Dietary Patterns and Incident Type 2 Diabetes …...was positively associated with incident T2DM in...

Dietary Patterns and Incident Type 2 Diabetes Mellitus in an Aboriginal Canadian Population

by

Jacqueline Kathleen Reeds

A thesis submitted in conformity with the requirements for the degree of Masters of Science

Department of Nutritional Sciences University of Toronto

© Copyright by Jacqueline Kathleen Reeds 2010

ii

Dietary Patterns and Incident Type 2 Diabetes Mellitus in an Aboriginal Canadian Population

Jacqueline Kathleen Reeds

Masters of Science

Department of Nutritional Sciences

University of Toronto

2010

Abstract

Type 2 diabetes (T2DM) is a growing concern worldwide, particularly among Aboriginal

Canadians. Diet has been associated with diabetes risk, and dietary pattern analysis (DPA)

provides a method in which whole dietary patterns may be explored in relation to disease.

Factor analysis (FA) and reduced rank regression (RRR) of data from the Sandy Lake Health

and Diabetes Project identified patterns associated with incident T2DM at follow-up. A RRR-

derived pattern characterized by tea, hot cereal, and peas, and low intake of high-sugar foods

and beef was positively associated with diabetes; however, the relationship was attenuated with

adjustment for age and other covariates. A FA-derived pattern characterized by processed foods

was positively associated with incident T2DM in a multivariate model (OR=1.38; CIs: 1.02,

1.86 per unit), suggesting intake of processed foods may predict T2DM risk.

iii

Acknowledgments

I would like to sincerely thank Dr. Anthony Hanley for his continuous support, understanding,

and invaluable input and guidance. Gratitude should also be expressed to Drs. Valerie Tarasuk

and Thomas Wolever for their input and for overseeing the completion of this project as

members of the Advisory Committee. Additional thanks are due to my lab mates, Rachel

Masters, Meredith MacKay, Sheena Kayaniyil, and Sylvia Ley, for their ongoing support and

encouragement. I would like to especially thank T.J. Reeds for his unconditional support,

encouragement, and understanding, as well as Karen and Arthur Thompson.

I would like to acknowledge the University of Toronto for the Mary H. Beatty Fellowship, and

the Ministry of Training, Colleges and Universities for the Ontario Graduate Scholarship which

were sources of funding for this project.

Thank you to all of the Sandy Lake Health and Diabetes Project study participants, leaders, and

study team members.

Funding for the Sandy Lake Health and Diabetes Project was provided by National Institute of

Health and the Ontario Ministry of Health.

iv

Table of Contents

List of Tables vii

List of Figures ix

List of Appendices x

Chapter One: Introduction and Review of the Literature 1

1.1: Introduction 1

1.2: Risk Factors for Type 2 Diabetes Mellitus 2 1.2.1: Traditional Risk Factors

1.2.1.1: Obesity

1.2.1.2: Dyslipidemia

1.2.13 Elevated Blood Pressure

1.2.1.4 Dysglycemia

1.2.1.5 Metabolic Syndrome

1.2.2. Non-Traditional Risk Factors

1.2.2.1 Adiponectin

1.2.2.2 C-Reactive Protein

1.2.2.3 Leptin

1.2.2.4 Interleukin-6

1.2.3 Genetics

1.2.4 Lifestyle Factors

1.2.4.1 Smoking

1.2.4.2Exercise

1.2.4.3. Diet

1.3 Diet and Type 2 Diabetes Mellitus 11

1.3.1 Dietary Components

1.3.2 Dietary Patterns

1.4 Dietary Pattern Analysis 15

1.4.1 A priori Approaches: Dietary Scores and Indices

1.4.2 A posteriori Approaches

1.4.2.1 Cluster Analysis

1.4.2.2 Factor Analysis

1.4.2.3 Reduced Rank Regression Analysis

1.5 Summary and Rationale 19

1.6 Research Objectives 20

1.7 Hypotheses 20

v

1.8 References 21

Chapter Two: Methods 30

2.1 Study Design 30

2.2 Subjects 30

2.3 Baseline Data Collection 31

2.3.1 Demographics and Risk Factors

2.3.2 Physical Activity and Physical Fitness

2.3.3 Dietary

2.3.4 Anthropometric Measures and Blood Pressure

2.3.5 Metabolic and Biochemical Measures

2.4 Follow-Up Data Collection 35

2.5 Statistical Analyses 35

2.5.1 Descriptive Statistics

2.5.2 Dietary Pattern Analysis



2.5.3 Logistic Regression Analysis

2.6 References 41

Chapter 3: Results 43

3.1 Descriptive Statistics 43

3.2 Factor Analysis 44

3.2.1 Associations of Factor Analysis-Derived Pattern Scores and

Incident Type 2 Diabetes Mellitus

3.3 Reduced Rank Regression Analysis 56

3.3.1 Associations of Reduced Rank Regression-Derived Pattern

Scores and Incident Type 2 Diabetes Mellitus

3.4 References 67

Chapter Four: Discussion 68

4.1 Summary of Findings 68

4.2 Results in Context of the Previous Literature 69

4.3 Dietary Pattern Analysis: Methodological Considerations 77

vi

4.4 Potential Mechanisms 78

4.5 Strengths and Limitations 80

4.5.1 Strengths

4.5.2 Limitations

4.6 Future Directions 82

4.7 Conclusion 82

4.8 References 84

Appendix A 86

Appendix B 91

Appendix C 97

Appendix D 103

Appendix E 110

Appendix F 118

Appendix G 119

Appendix H 120

Appendix I 127

Appendix J 128

vii

List of Tables

Table 1. Baseline characteristics of participants the Sandy Lake Health and Diabetes Project

according to diabetes status at follow-up.

Table 2.Spearman rank correlation coefficients between novel and traditional biomarkers of

participants of the Sandy Lake Health and Diabetes Project at baseline.

Table 3. Pattern names, FFQ items in each pattern, and percent common variation identified by

factor analysis using data from the Sandy Lake Health and Diabetes Project.

Table 4. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health

and Diabetes Project.

Table 5a. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Balanced Market Foods pattern score as determined by

exploratory factor analysis.

Table 5b. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Beef & Processed Foods pattern score as determined by


Table 5c. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Traditional Foods pattern score as determined by


Table 6. Spearman rank correlation coefficients of the relationship between baseline

characteristics and dietary patterns as determined using exploratory factor analysis

on FFQ data from the Sandy Lake Health and Diabetes Project.

Table 7. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor dietary

pattern scores and incident type 2 diabetes using data from the Sandy Lake Health

and Diabetes Project

Table 8. Pattern names, FFQ items in each pattern, and percent total variation explained by each

pattern, determined using reduced rank regression using data from the Sandy Lake

Health and Diabetes Project.

Table 9. Pattern loadings for each food as listed on the 34-item FFQ, as determined by reduced

rank regression analysis using data from the Sandy Lake Health and Diabetes Project.


according to tertiles of scores for the Tea & Fibre pattern as determined by reduced

rank regression.


according to tertiles of scores for the Traditional pattern as determined by reduced

rank regression.


according to tertiles of scores for the Proto-Historic pattern as determined by reduced

rank regression.


characteristics and patterns as determined using reduced rank regression analysis

using data from the Sandy Lake Health and Diabetes Project.

Table 12. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived dietary pattern scores and incident type 2 diabetes using data from

the Sandy Lake Health and Diabetes Project.

Table 13. Comparison of dietary patterns identified by factor analysis in current study to those

identified by Gittelsohn et al4.

viii

Table 14. Comparison of dietary patterns identified by reduced rank regression analysis in current

study to those identified by Heidemann et al7.

Table 15. Comparison of dietary patterns positively associated with an outcome of type 2 diabetes

mellitus, identified by reduced rank regression analysis.

Table A1. Pattern name, FFQ items in the pattern, and percent common variation identified by

reduced rank regression analysis using data from the Sandy Lake Health and

Diabetes Project.

