Deakin Universitydro.deakin.edu.au/eserv/DU:30112363/beckford-iodine...Acknowledgements There are...

Iodine intakes and food sources of

iodine in Victorian Schoolchildren

by

Kelsey Beckford

Bachelor of Food Science and Nutrition (Hons)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Deakin University

June 2018

lswan

Redacted stamp

Acknowledgements

There are many people who I wish to thank and acknowledge for helping

and supporting me throughout the past four years. First and foremost, I wish to

thank my beautiful family who have had to put up with my unpredictable mood

swings and continued ranting. Mum, Dad, Meghan and Truffels, I could not have

gotten through the past four years without you and I am eternally grateful for all

of your help, comfort and support.

Considerable mention must go out to each and every single person who has

been a friend and support in my immediate work environment, as at times I know

I have been a right pain to deal with on a daily basis. Particular shout outs go to

Sim, Jules, Penny, Andrew, Inga, Ajam, Kathryn, Peggy, Linda, Jen, Megs, Simi, Karen

and Rivkeh, I could not have done it without you.

I wish to extend my most heartfelt thanks to my team of supervisors. You

have all contributed uniquely to my PhD experience, and I definitely could not have

gotten through my PhD without the combined efforts of all 5 of you. Caryl, your

knowledge and expertise have added to my own immensely, and I have learnt so

much from working with you. Thank you for being a dedicated and steadfast

supervisor, always pushing me to do better. Lynn, thank you for your support,

guidance and advice throughout my PhD I definitely do not think I would have had

the belief in myself to conquer a PhD if it had not been for your encouragement

and support at the end of honours. Claire, thank you for letting my hide under your

desk when I was feeling stressed and overwhelmed, for being a rational confidant

and providing advice and support in my more stressed out moments. Carley, thank

you for taking the time out of your busy schedule to answer my questions and give

me direction and support, particularly when it came to the data analysis and the

re-entry of the food recalls. I have learnt so much from you and I hope we are able

to work together in the future. Finally, Sheila, although you were involved from

afar, thank you for your support and knowledge. I am so grateful for your warm

hospitality, kindness, patience and understanding when I called Otago home for 6

weeks. You, Murray and the team at Otago made me feel so welcome and made

living away from home a breeze (even though you tried to kill me at sandfly bay).

This brings me to the rest of the Otago uni crew. Anna, Zheng, Andrew,

Devonia, thank you for welcoming me into your circle and involving me in your

social activities. Your warmth and generosity was immensely appreciated. I also

wish to thank Michelle Harper and Ashley Duncan for their support in the lab as I

was learning the urine analysis method and to Claire who opened her home for me

to stay in.

Additional thanks goes out to Rachel West and the Deakin University

Librarians, who helped me during my systematic review. Karen Lamb for her

statistical knowledge and for spending hours with me trying to figure out the

analyses for all of my studies. I also wish to acknowledge Dorothy Mackerras whose

question at a Nutrition Society of Australia conference got the ball rolling for the

systematic review. Finally I wish to acknowledge all of the tech team and admin

team at Deakin, Joan, Sharon, Linda and Amanda, as well as the HDR co-ordinators

and Brad Aisbett, all of whom have provided support and who have contributed to

my PhD experience in some way.

I also wish to acknowledge the project manager of the SONIC study,

Manuela Rigo, as well as all of the research assistants, participants, parents and

schools involved in the SONIC study, without whom my PhD would not exist. I

would also like to acknowledge the National Heart Foundation of Australia Grant-

in-Aid, Helen MacPherson Smith Trust Fund Project Grant and Deakin University

Faculty Research Development grant, which funded the SONIC study, as well as the

Australian Postgraduate Award which supported me throughout my study.

The past four years of my life have been incredibly challenging, but I have

learnt so much and I am so grateful to everyone for helping me get through it all.

I would like to dedicate this thesis to my best friend and confidante, Toffy,

whom I had to say goodbye to towards the end of my PhD after 16 long years of

unconditional love and friendship. I will never forget your scraggly face and the

look of love in your eyes as I complained to you about things you would never

understand. I hope you are living a better life wherever you are.

List of Publications and Presentations

Manuscripts published:

Beckford K, Grimes C, Margerison C, Riddell L, Skeaff S, Nowson C. Iodine

Intakes of Victorian Schoolchildren Measured Using 24-h Urinary Iodine Excretion.

Nutrients. 2017;9(9):961.

Conference abstracts published:

International Conference on Diet and Activity Methods, Brisbane, Australia, 2015

Beckford K, Grimes CA, Riddell LJ, Margerison C, Skeaff S, Nowson C. 24-

hour Urinary Iodine Concentration and 24hr Urinary Iodine Excretion in a sample

of Australian primary school children. 2015. Poster Presentation.

Joint Annual Scientific Meeting of the Nutrition Society Australia and Nutrition

Society New Zealand, Wellington, New Zealand, 2015

Beckford K, Grimes CA, Riddell LJ, Margerison C, Skeaff S, Nowson C. 24-

hour Urinary Iodine Concentration and 24hr Urinary Iodine Excretion in a sample

of Australian primary school children. 2015. Poster Presentation.

Beckford K, Grimes CA, Riddell LJ, Margerison C, Skeaff S, Nowson C. The

association between 24-hour urinary sodium and iodine excretion in a sample of

Victorian school-aged children. 2015. Oral Presentation.

The Nutrition Society of Australia Annual Scientific Meeting, Melbourne, Australia,

2016

Beckford K, Grimes C, Margerison C, Riddell L, Skeaff S, Nowson C.

Relationship between urinary iodine excretion, milk and bread intake in Victorian

schoolchildren. Poster Presentation

Asia Pacific Conference on Clinical Nutrition, Adelaide, Australia, 2017

Beckford K, Grimes C, Margerison C, Riddell L, Skeaff S, Nowson C. Twenty-

four hour urinary volume of children: a systematic review of the literature. 2017.

Oral Presentation.

Contribution to Associated Publications

Grimes CA, Riddell LJ, Campbell KJ, Beckford K, Baxter JR, He FJ, et al. Dietary

intake and sources of sodium and potassium among Australian schoolchildren:

results from the cross-sectional Salt and Other Nutrients in Children (SONIC) study.

BMJ Open. 2017;7(10).

Specific contributions and tasks performed by

the Author

1. Conducted a systematic review and meta-analysis of studies reporting the 24-hour

urine volume of children and adolescents. This process involved:

Locating relevant articles

Screening for inclusion in data synthesis

Data extraction and collation

Meta-analysis of study findings

2. Utilising data from a larger study, the Salt and Other Nutrient Intake in Children

(SONIC) study the following tasks were undertaken by the author:

Locating previously frozen 10mL aliquots of 24-hour urine samples, collected

from the 750 participants of the SONIC study. Following this the Author

defrosted, re-aliquoted and immediately refroze the samples for iodine analysis.

These samples were then transported from Deakin University, Melbourne,

Australia to the University of Otago in Dunedin, New Zealand.

The author then learnt the method for determining the iodine concentration in

urine samples, a modification of the method established by Pino et. al, under

the supervision of Associate Professor Sheila Skeaff at the University of Otago.

Following this, the author analysed the 750 available urine samples for iodine

concentration, under the supervision of Associate Professor Skeaff.

The author was originally involved in the co-ordination and entry of over 650

24-hour food recalls collected as part of the SONIC study, under the supervision

of Dr. Carley Grimes. However, as these recalls were originally entered into a

version of FoodWorks which was linked to an outdated nutrient database, the

recalls for use in the present thesis (i.e. children aged >8 years of age, ~600

recalls) needed to be re-entered into the newer software, released in early

2017, as this was linked to a database which had been updated to reflect

changes to the iodine content of foods as a result of the introduction of

mandatory fortification of bread with iodised salt from October 2009. The

process of re-entry was undertaken solely by the Author, under the supervision

of Professor Caryl Nowson and Dr. Carley Grimes.

~ i ~

Table of Contents

ABSTRACT 1

CHAPTER ONE: INTRODUCTION 7

CHAPTER TWO: REVIEW OF THE LITERATURE 13

2.1 BACKGROUND ............................................................................................................. 13

2.2 IODINE IN THE BODY ..................................................................................................... 13

2.3 IODINE INTAKES AND HEALTH ......................................................................................... 15

2.3.1 Severe consequences of iodine deficiency................................................................ 15

2.3.2 Mild iodine deficiency ................................................................................................ 17

2.3.3 Excessive iodine intakes ............................................................................................. 19

2.3.4 Recommended Dietary Intakes for iodine: Australia and New Zealand and

determination of individual iodine requirements ..................................................... 20

2.4 ASSESSMENT OF IODINE NUTRITION ................................................................................ 21

2.4.1 Assessment of Thyroid size ........................................................................................ 21

2.4.2 Urinary iodine ............................................................................................................ 22

2.4.3 WHO recommendations for assessing the iodine status of populations .................. 28

2.5 IODINE NUTRITION IN AUSTRALIA ................................................................................... 33

2.5.1 History of Iodine deficiency in Australia .................................................................... 33

2.5.2 Mandatory fortification of bread with iodised salt ................................................... 35

2.5.3 Population Iodine nutrition in Australia post‐fortification of bread with iodised salt

36

2.5.4 Dietary assessment .................................................................................................... 37

2.6 FOOD SOURCES OF IODINE IN AUSTRALIA ......................................................................... 38

2.6.1 Bread .......................................................................................................................... 38

2.6.2 Milk as a dietary source of iodine .............................................................................. 39

2.6.3 The use of iodised salt ............................................................................................... 40

2.7 SODIUM REDUCTION TARGETS AND IODINE ....................................................................... 43

2.7.1 Sodium intakes and health of children and adolescents .......................................... 43

2.7.2 Global action to reduce sodium intake ..................................................................... 43

2.7.3 Australian sodium reduction strategies .................................................................... 44

2.8 SUMMARY .................................................................................................................. 46

CHAPTER THREE: STUDY 1: META‐ANALYSIS OF AVERAGE 24‐HOUR

URINARY VOLUME OF CHILDREN AND ADOLESCENTS 49

3.1 INTRODUCTION ........................................................................................................... 49

3.2 AIM .......................................................................................................................... 50

3.3 METHODS .................................................................................................................. 50

3.3.1 Information Sources and Search ............................................................................... 50

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3.3.2 Inclusion/Exclusion criteria ....................................................................................... 52

3.3.3 Data extraction and synthesis ................................................................................... 54

3.3.4 Quality Assessment ................................................................................................... 55

3.3.5 Statistical Analysis ..................................................................................................... 56

3.4 RESULTS .....................................................................................................................69

3.4.1 Summary of studies considered for primary analysis ............................................... 69

3.4.2 Quality of studies considered for primary analysis ................................................... 69

3.4.3 Initial analysis ............................................................................................................ 70

3.4.4 Primary analysis limited to studies with > 1 urine assessment criteria .................... 70

3.5 DISCUSSION ................................................................................................................75

3.6 CONCLUSION ...............................................................................................................78

CHAPTER FOUR: THE SALT AND OTHER NUTRIENT INTAKE IN

CHILDREN (SONIC) STUDY METHODOLOGY 81

4.1 PARTICIPANT RECRUITMENT ...........................................................................................81

4.1.1 Phase One: Victorian non‐government privately funded schools ............................ 82

4.1.2 Phase Two: Victorian government schools ............................................................... 82

4.2 TESTING PROCEDURES ..................................................................................................84

4.2.1 Demographic characteristics ..................................................................................... 84

4.2.2 Discretionary salt use characteristics ........................................................................ 85

4.2.3 Anthropometric measurements ............................................................................... 85

4.3 24‐HOUR URINE COLLECTION .........................................................................................86

4.3.1 Procedure .................................................................................................................. 86

4.3.2 Validity of urine samples ........................................................................................... 87

4.3.3 Biochemical Analysis ................................................................................................. 88

4.4 24‐HOUR FOOD RECALL .................................................................................................90

CHAPTER FIVE: STUDY 2: IODINE INTAKES OF VICTORIAN SCHOOL‐

AGED CHILDREN MEASURED USING 24‐HOUR URINARY IODINE

EXCRETION 93

5.1 INTRODUCTION ............................................................................................................93

5.2 AIM ...........................................................................................................................94

5.3 METHODS ...................................................................................................................94

5.3.1 Participants and recruitment .................................................................................... 94

5.3.2 24‐hour urine collection ............................................................................................ 94

5.3.3 Urinalysis ................................................................................................................... 94

5.3.4 Statistical analysis ...................................................................................................... 95

5.4 RESULTS .....................................................................................................................96

5.4.1 Demographic characteristics ..................................................................................... 96

5.4.2 Urinary parameters, by gender ................................................................................. 96

5.4.3 Urinary parameters, by age ....................................................................................... 98

5.4.4 Urinary parameters, by socioeconomic status ......................................................... 99

5.5 DISCUSSION ............................................................................................................. 100

5.6 CONCLUSIONS .......................................................................................................... 103

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CHAPTER SIX: STUDY 3: FOOD SOURCES OF IODINE AND

ASSOCIATION WITH URINARY IODINE EXCRETION 105

6.1 INTRODUCTION ......................................................................................................... 105

6.2 AIMS ....................................................................................................................... 106

6.3 METHODS ................................................................................................................ 106

6.3.1 Participants and recruitment ................................................................................... 106

6.3.2 Measures ................................................................................................................. 106

6.3.3 24‐hour food recall .................................................................................................. 106

6.3.4 24‐hour urine collection .......................................................................................... 107

6.3.5 Urinalysis .................................................................................................................. 107

6.3.6 Statistical analysis .................................................................................................... 107

6.4 RESULTS .................................................................................................................. 109

6.4.1 Demographic characteristics ................................................................................... 109

6.4.2 Iodine intakes ........................................................................................................... 109

6.4.3 Food sources of iodine ............................................................................................ 111

6.4.4 Relationship between food sources of iodine and UIE ........................................... 113

6.4.5 Differences in dietary iodine intake and UIE between consumers and non‐

consumers of bread and milk. ................................................................................. 117

6.5 DISCUSSION .............................................................................................................. 118

6.6 CONCLUSION ............................................................................................................ 121

CHAPTER SEVEN: STUDY 4: ASSOCIATION BETWEEN URINARY

EXCRETION OF SODIUM AND IODINE IN 24‐HOUR URINE SAMPLES

AND ASSOCIATION WITH DISCRETIONARY SALT USE 123

7.1 INTRODUCTION ......................................................................................................... 123

7.2 AIMS ....................................................................................................................... 124

7.3 METHODS ................................................................................................................ 124

7.3.1 Participants and recruitment ................................................................................... 124

7.3.2 Measures ................................................................................................................. 124

7.3.3 Discretionary salt use characteristics ...................................................................... 125

7.3.4 24‐hour urine collection .......................................................................................... 125

7.3.5 Urinalysis .................................................................................................................. 125

7.3.6 Statistical analysis .................................................................................................... 125

7.4 RESULTS .................................................................................................................. 126

7.4.1 Demographic characteristics ................................................................................... 126

7.4.2 Relationship between urinary sodium and urinary iodine excretion ...................... 127

7.4.3 Differences in urinary iodine excretion across discretionary salt use categories ... 128

7.5 DISCUSSION .............................................................................................................. 130

7.6 CONCLUSION ............................................................................................................ 132

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CHAPTER EIGHT: DISCUSSION 133

8.1 VICTORIAN CHILDREN HAVE SUFFICIENT IODINE INTAKES – A RESULT OF THE SUCCESSFUL

IMPLEMENTATION OF MANDATORY FORTIFICATION? ........................................................ 133

8.2 CURRENT METHODS FOR ASSESSING THE IODINE NUTRITION OF POPULATIONS – IS IT TIME FOR A

CHANGE?................................................................................................................. 137

8.3 CURRENT LACK OF POPULATION IODINE MONITORING IN AUSTRALIA .................................. 141

8.4 CONCLUDING REMARKS .............................................................................................. 143

REFERENCES 145

APPENDICES 165

~ v ~

List of Tables

Table 2.1: Daily iodine intake recommendations for children and

adolescents - Australia and New Zealand (µg/day) .................. 20

Table 2.3: World Health Organization epidemiological criteria for

assessing iodine nutrition based on median urinary iodine

concentrations from spot urines in different target groups ..... 32

Table 2.4: Studies documenting iodine deficiency in the Australian

population pre-fortification of bread with iodised salt .............. 34

Table. 2.5: Comparison of median UIC from spot urine samples of

schoolchildren in 2003-2004 and 2011-2013 ................................ 36

Table 3.1: Search criteria specifications for each database .......................... 51

Table 3.2: Data extracted from included studies ............................................ 54

Table 3.3: Characteristics of studies considered for primary analysis

where 24-hour urine volume was presented as mean (SD/SEM),

n=36 studies ...................................................................................... 59

Table 3.4: Characteristics of studies considered for the primary analysis

where 24-hour urine volume was presented as median (IQR),

n=7 studies ........................................................................................ 67

Table 5.1: Descriptive characteristics and urinary parameters of SONIC

participants ....................................................................................... 97

Table 5.2: Urinary parameters of SONIC participants, by age group

(n=650). .............................................................................................. 98

Table 5.3: Urinary parameters of SONIC participants by school postcode

socioeconomic status mean (SD) (n=650) ..................................... 99

Table 5.4: Urinary parameters of SONIC participants, by parental

education socioeconomic status mean (SD) (n=554) ................... 99

Table 6.2: Food sources of iodine in SONIC participants >8y/o with both a

valid food recall and urine sample (n=454) ............................... 111

Table 6.3: Urinary Iodine Excretion and dietary iodine intake by

consumption of bread and milk (n=454) .................................... 116

Table 7.1: Demographic characteristics of SONIC participants included in

sodium analysis (n=649) ............................................................... 127

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Table 7.2: Urinary excretion of iodine and sodium across discretionary salt

use categories (n=638), mean (SEM) or n (%) ............................ 129

Table 7.3: Relationship between urinary iodine excretion (μg/24-hour) and

discretionary salt use (n=638) ...................................................... 129

Table 8.1: Iodine Nutrient Reference Values for children and adults ...... 139

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List of Figures

Figure 2.1: Absorption and metabolism of dietary iodine ........................... 15

Figure 3.1: PRISMA flow chart of study selection ......................................... 53

Figure 3.2: Flow chart of studies included in analyses ................................. 58

Figure 3.3: Forest plot of studies assessing 24-hour urine volumes of 2-6

year olds with >1 urine assessment criteria (n=1084) ................. 72


year olds with >1 urine assessment criteria (n=3628 ) ................ 73


year olds with >1 urine assessment criteria (n=1438) ................. 74

Figure 4.1: Outline of recruitment and participation in the SONIC study 84

Figure 4.2: Flow chart of urines included in iodine analysis ....................... 88

Figure 6.1: Relationship between grams of bread consumed and 24-hour

urinary iodine excretion among SONIC participants with both

a valid food recall and urine sample (n=454) ............................ 114

Figure 6.2: Relationship between grams of milk consumed and 24-hour

urinary iodine excretion among SONIC participants with both

a valid food recall and urine sample (n=454) ............................ 114

Figure 6.3: Relationship between grams of bread and milk consumed and

24-hour urinary iodine excretion among SONIC participants

with both a valid food recall and urine sample (n=454) .......... 115

Figure 7.1: Relationship between urinary excretion of iodine and sodium

in the SONIC paticipants included in sodium analyses

(n=649) ............................................................................................. 128

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List of Appendices

Appendix A: Summary of Food and Health Dialogue food reformulation

targets (323) ........................................................................................ 165

Appendix B: Characteristics of studies which utilised the Dortmund

Nutritional and Anthropometric Longitudinally Designed

(DONALD) Study ......................................................................... 167

Appendix C: Modified Newcastle – Ottawa Quality Assessment Scale .. 176

Appendix D: Forest plot of studies assessing 24-hour urine volumes of 2-6

year olds ......................................................................................... 177

Appendix E: Forest plot of studies assessing 24-hour urine volumes of 7-

11 year olds .................................................................................... 178

Appendix F: Forest plot of studies assessing 24-hour urine volumes of 12-

19 year olds .................................................................................... 179

Appendix G: Subgroup analysis of relationship between food sources of

iodine and UIE ............................................................................... 180

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List of Abbreviations

2 – Chi-squared test

µg – microgram

ACT – Australian Capital Territory

AHS – Australian Health Survey

AWASH – Australian Division of the World Action on Salt and Health

BP – Blood Pressure

BMI – Body Mass Index1

CASH – Consensus Action on Salt and Health

CNPAS – Children’s Nutrition and Physical Activity Survey

CNS – Central Nervous System

CVD – Cardiovascular Disease

CV – Coefficient of Variation

EAR – Estimated Average Requirement

estBMR – estimated Basal Metabolic Rate

FHD – Food and Health Dialogue

FFQ – Food Frequency Questionnaire

FSANZ – Food Standards Australia New Zealand

GIT – Gastro-intestinal Tract

ICCIDD – International Council for the Control of Iodine Deficiency Disorders

I/CR – Iodine : Creatinine Ratio

IDD – Iodine Deficiency Disorders

IOM – Institute of Medicine

IQ – Intelligence Quotient

IQR – inter-quartile range

kg – kilogram

L – Liter

mg – milligram

mL – milliliter

mmol – millimole

Na – sodium2

NHMRC – National Health and Medical Research Council

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NINS – National Iodine Nutrition Survey

NRV – Nutrient Reference Value

PAHO – Pan American Health Organization

PRISMA – Preferred Reporting Items for Systematic Reviews and Meta-Analyses

r – Correlation co-efficient

R2 – co-efficient of determination

RCT – Randomised Control Trial

RDI – Recommended Dietary Intake

SD – Standard Deviation

SE – Standard Error

SEIFA – Socioeconomic Indexes for Areas

SES – Socioeconomic Status

SONIC – Salt and Other Nutrient Intake in Children study

T3 – 3,5,3’-Tri-iodothyronine

T4 – Thyroxine

Tg - Thyroglobulin

TSH – Thyroid Stimulating Hormone

UIC – Urinary Iodine Concentration

UIE – Urinary Iodine Excretion

UL – Upper Level of intake

UNICEF – United Nations Children’s Emergency Fund

USI – Universal Salt Iodisation

WHO – World Health Organization

Notes:

1 BMI was converted to age and gender adjusted z-scores using the least mean squares method

and the 2000 Centers for Disease Control and Prevention growth reference charts (1)

2 Ninety-five percent of sodium consumed is in the form of sodium chloride, commonly referred

to as salt. Throughout this thesis the term ‘salt’ has been used to refer to the presence of

sodium in foodstuffs. Where possible results for both sodium and salt excretion and/or intake

have been presented. The conversion factors for sodium to salt used throughout this thesis

are:

1 mmol sodium = 23 mg sodium

1 gram of salt (sodium chloride) = 390 mg of sodium (2)

~ 1 ~

Abstract

Iodine is an essential trace element that plays an important role in the

production of thyroid hormones, important in the growth and development of the

brain and central nervous system. As such, iodine deficiency during childhood has

been linked to impaired growth and cognition. Prior to the 1990s Australia was

believed to be iodine sufficient, however iodine deficiency was reported in children

and pregnant women in the late 1990s. In 2009 mandatory fortification of all

commercially produced yeast-leavened bread, excluding organic varieties, with

iodised salt was implemented. Studies conducted post-fortification using spot urine

samples have reported an improvement in iodine status among children and adults,

when compared to pre-fortification. More recently however, the successful

application of sodium reduction targets to bread, implemented as a part of a larger

sodium reduction strategy, has raised the concern that sodium reduction strategies

may have a negative impact on the iodine intakes of Australian schoolchildren.

As approximately 90% of ingested iodine is excreted within 24-48 hours the

measurement of urinary iodine excretion (UIE) in 24-hour urine samples can

provide an accurate estimate of total daily iodine intake, however their use in large

population based studies is limited. Historically however iodine nutrition has been

assessed through the measurement of median urinary iodine concentration (UIC)

measured in µg/L from spot urine samples, with the use of 24-hour urine samples

for measuring iodine intakes only becoming more common in larger population

groups in the late 20th century. Current recommendations for evaluating the

severity of iodine deficiency within a population provide criteria for defining iodine

status, whereby the median UIC estimated in spot urine samples from school-aged

children is used as a marker of iodine status. These recommendations define iodine

deficiency as a population median UIC <100 µg/L and are based on the assumption

that UIC is able to provide a surrogate estimate of the iodine intakes of the

population. Whilst this method may be appropriate for use in populations where

the daily urine volume is approximately 1L, its use as a surrogate index of iodine

intake in populations where the urine volume is less than 1L, such as younger

Abstract

~ 2 ~

children, could result in the overestimation of iodine intakes. To date however, no

studies have systematically evaluated the daily urine volume of school-aged

children, the population group recommended for use in iodine monitoring

programs.

Whilst spot urine samples are the easiest and most commonly used method

for assessing iodine nutrition in Australia, this method is not able to provide an

accurate estimate of actual iodine intake. To date there have been no studies

utilising 24-hour urine samples to estimate the iodine intakes of Australian school-

aged children post-fortification of bread with iodised salt. Furthermore, whilst the

recent Australian Health Survey was able to identify the main food sources of iodine

using 24-hour food recalls, to date there have been no studies examining the

association between the main food sources of iodine and an objective measure of

iodine intake, i.e. 24-hour urinary excretion of iodine. Such studies will help to

determine the success of iodine fortification strategies, as well as the potential

impact which sodium reduction strategies may have on the iodine intakes of

Australian schoolchildren.

The overall aims of this thesis were to: i) assess the average urine volume

of school-aged children to inform population estimates of iodine intake, through a

systematic review of the literature; ii) determine the iodine intakes of a sample of

Australian schoolchildren using an objective measure of total daily iodine intake,

post-fortification of bread with iodised salt, and identify socio-demographic

characteristics associated with iodine intake, and iii) to determine the association

between the major food sources of iodine, including bread, milk, and iodised salt,

and total iodine intake as measured using 24-hour urine samples.

The aim of Study 1 (Chapter 3) was to produce an estimate of the average

24-hour urine volume of children and adolescents, through a systematic review of

studies conducted in healthy children and adolescents (>1y and <19y). Of the 53

studies initially located a meta-analysis was conducted on 26 studies, which

included at least one criteria for assessing the completeness of urine collections.

Of these, 12 reported results for 2-6 year olds, 11 for 7-11 year olds and 7 for 12-

19 year olds. The overall mean (95% CI) 24-hour urine volume of 2-19 year olds was

774 (656, 893) mL/24-hours. When broken down by age group, mean (95% CI) 24-

Abstract

~ 3 ~

hour urine volume was 539 (468, 611), 786 (749, 824) and 1067 (855, 1279) mL/24-

hours for 2-6, 7-11 and 12-19 year olds, respectively (P<0.001). There was no

difference in average daily urine volume between genders. This study provides

evidence that the average urine volume of school-aged children is less than 1L,

which could result in the misclassification of iodine intakes when spot urine

samples are used as a surrogate index of iodine intakes in populations of school-

aged children.

The remaining three studies in this thesis utilised data from the Salt and

Other Nutrient Intakes in Children (SONIC) study, a cross-sectional study conducted

in a convenience sample of Victorian schoolchildren between June 2010 and May

2013. A single twenty-four hour urine sample was obtained from 650

schoolchildren aged 4-12 years (55% male) from 43 government and non-

government schools across Victoria, Australia. Completeness of collections was

assessed using collection time, total volume, and urinary creatinine. These samples

were analysed for urinary iodine using a modification of the ammonium persulfate

method. In a subset of participants aged >8 years of age (n=520), a single 24-hour

food recall was also collected.

The aim of Study 2 (Chapter 5) was to determine total daily iodine intake of

a sample of Victorian schoolchildren on one day utilising 24-hour UIE and to assess

differences across age, gender, and socioeconomic status. Valid 24-h urine samples

were provided by 650 children, mean (SD) age 9.3 (1.8) years (55% male). The mean

UIE of 4–8 and 9–13 year olds was 94 (48) and 111 (57) µg/24-hours, respectively,

with 29% and 26% of participants having a UIE below the age-specific Estimated

Average Requirement (EAR), respectively. The median (IQR) UIC was 124 (83,172)

µg/L, with 36% of participants having a UIC <100 µg/L. There was no difference in

UIE between the genders or by socioeconomic status. This convenience sample of

Victorian schoolchildren were found to be iodine replete, based on UIC and

estimated iodine intakes derived from 24-h urine collections, confirming the

findings of the 2011-13 Australian Health Survey.

The aims of Study 3 (Chapter 6) were to: i) determine the dietary intake and

food sources of iodine in a sample of Victorian schoolchildren, and ii) assess the

association between the major food sources of iodine, including bread and milk,

Abstract

~ 4 ~

and urinary excretion of iodine over a 24-hour period. A total of 454 participants

aged 8-12 years (mean (SD) 10 (1.3) years, 55% male) had both a valid 24-hour food

recall and a 24-hour urine sample analysed for iodine. Mean UIE and dietary iodine

were 108 (54) g/24-hour and 172 (74) µg/day, respectively. Major food sources

of dietary iodine were “regular breads, and bread rolls” (27%), “dairy milk” (25%)

and “mixed dishes where cereal is the major ingredient” (7%). The total amount of

bread consumed (g/d) was not associated with UIE, however there was a

significant, weak correlation between the amount of milk consumed (g/d) and UIE

(r=0.15, P=0.001). Furthermore, a 100 g/day increase in milk intake was associated

with a 4 µg/24-hour higher UIE and accounted for 2% of the variance in UIE

(R2=0.02, β=4.03 (0.90)(SE), P<0.001). This study demonstrated that both bread

and milk are important food sources of iodine, however only milk was significantly

associated with UIE.

As bread was a major source of iodine in this population most likely as a

result of the iodised salt added during processing, the aim of Study 4 (Chapter 7)

was to examine the relationship between urinary excretion of iodine (UIE) and

sodium (UrNa), as a measure of the intakes of these two nutrients. An additional

aim of this study was to assess whether there was an association between

discretionary salt use, i.e. salt added at the table or during cooking, and UIE. The

mean (SD) UIE and UrNa for all participants (n=649) were 104 (54) μg/24-hour and

104 (45) mmol/24-hour, respectively. There was a moderate, positive correlation

between UrNa and UIE (r=0.37, P<0.001). Regression analysis revealed that a 1

mg/24-hour increase in UrNa (0.003g salt) was associated with a 7.1 µg/24-hour

higher UIE and accounted for 14% of the variance in UIE (R2=0.14, β(SE)=7.12(0.80),

P<0.001). In the subsample of participants with both a complete 24-hour urine

sample and discretionary salt use data (n=638), 77% (n=493) of participants were

classified as a discretionary salt user, defined as using salt either at the table or

during cooking, with 28% (n=177) of participants adding salt both at the table and

during cooking. There was, however, no significant difference in UIE between those

who added salt either at the table or during cooking and those who did not. This

study did not detect a relationship between discretionary salt use and UIE,

suggesting that the positive association between UIE and UrNa is due to iodised

Abstract

~ 5 ~

salt present in the food supply. Regular monitoring of population iodine status is

essential as reductions in the amount of iodised salt added to key food sources of

iodine, i.e. bread, may adversely impact iodine intakes.

The results from these four studies provide a novel contribution to the

current evidence base surrounding assessment of iodine nutrition in Australia post-

fortification of bread with iodised salt. The findings of the systematic review

indicate that the urinary volume of children may need to be considered in studies

assessing the iodine nutrition of populations. In addition, the findings from the

remaining three studies which used an objective measure of total iodine intake, 24-

hour urinary excretion of iodine, confirmed the results of the recent 2011-2013

Australian Health Survey. Furthermore, the results from this thesis provide

evidence that the mandatory fortification of bread with iodised salt may be

contributing to the total iodine intakes of Australian schoolchildren. It is therefore

imperative that population sodium and iodine intakes continue to be monitored

using an objective measure of intake, i.e. 24-hour urinary excretion, in order to

ensure that a reduction in sodium intakes to decrease dietary related disease risk

does not put Australian schoolchildren at risk of iodine deficiency

~ 7 ~

Chapter One: Introduction

Iodine is an essential trace element found in low concentrations in the soil,

air and sea (3-5). Due to the important role which iodine plays in the production of

thyroid hormones, which are vital in the growth and development of the brain and

central nervous system (3-5), iodine deficiency has been linked to impaired growth

and cognition in schoolchildren (6, 7). In less economically developed regions of the

world, severe iodine deficiency during pregnancy and childhood has been linked

with the development of goiter and cretinism (8, 9).

The iodine content of foods varies depending on the iodine content of the

soil where it is grown (10). The concentration of iodine in the soil can further vary

depending on regional variations in soil solubility, as well as exposure to rain, snow

and glaciation (10). In Australia specifically, the iodine content of plant foods is

particularly low (<20-280 μg /kg fresh weight (11)). due to the impact of glaciation

on the iodine content of the soil during the last ice age (12). This, in combination

with the low iodine content of the drinking water (13), has contributed to the history

of iodine deficiency in Australia, particularly in the island state of Tasmania where

iodine deficiency was documented in the early 1960s (13, 14).

Historically, population monitoring of iodine nutrition was limited to the

assessment of thyroid size and goiter prevalence in school-aged children (15).

However, in the early 1990s it was established that the assessment of median

urinary iodine concentration (UIC) measured in spot urine samples provided an

accurate reflection of population iodine status. Whilst spot urine samples are the

easiest and most commonly used method for assessing iodine nutrition, they are

not able to provide an accurate estimate of the actual iodine intake of a population

as they do not capture variations in iodine excretion over the course of the day (16-

19) (20, 21). Comparatively however, the use of a 24-hour urine sample to determine

urinary iodine excretion (UIE) in μg/24-hour allows for a good estimate of recent

iodine intake as approximately 90% of ingested iodine is excreted within 24-48

hours (22-26). In order to measure daily iodine intake, a complete 24-hour urine

sample is recommended as it is able to capture variations in iodine excretion over

Introduction

~ 8 ~

the course of the day (25, 27, 28), however their use in large population based studies

is limited due to the high subject burden associated with collecting 24-hour urine

samples. Spot urine samples are the easiest and most commonly used method for

assessing iodine nutrition (29-35), with a small number of more recent studies using

24-hour urine samples to measure the iodine intakes of larger groups (36)

Current recommendations for assessing the severity of iodine deficiency of

populations define iodine sufficiency based on a population median UIC (µg/L),

measured in spot urine samples from school-aged children, >100µg/L (37). These

recommendations are based on the assumption that the median UIC can be used

as a surrogate index of the daily iodine intake of individuals within the population

(38). However a review by Zimmerman and Andersson (2012) highlighted that this

assumption would only be true if total daily urine volume was equivalent to 1L (15).

In populations where the urine volume is substantially less than 1L, for example

young children, the use of UIC as a surrogate index of daily iodine intake may result

in the overestimation of intakes. As such, it is currently not clear whether it is

appropriate for UIC to be used as a surrogate index of daily iodine intake in

populations such as young children or adults where the average urine volume may

be less than or in excess of 1L (15). Furthermore, the average urine volume of school-

aged children, the population group recommended for use in iodine monitoring

programs, has yet to be systematically evaluated. Such information could help in

determining appropriateness of spot urine samples for estimating the iodine

intakes of populations where the daily urine output is not 1L.

