A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately...
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Transcript of A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately...
A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents:
Accurately Measuring Diet and Body Composition in ALSPAC
P. K. Newby, ScD, MPH, MSAssociate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research Scientist
Boston University
http://www.pknewby.com
http://blog.pknewby.com
University of Bristol, UK
19 October 2011
Acknowledgments
Sabrina E. Noel, PhD, MS, RDSherman Bigornia, MAMichael LaValley, PhD
Lynn Moore, DScCarine Lenders, MD, ScD
Kate Northstone, PhD, MSPauline Emmett, PhD
Andy Ness, PhD, DPH (etc.)Li Benfield, PhD
Calum Mattocks, PhDChris Riddoch, PhD
Funding SourcesAmerican Diabetes Association
The UK Medical Research Council, Wellcome Trust, and the University of Bristol provide core support for ALSPAC.
Challenge 1: Measuring Diet– How to quantify dietary measurement errors?– Research example: Flavored milk and body fat
Challenge 2: Measuring Body Composition– Are we measuring what we think we’re measuring? – Research example: SSBs and body fat
Challenge 1: Dietary Reporting Errors
Significant misreporting of dietary intakes has been reported among children – Especially with increasing body weight and body fatness
Bandini et al, AJCN, 1990
r = -0.48, p < 0.001
Accounting for reporting errors is key for understanding diet-obesity relationships (but it is often overlooked)
Methods Used for Capturing Implausible Energy Reporters
• Premise: reported energy intake = energy expenditure under weight-stable conditions
• Direct measure of energy expenditure using doubly labeled water (DLW)– Compare reported intake to energy expenditure– Not feasible for large population
studies
• Equations to estimate implausible and plausible reporting– Compare reported intake to estimates of energy requirements
Goldberg et al, Eur J Clin Nutr, 1991; McCrory et al, Public Health Nutr, 2002; Huang et al, Obes Res 2004 & 2005DLW figure: http://www.iaea.org/newscenter/features/nutrition/energyintake.html
Capturing Implausible Reporters
• Age- and sex-specific cut-off for the ratio of reported energy intake to predicted energy requirements
• Predicted energy requirement equation (IOM)– Includes coefficients for age, physical activity (PA),
and weight and constants for sex and energy deposition during growth
Huang et al, 2004
Study Objective: Include objective measures of physical activity in equations used to predict energy requirements and quantify dietary reporting errors
- 2 methods used physical activity data from accelerometers- 1 assumed a low-active level
Three Variations of the PA Coefficient
IOM PA Category
IOM Description of Categories
IOM PA Coefficient
Categories based on mins of MVPA
Boys Girls
Sedentary Typical daily living activities
1.00 1.00 <30 minutes of MVPA
Low-active Sedentary + 30-60 min moderate activity
1.13 1.16 30 to <60 minutes of MVPA
Active Sedentary + 60 min moderate activity
1.26 1.31 >60 minutes to <120 minutes of MVPA
Very active Sedentary + 60 min moderate + 60 min vigorous or 120 min moderate activity
1.42 1.56 >120 minutes of MVPA
Percent Agreement between Methods
2. PAL Value Method 3. MVPA Method
1. Low-active Method
UR 51.8%
PR 37.9%
OR 10.3%
UR 37.1%
PR 42.4%
OR 20.4%
UR, 51.5% 88.0 15.5 0 97.4 36.1 0
PR, 40.8% 12.0 78.8 45.8 2.6 63.5 63.0
OR, 7.7% 0 5.7 54.2 0 0.4 37.0
к = 0.66 between the low-active and PAL value method; к = 0.53 between the low-active and MVPA method
Body Fatness Across Dietary Reporting Categories
Body Fat (%)
Method for Capturing Reporting Errors
a
bc
a
bc
a
bc
Comparison of Methods
0
10
20
30
40
50
60
UR
OR
DLW Studies 11- 15 y
Prediction Equations
% C
lass
ified
Our Methods
Conclusions and Next Steps
• All three methods were associated with sociodemographic and body composition measures as expected
• Inclusion of objectively measured physical activity as MVPA may have resulted in more reasonable estimates of plausible and implausible reporters
• Improving measurement of dietary reporting errors will improve precision and accuracy of results
• Future: Better quantification of MVPA using accelerometer data and direct comparisons with EE using DLW
Research example 1: Chocolate Milk, Body Fat, and Body Weight
Serving Size 1 cup (240mL)Amount per Serving
Calories 170Calories from Fat 25
% Daily ValuesTotal Fat 3g 4% Saturated Fat 2g 9% Trans Fat 0g Cholesterol 15mg 4%Sodium 170mg 7%Total Carbohydrate 28g
9%
Fiber <1g 3%Sugar 26g Protein 9g 17%
Vitamin A 10% Vitamin C 0%Calcium 50% Iron 4%Vitamin D 25%
http://www.hood.com/Products/prodDetail.aspx?id=639
Flavored milk consumers had less favorable changes in body fat
Means were adjusted for pubertal status, maternal BMI and educational attainment, changes in age, height, height squared, physical activity, and intakes of total fat, ready-to-eat cereal, 100% fruit juice, sugar-sweetened beverage, and plain milk. Plausible reporters only.