Table A2. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health


Table A3. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Tea & Hot Cereal pattern score as determined by reduced

rank regression analysis.

Table A4. Spearman rank correlation coefficients of the relationship between baseline



Table A5. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived dietary pattern score and incident type 2 diabetes using data from


Table B1. Pattern names, FFQ items in each pattern, and percent total variation explained by each



Table B2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by reduced


Table B3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Tea, Hot Cereal & Peas pattern score as determined by

reduced rank regression analysis.

Table B3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Cereal, Soup & Chocolate pattern score as determined by


Table B4. Spearman rank correlation coefficients of the relationship between baseline



Table B5. Odds ratios and 95% confidence intervals for the association between reduced rank



Table C1. Pattern names, FFQ items in each pattern, and percent total variation explained by each



Table C2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by reduced


Table C3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Regular Tea, Low Junk Foods pattern score as determined

by reduced rank regression analysis.

Table C3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Proto-Historic pattern score as determined by reduced

rank regression analysis

ix

Table C4. Spearman rank correlation coefficients of the relationship between baseline



Table C5. Odds ratios and 95% confidence intervals for the association between reduced rank



Table D1. Correlation Coefficients for Physical Activity and Fitness Measures.

Table D2. – Baseline characteristics of participants the Sandy Lake Health and Diabetes Project


Table D3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Balanced Market Foods pattern score as determined by


Table D3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Beef & Processed Foods pattern score as determined by


Table D3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Traditional Foods pattern score as determined by


Table D4. Spearman rank correlation coefficients of the relationship between baseline



Table D5. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor

dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake


Table E1. Pattern names, FFQ items in each pattern, and percent common variation identified by


Table E2. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health


Table E3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Balanced Market pattern score as determined by


Table E3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of the Beef & Processed pattern score as determined by


Table E3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Traditional pattern score as determined by


Table E3iv. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Tea/Proto-Historic pattern score as determined by


Table E4. Spearman rank correlation coefficients of the relationship between baseline



Table E5. Odds ratios and 95% confidence intervals (CIs) for association between 4-factor

dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake


x

Table F1. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived Tea & Fibre pattern scores and incident type 2 diabetes using data

from the Sandy Lake Health and Diabetes Project, sub-grouped by age.

Table G1. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived Traditional pattern scores and incident type 2 diabetes using data

from the Sandy Lake Health and Diabetes Project, sub-grouped by age.

Table H1. Pattern names, FFQ items in each pattern, and percent total variation explained by each



Table H2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by reduced


Table H3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of scores for the Hot Market Foods & Vegetables pattern as

determined by reduced rank regression.

Table H3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes Project

according to tertiles of scores for the Traditional Foods & Hot Cereal pattern as


Table H3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Modified Proto-Historic pattern as


Table H4. Spearman rank correlation coefficients of the relationship between baseline



Table H5. Odds ratios and 95% confidence intervals for the association between reduced rank



Table I1. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor dietary

pattern scores and incident type 2 diabetes using data from the Sandy Lake Health


Table J1. Odds ratios and 95% confidence intervals for the association between reduced rank



xi

List of Figures

Figure 1. Scree plot of eigenvalues by factor (pattern) from factor analysis of FFQ data from the

Sandy Lake Health and Diabetes Project.

xii

List of Appendices

Appendix A: Reduced Rank Regression with Only Age as an Intermediate Response Variable

Appendix B: Reduced Rank Regression Analysis with Highly Correlated Variables (Waist

Circumference and Fasting Serum Insulin) as Intermediate Response Variables

Appendix C: Reduced Rank Regression Analysis with Uncorrelated Variables (Systolic Blood

Pressure and Adiponectin) as Intermediate Response Variables

Appendix D: Sensitivity Analyses Considering Physical Activity, Physical Fitness, and Current

Smoking Status as Covariates

Appendix E: Four-Factor Factor Analysis Solution

Appendix F: Subgroup Logistic Regression by Age for the Reduced Rank Regression-Driven

Tea & Fibre Pattern

Appendix G: Subgroup Logistic Regression by Age for the Reduced Rank Regression-Driven

Traditional Pattern

Appendix H: Reduced Rank Regression Analysis Using Log-Transformed Non-Normally

Distributed Intermediate Response Variables

Appendix I: Logistic Regression, Adjusted for Dietary Patterns Derived by Factor Analysis

Appendix J: Logistic Regression, Adjusted for Dietary Patterns Derived by Reduced Rank

Regression Analysis

1

1

Chapter 1

Introduction and Review of the Literature

1.1 Introduction

Type 2 diabetes mellitus (T2DM) and its macro- and micro-vascular complications are a

growing concern worldwide. In November of 2009, the International Diabetes Federation1

reported a projected worldwide prevalence of 6.6% in 2010 for diabetes in adults (aged 20 to 79

years) based on regional estimates. By 2030, the worldwide prevalence is expected to reach

7.7%1. Canadian estimates, however, are higher, with an estimated diabetes prevalence of

11.6% in adults in 20101. Again, this number is expected to increase by 2030, reaching an

estimated 13.9%1. In 2002, Hux et al

2 reported T2DM prevalence rates in counties of Ontario

ranging from 4.58 to 9.58% using administrative data. Although overall prevalence of diabetes

is rising in Ontario, Aboriginal Canadian populations have even higher prevalence rates3,4

. Since

the 1950s, the prevalence of diabetes in Aboriginal Canadian populations has been steadily

increasing4. In 1995, Delisle et al

5 reported an age-standardized prevalence of 48.6% and 23.9%

in Algonquin women of Lac Simon, and River Desert, Quebec, respectively. Age-standardized

prevalence of T2DM in men was 23.9% and 16.3% in Lac Simon and River Desert,

respectively5. In Sandy Lake, a northwestern Ontario First Nations community, the age-

standardized prevalence of T2DM was 26.1% in 1995 as determined by oral glucose tolerance

testing (OGTT)3. This high prevalence of type 2 diabetes and its related complications in

Aboriginal Canadian communities has risen dramatically over the past twenty to thirty years4.

T2DM has been associated with a number of risk factors, including metabolic risk factors,

genetics, and lifestyle factors6-16

. Among lifestyle factors, the literature examining the role of

diet in T2DM development is largely inconsistent and equivocal17

. A recent literature examining

dietary patterns and their association with chronic disease, such as T2DM, has emerged, and

shows potential in describing dietary habits which predict disease outcomes13, 17-23

. Despite

progress in diet studies of white, non-Hispanic North Americans,18,22, 23

the association between

diet and T2DM in Aboriginal Canadian populations has received little attention. Further, dietary

patterns of Aboriginal Canadians have not been examined. Therefore, a gap in the literature

2

exists where there is a need to examine the dietary patterns of Aboriginal Canadians and the

associations between these patterns and incident T2DM.

1.2 Risk Factors for Type 2 Diabetes Mellitus

1.2.1 Traditional Risk Factors

A number of metabolic disorders have been consistently associated with the development of

T2DM and are considered to be risk factors. In many populations, risk factors for T2DM

include: obesity, dyslipidemia, elevated blood pressure, and elevated fasting blood glucose15

.

When taken together, these risk factors combine to form what has been named “Metabolic

Syndrome (MetS)” or “Insulin Resistance Syndrome.”