Prior to the 21st century, monitoring of iodine deficiency in Australia was

scarce and was limited to studies in small sample sizes (<100 participants) (12, 13, 39).

As endemic goiter was largely limited to Tasmania, monitoring of iodine nutrition

was focused on the island state until the early 1990s (12-14, 40). However, following

the detection of iodine deficiency in a small sample of pregnant women in Sydney

in the late 1990s (33) a number of studies began to document iodine deficiency in

other regions of Australia, using spot urine samples in school-aged children (34, 41-

43). In response to this, Food Standards Australia New Zealand (FSANZ) legislated

that all salt added during commercial bread production be replaced by iodised salt

from October 2009 (44). Whilst this fortification strategy was limited to bread

Introduction

~ 9 ~

products, excluding organic and homemade varieties, it was also recommended

that iodised salt replace non-iodised salt in other foods, where possible, at the

manufacturer’s discretion.

Studies conducted post-fortification have determined that the iodine status

of the Australian population has returned to sufficiency (29, 45-47). Specifically, the

2011-13 Australian Health Survey (AHS) observed a median population UIC of 2-18

year olds 16% higher than the population median UIC determined as part of the

National Iodine Nutrition study in 2003/4 (124 µg/L vs. 104 µg/L) (34, 48). It is

important to note, however, that these studies have focused only on estimating

median UIC from spot urine samples, with no studies using 24-hour urine samples

to determine the iodine intakes of a sample of Australian schoolchildren post-

fortification. The 2011-13 AHS collected 24-hour food recalls and spot urine

samples from a representative sample of the population (45). Whilst these methods

are able to provide valuable insight into the food sources of iodine and the iodine

status of the population, they may not provide an accurate estimate of daily iodine

intake. Due to the wide variation in iodine excretion both within and between

individuals throughout the day (16, 18, 19), spot urine samples are unable to provide

an accurate reflection of daily iodine intake. In addition, limitations associated with

the use of food databases for estimating the iodine content of foods impact the

ability of food recalls for estimating total iodine intake (15, 49, 50). Food recalls, can

however, provide an indication of the main food sources of iodine in a population.

Prior to the introduction of mandatory fortification of bread with iodised

salt in 2009, milk was the main food source of iodine (~30-60%) amongst Australian

school-aged children (51), as iodine containing sanitisers used during milk processing

leached into the milk increasing its iodine content (52). However, after the detection

of thyrotoxicosis in two groups of Tasmanian adults in the 1970s (40, 53), the use of

these sanitisers declined over the next two decades (13). This was believed to be the

primary factor influencing the emergence of iodine deficiency in the Australian

population in the late 1990s (52). However, the 2011-13 AHS observed that milk was

still the highest contributor to total daily iodine intake amongst school-aged

children, as measured using 24-hour food recalls, with 27% and 25% of dietary

iodine coming from dairy milk amongst 4-8 and 9-13 year olds, respectively (45). The

Introduction

~ 10 ~

contribution of milk to total dietary iodine was similar to that observed for bread,

which accounted for 27% and 23% of daily iodine intake, among 4-8 and 9-13 year

olds, respectively (45).

The iodine content of bread in Australia is primarily influenced by the

iodised salt added during processing (54). Whist there is no recommended amount

of salt which should be added, the salt content of commercially available breads in

Australia ranges from 0.5-2mg/100g (55). In addition, the iodine concentration of

this salt varies between 25-65 mg/kg of salt (15-40 ppm iodine) (56), indicating that

the iodine content of commercially produced bread can vary up to tenfold (0.013 -

0.13 µg I/100g bread). Due to the well-established links between high salt intakes

and adverse health outcomes, including cardiovascular disease (57), it has become

increasingly important to implement strategies to reduce population salt intake.

The World Health Organization (WHO) recommends a reduction in salt intake to

<5g (2 g sodium)/day among adults by 2025 worldwide with a further reduction to

<2g (0.8g sodium)/day recommended for children (58). In Australia, the

establishment of salt reduction targets for nine food categories, including bread,

by the Federal Government’s former Food and Health Dialogue (FHD) in 2009

resulted in a 10% reduction in the sodium content of commercially available breads

over four years (59). As such strategies may have an adverse impact on iodine

intakes, it is important to understand the association between the major food

sources of iodine, including iodised salt added both during processing and as

discretionary salt (i.e. added at the table or during cooking), and iodine intakes of

Australian school-aged children.

It is important to understand the relationship between the major food

sources of iodine, particularly bread, milk and iodised salt added either during

processing or as discretionary salt (i.e. at the table and/or during cooking), and

iodine intakes. To date, there have been no studies examining the relationship

between these food sources and an objective measure of total iodine intake, i.e.

24-hour urinary excretion of iodine. Such studies will help to determine the success

of iodine fortification strategies, as well as the potential impact which sodium

reduction strategies may have on the iodine intakes of Australian schoolchildren.

Furthermore, it is important to establish the relationship between foods which are

Introduction

~ 11 ~

not reliant on iodised salt, such as milk, and total iodine intake, as an increase in

the consumption of these foods could help alleviate the impact which a reduction

in the iodised salt content of bread may have on iodine intakes.

The overall aims of this thesis were to i) assess the average urine volume of

school-aged children to inform population estimates of iodine intake; ii) determine

the iodine intakes of a sample of Australian schoolchildren using an objective

measure of total daily iodine intake, post-fortification of bread with iodised salt,

and identify socio-demographic characteristics associated with iodine intake, and

iii) determine the association between the major food sources of iodine and total

iodine intake as measured using 24-hour urine samples. More specifically, Study 1

(Chapter 3) estimated the average 24-hour urine volume of children and

adolescents, through a meta-analysis of studies conducted in healthy children and

adolescents (>1y and <19y). The remaining studies in this thesis utilised data from

the Salt and Other Nutrient Intake in Children (SONIC) study, described in Chapter

4. The first study using this data, Study 2 (Chapter 5) determined the total daily

iodine intake of a sample of Victorian schoolchildren on one day utilising 24-hour

UIE and to assess differences across age, gender, and socioeconomic status. Study

3 (Chapter 6) built on the findings of Study 2, by: i) determining the dietary intake

and food sources of iodine in a sample of Victorian schoolchildren, and ii) assessing

the association between the major food sources of iodine, including bread and

milk, and urinary excretion of iodine over a 24-hour period. Finally, Study 4 (Chapter

7) combined the findings of studies 3 and 4 to examine the relationship between

urinary excretion of iodine (UIE) and sodium (UrNa), as a measure of the intakes of

these two nutrients. An additional aim of this study was to assess whether there

was an association between discretionary salt use, i.e. salt added at the table or

during cooking, and UIE.

~ 12 ~

~ 13 ~

Chapter Two:

Review of the Literature

2.1 Background

Iodine is an essential trace element found in low concentrations in soil, air

and the sea (3-5). Iodine is most abundant in oceans, however movement of iodine

from the ocean through glaciation, rain or snow and subsequent transportation by

rivers, floods or wind has led to the accumulation of iodine in surface soil (3-5). The

most abundant source of iodine is seawater, and foods of marine origin have

greater amounts of iodine than plant foods due to the ability of marine animals to

concentrate iodine from seawater (60). Therefore the iodine content of fish varies

based on the seawater which they inhabit (<1 – 400 µg/kg fresh weight (61). In

Australia, the iodine content of plant foods is relatively low and reflects the

concentration of iodine in the soil in which they are grown (<20-280 µg/kg fresh

weight (11)). The iodine concentration of soil is dependent on regional variations in

soil solubility and can be affected by exposure to rain, snow, and glaciation (10).

Areas which experience frequent flooding or soil erosion, such as mountainous

regions or river deltas generally produce foods with poor iodine content, and these

areas are generally associated with iodine deficient populations (62). As such, the

major food sources of iodine can vary considerably both within and between

countries.

2.2 Iodine in the body

Whilst iodine in foods is present mainly in the inorganic form as iodide,

organically bound iodine is most abundant within tissues, with inorganic iodide

present in very low concentrations (3, 5). Therefore, inorganically bound iodide is

easily absorbed in the stomach and upper small intestine and converted to

organically bound iodine (3-5). Organically bound iodine is an essential component

in thyroid hormones, which are synthesised in the body by the thyroid gland (3, 5).

The adult human body contains between 15 and 20 mg of iodine (63). Approximately


~ 14 ~

70% of iodine in the body is found in the thyroid gland, nearly 40 times that found

in the plasma under normal circumstances (63). Iodine is an essential component of

the thyroid hormones Thyroxine (T4) (5) and 3,5,3’-Tri-iodothyronine (T3) (5). These

hormones are iodinated derivatives of the amino acid tyrosine, and are essential

for normal growth, metabolism and maturation of the whole body (3, 5).

Specific functions of these hormones include the growth and development

of tissues in the central nervous system (CNS), maintenance of basal metabolic rate

and macronutrient metabolism (3-5). Iodine also plays an important role in a variety

of enzymatic processes in the body, including lipolysis, glycogen synthesis and the

uptake of glucose and galactose from the gastro-intestinal tract (GIT) (4). Iodine is

an essential element, meaning that humans cannot synthesise it and need to obtain

iodine from their diet (3). Once absorbed by the stomach and converted to

organically bound iodine in the upper small intestine, iodine is transported in the

blood to several tissues in the body, including the thyroid gland. In the thyroid gland

iodine is oxidised and added to the thyroxine residues on thyroglobulin to form

iodinated thyroglobulin. This iodinated thyroglobulin then participates in a complex

series of reactions to produce the thyroid hormones T3 and T4 (5). These hormones

are stored in the thyroid gland, with production and secretion controlled by the

level of thyroid-stimulating hormone (TSH) in circulation (5). The absorption and

metabolism of iodine is outlined in Figure 2.1.

When the circulating levels of thyroid hormones are adequate, feedback on

the pituitary gland regulates the production of TSH within normal levels. During

periods of mild iodine deficiency, a decrease in circulating levels of T4 will result in

an increase in the production of TSH by the pituitary gland. This leads to an increase

in iodine uptake by the thyroid gland in order to promote T4 production (5). When

deficiency is severe, circulating levels of both T4 and T3 decline resulting in a

prolonged increase in secretion of TSH by the pituitary. If stimulation of the thyroid

becomes chronic, as in the case of continued severe iodine deficiency, the thyroid

can become enlarged and produce a goiter (9). De-iodination of T4 to T3 is an

irreversible step and results in a release of free iodide back into the plasma iodine


~ 15 ~

pool. Here iodide is either taken up by the thyroid gland to produce more thyroid

hormones or it is transported to the kidneys and excreted in the urine (64).

2.3 Iodine intakes and health

2.3.1 Severe consequences of iodine deficiency

In populations where the food supply is not secure or where multiple

nutritional inadequacies are observed, iodine deficicency is often prevalent in its

severest form (8). When iodine intakes are extremely low, severe iodine deficiency

can impact health in a number of different ways. The disorders associated with

severe iodine deficiency are termed “Iodine Deficiency Disorders” (IDD), which can

impact people from all life stages (9). The most severe consequences of iodine

deficiency are endemic goiter and cretinism (9). Severe deficiency during childhood

Figure 2.1: Absorption and metabolism of dietary iodine Adapted from (3-5)


~ 16 ~

and adolescence generally presents as endemic goiter (9, 65). The most severe

consequence of iodine deficiency is endemic cretinism, which can manifest as a

result of severe deficiency during pregnancy. Endemic cretinism usually manifests

as severe and irreversible mental retardation, and a combination of one or more

neurological impairments, including deaf mutism, squint, spastic diplegia, motor

rigidity or dwarfism (66).

Whilst endemic goiter remains the most visible consequence of severe

iodine deficiency, of more concern is the impact of iodine deficiency on the brain,

which can occur even in the absence of goiter (65). As thyroid hormones are

essential in the growth and development of tissues in the CNS, a deficiency in

iodine during critical periods of brain development can lead to neurological

impairment, intellectual disability, retarded growth, delayed onset of puberty and

sexual immaturity (65, 67). The severity of endemic cretinism in the newborn can be

impacted by maternal iodine status, as well as the severity and duration of

postnatal iodine deficiency (68). Whilst thyroid goiter may be reversible with long-

term treatment of iodine (69), endemic cretinism is irreversible (70), and is better

treated by prevention and intervention prior to pregnancy, through iodine

supplementation (71, 72).

A recent systematic review of randomised control trials (RCTs) conducted

by Zhou et. al (2013) highlighted the current lack of quality evidence regarding the

effect of iodine supplementation during pregnancy on growth and cognitive

functions in children (72). In this review, 8 of the 14 included studies were high

quality RCTs, of which only two were conducted in areas of severe iodine

deficiency. In these studies it was demonstrated that iodine supplementation

during pregnancy or the peri-conception period reduced risk of cretinism, however

there were no improvements in childhood intelligence, gross development,

growth, or pregnancy outcomes (72). The remaining 6 studies were conducted in

areas of mild to moderate iodine deficiency, 5 in Europe and 1 in rural Chile (72)

however none of these studies reported childhood development or growth

outcomes as a result of iodine supplementation. Instead these studies focused on

the impact of iodine supplementation on the thyroid function of children and their


~ 17 ~

mothers. The findings of this review were inconsistent, with no obvious dose-

response relationship demonstrated by any of the 6 studies (72). Whilst this review

demonstrates that there is currently a lack of evidence linking iodine

supplementation to improvements in child growth and development in areas of

mild to moderate iodine deficiency, the results from studies in areas of severe

deficiency highlight the importance of monitoring iodine intakes during pregnancy,

in order to prevent the development of cretinism in the newborn (72).

2.3.2 Mild iodine deficiency

In economically developed countries such as Australia and New Zealand

where the food supply is secure and malnutrition is rare, mild iodine deficiency and

long-term exposure to low level mild iodine deficiencyis of greater concern than

the consequences associated with severe deficiency, as this is seldom observed (8,

15). Deficiency observed in these countries is typically associated with a low

concentration of iodine in the soil, which results in a low concentration of iodine in

food (4, 11). A recent systematic review and meta-analysis of nine RCTs and 8

observational studies investigated the impact of iodine supplementation on

maternal and newborn thyroid function, infant neurodevelopment, and cognitive

performance in school-aged children in populations with mild-moderate iodine

deficiency (73). In this review it was concluded that iodine supplementation was

associated with beneficial effects on cognitive function, even in marginally iodine

deficient areas (73). It is important to note that this review only included 2 RCTs

examining the impact of iodine supplementation on cognitive function in school-

aged children from areas of mild to moderate iodine deficiency and that current

research into the effects of mild iodine deficiency during childhood and

adolescence is fairly limited (6, 7, 73-76). Furthermore, the potential impact of long-

term exposure to mild iodine deficiency in school-aged children using prospective

studies has yet to be investigated.

Most of the literature available investigating the consequences of mild

iodine deficiency in school-aged children consists of cross-sectional and

observational studies (62, 73, 77). One cross-sectional study carried out in a


~ 18 ~

representative sample of school-aged children (n=1221, 6-16 years old) in an

economically developed province of southern Spain, found that Intelligence

quotient (IQ) was significantly higher in children with urinary iodine levels above

100 µg/liter (P=0.01) (78). Similar observational studies in areas of mild to moderate

iodine deficiency have also made links between lower iodine intakes and impaired

intellectual function and fine motor skills (79, 80). A study conducted in an iodine

deficient region of Sicily (n=719, 6-12 years old) found that the proportion of

children with learning defects was significantly higher when compared to an iodine

sufficient, goiter free control area in the same region (13.8% vs 3.5%, P<0.0001)(79).

High quality RCTs have attempted to examine the impact of mild iodine

deficiency during childhood (6, 7, 74). One of these studies, a double-blind

intervention trial in 10-12 year old children (n=310) in a mildly iodine deficient

region of rural Albania, found that treatment of iodine deficiency with

supplementation resulted in an improvement in information processing, fine motor

skills and visual problem solving (7). After 24 weeks of supplementation with a single

dose of oral iodised oil (400 mg iodine) it was found that the supplemented group

had significantly improved in 4 out of the 7 cognitive tests, when compared to the

placebo group. Iodine treatment was associated with highly significant

improvements in test scores (P<0.0001) in measures of ability to reason and solve

problems, namely rapid target marking, symbol search, rapid object naming and

Raven’s Coloured Progressive Matrices (mean (95%CI) adjusted treatment effects:

2.8 (1.6, 4.0), 2.8 (1.9, 3.6), 4.5 (2.3, 6.6) & 4.7 (3.8, 5.8), respectively) (7). Although

significant differences in the baseline measurements for the cognitive tests existed

between the two groups (7), with the control group scoring higher in four of the

seven tests of cognitive and motor skills, this study provides strong evidence for

the relationship between iodine intake and cognition.

No RCTs have been conducted investigating the impact of mild iodine

deficiency in Australian children, however one study has been conducted in a

sample of mildly deficient school-aged children from New Zealand (6). This study, a

randomised placebo-controlled double-blind trial, was conducted in 184 10-13

year old children. Cognitive performance was measured using four subtests from


~ 19 ~

the Wechsler Intelligence Scale for Children, at baseline and after a 28 week

treatment period. The treatment consisted of a daily supplement of either 150 μg

iodine (equivalent to the recommended daily intake (RDI) for children aged 9-13 (2))

or a placebo. The study found that children in the supplemented group had

significantly improved scores for two of the four cognitive tests, when compared

to the placebo group. Iodine supplementation was found to significantly improve

perceptual reasoning (P<0.05), and the supplemented group had a higher overall

cognitive score when compared to the placebo group after supplementation

(+0.19SDs, P=0.011) (6). Although the study may have been underpowered due to

difficulties recruiting participants and time restraints relating to the introduction of

mandatory fortification, this study does provide evidence that supplementation

with iodine in areas of mild iodine deficiency has a beneficial effect on cognition in

school-aged children. Furthermore, it highlights the impact which iodine deficiency

may have on the growth and development of school-aged children.

Methodological differences and limitations of these studies make it difficult

to compare the results and make an accurate conclusion regarding the potential

impact of mild iodine deficiency on cognitive development in childhood. Both

studies described above were relatively short in duration, and longer treatment

periods may be necessary in order to adequately examine the impact which mild

iodine deficiency may have on cognition in children.

2.3.3 Excessive iodine intakes

Although iodine is an important nutrient component of multiple processes

and functions in the body, excessive intakes have also been linked with increased

prevalence of goiter (81, 82) and impaired thyroid function (83, 84). Such intakes (>300

µg/day) are generally observed in populations where consumption of iodine-rich

seaweeds is high, such as China (81, 82), and are not usually observed in countries

such as Australia and New Zealand. Recently however, the impact of mandatory

fortification of foods with iodised salt has been linked with an increase in the

prevalence of excessive iodine intakes in America (85), Brazil (86), and Australia (47).

Long-term increased thyroid hormone levels could lead to the development of


~ 20 ~

hyperthyroidism and other autoimmune disorders, which may have a life-long

impact on health and well-being (87).

2.3.4 Recommended Dietary Intakes for iodine: Australia and New

Zealand and determination of individual iodine requirements

The Australian National Health and Medical Research Council (NHMRC)

have provided a set of iodine intake requirements (Table 2.1) (2). These individual

based recommendations vary based on age group and gender, as iodine

requirements differ across the lifespan (2). The iodine Estimated Average

Requirement (EAR) for adults was determined from accumulation and turnover

studies which related urinary iodide to thyroid volume and indicated that iodine

balance is achieved at intakes between 85-100µg/day (63, 88). As such, the EAR was

set at 100µg/day. The RDI was set assuming a CV of 20% for the EAR, and rounded

up to reflect the possible influence of natural goitrogens (2, 89). The Upper Level of

intake for adults was set based on studies which observed extremely low serum

TSH levels (associated with impaired thyroid function) in adults after administration

of supplemental iodine at 1,800 µg/day and 1,700 µg/day (90, 91).

Recommendations for children were based on iodine balance studies for

the age groups 1–3 years, 4–8 years and 14–18 years (92, 93) and by extrapolation

from adults on the basis of metabolic weight [(bodyweight)0.75] for 9–13 year olds

(92, 93). The RDI was set assuming a CV of 20% for the EAR from studies in adults and

rounded (2). Finally, the UL for children and adolescents was determined through

extrapolation of the adult recommendation on a metabolic weight basis (2).

Table 2.1: Daily iodine intake recommendations for children and adolescents - Australia and New Zealand (µg/day) (2)

EAR RDI UL

1-3 y/ols 65 90 200

4-8 y/old 65 90 300

9-13 y/old 75 120 600

14-18 y/old 95 150 900

EAR: estimated average requirement; RDI: recommended dietary intake,

UL: upper level of intake


~ 21 ~

2.4 Assessment of iodine nutrition

Since the 1960s school-aged children, usually defined as children 6-12 years

of age, have been the most commonly used target group for iodine monitoring

surveys as they are readily accessible, a representative sample can be obtained as

most children attend school even in low-income countries, they are particularly

vulnerable to goiter, and goiter is more responsive to treatment in children when

compared to adults (15). Historically, the iodine nutrition of populations was

primarily assessed using assessment of thyroid size to determine goiter rate and

urinary excretion of iodine (as approximately 90% of ingested iodine is excreted in

the urine within 24-48 hours (64, 94, 95)). Whilst urinary iodine concentration

measured in random spot urine samples continues to be the preferred method for

assessing the iodine nutrition of populations, the assessment of thyroid size is more

commonly used at a clinical level to assess the iodine nutrition of individuals, as it

is more reflective of long-term iodine nutrition (months to years)(96, 97). At the

individual level, assessment of serum thyroid hormone levels (TSH and

Thyroglobulin (Tg)) are used to provide an indicator of intermediate iodine

nutrition (weeks to months)(98). The following sections will focus on the methods of

assessing the iodine nutrition of population: measurement of thyroid size and

urinary iodine excretion.

2.4.1 Assessment of Thyroid size

Prior to the 1980s assessment of thyroid size by palpation or ultrasound to

determine goiter rate was the primary method used to assess the iodine nutrition

of populations (15). The use of goiter rate in iodine nutrition monitoring can be

traced back to the early 1500s (99, 100), however it was not until a joint Pan American

Health Organization (PAHO) and World Health Organization (WHO) meeting in

1983 that the first official system of classification of goiter rate by thyroid palpation

was proposed (101). Following this the WHO, along with the United Nations

Children’s Fund (UNICEF) and the International Council for the Control of Iodine

Deficiency Disorders (ICCIDD), published a simplified grading system in 1993, which

formed the basis of the current day goiter classification criteria (102, 103). In addition,


~ 22 ~

it was recommended that the goiter rate within populations also be used to define

iodine deficiency as follows: <5%, iodine sufficiency; 5–19%, mild deficiency; 20–

29%, moderate deficiency; and >30%, severe deficiency (103).

Following recommendations by the WHO in 1993 (102), a number of

countries introduced universal salt iodisation (USI) as a strategy to address the

growing concern of iodine deficiency (8). The implementation of these strategies

lead to the observation that although thyroid size decreased as iodine intakes

increased, it could take months or even years for a thyroid to return to normal after

correction of iodine deficiency (104-106). In addition, there is considerable

intraindividual variation in the ability of the thyroid to adapt to iodine prophylaxis

(107). Furthermore, measurement of thyroid size requires substantial training and

differences in technique can result in large intraobserver errors (108). As such, the

use of goiter rate to assess the iodine nutrition of populations where salt iodisation

strategies have been introduced could result in an overestimation of the

prevalence of iodine deficiency (104-106).

2.4.2 Urinary iodine

It is estimated that approximately 90% of ingested iodine is excreted in the

urine within 24-48 hours, with only minimal losses in sweat and faeces (64, 95, 109). As

such, 24-hour urine samples are able to provide a good estimate of recent iodine

intake. This method allows for the determination of 24-hour urinary iodine

concentration (24-hour UIC, µg/L) and 24-hour urinary iodine excretion (24-hour

UIE), expressed as µg/24-hour. The determination of 24-hour UIE is widely

regarded as the most appropriate method for measuring the daily iodine intake of

a population (23, 25, 26, 110, 111).

Although it is recognised that to measure the iodine intake of an individual

a minimum of 10 days of 24-hour urine collections are required (23), for the

assessment of mean intake within a population a single 24-hour urine can be used

for group level assessment (28). Early recommendations by the WHO suggested the

use of 40-50 subjects to determine the mean concentration of iodine in a given


~ 23 ~

region (112), however more recent recommendations by the WHO do not specify

the recommended number of participants for use in population studies (37, 38).

Due to the practical difficulties associated with collecting 24-hour urine

samples, such as high participant burden and financial costs, the WHO recommend

the use of casual, spot urine samples as an appropriate indicator of iodine status in

populations (37). Iodine excretion from spot urine samples is most often expressed

as a concentration (spot UIC, µg/L) (37), or as a ratio to creatinine excretion (I/Cr, µg

iodine/ g creatinine) (113-115). Estimation of daily iodine intake from spot urine

samples through extrapolation based on daily creatinine excretion was a commonly

used method for determining the iodine status of populations prior to the 1990s

(101, 116-118). This method involves the calculation of estimated daily UIE from spot

urine samples on an individual basis, based on standard creatinine excretion values

(119, 120) using the following formula (111):

The use of I/Cr to estimate daily iodine intake was believed to provide an

accurate estimate of daily iodine excretion from spot urine samples as creatinine,

an endogenous indicator of lean body mass, is relatively constant from day to day

in healthy adult populations (121, 122). Whilst this is true in adults, estimating

expected creatinine excretion values for children can be difficult as creatinine

excretion can be affected by muscle mass, age, gender, ethnicity and onset of

puberty (123). Some equations for estimating daily creatinine excretion are able to

account for these factors, whilst others provide a more crude estimate of daily

creatinine excretion (111).

Whilst some studies have determined that 24-hour urine samples are more

likely to capture the full range of iodine excretion throughout the day when

compared to UIE estimated from spot urine samples (18, 23, 113), there are

inconsistencies in the methods used between studies. Table 2.2. provides a

comprehensive summary of studies which have compared UIE estimated from spot

Estimated 24-hour urinary iodine excretion (estUIE)

measured in spot urine samples

Creatinine (g/L)

Iodine (µg/L) X

Predicted 24-hour Cr excretion (g/day)

from established reference values

(119 ,120)

=


~ 24 ~

urine samples to daily iodine intake measured in 24-hour urine samples (18, 23, 111,

113-115, 124-127). These studies have estimated daily iodine intake from spot urine

samples (estUIE) through extrapolation based on creatinine excretion. Whilst most

studies utilised spot and 24-hour urine samples collected on the same day (18, 23, 111,

113, 115, 124, 125), some studies collected the spot and 24-hour samples on different

days (114, 126-128). Furthermore, there are discrepancies in the use of fasting and non-

fasting spot urine samples between studies. Finally, these studies were also limited

to relatively small sample sizes and the inclusion of wide age ranges. Aside from

these limitations, the studies described in Table 2.2. demonstrate that when daily

iodine intake is estimated from spot urine samples, through extrapolation based

on creatinine excretion, there is considerably more within person variation than

iodine intake measured in 24-hour urine samples (18, 23, 111, 113-115, 124-126). Studies

utilising both morning fasting (18, 23) and random non-fasting (113) spot urine samples

have found that, on average, iodine intake determined from spot urine samples is

significantly lower than iodine intake determined from 24-hour urine samples by

up to 20% (range 10-20%, P<0.001), and is largely dependant on the time of day

when the urine sample is collected(18, 23, 113).

A 2014 study by Perrine et.al. demonstrated that the I/Cr should not be

used interchangeably with observed UIE when estimating the iodine intakes of a

population (111). In this study of 18-39 year olds (n=400), UIE and UIC were both

determined from 24-hour urine samples and compared to daily iodine intake

estimated from morning spot urine samples and spot UIC. It was determined that

the the estimated daily iodine intake was consistently and substantially lower than

daily iodine intake determined in the 24-hour urine samples and varied significantly

by age, sex, ethnicity and anthropometric measures (111). Furthermore it has been

demonstrated in children that as age increases, so too does the I/Cr as a result of

the decrease in creatinine excretion associated with an age related decline in lean

body mass(114). As this increase is related more to a change in creatinine excretion

and less to an increase in iodine excretion, the use of the I/Cr could result in the

misclassification of the iodine intakes of populations of children (129).


~ 25 ~

Whilst the variation in individual iodine excretion between days is largely

dependant on the iodine content of the diet, iodine excretion has also been found

to vary over the course of the day in individuals (18, 23, 111, 113-115, 124-126). One study

conducted in 42 adults and children (aged 4-60 years) found that urinary iodine

excretion varied significantly by timing of collection (P<0.001), with lowest levels

occurring in the morning and peaks observed following meals (16). Whilst both inter

and intra individual variations in iodine excretion have been examined in adult

populations (18, 23, 111, 113-115, 124-126), to date there have been no studies examining

the inter and intra individual variation in iodine excretion in children.

A recent systematic review of studies comparing spot and 24-hour urine

samples for estimating the iodine intakes of a population concluded that there is

currently not enough evidence to determine whether iodine intake determined

from spot urine samples provides an accurate reflection of daily iodine intake, as

measured using 24-hour urine samples (27). As such, the measurement of observed

24-hour urinary iodine excretion (24-hour UIE) is currently the most appropriate

method for determining the iodine intakes of children (15). Spot urines continue to

be the most widely used method of measuring the iodine status of school-aged

children in Australia (6, 29, 32, 34, 35, 42, 130-132) and New Zealand (35, 133-136), due to their

low cost and minimal respondent burden. To date there have been no studies

utilising 24-hour urine samples to measure the iodine intakes of school-aged

children in Australia. Such studies will help identify populations at risk of iodine

deficiency and/or excess and allow for the implementation of appropriate

strategies. In addition such studies can help evaluate the impact of various public

health strategies, such as iodine fortification or the application of sodium reduction

targets to major food sources of iodine, on the iodine intakes of the population.

Stu

dy

Co

un

try

Sam

ple

M

eth

od

olo

gy

Mai

n f

ind

ing

Vo

ugh

t et

. al.

(19

63)

(124

) U

.S.

n=6

(4

mal

e, 2

fem

ale)

yo

un

g ad

ult

s

(age

no

t d

escr

ibed

)

Twen

ty-f

ou

r h

ou

r u

rin

es

colle

cted

dai

ly f

or

24

day

s.

Co

ncu

rren

t d

aily

sp

ot

uri

ne

sam

ple

s id

enti

fie

d a

s fa

stin

g,

mid

day

or

nig

ht

ob

tain

ed

.

estU

IE f

rom

all

spo

t u

rin

e sa

mp

les

no

t si

gnif

ican

tly

dif

fere

nt

fro

m o

bse

rved

24

-ho

ur

UIE

.

Frey

et.

al.

(19

73)

(125

) N

orw

ay

n=6

2

(33

mal

e, 2

9 f

emal

e)

20

-64

y/o

ld

Sin

gle

24

-ho

ur

uri

ne

spec

imen

co

llect

ed.

5m

L o

f u

rin

e fr

om

a s

ingl

e vo

id b

etw

een

no

on

an

d 6

p. m

. o

n t

he

sam

e d

ay t

ake

n a

s n

on

-fas

tin

g af

tern

oo

n s

po

t u

rin

e sa

mp

le

Stro

ng,

sig

nif

ican

t co

rrel

atio

n b

etw

een

ob

serv

ed 2

4-h

ou

r U

IE e

stU

IE f

rom

no

n-f

asti

ng

afte

rno

on

sp

ot

sam

ple

(m

ales

: r=0

.69

, P=0

.00

1; f

emal

es: r

=0.6

8, P

=0.0

05

).

Kon

no

et.

al.

(19

93)

(114

) Ja

pan

n=2

2

(9 m

ale,

13

fem

ale)

m

ean

(SD

) ag

e 5

2 (

19

) ye

ars

Mo

rnin

g fa

stin

g sp

ot

uri

ne

colle

cted

pri

or

to 2

4-h

ou

r u

rin

e co

llect

ion

. All

uri

ne

void

ed o

ver

follo

win

g 2

4-h

ou

rs

colle

cted

Stro

ng,

sig

nif

ican

t co

rrel

atio

n b

etw

een

ob

serv

ed 2

4-h

ou

r U

IE a

nd

est

UIE

fro

m m

orn

ing

fast

ing

spo

t sa

mp

le (

r=0

.83

, P

<0.0

01

). N

o s

ign

ific

ant

corr

elat

ion

bet

wee

n e

stU

IE f

rom

n

on

-fas

tin

g sp

ot

sam

ple

s an

d o

bse

rved

24

-ho

ur

UIE

Tho

mso

n e

t.

al. (

1996

) (1

23)

New

Ze

alan

d

n=6

1

(30

mal

e, 3

1 f

emal

e)

18

-58

y/o

ld

Mo

rnin

g fa

stin

g sp

ot

uri

ne

colle

cted

pri

or

to 2

4-h

ou

r u

rin

e co

llect

ion

. All

uri

ne

void

ed o

ver

follo

win

g 2

4-h

ou

rs

colle

cted

into

sep

arat

e co

nta

iner

s. T

he

last

of

the

se

sam

ple

s w

as a

mo

rnin

g fa

stin

g u

rin

e. E

ach

vo

id

con

sid

ere

d a

sin

gle

spo

t u

rin

e sa

mp

le. O

ne

-fif

th o

f ea

ch

void

was

po

ole

d t

o g

ive

a re

pre

sen

tati

ve 2

4-h

ou

r sa

mp

le.

Stro

ng,

sig

nif

ican

t co

rrel

atio

n b

etw

een

est

UIE

fro

m

mo

rnin

g fa

stin

g sp

ot

sam

ple

an

d o

bse

rved

24

-ho

ur

UIE

(r

=0.7

4, P

<0

.00

1).

N

o s

ign

ific

ant

corr

elat

ion

be

twee

n o

bse

rved

24

-ho

ur

UIE

an

d e

stU

IE f

rom

no

n-f

asti

ng

spo

t sa

mp

les.

Ras

mu

ssen

et.

al

. (19

99)

(18)

Den

mar

k n

=22

(9

mal

e, 1

3 f

emal

e)

30

-55

y/o

ld

All

uri

ne

void

ed

ove

r 2

4 h

ou

rs c

olle

cted

into

sep

arat

e co

nta

iner

s. E

ach

vo

id c

on

sid

ered

a s

ingl

e sp

ot

sam

ple

. Io

din

e ex

cre

tio

n in

eac

h v

oid

fro

m e

ach

su

bje

ct

sum

mar

ised

to

de

term

ine

ob

serv

ed 2

4-h

ou

r U

IE.

Ob

serv

ed 2

4-h

ou

r U

IE c

om

par

ed w

ith

est

UIE

det

erm

ined

fr

om

sp

ot

uri

ne

sam

ple

s at

dif

fere

nt

tim

es

of

the

day

.

estU

IE f

rom

mo

rnin

g fa

stin

g sp

ot

uri

ne

sam

ple

si

gnif

ican

tly

low

er t

han

ob

serv

ed 2

4-h

ou

r U

IE (

mea

n±S

D

11

1±5

8 v

s. 1

39

±59

µg/

day

, P=

0.0

06

).