Conclusions and Next Steps
• Less favorable changes in body fat and weight were seen for overweight children consuming flavored milk compared with non-consumers over a 2 year period
• Associations were strengthened when reporting errors were considered.
• These results limit recommendations that promote flavored milk consumption among children, especially those who are overweight or obese
• Future: Repeating study with greater variability in intakes and conducting an analysis looking at total dairy
Challenge 2: How to Measure Body Fat
• Central adiposity is an important chronic disease risk factor in adults
• Studies in children suggest correlations between central and total adiposity are high due to limited accrual of visceral fat
• Little is known how these relationships change as children move through puberty.
Study Objectives:1. Examine relationships between central and total adiposity
assessed by anthropometry, DXA and MRI (11 and 13 y only) at 9, 11, 13, and 15 y of age
2. Compare how measures of central and total adiposity were associated with SSBs and systolic blood pressure
Methods
Body composition• Total adiposity: BMI (kg/m2) and total body fat mass
(TBFM, g) by DXA• Central adiposity: waist circumference (WC, cm),
trunk fat mass (TFM, g) by DXA, and intra-abdominal adipose tissue (IAAT, cm3) by MRI
Sexual Maturity • Self-reported tanner stage (5 levels) collapsed to
pre (1), early (2-3), and late (4-5).
Relationships between central and total adiposity measures among children at ages 9, 11, 13, and 15 y.*
*WC, waist circumference; TBFM, total body fat mass, TFM, trunk fat mass† Values are the partial variances (%) accounted by select adiposity measures by multivariate linear regression with adjustment for age, height , and pubertal stage (pre-, early, and late).
n=2031 n=1816 n=1616 n=962 n=437 n=505 n=370 n=192
n=672 n=646 n=486 n=228 n=2183 n=2079 n=1824 n=1173
Relationships between adiposity measures and intra-abdominal adipose tissue volume at ages 11 and 13 y*
*Data are Pearson’s partial correlation coefficients adjusted for age and height. P < 0.05 for all values.
†MRI data were collected at 11 and 13 on a subset of ALSPAC participants.
Conclusions
• Central and total fat measures were strongly correlated at all ages and modestly attenuated at age13 and 15 years.
• BMI, WC, TBFM, and TFM correlations with IAAT were comparable.
• Similar associations were observed with SBP (data not shown).
• Our findings have implications for the interpretation of epidemiological studies examining central adiposity on metabolic outcomes in late childhood and early adolescence, highlighting the need to also consider associations with total adiposity as they explain a large amount of variation in central adiposity
Research Example 2: SSBs and Body Composition
1) Examine the effect of change in SSB intake from 10 to 13 y (∆SSB) on total adiposity (BMI and total body fat) at 13 y
1) Determine whether SSB consumption has similar and additional effects on measures of total and central adiposity (waist circumference)
2) Adjust for dietary reporting errors
Methods
Diet • 3 day diet records at 10 and 13 y • Sugar-sweetened beverages (SSB): fruit squashes,
cordials and fizzy drinks (i.e. soda) with added sugar. 140 g water assumed for every 40 g of concentrate. 180 g = 1 serving
• Change in SSB (∆SSB) = SSB 13 – SSB 11
Adiposity • BMI, waist circumference (WC), and total body fat
mass (TBFM) at 13 y as previously described
∆SSBs (servings/d) and central and total adiposity at 13 y (n=2,455)
Adiposity at 13
Model1
Change in adiposity per
∆SSB (servings/d)2
Standardized Beta
P value
BMI, kg/m2 1 0.07 (0.03) 0.028 0.0252 0.09 (0.03) 0.039 0.0023 0.16 (0.04) 0.074 <0.001
Waist, cm 1 0.13 (0.10) 0.020 0.1882 0.22 (0.10) 0.034 0.0253 0.55 (0.14) 0.097 <0.001
Total body fat, kg
10.10 (0.08) 0.017 0.203
2 0.19 (0.08) 0.033 0.0113 0.33 (0.11) 0.065 0.003
∆SSBs (servings/d) and central adiposity at 13 y (n=2,455)
General adiposity at 13
adjustmentModel
Change in adiposity per ∆SSB
(servings/d)2
Standardized Beta
P value
Waist, cm BMI, kg/m2
1 0.07 (0.07) 0.011 0.292 0.06 (0.07) 0.010 0.373 0.24 (0.10) 0.042 0.02
Waist, cm
Total body fat, kg 1 0.11 (0.07) 0.018 0.10
2 0.08 (0.07) 0.013 0.223 0.27 (0.11) 0.048 0.01
Conclusions
• Increased SSB intakes over 3 y was associated with higher BMI and fat mass at 13 y supporting recommendations to limit SSB consumption to combat excess weight gain
• SSBs have somewhat stronger and additional effects on WC independent of total adiposity but these are likely not clinically meaningful
• Accounting for dietary reporting errors uniformly strengthened effect estimates, highlighting the importance of measuring and accounting for these errors.