1.2.1.1 Obesity

Obesity is defined as a body mass index (BMI) of greater than or equal to 30 kg/m2 (24)

and is the

most well-described risk factor for T2DM. Obesity has been linked with hyperinsulinemia and

insulin resistance, which are both recognized as pre-cursors to T2DM25, 26

. Abdominal obesity,

in particular, is a well-documented risk factor for T2DM24

, and is defined as a waist

circumference of greater than 88 cm for females, and greater than 102 cm for males27

. Adipose

tissue is known to produce cytokines, hormones and metabolites that have been linked with

T2DM but the molecular basis for the association between obesity and T2DM is not well-

understood25

. Plasma free fatty acid (FFA) levels are generally higher in obese individuals and

elevated levels have been shown to inhibit insulin-dependent peripheral glucose uptake in a

dose-dependent manner28

. FFAs also stimulate insulin secretion, thus compensating for the

formerly described inhibition of glucose uptake28

. However, the increased insulin secretion and

reduced clearance may lead to hyperinsulinemia, and the FFAs are believed to eventually fail in

stimulating insulin secretion, with chronic elevations in FFA eventually resulting in

morphological and functional changes to the beta cell and suppression of insulin secretion,

finally resulting in overt T2DM28

. In addition to the effects of FFAs released by adipose tissue,

the expression of adipocytokines such as interleukin-6 (IL-6) is believed to play a role25

.

Cytokines have been shown to have direct effects on the insulin signaling pathway (eg. tumour

3

necrosis factor-alpha – TNF-α), the fibronolytic pathway (plasminogen activator inhibitor-1 –

PAI-1), and cellular adhesion (IL-6)25

. Clinical studies have shown associations between

adipocytokines and insulin sensitivity and endothelial function in human subjects25

. More

information on the role of adipocytokines may be found on page 6, section 1.2.2.

The 2008 Canadian Diabetes Association Clinical Practice Guidelines for the Prevention and

Treatment of Diabetes in Canada recommend a modest weight loss of 5 to 10% to improve

insulin sensitivity and glycemic control, and to lower blood pressure and cholesterol24

.

1.2.1.2 Dyslipidemia

Dyslipidemia has been consistently linked with T2DM, and is documented as a risk factor for its

development24, 29

. As described in the previous section, high levels of circulating FFAs have an

inhibitory effect on insulin-induced peripheral glucose uptake. High circulating levels of serum

triglycerides (TG) (hypertriglyceridemia) is also a common disorder in the etiologic pathway of

T2DM30

. Deposition of the lipids in muscle tissue has been negatively associated with insulin

sensitivity30, 31

. Intramyocellular lipid concentrations have been shown to be very closely related

to insulin sensitivity; therefore, in addition to circulating lipid levels, the location of lipid

deposition appears to be linked with development of T2DM30

.

Inverse relationships (independent of body mass index [BMI] and other risk factors) have been

shown between high-density lipoprotein cholesterol (HDL-C) and incident T2DM32

; however,

the relationship appears to be stronger in women than in men33-35

. There are a number of sub-

fractions of HDL-C, each of which appear to be influenced by different factors35

. Alcohol

consumption has been shown to influence the HDL-C3 sub-fraction36

, while physical activity

influences the HDL-C2 sub-fraction35

. Both obesity and insulin resistance are associated with

decreased levels of total HDL-C and the HDL-C2 sub-fraction35, 37, 38

. Exogenous estrogen has

been shown to increase HDL-C2 levels38

, while testosterone appears to decrease HDL-C2

levels35, 40

. In a study of Pima Indians, Fagot-Campagna et al35

showed a protective effect of

total HDL-C, HDL-C2, and HDL-C3 against T2DM in females. Conversely, total HDL-C and

HDL-C3 were positively associated with outcomes of T2DM in male subjects; however, this

association appeared to be due to alcohol consumption35

. Additionally, interventions known to

4

improve insulin sensitivity, such as weight loss, physical activity and administration of

glitazones have been shown to cause moderate increases in HDL-C41

.

There has been controversy as to whether low HDL-C is causal in the etiology of T2DM or

whether it simply coincides with it41

. Direct insulin effects, as well as the elevated levels of

FFAs mentioned previously, appear to play a role in decreased levels of HDL-C in those

developing insulin resistance and T2DM41

. Decreased hepatic production of HDL-C caused by

excess FFAs, as well as the elevated levels of circulating TG result in low HDL-C

concentrations in the blood41

. Low HDL-C and hypertriglyceridemia have also been shown as

markers of a β-cell-toxic metabolic state, contributing to β-cell failure and resultant T2DM41

.

1.2.1.3 Elevated Blood Pressure

Elevated blood pressure, or hypertension, is a long-established risk factor for T2DM24, 29

. A

recent study by Conen et al42

has shown that elevated blood pressure (BP) is an independent

predictor of incident T2DM in women. Obese women were at greatest risk for T2DM; however,

obese women with high BP were at an even higher risk42

. A study by Gress et al43

also showed a

predictive effect of high BP and T2DM; however, there was no multivariate adjustment. The

mechanism which explains the relationship between high BP and T2DM is poorly understood;

however, endothelial dysfunction and inflammation (both of which are highly correlated with

BP and T2DM) may play a role42

.

1.2.1.4 Dysglycemia

It has been long understood that impaired glucose metabolism and hyperglycemia in the pre-

diabetic range are strong independent predictors of T2DM24, 29, 44, 45

. Where impaired fasting

glucose (IFG) was once considered a strong predictor of T2DM risk, impaired glucose tolerance

(IGT), as determined using an oral glucose tolerance test (OGTT), has proven to be even more

predictive46

. However, since dysglycemia is not the sole predictor of T2DM risk, it is important

to examine several of the risk factors for T2DM in combination to identify those at greatest risk

for conversion to T2DM.

5

1.2.1.5 Metabolic Syndrome

The Metabolic Syndrome (MetS), as mentioned previously, is a clustering of well-documented

risk factors for T2DM (as well as coronary artery disease)15, 45

. According to the National

Cholesterol Education Program Adult Treatment Panel III27

criteria, the MetS is characterized

by three or more of the following: abdominal obesity (waist circumference > 88 cm for females,

>102 cm for males), hypertriglyceridemia (TG >= 1.69 mmol/L or 150 mg/dL), low HDL-C (<

1.29 mmol/L or 50 mg/dL in females, < 1.04 mmol/L or 40 mg/dL in males), elevated BP (>=

130/85 mmHg or on antihypertensive medication), and high fasting glucose (>= 6.1 mmol/L or

110 mg/dL)15, 45

.

The prevalence and definitions of components of the MetS have been well-established in

Caucasian North American adults27, 45, 47

; however, it is important to consider any differences in

risk factors that may exist for Canadians of Aboriginal origin. A recent study by Lear et al48

showed that current cut-points for central obesity, namely waist circumference (WC), are

consistent for both Canadians of European descent and those of Aboriginal descent in their

ability to predict cardiovascular disease (CVD). A study by Pollex et al16

in the Oji-Cree of

northwestern Ontario found that abdominal obesity and low HDL-C were the most prevalent

components of the MetS, while high BP was least prevalent. A study of Oji-Cree from Ontario

and Manitoba, Inuit from the Keewatin region of the Northwest Territories, and non-Aboriginal

Canadians (primarily of European descent) from Manitoba, showed that MetS is prevalent in

diverse ethnic groups across Canada, but that different components of the syndrome are more

common in specific population groups49

. For example, the Oji-Cree study participants had

higher rates of abdominal obesity and hyperglycemia when compared to non-Aboriginal

participants, and Inuit participants had a better metabolic profile, but more abdominal obesity49

.

Overall, Oji-Cree participants had the highest prevalence of MetS despite similar prevalence of

abdominal obesity to Inuit participants49

. Interestingly, women of Aboriginal origin have a

higher prevalence of MetS than their male counterparts, whereas there appears to be no gender

effect in non-Aboriginal Canadians49

. Recently, Ley et al29

found that both MetS and its

individual components had significant positive associations with incident T2DM in the Oji-Cree

of Sandy Lake, ON.

6

In summary, the clustering of a number of risk factors for T2DM, known as the MetS, is

predictive of T2DM in diverse ethnic populations; however, the patterns in which they are

expressed differ among population groups.