No

sig

nif

ican

t d

iffe

ren

ce b

etw

een

ob

serv

ed 2

4-h

ou

r U

IE

and

est

UIE

fro

m n

on

-fas

tin

g u

rin

e sa

mp

les.

St

ron

g, s

ign

ific

ant

corr

elat

ion

bet

wee

n e

stU

IE f

rom

m

orn

ing

fast

ing

sam

ple

, fir

st s

amp

le a

fter

mo

rnin

g an

d

even

ing

sam

ple

s an

d o

bse

rve

d 2

4-h

ou

r U

IE. (

r=0

.70

, P

<0.0

01

, r=0

.74

, P<0

.00

1, r

=0.6

7, P

=0.0

01

, res

pec

tive

ly).

~ 26 ~


Tabl

e 2.

2: S

tud

ies

com

pari

ng

spo

t vs

. 24

-ho

ur

uri

nes

as

a m

easu

re o

f u

rina

ry io

din

e ex

cret

ion

(U

IE)

Stu

dy

Co

un

try

Sam

ple

M

eth

od

olo

gy

Mai

n f

ind

ing

Knu

dse

n e

t. a

l. (2

000

) (1

13)

Den

mar

k

n=3

1

(24

mal

e, 7

fem

ale)

2

7-7

1 y

/old

All

uri

ne

void

ed

ove

r 2

4-h

ou

rs c

olle

cted

be

twee

n 2

-6

tim

es p

er p

arti

cip

ant.

Si

ngl

e co

ncu

rren

t ra

nd

om

no

n-f

asti

ng

spo

t u

rin

e sa

mp

le

colle

cted

on

th

e sa

me

day

as

each

24

-ho

ur

uri

ne

sam

ple

.

est

UIE

fro

m s

po

t sa

mp

les

sign

ific

antl

y lo

wer

th

an

ob

serv

ed 2

4-h

ou

r U

IE (

med

ian

(P

5 –

P9

5)

12

6 (

58

-31

9)

vs.

14

3 (

75

-29

7)

µg/

day

, P=0

.03

) St

ron

g si

gnif

ican

t co

rrel

atio

n b

etw

een

est

UIE

fro

m n

on

-fa

stin

g sp

ot

uri

ne

sam

ple

s an

d o

bse

rved

24

-ho

ur

UIE

(r

=0.6

2, P

<0

.05

).

Kön

ig e

t. a

l. (2

011

)(23)

Swit

zerl

and

n

=22

fem

ales

5

2-7

7 y

/old

All

uri

ne

void

ed

ove

r 2

4-h

ou

rs c

olle

cted

bi-

we

ekly

fo

r 1

5

mo

nth

s

Smal

l am

ou

nt

of

corr

esp

on

din

g fa

stin

g, s

eco

nd

-vo

id,

mo

rnin

g sp

ot

uri

ne

sam

ple

s co

llect

ed

on

th

e sa

me

day

as

the

24

-ho

ur

uri

ne

sam

ple

use

d a

s th

e fa

stin

g sp

ot

sam

ple

fo

r es

tUIE

de

term

inat

ion

.

estU

IE f

rom

fas

tin

g sp

ot

sam

ple

s w

as 1

6%

low

er t

han

th

e o

bse

rved

24

-ho

ur

UIE

(m

ean

±SD

86

±33

vs.

10

3±2

8

µg/

day

, P<0

.00

1).

Bla

nd

-Alt

man

an

alys

is r

evea

led

th

at

estU

IE f

rom

fas

tin

g sp

ot

sam

ple

was

16

mg/

24

-ho

ur

low

er

than

ob

serv

ed 2

4-h

ou

r U

IE. T

he

95

% li

mit

s o

f ag

reem

ent

(±1

.96

SD

) w

ere

-57

an

d 2

4 µ

g/2

4-h

ou

r o

f th

e in

div

idu

al

mea

ns

and

-1

25

an

d 9

3 µ

g/2

4 h

of

the

ove

rall

mea

ns.

Per

rin

e et

. al.

(20

14)(1

11)

U.S

.

n=3

97

(1

85

mal

e, 2

12

fe

mal

e)

18

-39

y/o

ld

All

uri

ne

void

ed

ove

r 2

4-h

ou

rs c

olle

cted

into

sep

arat

e co

nta

iner

s. A

pro

po

rtio

nal

aliq

uo

t fr

om

eac

h v

oid

was

ta

ken

to

cre

ate

a co

mp

osi

te 2

4-h

ou

r u

rin

e sa

mp

le.

Fou

r n

on

-fas

tin

g ti

me

d s

po

t u

rin

e sa

mp

les

wer

e al

iqu

ote

d f

or

anal

ysis

.

estU

IE f

rom

sp

ot

sam

ple

s n

ot

sign

ific

antl

y d

iffe

ren

t fr

om

ob

serv

ed 2

4-h

ou

r U

IE a

t an

y o

f th

e ti

me

po

ints

(m

ean

(95

% C

I) c

ross

ed 0

fo

r al

l tim

e p

oin

ts).

Mo

nte

neg

ro-

Bet

han

cou

rt

et. a

l. (2

015

) (1

15)

Ger

man

y n

=18

0

(90

mal

e, 9

0 f

emal

e)

6-1

8 y

/old

All

uri

ne

void

ed

ove

r 2

4-h

ou

rs c

olle

cted

. C

on

curr

ent,

ran

do

m n

on

-fas

tin

g sp

ot

uri

ne

sam

ple

s w

ere

colle

cted

on

th

e sa

me

day

as

the

24

-ho

ur

colle

ctio

n.

Mo

der

ate,

sig

nif

ican

t co

rrel

atio

n b

etw

een

ob

serv

ed 2

4-

ho

ur

UIE

an

d e

stU

IE f

rom

no

n-f

asti

ng

spo

t u

rin

e sa

mp

le

(6-1

2 y

/old

s: r

=0

.41

, P<

0.0

01

; 1

3-1

8 y

/old

s: r

=0.4

7,

P<0

.00

1).

~ 27 ~


Tabl

e 2.

2: S

tud

ies

com

pari

ng

spo

t vs

. 24

-ho

ur

uri

nes

as

a m

easu

re o

f u

rina

ry io

din

e ex

cret

ion

(U

IE)


~ 28 ~

2.4.3 WHO recommendations for assessing the iodine status of

populations

The development of the current recommendations for assessing the iodine

nutrition of populations using spot urine samples evolved over a number of years,

however the use of UI as an indicator of the iodine status of populations was first

introduced in 1983 by the PAHO and WHO working group on iodine deficiency (101).

Recognising the issues associated with focusing on goiter assessment alone, these

recommendations proposed that iodine nutrition be described by a combination

of goiter rate and measurement of UI in spot urine samples. As spot samples are

unable to provide an estimate of total iodine intake, initial recommendations were

based on excretion of iodine per gram of creatinine, as this was believed to provide

a proxy estimate of daily iodine excretion and thus intake (101).

A key study informing the recommendations issued by the PAHO/WHO

working group in 1983 was conducted in 1970 in a study that included both children

and adults (n=21,000) from 186 regions of Central America (117). This study

examined the relationship between estUIE from spot urine samples, and goiter rate

estimated by thyroid palpation. The study found that goiter rate was >10% in i) all

areas in which the mean estUIE was <25 µg/day; ii) most areas in which the mean

estUIE was 25-49 µg/day; iii) approximately a third of areas where the mean estUIE

was 50-99 µg/day and; iv) virtually none of the areas in which the mean estUIE was

>100 µg/day (117). It is important to note, however that the exact creatinine

excretion values used to determine the estUIE in this study are not clear, nor

whether intakes determined were adjusted for the age of the participants.

Furthermore, the intakes of 25, 50, and 100 µg/day associated with varying levels

of goiter rate in the population were determined for the population as a whole,

with the results for both children and adults pooled together in one overall

estimate. Whilst the study did report a negative correlation between age and goiter

rate, the relationship between estUIE and goiter rate was not broken down by age

(117). Therefore, as iodine requirements vary with age (2), the mean estUIE of 100

µg/day associated with reduced goiter prevalence in this population of children and


~ 29 ~

adults may not be reflective of the true estUIE associated with reduced goiter

prevalence in either children or adults independently.

Regardless of these potential inaccuracies, the PAHO/WHO recommended

in 1986 that the degree of iodine deficiency in a population be defined using spot

urine samples collected from school-aged children, and were based on the cutoffs

of 25, 50, and 100µg/day determined by the study in Central America (101). The

recommendations were as follows: “Grade 1: goiter endemias with an estimated

mean UIE of greater than 50 mg/g creatinine - at this level of iodine intake, no

thyroidal or developmental abnormalities are anticipated; Grade 2: goiter

endemias with an estimated mean UIE in the range of 25–50 mg/g creatinine - this

group is at risk for hypothyroidism but not for overt cretinism; and grade 3: goiter

endemias with an estimated mean UIE of <25 mg/g creatinine - there is a high risk

for endemic cretinism in such a population.”(101)

Following the publication of these recommendations in 1986, a study by

Pierre Bourdoux in 1988, demonstrated the inaccuracies associated with the use of

estUIE from spot urine samples for classifying daily iodine intakes of populations

(118). Furthermore, Bourdoux demonstrated that UIC did not follow a normal

distribution in populations, and that the use of mean UIC to describe the severity

of iodine deficiency in a population was inappropriate. As such, Bourdoux

suggested that UI determined from casual urine samples in children be expressed

solely as the median concentration, in µg iodine/L urine. In addition, he

recommended that the severity of iodine deficiency be described based on the

proportion of individuals, either children or adults, within a population with a UIC

below 20, 50 and 100 µg/L, as this would provide researchers with a more complete

picture of the true extent of iodine deficiency within the population (118). Based

primarily on these two studies and the finding that goiter rate was <10% in

populations where mean estimated UIE was >100 µg/day, in 1993 the WHO

endorsed the use of a median UIC of 100 µg/L as an indicator of iodine sufficiency

within a population (102). Furthermore, it was recommended that the prevalence of

median UIC <50 µg/L (based on a minimum daily iodine requirement of 50 µg (112,

137)) should not exceed 20% within a population (102).


~ 30 ~

One of the key studies which supported the new WHO cut-offs as being

appropriate was a multi-center study conducted in 5,709 schoolchildren (7-15

years) from The Netherlands, Belgium, Luxembourg, France, Germany, Austria,

Italy, Poland, the Czech and Slovak Republics (1997) (107). This study collected spot

urine samples and measured thyroid volume using thyroid ultrasound to determine

iodine status and assess the relationship between UIC and goiter rate. Iodine

deficiency was defined on the basis of the 1993 WHO recommendations as i)

prevalence of >5% of clinically detectable goiter or of thyroid volume determined

by ultrasonography above the 97th percentile for a population of children with

normal iodine intake and; ii) a median UIC below 100 µg/L. Median UIC ranged from

20µg/L in Poland to 160µg/L in the Netherlands, however goiter rate was above 5%

in all sites, including iodine sufficient areas (as determined using UIC). In addition,

goiter rate was systematically below 5% in areas where the median UIC was above

100 µg/L, indicating an association between UIC and thyroid size and supporting

the recommendations proposed by the WHO (107).

Whilst this study provided strong evidence in support of the WHO

recommendations and formed the basis of thyroid volume recommendations

published by the WHO in 1997 (138), subsequent reports suggested that the study

had overestimated the goiter rate (139-141). Studies in American (141), Swiss (140) and

Malaysian (139) school-aged children observed significantly lower goiter rates, less

than 1%, when compared to the European study where a goiter prevalence rate of

>5% was observed in all sites, even in areas with normal iodine intakes (107). It was

first suggested that this may have been a result of the long standing history of

iodine deficiency in Europe, resulting in a slower response by the thyroid gland to

iodine prophylaxis as a result of long-term exposure to iodine deficiency (141).

However in 2000 a WHO/ICCIDD workshop on thyroid ultrasound uncovered

several sources of inter-observer and inter-equipment error, even in experienced

examiners (108). It was suggested that this systematic error in the thyroid ultrasound

methodology used in the 1997 study was the reason for the observed discrepancies

in goiter prevalence estimates (108). Whilst this led to the re-development of


~ 31 ~

recommended reference values for thyroid volume by ultrasound (97), the UIC cut-

off of 100 µg/L remained unchanged (37, 38, 142).

Whilst the 1993 recommendations supported the use of median UIC from

spot urine samples, it was still recommended for use in combination with goiter

rate (102). As previously discussed, however, goiter can take months, if not years to

respond to iodine prophylaxis, and studies conducted in school-aged children in

the late 1990s observed that spot UIC responded to iodine supplementation much

faster than thyroid size (69, 143). One such study conducted in 671 6, 12, and 15 year

old children in South Africa 1 year after the introduction of USI, observed that

goiter rate ranged from 5%-29%, even though median UIC from spot urine samples

was >100 µg/L (143). Similarly, a 5 year prospective study in 5-14 year old children

from West Africa, measured median UIC in spot urine samples alongside goiter

prevalence determined using thyroid palpation 6 months before the introduction

of USI and annually for 4 years after (7). In this study it was observed that although

median UIC had increased to within normal levels, goiter rate remained elevated

four years after the introduction of USI. As such, it was recognised that the use of

goiter rate could result in the misclassification of iodine status, and population

monitoring surveys began to focus more on using median UIC, estimated from spot

urine samples in school-aged children, with a subsequent shift away from the use

of goiter rate (144). This led to the WHO endorsing the use of median UIC determined

from spot urine samples in school-aged children as the primary indicator for

assessing the iodine status of populations in 2001 (142). Since the publication of

these recommendations in 2001, they have remained largely unchanged (Table 2.3)

(38).


~ 32 ~

Whilst the use of UIC from spot urine samples has been valiadated for use

as a tool for identifying iodine deficiency, its use as a measure of intake is based on

a number of assumptions (15). Specifically, only in populations where daily urine

volume is equal to 1L can spot UIC be extrapolated to equal daily iodine intake (15).

For example, in a population of adolescents with a daily urine volume of 1L, a UIC

of 100 µg/L would be equivalent to a daily iodine intake of 100 µg/24-hours.

However in a population of children with a daily urine volume closer to 0.5L, the

same UIC would be indicative of a daily intake of approximately 50 µg/24-hours. As

such, this population would be classified as being iodine sufficient, based on a UIC

of <100 µg/L, when their daily intake may in fact be lower than the 100 µg/day

originally associated with reduced goiter prevalence (117). Such misclassification

might prevent the implementation of appropriate strategies to improve iodine

intakes in this population and result in the subsequent development of iodine

deficiency disorders.

The assumption of a daily urinary output of 1L in children was first identified

by a 1995 study in 206 children aged 7 to 15 years. In this study the median 24-

hour urine volume for aged 7 -15 years was estimated to be approximately 0.9

mL/hr/kg (145). However daily urinary excretion varied between 0.3-3.2 mL/kg/hour,

indicating a wide range. Based on the findings of this study, however, a number of

studies have assumed a daily urine output of 1L amongst children and have utilised

Table 2.3: World Health Organization epidemiological criteria for assessing iodine nutrition based on median urinary iodine concentrations from spot urines in different target groups (38) Median Urinary Iodine Concentration (µg/L)

Iodine intake Iodine status

School-age children (6 years or older) a

<20 Insufficient Severe iodine deficiency

20-49 Insufficient Moderate iodine deficiency

50-99 Insufficient Mild iodine deficiency

100-199 Adequate Adequate iodine nutrition

200-299 Above requirements May pose a slight risk of more than adequate iodine intake in these populations

≥300 Excessive b

Risk of adverse health consequences (Iodine induced hyperthyroidism, autoimmune thyroid disease)

a Applies to adults but not to pregnant and lactating women b The term “excessive” means in excess of the amount required to prevent and control iodine deficiency


~ 33 ~

UIC as a surrogate index of daily iodine intakes amongst populations of

schoolchildren (8, 144, 146-149) . To date, however, there has been no systematic

evaluation of the average 24-hour urine volumes of children and adolescents from

around the world. This information would assist in determining the

appropriateness of the assumption that UIC can be used as a surrogate marker of

daily iodine intake in children and adolescents, and whether such assumptions

might result in the misclassification of populations with inadequate iodine intakes

as iodine sufficient.

2.5 Iodine Nutrition in Australia

2.5.1 History of Iodine deficiency in Australia

Monitoring of iodine nutrition in Australia prior to the 21st century was

scarce, and was limited to very small sample sizes (13, 39, 40, 53, 130). Prior to the 1990s

Australia was believed to be iodine-sufficient, with the exception of the Australian

Capital Territory (ACT) and the island of Tasmania where endemic goiter was

prevalent (12, 13). In order to address this, public health experts in Australia

recommended the substitution of potassium bromate for potassium iodate as the

‘improver’ added to flour before bread was baked, following the successful

implementation of a similar program in the Netherlands (13). This fortification

program was subsequently introduced in the ACT in 1963 and Tasmania in 1966

and was estimated to result in an average intake of approximately 100 µg of iodine

per day in school-aged children. This, in conjunction with the serendipitous

contamination of milk by newly-introduced iodine containing sanitisers in the dairy

industry, saw a dramatic increase in iodine intakes (12, 13, 40, 53). The detection of mild

thyrotoxicosis in a sample of Tasmanian adults in the 1970s (40, 53) led to a reduction

in the use of these sanitisers by the dairy industry over the next two decades (13, 52,

150). This was believed to be the reason for the re-emergence of iodine deficiency

in the Australian population, first observed in a small sample of pregnant women

from Sydney in the late 1990s (33). Following this, a number of studies documented

iodine deficiency in Australian children (14, 34, 42, 43, 130) (Table 2.4) .

Tabl

e 2.

4: S

tud

ies

do

cum

enti

ng

iod

ine

defi

cien

cy in

th

e A

ust

ralia

n p

op

ulat

ion

pre

-fo

rtif

icat

ion

of

bre

ad w

ith

iod

ised

sal

t

Au

tho

r St

udy

Yea

r Lo

cati

on

Part

icip

ants

M

eth

ods

Res

ult

s

Gu

ttik

ond

a (1

4)

200

0

Tasm

ania

n

=2

25

4

-17

y/o

ld

99

gir

ls, 1

26

bo

ys

spo

t U

IC d

eter

min

ed

fro

m m

orn

ing

no

n-

fast

ing

spo

t sa

mp

les

med

ian

(IQ

R)

UIC

= 8

4 (

57

-11

0)

µg/

L

[gir

ls: 8

1 (

54

-11

1)

µg/

L;

bo

ys: 8

7 (

59

-11

1)

µg/

L (P

>0.0

5)]

Gu

ttik

ond

a (4

2)

200

0

Cen

tral

Co

ast,

N

ew S

ou

th W

ales

n=

30

1

5-1

3 y

/old

1

38

gir

ls, 1

60

bo

ys

spo

t U

IC d

eter

min

ed

fro

m m

orn

ing

no

n-

fast

ing

spo

t sa

mp

les

m

edia

n (

IQR

) U

IC =

82

(6

1-1

09

) µ

g/L

McD

onn

ell (4

3)

200

1

Mel

bo

urn

e, V

icto

ria

n=

57

7

11

-18

y/o

ld

41

0 g

irls

, 16

7 b

oys

spo

t U

IC d

eter

min

ed

fro

m m

orn

ing

no

n-

fast

ing

spo

t sa

mp

les

med

ian

UIC

= 7

0 µ

g/L

[g

irls

: 64

µg/

L; b

oys

: 82

µg/

L (P

<0.0

02

)]

Hyn

es (1

30)

bas

elin

e:

199

8/1

999

Ta

sman

ia

n=

24

1 4

-12

y/o

ld

13

3 g

irls

, 12

8 b

oys

sp

ot

UIC

det

erm

ined

fr

om

fas

tin

g, f

irst

vo

id

spo

t sa

mp

les

med

ian

(ra

nge

) U

IC =

75

(1

5-2

40

) µ

g/L

follo

w-u

p:

200

0-2

001

n

=17

0 5

-14

y/o

ld

75

gir

ls, 9

5 b

oys

m

edia

n (

ran

ge)

UIC

= 7

6 (

18

-48

0)

µg/

L

Li (3

4)

200

3-2

004

New

So

uth

Wal

es,

Vic

tori

a,

Sou

th A

ust

ralia

, Wes

tern

A

ust

ralia

an

d Q

uee

nsl

and

n=

17

09

8

-10

y/o

ld

82

8 g

irls

, 88

1 b

oys

spo

t U

IC d

eter

min

ed

fro

m n

on

-fas

tin

g ra

nd

om

sp

ot

uri

ne

sam

ple

s

ove

rall

med

ian

(IQ

R)

UIC

= 1

04

(7

1-1

47

) µ

g/L

NSW

: 89

(6

5-1

24

) µ

g/L

VIC

: 74

(5

3-1

04

) µ

g/L

SA: 1

01

(7

4-1

30

) µ

g/L

WA

: 14

3 (

10

4-2

14

) µ

g/L

QLD

: 13

7 (

10

4-1

84

) µ

g/L

UIC

–U

rin

ary

Iod

ine

Co

nce

ntr

atio

n

~ 34 ~



~ 35 ~

2.5.2 Mandatory fortification of bread with iodised salt

In response to the increased concern over the re-emergence of iodine

deficiency in Australia and New Zealand (33, 35, 41, 150, 151), Food Standards Australia

and New Zealand (FSANZ) introduced mandatory fortification of bread with iodised

salt in October 2009 (44). The primary strategy for preventing and controlling iodine

deficiency disorders promoted by the WHO since 1993 is the implementation of

USI (152). This involves the “fortification of all food-grade salt, used in household and

food processing” with iodine at a level of 15-40 ppm iodine (153). FSANZ chose not

to implement the USI policy recommended by the WHO (152) due to a concern over

the risks associated with a rapid increase in iodine consumption (154). Additionally,

FSANZ was concerned that the implementation of a USI policy may restrict the

ability of Australia to export and trade with countries where a USI policy had not

been established (154). As a result, FSANZ chose to limit salt iodisation to salt used

in bread manufacture, and since October 2009 all bread products, excluding

organic varieties and bread mixes for making bread at home (44), should have

iodised salt added where non-iodised salt would otherwise be used (155). It is also

recommended that iodised salt be added to other foods, however this is at the

manufacturers discretion (44). There is no recommended amount of salt which

should be added however salt the current level of iodine in salt added to bread in

Australia varies more than two fold: between 25-65 mg iodine per kilogram of salt

(56). Furthermore the sodium content of bread ranges between 200-800 mg/100g

(55), resulting in substantial variations in the iodine content of bread products both

within and between varieties (56). Bread was specifically chosen it was believed to

be a commonly consumed food product amongst children and pregnant women,

the two main target groups for iodine intervention (132). Studies conducted in

school-aged children since the introduction of mandatory fortification of bread in

2009 (44) have reported an improvement in iodine status, measured using spot

urine samples (29, 45-47).


~ 36 ~

2.5.3 Population Iodine nutrition in Australia post-fortification of

bread with iodised salt

The recent Australian Health Survey (AHS) reported that the Australian

population was iodine replete, based on a population median spot UIC of 124 µg/L

(45). Median UIC determined from ‘random’ spot samples in the AHS was found to

be significantly higher than UIC estimates from the 2003-2004 Australian National

Iodine Nutrition Study (NINS), conducted prior to the introduction of mandatory

fortification (34) and was found to vary by state (Table 2.5). Specifically, the median

population UIC of 2-18 year olds was 16% higher than the population median UIC

determined as part of the NINS in 2003/4 (124 µg/L vs. 104 µg/L) (34, 48). Median

UIC was highest in Western Australia and lowest in South Australia and Victoria (156),

indicating possible regional differences in iodine intakes (34). Of increasing concern

was the finding that the median spot UIC observed in Western Australia, was

nearing the WHO’s upper level recommendation of 300 µg/L. In another study in a

sample of Queensland preschoolers (n=51, 2-4 years old) (47), 18% of the population

had a spot UIC exceeding 300 µg/L, which puts children at risk of developing iodine-

induced hyperthyroidism and other autoimmune disorders(87).

The findings from the AHS are supported by a study in Tasmanian

schoolchildren 8-13 years of age (n=320) which observed that median spot UIC was

significantly higher in 2011 when compared to the period prior to fortification (129

µg/L vs. 108 µg/L, P<0.001)(29). Whilst this study compared data from different

cohorts, it does provide evidence that population iodine nutrition has improved

Table. 2.5: Comparison of median UIC from spot urine samples of schoolchildren in 2003-2004 and 2011-2013 (34)

State Median UIC (µg/L)

2003-2004 2011-2012

New South Wales 89 177

Victoria 74 163

Queensland 137 166

South Australia 101 150

Western Australia 143 261

UIC – urinary iodine concentration


~ 37 ~

following the introduction of mandatory fortification. It is important to note that

both the AHS and the studies in Tasmania and Queensland utilised spot urine

samples to assess iodine status as opposed to iodine intake. To date there have

been no studies examining the iodine intakes of Australian schoolchildren utilising

24-hour urine samples, post-fortification of bread with iodised salt.

2.5.4 Dietary assessment

Whilst dietary assessment methodologies may be more cost-effective than

24-hour urines, enabling their use in large population based studies, they are not

considered as accurate as urine samples for estimating iodine intakes (15). In

addition to the recall and subject biases associated with dietary assessment

methodologies (157) food composition databases often lack specificity regarding the

use of iodised salt during food production (49, 50). For example in Australia the

iodised salt added to bread during production contributes a significant proportion

of total iodine intake, as bread is one of the main sources of iodine (44, 54). The

concentration of iodine bound to the salt added to bread can vary more than two

fold, depending primarily on the amount of potassium iodate added during

production (56). Furthermore, the sodium content of bread can range between 200-

800 mg/100g (55), however most food databases used to estimate iodine intakes

from food recalls and food records utilise the mean sodium and iodine content of

each bread product and do not account for the variation in either sodium or iodine

contents of breads (158). Additional variations in the iodine content of other foods

such as milk, another major food source of iodine in the Australian population (45),

which can vary from 90-300 µg iodine /kg milk (11) are unlikely to be captured by

food composition databases.

This lack of specificity regarding the iodine content of foods can result in

inaccurate estimations of daily iodine intakes when dietary assessment

methodologies are used (15, 159). Therefore dietary assessment methodologies are

more commonly used for identifying the food sources of iodine in populations (46,

159-162). Whilst the recent AHS utilised 24-hour food recalls primarily to identify the

main food sources of iodine, these recalls were also used to estimate the daily


~ 38 ~

iodine intakes of the population. A recent analysis of the AHS dietary data

confirmed the improvement in population iodine intake with only a small

proportion of children (8%, n=144/1772) having inadequate iodine intakes,

assessed by dietary recall, below the age-specific EAR (46).

2.6 Food sources of iodine in Australia

2.6.1 Bread

Prior to the introduction of mandatory fortification in 2009, bread was not

considered a major food source of iodine. In the 2007 Australian Children’s

Nutrition and Physical Activity Survey (CNPAS), cereal products and dishes

contributed <10% of total iodine intake (51). Comparatively, the 2011-2013 AHS

found that cereals and cereal products contributed 33% and 30% of total dietary

iodine in 4-8 and 9-13 year olds, respectively (45). Furthermore, the AHS found that

regular bread and bread rolls specifically contributed 27% of 4-8 year olds and 23%

of 9-13 year olds’ total dietary iodine. Additional analyses of the AHS data indicated

that, amongst children and adolescents (2-18 year olds), consumption of 100g of

bread was associated with 12 times greater odds of achieving an adequate dietary

iodine dietary iodine intake (OR 12.34, 95% CI 1.71–89.26; P<0.001) compared to

children who consumed <100g bread per day, using 24-hour food recalls (46).

Similarly, a recent study in 147, 8-10 year old New Zealand schoolchildren,

post-fortification of bread with iodised salt, estimated total dietary iodine intake

and the food sources of iodine using an iodine-specific food frequency

questionnaire (FFQ) (133). In addition, this study collected random spot urine

samples, in order to determine the association between the food sources of iodine

and spot UIC. In this study bread was found to contribute 28% of total dietary iodine

intake (determined using the FFQ), compared to 1% in 2002. Furthermore, the

percentage contribution of iodine from bread was significantly associated with UIC

(P=0.017) (133). The results of this study are limited, however, as spot UIC does not

provide an accurate estimate of daily iodine intake. Studies assessing the

association between bread consumption and iodine intake assessed using an

objective measure of iodine intake, i.e. observed 24-hour UIE, are needed to


~ 39 ~

confirm these findings and further elucidate the relationship between bread

consumption and total daily iodine intake. Such a study will help to evaluate the

impact of the current fortification strategy and determined whether it has been

effective in improving the iodine intakes of Australian population. To date, no

studies have examined this association in a sample of Australian schoolchildren,

post-fortification of bread with iodised salt.

2.6.2 Milk as a dietary source of iodine

In Australia and New Zealand between 1975 and 2009 one of the most

abundant food sources of iodine was milk and dairy products (51, 52, 130, 163, 164), as

iodophores, used as sanitisers during dairy processing, leached into the milk

increasing its concentration (165). Whilst concerns have been raised that a change

in the use of sanitisers by the dairy industry may have led to the observed reduction

in iodine intakes of the Australian population (52), a recent study by Tinggi et. al.

(2012) found that the iodine content of Australian milk is comparable with that in

iodine sufficient countries such as Switzerland, where iodine containing sanitisers

are still used (11). Furthermore, the 2011-13 AHS observed that milk was the

greatest contributor to total daily iodine intake in children, as measured using 24-

hour food recalls, with 27% and 25% of dietary iodine coming from dairy milk

amongst 4-8 and 9-13 year olds, respectively (45). Similarly in adults, milk products

and dishes was the second highest food source of iodine, after bread, contributing

26% of total dietary iodine (45).

The iodine content of milk appears to be dependant mainly on

contamination from iodophores (165), although there is some naturally occurring

iodine which is present within milk (166). This naturally occurring iodine is, however,

highly variable and is largely dependent on the iodine intake of the cow itself (165-

167). Factors such as the supplementation of cow feed and salt licks, the cow species

and farm location can impact the level of naturally occurring iodine in milk (165, 166,

168). Currently within Australia iodophores are still permitted for use in the dairy

industry, however their use is not monitored, and the proportion of farmers

currently using them is not known, nor is the concentration at which they are used


~ 40 ~

(Kathryn Davis, Dairy Australia, Personal Communication, January 2017). As such,

it is difficult to determine the level of contamination in milk by iodophores, and this

in combination with the variation in the naturally occurring iodine content of milk,

means that the iodine content of Australian milk could vary considerably both

within and between producers.

A recent study in a sample of 168 children aged 8-10 years from three areas

of the United Kingdom (Omagh, Northern Ireland; Glasgow, Scotland, and

Guildford, South-East England) observed a significant positive association between

milk intake and spot UIC (P=0.006) (169). Furthermore, consumption of milk <140mL

per day was associated with a 20% lower median spot UIC compared to those

children who consumed 140-280 mL per day (P=0.03) (169). Similarly, a study in

German school-aged children (n=221, 3-6 y/old) observed that milk intake,

assessed from 3-day weighed food records, was a significant predictor of observed

24-hour UIE (160). In this study a 1g/day increase in milk intake was associated with

a 0.08 µg/24-hour increase in UIE (=0.08, P<0.001) (160). It is important to note,

however, that milk is the main food source of iodine in both the U.K. and Germany

, primarily due to contamination of milk with iodine containing sanitisers, as neither

country currently has a mandatory iodine fortification strategy in place (160, 170).

These two studies (160, 169) indicate that milk is a significant contributor to iodine

intakes among school-aged children in the Germany and U.K., however the

contribution of milk to iodine intake, measured using 24-hour urine samples, has

not been assessed in Australian school-aged children. Establishing the association

between milk consumption and observed 24-hour UIE would help to confirm the

AHS finding that milk remains a significant source of iodine in Australia.

2.6.3 The use of iodised salt

Iodine can be fortified into a variety of food products, including oil, milk,

sugar or animal feed (5). As salt remains one of the few foodstuffs consumed by

people worldwide it provides a unique medium through which to correct and/or

prevent iodine deficiency in multiple regions of the globe (50). Additionally salt

consumption remains fairly stable throughout the year, is not affected by


~ 41 ~

seasonality, and salt production/importation is limited to a few sources in most

countries (50). Finally, iodisation technology is relatively easy to implement and

monitor, with low costs and no impact on the overall flavour and colour of the salt

itself (50). Therefore salt provides a good medium for iodine supplementation.

Furthermore, a recent systematic review and meta-analysis of 89 studies found

that exposure to iodised salt was significantly associated with reduced risk or

prevalence of goiter, cretinism, low intelligence and low observed 24-hour UIE (171).

This analysis also did not find an association between exposure to iodised salt and

prevalence/risk of adverse effects, including increased hypothyroidism or

hyperthyroidism, indicating that iodised salt is both effective and safe for use as a

fortification strategy (171).

It has been estimated that approximately 70% of households worldwide

have access to iodised salt, with regions in the Western Pacific and Americas having

the greatest access, and parts of the Eastern Mediterranean having the least access

(148). In Australia specifically, monitoring of iodised salt consumption has been

limited to data from salt sales (172) or questions related to engagement in

discretionary salt use behaviours (including the AHS) (45). Iodised salt has been

available in supermarkets and grocery stores in Australia since the 1960s, however

consumption has remained relatively low, despite the prevalence of iodine

deficiency (172). Previous research has found that the main reason for low iodised

salt sales is that consumers are generally uneducated regarding the benefits of

iodised salt (172). A 2008 study conducted by Li et. al. examined national data on

iodised salt sales, obtained from national point-of-purchase bar code scanning for

all brands of iodised salt from major supermarket stores and independent stores

throughout Australia from January 1, 1997 – April 30, 2006 (172). There was an

overall increase in iodised salt sales between 1997 and 2006, with the most

noticeable increase occurring in the three years following the NINS in 2003-2004.

It was estimated that during this three year period, iodised salt sales increased

approximately 29%, with a 5% increase in sales in the month immediately following

the broadcast of a science program on iodine deficiency disorders in November

2005.


~ 42 ~

It has been estimated that discretionary salt use contributes 10-15% of total

dietary salt (173) and it has been found that adults who report adding salt at the

table and in cooking have higher total salt intakes compared to those not using

discretionary salt (174). The 2011-13 AHS reported that 50% of 4-8 year olds and 57%

of 9-13 year olds reported using salt in cooking “rarely/occasionally/very often”

with 17% of 4-8 year olds and 36% of 9-13 year olds adding salt at the table(45). Of

those who reported adding salt during cooking, 24% of 4-8 year olds (parental

report) and 25% of 9-13 year olds (self-report) reported that the salt used was

iodised. In addition, 8% of 4-8 year olds and 13% of 9-13 year olds who reported

adding salt at the table indicated iodised salt was used (45). However, the AHS did

not include a direct estimate of daily salt intake, with data pertaining to

engagement in salt use behaviours collected via self-report by the participant or a

proxy (in the case of young children). As such there may have been some

underreporting or overreporting of engagement in these behaviours (175, 176). This

makes it difficult to ascertain the contribution of discretionary salt to total dietary

iodine intakes among Australian school-aged children.