Publications (Published and In Progress)
Noel SE, Ness AR, Northstone K, Emmett PE, Newby PK. Flavored milk consumption and changes in body fat in children: a prospective study. Journal of Nutrition. Submitted.
Bigornia SJ, Noel SE, LaValley MP, Moore LL, Ness AR, Newby PK. Sugar-sweetened beverage intake among children from 10 to 13 years of age and central and total adiposity: a prospective population based cohort study. International Journal of Obesity. Submitted.
Bigornia SJ, LaValley MP, Benfield LL, Ness AR, Newby PK. Relationships between direct and indirect measures of central and total adiposity in children at 9, 11, 13, and 15 years of age. American Journal of Clinical Nutrition. Submitted.
Noel SE, Ness AR, Northstone K, Emmett P, Newby PK. Milk intakes are not associated with percent body fat in children from ages 10 to 13 years. Journal of Nutrition 2011; Sept 21. [Epub ahead of print]
Noel SA, Mattocks C, Riddoch C, Emmett PE, Ness AR, Newby PK. Use of accelerometer data in prediction equations for capturing implausible dietary intakes among adolescents. American Journal of Clinical Nutrition 2010;92(6):1436-45.
Thank you for your attention!
P. K. Newby, ScD, MPH, MSAssociate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research Scientist
Boston University
http://www.pknewby.com
http://blog.pknewby.com
University of Bristol, UK
19 October 2011
Supplemental Slides
Sample characteristics by flavored milk consumption
Sample Characteristics
Flavored milk non-consumers,
age 10 y
Flavored milk consumers, age
10 y
P value
Girls, % 55.8 49.0 0.01
Body fat, %
11 y 25.5 ± 9.1 25.5 ± 9.3 0.98
13 y 24.4 ± 10.1 24.8 ± 10.7 0.50
Physical activity
11 y 587.8 ± 171.9 585.5 ± 165.6 0.80
13 y 536.0 ± 193.5 534.5 ± 177.9 0.89
Dieting at age 13 y, %
25.7 19.4 0.02
Maternal body mass index, kg/m2
24.5 ± 4.4 24.6 ± 4.8 0.73
Table 2. Adjusted means of daily total energy and selected nutrient & food intakes
Energy, nutrient and food group intake
Flavored milk non-consumers, age 10
y (n=1890)
Flavored milk consumers, age 10 y
(n=380)
P value
Total energy, kcal 1917 ± 11 2064 ± 24 <0.001
Fat, g 75.6 ± 0.32 77.5 ± 0.71 0.01
Saturated fat, g 29.2 ± 0.16 30.6 ± 0.37 <0.001
Carbohydrate, g 251.0 ± .86 258.0 ± 1.9 0.001
Fiber, g 11.8 ± 0.07 11.2 ± 0.16 0.002
Added sugars, g 89.1 ± 0.67 85.9 ± 1.5 0.05
Dietary calcium, g 796.1 ± 5.7 917.4 ± 12.8 <0.001
Sugar-sweetened beverages3, g
106.8 ± 3.31 92.6 ± 7.39 0.08
Means for total energy intake were adjusted for sex only. Means for all other nutrients and food groups were adjusted for sex and total energy intake.
Flavored milk non-consumers, age 10
(n=1890)
Flavored milk consumers, age 10
(n=380)
P value
Mean 95% CI Mean 95% CI
Normal weight childrenChange in % body
fat, (n=1,715) Model 1 -0.83 -1.42, -0.24 -0.63 -1.37, 0.12 0.48
Model 2 -0.86 -1.44, -0.27 -0.60 -1.35, 0.14 0.40
Overweight/obese childrenChange in % body
fat, (n=449)Model 1 -2.64 -3.82, -1.45 -1.09 -2.60, 0.41 0.01
Model 2 -2.64 -3.83, -1.45 -1.11 -2.62, 0.40 0.01
Model 1 was adjusted for change in counts per minute, pubertal status, maternal BMI and educational attainment, change in total fat intake, and change in ready-to-eat cereal, 100% fruit juice and SSB intake. Model 2 also included change in total milk intake.
Pearson’s partial correlations between systolic blood pressure and BMI, WC, TBFM and TFM from 9 to 15 y adjusted for age and height