1.2.2 Non-Traditional Risk Factors

In addition to the traditional risk factors encapsulated by the MetS, T2DM is associated with an

acute-phase, cytokine-associated immune response7. Low grade chronic inflammation is being

examined for its potential causal role in the development of MetS and subsequent T2DM6.

Regardless of its role in the causality of T2DM, biochemical markers of inflammation have been

consistently linked with T2DM6, 7, 9, 12, 14, 50

. Non-traditional risk factors for T2DM include: low

adiponectin, high C-reactive protein (CRP), and high interleukin-6 (IL-6)6, 7, 9-12, 14

.

1.2.2.1 Adiponectin

Adiponectin is a fat-derived collagen-like plasma protein which has been shown to exert anti-

atherogenic, anti-inflammatory and insulin-sensitizing effects51-54

. Its anti-atherogenic and anti-

inflammatory effects have been attributed to its suppression of the expression of adhesion

molecules in vascular endothelial cells52

. Adiponectin also suppresses cytokine production of

macrophages, thereby inhibiting the inflammatory processes that occur in the development of

atherosclerosis52

. In mouse models, adiponectin has been shown to act on skeletal muscle,

increasing the influx and combustion of FFAs, thereby reducing TG content which may be

responsible for inhibiting glucose uptake into the muscle by glucose-transporter protein 451

. The

result of the increased oxidation of FFAs is an enhanced ability to absorb glucose into the

muscles, thereby reducing circulating glucose levels and improving insulin-sensitivity51

.

Concentrations of this hormone are typically lower in individuals with T2DM, CVD,

hypertension and dyslipidemia when compared to healthy individuals32,

52-55

. Plasma

adiponectin has been positively correlated with HDL-C55

and direct measures of insulin

sensitivity52

and negatively correlated with BMI, percent body fat, waist-to-hip ratio (WHR),

glucose, insulin, and TG10, 52, 55, 56

. Weight reduction results in increased plasma adiponectin

levels10, 52

, and a diet high in whole grains has been associated with higher adiponectin levels56

.

7

The inverse relationship between obesity and expression of adiponectin may partially explain

the strong association between obesity and incident T2DM10, 52, 55, 56

. A recent review and meta-

analysis by Li et al54

observed an inverse relationship between adiponectin and T2DM across

different ethnicities, including Caucasians, East Asians, Asian Indians, African Americans, and

Native Americans. Furthermore, a recent study by Ley et al32

showed a significant inverse

association between total plasma adiponectin and incident T2DM independent of BMI and other

covariates in Aboriginal Canadians residing in Sandy Lake, Ontario. The strong independent

associations between adiponectin and T2DM and its risk factors make adiponectin a novel

biomarker of risk of T2DM.

1.2.2.2 C-Reactive Protein

C-reactive protein (CRP) is an acute-phase reactant plasma protein and a marker of low-grade

inflammation which is derived from IL-6-dependent hepatic biosynthesis12, 49, 57

. In healthy

individuals, CRP circulates at low levels; however, upon injury, infection, or inflammation,

CRP concentrations rise dramatically9. Elevated CRP levels have been shown to be consistently

positively associated with obesity, insulin resistance, and glucose intolerance, suggesting that

CRP may be a marker of risk for developing T2DM9, 12, 49, 58-62

. Cross-sectionally and

prospectively, CRP has been shown to be closely associated with obesity, as evidenced by

correlational and logistic regression analyses by Ford9, Frohlich et al

58, Festa et al

59, and Visser

et al63

. A multiple logistic regression analysis of the NHANES III data by Ford9 found that both

newly- and previously-diagnosed T2DM were significantly positively associated with CRP

independent of BMI. Similarly, Pradhan et al12

, in a prospective, nested case-control study,

found that CRP was a significant predictor of T2DM independent of BMI and physical activity,

as did Spranger et al64

in a nested case-control study using EPIC-Potsdam data. Conversely,

some studies have shown that while CRP may predict T2DM, it may not do so independently of

markers of obesity, such as BMI32, 50, 65

. In fact, studies of lifestyle interventions including

exercise and weight loss have shown decreased CRP concentrations in women, suggesting that

CRP may indicate risk of T2DM by way of obesity66, 67

. Nonetheless, the positive relationship

between CRP and T2DM has been reinforced by drug intervention studies. For example, studies

8

in which statins have been used to treat dyslipidemia in those at risk for T2DM have shown

reductions in CRP and reduced risk of T2DM68, 69

.

The mechanism by which CRP plays a role in T2DM is not well understood. CRP as a marker

of low-grade inflammation may have indirect effects on insulin resistance and insulin secretion

caused by changes in immune response to inflammation50

. Since individuals with T2DM are at

increased risk of developing CVD, the high CRP concentrations seen in diabetic individuals

may reflect the inflammatory response to the concurrent development of atherosclerosis9. The

link between CRP and obesity in the development of T2DM is described by Mendall et al70

.

Adipocytes in obese individuals tend to overproduce tumour necrosis factor alpha (TNF-α)71

.

TNF-α induces production of IL-6 in various cell types, which in turn stimulates synthesis of

CRP, resulting in increased serum CRP concentrations70, 71

.

1.2.2.3 Leptin

Leptin is an adipocyte-derived hormone which is secreted into the serum72, 73

. Subcutaneous fat

is responsible for approximately 80% of all leptin production73

. Leptin levels are positively

associated with obesity and are directly related to its severity72, 73

. Studies have also shown

positive associations between circulating leptin and incident T2DM32, 74, 75

. A recent study by

Ley et al32

showed a significant positive association between leptin and incident T2DM in an

Aboriginal Canadian population after adjusting for age, sex, hypertension, IGT, TG, and HDL-

C. However, adjustment for abdominal obesity attenuated the association32

. Similar results were

observed in a study of Japanese men by McNeely et al74

. While leptin has shown some effects in

enhancing insulin sensitivity72

the mechanism is unclear73

and high leptin levels in individuals

with, and at risk, of T2DM may be the result of leptin resistance. Inflammatory cytokines, such

as IL-6 and TNF-α stimulate leptin production in adipocytes73

. As such, chronic low-grade

inflammation may contribute to increasing levels of leptin in obesity and T2DM.

1.2.2.4 Interleukin-6

IL-6 is a pro-inflammatory cytokine and is a major mediator of acute-phase reactants6, 14

. As

mentioned previously, IL-6 is responsible for some hepatic lipogenesis58

and biosynthesis of

9

CRP12

. It is produced in leukocytes, adipocytes (responsible for approximately 30% of total IL-6

production76

), and endothelial cells12

. Elevated blood levels of IL-6 have been associated with

IGT14

, the MetS6, 12

and T2DM6, 12, 64

. In 2003, Spranger et al64

, in a nested case-control study,

reported that IL-6 was a predictor of T2DM independent of BMI, WHR, physical activity, age,

sex, smoking status, education, alcohol consumption, and glycosylated hemoglobin. However,

in further analysis, it was found that participants with combined elevated IL-6 and IL-1β had an

approximately three-fold increased risk of developing T2DM, while elevated IL-6 with

undetectable IL-1β did not predict T2DM64

. Interestingly, recombinant human IL-6

administration has been associated with dose-dependent increases in fasting blood glucose,

perhaps due to stimulating the release of glucagon and/or by inducing peripheral insulin

resistance77

; thereby contributing to increased risk of T2DM.

1.2.3 Genetics

Genetic factors are believed to play an important role in predisposing individuals to disease,

such as T2DM. Individuals with a first-degree relative with T2DM are at an increased risk of

developing T2DM24

. Studies have shown that specific population groups are at greater risk of

developing T2DM and that this susceptibility may be, in part, attributable to genetics8. For

example, the Pima Indians of the United States have experienced a considerable rise in

prevalence of T2DM8. The recent emergence of genome-wide association studies (GWAS)

made possible by technological advances and the availability of large cohorts has lead to an

increased understanding of the impact of genetics on T2DM risk. A review by Wolfs et al78

published in 2009 reports that genetic variants on 19 loci have been identified and the number

identified is expected to increase as more GWAS are reported. Thus far, the majority of the

genetic variants identified are related to pancreatic β-cell growth and development78

. In the Oji-

Cree of northern Ontario and Manitoba, there is evidence of a private mutation (HNF1A G319S)

which is associated with an increased risk of T2DM16

.