Some studies have utilised 24-hour urinary sodium excretion as an indicator

of total sodium, and thus salt, intake as a means of assessing the relationship

between total salt intake and iodine intake (160, 177-179). Whilst the results of these

studies are varied, with studies in adults failing to determine an association

between 24-hour excretion of urinary sodium and iodine (178, 179), one study in a

sample of mildly iodine deficient German children (n=378, 3-6 year olds) estimated

that total salt intake, measured using 24-hour urinary sodium excretion,

contributed 42% of total dietary iodine, measured using observed 24-hour UIE (160).

In addition, the study observed a moderate positive correlation between observed

24-hour UIE and 24-hour excretion of sodium (r=0.43, P<0.05) (160). To date no

studies have examined the association between daily intakes of sodium (as a

marker of total salt intake) and iodine measured using 24-hour urinary excretion in

a sample of Australian school-aged children. Such a study could help to establish

the relationship between iodine intake and salt intake, in order to further

understand the role which iodised salt, added either during processing or as


~ 43 ~

discretionary salt (i.e. during cooking and at the table), plays in the iodine intakes

of Australian school-aged children.

2.7 Sodium reduction targets and iodine

2.7.1 Sodium intakes and health of children and adolescents

The relationship between sodium intake and blood pressure is well

established in adults (57, 180-182). Similar associations have also been reported in

children, with a 2006 meta-analysis of controlled trials, encompassing 10 trials and

966 children and adolescents aged 8-16 years observed a significant reduction in

both systolic, -1.17 mm Hg (95% CI: -1.78 to -0.56 mm Hg; P<0.001), and diastolic,

-1.29 mm Hg (95% CI: -1.94 to -0.65 mm Hg; P<0.0001), blood pressure (BP) with a

reduction in salt intake (median (IQR) salt reduction 42% (7%-58%) (181). Similarly,

a review of 25 observational and 12 intervention studies conducted by Simons-

Morton & Obarzanek (1997) concluded that a greater sodium intake was

associated with higher BP in children and adolescents (183). More recently, a

possible link between high sodium intakes and sugar sweetened beverage

consumption has been observed in studies in Britain (184), the United States (185) and

Australia (186). The mechanism behind this relationship lies in the homeostatic

trigger of thirst in response to the ingestion of dietary salt (187). It is suggested that

in an environment where soft drinks are readily available, a high salt diet may

encourage greater consumption of sugar sweetened beverages in children (187).

Increased consumption of sugar sweetened beverages has been linked to an

increased risk of obesity amongst Australian children and adolescents (186).

Furthermore, recent cross-sectional studies in Australia have also observed an

association between salt intake and obesity (188). Together these studies highlight

the adverse effects which excessive salt intakes may have on the health of children

and adolescents.

2.7.2 Global action to reduce sodium intake

Due to the adverse health effects known to be associated with high salt

intakes (57, 180, 181, 189) the WHO recommends a reduction in salt intake to <5g/day


~ 44 ~

among adults by 2025 worldwide (58) with a further reduction to <2g/day

recommended for children based on the energy requirements of children relative

to those of adults (190). A recent systematic review of 55 peer-reviewed articles and

170 grey literature documents and websites conducted in 2014 found that 75

countries had implemented national salt reduction strategies with a further 9

studies in the planning stages (191). The most prominent strategy implemented to

reduce sodium reductions was food reformulation, with 81% of national salt

reduction strategies focusing on engaging food industry to reduce the sodium

content of processed foods (191).

The most successful example of a population based sodium reduction

strategy comes from the United Kingdom (192). Initially, the strategy aimed to

reduce average population salt intakes to <6 g per day by 2015, through a

combination of engagement with the food industry to reduce the amount of salt

added to processed, restaurant and fast food, as well as a public health campaign

to increase awareness around the harmful effects of high salt intakes (192). Since the

introduction of the strategy in 2003, the U.K. has observed a 15% reduction in the

average salt consumption, with an estimated 9000 fewer cardiovascular deaths per

year (192). The success of the strategy has led to the subsequent revision of the

original target, with the program now aiming to reduce population salt intake to 3g

per day by 2025 (193).

2.7.3 Australian sodium reduction strategies

Currently within Australia, a number of sodium reduction initiatives are

being implemented by different organizations, however there is no national

governmental policy related to reducing sodium intake at the population level (194).

A key stakeholder in the development of sodium reduction strategies in Australia

is the Australian Division of World Action on Salt and Health (AWASH). AWASH was

originally modelled on the United Kingdom’s Consensus Action on Salt and Health

(CASH), and was first established in 2005 (195). AWASH comprises of a growing body

of experts in public health, marketing and communications, and project

management, with wider stakeholder input from research organisations, the food


~ 45 ~

industry, health and consumer entities, government bodies and food and health

consultants (195). In 2007 AWASH launched its ‘Drop the Salt!’ campaign, a multi-

pronged initiative modelled on the strategy implemented in the U.K. (194). The

primary aim of this campaign was a reduction in the average salt consumption of

the Australian population to 6 grams per day over 5 years through the

reformulation of processed and catered foods, and advocacy to improve

population knowledge regarding the health benefits of limiting salt intakes.

Furthermore, the campaign aimed to raise the profile of salt reduction on the

agenda of both State and Federal governments across Australia (194).

This was followed in 2009 by the establishment of the Federal

Government’s Food and Health Dialogue (FHD) between government, food

industry, the Heart Foundation and Public Health Association which saw the

establishment of salt reduction targets for nine food categories, including bread

(Appendix A) (194). Since the establishment of the FHD in 2009, Australia has

observed a 10% reduction in the sodium content of commercially available breads

(59). In addition the number of bread products with a sodium content below the

maximum recommended level of 400 mg sodium/100 g tripled from 28% in 2009

to 86% in 2015(59). However, during this same period (2010-2013) FSANZ also

reported a reduction in the iodine contents of white and wholemeal bread

products by 11% and 25%, respectively (196). This reduction in the iodine content of

bread products is most likely a result of the successful implementation of sodium

reduction targets, and raises the concern that a further reduction in the sodium

content of bread products could result in a subsequent reduction in the iodine

content of bread. As bread was found to be a significant contributor to total dietary

iodine intake in the 2011-13 AHS, due to the addition of iodised salt to bread during

processing, there are concerns that further reductions in the salt content of bread

could have a negative impact on the iodine intakes of Australians (197).

It is, however, important that sodium reduction strategies remain a public

health priority in Australia due to the negative health outcomes associated with

high salt intakes intakes (57, 180, 181, 189). Whilst there is currently no co-ordinated

national strategy for sodium reduction in Australia, the ‘Healthy Food Partnership’


~ 46 ~

established in November 2015 (198) aims to build on the FHD’s reformulation

targets, with the overarching aim of reducing average population salt intakes by

30% by 2025 as part of a broader international commitment to reducing the

prevalence of non-communicable diseases (199). It is therefore important to

continually monitor the iodine intakes of Australian schoolchildren, to ensure they

are not being placed at risk of iodine deficiency as a result of a reduction in the

amount of iodised salt added to bread during processing. Furthermore, establishing

the relationship between iodine intakes determined using an objective measure of

intake (i.e. 24-hour UIE), food sources of iodine, including iodised salt, and

discretionary salt use will help to determine the role which iodised salt plays in the

iodine intakes of Australian school-aged children. This information is important

when evaluating the potential impact which sodium reduction targets might have

on the iodine intakes of Australian school-aged children. Furthermore, the

identification of alternative food sources of iodine which are not dependent on

iodised salt could help in the development of strategies to ameliorate the potential

impact of sodium reduction strategies on iodine intakes.

2.8 Summary

Iodine is an essential nutrient in the synthesis of thyroid hormones which

are important for normal growth and development (62). These hormones are

particularly important in periods of rapid growth and development, such as

childhood, with studies in children observing a significant association between

iodine intakes and cognition (6, 7). Current recommendations by the WHO endorse

the use of spot urine samples to determine median UIC as the most appropriate

method for determining the iodine status of a population (142). These

recommendations are based on the assumption that spot UIC can be used as a

surrogate measure of the iodine intakes of individuals within the population.

However, only in populations where daily urine volume is equal to 1L would spot

UIC be equivalent to daily iodine intake (15). Whilst the use of UIC has been validated

for identifying populations at risk of deficiency, its use as an indicator of daily intake

is less reliable as it is based on the assumption of a daily urine excretion of 1L.


~ 47 ~

However, in populations where daily urine volume is less than 1L, such as children,

the overestimation of daily iodine intake may occur which could result in the

misclassification of children with inadequate iodine intakes as iodine sufficient. To

date, however, there has been no systematic evaluation of the 24-hour urine

volume of school-aged children to determine the appropriateness of current

recommendations for assessing the iodine nutrition of schoolchildren.

Following the detection of iodine deficiency in a proportion of the

Australian population in the late 1990s (33), mandatory fortification of all

commercially produced bread, excluding organic and homemade varieties, with

iodised salt was introduced in 2009 (44). Whilst studies utilising spot urine samples

have observed an improvement in the iodine status of the Australian school-aged

children since the implementation of fortification in 2009 (14, 34, 42, 43, 130), no studies

have examined the iodine intakes of a sample of Australian school-aged children

determined using 24-hour urine samples. Although the 2011-13 AHS utilised 24-

hour food recalls to identify the food sources of iodine and estimate daily dietary

iodine intake, variations in the iodine content of foods not captured by food

databases may have resulted in an inaccurate estimate of daily iodine intake.

Furthermore dietary assessment does not account for discretionary use of iodised

salt, which might lead to the underestimation of total iodine intake. Understanding

the food sources of iodine can assist in determining the success of fortification

strategies as well as help in identifying alternative food sources of iodine which are

less reliant on iodised salt for their iodine content.


intakes (57, 180, 181, 189) the WHO recommends a reduction in salt intake to <5g/day

among adults by 2025 worldwide (58) with further reduction to <2g/day

recommended for children based on the energy requirements of children relative

to those of adults (190). The most prominent strategy implemented to reduce

sodium reductions is food reformulation, with the implementation of sodium

reduction targets for nine food categories, including bread, in 2009 resulting in a

10% reduction in the sodium content of commercially available breads in Australia

(59). As bread was found to be a significant contributor to total dietary iodine intake


~ 48 ~

in the 2011-13 AHS, due to the addition of iodised salt to bread during processing,

there are concerns that further reductions in iodised salt use during bread

processing as a result of sodium reduction strategies could have a negative impact

on the iodine intakes of Australians (197). Establishing the relationship between

iodine and sodium intakes determined using an objective measure of intake (i.e.

observed 24-hour UIE) will help to determine the role which iodised salt, added

either during processing or as discretionary salt (i.e added at the table or during

cooking) plays in the iodine intakes of Australian schoolchildren. This information

is important when evaluating the potential impact which sodium reduction

strategies, including product reformulation, might have on the iodine intakes of

Australian school-aged children. The lack of data pertaining to daily iodine intakes

of Australian school-aged children measured using 24-hour urine samples, an

objective measure of intake, along with limited data exploring the relationship

between food sources of iodine and iodine intakes formed the rationale for the

work in this thesis.

~ 49 ~

Chapter Three:

Study 1: Meta-analysis of average 24-hour

urinary volume of children and adolescents

3.1 Introduction

Monitoring the iodine nutrition of populations and individuals is important

in order to identify those at risk of deficiency, as deficiencies during childhood have

been linked with impaired cognitive and motor functions in schoolchildren (6, 7).

Current recommendations for assessing the iodine nutrition of populations using

spot urine samples from school-aged children were developed by the World Health

Organization over a number of years (1986-2007) (37, 38, 102, 112, 142, 152). Whilst the

original version of these recommendations were based on the association between

a daily estimated iodine intake, extrapolated from creatinine excretion, equivalent

to 100 µg and reduced goiter prevalence among children and adults (117), later

iterations extrapolated this value to represent a concentration, urinary iodine

concentration (UIC) expressed as µg/L(37). In some instances where UIC is used as a

marker of iodine intake, issues may arise as UIC would only be reflective of daily

intake if urine volume was equivalent to 1L. For example, in a population with a

daily urinary volume of 1L, a UIC of 100 µg/L could be extrapolated to be indicative

of a daily iodine intake of 100 µg/24-hours. As this value is equal to the daily intake

originally associated with reduced goiter prevalence (117), this population can be

classified as having sufficient iodine intakes. However, in a population of children

with a daily urine volume closer to 0.5L a UIC of 100 µg/L would be indicative of a

daily iodine intake closer to 50 µg/24-hours. This could result in the

misclassification of a population as iodine sufficient when their daily iodine intakes

may, in fact, be placing them at risk of developing iodine deficiency disorders.

Therefore, iodine monitoring programs which have extrapolated daily

iodine intake from UIC determined from spot urine samples in populations of

children and adolescents (8, 144, 146-149) and have not taken the lower daily urinary

output into account, may be inaccurately estimating and reporting iodine intakes.

As such, this may have resulted in the misclassification of populations as having

Study 1: Meta-analysis of average 24-hour urinary volume of children and adolescents

~ 50 ~

sufficient iodine intakes based on a UIC > 100 µg/L, when their true intakes may be

lower than the 100µg/day originally associated with reduced goiter prevalence.

Such misclassifications may have prevented the implementation of necessary

iodine fortification programs which may have led to the subsequent development

of iodine deficiency disorders in these populations. To date, there has been no

systematic evaluation of the average 24-hour urine volume of children and

adolescents from around the world. This information would be useful to help

researchers interpret the interplay between UIC determined from spot urine

samples and iodine intakes, in order to understand the appropriateness of the

assumption that UIC can be used as a marker of the iodine intakes of children and

adolescents.

3.2 Aim

Using a systematic review, to produce an estimate of the average 24-hour urine

volume of healthy children and adolescents across the world

3.3 Methods

This protocol adheres to the Preferred Reporting Items for Systematic

Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement (200) and was

registered with the International Prospective Register of Systematic Reviews

(PROSPERO) (registration number CRD42016033682).

3.3.1 Information Sources and Search

A search strategy was developed to identify papers which have reported

the 24-hour urine volume of children and adolescents (>1y and 19y). An electronic

literature search of EBSCOHOST (MEDLINE complete, CINAHL, Academic Search

Complete and Global Health) and EMBASE databases was conducted. The search

strategy was developed in consultation with a research librarian. Free text

keywords were used to conduct the search. Search criteria specific to each

database are outlined in Table 3.1. The search strategy was piloted across each

database to improve the effectiveness of the final search. Only peer-reviewed


~ 51 ~

original research articles published in English and conducted in humans were

included. It is beyond the scope of this review to include and examine sources from

‘grey’ literature. The reference lists of included studies identified through the

search were also reviewed. The search was then re-run in October 2017, to identify

any new studies published since the initial search in June 2016.

Table 3.1: Search criteria specifications for each database

Database Search options Search terms

EBSCOHOST

Academic Search Complete

Limiters - Full Text; Scholarly (Peer Reviewed) Journals; Language: English Search modes - Boolean/Phrase

(“24 hour urin*” OR “twenty four hour urin*” OR "24 h urin*") AND (sampl* OR collection* OR volume* OR excretion* OR output*) AND (schoolchild* OR child* OR adolescen* OR teen*)

CINAHL Complete

Limiters - English Language; Peer Reviewed Search modes - Boolean/Phrase

Global Health

Limiters - Language: English Search modes - Boolean/Phrase

MEDLINE Complete

Limiters - English Language; Human Search modes - Boolean/Phrase

EMBASE

Advanced search No mapping options used No date limits specified Sources: Embase only (Medline not selected as separate search) Field labels: abstract, article title, index term and subheading Quick limits: human, only in English Publication types: article, article in press EBM, gender, age and animal advanced options left blank

#1: '24 hour urin*' OR 'twenty four hour urin*' OR '24 h urin*' OR 'twenty four h urin*' #2: sampl* OR collection* OR volume* OR excretion*or AND output* #3: child* OR adolescen* OR teen* #4: #1 AND #2 AND #3


~ 52 ~

3.3.2 Inclusion/Exclusion criteria

Only peer-reviewed original research studies were included and any

reviews, meta-analyses, editorials, case reports, conference proceedings or other

grey literature identified through the search were excluded at the screening stage.

As the primary focus of this review was to determine the average urinary output of

children and adolescents, only studies which reported the 24-hour urinary output

of children and adolescents >1 and 19 years of age were included and studies

outside of this age range or studies where spot urine samples were used were

excluded. Finally, only studies in healthy participants were included, and any study

in a sample where an illness or diseased state (including diabetes, malnourishment,

kidney disease, liver failure, cancer, chronic asthma) existed was excluded. If

multiple published reports were available from the same study, the most recently

published and/or the study with the largest sample size was included.

All papers identified from the initial electronic search process were

imported into an Endnote library, and duplicates removed. Titles and abstracts

were screened and studies included based on the eligibility criteria as outlined

above. Following this screening process, the full text of eligible studies was

retrieved and studies which collected 24-hour urine samples but did not report the

final 24-hour urine volume were excluded. At this stage, the reference lists of

included studies were scanned, and the full text of any relevant studies retrieved

and reviewed for inclusion. The PRISMA flow chart (201) was used to document the

number of studies identified during the search process and those excluded and

included according to the outlined eligibility criteria (Figure 3.1).


~ 53 ~

As multiple studies had utilised data from the Dortmund Nutritional and

Anthropometric Longitudinally Designed (DONALD) Study (202), cross-checking of

study dates and participant characteristics was carried out to minimise participant

overlap between the studies. Some studies (n=7) (203-209) were excluded from the

final analysis as they did not report which years of data collection had been

analysed, therefore possible participant overlap with other studies could not be

determined. Authors were contacted for information where possible, however no

additional information was collected. Where participant overlap was possible, the

study with the larger participant number was included in the final analysis. Of the

Figure 3.1: PRISMA flow chart of study selection


~ 54 ~

27 DONALD studies initially identified, 6 studies (115, 176, 210-213) were included in the

final analyses as they captured the full range of data collection years and included

the largest number of participants whilst minimising possible participant overlap

(Appendix. B).

3.3.3 Data extraction and synthesis

Data extraction was completed using a data extraction template (Table 3.2).

The template was initially piloted on 5 eligible studies and modifications made

where necessary. As 24-hour urinary creatinine excretion either alone, in relation

to expected creatinine based on sex and/or weight, is often used as a marker for

complete urine collection under the assumption that urinary creatinine excretion,

as an indicator of body mass is stable within individuals from day to day (121, 214, 215),

data pertaining to 24-hour excretion of creatinine was also extracted where

reported.

Table 3.2: Data extracted from included studies

Study overview

Aim(s) Study design Study year(s) Description of country where the study was conducted Season of the year Setting (i.e. home/school)

Methods

Recruitment method Inclusion criteria (main study - not urine specific) Specific exclusion criteria (main study - not urine specific) Data collection overview

24-hour urine collection protocol

24-hr urine protocol description Criteria for completeness of urine samples

- time of collection (hours) - volume of collection - creatinine cut-off - number of missed collections reported by child/parent

Specific exclusion criteria for urine samples Adjustment for collection time/normalised? Other nutrients analysed


~ 55 ~

Participant characteristics

Total no. participating No. exclusions No. withdrawals Final n included in analyses % male participants Total no. urine samples Age, mean and range Weight category/BMI Race/Ethnicity SES (include description of SES definition)

Results

Urine volume (L/24h) - Mean, SD/SEM, Median, Range Creatinine - Mean, SD/SEM, Median, Range

3.3.4 Quality Assessment

The quality of the studies included in this review was assessed using a

modified version of the Newcastle-Ottawa scale (NOS) for cohort studies (216), as all

studies included in the final synthesis were of a cross-sectional study design

(Appendix C). The NOS was modified to suit the context of the studies included in

the review and particular consideration was made towards the 24-hour urine

collection methods used in each study. This scale assigns stars to indicate higher

quality based on three broad criteria specific to the design of the study: i) Selection

(representativeness of the study sample); ii) Comparability of the findings

(normalisation of the results to a 24-hour period); and iii) Assessment of outcome

(quality of the reported 24-hour urine collection methodology). Studies were

categorised as ‘high’ ‘medium’ or ‘low’ quality according to the number of stars

which they received (out of a maximum of 10 stars: low: 0-3; medium: 4-7 ; high 8-

10). As only 2 studies provided sufficient detail on their urine collection protocol to

be classified as “high” quality (188, 217), we included a second category of quality

assessment, based on studies which had reported at least one criteria for the

assessment of urine collection completeness.


~ 56 ~

3.3.5 Statistical Analysis

Following data extraction, data was collated and imported into STATA/SE

15.0 (StataCorp LP, College Station, TX, USA) for analysis. The main outcome

variable was 24-hour urine volume, presented in mL/24-hours. Of the 53 studies

originally included, five did not include a measure of spread/dispersion (218-222) and

were subsequently excluded (Figure 3.2). Most studies (n=36) reported urine

volume as mean (SD) or mean (SEM) (188, 217, 223-256) (Table 3.3). Thirteen studies

reported urine volume as either median (min, max)(20, 257-259), median (IQR)(115, 176,

210, 212, 213, 260, 261), median (P3, P97)(262) or median (no range) (218). In seven studies,

the 24-hour urine volume was reported as median (IQR) (n=7) (115, 176, 210, 212, 213, 260,

261) (Table 3.4) . The mean (SD) was extrapolated from the median (IQR) by using

the median as a proxy for the mean and the IQR as a proxy for the SD (i.e. P75-

P25=SD)(263). The calculated mean (SD) for these studies was then pooled with the

results of those studies which reported urine volume as mean (SD). As such, a total

of 43 studies were considered for the primary analysis.

Due to the wide age range of participants of the included studies, studies

were grouped into three groups according to the age of the participants; 2-6 (n=19)

(176, 212, 217, 223-225, 230, 231, 234-237, 240, 242, 248, 252, 254, 255, 262), 7-11 (n=19) (188, 210, 213, 217, 225-

228, 232, 234, 238, 239, 243-246, 251, 256, 260) and 12-19 years (n=12) (115, 176, 210, 217, 229, 233, 234,

243, 245, 247, 249, 260). These cut-points were chosen based on the age range of the

individual studies, with the cut-point of 12 years for the older age group as children

typically begin secondary education at 12 years of age. As some studies presented

results broken down by age group, a single study may have been assigned to

multiple age groups. Studies which crossed the age group cut-offs were assigned

to the age group in which the majority of the participants would fall (e.g. a study

with participants 9-13 years (239) would fall into the 7-11 year age group. Three

studies (250, 253, 261) which encompassed very large age ranges (i.e. >8 years) were

excluded from the sub group analyses using age cut offs.


~ 57 ~

Initial analysis

The overall mean (95% confidence interval (CI)) estimate of urine volume

for all 43 studies was determined using a random effects model and presented for

the group as a whole (i.e 2-19 year olds), as well as broken down by age group.

Primary analysis – studies with ≥1 criteria for assessing the completeness

of urine samples

As the inclusion of criteria for assessing the completeness of urine

collections can result in the exclusion of over/under collectors, the primary analysis

was limited to only those studies which reported at least one criteria for assessing

the completeness of the included urine samples (n=26, “Primary Analysis, Figure

3.2). The overall mean (95%CI) estimate of urine volume was determined using a

random effects model and displayed in forest plots, broken down by age group.

A one-way ANOVA was used to assess the differences in urine volume

across the three age groups. In addition, a one-way ANOVA was used to assess

differences in the urine volume between those studies which did not report any

criteria for assessing the completeness of included urine samples and those which

reported >1 and >2 criteria. A two-sample t-test used to assess the difference in

volumes determined for the initial analysis compared to the primary analysis (i.e.

limited to studies with >1 criteria for assessing the completeness of the urine

samples) across the 3 age groups. As seven studies had presented the results

broken down by gender (115, 176, 212, 213, 246, 250, 260), a two-sample t-test was used to

evaluate the difference in 24-hour urine volume between genders.


~ 58 ~

*Some studies reported results for more than one age group

Figure 3.2: Flow chart of studies included in analyses

Tab

le 3

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(SD

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), n

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Year

(s)

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n(%

)

Val

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Age

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(y

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)

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216

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3

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(0

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(2

24)

n

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32

n

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32

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) Sa

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(198

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.9

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1

Ap

aric

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(201

5)

(22

6)

201

4

205

1

09 (

53%

) 2

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7

-11

8

.8 (

1.2

) Sp

ain

cr

eati

nin

e ex

cret

ion

<0

.1m

mo

l/kg

/day

0


~ 59 ~

Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Bal

lau

ff

(198

8) (2

27)

n

ot

des

crib

ed

21

1

0 (

48%

) 2

1

6-1

1

8.8

(1

.4)

no

t d

escr

ibed

n

ot

des

crib

ed

4

Cla

rk

(199

6) (2

28)

no

t d

escr

ibed

2

04

89

(47

%)

19

0

9-1

0

9.6

(0

.2)

Salis

bu

ry, U

nit

ed

Kin

gdo

m

"Fo

urt

een

uri

ne

sam

ple

s w

ere

excl

ud

ed b

eca

use

of

an

in

corr

ect

colle

ctio

n p

erio

d

(n=4

), a

uri

ne

volu

me

alm

ost

4

sta

nd

ard

dev

iati

on

s b

elo

w t

he

geo

met

ric

mea

n (

n=1

), o

r b

act

eria

l or

oth

er

con

tam

ina

tio

n (

n=9

)"

4

Fujis

him

a (1

983)

(22

9)

198

0

84

8

4 (

100

%)

76

1

5-1

8

no

t d

escr

ibed

Ka

nag

awa,

Jap

an

no

t d

escr

ibed

2

Gri

mes

(2

016)

(18

8)

201

0 -

201

3

168

9

6 (

57%

) 1

68

4

- 7

.9

6.9

(0

.7)

Mel

bo

urn

e,

Au

stra

lia

On

e o

r m

ore

of

the

follo

win

g cr

iter

ia:

i) c

olle

ctio

n t

ime

<20

or

>28

h

ii) t

ota

l vo

lum

e <

30

0m

l iii

) p

arti

cip

ant

rep

ort

ed

mis

sin

g >1

co

llect

ion

, or

iv

) u

rin

ary

crea

tin

ine

excr

etio

n

<0.1

mm

ol/

kg/d

ay

10

498

2

69 (

54%

) 4

98

8

- 1

2

10

.1 (

1.2

)

~ 60 ~


Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Haf

tenb

erge

r (2

001)

(23

0)

199

8

11

6

(5

5%)

11

3

-6

4.2

(1

.3)

Dre

sden

, G

erm

any

no

t d

escr

ibed

0

Hag

a (2

010)

(23

1)

200

5

29

1

3 (

45%

) 2

9

3 -

4

no

t d

escr

ibed

M

iyag

i, Ja

pan

n

ot

des

crib

ed

2 3

3

18

(55

%)

33

4

.1 -

5

17

8

(4

7%)

17

5

.1 -

6

He

(201

5) (2

32)

2

013

138

6

7 (

49%

) 1

38

9

-11

1

0.2

(0

.5)

Ch

angz

hi,

Ch

ina

i) u

rin

e vo

lum

e <3

00

ml/

24

-h

ou

r o

r

ii) c

reat

inin

e <

5th

cen

tile

(<2

.5

mm

ol/

24

-ho

ur

for

girl

s an

d

<2.9

mm

ol/

24

-ho

ur

for

bo

ys)

7

141

6

7 (

48%

) 1

41

9

-12

1

0.0

(0

.5)

Hes

se

(198

6) (2

33)

n

ot

des

crib

ed

25

2

5 (

100

%)

25

1

5-1

9

no

t d

escr

ibed

n

ot

des

crib

ed

no

t d

escr

ibed

0

25

0

(0

%)

25

1

5-1

9

Hö

llrie

gl

(201

1) (2

34)

200

8

9

no

t d

escr

ibed

8 3

-5

no

t d

escr

ibed

A

kure

, Nig

eria

uri

nar

y cr

eati

nin

e va

lues

o

uts

ide

of

the

ran

ge o

f 0

.2-

3.0

g/L

3 2

8

24

6

-10

12

5

11

-15

~ 61 ~


Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Juar

ez-L

ope

z (2

016)

(23

5)

no

t d

escr

ibed

2

0

30

(54

%)

20

3

-5

3.8

(0

.9)

Stat

e o

f M

exic

o

excr

etio

n r

ate

bel

ow

9 μ

g F/

hr

3

Ketl

ey

(200

1) (2

37)

n

ot

des

crib

ed

13

n

ot

des

crib

ed

65

5

-6

5.8

(0

.5)

Mer

seys

ide,

En

glan

d

uri

nar

y fl

ow

rat

e <9

mL/

h

3

Ketl

ey

(200

4) (2

36)

199

4-1

996

19

no

t d

escr

ibed

19

1

.5-3

.5

3.4

(0

.2)

Co

rk, I

rela

nd

uri

nar

y fl

ow

rat

e o

f <

9 m

l/h

or

>42

0 m

l/h

6

18

1

8

1.5

-3.5

3

.1 (

0.6

) Kn

ow

sley

, En

glan

d

18

1

8

1.5

-3.5

3

.1 (

0.4

) O

ulu

, Fin

lan

d

4

4 1

.5-3

.5

3.6

(0

.3)

Rey

kjav

ik, I

cela

nd

6

6 1

.5-3

.5

3.2

(0

.5)

Haa

rlem

, th

e N

eth

erla

nd

s

21

2

1

1.5

-3.5

3

.1 (

0.4

) A

lmad

a/Se

tub

al,

Po

rtu

gal

Khan

dare

(2

002)

(23

8)

no

t d

escr

ibed

1

8

18

(10

0%

) 1

8

9-1

1

10

.7 (

1.4

) A

nd

hra

Pra

des

h,

Ind

ia

no

t d

escr

ibed

0

Kird

pon

(2

006)

(23

9)

no

t d

escr

ibed

1

3

13

(10

0%

) 1

3

9-1

3

12

.4 (

0.3

) n

ot

des

crib

ed

no

t d

escr

ibed

0

~ 62 ~


Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Kris

tbjo

rnsd

ott

ir

(201

2) (2

40)

n

ot

des

crib

ed

76

3

0 (

52%

) 5

8

5-6

6

(0

.1)

Rey

kjav

ik, I

cela

nd

P

AB

A r

eco

very

<8

5%

5

Mag

uir

e (2

013)

(24

1)

no

t d

escr

ibed

12

no

t d

escr

ibed

12

6

-7

6.6

(0

.3)

No

rth

ern

En

glan

d

i) u

rin

ary

flo

w <

5m

l/h

ou

r fo

r <

6 y

ear

old

ss a

nd

< 9

mL/

ho

ur

for

child

ren

≥6

yea

rs

ii) c

reat

inin

e ex

cret

ion

<1

1.3

m

g/kg

/day

or

>21

.1 m

g/kg

/day

7 1

1

11

6

-7

6.6

(0

.5)

13

1

3

6-7

7

.0 (

0.7

)

Mar

rero

(201

4) (2

17)

200

7-2

010

150

6

1 (

52%

) 1

26

5

-6

5.5

(0

.6)

Lon

do

n, E

ngl

and

i) t

he

par

tici

pan

t ad

mit

ted

to

h

ave

mis

sed

at

leas

t o

ne

uri

ne

colle

ctio

n

ii) 2

4-h

ou

r u

rin

ary

crea

tin

ine

of

< 0

.1m

mo

l/kg

/d

iii)

uri

ne

ou

tpu

t o

f <0

.5m

l/kg

/h

for

5-6

an

d 8

-9 y

ear

old

s o

r <5

00

mL/

24

-ho

ur

for

13

-17

ye

ar o

lds,

an

d/o

r

iv)

if t

he

tim

ing

of

the

colle

ctio

n w

as <

20

ho

urs

or

>

28

ho

urs

8 1

67

56

(50

%)

11

1

8-9

8

.5 (

0.5

)

125

5

7 (

55%

) 1

03

1

3-1

7

14

.6 (

1.3

)

~ 63 ~


Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Mar

tins

(2

011)

(24

2)

200

8

11

5

(4

5%)

11

2

-4

3.7

Ib

iá, B

razi

l(24

2)

no

t d

escr

ibed

2

Mar

uha

ma

(198

6) (2

43)

n

ot

des

crib

ed

11

6

(5

5%)

11

6

-9.9

8

(1

.3)

no

t d

escr

ibed

n

ot

des

crib

ed

0 1

1

5 (

45%

) 1

1

10

-18

1

4 (

1.9

)

Mat

kovi

c (1

995)

(24

4)

no

t d

escr

ibed

3

81

0 (

0%

) 3

59

8

-13

1

0.9

(7

.7)

Oh

io, U

nit

ed

Stat

es

"in

ap

pro

pri

ate

uri

ne

colle

ctio

ns

iden

tifi

ed b

y re

sid

ua

l an

aly

sis

aft

er f

itti

ng

a

reg

ress

ion

eq

ua

tio

n o

f 2

4-h

cr

eati

nin

e ex

cret

ion

on

LB

M a

s o

bta

ined

by

du

al-

ener

gy

X-r

ay

ab

sorp

tio

met

ry"

5

Mel

se-B

oo

nst

ra

(199

8) (2

56)

1

996

9

7

(1

00%

) 7

6-9

8

.1

Gu

atem

ala,

C

entr

al A

mer

ica

no

t d

escr

ibed

2

13

1

3(10

0%

) 1

3

8-1

2

9.7

B

enin

, Wes

t A

fric

a

Mo

ri

(201

0) (2

45)

2

002

56

5

4 (

46%

) 5

4

6-1

1

8.1

(1

.4)

Jap

an

no

t d

escr

ibed

3

364

0

(0

%)

19

3

13

-18

1

6.7

(1

.3)


~ 64 ~

Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Pad

rao

(201

6) (2

46)

201

4

86

8

6 (

100

%)

86

7

-11

8

.7 (

0.8

) P

ort

uga

l cr

eati

nin

e ex

cret

ion

<0

.1m

mo

l/kg

/day

6

86

0

(0

%)

86

7

-11

Raf

ie

(201

7) (2

47)

201

5 -

201

6

128

4

2 (

33%

) 1

28

1

1-1

8

14

.4 (

2.0

)

Isaf

ahn

, Ira

n

i) v

olu

me

<50

0m

L, a

nd

/or

ii) c

reat

inin

e ex

cret

ion

<0

.1m

mo

l/kg

/day

4

129

5

7 (

44%

) 1

29

1

1-1

8

14

.3 (

2.2

)

117

5

5 (

47%

) 1

17

1

1-1

8

14

.6 (

2.1

)

Ru

gg-G

unn

(199

3) (2

48)

no

t d

escr

ibed

27

2

7 (

100

%)

27

4

-6

no

t d

escr

ibed

Dam

bu

lla, S

ri

Lan

ka

no

t d

escr

ibed

4

26

0

(0

%)

26

4

-6

20

2

0 (

100

%)

20

4

-6

New

cast

le,

Engl

and

2

4

0 (

0%

) 2

4

4-6

Sin

aiko

(1

994)

(24

9)

no

t d

escr

ibed

40

2

0 (

50%

) 4

0

11

-14

1

3.9

(1

.3)

Min

nes

ota

, U

nit

ed S

tate

s n

ot

des

crib

ed

1

243

1

22 (

50%

) 2

43

1

1-1

4

13

.3 (

1.6

)


~ 65 ~

Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

unt

ry

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Stae

ssen

(1

983)

(25

0)

197

9 -

198

1

82

8

2 (

100

%)

82

1

0-1

9

14

.3 (

3.0

)

Bel

giu

m

"Uri

na

ry c

rea

tin

ine

excr

etio

n

wa

s p

lott

ed a

ga

inst

bo

dy

wei

gh

t af

ter

stra

tifi

cati

on

fo

r a

ge

an

d s

ex. S

ub

ject

s w

ho f

ell

ou

tsid

e th

e tw

o s

tan

da

rd

dev

iati

on

ra

ng

e o

f th

e re

gre

ssio

n li

nes

wer

e ex

clu

ded

"

4

78

0

(0

%)

78

1

0-1

9

14

.4 (

2.6

)

Tou

itou

(2

010)

(25

1)

no

t d

escr

ibed

9

9

(1

00%

) 9

9-1

1

10

.8 (

0.1

1)

Fran

ce

no

t d

escr

ibed

0

Vill

a (2

000)

(25

2)

no

t d

escr

ibed

2

0

20

(10

0%

) 2

0

3-6

4

.4 (

0.8

) Sa

nti

ago

, Ch

ile

no

t d

escr

ibed

3

Zhan

g (2

015)

(25

3)

201

2

10

5

(5

0%)

16

2

-11

1

0.0

(3

.2)

Ch

ina

uri

nar

y cr

eati

nin

e u

sed

- c

uto

ff

no

t d

escr

ibed

3

Zoh

ou

ri

(200

0) (2

54)

1

995

-1

996

7

8

no

t d

escr

ibed

7

8

3-4

.9

no

t d

escr

ibed

Fa

rs P

rovi

nce

, Ir

an

crea

tin

ine

excr

etio

n <

14

m

g/kg

/day

or

>20

mg/

kg/d

ay

3

Zoh

ou

ri

(200

6) (2

55)

200

2

7

5 (

71%

) 7

1-3

3

(0

.6)

New

cast

le,

Engl

and

" va

lida

ted

by

mea

suri

ng

th

e u

rin

ary

exc

reti

on

of

crea

tin

ine

in e

ach

24

-h u

rin

e sa

mp

le a

nd

co

mp

ari

ng

th

is w

ith

th

e st

an

da

rd r

efer

ence

va

lue

for

crea

tin

ine

excr

etio

n"

1

NO

S –

New

cast

le-O

tta

wa

Sca

le f

or

coho

rt s

tud

ies

~ 66 ~


Tabl

e 3.

4: C

hara

cter

isti

cs o

f st

ud

ies

con

side

red

fo

r th

e p

rim

ary

anal

ysis

wh

ere

24-h

ou

r u

rine

vo

lum

e w

as p

rese

nted

as

med

ian

(IQ

R),

n=7

stu

die

s

Stu

dy

Stu

dy

Year

(s)

n

Mal

e,

n(%

)

Val

id

uri

ne

sam

ple

s

Age

ra

nge

(y

ears

)

Age

, m

ean

(SD

) ye

ars

Co

un

try

Excl

usi

on

crit

eria

fo

r u

rine

sa

mp

les

NO

S sc

ore

(m

ax 1

0 p

oin

ts)

Cam

pan

ozz

i (2

015)

(26

0)

no

t d

escr

ibed

303

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~ 67 ~


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~ 68 ~


7


~ 69 ~

3.4 Results

3.4.1 Summary of studies considered for primary analysis

The 43 studies considered for inclusion in the primary analysis were

published from 1981-2017 (Tables 3.3 & 3.4). Of these, 14 reported findings from

Europe (115, 176, 210, 212, 213, 226, 230, 236, 240, 246, 250, 251, 260, 261), seven from the United

Kingdom (217, 228, 236, 237, 241, 248, 255), seven from Asia (229, 231, 232, 238, 245, 248, 253), four

from North America (235, 244, 249, 256), three from South America (224, 242, 252), three

from the Middle East (223, 247, 254), two from Africa (234, 256), and one from Australia

(188). Twenty-four studies reported creatinine excretion (20, 115, 188, 210, 217, 223, 225, 226,

229, 232-234, 238, 241, 243, 244, 247, 249-251, 253, 255, 257, 260), 17 as 24-hour excretion

(mmol/24hours, mg/24hours, g/24hours) (115, 188, 217, 225, 226, 229, 232-234, 238, 243, 244, 249,

250, 253, 260), six as a ratio of creatinine to body weight (mmol/kg, mg/kg) (210, 241, 247,

251, 255, 257) and two as a ratio to urine volume (20, 223).

3.4.2 Quality of studies considered for primary analysis

The criteria used to evaluate the completeness of the urine samples was

inconsistent between studies, with 18 of the studies not providing information on

criteria used to assess the completeness of the urine samples (224, 225, 227, 229-231, 233,

238, 239, 242, 243, 245, 248, 249, 251, 252, 256, 261). Of the 25 studies which reported their urine

assessment criteria, 20 used creatinine excretion per kilogram of bodyweight (115,

176, 188, 210, 212, 213, 217, 223, 226, 232, 234, 241, 244, 246, 247, 250, 253-255, 260) based on established

age and gender specific cutoffs (116). The remaining five studies relied solely on

urine volume (236, 237), the number of reported missed collections (228), the excretion

of other nutrients (i.e. fluoride)(235), and one study utilised paraminobenzoic acid

(PABA) recovery as a measure of completeness (240). A total of nine studies utilised

urinary volume as an indicator of completeness (188, 217, 223, 228, 232, 236, 237, 241, 247), two

(188, 232) used the cut-off of 300mL/24hour based on previously published criteria

(264), two studies (236, 241) utilised the WHO criteria of <5mL/hour and <9mL/hour for

<6 and 6 year olds respectively, and two studies (217, 247) in older children (13-19

years) used the cutoff of <500mL/24hour, based on previously published criteria


~ 70 ~

(264). The cut-offs used by the remaining three studies (223, 228, 235) were based on the

distribution of volume in the sample, enabling the exclusion of extreme outliers

(e.g. 4SDs below the geometric mean (228)). Of the 25 studies which reported the

urine exclusion criteria, 6 did not report the number of urine samples excluded

from the final analysis (210, 234, 253-255, 261).

As a result of the inconsistency in the criteria used to assess the

completeness of the urine samples between studies, 23 studies (54%) scored low

on the NOS quality scale (224-226, 229-231, 233-235, 237-239, 242, 243, 245, 249, 251-256, 261) (Tables

3.3 & 3.4). Eighteen studies (42%) were classified as “medium” (115, 176, 210, 212, 223, 227,

228, 232, 236, 240, 241, 244, 246-248, 250, 260), and only two studies provided sufficient detail

regarding the 24-hour urine collection procedure to be classified as “high” quality

(188, 217) (Tables 3.3 & 3.4).

3.4.3 Initial analysis

The overall urine volume estimate (95% CI) for all 43 studies was 718 (682,

755) mL/24-hours. When broken down by age group, 19 studies reported results

for 2-6 year olds (n=1340, 1593 urine samples), 19 for 7-11 year olds (n=3736, 5174

urine samples) and 12 for 12-19 year olds (n=2230, 2359 urine samples). The

overall estimate (95% CI) for each of the three age groups were 470 (424, 516)

(Appendix D), 769 (735, 802) (Appendix E) and 1048 (973, 1123) (Appendix F)

mL/24-hours, respectively. There was a significant difference in the mean urine

volume across the three age groups (P<0.001).

3.4.4 Primary analysis limited to studies with > 1 urine assessment

criteria

Twenty-five studies reported >1 criteria for assessing the completeness of

the urine samples (n=6191, 8200 urine samples) (115, 176, 188, 210, 212, 213, 217, 223, 226, 228,

235-237, 240, 241, 244, 246, 247, 250, 253-255, 260). The overall urine volume estimate (95% CI) for

these studies was 774 (654, 893) mL/24-hours. When broken down by age group,

12 studies reported results for 2-6 year olds (n=1084, 1337 urine samples) (176, 212,

217, 223, 234-237, 240, 241, 254, 255), 11 for 7-11 year olds (n=3628, 5070 urine samples) (188,


~ 71 ~

210, 213, 217, 226, 228, 232, 234, 244, 246, 260) and seven for 12-19 year olds (n=1438, 1746 urine

samples) (115, 176, 210, 217, 234, 247, 260). The overall estimate (95% CI) for each of the

three age groups were 539 (468, 611) (Figure 3.3), 786 (749, 823) (Figure 3.4), and

1067 (855, 1279) (Figure 3.5) mL/24-hours, respectively. There was a significant

difference in the mean urine volume across the three age groups (P<0.001).

Children in the oldest age group had a 26% higher 24-hour urine volume compared

to those aged 7-11 years (1067 vs. 786 mL/24-hours, P<0.001) and a 49% higher

urine volume compared to those aged 2-6 years (1067 vs. 539 mL/24-hours,

P<0.001). Similarly, those aged 7-11 had a 31% higher volume compared to 2-6

year olds (786 vs. 539 mL/24hours, P<0.001).

The only difference in mean urine volume between those studies which had

reported at least one criteria for assessing the completeness of urine samples and

those which had reported none was amongst the 2-6 year old age group. The mean

urine volume in the initial analysis (i.e. all studies were included) was 13% lower

compared to the primary analysis (i.e. when the analysis was limited to only those

studies with >1 criteria for assessing the completeness of urine samples (470 vs.

539 mL/24-hours, P=0.04). There was no significant difference in the overall urine

volume estimate and the estimates for the other two age groups between the

initial and primary analyses (overall: 718 vs. 774 mL/24-hours; 7-11 y/olds: 769 vs.

and 786 mL/24-hours; and 12-19 y/olds: 1048 vs. 1067 mL/24-hours; all P>0.05).

Of the 25 studies with more than one criteria for assessing the

completeness of the urine samples, only eleven (115, 188, 210, 212, 217, 223, 228, 232, 235, 241,

244) had more than two urine criteria (n=2736, 3060 urine samples). In these eleven

studies, the mean (95% CI urine volume estimate was 725 (619, 832) mL/24-hours.

There was no difference in the mean (95% CI) urine volume estimate of these

studies, compared to those with only one assessment criteria (n= 14, 798 (664,

932) mL/24-hours, P=0.33) or those with none (n= 18, 635 (577, 692) mL/24-hours

P=0.28).

~ 72 ~


Figu

re 3

.3: F

ore

st p

lot

of

stud

ies

asse

ssin

g 2

4-h

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r u

rin

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~ 73 ~


Figu

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.4: F

ore

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lot

of

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ies

asse

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fect

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, P<0

.00

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~ 74 ~


Figu

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.5: F

ore

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lot

of

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ies

asse

ssin

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4-h

ou

r u

rin

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38)

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52

.9, d

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Te

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=9.8

9, P

<0.0

01


~ 75 ~

3.5 Discussion

This is the first study to systematically review the 24-hour urine volume of

children and adolescents. The overall 24-hour urine volume of 2-19 year olds was

less than 1L. As expected, older children had higher urine volumes with children in

the oldest age group having a 26% higher 24-hour urine volume compared to those

aged 7-11 years and a 49% higher urine volume compared to those aged 2-6 years.

Similarly, those aged 7-11 had a 31% higher volume compared to 2-6 year olds.

As approximately 90% of ingested iodine is excreted in the urine within 24-

48 hours (64), current recommendations for assessing the severity of iodine

deficiency within a population are based on the measurement of urinary iodine

concentration (UIC), expressed as µg iodine per liter of urine, in random ‘spot’ urine

samples collected from school-aged children (i.e 6-12 years (37)). Although these

recommendations were originally based on the observation that goiter prevalence

was <10% in populations of children and adults where the average daily iodine

intakes were >100 µg, later iterations extrapolated this value to represent a

concentration, expressed as µg/L. However, only in populations where mean urine

volume is equal to 1L would UIC provide an accurate reflection of total daily iodine

intake (15). Results from this analysis indicate that the average 24-hour urine volume

of school-aged children, the group commonly recommended for use in population

ioding monitoring, may not be 1L. This finding may impact on the findings of studies

conducted in children and adolescents, whereby spot UIC has been used as an

estimate of daily iodine intake as UIC would not be equivalent to daily iodine

excretion.

Furthermore, whilst these recommendations are primarily meant for use in

school-aged children, and were derived primarily on data based on goiter

prevalence estimates in school-aged children, they are often used to define the

iodine status of adult populations (265-268). Issues concerning different urinary

volume outputs among different subsets of the population and implications for

iodine nutrition assessment have previously been identified by Zimmerman and

Andersson (15). In this case, it was highlighted that as the urine volume of adults is


~ 76 ~

closer to 1.5L (269, 270), the use of the median UIC determined using spot urine

samples could result in the underestimation of the iodine intakes of individuals

within the population (15). This was demonstrated in a recent study in 301 adults

(18-64 years) from New Zealand, which compared median UIC from 24-hour urine

samples to the WHO criteria, both with and without adjustment for total urine

volume (266). This sample of adults was classified as iodine deficient using the WHO

criteria, based on a median UIC of 73 µg/L. However, the measured 24-hour UIE,

which accounts for urine volume, was closer to 127 µg/day (266). This value is in

excess of the 100 µg/day originally associated with reduced goiter prevalence (117),

and would indicate that the iodine intakes of this group of adults may be sufficient

(117). This study demonstrates the potential impact which not accounting for the

daily urine volume may have on the assessment of iodine deficiency in populations,

when UIC determined from spot urine samples is used as a surrogate index of

iodine intake.

Whilst UIC may provide an index of daily iodine intake in populations where

the daily urine volume is 1L , problems may arise if the assumption of 1L is made in

populations where the urine volume is less than 1L. For example, if we were to

assume that the average 24-hour urinary output of a population of school aged

children was 786 mL/24 hours as was determined for the 7-11 year age group, a

median UIC of 100 g/L, which indicates iodine adequacy according to the WHO

criteria (38), would correspond to an actual iodine intake of approximately 79

g/day. This level of intake is below the 100 µg/24-hours originally associated with

reduced goiter prevalence (117), and could suggest that this population may be at

risk of mild iodine deficiency. Therefore, studies which have utilised median UIC

determined from spot urine samples, as a surrogate marker of the iodine intake of

populations where the daily urinary output is less than 1L could be inaccurately

estimating and reporting iodine intakes when total daily urine volume is not

accounted for.

It is important to note that the included studies did not consistently report

the 24-hour urine collection protocol. As over/under collection of 24-hour urine

samples can bias results, studies which reported having indicators for assessing the


~ 77 ~

completeness of the included urine samples could be considered more reliable as

they are more likely to excluded over/under collectors. In the present review, 17

studies (40%) did not report at least one indicator for assessing the completeness

of the included urine samples. However, the only difference in the mean urine

volume estimate between the initial analysis including all studies and the primary

analysis limited to those studies with 1 indicator for assessing the completeness

of urine samples, was amongst the youngest age group. A recent systematic review

of methods for assessing the completeness of 24-hour urine collections in adults

and children (15-89 years) concluded that that the use of two or more indicators

for assessing urine completeness increases the likelihood of detecting incomplete

samples, thus increasing the validity of the results (215). In the present review, only

11 studies utilised more than one criteria (115, 188, 210, 212, 217, 223, 228, 232, 235, 241, 244),

however there was no difference in mean 24-hour urine volume estimated from

these studies compared to the overall estimate from all 43 studies. As such,

although the inclusion of only studies with >2 urine assessment criteria may result

in a more accurate overall volume, limiting the analysis to studies with a minimum

of 1 assessment criteria is sufficient to determine the overall urine volume of this

sample of children and adolescents.

It is also important to note that there was much wider variation in the 24-

hour urinary excretion of the oldest age group (12-19 years), compared to the

younger age groups. This is likely due to the fact that this age group encompassed

a much larger range of ages (i.e. 7 years), when compared to the other two age

groups. In this analysis it was not possible to further break this age group down into

two smaller groups, due to the age range of the included studies, and this is a

limitation of the present review which should be considered when interpreting its

findings. In addition, the adolescent growth spurt during the teenage years can

result in a rapid increase in growth over a relatively short period of time as well as

considerable variations in body size both within and between age and gender

groups (116). These variations in body size could result in subsequent variations in

overall fluid intake and as a result, variations in overall urine volume (264).


~ 78 ~

The impact of these findings on the use of UIC as an indicator of iodine

nutrition should be interpreted with caution, as urinary excretion of iodine is

dependent not only on urine volume, and can be influenced by a number of other

factors including gender, age, socio-cultural and dietary factors, drug interferences,

geographical location, and season (3-5, 18). In addition, another factor which may

need to be considered is the variability in iodine excretion within days. A number

of studies have demonstrated the wide variation in iodine excretion both within

and between days in individuals (16, 18, 26, 111, 126, 271). One study conducted in 42

adults and children (aged 4-60 years) found that urinary iodine excretion varied

significantly by timing of collection (P<0.001), with lowest levels occurring in the

morning and peaks observed following meals (16). Furthermore, a study in adults

observed that UIC determined from a fasting spot urine samples was 10% lower

than that determined in a non-fasting spot urine sample (18). Although this variation

has yet to be assessed in children, this study indicates that the timing of the spot

urine used to estimate the iodine intakes of a population may also have a significant

impact on the overall assessment of iodine nutrition. Therefore it is clear that a

number of factors need to be considered when assessing the suitability of current

recommendations for assessing the iodine nutrition of different age groups.

3.6 Conclusion

This is the first systematic review of the average 24-hour urine volume of

children and adolescents. Our ability to combine data and compare different

studies was limited by inconsistencies in the published urine collection protocol

and criteria used to define incomplete 24-hour urine collections. However, the

large number of studies representing 9,407 individuals demonstrate that the mean

urine volume of school-aged children (7-11 years) was less than 1L. This finding has

potential implications for the use of median UIC from spot urine samples as a

surrogate index of the iodine intakes of populations of children, as only in

populations where the mean urine volume is 1L would UIC provide an accurate

reflection of the iodine intakes of that population. More research is needed to

evaluate the appropriateness of current recommendations for assessing the iodine


~ 79 ~

status of populations where the average daily urine volume is not 1L. Future

research should focus on the use of functional tests of iodine deficiency, such as

measurement of serum thyroid hormone levels or thyroid volume, alongside

determination of UIC from spot urine samples in different subgroups of the

population. Such studies would help in the development more specific cut-offs for

assessing the iodine nutrition of populations where the daily urine output is not

equal to 1L using spot urine samples.

~ 80 ~

~ 81 ~

Chapter Four:

The Salt and Other Nutrient Intake in Children

(SONIC) Study Methodology

Components of this chapter have been reproduced directly from the methodological

paper describing the SONIC study published by Grimes CA, Baxter JR, Campbell KJ,

Riddell LJ, Rigo M, Liem DG, et al. Cross-Sectional Study of 24-Hour Urinary

Electrolyte Excretion and Associated Health Outcomes in a Convenience Sample of

Australian Primary Schoolchildren: The Salt and Other Nutrients in Children (SONIC)

Study Protocol. JMIR Res Protoc. 2015;4(1):e7.

4.1 Participant recruitment

The SONIC study was a cross-sectional study conducted within primary

schools located in Victoria, Australia from 2009-2013. Ethics approval was obtained

by the Deakin University Human Research Ethics Committee (Project No: EC 62-

2009).Victorian school-aged children attending government and non-government

primary schools were targeted for the Salt and Other Nutrient Intakes in Children

(SONIC) study. To enable a representative sample of children across different

socioeconomic groups, this study was initially designed to take place in government

schools. However, as this was the first Australian study to propose the collection of

24-hour urine collections within the school sector, there were difficulties in

obtaining approval from the governing education authority to conduct the study in

government schools. In order, to demonstrate the feasibility of collecting 24-hour

urine samples from schoolchildren, the study was piloted in a smaller sample of

children within the non-government school sector. This was possible as permission

to conduct research within non-government schools is granted on a school-by-

school basis by either the school board or principal.

After the successful completion of Phase 1 within non-government schools,

permission to enter government schools was granted by the Victorian Department

of Education and Early Childhood Development (2011_001151). Due to the overall

difficulties in recruiting schools to participate, a convenience sampling framework

The Salt and Other Nutrient Intake in Children (SONIC) Study Methodology

~ 82 ~

was used. To maximise recruitment, participation was open to all children

attending the primary school, however in some instances, the school principal did

not think it was appropriate for very young (i.e. prep – year 2) children to participate

in the study. Therefore, with respect to the requests of individual schools, the grades

invited to participate varied across schools. Participants were recruited in two

phases, first in non-government privately funded schools, and secondly in Victorian

government schools.

4.1.1 Phase One: Victorian non-government privately funded

schools

A web-based school locater search engine was used to identify all non-

government Victorian schools with enrolments of primary school children (n=193).

From this list a convenience sample of schools was selected and the principal of the

school contacted where the school was invited to participate in the study. In total

104 schools were contacted between June 2010 and June 2011, of which nine

schools agreed to participate in the study (response rate=9%). A short presentation

outlining the purpose of the study, was then presented at the school assembly.

Children were provided with information packs containing written materials

addressed to parents inviting them to take part in the study. Of the potential 1464

students, 269 agreed to participate (response rate 18%) of which 9 later dropped

out of the study. Data from six participating children were later excluded, as they

did not fall within the target age group (i.e primary school children,<13 years of

age). Written consent was obtained from the child, as well as the child’s

parent/caretaker. Children received a $20 book voucher to thank them for their

participation in the study.

4.1.2 Phase Two: Victorian government schools

Victorian government schools with enrolments for primary school children

(n=649) were identified using the Department of Education and Early Childhood

Development online school locater. A convenience sample of schools was selected

from the Eastern Metropolitan and Northern Metropolitan regions of Victoria due

to their proximity to the Burwood campus of Deakin University. No schools from


~ 83 ~

the Western Metropolitan region were recruited as by this stage the study had

reached its funding capacity. Schools were contacted in the same manner as those

in Phase 1. In total 316 Victorian government schools were contacted between

November 2011 and May 2013, of which 47 agreed to participate. The recruitment

strategy for children differed between participating government schools. Due to

constraints on the amount of access given to the project officer within each school,

it was not always possible for the short presentation to be given in some of the

schools, whilst the information pack was not distributed to all the children at other

schools. In these instances the project officer provided a ‘recruitment starter pack’

to the school principal. These packs contained the study information packs, which

were distributed to the children at the discretion of the school principal. Across all

schools the study was advertised in the school newsletter and parents could then

contact the project officer directly to express their interest in the study and request

an information pack.

Of the potential 13,045 students invited to participate in the study, 582

agreed to participate (response rate=4.5%). Children from schools where there

were less than 10 participants (n=11) were informed that to participate they would

need to travel to Deakin University to complete testing on a Saturday. Of the 40

children that this applied to, 15 agreed to visit Deakin University; however, 7 of

these did not attend (i.e. dropouts) on the scheduled day of data collection. A

further 25 children dropped out of the study; reasons for attrition included no

longer interested in participating (n=7) or being absent on the day of data collection

(n=18). Finally, one child from a school that refused to participate attended the

Deakin University data collection day. This child was aware of the study as their

mother worked at Deakin University. Written consent was obtained from the child

as well as their parent/caretaker. Due to guidelines for inducements for study

participation in government schools, either a book voucher to the value of $20 per

participant or $100 per school was issued to the school library to thank the children

for their participation. In total 254 children from Phase 1 and 526 children from

Phase 2 participated in the study, totalling 780 children. Figure 4.1 outlines the flow

of recruitment and participation in the SONIC study (272).


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4.2 Testing Procedures

Testing procedures were conducted at the participating schools by a team

of research staff, with the exception of the one day of data collection completed at

Deakin University. Data for Phase 1 were collected across two time blocks, June-

December 2010 and May-June 2011. Data for Phase 2 were collected as schools

were recruited between November 2011 and May 2013.

4.2.1 Demographic characteristics

A health and medical questionnaire was used to collect information on the

child’s age, gender, birth weight, existing medical conditions, and the use of

medication and dietary supplements, and was completed by the child’s primary

Figure 4.1: Outline of recruitment and participation in the SONIC study


~ 85 ~

caregiver. The questionnaire also collected information on the highest level of

education attained by the child’s primary caregiver, as a measure of Socioeconomic

Status (SES). The use of parental education as a marker of SES has consistently been

used in dietary studies in Australian children and adolescents (273-275). For the

present analyses SES will be defined as: i) high: includes those with a

university/tertiary qualification, ii) mid: includes those with an advanced diploma,

diploma or certificate III/IV or trade certificate and iii) low: includes those with a

high school diploma or lower. As this data was not available for all participants,

Socio-Economic Indexes for Areas, Index of Relative Socio-Economic Disadvantage

(SEIFA) , was used to group schools, based on school postcode, into tertiles of socio-

economic disadvantage. This marker was used as a second marker of SES, whereby

the participant was grouped as either low, mid or high SES with reference to the

tertile that the school they attended fell within.

4.2.2 Discretionary salt use characteristics

The demographic questionnaire included questions assessing the

participant’s discretionary salt use habits, relating to the primary caregiver’s use of

salt during cooking, “Do you add salt during cooking?”, as well as their child’s

addition of salt at meal times, “Does your child add salt to their meal at the table or

sandwich preparation?”. In addition, the child was also asked about their addition

of salt at the table, “Do you add salt to your meal at the table?” Responses for these

questions included ‘yes, usually’, ‘yes, sometimes’, ‘no’ or ‘don’t know’.

4.2.3 Anthropometric measurements

Height and weight were measured in participants wearing light clothing,

with shoes removed, according to standardised protocol (276). A calibrated portable

stadiometer SECA model 220 (Hamburg, Germany) was used to measure height to

the nearest 0.1cm and calibrated Scaleman FS-127-BRW portable electronic scales

(Bradman, MA, USA) were used to measure weight to the nearest 0.1 kg. A

minimum of two measurements were taken for height and weight. BMI was

calculated as body weight (kg) divided by the square of body height (m2).

Participants were grouped into weight categories (very underweight, underweight,


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healthy weight, overweight, obese) using the International Obesity Taskforce BMI

reference cut-offs for children (277, 278). Waist and hip circumference were measured

to the nearest 0.1 cm using a Lufkin Executive Thinline W606PM pocket tape

(Sparks, MD, USA) according to standardised protocol (276). A minimum of two

measurements were taken for waist and hip circumference.

4.3 24-hour urine collection

4.3.1 Procedure

For the 24-hour urine collection, each participant could elect to commence

his or her 24-hour urine at school on a weekday or at home on the weekend.

Written instructions, tailored for either a school or weekend day collection, were

provided for parents, with simplified pictorial instructions provided for the

children. Each participant was instructed to commence the collection by first

emptying their bladder, this urine was discarded and the time recorded as the 24-

hour collection start time. All urine voided during the following 24-hours was

collected into a 2.4 L wide-rimmed mouthed propylene bottle. Additional 500mL

plastic handled jugs were provided to assist with urine collection. Any missed

collections or spillages were recorded on a urine collection slip, returned with the

24-hour urine sample.

School day collections commenced at the start of the school day (i.e. 8:30-

9:30 am), with children collecting their materials to continue the collection at home

at the end of the school day (i.e. 3:00-3:30 pm). At the commencement of school

on the following day, children were instructed to visit the research staff, where

they completed the 24-hour collection by providing one final void. Children who

chose to complete the 24-hour urine collection at home on the weekend were able

to complete the collection over any 24-hour period over the weekend. Parents

were instructed to record the start and finish times on both the urine collection

bottle, as well as the urine collection slip. The following Monday morning children

returned the completed samples to the research staff at their school. The samples

were then weighed to determine the total 24-hour urine volume and following the


~ 87 ~

original analysis for sodium (described below), 2 x 10 mL aliquots were taken per

participant and transferred to -80°C conditions for storage.

4.3.2 Validity of urine samples

If the duration of the collection was not exactly 24-hours (but within 20-28

hours) urinary sodium, iodine, creatinine and total volume were normalised to a

24-hour period (i.e. (24 h/urine duration (h)) × urinary measure). Following this

urine collections were considered incomplete and excluded if they met one or

more of the following criteria: i) the timing of the collection was <20 hours or >28

hours ;ii) total volume was <300 mL; iii) the participant reported missing more than

one collection; iv) urinary creatinine excretion was less than 0.1 mmol/Kg body

weight/day (116). Fifteen participants did not return their urine collection slip, so it

was not possible to confirm if they had missed any collections or spillages. In these

children, the completeness of the 24h urine collection was assessed based on the

other three criteria. Of the 780 participants, 757 (97%) had available results from

the 24-hour urine collection. Based on the completeness criteria 90 participants

(12%) were classified as providing invalid 24-hour urine samples and were excluded

from analyses (Figure 4.2). From the 667 participants who provided valid 24-hour

urines, 650 had at least one 10mL aliquot of urine stored in -80°C conditions. Thus,

a final sample size of 650 participants with a valid 24-hour urine sample was used

for the analyses in the present thesis.


~ 88 ~

4.3.3 Biochemical Analysis

Sodium

Urinary sodium concentration was assessed using indirect ion selective

electrodes and urinary creatinine concentration was assessed using the Jaffe

reaction on the Siemens Advia 2400 analyser (Dorevitch Pathology, Melbourne,

Vic, National Association of Testing Authorities and Royal College of Pathologists of

Australia, accredited pathology laboratory). In the analyses involving urinary

sodium excretion, one extreme outlier for sodium excretion (mean (SD) 531 (9)

mmol/24-hour) was excluded, leaving a sample size of 649 participants.

Figure 4.2: Flow chart of urines included in iodine analysis


~ 89 ~

Iodine

A single 10ml aliquot per participant was defrosted at room temperature

and re-aliquoted into 2mL HDPE, preservative free tubes. These samples were

immediately re-frozen and shipped on dry ice to the University of Otago, Dunedin,

New Zealand, where they were stored at -80°C until analysis under the supervision

of Associate Professor Sheila Skeaff in February-March 2015.

A modification of the method established by Pino et al (1998) (279) was used

to determine the iodine concentration of the urine samples. First, each 2mL sample

was digested with ammonium persulfate at 110°C for one hour. Arsenious acid and

ceric ammonium sulphate were then added to the cooled digested samples. During

this reaction iodine catalyses the reduction of ceric ammonium sulphate (yellow

colour) to the cerous form (colorless), known as the Sandell-Kolthoff reaction (280).

The urinary iodine concentration (UIC) of the sample (µg/L) was then determined

by measuring the extinction of the samples in a microplate reader at 405nm, and

plotting against a standard curve constructed with known amounts of iodine (range

0-300 µg/L). The internal standard used was a pooled urine sample (mean (SD)

iodine concentration of 84.1(3.8) µg/L) which gave a CV of 4.5% (n=54). Seronorm

(Sero As, Billingstad, Norway) was used as an external standard, which had a mean

(SD) iodine concentration of 131.2 (1.3) µg/L (expected range 131-150 µg/L) and a

CV of 1.0% (n=54). To determine the iodine intakes of the study population,

adjusting for the urine volume and the fact that only ~92% of iodine ingested is

excreted in urine (64), urinary iodine excretion expressed as µg/24-hour, was

determined from the urinary iodine concentration (µg/L) using the following

equation :

UIE (µg/24-hour) = UIC (μg L⁄ )×24-hour urine volume (L 24⁄ hour)

0.92


~ 90 ~

4.4 24-hour food recall

A single three pass 24-hour dietary recall was used to determine all food

and beverages consumed from midnight to midnight on the day prior to the

interview. Recalls were completed on the day of testing at an allocated space within

the school. The method used was consistent with that used in the 2007 Australian

Children’s National Nutrition and Physical Activity Survey (CNPAS) (281). The three-

pass method includes the following stages:

1. First Pass: A ‘quick list’ of all foods and beverages and dietary supplements

consumed from midnight to midnight the day before the interview. Each ‘quick

list’ item has a unique series of probe questions which follow in the second

pass.

2. Second Pass: The ‘probe questions’ allow for further, more detailed

information, on each of the food items consumed to be collected. This includes

the time and place of consumption, the amount consumed, any additions to

the food item, brand and product name (if relevant), and the meal occasion

(i.e. breakfast, morning tea, lunch, afternoon tea, dinner, or supper, as defined

by the participant). Portion sizes were estimated using a validated food model

booklet and standard household measures. All items were hand recorded on a

food intake form by research staff with a nutrition background, who had

received training from an Accredited Practising Dietitian.

3. Third Pass: The ‘recall review’ is used to validate all information collected, and

to make any necessary corrections or additions. The interviewer reads aloud

all items consumed, as well as the place and time of consumption, brand and

product name, recipe details and portion size. All elements of the recall could

be edited, items deleted and new items added.

Children aged 8 years and older are recognised as having the cognitive

ability required to recall their own food and beverage intake (282). Children younger

than 8 years of age require a proxy (i.e. parent or caretaker) to assist in recalling

intake. Proxy interviews were not used in the SONIC study, as the dietary recall

component was completed during school time. However, all children completed

the 24-hour recall component, in order to avoid a feeling of exclusion amongst the


~ 91 ~

younger participants. In total 776 participants completed the 24-hour recall

component. Of these, 7 were excluded due to the participant having issues

recalling their intake and 217 were excluded as they were below the age of 8 years.

A total of 552 valid 24-hour recalls completed by participants over 8 years of age

were used for the dietary analysis..

The completed 24-hour recalls were entered into the nutrient analysis

software FoodWorks (version 8, Xyris, Australia), which is linked to the AUSNUT

2011-13 nutrient database , to determine total energy (kJ/day) and iodine (µg/day)

intakes. Each food and beverage item recorded was matched to an eight-digit food

code, which corresponded to a set of nutrient data. Each eight-digit food code was

derived from a five-digit food code that represented ‘minor’ food categories (e.g.

“12201: Breads, and bread rolls, white, mandatorily fortified”). Each minor food

category falls under a three-digit sub-major food category (e.g. “122: Regular

breads, and bread rolls (plain/unfilled/untopped varieties)”), which then falls

within a two-digit major food category (e.g. “12: Cereals and cereal products”). A

detailed list of the food group classification system can be found in the 2011-13

Australian Health Survey (AHS) user guide (158). This food group coding system was

used to calculate the contribution of iodine from major, sub-major, and minor food

groups, using the population proportion method (283).

The paediatric adjusted Goldberg cut-off method was used to identify

participants with implausible energy intakes. This method compares the

participant’s ratio of reported energy intake to estimated basal metabolic rate

(estBMR) with the Goldberg cut-off value. Age- and sex-specific Goldberg cut-off

values were determined using the original adult Goldberg equation (28). On this

basis, participants (n=32, 5.8%) with an EI:estBMR ratio less than 0.87 for 8-12 year

old males and 0.84 for 8-12 year old females were classified as a low energy

reporter and excluded. Thus, a total of 520 valid 24-hour recalls were included in

the final dietary analyses.