10

1.2.4 Lifestyle Factors

A transition to a “westernized” lifestyle has been blamed for changes in dietary patterns and

exercise habits leading to increased prevalence of T2DM8.

While exercise has been associated with improved insulin sensitivity, weight maintenance and

weight loss, a lack of exercise may be predictive of T2DM79

. Poor dietary habits and exercise

habits are significant risk factors for development of T2DM even independently of BMI80

.

1.2.4.1 Smoking

Smoking has been associated with increased risk of T2DM in several large prospective studies,

including the Health Professionals’ Follow-up Study81

, the Physicians’ Health Study82

, and the

Nurses’ Health Study83

. As well, a review and meta-analysis published in 2007 examined the

results of 25 prospective cohort studies, reporting a pooled adjusted relative risk of 1.44 (95%

CI: 1.31, 1.58), and a dose-dependent positive association between smoking and incident

T2DM84

.

1.2.4.2 Exercise

The Nurses’ Health Study and the Physicians’ Health Study have shown evidence of protective

effects of exercise against the development of T2DM85, 86

. A review by Jeon et al87

found that

moderate physical activity, such as brisk walking, had a significant negative association with

development of T2DM independent of BMI. Jeon et al87

found that individuals participating in

moderate physical activity had a 30% (17% after adjusting for BMI) lower risk of developing

T2DM compared to sedentary individuals. While exercise is an important risk factor for T2DM,

diet plays a critical role and will be the focus of discussion in the sections presented below.

1.2.4.3 Diet

Diet has been blamed for rising obesity prevalence in North America; however, a review by

Weinsier et al90

, describing the results of USDA Nationwide Food Consumption Survey and

NHANES explains that while total energy and dietary fat intake are reportedly declining,

11

obesity prevalence continues to rise. It is noted, however, that underreporting with increasing

adiposity in females may be a factor in reportedly lower energy and fat intakes89

. Bray and

Popkin90

have also suggested that energy and fat intake may be underestimated due to lack of

data on fat added to foods during preparation. Additionally, some studies have shown that diets

high in fat and simple carbohydrates, or foods with a high glycemic index (GI) and low in fibre

and whole grains are associated with increased risk of obesity and T2DM11, 91

. Recently, specific

food components and nutrients have been investigated for their role in T2DM development,

some as promoters of disease, and some as protective factors against its development.

1.3 Diet and Type 2 Diabetes Mellitus

Over the past fifteen years, there has been increased interest in studies of diet and T2DM

prevention and treatment. Many studies have examined single nutrients, foods, and food

components. Though this research has proven interesting and informative, the results have been

largely inconsistent, making it difficult to attribute the development of T2DM to specific foods

and/or their components.

In Aboriginal Canadian populations, a transition from a hunter-gatherer lifestyle to that which is

much more sedentary has occurred over the past century92

. This lifestyle transition has been

accompanied by a change in dietary intake from a diet high in wild meats, roots, and berries, to

one high in fat (especially saturated fatty acids) and simple and high GI carbohydrates, and low

fibre92, 93

. Diet and lifestyle changes have also been accompanied by increasing prevalence of

obesity, IGT, and T2DM in these populations3, 4

. A study by Wolever et al92

found that

Aboriginal Canadians of the Sandy Lake community in northern Ontario, in general, eat a diet

high in total and saturated fat. Fat intake was relatively consistent across age groups; however, a

high GI diet was more common in subjects under 50 years of age92

. A study by Harris et al3

found positive associations between obesity and T2DM in subjects aged 18 to 49. Those over 50

years of age tended to have a diet higher in cholesterol and protein, implying that a more

traditional diet of wild meat and fat is common in this age group3.

12

1.3.1 Dietary Components

Dietary fat has been investigated for its role in the development of obesity as well as T2DM.

While some animal studies have shown beneficial effects of monounsaturated (MUFAs) and

polyunsaturated fatty acids (PUFAs) over saturated (SFAs) and trans fatty acids (TFAs),

epidemiologic human studies have failed to maintain the trend11

. Difficulties in understanding

interactions and controlling for possible confounders have been blamed for inconsistencies in

study results. Studies of dietary fat and its associations with obesity and T2DM have led to

examination of specific fatty acids, such as long-chain omega-3 fatty acids. A study by Das et

al94

found that, compared with control subjects (not of South Indian descent, non-diabetic),

South Indians (also non-diabetic) had lower plasma concentrations of arachidonic acid,

eicosapentaenoic acid and docosahexaenoic acid, and higher plasma concentrations of SFAs.

The result of the study by Das et al94

shows that a difference may exist in plasma concentrations

of fatty acids differs across ethnicities. Long-chain omega-3 fatty acids found in fish oil have

been associated with improved insulin sensitivity in rats as well as humans95-97

. In fact, a study

by Pan et al96

, in Pima Indians, showed that Δ5 desaturase (an enzyme involved in converting

dietary long-chain omega-3 fatty acids into useful muscle membrane components) activity is

independently negatively associated with both obesity and insulin resistance. However, a

randomized control study by Vessby et al98

showed no clear association between addition of

dietary omega-3s and insulin sensitivity or insulin secretion. According to a review by Hu et

al11

, the evidence for omega-3 fatty acids’ role in improving insulin resistance is promising;

however, further research is necessary.

The quantity and quality of carbohydrates, as measured by GI and glycemic load (GL) in the

diet have been shown to play a role in insulin resistance as well as obesity11

. The GI is based on

the degree of elevation in blood glucose levels following ingestion of 50 grams of a test food

compared to the elevation associated with ingestion of 50 grams of a reference food, such as

white bread11, 99, 100

. GL is derived from the product of the GI and the carbohydrate content of

the food11

. Animal studies have shown increased insulin resistance in rats fed high GI diets

compared to those fed low GI diets11

. Epidemiologic studies, such as the Health Professionals’

Follow-up Study101

and the Nurses’ Health Study102

, have shown positive associations between

higher GL diets and incidence of T2DM, especially in subjects consuming a diet low in cereal

fibre. Additionally, consumption of low-GI foods has been associated with increased satiety in

13

humans103

, while high-GI foods have been associated with increased fat synthesis in animal

models11

. Therefore, it is possible that carbohydrate quantity and quality may promote

development of T2DM through adiposity as well as outright insulin resistance.

Whole grains are examples of food with a low GI, and thus produce lower grade insulin and

glycemic responses than refined grains, such as white bread11

. The Iowa Women’s Health

Study, which stratified women (aged 55 to 69 years) based on their whole grain consumption,

found that women in the group consuming the greatest quantity of whole grains had the lowest

self-reported diabetes104

. A similar finding appeared in a paper by Liu et al105

, based on the

Nurses’ Health Study. This study contrasted women (aged 38 to 63 years) consuming diets high

in whole grains with women consuming diets high in refined carbohydrates, finding that the

latter had a greater incidence of T2DM105

. The impact of whole grains on glycemic and insulin

response may be due, in part, to their fibre content. However, a study by Wolever106

found that

total dietary fibre accounts for only 21% of variability in GI. A review paper by Hu et al11

has

highlighted three prospective cohort studies in which diets high in dietary fiber have been

associated with decreased risk of T2DM development. In reviewing the three studies, Hu et al11

found that cereal fiber, in particular, showed the greatest inverse relationship with development

of T2DM. These findings are consistent with those of Schulze et al107

in a meta-analysis of fiber

and magnesium in incident T2DM. In this meta-analysis, results based on insoluble versus

soluble fiber in the prevention of T2DM were largely inconsistent, with no certainty in possible

mechanisms of protection107

. Results for magnesium were also inconsistent and difficult to

quantify from food frequency questionnaires and to separate from the effects of dietary fiber107

.

A study by Kao et al108

found that while low serum magnesium was associated with increased

risk of development of T2DM in white subjects, the same association was not present in black

subjects. Additionally, where the relationship between serum magnesium and T2DM was strong

cross-sectionally, it was significantly weaker when examined prospectively108

. This finding

implies that low serum magnesium may be a result of, rather than a causal factor, in the

development of T2DM. Finally, in both black and white subjects, there was no association

between dietary magnesium and T2DM108

.

Over the past decade, dairy consumption has been studied with respect to risk of chronic

diseases, such as obesity, MetS, and T2DM. As the prevalence of these diseases increases, dairy

consumption appears to be decreasing109

. Mechanisms for the role of dairy in the prevention and

14

treatment of adiposity have been proposed110

; however, results of utilization of dairy in weight

loss studies are inconsistent and are hindered by small sample sizes111

. A prospective study by

Pereira et al112

found that among overweight subjects (both black and white), dairy consumption

was inversely associated with MetS. This result is consistent with a cross-sectional study by

Mennen et al113

which found an inverse association between dairy and the metabolic syndrome

in men aged 30 to 64 years. Higher dairy consumption has also been associated with decreased

risk of T2DM in men, as reported by Choi et al114

. A 9% lower risk of T2DM was associated

with a serving-per-day increase in total dairy; however, the inverse relationship between dairy

and T2DM was strongest for low-fat dairy products, most notably, skim milk114

. More recently,

a study by Liu et al115

examined the relationship between dairy consumption and development

of T2DM in middle-aged women. Results were similar to those for men, where lower-fat dairy

was more strongly associated with decreased risk of T2DM115

. With each serving-per-day

increase, Liu et al observed a 4% decrease in risk for T2DM115

. Two of milk’s most well-known

nutrients have received some recent attention in diabetes research. Calcium and vitamin D have

both shown inverse associations with T2DM and MetS116

. Some mechanisms for their role in

prevention of T2DM and MetS have been postulated, but there has been no clear conclusion116

.

Unfortunately, the lack of understanding of calcium and vitamin D in disease prevention is

consistent with that of dairy products in general.

Over the past five years, coffee has become a topic of interest in research concerning T2DM117

.

A review by van Dam and Hu117

describes a significant inverse association between coffee

consumption and T2DM. This association remains relatively consistent with respect to IGT;

however, there appears to be little or no effect on FPG concentrations117

. An American

prospective study of postmenopausal women found that the inverse association between coffee

and T2DM was much stronger for decaffeinated coffee as compared to caffeinated coffee118

.

The mechanism by which coffee may protect against development of T2DM is unknown;

however, there has been some speculation of the role of polyphenols and antioxidants118

.

1.3.2 Dietary Patterns

Dietary pattern analysis has been highlighted recently for its ability to examine diets as a whole.

Though there is great value in examining specific nutrients, food components and qualities, it is

difficult to separate the effects of each of them and to understand their sometimes subtle and/or

15

complex interactions. Confounding, a serious concern in epidemiologic research, may also arise

in studies focusing on single dietary components, as whole dietary patterns may act as

confounders. Additionally, it may be these complex interactions of nutrients and their

derivatives that either protect against chronic diseases such as T2DM, or contribute to their

development. The Dietary Approaches to Stop Hypertension (DASH) 119

diet is a well-known

example of a study in which a dietary pattern (characterized by fruits, vegetables, and low-fat

dairy) has been associated with positive health outcomes, including a reduction in blood

pressure.

1.4 Dietary Pattern Analysis

There are two major approaches to dietary pattern analysis (DPA): a priori, and a posteriori. In

the a priori approach, previously proposed concepts are applied to the characterization of diet

patterns prior to performing the analyses. Examples of a priori approaches include food scores

and diet quality indices.

1.4.1 A priori Approaches: Dietary Scores and Indices

The Healthy Eating Index is a well-known diet quality score which was developed by the United

States Department of Agriculture (USDA) in 1995 to measure compliance to USDA dietary

guidelines120, 121

. This index has been employed in a number of studies to determine associations

of dietary patterns with blood biomarkers122

, cardiovascular disease, and cancer risk123, 124

.

There are a variety of other diet quality indices and food scores, including the Alternative

Healthy Eating Index (AHEI)125

, the Recommended Foods Score (RFS)126

, the Diet Quality

Index127

, the Dietary Diversity Score (DDS)128

, and the Mediterranean Diet Index (MED)129

.

Some scores and indices award certain numbers of points for consumption of foods perceived as

healthy or desirable, while others are simply tallies of foods recommended by current

guidelines13

. A study by Fung et al130

found that the AHEI and the alternate Mediterranean Diet

Index (aMED) were inversely associated with markers of inflammation and endothelial

dysfunction, such as interleukin-6. Therefore the AHEI and aMED may be effective in

predicting outcomes based on the blood biomarkers for inflammation and endothelial

16

dysfunction. This type of model may be useful in determining dietary patterns associated with

decreased risk of chronic disease with low-grade inflammation, such as T2DM. Dietary scores

and indices, however, have a number of limitations. Since dietary scores and patterns are a

priori approaches, they rely heavily upon current knowledge of diets in regard to disease

prevention and health promotion13

. Although indices and scores typically reflect current national

dietary guidelines and recommendations, the scientific basis for the recommendations may be

out of date and no longer accurate13

. As well, the scoring of indices may be criticized for its

subjectivity and lack of consistency across indices.

1.4.2 A posteriori Approaches

A posteriori approaches include cluster analysis, factor analysis, and reduced rank regression

analysis131, 132

. These approaches are primarily data-driven and consider the foods and their

patterns as consumed by study subjects, and the relationships of these food consumption

patterns to outcome variables, such as markers of chronic disease131

. Since the food patterns in

an a posteriori approach are identified using multivariate techniques, they may not necessarily

reflect common food consumption patterns13

. As a result, food patterns identified by a posteriori

approaches may not be readily accepted by health educators and communicators. As well,

dietary patterns as determined by a posteriori methods may not be generalizable as they pertain

only to the study population133

. The advantage of a posteriori approaches; however, is that since

they are not based on previous knowledge, they have the potential to discover unrealized diet-

disease links, provoking further study of novel foods and food patterns. The following is an

examination of each of the aforementioned a posteriori approaches.

1.4.2.1 Cluster Analysis

Cluster analysis identifies groups of individuals based on their dietary intake patterns. The

groups are mutually exclusive and the goal of grouping based on intake is to find similarities in

intake related to disease outcome among groups, and differences in intake and disease outcome

between groups133

. The number of groups is predetermined through experimentation with

different numbers of clusters, selecting the number which presents the most desirable between-

17

cluster and within-cluster variance ratios133

. For thorough comparisons, clusters may be

stratified based on sex, age intervals, or other categorical variables133

. Examples of groups may

include “dark bread,” “wine,” “fruit,” and “fries” groups, where subjects belonging to each

group eat a great deal of the named food and related foods133

.