~ 92 ~

~ 93 ~

Chapter Five:

Study 2: Iodine intakes of Victorian school-aged

children measured using 24-hour urinary iodine

excretion

Components of this chapter have been published in: Beckford K, Grimes C,

Margerison C, Riddell L, Skeaff S, Nowson C. Iodine Intakes of Victorian

Schoolchildren Measured Using 24-hour Urinary Iodine Excretion. Nutrients.

2017;9(9):961.

5.1 Introduction

Prior to the 1990s Australia was believed to be iodine-sufficient, with the

exception of the Australian Capital Territory (ACT) and the island of Tasmania

where endemic goiter was prevalent until the introduction of an iodine

supplementation program in 1950 (12, 13). However, studies conducted in a small

sample of pregnant women (33) and Tasmanian school-aged children (130) reported

iodine deficiency in the late 1990s. Following this, a number of studies documented

iodine deficiency in Australian school-aged children (34, 35, 151, 284, 285) and adults (178,

286-289). In order to combat the re-emergence of deficiency, mandatory fortification

of all commercially produced yeast leavened bread, excluding organic varieties,

with iodised salt was implemented from October 2009 (44). Studies conducted post-

fortification utilising spot urine samples, including the 2011–2013 Australian Health

Survey (AHS), have reported an improvement in iodine status among school-aged

children and adults, when compared to pre-fortification (29, 30, 46, 47). The most

common method for monitoring the iodine status of a population is usinf spot urine

samples, as approximately 90% of ingested iodine is excreted in the urine within

24-48 hours (64, 95, 109), with a median urinary iodine concentration (UIC) ≥100 μg/L

measured in spot urine samples indicative of sufficiency (38). Due to the

considerable variation in iodine excretion over the course of the day (16-19), spot

samples are not able to provide an accurate estimate of the actual iodine intake of

a population (20, 21), however the use of a 24-hour urine sample to determine

Study 2: Iodine intakes of Victorian school-aged children measured using 24-hour urinary iodine

excretion

~ 94 ~

urinary iodine excretion (UIE) in μg/24-hour allows for a good estimate of recent

iodine intake (22-26). Whilst the 2011-13 AHS was able to estimate iodine intakes

using 24-hour food recalls , dietary assessment methodologies are not considered

as accurate as urine samples for estimating iodine intakes due to the wide

variations in the iodine content of foods such as iodised salt, bread and milk, which

are unlikely to be captured by food databases (15, 159). A single 24-hour urine sample

is able to provide a more objective measure of actual iodine intake. To date there

have been no studies utilising 24-hour urine samples to estimate the iodine intakes

of a sample of Australian school-aged children, post fortification of bread.

5.2 Aim

To determine the total daily iodine intake of a sample of Victorian school-

aged children on one day utilising 24-hour urinary excretion of iodine and to

assess differences across age, gender, and socioeconomic status.

5.3 Methods

The study design and full methodology for this study has been described in

detail in Chapter Four

5.3.1 Participants and recruitment

See section 4.1 Participant recruitment for details.

5.3.2 24-hour urine collection

See section 4.3 24-hour urine collection procedure for details.

5.3.3 Urinalysis

See section 4.3 Biochemical analysis for details. A total of 650 participants

provided a valid 24-hour urine samples which was analysed for iodine and included

in these analyses.


excretion

~ 95 ~

5.3.4 Statistical analysis

All analyses were completed with STATA/SE 14.0 software (StataCorp LP,

College Station, TX, USA), and a P value <0.05 was considered statistically

significant. For analysis children were grouped into age groups (4–8 years and 9–

12 years) that are consistent with the National Health and Medical Research

Council (NHMRC) Nutrient Reference Values (NRVs) for Australia and New Zealand

reference age groups (2). In order to make comparisons with results from the 2011-

13 AHS (156), results for the subgroup of participants within the age range of 5–11

years have also been reported. Initially socioeconomic status (SES) was defined

based on the highest level of education attained by the child’s primary caregiver as

follows: i) high: includes those with a university/tertiary qualification, ii) mid:

includes those with an advanced diploma, diploma or certificate III/IV or trade

certificate and iii) low: includes those with some or no level of high school

education. However, as this data was not available for all participants (n=554, 85%),

an alternative measure of SES was also used. Socio-Economic Indexes for Areas,

Index of Relative Socio-Economic Disadvantage (SEIFA) , was used to group schools,

based on school postcode, into tertiles of socio-economic disadvantage. This

marker was used to define SES, whereby the participant was grouped as either low,

mid or high SES with reference to the tertile that the school they attended fell

within.

Descriptive statistics (mean values and standard deviations or proportions

and numbers) were used to describe participant characteristics, stratified by

gender. Normality of continuous variables was assessed using box plots, histograms

and the Shapiro-Wilk test. UIE (μg/24-hour), UIC (ug/L), creatinine (mmol/24-hour),

iodine:creatinine ratio (µg/mmol) and volume (L/24-hour) were expressed as mean

(SD) as they were determined to be sufficiently normally distributed. As UIC (μg/L)

is commonly expressed as median (IQR), this has also been reported to enable

comparisons to the literature. Differences in the proportion of participants by

gender and across sociodemographic characteristics was determined using χ2 tests.

Multiple linear regression, with adjustment for school cluster was used to assess

differences in urinary parameters between age groups and socioeconomic groups.


excretion

~ 96 ~

Linear regression, adjusted for school clustering, was used to assess differences

across socioeconomic groups (adjusted for age and gender). The unstandardised

beta co-efficient (β) has been presented for both the adjusted and unadjusted

models. The underlying assumptions of linear regression (i.e., homoscedasticity,

normality) were confirmed using regression plots of residuals.

To assess the adequacy of UIE compared to dietary recommendations, the

proportion of the population with a UIE below the age and gender specific

estimated average requirement (EAR) (i.e. 65 μg/day for 4–8 year olds and 75

μg/day for 9–12 year olds) (2) was determined using the EAR cut-point method (5)

and expressed as n (%), and chi-square (χ2) test used to assess the difference in

proportions between age and gender groups exceeding the EAR. The proportion of

the population falling below the recommended UIC of 100 μg/L and 50 μg/L, as

recommended by the World Healt Organization (WHO) (38) was expressed as n (%),

and χ2 test used to assess the differences in proportions between age and gender

groups.

5.4 Results


Overall 55% of children were male, the average age was 9.3 years and 62%

and 61% were of high socio-economic background based on school postcode SEIFA

and primary caregiver education index, respectively (Table 5.1). In total, 17% of

children were either overweight or obese and more than half completed the 24-

hour urine collection on a non-school day.

5.4.2 Urinary parameters, by gender

The mean (SD) UIE and median (IQR) UIC for all participants were 104 (54)

μg/24-hour and 124 (83, 172) μg/L, respectively (Table 5.1). Whilst only 2% of

participants (n=14) had a UIC in excess of 300 µg/L, 73% of participants had a UIE

greater than the recommended EAR for their age and gender group (2). The

proportion of participants falling below the WHO recommendation of 100 μg/L


excretion

~ 97 ~

based on UIC (38) was less than 50%, with only 8% of the population below 50 μg/L.

Overall, males had higher UIE and UIC, compared to females (P<0.01), and more

females had a UIE below the recommended age-specific EAR.(2). In addition, a

higher proportion of females fell below the WHO recommended UIC cut-offs of 50

μg/L and 100 μg/L (38) (Table 5.1).The mean UIE of participants classified as having

a UIC below 50 μg/L (n=49) was 42 (26) μg/24-hour and 73 (38) μg/24-hour for

those with a UIC below 100 μg/L (n=233).

Table 5.1: Descriptive characteristics and urinary parameters of SONIC participants All Boys Girls

Participants, n (%) 650 359 (55) 291 (45) Age (mean (SD), years) 9.3 (1.8) 9.3 (1.9) 9.2 (1.7) Age group

4–8 years, n (%) 280 (43) 157 (44) 123 (42) 9–12 years, n (%) 370 (57) 202 (56) 168 (58) SES 1

Bottom tertile, n (%) 105 (16) 45 (13) 60 (21)a

Mid tertile, n (%) 144 (22) 77 (22) 67 (23) High tertile, n (%) 401 (62) 237 (66)a 164 (56)

SES 2 Bottom tertile, n (%) 134 (24) 77 (25) 57 (24) Mid tertile, n(%) 84 (15) 54 (17) 30 (12) High tertile, n (%) 336 (61) 183 (58) 153 (64) BMI category

Underweight, n (%) 67 (10) 34 (9) 33 (11) Healthy, n (%) 473 (73) 270 (75) 202 (69) Overweight, n (%) 91 (14) 45 (13) 46 (16) Obese, n (%) 20 (3) 10 (3) 10 (4) Day of urine collection

School day, n (%) 304 (47) 168 (47) 136 (47) Non-school day, n (%) 346 (53) 191 (53) 155 (54) UVol (mean (SD), mL/24-hour) 873 (424) 897 (453) 843 (384) UCr (mean (SD), mmol/24-hour) 5.6 (2.0) 5.8 (2.1)b 5.3 (1.9)

I:Cr ratio (mean (SD), µg/mmol) 28 (19) 29 (20) 26 (18) UIE (mean (SD), μg/24-hour) 104 (54) 112 (57)b 93 (48)

<EAR *, n (%) 177 (27) 77 (21) 100 (34)a

>EAR *, n (%) 473 (73) 282 (79)a 191 (66)

UIC (μg/L) mean (SD) 133 (68) 141 (70)b 123 (64) median (IQR) 124 (83,172) 135 (88,180) 116 (75,161) <100 μg/L, n (%) 233 (36) 114 (32) 119 (41)a

<50 μg/L, n (%) 49 (8) 20 (6) 29 (10)a

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); SES: Socioeconomic status; BMI: body mass index; UVol: urine volume; UCr: urinary creatinine; UIE: urinary iodine excretion; EAR: estimated average requirement; UIC: urinary iodine concentration * EAR for 4–8 year olds: 65 μg/day, 9–13 year olds: 75 μg/day (2) 1 SES based on state-based socioeconomic indexes for areas, index of relative socioeconomic disadvantage ; 2 SES based on primary caregiver education—data not available for all 650 participants, n=554 a χ2 test p<0.05 for differences between genders; b Multiple regression adjusted for age and school cluster p<0.05 for difference between genders.


excretion

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5.4.3 Urinary parameters, by age

A 1 year increase in age was associated with a 6 μg/24-hour increase in

iodine excretion and accounted for 4% of the variance in UIE (R2=0.04, β(SE)=5.83

(1.40, P<0.001). This association remained significant after adjustment for gender

(R2=0.07, β=5.71 (1.37), P<0.001). Children in the 9–12 year age group had a 15%

higher mean UIE compared to the 4–8 year old participants (Table 5.2, P<0.001).

There was no significant difference in UIC or the proportion of participants falling

below 100 and 50 μg/L between the two age groups, nor was there a difference in

the proportion of participants with a UIE meeting the age-specific EAR. When

broken down into the AHS age group of 5–11 year olds (n=617), the median (IQR)

UIC was 124 (83, 172) μg/L and mean (SD) UIC and UIE were 133 (67) μg/L and 102

(52) μg/24-hour, respectively. The proportion of 5–11 year old participants with

median UIC below 50 and 100 μg/L were 36% and 7%, respectively.

Table 5.2: Urinary parameters of SONIC participants, by age group (n=650). Age Group (Years)

4–8 (n=280)

9–12 (n=370)

UVol (mean (SD) , mL/24-hour) 749 (341) 966 (456) a

UCr (mean (SD), mmol/24-hour) 4.3 (1.3) 6.5 (2.0) a

I:Cr ratio (mean (SD), µg/mmol) 35 (23) 22 (13) a UIE (mean (SD), μg/24-hour) 94 (48) 111 (57) a

<EAR *, n (%) 80 (29) 97 (26)

>EAR *, n (%) 200 (71) 273 (74)

UIC (mean (SD), μg/L) mean (SD) 138 (69) 129 (67)

median (IQR) 127 (86,175) 122.3 (80,170) <100 μg/L, n (%) 89 (32) 144 (39)

<50 μg/L, n (%) 19 (7) 30 (8)

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); UVol: urine volume; UCr: urinary

creatinine; UIE: urinary iodine excretion; EAR: estimated average requirement; UIC: urinary iodine

concentration

* EAR for 4–8 year olds: 65 μg/day, 9–13 year olds: 75 μg/day (2) a Linear regression (adjusted for gender and school cluster) p < 0.05 for differences between age

groups


excretion

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5.4.4 Urinary parameters, by socioeconomic status

There was no significant effect of SES based on school postcode on UIC, UIE,

or the proportion meeting the recommendations for EAR or UIC cut-offs (Table 5.3).

When SES was based on primary caregiver education there was no change in these

findings (Table 5.4).

Table 5.3: Urinary parameters of SONIC participants by school postcode socioeconomic status mean (SD) (n=650)

Table 5.4: Urinary parameters of SONIC participants, by parental education socioeconomic status mean (SD) (n=554)1

SES category

Bottom tertile

(n=134)

Mid tertile

(n=84)

Top Tertile

(n=336)

UVol (mean (SD), ml/24-hour) 817 (408) 897 (366) 868 (419)

UCr (mean (SD), mmol/24-hour) 5.7 (2.3) 5.5 (1.7) 5.3 (2.0)

I:Cr ratio (mean (SD), µg/mmol) 28 (23) 25 (18) 29 (19)

UIE (mean (SD), mg/24-hour) 97 (46) 99 (52) 103 (56)

<EAR*, n(%) 38 (29) 25 (30) 98 (29)

>EAR*, n(%) 96 (72) 59 (70) 238 (71)

UIC (mg/L)

mean (SD) 135 (71) 124 (72) 133 (67)

median (IQR) 131 (75, 174) 109 (67, 172) 122 (84, 171)

<100mg/L, n(%) 46 (35) 38 (45) 123 (37

<50mg/L, n(%) 12 (9) 10 (12) 23 (7)

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); UVol: Urine volume; UCr: Urinary

creatinine; UIE: Urinary Iodine Excretion; EAR: Estimated Average Requirement; UIC: Urinary Iodine

Concentration 1 Data not available for all 650 participants; * EAR for 4-8 year olds: 65mg/day, 9-13 year olds: 75mg/day

SES Category

Bottom Tertile (n=105)

Mid Tertile (n=144)

Top Tertile (n=401)

UVol (mean (SD), mL/24-hour) 868 (478) 813 (405) 896 (415) UCr (mean (SD), mmol/24-hour) 5.7 (2.1) 5.1 (1.7) 5.7 (2.1) I:Cr ratio (mean (SD), µg/mmol) 29 (18) 31 (22) 26 (18) UIE (mean (SD), μg/24-hour) 115 (53) 100 (47) 102 (56) <EAR *, n (%) 20 (20) 38 (26) 119 (30) >EAR *, n (%) 85 (81) 106 (74) 282 (70) UIC (μg/L) mean (SD) 150 (70) 139 (72) 127 (65) median (IQR) 136 (89,195) 130 (85,175) 121(78,166) <100 μg/L, n (%) 32 (30%) 48 (33) 153 (38) <50 μg/L, n (%) 2 (2%) 11 (8) 36 (9)

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); UVol: urine volume;

UCr: urinary creatinine; UIE: urinary iodine excretion; EAR: estimated average requirement; UIC:

urinary iodine concentration

* EAR for 4–8 year olds: 65 μg/day, 9–13 year olds: 75 μg/day (2).


excretion

~ 100 ~

5.5 Discussion

The mean UIE of both age groups exceeded the age-specific EAR indicating

that, overall, the iodine intakes of this group of schoolchildren are sufficient. These

data are supported by the observation that the median UIC of 124 μg/L exceeds

the recommended 100 μg/L set by the WHO indicative of adequate iodine status.

Age and male gender were significant predictors of UIE, albeit minimally, and this

is most likely due to a greater overall food intake in boys. There was, however, no

difference in UIE between the different socioeconomic groups. These findings,

along with those of other studies conducted post-fortification of bread with iodised

salt (32, 47), provide evidence that compared to pre-fortification data Australian

schoolchildren are now iodine replete. Therefore, it appears that the iodine intakes

of Australian schoolchildren have increased to sufficiency since the introduction of

mandatory fortification of bread with iodised salt.

The mean UIC of 5–11 year olds in our study, as determined using 24-hour

urine samples, was 124 μg/L compared to 178 μg/L in the same age group assessed

by spot urine samples in the 2011-13 AHS, with a higher proportion of participants

falling below the recommended cut-off of 100 μg/L when compared to the AHS

(36% vs. 20%) (45). It is not unusual to observe lower iodine excretion rates in

Victoria, when compared to the rest of Australia, with studies conducted both pre-

and post-fortification observing significantly lower iodine excretion amongst

Victorians (13, 34, 43, 156, 285). In the AHS in particular, Victorians aged 18 years and over

had a 10% lower median UIC compared to the Australian average (113 vs. 124 μg/L)

as well as 11% more participants with a UIC below 100 μg/L (42% vs. 37%)(156).

Reasons for the regional variation in iodine intakes within Australia are not well

established, but could be related to possible regional variations in the iodine

concentration of soil, milk, and differences in water iodine levels (290).

Another reason for the differences between the results in the present study

and those of the AHS may be due to the differences in the urinary methodologies.

Whilst the AHS utilised spot urine samples, the present study measured UIC in 24-

hour urine samples (16, 18). Previous studies comparing 24-hour UIC and spot UIC in

schoolchildren (20, 115) have found that spot UIC tends to be lower than 24-hour UIC,


excretion

~ 101 ~

by approximately 10% (20, 115). Conversely, the 24-hour UIC observed in the present

study was 30% lower than the spot UIC observed in the AHS. This could be due to

the timing of the spot samples in the AHS compared to previous studies, as it has

been demonstrated that spot UIC can vary significantly depending on the timing of

the sample collection (18, 19, 111, 126). One study in 60 Brazilian adults observed a 30%

lower UIC in morning spot samples, compared to overnight samples (183 vs. 253

μg/24-hour, P<0.001) (19). It is important to note that the studies comparing spot

and 24-hour UIC in school-aged children utilised first morning spot samples (20, 115)

whereas the AHS did not specify a time for the spot urine collection (291). Therefore

the urine samples in the AHS could have been collected overnight when iodine

excretion is highest, and this might explain the higher UIC observed in the AHS,

when compared to the 24-hour UIC observed in the present study.

A recent systematic review of studies comparing spot and 24-hour urines

for estimating the iodine intakes of a population concluded that there is currently

not enough evidence to determine whether UIC determined from spot urine

samples provides an accurate reflection of 24-hour UIE (27). Subsequently the

authors of this review recommend that, whilst spot urines provide a good reflection

of the iodine status of a population, 24-hour urine samples should be used to

determine the iodine intakes of a population. In the present study, the mean 24-

hour UIE, as an indicator of daily iodine intake, of both age groups exceeded the

EAR without exceeding the recommended upper level of intake of 300 μg/day (2).

The iodine intakes determined using 24-hour UIE in the present study were 40%

lower than the iodine intakes determined using 24-hour food recalls in the AHS (i.e.

94 vs. 156 μg/day for 4–8 year olds and 111 vs. 179 μg/day in 9–13 year olds) (45).

As previously discussed, it is not unusual to observe lower iodine intakes in

Victorians and this could explain the lower intakes observed in the present study

compared to the AHS. Furthermore, the present study utilised 24-hour urine

samples which are able to provide a more objective measure of actual iodine intake

when compared to 24-hour food recalls. In addition to the recall and subject biases

associated with food recalls food composition databases often lack specificity

regarding the use of iodised salt during food production and the salt iodine content


excretion

~ 102 ~

(49, 50, 159). Furthermore, the iodine content of dairy products in particular is highly

variable and can depend on a number of factors including the breed of cow,

supplementation of feed, the use of salt licks, farm location and contamination by

iodophore sanitisers during processing (165, 166, 168). These variations are often not

captured by food composition databases and this can result in inaccurate

estimations of iodine intakes when dietary assessment methodologies are used (15,

159, 167).

The present study also determined that iodine intake, as measured using

UIE, was significantly lower in females, with a higher proportion of female

participants having a UIE below the age-specific EAR. To date, this is the first

Australian study to utilise 24-hour urine samples to assess differences in iodine

intakes, as measured using UIE, between genders. The lower intake of iodine in

females is of concern as dietary habits tend to track into adulthood and low iodine

intakes during childhood could lead to females entering pregnancy with suboptimal

iodine status. Iodine is an important nutrient during pregnancy, and deficiency

during this time has been linked with impaired growth and cognition during

childhood (66, 292). Therefore, it is important that the iodine intakes of school-aged

children, particularly females, continue to be monitored using 24-hour urine

samples.

The major strength of the present study is the use of 24-hour urine samples

to objectively measure iodine intake in a relatively large number of Victorian

schoolchildren. A stringent 24-hour urine collection protocol, specifically tailored

to children was used to ensure the completeness of the samples. Limitations of this

study include the convenience sampling method, which resulted in the over-

representation of participants from a higher socioeconomic background.

Regardless of which SES definition was used, almost two-thirds of the participants

were from a high socioeconomic background, compared to 28% of 5–11 year olds

(based on parental education) in the AHS (293), and this limits applicability of the

findings to the general population. Finally, as the present study was limited to a

single 24-h urine collection, adjustments for the within person variation in 24-hour


excretion

~ 103 ~

urinary iodine excretion could not be made, in order to obtain a measure of long-

term ‘usual’ iodine intake.

5.6 Conclusions

In conclusion, this is the first study to assess the iodine intakes of Australian

school-aged children post- fortification of bread with iodised salt using 24-hour

urine samples. Both daily UIE and UIC determined from 24-hour samples indicate

that the iodine intakes of this population are sufficient. These results confirm the

results of the AHS and indicate that the iodine intakes of Victorian schoolchildren

have increased to adequacy since mandatory fortification of bread with iodised salt

was introduced in 2009. However, continued monitoring of iodine intakes using 24-

hour urine samples is needed in order to ensure that the actual iodine intakes of

school children are sufficient.

~ 104 ~

~ 105 ~

Chapter Six:

Study 3: Food sources of iodine and association

with urinary iodine excretion

6.1 Introduction

Iodine is a trace element found in low concentrations in soil, air and the sea

(3-5). In the past, the most abundant food source of iodine was milk, both through

naturally occurring iodine as well as the contamination of milk by iodophor-

containing sanitisers used during milk processing (51, 130, 163, 164). Following the re-

emergence of iodine deficiency in Australia in the late 1990s (14, 34, 35, 52, 287, 294) a

number of fortification strategies were discussed by experts and government

bodies (154). From October 2009 mandatory fortification of all commercially

produced yeast leavened bread, excluding organic varieties, with iodised salt was

implemented nationally in Australia , due to the success of a similar voluntary

fortification strategy in Tasmania between 2001-2005 (32). Whilst the legislation

also stipulates that iodised salt may replace non-iodised salt in other baked

products, its addition during processing is at the discretion of the manufacturer (44).

Utilising dietary information obtained from 24-hour recalls, the 2011-13

Australian Health Survey (AHS) reported that bread and milk were the major food

sources of iodine in the Australian population (45). Furthermore, a recent analysis of

the AHS found that consumption of 100g of bread per day was associated with 12

times greater odds of achieving adequate dietary iodine intakes above the age

specific Estimated Average Requirement (EAR), amongst 2-18 year olds (46). The

iodine intakes determined in the AHS were based purely on dietary intake derived

from 24-hour dietary recalls, however 24-hour urine samples are able to provide

an objective measure of iodine intakes as approximately 90% of dietary iodine is

excreted in the urine (64, 94).To date no studies have examined the association

between food sources of iodine and urinary iodine excretion (UIE) over a 24-hour

period. Understanding the contribution of key food sources of iodine to urinary

iodine excretion, as an objective measure of total daily iodine intake, is important

Study 3: Food sources of iodine and association with urinary iodine excretion

~ 106 ~

when assessing the success of fortification strategies, as well determining how food

choices may influence iodine intake among Australian school-aged children.

6.2 Aims

i) To determine the dietary intake and food sources of iodine in a sample of

Victorian school-aged children.

ii) To assess the relationship between dietary intake of iodine and the major food

sources of iodine, including bread and milk, and urinary excretion of iodine over a

24-hour period.

6.3 Methods

The study design for this study has been described in Chapter Four


See section 4.1 Participants and recruitment for details. Of the 780

participants in the Salt and Other Nutrient Intake in Children (SONIC) study, 454

had both a valid 24-hour food recall and a 24-hour urine sample analysed for iodine

and have been included in these analyses. Logistically it was not possible to

concurrently collect 24-hour dietary recalls alongside the 24-hour urine collection.

Instead 24-hour recalls were collected with children on the day of testing

procedures completed at the school site. As such, n=12 (3%) provided their dietary

recall and 24-hour urine collection on the same day, n=75 (17%) within two days,

n=29 (7%) collected within a 2-week period, and n=14 (3%) within a 6-week period)

(272).

6.3.2 Measures

See section 4.2 Testing procedures for details

6.3.3 24-hour food recall

See section 4.4 24-hour food recall for details.


~ 107 ~


See section 4.3 24-hour urine collection for details.

6.3.5 Urinalysis

See section 4.3.3 Biochemical analysis for details.


Analyses were completed with STATA/SE 14.0 software (StataCorp LP), and

a P value <0·05 was considered statistically significant. Descriptive statistics (mean

values and standard deviations or proportions and numbers) were used to describe

participant characteristics, stratified by gender. Normality of continuous variables

was assessed and confirmed using box plots, histograms and the Shapiro-Wilk test.

Differences in the proportion of participants by gender across sociodemographic

characteristics was determined using 2 tests. Multiple linear regression, which

allows for the adjustment for clustering of students within schools, was used to

examine the differences in energy intake, dietary iodine intake and UIE between

genders. Pearsons correlation and multiple linear regression were used to assess

the association between the two methods of estimating iodine intake (dietary

assessment vs. urinary excretion). The unstandardised beta coefficient (β) and

standard error (SE) have been presented for all regression analyses. To assess the

adequacy of UIE and dietary iodine compared to dietary recommendations, the

proportion of the population with a UIE or Dietary iodine intake below the age and

gender specific estimated average requirement (EAR) (i.e. 65 μg/day for 4–8 year

olds and 75 μg/day for 9–12 year olds) (2) was determined using the EAR cut-point

method (5) and expressed as n (%), and chi-square (χ2) test used to assess the

difference in proportions between bread and milk consumption groups exceeding

the EAR.

Previous analyses in this sample determined that salt intakes were

significantly higher on non-school days compared to school days (295). Therefore,

we examined the differences in iodine intakes (dietary assessment and urinary

excretion) and food sources between school days and non-school days using


~ 108 ~

multiple linear regression (adjusted for age, gender, energy intake and school

clustering). Contribution of food groups to total iodine intake was expressed as

mean %. A ‘consumer’ was defined as any participant who consumed >0g of the

specified food on the day of the recall. Pearsons correlation and multiple regression

(adjusted for age, gender, energy intake and school cluster) were used to assess

the association between intake of food sources which contributed >1% of total

iodine (g/day) and UIE.

As the primary vehicle for iodine fortification in Australia is iodised salt

added to bread , we further examined the relationship between known iodine

fortified bread products and UIE specifically. For the purpose of these analyses,

‘Bread’ (g/day) includes all food items eaten within the 3-digit food group “122:

Regular breads, and bread rolls (plain/unfilled/untopped varieties)” and “123:

English-style muffins, flat breads, and savoury and sweet breads” food groups,

excluding any homemade and organic varieties in accordance with the foods which

are required to have iodised salt added to them under the 2009 mandatory

fortification legislation (44). This definition does not include bread which may have

been consumed as a commercially produced sandwich/hamburger which would fall

within the “135: Mixed dishes where cereal is the main ingredient category” and

would encompass the contribution of iodine from all components of the dish, as

opposed to the bread alone. In addition, as recent analyses of the AHS revealed

that milk is still a major contributor to iodine intakes (46), we also examined the

relationship between milk intake and UIE. ‘Milk’ g/d) includes all items consumed

within the 3-digit sub-major food group “191: Dairy milk (cow, sheep and goat)”

(158). The association between consumption of >100g bread or milk per day and UIE

was assessed using multiple linear regression, adjusted for age, gender, energy

intake and school cluster.

To further examine the relationship between bread and/or milk

consumption and UIE, multiple linear regression, adjusted for age, gender, energy

intake and school clustering, was used to compare differences in UIE and iodine

intake as assessed by 24-hour recall between consumers and non-consumers of

bread or milk. We also conducted a second analysis whereby consumers were


~ 109 ~

further broken down into consumers of neither (i.e. consumed neither bread nor

milk on the day of the survey), either (i.e. reported consuming either bread or milk

on the day of the recall) or both (i.e. consumed both bread and milk on the day of

the recall). In this model which included multiple comparisons between the

different consumption groups, non-consumers (i.e. did not report consuming both

bread and milk on the day of the recall) were used as the reference group and the

Bonferroni correction for P-values was used.

6.4 Results


Of the 454 participants who provided valid urinary and dietary data, 55%

were male with an average age of 10 years (Table 6.1). Most (81%) participants

completed the 24-hour recall on a school day, whereas just under half (49%)

completed the 24-hour urine collection on a school day. The number of days

between dietary recall and urine collection ranged between 0 and 10 (median 8)

days: 49% of participants (n=224) completed the urine and dietary recall on the

same type of day (i.e. school day/non-school day), with a further 84% (n=380)

completing dietary assessment and urine collections within a week of each other

and 17% (n=75) completing the two components within two days of one another.

Mean energy intake was 8561 (2660) kJ/day, with males having a significantly

higher energy intake than females (Table 6.1).

6.4.2 Iodine intakes

Mean (SD) UIE and dietary iodine were 108 (54) µg/24-hour and 172 (74)

µg/day, respectively (Table 6.1.) There was a significant, weak, positive relationship

between iodine intake determined using the food recall and UIE (r=0.23 P<0.001).

Regression analysis revealed that a 10 µg/day increase in dietary iodine was

associated with a 3.2 µg/24hour increase in UIE (R2=0.054, =3.22 (0.63)(SE),

P<0.001). In participants who completed the recall and urine components on the

same day (n=12, 3%), a 10 µg/day increase in dietary iodine was associated with a

6.3 µg/24-hour increase in UIE (R2=0.26, =6.32 (1.81)(SE), P=0.04).


~ 110 ~

Males had significantly higher intakes of dietary iodine and UIE (both

P<0.001, Table 6.1). Whilst UIE was 10% higher on non-school days compared to

school days (Table 6.1), there was no difference in dietary iodine intake assessed

by 24-hour food recall between the different day types.

Table 6.1: Descriptive characteristics of SONIC participants with both a valid food recall and urine sample (n=454)

All Boys Girls

Participants, n (%) 454 249 (55%) 205 (45%) Age (mean (SD), years) 10.1 (1.25) 10.3 (1.3) 9.9 (1.2) SES1 Lowest tertile, n (%) 82 (18%) 36 (14%) 46 (22%) Mid tertile, n (%) 88 (19%) 42 (17%) 46 (22%) High tertile, n (%) 284 (63%) 171 (69%)a 113 (56%) BMI category Underweight, n (%) 33 (7%) 14 (6%) 19 (9%) Healthy, n (%) 330 (73%) 191 (77%) 139 (68%) Overweight, n (%) 76 (17%) 36 (14%) 40 (20%) Obese, n (%) 15 (3%) 8 (3%) 7 (3%) Day type of recall School day, n (%) 369 (81%) 206 (83%) 163 (80%) Non-school day, n (%) 58 (19%) 43 (17%) 42 (20%) Energy intake (kj/day) 8561 (2660) 8897 (2815)b 8152 (2402) Iodine intake (µg/day) 172 (74) 183 (80)b 159 (64) School day (µg/day) 172 (75) 182 (81)b 160 (65) Non-school day (µg/day) 169 (70) 185 (77)b 154 (60) Day type of urine School day 221 (49%) 124 (50%) 97 (47%) Non-school day 233 (51%) 125 (50%) 108 (53%) UIE (µg/24-hour) 108 (54) 118 (57)b 95 (47) School day (µg/24-hour) 102 (50)c 111 (52)b,c 89.9 (45)c

Non-school day (µg/24-hour) 113 (57)c 125 (61) b,c 99.5 (48)c

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); SES: socioeconomic status;

BMI: body mass index; UIE: Urinary Iodine Excretion 1 SES based on state-based socioeconomic indexes for areas, index of relative socioeconomic

disadvantage (296) a 2 p-value <0.01; b Multiple regression adjusted for age and school cluster P<0.05 for

difference between genders; c Multiple regression adjusted for age, gender and school cluster

P<0.05 for difference between school day and non-school day


~ 111 ~

6.4.3 Food sources of iodine

Table 6.2 presents the food groups which contributed 1% of total dietary

iodine. The two major sources of dietary iodine were ‘Cereals and cereal products’

(35%) and ‘milk products and dishes’ (35%). Ninety-eight percent of participants

(n=445) reported consuming a food from the ‘cereals and cereal products’ major

food category on the day of the recall and 89% (n=402) consumed an item from

the ‘milk products and dishes’ major food category. ‘Regular breads and bread rolls’

contributed 27% of total dietary iodine intake, with 78% of participants consuming

an item from this category (Table 6.2). Similarly, ‘Dairy milk (cow sheep and goat)’

contributed 25% of total dietary iodine, with 69% of participants consuming dairy

milk on the day of the recall.

When intake of iodine fortified bread products (i.e. based on the previously

defined ‘bread’ definition) was examined, bread contributed 31% of total dietary

iodine intake. Whilst consumption of an iodine fortified bread product was

significantly higher on school days compared to non-school days (93 (67) vs. 70 (66)

g/day, P=0.02), there was no difference in milk intake between school days and

non-school days (196 (205) vs. 171 (182) g/day, P=0.2).

Table 6.2: Food sources of iodine in SONIC participants >8y/o with both a valid food recall and urine sample (n=454)

Food Group Name (158) %

Consuming %

Contribution

Non-alcoholic beverages (total %) 94 5.5

Fruit and vegetable juices, and drinks 37 1.4

Fruit juices, commercially prepared 32 1.2

Waters, municipal and bottled, unflavoured 77 1.9

Domestic water (including tap, tank/rain water) 77 1.9

Cereals and cereal products (total %) 98 35.4 Regular breads, and bread rolls (plain/unfilled/untopped varieties)a

78 26.7

Breads, and bread rolls, white, mandatorily fortified 18 5.8 Breads, and bread rolls, white, not stated as to

fortification 35 9.1

Breads, and bread rolls, mixed grain, mandatorily fortified

5 1.2

Breads, and bread rolls, mixed grain, not stated as to fortification

8 2.2

Breads, and bread rolls, wholemeal and brown, mandatorily fortified

8 2.5


~ 112 ~

Table 6.2: Food sources of iodine in SONIC participants >8y/o with both a valid food recall and urine sample (n=454)

Food Group Name (158) %

Consuming %

Contribution Breads, and bread rolls, wholemeal, not stated as to

fortification 11 3.7

English-style muffins, flat breads, and savoury and sweet breads a

21 5.0

Flat breads (e.g. Pita bread), wheat based 6 1.3

Savoury filled or topped breads and bread rolls 5 1.2

Breakfast cereals, hot porridge style 7 1.7

Porridge style, oat based 7 1.7

Cereal based products and dishes (total %) 86 11.7

Cakes, muffins, scones, cake-type desserts 28 2.7

Mixed dishes where cereal is the major ingredient 31 6.5

Pizza, saturated fat ≤5 g/100 g 7 1.7 Savoury pasta/noodle and sauce dishes, saturated fat ≤5 g/100 g

9 1.1

Fish and seafood products and dishes (total %) 14 2.2

Fish and seafood products (homemade and takeaway) 4 1.0

Egg products and dishes (total %) 9 2.0

Eggs 7 1.3

Eggs, chicken 7 1.3

Meat, poultry and game products and dishes (total %) 75 2.5 Mixed dishes where poultry or feathered game is the major component

11 1.1

Milk products and dishes (total %) 89 35.3

Dairy milk (cow, sheep and goat) 69 24.9

Milk, cow, fluid, regular whole, full fat 32 11.9

Milk, cow, fluid, reduced fat, <2 g/100g 21 7.6

Milk, fluid, unspecified 14 4.0

Yoghurt 15 2.6

Yoghurt, flavoured or added fruit, full fat 9 1.7

Cheese 40 2.0

Cheese, hard cheese ripened styles 29 1.6

Frozen milk products 22 2.9

Ice cream, tub varieties, fat content >10 g/100 g 13 1.9

Flavoured milks and milkshakes 6 2.0

Milk, coffee/chocolate flavoured and milk-based drinks, full fat

4 1.5

Vegetable products and dishes (total %) 69 1.7

Other* - 3.7

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria) *Other includes foods contributing less than 1% iodine - fats and oils; fruit products and dishes; dairy & meat substitutes; soup; seed and nut products and dishes; savoury sauces and condiments; legume and pulse products and dishes; snack foods; sugar products and dishes; alcoholic beverages; special dietary foods; miscellaneous; and infant formulae and foods a Included in “bread” definition as included in mandatory fortification legislation


~ 113 ~

6.4.4 Relationship between food sources of iodine and UIE

The total amount of bread consumed (g/day) was not associated with UIE

(Figure 6.1), however there was a significant, weak correlation between the

amount of milk consumed (g/day) and UIE (r=0.15, P=0.001, (Figure 6.2)).

Regression analysis indicated that a 100 g/day increase in milk intake was

associated with a 4 µg/24-hour higher UIE and accounted for 2% of the variance in

UIE (R2=0.02, β=4.03 (0.90)(SE), P<0.001). The combined weight of bread and milk

(g/day) consumed was also associated with UIE (r=0.67, P<0.001, (Figure 6.3)), with

a 100g increased intake of bread and milk combined associated with a 4 µg/24-

hour higher UIE and accounted for 3% of the variance in UIE (R2=0.03, β=4.21

(0.90)(SE), P<0.001). Both associations remained significant after adjustment for

age, gender and energy intake. There was no difference in the association between

UIE and bread, milk and combined bread and milk intake in consumers whose

dietary recall and urine were completed within one week of each other compared

to those whose recall and urine were completed >1 week apart. (Appendix G)

Thirty-seven percent of participants consumed >100 g bread/day however

consumption of >100 g/day was not significantly associated with UIE (P=0.26).

Comparatively, 62% of participants reported consuming >100 g milk/day and

regression analyses indicated that consumption of >100 g milk/day was associated

with a 10.8 µg/24-hour increase in UIE after adjustment for age, gender, energy

intake and school cluster (R2=0.09, β=10.79 (4.60)(SE), P=0.02).


~ 114 ~

Figure 6.1: Relationship between grams of bread consumed and 24-hour urinary iodine

excretion among SONIC participants with both a valid food recall and urine sample

(n=454)

Figure 6.2: Relationship between grams of milk consumed and 24-hour urinary iodine

excretion among SONIC participants with both a valid food recall and urine sample

(n=454)


~ 115 ~

Figure 6.3: Relationship between grams of bread and milk consumed and 24-hour

urinary iodine excretion among SONIC participants with both a valid food recall and

urine sample (n=454)

Tabl

e 6.

3: U

rina

ry Io

din

e Ex

cret

ion

an

d d

ieta

ry io

din

e in

take

by

con

sum

ptio

n o

f b

read

an

d m

ilk (

n=4

54)

B

read

1 M

ilk1

Bre

ad &

Milk

2

No

n-c

ons

umer

s C

on

sum

ers

No

n-c

on

sum

ers

Co

nsu

mer

s N

eith

er

Eith

er

Bo

th

n (

%)

68

(15

%)

38

6 (

85

%)

14

1 (

31

%)

31

3 (

69

%)

11

(3

%)

18

7 (

41

%)

25

6 (

56

%)

Die

tary

iod

ine

(mea

n (

SD),

µg/

24-h

ou

r)

138

(64

) 1

78

(7

4)a

14

0 (

55

) 1

86

(7

7)a

10

1 (

53

) 1

44

(5

7)b

19

5 (

77

)c,d

<E

AR

*, n

(%

) 8

(1

2%)

13

(3

%)e

15

(1

1%

) 6

(2

%)e

4 (

36

%)

15

(8

%)

2 (

1%

)

≥

EAR

*, n

(%

) 6

0 (

88%

) 3

73

(9

7%

)e 1

26

(8

9%

) 3

07

(9

8%

)e 7

(6

4%

) 1

72

(9

2%

) 2

54

(9

9%

)

UIE

(m

ean

(SD

), µ

g/24

-ho

ur)

1

02 (

59)

10

9 (

53

) 9

8 (

51

) 1

12

(5

5)a

70

(3

7)

10

3 (

54

)b 1

13

(5

3)c

<E

AR

*, n

(%

) 2

3 (

34%

) 9

1 (

24

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~ 116 ~



~ 117 ~

6.4.5 Differences in dietary iodine intake and UIE between

consumers and non-consumers of bread and milk.

Eighty-five percent of participants reported consuming a known iodine

fortified bread product on the day of the recall (Table 6.3). Consumers of these

products had 22% higher dietary iodine intake compared to non-consumers

(P<0.001 Table 6.3), however there was no difference in UIE. In contrast,

consumers of milk (n=313, 69% of participants) had 55% higher dietary iodine

intake which was reflected by a 12% greater UIE, compared to non-consumers

(P<0.001 Table 6.3). Consumers of bread and milk were significantly more likely to

have dietary iodine intakes exceeding the age and gender specific EAR compared

to non-consumers (P<0.001 Table 6.3). There was, however, no difference in the

proportion of participants with a UIE excceding the age and gender specific EAR

between consumers and non-consumers of either bread or milk.

Participants who consumed either bread or milk on the day of the survey

had 32% higher UIE and 30% higher total dietary iodine intake than participants

who consumed neither (both P<0.001, Table 6.3). Participants who reported

consuming both bread and milk on the day of the recall had 38% higher UIE and

48% higher total dietary iodine intake than participants who consumed neither

bread nor milk on the day of the recall (both P<0.001 Table 6.3). Although total

dietary iodine intake was 26% higher in participants who consumed both bread and

milk compared to those who only consumed either bread or milk (P<0.001 Table

6.3), there was no difference in UIE.


~ 118 ~

6.5 Discussion

In a sample of Victorian schoolchildren following the mandatory

fortification of bread with iodised salt, the mean daily intakes of iodine estimated

from both 24-hour dietary recalls and 24-hour urine samples for 4-8 and 9-13 year

olds were in excess of the age-specific EARs of 65µg/day and 75µg/day, respectively

(2). In this population consuming adequate dietary iodine, the major contributors to

dietary iodine intakes, determined using 24-hour food recalls, were bread and

bread products (31%) and milk (25%). These results are similar to the findings of

the 2011-13 AHS, whereby bread and milk were the major sources of iodine (25%

and 26%, respectively) (45). Although milk intake was significantly associated with

UIE, the present study did not determine an association between total bread

intake, a major source of dietary iodine due to the mandatory use of iodised salt in

bread, and UIE.

Whilst mandatory fortification of bread with iodised salt appears to have

been successful in improving iodine intakes, with bread being a significant

contributor to iodine intakes in this study as well as the 2011-13 AHS (46, 163), the

present study found no association between bread consumption and UIE. In

addition, the present study was unable to confirm the findings of a previous

analysis of the 2011-13 AHS which found that consumption of >100g of bread/day

was associated with twelve times greater odds of achieving an adequate dietary

iodine intake in 2-18 year olds (46). This study, conducted by Charlton et. al. (2016),

utilised data from two 24-hour recalls, which may have reduced the variability in

bread intake (46). Within the present study we determined that 37% of participants

consumed >100 g of bread per day using a single 24-hour food recall, however

there was no association between consumption of greater than 100 g and UIE. The

use of only one 24-hour recall in the present study compared to two in the AHS

analysis may explain the differences in the findings as two 24-hour recalls are more

likely to capture usual intake (5, 157). Although the present study was unable to

determine an association between UIE and bread intake, this study does provide

evidence that bread and bread products contribute a significant proportion of

dietary iodine amongst schoolchildren in Australia.


~ 119 ~

Although it has been hypothesised that a reduction in the use of iodophores

by the dairy industry was the reason for the re-emergence of iodine deficiency in

Australia in the late 1990’s (52), the present study found that milk contributed 25%

of total iodine intake and was significantly associated with UIE, even after

adjustment for age, gender and energy intake. The proportion of dietary iodine

from milk products observed in Australian schoolchildren is comparable to that

observed in New Zealand (162), the United Kingdom (169), Germany (160) and the

United States (161), and indicates that milk is still a valuable source of iodine amongst

Australian schoolchildren. Similar to the present study, the studies in the U.K.,

Germany and the U.S. found that higher milk intakes were associated with an

increase in iodine excretion (160, 161, 169).

The iodine content of milk is dependent mainly on contamination from

iodophores (165), however there is some naturally occurring iodine present (166). This

naturally occurring iodine is, however, highly variable and is largely dependent on

the iodine intake of the cow itself (165-167). Factors such as the supplementation of

cow feed and salt licks, the cow species and farm location can impact the level of

naturally occurring iodine in milk (165, 166, 168). Currently within Australia iodophores

are still permitted for use in the dairy industry, however their use is not monitored,

and the proportion of farmers currently using them is not known, nor is the

concentration at which they are used (Kathryn Davis, Dairy Australia, Personal

Communication, January 2017). As such, it is difficult to determine the level of

contamination of milk by iodophores, and this in combination with the variation in

the naturally occurring iodine content of milk, means that the iodine content of

Australian milk could vary considerably both within and between producers.

However, the association between dietary iodine and UIE observed in the present

study provides evidence that milk remains a major contributor to daily iodine

excretion, and accordingly dietary intake among Australian schoolchildren. It is also

important to note that although there was a significant relationship between both

milk and combined bread and milk intake and UIE, consumption of these foods only

explained 2% and 3% of the variance in UIE, respectively. This indicates that there

may be a number of other factors influencing UIE aside from diet alone.


~ 120 ~

The major strength of the present study is the use of both 24-hour recalls

and 24-hour urine samples for the assessment of daily iodine intake. A stringent

24-hour urine collection protocol, specifically tailored to children was used to

ensure the completeness of the samples. However, as the present study was

limited to a single 24-h urine collection, adjustments for within and between

person variations in 24-hour urinary iodine excretion(18, 27, 297) could not be made,

in order to obtain a measure of long-term ‘usual’ iodine intake. In addition, the

present study only collected a single 24-hour recall further impacting our ability to

determine ‘usual’ intake. Whilst a single 24-hour recall is able to provide an

indication of the mean intake of a group, the use of only one 24-hour recall may

have impacted our ability to measure consumption of foods which are consumed

less frquently, such as fish (298). Fish and fish products contributed less than 1% of

total dietary iodine, compared to 3% in the Australian Health Survey (45). This study

does, provide a snapshot of current dietary patterns and provides evidence that

bread and milk are major food sources of iodine in this group of school children.

There are some limitations to this study, namely that the 24-hour recalls

over the same time period and 24-hour urine collection were not collected

concurrently. As such, some children completed the urine samples over a week

after the original 24-hour recall (i.e. n=29, 7% collected within a 2-week period)

(272). The variation in the number of days between the urine collection may have

contributed to the inability to detect an association between bread intake and UIE.

Furthermore, the observation that there was a significant difference in bread, but

not milk intake, between school days and non-school days indicates that bread

intake may be more variable than milk intake. This should be interpreted with

caution, however, due to the small number of participants (19%) who completed

the recall on a non-school day.

In addition, there are some inaccuracies inherent with the dietary

assessment methodologies used to assess the iodine content of food (299). A recent

review by Ershow et. Al. (2018) describes the wide variation in the iodine content

of food, and highlights the importance of maintaining accurate food databases for

determining the iodine intake of populations (299). This review also highlights the


~ 121 ~

difficulties associated with developing such a database due to the wide variations

in iodine content of food both within and between food types (299). Errors in the

database used to determine the food sources of iodine in the present study may

have limited our ability to capture associations between foods consumed (such as

bread) and UIE.

In Australia, the main source of iodine in bread is the iodised salt added

during production and the current level of iodine in salt added to bread in Australia

varies more than two fold: between 25-65 mg iodine per kilogram of salt (56).

Furthermore, the sodium content of bread ranges between 200-800 mg/100g (55)

and the iodine content of iodised salt may differ from the reported iodine content

due to losses related to storage and humidity (299). AUSNUT 2011-13, the food

database used in the present study, utilises the mean sodium and iodine content

of each of the bread products and does not account for the variation in either

sodium or iodine contents of breads , which may result in the inaccurate estimation

of the iodine content of foods, particularly bread and bread products which contain

iodised salt. Finally, the convenience sampling method used to recruit the

participants for this study may have resulted in the over-representation of

participants from higher socioeconomic areas (293).

6.6 Conclusion

This is the first Australian study to assess the contribution of food sources

of iodine to 24-hour urinary iodine excretion, as an objective measure of daily

iodine intake. In this sample of Victorian school-aged children, the major food

sources of dietary iodine were bread and milk. These results are comparable to the

results from the 2011-13 Australian Health Survey. The present study also found

that milk, but not bread intake, was associated with 24-hour urinary iodine

excretion. Future longitudinal research whereby 24-hour recalls and 24-hour urine

samples are collected concurrently, preferably on more than one day, is needed to

confirm these findings and to further elucidate the association between food

sources of iodine and urinary iodine excretion. Such studies are important for

evaluating the success of iodine fortification programs, as well as the potential


~ 122 ~

impact which the reformulation of major food sources of iodine, particularly

sodium reduction strategies, might have on the iodine intakes of Australian

schoolchildren.

~ 123 ~

Chapter Seven:

Study 4: Association between urinary excretion

of sodium and iodine in 24-hour urine samples

and association with discretionary salt use

7.1 Introduction


intakes (57, 180, 181, 189), the World Health Organization (WHO) recommends a

reduction in salt intake to <5g (2 g sodium) /day among adults by 2025 worldwide

with further reduction to <2g (0.8g sodium) /day recommended for children (190).

In Australia specifically, the establishment of salt reduction targets for nine food

categories, including bread, by the Federal Government’s Food and Health

Dialogue (FHD) in 2009 (194) resulted in a 10% reduction in the sodium content of

commercially available breads over 4 years (59). In addition the number of bread

products with a sodium content below the maximum recommended level of 400mg

sodium/100g tripled from 28% in 2009 to 86% in 2015 (59). During this same period

(2009-2015) Food Standards Australia New Zealand (FSANZ) reported an increase

in the iodine content of bread products from <2 µg/100g in 2006 to 60 µg/100g in

2015, as a result of mandatory fortification (196). However, it should be noted that

FSANZ also reported a subsequent reduction in the iodine contents of white and

wholemeal bread products by 11% and 25%, respectively, between 2010 and 2013

(196).

This reduction in the iodine content of bread products could be a result of

the successful implementation of sodium reduction targets. This raises the concern

that a further reduction in the sodium content of bread products could result in a

subsequent reduction in the iodine content of bread. As the 2011-13 Australian

Health Survey reported that bread and bread products contributed approximately

25% of total dietary iodine intake amongst 5-11 year olds (46), due to the iodised

salt added during processing, a reduction in the iodine content of bread as result

Study 4: Association between urinary excretion of sodium and iodine in 24-hour urine

samples and association with discretionary salt use

~ 124 ~

of sodium reduction strategies could have a subsequent negative impact on the

iodine intakes of Australians.

Therefore, it is important to establish the association between intakes of

iodine and sodium, in order to understand the role which iodised salt, added either

during processing or as discretionary salt (added during cooking and/or at the

table), plays in the iodine intakes of Australians. Studies in Germany (160) and Korea

(177) have observed modest correlations between urinary excretion of iodine and

sodium, however to date there have been no studies utilising 24-hour urine

samples, the most objective measure of both sodium and iodine intakes (64, 94), to

assess the relationship between iodine and sodium intake in a sample of Australian

schoolchildren.

7.2 Aims

i) To examine the relationship between excretion of iodine and sodium in a sample

of Victorian school-aged children, using 24-hour urine samples

ii) To assess differences in iodine intakes, determined using urinary iodine

excretion, across reported categories of discretionary salt use.

7.3 Methods

The study design for this study has been described in Chapter Four


See section 4.1 Participant recruitment for details. In these analyses which

involved urinary sodium excretion, one extreme outlier for sodium excretion

(mean(SD) 531(9) mmol/24-hour) was excluded, leaving a final sample size of 649

participants with a valid 24-hour urine sample analysed for both sodium and iodine.

7.3.2 Measures

See section 4.2 Testing procedures for details



~ 125 ~

7.3.3 Discretionary salt use characteristics

This has been described in section 4.2.2 Discretionary salt use

characteristics. For the purpose of this analysis the responses for the questions

relating to discretionary salt use were dichotomised into “Yes”, encompassing “Yes,

usually” and “Yes, sometimes”, and “No”. Missing (n=7) and “Don’t know” (n=4)

responses were excluded from analysis, leaving a total of 638 participants with

both valid urine and completed discretionary salt use data. No information on

whether the discretionary salt used was iodised was collected. A discretionary salt

user was defined based on parent reported addition of salt during cooking and child

reported salt use at the table. Participants were classified as consuming “neither’

(i.e. salt was not added during cooking or at the table), “either” (reported adding

salt either during cooking or at the table), or “both” (reported adding salt during

cooking and at the table).


See section 4.3 24-hour urine collection for details.

7.3.5 Urinalysis

See section 4.3.3 Biochemical analysis for details.


All analyses were completed with STATA/SE 14.0 software (StataCorp LP),

and a P value <0·05 was considered statistically significant. Normality of continuous

variables was assessed and confirmed using box plots, histograms and the Shapiro-

Wilk test and have been presented as mean (SEM). The underlying assumptions of

linear regression (i.e. homoscedasticity, normality) were confirmed using

regression plots of residuals. Pearson’s correlation and multiple regression

(adjusted for age, gender and school cluster) were used to assess the relationship

between urinary iodine excretion (UIE) and urinary sodium (UrNa).

Paired t-tests were used to assess differences in UIE between those who

did and did not report engaging in each of the three dichotomous discretionary salt



~ 126 ~

use behaviours. Linear regression, adjusted for age, gender and school cluster, was

used to further assess differences in UIE across the different levels of discretionary

salt use, whereby discretionary salt use category was entered as the indicator

variable with “neither” used as the reference group. As this model included

multiple comparisons between sub-groups, the Bonferroni correction for P-values

was also reported. Both the adjusted and unadjusted models have been presented.

As previous analyses have determined that sodium excretion was significantly

higher on non-school days, compared to school days (295), all regression analyses

have also been adjusted for urine day type. The unstandardised beta co-efficient

(β) and standard error (SE) have been presented for all regression models.

7.4 Results


Overall 55% of children were male, the average age was 9 years and 4

months and 62% were of from a high socioeconomic area (Table 7.1). In total, 17%

of children were either overweight or obese and 53% completed the 24-hour urine

collection on a non-school day.



~ 127 ~

Table 7.1: Demographic characteristics of SONIC participants included in sodium analysis (n=649)1

All Boys Girls

Number of participants, n(%) 649 358 (55%) 291 (45%) Age (mean (SD), years) 9.3 (1.8) 9.3 (1.9) 9.2 (1.7) Age group

4-8 years, n(%) 280 (43%) 157 (44%) 123 (42%) 9-12 years, n(%) 368 (57%) 201 (56%) 167 (58%)

SES2 Low tertile, n(%) 105 (16%) 45 (12%)a 60 (21%)a

Mid tertile, n(%) 144 (22%) 77 (22%) 67 (23%) Top tertile, n(%) 399 (62%) 236 (66%)a 163 (56%)a

BMI category Underweight, n(%) 67 (10%) 34 (10%) 33 (11%) Healthy, n(%) 471 (73%) 270 (75%) 201 (69%) Overweight, n(%) 91 (14%) 45 (13%) 46 (16%) Obese, n(%) 19 (3%) 9 (2%) 10 (4%)

Day of urine collection School day, n(%) 303 (47%) 168 (47%) 135 (47%) Non-school day, n(%) 345 (53%) 190 (53%) 155 (53%)

Urvol (mean (SD), ml/24-hour) 870 (16.4) 892 (23.5) 844 (22.5) UrCr (mean (SD), mmol/24-hour) 5.5 (0.1) 5.8 (0.1)b 5.3 (0.1)b

UIE (mean (SD), µg/24-hour) 104 (2.1) 112 (3.0)b 93 (2.8)b

UrNa (mean (SD), mmol/24-hour) 104 (1.8) 109 (2.5)b 97 (2.5)b

>Na UL2, n(%) 468 (72%) 196 (67%)a 272 (76%)a Salt equivalent (mean (SD), g/24-hour) 6.1 (0.1) 6.4 (1.5)b 5.7 (0.1)b

SONIC: Salt and Other Nutrient Intakes in Children study (Victoria); SES: socioeconomic status; BMI: body mass index; UrVol: Urine volume; UrCr: Urinary creatinine; UIE: urinary iodine excretion; UrNa: Urinary sodium; UL: Upper Level of intake 1 One extreme outlier for sodium excretion (mean(SD) 531(9) mmol/24-hour) excluded; 2 SES based on state-based socioeconomic indexes for areas, index of relative socioeconomic disadvantage 2 Upper Level of intake for children aged 4-8 years: 60 mmol Na/24-hour and 9-13 years: 86 mmol Na/24-hour a 2 Test P<0.05 for differences between genders; b Multiple regression adjusted for age, urine day type and school cluster P<0.05 for difference between genders

7.4.2 Relationship between urinary sodium and urinary iodine

excretion

The mean (SEM) UIE and UrNa for all participants were 104 (2.1) μg/24-

hour and 104 (1.8) mmol/24-hour, respectively (Table 7.1). Seventy-two percent of

participants exceeded the upper level of intake for sodium (Table 7.1). Children

who exceeded the UL for sodium had a 28% higher UIE compared to those whose

urinary sodium was equal to or below the recommendation (mean (SD) 81 (38) vs.

112 (56) mmol/24-hour, P<0.001). There was a moderate, positive correlation

between UIE and UrNa (r=0.37, P<0.001; Figure 7.1). Regression analysis revealed



~ 128 ~

that one milligram/24-hour (0.003mg salt) of UrNa was associated with a 7.1

g/24-hour higher UIE and accounted for 14% of the variance in UIE (R2=0.14,

β(SE)=7.12(0.80), P<0.001). When adjusted for age, gender and day type of urine

collection, this association remained significant (R2=0.19, β(SE)=5.87(0.90),

P<0.001).

7.4.3 Differences in urinary iodine excretion across discretionary

salt use categories

Sixty-five percent of participant’s parents reported using salt during cooking

and 29% reported that their child added salt at the table (Table 7.2). Conversely,

significantly more children, 40%, reported that they added salt at the table (Table

7.2, P<0.001). Seventy-seven percent (n=493) of participants were classified as a

discretionary salt user, with 28% (n=177) of participants adding salt both at the

table and during cooking (Table 7.2).

Figure 7.1: Relationship between urinary excretion of iodine and sodium in the SONIC

paticipants included in sodium analyses (n=649)



~ 129 ~

Urinary sodium excretion was significantly higher in participants whose

parents reported not using salt during cooking (110.3 (46) vs. 101.6(49) mmol/24-

hour, P<0.05). There was, however, no significant difference in UIE across any of

the discretionary salt use categories (Table 7.2 & 7.3).

Table 7.2: Urinary excretion of iodine and sodium across discretionary salt use categories (n=638), mean (SEM) or n (%)

n (%)

UIE (µg/24-hour)

UrNa (mmol/24-hour)

salt added at the table (child defined) yes (yes, usually/yes, sometimes) 253 (40%) 104 (4.8) 107.2 (3.2) no 385 (60%) 103 (4.7) 102.2 (2.4)

salt added at the table (parent defined) yes (yes, usually/yes, sometimes) 184 (29%) 105 (4.4) 106.5 (3.9) no 454 (71%) 103 (4.4) 103.2 (2.3)

salt added during cooking (parent defined) yes (yes, usually/yes, sometimes) 417 (65%) 102 (4.1) 100.7 (2.3) no 221 (35%) 107 (5.5) 110.7 (3.4)a

discretionary salt user 1 salt not used either at the table or during cooking

145 (23%) 108 (6.1)b 110.9 (4.1)b

salt used either at the table or during cooking

316 (50%) 101 (4.4) 100.2 (2.5)

salt used both at the table and during cooking

177 (27%) 104 (5.1) 105.7 (3.2)

UIE: urinary iodine excretion; UrNa: urinary sodium excretion a Paired T-test used to examine difference between ‘yes’ and ‘no’, P<0.05; b Linear regression with adjustment for age, gender, urine day type and school cluster used to assess differences between subgroups, with Bonferroni correction for multiple comparisons P>0.05

1 Defined using parent reported use of salt during cooking and child reported use of salt at the table

Table 7.3: Relationship between urinary iodine excretion (μg/24-hour) and discretionary salt use (n=638)1

β (SE) P-value Adjusted P-Value2

Model 1 used neither (reference)

used either -7.0 (5.4) 0.2 0.4 used both -4.6 (5.7) 0.4 0.9

Model 2 used neither (reference)

used either -4.8 (5.4) 0.4 0.8

used both -4.6 (5.9) 0.4 0.9

Model 1: Unadjusted; Model 2: Adjusted for age, gender, and urine day type 1 Multiple linear regression used to assess differences in urinary electrolyte excretion by discretionary salt use, as defined using parent reported salt use during cooking and child reported salt use at the table;2 P-value adjusted for multiple comparisons between sub-groups using Bonferroni correction.



~ 130 ~

7.5 Discussion

In this sample of Victorian school-aged children post fortification of bread

with iodised salt there was a moderate, positive association between UIE and UrNa

but no association between UIE and reported discretionary salt use. The positive

association between urinary excretion of iodine and sodium is consistent with that

previously reported using 24-hour urine collections in a sample of mildly iodine

deficient German children aged 3-6 years (r=0.43, P<0.05) (160). In both Germany

and Australia, iodised salt added to foods during processing is a significant

contributor to iodine intake (45, 300), and this might explain the relationship between

UIE and UrNa observed in these studies. Conversely, however, a study conducted

in a sample of mildly iodine deficient Australian women prior to the introduction of

mandatory fortification of bread with iodised salt, was unable to detect an

association between UIE and UrNa (178). This may have been due to the fact that

bread, a food which is a major contributor to daily sodium intake, was at the time

not a source of iodine. Similarly, a study conducted in South African adults was also

unable to establish a relationship between the two nutrients as assessed by 24-

hour urinary excretion (179). Although universal salt iodisation (USI) has been

implemented in South Africa since 1995, salt added to food during processing is

exempt from mandatory iodisation (301). Therefore despite bread and bread

products being major contributors to sodium intake they may not be a source of

iodine in South Africa. The findings from these studies indicate that iodine and

sodium intake are more likely to track together in populations where foods that are

major sources of sodium are produced using iodised salt.

It has been estimated that discretionary salt use contributes 10-15% of total

dietary sodium and it has been found that adults who report adding salt at the

table and in cooking have higher total salt intakes compared to those not using

discretionary salt (174). Sixty-five percent of participants in the present study

reported that salt was added during cooking (parent reported) and 40% added it to

their food at the table (child reported). These results are slightly higher than that

observed in the 2011-13 AHS, whereby 50% of 4-8 year olds and 57% of 9-13 year



~ 131 ~

olds reported using salt in cooking rarely/occasionally/very often with 17% of 4-8

year olds and 36% of 9-13 year olds adding salt at the table (45).

Whilst reported discretionary salt use was high in the present study,

children whose parents reported adding salt during cooking had 10% lower urinary

sodium excretion compared to those children whose parents reported not adding

salt during cooking. In addition, the present study found that both UIE and UrNa

were significantly higher in participants who did not report adding salt at the table

or during cooking, compared to those participants who engaged in one or both of

these behaviours. It is possible that that those who added salt in cooking might be

doing more cooking at home and, although they added salt in cooking, the amount

added would be less than that found in pre-prepared/take-away meals. The

discrepancy could also be related to the use of one 24-hour urine collection for

assessment of daily sodium excretion compared with questions relating to general

use of discretionary salt. Furthermore, assessment of discretionary salt use can be

challenging due to recall and self-reporting biases (175).

Although discretionary salt use among Australian schoolchildren is still

relatively high, the present study did not find an an association between reported

discretionary salt use behaviours and UIE. This finding is similar to that observed in

a 2016 multi-center study of 8-10 year old iodine sufficient children (n=132) in the

United Kingdom (169). Compared to the study in the U.K. however, the present study

did not ask participants whether the salt used during cooking or at the table was

iodised. Furthermore, neither the present study nor the study in the U.K. included

a direct measure of iodised salt intake, and this may be the reason for the lack of

association between iodine excretion and discretionary salt use in both studies.

Further studies whereby discretionary use of iodised salt is directly measured

alongside a 24-hour urine sample are needed to further elucidate whether

discretionary use of iodised salt contributes to the daily iodine intake of Australian

schoolchildren

The main strength of the present study is the use of 24-hour urine samples

to estimate iodine and sodium intakes, as this is the most robust way to accurately

capture total daily intakes of both nutrients (20, 58). A stringent 24-hour urine



~ 132 ~

collection protocol, specifically tailored to children was used to ensure the

completeness of the samples. Limitations of this study include the convenience

sampling method, which resulted in some over-representation of participants from

higher socioeconomic areas (293). In addition, the study did not include a direct

measure of discretionary salt use, nor did it collect information regarding the use

of iodised salt specifically, which may have limited the ability to determine a

relationship between UIE and discretionary salt use. Finally, as the present study

was limited to a single 24-hour urine collection, adjustment for the within person

variation in 24-hour urinary iodine and sodium excretion could not be made in

order to obtain a measure of long-term ‘usual’ iodine intake.

7.6 Conclusion

In summary, this study found that in a sample of iodine sufficient Victorian

schoolchildren there was a moderate, positive association between urinary sodium

and urinary iodine excretion, but no association between urinary iodine and

reported discretionary salt use. Therefore, it is important to implement an ongoing

program to monitor population sodium and iodine intakes, in order to ensure that

a reduction in sodium intakes to decrease dietary related disease risk does not put

Australian schoolchildren at risk of iodine deficiency.

~ 133 ~

Chapter Eight: Discussion

8.1 Victorian children have sufficient iodine intakes – a

result of the successful implementation of mandatory

fortification?

Collectively the findings from this thesis build on the results of the 2011-13

Australian Health Survey (AHS), and confirm that Victorian school-aged children are

now iodine sufficient. The average iodine intakes in this sample of Victorian school-

aged children, assessed by 24-hour urine samples, were 94 and 111 µg/24-hours

for 4-8 and 9-13 year olds, respectively, exceeded the age-specific Estimated

Average Requirement (EAR) (2). Less than 30% of children having intakes below the

age-specific EAR. Furthermore, the median 24-hour urinary iodine concentration

(UIC) of 124µg/L observed in exceeded 100µg/L as recommended by the World

Health Organization (WHO) (38), indicating iodine sufficiency in the population.

The UIC determined using 24-hour urine samples was 41% higher than the

spot UIC observed in Victoria in the 2003-2004 National Iodine Nutrition (NINS)

study conducted prior to the introduction of mandatory fortification of bread with

iodised salt (34). This difference in UIC can be interpreted to indicate that

mandatory fortification of commercially produced bread, excluding organic and

homemade varieties, with iodised salt in 2009 has been effective in improving the

iodine status of Victorian school-aged children. Furthermore, it was found that

nearly 80% of participants consumed an iodine fortified bread product on the day

of the survey. This finding is important as it indicates that a large proportion of the

school-aged children in this sample were exposed to iodine fortification and that

iodine fortification is reaching the target audience identified when the strategy was

first proposed in 2004 (154). However, although it was observed that the main food

source of iodine was ‘regular breads and bread rolls’, contributing 27% of total

dietary iodine intake in these children, there was no association between the

amount of bread consumed and the amount of iodine excreted over 24-hours.

Discussion

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Although we were unable to determine an association between bread

consumption and UIE, the finding that bread was the main food source of dietary

iodine in this sample of Victorian school-aged children is of significant importance

when considering the implementation of sodium reduction targets to processed

foods. As previously discussed, the application of sodium reduction targets to

bread as part of the Food and Health Dialogue (FHD), saw a 10% reduction in the

sodium content of bread between 2009-2015 (196). Whilst the successful

implementation of these targets is important for the reduction of excessive sodium

intakes, further reductions in the sodium content of bread may negatively impact

the iodine intakes of Australian school-aged children (44). A recent report by Food

Standards Australia New Zealand (FSANZ) observed that whilst the iodine content

of bread is considerably higher than compared to the period prior to fortification,

there has been a subsequent decline in bread iodine content since 2009 (196). This

decline is likely due to the application of sodium reduction targets to processed

foods, and a subsequent reduction in the amount of iodised salt added by bread

manufacturers. Furthermore, a 2016 report by FSANZ which evaluated the iodine

content of 95 commercially available breads in 2013, observed that the iodine

content ranged from 20-110 µg/100g (196). This range is very broad, and indicates

significant variation in the iodine content of breads currently available on the

Australian market. These variations are likely due to differences in both the amount

of salt added during bread production (range: 200-800mg/100g ) as well as the

level of salt iodisation specifically. Current salt iodisation legislation in Australia

state that iodised salt for use during bread manufacture should be iodised at a level

between 25-65 mg/kg of salt (56). It is therefore important to note that the exact

amount of iodine added to bread can vary substantially both within and between

bread varieties. Furthermore, in Australia there is currently no legislation ensuring

adequate monitoring of the amount of iodine in commercially produced bread and

bread products.