Factor analysis (FA) is a multivariate data-reduction technique which, when applied to DPA,

identifies underlying themes amongst predictor variables (typically FFQ items, or food

categories developed by collapsing FFQ items by culinary usage and similar nutrient profiles)13,

18. It is closely related to principal component analysis (PCA); however, PCA is designed to

simply reduce the number of variables in the model based on the variation observed, where FA

assumes that there are underlying factors or themes that explain the variation observed134

. The

themes, which are defined as dietary patterns, are identified by their role in explaining the

common variance amongst the predictor variables13, 18, 134

. In exploratory FA, the number of

initially-identified themes or patterns is equal to the number of predictor variables included in

the model134

. The degree to which a predictor variable belongs within an identified pattern is

measured by its factor or pattern loading134

. A loading may be deemed to be significant based on

the calculation of a critical value for pattern loadings134

, or a cut-point may be decided a priori,

such as |0.30|135

. The next step is to decide on the number of initial factors to retain for

subsequent analyses. A scree plot may be used to examine the patterns and their respective

proportions of explained common variance amongst predictor variables134, 136

. Patterns

appearing before the break (scree) in the sloping pattern of the data points on the scree plot are

usually considered for retention in subsequent analyses134, 136

. In addition to examining the scree

plot, a decision of which factors to be retained may be based on interpretability criteria which

state that 1) each pattern must contain at least 3 predictor variables (FFQ items) with significant

loadings; 2) the predictor variables loading on a given pattern must share some conceptual

meaning to fulfill the theme of the pattern 3) the predictor variables loading on different patterns

do so because they are fundamentally different in some way; and 4) predictor variables with

high loadings on one pattern do not have high loadings on other patterns134, 137

. It is important to

note that all predictor variables load onto each and every pattern; however, they differ in their

degree of loading, and consequently the significance of the loading. Once a number of patterns

18

for retention is decided upon, FA is re-applied to the dataset, specifying the number of patterns

to retain. In restricting the analysis to a set number of patterns, predictor variables which

previously loaded significantly onto other un-retained patterns, may begin to load on one of the

retained patterns. Once the patterns have been identified, the solution is rotated either

orthogonally (varimax rotation) or using an oblique (promax) rotation to improve the

interpretability of the patterns134

. Where an orthogonal rotation causes the patterns to be

uncorrelated, an oblique rotation allows the patterns to remain correlated with one another, and

is preferred in situations where the patterns are believed to be correlated134

. Once the rotation

has been completed, the loadings for each pattern may be interpreted and the patterns may be

named based on the apparent theme amongst the predictor variables in a given pattern. Factor or

pattern scores may be calculated for each study participant by multiplying the participant’s

frequency of consumption of each of the FFQ items (predictor variables) by the weight or

loading of that item in the given pattern134, 138

. The resulting pattern scores may be used to rank

participants’ consumption of a given pattern and may be used as an exposure variable in

subsequent analyses, such as logistic regression analysis13

.

Where cluster analysis forms groups of subjects based on their dietary intake, FA groups foods

based on their consumption patterns. Two of the most commonly reported patterns, or factors,

identified by nutritional epidemiological studies using FA are the “prudent” and “western”

dietary patterns18

. The “prudent” dietary pattern is typically characterized by whole grains, fruits

and vegetables, whereas the “western” dietary pattern is usually characterized by red meat,

potatoes, and high fat, processed foods18

.


Reduced rank regression (RRR) is relatively recently applied methodology in DPA. Though

RRR is classified as an a posteriori approach, it has an a priori component to it, as it does rely

upon some previous knowledge of intermediate markers which may be linked to the disease

being studied20

. Although RRR is similar to FA in its extraction of patterns, the patterns

identified in RRR do not explain common variance amongst FFQ items or foods, but rather the

common variance amongst the selected intermediate biomarkers of the primary outcome, such

19

as disease139

. As such, the dietary patterns elucidated by RRR may not reflect dietary patterns

commonly consumed by individuals in the study or the general population139

.

A German study used RRR to relate diet to biomarkers of T2DM19

. Heidemann et al19

used

glycosylated hemoglobin (HbA1c), HDL-C, CRP and adiponectin as biomarkers of T2DM. A

high pattern score reflected a diet high in fresh fruit, low in high-calorie soft drinks, beer, red

meat, processed meat, poultry, legumes, and bread (excluding whole grain bread)19

. Subjects

with higher dietary pattern scores were more likely to have higher plasma concentrations of

HDL-C and adiponectin and lower plasma concentrations of HbA1c and CRP, and a 70% lower

risk of T2DM19

. The study was limited by possible misreporting on the administered FFQ and

the issue of legumes being found primarily in stews containing bacon, pork, sausages, or beef,

resulting in likely under-representation of legumes in the lower-risk dietary pattern19

. Although

RRR may be limited by the availability of biomarker data and prior knowledge of biomarkers

related to disease, it shows promise in its ability to extract dietary patterns which may protect

against disease outcomes and disease development20

.

1.5 Summary and Rationale

T2DM is a growing concern worldwide, particularly in Aboriginal Canadian populations. The

literature indicates that diet may play a role in triggering the development of the disease,

although research on specific nutrients, foods and food groups has yielded inconsistent results.

DPA provides a method in which whole dietary intake may be explored in relation to incident

disease. To date, there are no known prospective studies examining the dietary patterns of

Aboriginal Canadians and their relationship with T2DM development. Available dietary,

biochemical, anthropometric, and T2DM status data from the ten-year prospective Sandy Lake

Health and Diabetes Project study provide a unique opportunity to explore dietary patterns

within a well-characterized Aboriginal Canadian population, and their relationship with incident

T2DM.

20

1.6 Research Objectives

1. To characterize distinct dietary patterns in an Aboriginal Canadian population using factor

analysis and reduced rank regression.

2. To determine whether dietary patterns identified using factor analysis and reduced rank

regression predict incident type 2 diabetes.

1.7 Hypotheses

1. Dietary patterns identified will include those characterized by traditional foods, and energy-

dense market foods.

2. Dietary patterns identified by factor analysis and reduced rank regression will predict

incident type 2 diabetes.

21

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30

Chapter 2

Methods

2.1 Study Design

The Sandy Lake Health and Diabetes Project (SLDHP) is a prospective cohort study focusing

on incident T2DM and associated risk factors in an Aboriginal Canadian population1. Baseline

data collection occurred between 1993 and 1995 with follow-up assessments of non-diabetic

cohort participants occurring between 2003 and 20052.

2.2 Subjects

All subjects were recruited at baseline (1993-1995) from Sandy Lake, an isolated Oji-Cree First

Nation community in the sub-arctic boreal forest region of northwestern Ontario1. Eligible study

subjects were Sandy Lake Band members who had lived in Sandy Lake for at least 6 months of

the previous calendar year, or members of other bands living in Sandy Lake households1.

Subjects were identified using band lists from the federal Department of Indian Affairs and

Northern Development1. Community maps and local surveyors’ knowledge, as well as

household demographics questionnaires, were also used to identify eligible subjects1. Full

details of the study design have been presented previously1. Of 1018 eligible subjects aged 10 to

79 years, 728 (72%) provided baseline measures1. Participants and non-participants did not

differ significantly; however, men aged 40-49 were least likely to participate1. Of the 728

individuals who participated, 606 were free of T2DM at baseline and thus were considered at

risk for development of T2DM over the follow-up period3. 540 of the individuals at risk of

T2DM were contacted at follow-up (2003-2005), resulting in a follow-up rate of 89%2.

Individuals contacted for follow-up (compared to the 66 individuals who did not return) were

slightly older with slightly lower plasma adiponectin concentration, but otherwise did not differ

by sex, BMI or non-traditional risk factors at baseline2. Of the 540 participants contacted for

follow-up, 27 deaths were reported, due to cancer (n=6), pneumonia (n=5), liver cirrhosis (n=3),

cardiovascular disease (n=2), brain tumour or aneurysm (n=2), suicide (n=2), or other causes,

including accidents (n=7)2. For the present analysis, nine subjects were excluded because they

had T2DM at baseline based on the revised 1999 WHO diagnostic criteria, as were the 27

31

31

individuals who died during follow-up. In addition, those who were missing baseline fasting

glucose and 2-hour postprandial glucose values (n=12)2 were excluded, leaving 492 participants

for the current analysis.

2.3 Baseline Data Collection

Data collected at baseline include demographic, physical activity, dietary, anthropometric, and

metabolic/biochemical measures1.