The wide variation in the iodine content of commercially available bread

and bread products makes it difficult to accurately estimate any potential impact

of sodium reduction targets on the iodine content of bread and the subsequent

impact on the iodine intakes of Australian school-aged children. Therefore, it is

Discussion

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important that the iodine intakes of Australian school-aged children continue to be

monitored, using 24-hour urine samples, in order to monitor the potential impact

which the application of sodium reduction targets to bread might have and prevent

the re-emergence of iodine deficiency. Whilst mandatory fortification of bread with

iodised salt appears to have been effective in improving the iodine nutrition of

Victorian school-aged children it is perhaps not ideal to tag an essential nutrient,

being iodine, to sodium, a substance which is known to have long-term adverse

health effects when consumed in excess. Therefore, strategies to improve iodine

intakes through foods which are less reliant on iodised salt for their iodine content

need to be considered by government bodies and health experts.

One such strategy could be the promotion of milk and milk products as a

source of iodine, as it was observed that in this sample of Victorian school-aged

children ‘milk products and dishes’, including dairy milk, cheese and yoghurt,

contributed the same amount to total dietary iodine as ‘cereal products and

dishes’, 35%. Furthermore, whilst consumption of ‘cereal products and dishes’ was

not associated with daily iodine excretion, the consumption of ‘milk products and

dishes’ was significantly associated with an increase in excretion of iodine over 24-

hours. Similar findings have been observed in American (161), British (169), Portuguese

(302) and German (160) school-aged children. In all of these countries the major food

source of iodine was milk and an increase in milk and/or dairy consumption was

associated with a significant increase in urinary iodine levels. It should be noted,

however that only the German study assessed the relationship between observed

24-hour UIE and dairy consumption (160), with the remaining three studies utilising

spot urine samples to assess the association between dairy consumption and UIC

(161, 169, 302). Differences in methodologies aside, the results from these studies

indicate that consumption of dairy products can have a significant impact on the

iodine nutrition of school-aged children.

Although dairy products contributed a large proportion of total dietary

iodine in the present study, the recent Australian Health Survey determined that

children aged 4-13 years were not meeting the recommended daily serves of dairy.

Children aged 4-8, 9-11 and 12-13 years were consuming, on average, 1.6, 1.6 and

1.8 serves of dairy per day, respectively, considerably less than the 2, 2.5 and 3.5

Discussion

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serves recommended for each (303). As the iodine content of milk is not dependent

on iodised salt, increasing population consumption of milk and/or dairy products

could present a viable alternative for maintaining the iodine intakes of the

population whilst also reducing population sodium intakes through a reduction in

the sodium content of processed foods, including bread. The consumption of an

additional 1 serve of full fat dairy milk per day would increase daily iodine intake by

approximately 57µg, equivalent to two slices of regular fortified white bread(54). If

dairy consumption was increased to meet recommendations, children would be

more likely to meet their daily iodine intake requirements, without the reliance on

foods fortified with iodised salt.

The present study is the first Australian study to evaluate the iodine intakes

of a sample of Australian schoolchildren using 24-hour urine samples. In addition,

the collection of 24-hour food recalls alongside these urine samples provides

valuable insight into the current state of iodine nutrition in Australia. It should be

acknowledged that the SONIC study was carried out between 2010-2013, relatively

soon after the introduction of mandatory fortification in 2009. Since the

introduction of mandatory fortification in 2009, the number of commercially

available iodine fortified products has increased substantially, particularly amongst

commercially produced bread and bread products (196). As such, the current

analysis may not be representative of iodine nutrition in Australia in 2018. It should

also be acknowledged that due to the convenience nature of participant

recruitment and the low response rate, the findings from the studies in this thesis

can not be extrapolated to the wider population. It is possible that participants

were from families that were more health conscious than non-respondents.

Despite efforts to reach all socioeconomic backgrounds, close to two-thirds of

children were from a high socioeconomic background. In addition, compared with

national estimates, our sample had fewer children who were overweight or obese,

which may reflect different dietary patterns among our study participants.

Discussion

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8.2 Current methods for assessing the iodine nutrition of

populations – is it time for a change?

Current recommendations for assessing the iodine nutrition of populations

are based on the measurement of UIC in spot urine samples, collected from school-

aged children (37). As discussed in Chapter Three these recommendations were

established over a number of years, however the initial cut-offs were based on a

study conducted in Spanish children and adults in the 1970s (117). Whilst this study

observed that goiter prevalence was <10% in areas where daily iodine intakes,

estimated using the iodine/creatinine ratio (I/Cr) measured in spot urine samples,

were greater than 100µg/day the results from this study were for the population

as a whole and there was no separation of the findings based on age (117).

Irrespective of this, the World Health Organization (WHO) subsequently used the

estimated intakes determined in this study to inform their recommendations for

assessing the severity of iodine deficiency within a population using spot urine

samples in school-aged children in 1986 (101). Additionally, although these

recommendations were initially based on estimated daily iodine intake determined

from spot urine samples through extrapolation based on creatinine excretion, later

iterations extrapolated these cutoffs to represent a concentration, reported as

µg/L (37).

There are a number of issues with the establishment of these

recommendations, as well as their current use in population monitoring. Firstly, as

discussed in Study 1, only in cases where the mean daily urine volume equals one

liter would spot UIC be indicative of daily iodine intake. As the average urine

volume of school-aged children determined in Study 1 was closer to 0.75L, the

application of the current cutoffs as a surrogate index of the iodine intakes of this

population of school-aged children might result in the misclassification of iodine

deficient children within the population as iodine sufficient. Furthermore, Study 4

determined that the average urine volume of 4-12 year olds in the SONIC study was

873 mL/day. As such, the iodine concentration determined from 24-hour samples

in this study cannot be used as a surrogate measure of iodine intake in this group

of children as it would overestimate true intakes. If a daily urine volume of 1L was

Discussion

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assumed, and the iodine concentration measured in the 24-hour urine sample (133

µg/L) used as a surrogate indicator of the daily iodine intake of this group of

schoolchildren, it would overestimate the iodine intakes by 22% when compared

to the iodine intake calculated using the true urine volume of the 24-hour urine

samples (104 µg/24-hours). Although the daily iodine intake of this group of

children was still in excess of the iodine intake of 100 µg/day originally associated

with reduced goiter prevalence (101, 117), such extrapolations in populations with a

UIC closer to 100 might result in the misclassification of a population with

inadequate iodine intakes as being iodine sufficient. For example, a population of

children with a UIC of 100 µg/L and a urine volume of 873 mL, true intakes would

lie closer to 87 µg/day which is 13% lower than the 100 µg/day originally associated

with reduced goiter prevalence(101, 117). As a result, such a population would not be

identified as at risk of developing the adverse health outcomes associated with

iodine deficiency, preventing the implementation of strategies to improve iodine

intakes. It may be worthwhile for future studies to consider collecting 24-hour urine

samples in a subset of the population under investigation, as a means of

determining the average urine volume of that population. This would allow for

studies to account for the average urine volume in their interpretation of the

results and the overall assessment of the iodine status of the population.

Secondly, the current recommendations were based on estimates from a

population of adults and children “of all ages” from Central America in the 1970s

(117). However, this study did not examine whether the association between

estimated daily iodine intakes and goiter prevalence was different in adults

compared to children. As such, the WHO recommendations state that whilst

school-aged children are the preferable target group for iodine monitoring studies,

they “appl[y] to adults, but not to pregnant and lactating women” (38). This implies

that the iodine requirements for adults (excluding pregnant and lactating women)

are the same as those for children, which is incorrect. The Nutrient Reference

Values for Australia New Zealand (NRVs) provide separate EAR recommendations

for different age groups (Table 8.1), and indicate that adults (excluding pregnant

and lactating women) have a 40% higher iodine requirement compared to 4-8 year

olds and a 20% higher requirement compared to 9-13 year olds (2). This presents

Discussion

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challenges when the same spot UIC cut-offs are used to determine the iodine status

of both adults and children. Although deficiency during adulthood may not impact

growth and cognition as it would in childhood, iodine deficiency during adulthood

could still have significant effects on thyroid function (100). Furthermore, iodine

deficiency among women of child-bearing age could lead to females entering

pregnancy with suboptimal iodine status. Iodine is an important nutrient during

pregnancy, and deficiency during this time has been linked with impaired growth

and cognition (66, 292). As such, it is important that monitoring of iodine nutrition is

not limited only to children and pregnant women and appropriate

recommendations are needed for assessing the iodine nutrition of adult

populations.

Table 8.1: Iodine Nutrient Reference Values for children and adults (2)

Age group (years) EAR (µg/day)

1-3 65 4-8 65

9-13 75 14-18 95 19+* 100

EAR: Estimated Average Requirement *does not apply to pregnant/lactating women

A recent review by Zimmerman and Andersson raised the concern that the

application of the current spot UIC recommendations to assess the iodine status of

a population of adults, could result in the misclassification of the intakes of

individuals within adult populations (15). It has been estimated that the daily urinary

output of adults is approximately 1.5L (269), based on a water balance study

conducted in a sample of 1528 German adults aged 18-88 years, almost 30 years

ago (269). More recently however, a study in a sample of New Zealand adults

observed a mean daily urine output of 2L (266). In this sample of adults, a daily iodine

intake of 100µg/day (equivalent to the EAR for adults aged 19 years and over)

would correspond to a UIC of 50µg/L. As such, the application of the 100µg/L cut-

off as the indicator of iodine deficiency in this population could result in the

misclassification of iodine sufficient adults as iodine deficient. Whilst this may not

be as detrimental to health as the misclassification of iodine deficient populations

Discussion

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as iodine sufficient, it could still have an impact on ID prevalence estimates. This

could result in the implementation of ID control programs in countries where they

are not needed, and potentially expose individuals to excessive iodine intakes and

subsequent adverse health outcomes. Therefore, it might be appropriate for the

current recommendations for assessing the iodine status of populations using spot

urine sample to be revised to account for the higher iodine requirements of adults.

Furthermore, more research into the daily urine volume of adults is needed in

order to inform the development of these recommendations.

Finally, it is important to note that the current spot UIC cut-offs were based

primarily on goiter prevalence estimates in an area of long-term moderate iodine

deficiency (117). Using goiter prevalence as a functional indicator of iodine deficiency

within a population comes with its own limitations, as both inter and intra observer

errors in the measurement of thyroid size can have a significant impact on goiter

prevalence estimates. This was demonstrated by a multi-centre study of the iodine

nutrition of 6-15 year old children from across Europe in 1995, using a combination

of thyroid ultrasound and the measurement of UIC in spot urine samples (107).

Whilst this study observed that a spot UIC >100µg/L was associated with reduced

goiter prevalence, thus providing support for the 1993 WHO recommendation for

assessing the iodine nutrition of populations based on a population median UIC of

100µg/L, it was later determined that the goiter prevalence estimates in the

European study had been overestimated (107). Whilst it was first suggested that this

may have been a result of the long standing history of iodine deficiency in Europe,

resulting in a slower response by the thyroid gland to iodine prophylaxis (141), it was

eventually established that systematic errors in the thyroid ultrasound

methodology used were the reason for the observed discrepancies in goiter

prevalence estimates (108). Furthermore, studies conducted in South Africa (143) and

Côte d’Ivoire (69) have demonstrated that goiter prevalence can remain elevated in

populations for up to 4 years after spot UIC returns to normal levels. As such, the

measurement of thyroid size, and subsequently goiter prevalence, may not be the

most sensitive functional outcome to use as the indicator of iodine deficiency when

forming recommendations for assessing the iodine nutrition of populations.

Alternatives to thyroid size assessment have been investigated by a number of

Discussion

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researchers (136, 304-307). The most promising of these biomarkers is serum

thyroglobulin (Tg), which studies have demonstrated to be a sensitive index of

iodine nutrition in both children (136, 307) and adults (308-310).

8.3 Current lack of population iodine monitoring in

Australia

The findings from this thesis add to the limited pool of research assessing

the current state of iodine nutrition in Australia. Prior to the 2011-2013 AHS the

most recent national survey of iodine nutrition encompassing both children and

adults was conducted in 1995 (311). This survey utilised 24-hour food recall data to

determine total dietary iodine intake, however did not include urinary assessment

of iodine status (311). Eight years later, the 2003-2004 NINS utilised spot urine

samples and thyroid ultrasound to assess the prevalence of iodine deficiency

among 1,709 8-10 year old children from across the five mainland states of

Australia, in order to confirm the findings of iodine deficiency among school-aged

children by a number of smaller studies in various regions across Australia (14, 41-43).

Following on from this in 2007 the Australian Children’s National Nutrition and

Physical Activity Survey (CNPAS) utilised 24-hour food recalls to estimate the iodine

intakes of a representative sample of 4,487 2-16 year old children from across

Australia (281). Although mandatory fortification of bread with iodised salt was

introduced in 2009 as a means of combatting the iodine deficiency observed in

Australia between 1999-2007, few studies have been conducted post fortification

to evaluate the effectiveness of the strategy and monitor the iodine nutrition of

the population (29, 47, 312-317). Furthermore, only three of these studies were

conducted in children (29, 47, 315), with the majority of studies focusing on pregnant

or lactating women (312-314, 316, 317).

Whilst the 2011-13 AHS utilised spot urine samples to assess the iodine

status of the population, to date there have been no studies utilising 24-hour urine

samples to assess the iodine intakes of a nationally representative sample of

Australian children and adults as a means of evaluating the effectiveness of

mandatory fortification. Such a program is unlikely as the collection of 24-hour

Discussion

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urine samples from populations is considered difficult, when compared to the

collection of spot urine samples. Globally, monitoring of the iodine nutrition of

populations is largely carried out using spot urine samples to determine iodine

status (318, 319), including the American National Health and Nutrition Examination

Survey (NHANES) (85), China’s National Iodine Deficiency Disorders Survey (320, 321)

and the United Kingdom’s National Diet and Nutrition Survey (NDNS) (322).

Additionally, these large-scale national surveys also collect dietary data to

determine dietary intakes and food sources of iodine within populations (85, 320-322).

Whilst spot urine samples and dietary methods are adequate for assessing

population risk of iodine deficiency, the inherent inaccuracies associated with

these methods mean that they are not able to provide an accurate estimate of daily

iodine intake. In order to accurately assess the iodine intakes of individuals and

populations 24-hour urine samples are needed (111). Globally, there are currently

no ongoing national surveys utilising 24-hour urine samples to monitor the iodine

intakes of a population. The findings from the present thesis demonstrate that with

appropriate instructions and resources, collection of 24-hour urine samples is

feasible among school-aged children. The use of 24-hour urine samples for the

assessment of the iodine intakes of a large sample has been successfully

demonstrated by the ongoing Dortmund Nutritional and Anthropometric

Longitudinally Designed (DONALD) Study which has effectively collected 24-hour

urine samples from German children and adolescents aged 2-18 years old at yearly

intervals since its establishment in 1985 (202).

The history of iodine deficiency in Australia in combination with the

evidence linking both iodine deficiency and excess to adverse health outcomes in

both children and adults provides sufficient rationale for the implementation of an

ongoing iodine monitoring program in Australia. There is currently no ongoing

monitoring of the iodine nutrition of both children and adults in Australia.

Furthermore, the use of 24-hour urine samples is needed to determine the iodine

intakes of the population as opposed to simply relying on spot urine samples for

assessing iodine status. Such a program would help monitor the impact of various

public health strategies, such as mandatory iodine fortification of bread and the

application of sodium reduction targets to processed foods, on the iodine nutrition

Discussion

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of the population. Furthermore, as the use of 24-hour urine samples is

recommended as the most appropriate method for monitoring the sodium intakes

of the population, it might be worthwhile for future studies assessing the sodium

intakes of Australian to consider assessing iodine intakes concurrently, as this will

assist in monitoring the impact of sodium reduction strategies on both sodium and

iodine intakes of the population.

8.4 Concluding remarks

The findings from this thesis provide a unique contribution to nutrition

science and add important information to the limited available data regarding the

iodine nutrition of Victorian school-aged children post fortification of bread with

iodised salt. Furthermore, the studies included in this thesis have highlighted a

number of challenges to overcome with regards to the current methods used for

assessing the iodine nutrition of populations. Therefore, validation of current

recommendations is needed using a more sensitive biomarker of iodine nutrition,

such as thyroglobulin, to ensure that the iodine nutrition of populations is being

accurately assessed. Based on the findings from this thesis, it would appear that

the iodine intakes of Victorian school-aged children have improved to sufficient

levels since the introduction of mandatory iodine fortification in 2009, with bread

and milk being the main contributors to total iodine intake. The main source of

iodine within bread comes from the iodised salt added during processing, therefore

the implementation of sodium reduction targets as a means of reducing population

sodium intakes, may have a negative impact on the iodine status of Victorian

school-aged children. As sodium reduction should remain an important public

health initiative for reducing the risk of cardiovascular disease, such targets should

also take into account the potential impact which their implementation may have

on the iodine content of foods such as bread. In addition, alternative strategies for

improving iodine intakes through foods which are less reliant on iodised salt for

their iodine content, such as milk, need to be considered by health professionals

and government bodies. In summary, the evidence linking both iodine deficiency

and excess to adverse health outcomes in school-aged children provides

Discussion

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substantial rationale for the implementation of an ongoing population iodine

monitoring program in Australia. Furthermore, such a program will help to further

elucidate the success of mandatory fortification and enable the monitoring of the

impact of various public health strategies, such as the application of sodium

reduction targets to bread, on the iodine nutrition of the population.

~ 145 ~

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310. Rasmussen LB, Ovesen L, Bulow I, Jorgensen T, Knudsen N, Laurberg P, et al.Relations between various measures of iodine intake and thyroid volume, thyroidnodularity, and serum thyroglobulin. Am J Clin Nutr. 2002;76(5):1069-76.

311. Australian Bureau of Statistics. 1995 National Nutrition Survey Users' Guide.Canberra: Australian Bureau of Statistics, 1998.

312. Axford S, Charlton K, Yeatman H, Ma G. Poor knowledge and dietary practicesrelated to iodine in breastfeeding mothers a year after introduction of mandatoryfortification. Nutr Diet. 2012;69(2):91-4 4p.

313. Charlton KE, Yeatman H, Brock E, Lucas C, Gemming L, Goodfellow A, et al.Improvement in iodine status of pregnant Australian women 3 years afterintroduction of a mandatory iodine fortification programme. Preventive medicine.2013;57(1):26-30.

314. Condo D, Huyhn D, Anderson AJ, Skeaff S, Ryan P, Makrides M, et al. Iodine statusof pregnant women in South Australia after mandatory iodine fortification of breadand the recommendation for iodine supplementation. Matern Child Nutr.2016:e12410-n/a.

315. Samidurai AJ, Ware RS, Davies PSW, Davies PS. The influence of mandatory iodinefortification on the iodine status of Australian school children residing in an iodinesufficient region. Asia Pacific Journal of Clinical Nutrition. 2017;26(4):680-5.

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329. Remer T, Manz F, Alexy U, Schoenau E, Wudy SA, Shi L. Long-term high urinarypotential renal acid load and low nitrogen excretion predict reduced diaphysealbone mass and bone size in children. J Clin Endocrinol Metab. 2011;96(9):2861-8.

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331. Shi L, Wudy SA, Buyken AE, Hartmann MF, Remer T. Body fat and animal proteinintakes are associated with adrenal androgen secretion in children. Am J Clin Nutr.2009;90(5):1321-8.

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References

~ 165 ~

Appendices

Appendix A: Summary of Food and Health Dialogue food reformulation targets (323)

Food Category Subcategory Reformulation Targets Timeframe for Action

Breads n/a Maximum sodium target of 400mg/100g

May 2010 - December 2013

Ready-to-eat Breakfast Cereals

n/a 15% reduction in sodium across those products with sodium levels exceeding 400mg/100g

May 2010 - December 2013

Simmer Sauces

Asian style sauces 15% reduction in sodium across simmer sauces with sodium levels exceeding 680mg/100g

January 2011 - December 2014

Indian style sauces Pasta Sauces Other (simmer-type) sauces

15% reduction in sodium across simmer sauces with sodium levels exceeding 420mg/100g

Processed meats

Bacon Ham and other cured meat products

Maximum sodium target of 1090mg/100g

January 2011 - December 2013

Emulsified luncheon meats


Soups

Dry soup products Maximum sodium target of 290mg/100g

December 2011 - December 2014

Wet/condensed soup products

Average sodium target of 290mg/100g AND a maximum target of 300mg/100g

Savoury Pies

Wet savoury pies and pastries

10% reduction in sodium across those with sodium levels exceeding 400mg/100g March 2012 -

March 2014

Dry savoury pies and pastries

10% reduction in sodium across those with sodium levels exceeding 500mg/100g

Appendices

~ 166 ~

Food Category Subcategory Reformulation Targets Timeframe for Action

Potato/Corn/ Extruded Snacks

Cereal-based snacks

Average sodium target of 550mg/100g AND maximum target of 700mg/100g


Potato chips Average sodium target of 550mg/100g AND max. target 800mg/100

Extruded snacks Average sodium target of 950mg/100g AND maximum target of 1250mg/100g

Salt and vinegar based snacks

Average sodium target of 850mg/100g AND maximum target of 1100mg/100g

Savoury crackers

Flavoured crackers (flour-based)

Maximum sodium target of 1000mg/100g OR 15% reduction in sodium towards the maximum target for products with sodium levels significantly above the agreed maximum targets


Plain crackers (flour based) Flavoured rice crackers/cakes/ corncakes

Maximum sodium target of 850mg/100g OR 15% reduction in sodium towards the maximum targets for products with sodium levels significantly above the agreed maximum targets

Cheese

Cheddar and cheddar style cheeses


March 2013 - March 2017

Low moisture mozarella cheeses


Chilled processed cheeses

Maximum sodium target of 1270mg/100mg OR 10-15% reduction in sodium towards the maximum target for those products with sodium levels significantly above the agreed maximum target of 1270mg/100g.

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har

acte

rize

th

e cu

rren

t io

din

e su

pp

ly o

f G

erm

an p

resc

ho

ol

child

ren

in d

etai

l, in

vest

igat

e it

s d

evel

op

men

t d

uri

ng

the

last

yea

rs

and

qu

anti

fy t

he

imp

act

of

dif

fere

nt

foo

d g

rou

ps

on

iod

ine

nu

trit

ion

in t

his

age

gro

up

.

20

03

-20

10

3

-<6

y/o

2

22

3

78

Li

bu

da_

201

2 N

o

Rem

er_2

006

(25

)

To s

pe

cifi

cally

exa

min

e th

e lo

ng-

term

tre

nd

s in

iod

ine

stat

us

fro

m

19

96

to

19

99

an

d f

rom

20

00

to

2

00

3, w

e st

ud

ied

th

e 2

4-h

uri

nar

y io

din

e ex

cre

tio

n (

24

-h U

I) in

a

sam

ple

of

Ger

man

sch

oo

lch

ildre

n

wh

o w

ere

follo

wed

lon

gitu

din

ally

o

ver

the

resp

ect

ive

8-y

per

iod

.

19

96

-20

03

6

-12

y/o

4

26

1

04

6

Mo

nte

neg

ro-B

eth

anco

urt

_201

5 M

on

ten

egro

-B

eth

anco

urt

_20

15b

- 1

99

6-

20

03

No

Rem

er_2

011

(329

)

To e

xam

ine

wh

eth

er t

he

asso

ciat

ion

of

lon

g-te

rm d

ieta

ry

acid

load

an

d p

rote

in in

take

wit

h

child

ren

’s b

on

e st

atu

s ca

n b

e co

nfi

rmed

usi

ng

app

rove

d u

rin

ary

bio

mar

kers

an

d w

het

her

th

ese

die

t in

flu

ence

s m

ay b

e in

dep

end

ent

of

po

ten

tial

bo

ne

-an

abo

lic s

ex

ster

oid

s.

July

19

98

-

Jun

e19

99

6

-18

y/o

1

97

7

89

M

on

ten

egro

-Bet

han

cou

rt_2

015

Mo

nte

neg

ro-

Bet

han

cou

rt_2

015

b

No

~ 171 ~

Stu

dy

ID

Appendices

Stu

dy

ID

Aim

(s)

Stu

dy

year

(s)

Age

ra

nge

N

N

um

ber

of

uri

ne

sam

ple

s

Par

tici

pan

t o

verl

ap w

ith

oth

er

stu

die

s

Incl

ud

ed in

fi

nal

dat

a sy

nth

esis

?

To e

xam

ine

wh

eth

er u

rin

ary

free

co

rtis

ol (

UFF

), c

ort

iso

ne

(UFE

),

and

glu

coco

rtic

oid

sec

reti

on

m

arke

r ar

e af

fect

ed b

y u

rin

e vo

lum

e in

hea

lth

y ch

ildre

n (3

30)

19

90

-20

02

8

-9, 1

2-

14

y/o

2

00

2

00

M

anz_

200

2 -

19

90

-19

97

M

on

ten

egro

-B

eth

anco

urt

_20

15b

- 1

99

3-2

00

2

No

Shi_

2009

(331

)

To in

vest

igat

e w

het

her

a r

elev

ant

par

t o

f th

e va

riat

ion

in 2

4-h

AA

se

cret

ion

may

als

o b

e ex

pla

ined

b

y b

od

y co

mp

osi

tio

n a

nd

die

tary

in

take

s in

he

alth

y ch

ildre

n

19

96

-20

03

3

-12

y/o

3

82

1

87

Mo

nte

neg

ro-B

eth

anco

urt

_201

5

- 6

-12

y/o

M

on

ten

egro

-B

eth

anco

urt

_20

15b

- 6

-12

y/o

No

Shi_

2010

(332

)

To e

xam

ine

the

exte

nt

to w

hic

h

24

-ho

ur

uri

nar

y ex

cret

ion

of

calc

ium

, oxa

late

, an

d u

ric

acid

is

asso

ciat

ed w

ith

bo

dy

fat

as w

ell

as e

nd

oge

no

us

glu

coco

rtic

oid

s in

fr

ee-l

ivin

g ch

ildre

n, t

akin

g re

leva

nt

nu

trit

ion

al a

nd

aci

d-b

ase

fact

ors

into

acc

ou

nt.

19

96

-20

03

4

-5, 8

-9,

12

-14

y/o

3

00

3

00

Mo

nte

neg

ro-B

eth

anco

urt

_201

5

- 8

-9y/

o, 1

2-1

4y/

o

Mo

nte

neg

ro-

Bet

han

cou

rt_2

015

b -

8-9

y/o

M

on

ten

egro

-Bet

han

cou

rt_2

013

-

4-6

y/o

No

~ 172 ~

Shi_

2008

(316

)

Appendices

Aim

(s)

Stu

dy

year

(s)

Age

ra

nge

N

N

um

ber

of

uri

ne

sam

ple

s

Par

tici

pan

t o

verl

ap w

ith

oth

er

stu

die

s

Incl

ud

ed in

fi

nal

dat

a sy

nth

esis

?

Shi_

2011

(333

)

To e

xam

ine

the

rela

tio

nsh

ip

bet

wee

n p

rep

ub

erta

l gl

uco

cort

ico

id s

tatu

s an

d e

arly

or

late

pu

ber

tal m

arke

rs,

ind

epen

de

nt

of

adre

nar

chal

an

d

nu

trit

ion

al s

tatu

s.

19

96

-20

03

7

-10

y/o

3

76

1

11

M

on

ten

egro

-Bet

han

cou

rt_2

015

Mo

nte

neg

ro-

Bet

han

cou

rt_2

015

b

No

Shi_

2011

b (3

34)

To e

xam

ine

wh

eth

er a

dre

nal

an

dro

gen

(A

A)

met

abo

lites

may

b

e al

so a

ffec

ted

by

uri

ne

volu

me

in h

ealt

hy

child

ren

19

96

-20

03

4

-10

y/o

1

69

1

20

Mo

nte

neg

ro-B

eth

anco

urt

_201

5

- 6

-10

y/o

M

on

ten

egro

-B

eth

anco

urt

_20

15b

- 6

-10

y/o

No

Shi_

2015

(335

)

To e

xam

ine

the

asso

ciat

ion

of

uri

nar

y m

easu

res

of

glu

coco

rtic

oid

sta

tus

and

co

rtic

al

bo

ne

in h

ealt

hy

no

n-o

be

se

child

ren

, aft

er p

arti

cula

rly

con

tro

llin

g fo

r p

rote

in in

take

.

July

19

98

-

Jun

e19

99

6

-18

y/o

1

75

1

75

M

on

ten

egro

-Bet

han

cou

rt_2

015

Mo

nte

neg

ro-

Bet

han

cou

rt_2

015

b

No

~ 173 ~

Stu

dy

ID

Appendices

A

im(s

) St

ud

y ye

ar(s

) A

ge

ran

ge

N

Nu

mb

er o

f u

rin

e sa

mp

les

Par

tici

pan

t o

verl

ap w

ith

oth

er

stu

die

s

Incl

ud

ed in

fi

nal

dat

a sy

nth

esis

?

Bo

kho

f_2

010

(203

)

To a

sse

ss t

he

valid

ity

of

die

tary

p

rote

in in

take

rec

ord

ed o

n t

he

thir

d 2

4h

of

a 3

d w

eig

he

d d

ieta

ry

reco

rd a

gain

st t

he

pro

tein

inta

ke

esti

mat

ed f

rom

sim

ult

ane

ou

sly

colle

cted

24

h u

rin

e sa

mp

les.

no

t p

rovi

ded

3

-4, 7

-8,

11

-13

y/o

4

39

3

36

u

ncl

ear

No

Dim

itri

ou

_20

01 (2

04)

To in

vest

igat

e w

het

her

uri

nar

y le

pti

n m

easu

rem

ents

co

uld

be

u

sed

as

a n

on

inva

sive

bio

che

mic

al

mar

ker

to a

sses

s th

e le

pti

n s

tatu

s in

ch

ildre

n.

no

t p

rovi

ded

4

-18

y/o

1

55

1

55

u

ncl

ear

No

Ebn

er_2

002

(205

) To

an

alys

e th

e o

rigi

n o

f th

e se

x d

iffe

ren

ce in

uri

ne

osm

ola

lity

usi

ng

the

DO

NA

LD c

oh

ort

n

ot

pro

vid

ed

4-1

4.9

y/

o

49

5

49

5

un

clea

r N

o

Esch

e_20

16

(206

)

To e

xam

ine

the

asso

ciat

ion

b

etw

een

uri

nar

y ci

trat

e ex

cret

ion

an

d b

on

e st

ren

gth

as

wel

l as

lon

g-te

rm f

ract

ure

ris

k.

no

t p

rovi

ded

6

-18

y/o

2

31

8

57

u

ncl

ear

No

~ 174 ~

Stu

dy

ID

Appendices

Aim

(s)

Stu

dy

year

(s)

Age

ra

nge

N

N

um

ber

of

uri

ne

sam

ple

s

Par

tici

pan

t o

verl

ap w

ith

oth

er

stu

die

s

Incl

ud

ed in

fi

nal

dat

a sy

nth

esis

?

Kru

pp

_201

2 (2

08)

To in

vest

igat

e w

het

her

Hip

pu

ric

Aci

d m

ay a

ctu

ally

rep

rese

nt

an

app

rop

riat

e b

iom

arke

r fo

r Fr

uit

an

d V

ege

tab

le c

on

sum

pti

on

in

hea

lth

y ch

ildre

n a

nd

ad

ole

sce

nts

.

no

t p

rovi

ded

9

-10

&

13

-15

y/o

2

40

2

40

u

ncl

ear

No

Pen

czyn

ski_

2015

(207

)

To in

vest

igat

e th

e re

lati

ve v

alid

ity

of

24

-h u

rin

ary

Hip

pu

ric

A

excr

etio

n a

s a

bio

mar

ker

for

hab

itu

al f

lavo

no

id f

rom

Fru

it a

nd

V

eget

able

co

nsu

mp

tio

n b

y co

mp

aris

on

wit

h it

s in

take

es

tim

ated

fro

m m

ult

iple

3-2

4h

w

eigh

ed

die

tary

rec

ord

s in

h

ealt

hy

ado

lesc

ents

.

no

t p

rovi

ded

9

-16

y/o

2

87

1

09

8

un

clea

r N

o

Rem

er_2

010

(209

)

To c

hec

k w

he

ther

th

e b

iom

arke

r-va

lidat

ed d

iet

effe

cts

on

bo

ne

are

ind

epen

de

nt

of

the

infl

ue

nce

of

adre

nar

chal

sex

ste

roid

s, w

e al

so

exam

ined

an

d c

on

tro

lled

fo

r u

rin

ary

24

-h D

HEA

met

abo

lite

excr

etio

n r

ate

s.

no

t p

rovi

ded

6

-10

y/o

1

09

1

09

u

ncl

ear

No

~ 175 ~

Stu

dy

ID

Appendices

Appendices

~ 176 ~

Appendix C: Modified Newcastle – Ottawa Quality Assessment Scale

TOTAL OUT OF 10 STARS

Selection (maximum 2 stars)

1) Representativeness of the study sample (maximum 2 stars) a. truly representative of the source population b. somewhat representative of the source population c. selected group of users eg. small age range, single gender, small sample

size d. no description of the derivation of the cohort

Comparability (maximum 1 star)

1) Normalisation of urine collection time a. collection normalised to 24-hour period b. some other normalization applied to results c. results not normalised d. unclear as to whether results were normalised

Assessment of Outcome: 24-hour urine collection (maximum 7 stars)

1) Instructions given to participants (maximum 2 stars) a. clear instructions given to both the parents and the child participant b. instructions only given to either the parent or the participant c. unclear as to the level of instruction given

2) Start/stop time of urine collection (maximum 1 star)

a. parent or researcher recorded the start and stop time b. child reported start and stop time c. no information provided

3) Sample inclusion/exclusion criteria (maximum 4 stars)

a. time of collection b. volume of collection c. number of missed samples/spillages d. creatinine cutoff e. no description of urine exclusion/inclusion criteria

~ 177 ~

Appendices

Ap

pen

dix

D:

Fore

st p

lot

of

stu

dies

ass

essi

ng

24-h

ou

r u

rin

e vo

lum

es o

f 2

-6 y

ear

old

s

~ 178 ~

Appendices

Ap

pen

dix

E: F

ore

st p

lot

of

stu

dies

ass

essi

ng

24-h

ou

r u

rin

e vo

lum

es o

f 7

-11

year

old

s

~ 179 ~

Appendices

Ap

pen

dix

F: F

ore

st p

lot

of

stu

dies

ass

essi

ng

24-h

ou

r u

rin

e vo

lum

es o

f 12

-19

year

old

s

Appendices

~ 180 ~

Figure G.1: Correlation between UIE and bread intake in bread

consumers whose recall and urine were collected within one week of

each other (n=325)

Figure G.2: Correlation between UIE and bread intake in bread

consumers whose recall and urine were collected >1 week apart (n=61)

Appendix G: Subgroup analysis of relationship between food sources of iodine and UIE

Appendices

~ 181 ~

Figure G.3: Correlation between UIE and milk intake in milk consumers

whose recall and urine were collected within one week of each other

(n=262)

Figure G.4: Correlation between UIE and milk intake in milk consumers

whose recall and urine were collected >1 week apart (n=51)

Appendices

~ 182 ~

Figure G.5: Correlation between UIE and combined bread and milk intake

in bread and milk consumers whose recall and urine were collected

within one week of each other (n=372)

Figure G.6: Correlation between UIE and combined bread and milk intake

in bread and milk consumers whose recall and urine were collected >1

week apart (n=71)

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