2.3.1 Demographics and Risk Factors

Individual questionnaires were administered at the research centre between baseline fasting and

2-hour post-prandial plasma glucose sampling and took 1.5 to 2 hours to complete1. The

questionnaire collected information about demographics and T2DM risk factors1. The

questionnaire inquired about gender, date of birth, marital status, education, occupation (past

and present), band number, languages spoken, and travel outside the community1. A series of

questions in the individual risk factor questionnaire asked subjects about their knowledge beliefs

with regard to diabetes and food and food preparation1. Family history of diabetes was assessed

by asking about prevalent diabetes in first-degree relatives, half-siblings, and grandparents1.

Information about tobacco use and exposure to second-hand smoke was collected1. Current

smokers were asked about duration of smoking and number of cigarettes smoked per day1.

Former smokers were queried about duration of smoking and time elapsed since smoking

cessation1.

2.3.2 Physical Activity and Physical Fitness

Self-reported occupational and leisure physical activity were assessed using a modified version

of the interviewer-administered Modifiable Activity Questionnaire (MAQ), an instrument

developed and validated (in both adults and adolescents) for use in the prospective Pima Indian

study in Arizona1, 4, 5

. The instrument was modified to make it locally applicable to the Sandy

32

Lake setting by deleting activities not applicable to the community, as well as by adding local

activities5. Data collected from the MAQ were parameterized as hours per week of physical

activity over the previous year, and by metabolic equivalents (METs), calculated by dividing the

estimated working metabolic rate of each activity by the estimated resting metabolic rate5.

Physical fitness was estimated using maximal oxygen uptake (V02max), which is a measure of

cardiovascular fitness5. A step test which was developed and validated by Siconolfi et al

6 was

used, requiring participants to step on a 25.4-centimetre exercise stepper, 3 minutes per stage,

for up to 3 stages5. A finger clip pulse monitor was used to monitor heart rate during the last 30

seconds of each phase5. Exclusion criteria, including a medical history of cardiovascular,

respiratory, severe muscular-skeletal disease, or an unwillingness to complete the test permitted

72% of male, and 61% of female adult study participants (≥ 18 years) to participate in the step

test.

At the time of the current analysis, physical activity data were not available for study

participants less than 18 years of age.

2.3.3 Dietary

Two instruments were used to assess diet at baseline: a single 24-hour dietary recall to assess

actual dietary intake, and a 34-item food frequency questionnaire (FFQ) to assess usual dietary

intake over the preceding 3 months1.

The 24-hour dietary recall was administered between fasting and 2-hour post-prandial plasma

glucose samples at the research centre1. Subjects were asked to recall their dietary intake over

the previous 24 hours, using measuring cups and spoons, 3-dimensional rubber food models,

and validated 2-dimensional food models to estimate portion size1. Interviewers prompted

subjects to recall added fat and sugar, as well as snacks consumed1. Recipes were collected for

multi-ingredient dishes and copies of the recalls were sent to the Department of Nutritional

Sciences at the University of Toronto for coding1,7

.

The 34-item FFQ asked subjects to recall their usual diet over the previous 3 months1. It was

developed using ethnographic interviews and was pilot-tested to ensure that it was culturally

33

appropriate1. The FFQ included both traditional foods, such as moose, rabbit and wild berries,

as well as market foods, including fruit, vegetables, baked goods, and candy1. Respondents

selected a frequency of consumption of “more than once per day,” “once per day,” “3-6 times

per week,” “1-2 times per week,” “1-3 times per month,” or “rare or never”8. For each item,

respondents were asked if their frequency of consumption varied by season1.

2.3.4 Anthropometric Measures and Blood Pressure

Anthropometric data were collected in the morning during the baseline research centre visit1.

All measurements were made without shoes and in cotton examination gowns or light athletic

clothing1. All measures were performed twice (including systolic and diastolic blood pressure),

and the average of the two measures was used for analyses. Height was measured to the nearest

0.1 cm using an Accustat wall-mounted stadiometer (Genentech Inc., San Francisco, California)

with heels together and buttocks, back, shoulders, and head touching the wall1. Weight was

measured to the nearest 0.1 kg using a standard hospital balance beam scale (Health-o-Meter

Inc., Bridgeview, Illionois) 1. Body mass index (BMI) was calculated using weight (in

kilograms) divided by squared height (in metres) as a measure of obesity1. Non-elastic

measuring tapes were used to measure waist and hip circumferences to the nearest 0.5 cm1.

Waist circumference (WC) was measured at the natural waist (minimal circumference between

umbilicus and xiphoid process) 1.

Percent body fat and lean body mass were estimated using bioelectrical impedance analysis

(BIA) using the Tanita TBF-201 Body Fat Analyzer (Tanita Corporation Inc., Tokyo, Japan) 1.

Dual energy x-ray absorptiometry (DEXA) is considered to be the gold standard for measures of

body composition9; however, these instruments are expensive and not easily transported. A

comparison of body composition analysis techniques by Rubiano et al9 found a strong

correlation of 0.94 between percent body fat as assessed by DEXA and the Tanita Body Fat

Analyzer. Similarly, Tsiu et al10

found a correlation of 0.89 between percent body fat measured

by DEXA versus the Tanita Body Fat Analyzer in participants with diagnosed T2DM. High

reproducibility (intra-class correlation coefficient 0.99) of the Tanita Body Fat Analyzer has

been documented in a sub-sample of the Sandy Lake population11

.

34

Blood pressure was measured in a seated position, on the right arm, using a hand-held aneroid

sphygmomanometer1. The project coordinator or a qualified surveyor measured blood pressure

to the nearest 2 mmHg for both systolic and diastolic pressure1. Systolic blood pressure (SBP)

was measured at the first Korotkoff sound (phase I), and diastolic at the fifth Korotkoff sound

(phase V) 1.

2.3.5 Metabolic and Biochemical Measures

At baseline, metabolic and biochemical data were collected during a morning visit to the

research centre following an 8 to 12-hour overnight fast1.

A standard oral glucose tolerance test (OGTT) was used to assess glucose tolerance status1. A

fasting blood sample was taken prior to ingestion of a 75-gram oral glucose load (Glucodex –

Rougier Inc., Chambly, Quebec) 1. A second blood sample was collected 120 minutes following

the glucose load1. Participants with previous physician-diagnosed diabetes and taking insulin or

oral hypoglycemic agents, or with physician-diagnosed diabetes and fasting plasma glucose

(FPG) of >11.1 mmol/L were excluded from the OGTT1.

All blood samples were centrifuged, aliquoted, and frozen on site, then shipped off-site for

analysis1.

Plasma for glucose was sent to the Sioux Lookout Zone Hospital laboratory for analysis using

the glucose oxidase method1, 2

. Serum samples were also sent to the Banting and Best Diabetes

Centre Core Lab in Toronto for measurement of fasting serum insulin (FI) using radio-

immunoassay techniques1. Plasma samples for lipid and lipoprotein analyses were sent to the

University of Toronto Lipid Research Laboratory1. Plasma levels of high-density lipoprotein

cholesterol (HDL-C) were measured using standard methods described by the Lipid Research

Clinic’s manual of operations1, 12

. Radio-immunoassay techniques (Linco Research, St. Louis,

MO) were used to measure serum leptin (inter-assay coefficient of variation [CV] 4.7% at 10.4

μg/L)13

and adiponectin (inter-assay CV 9.3% at 7.5 μg/L)14

. An enzyme-linked immunosorbent

assay (BioSource International, Camarillo, CA) was used to determine levels of interleukin-6

(IL-6) (inter-assay CV 10% at 2 ng/L)15

. Serum C-reactive protein (CRP) concentration (inter-

35

assay CV 5% at 12.8 mg/L) was determined using the Behring BN 100 and N high-sensitivity

CRP reagent (Dade-Behring, Mississauga, ON) 15

.

2.4 Follow-Up Data Collection

Diabetes status at follow-up was ascertained using an OGTT, or surrogate measures where an

OGTT was not possible. Incident diabetes was defined as FPG ≥7.0 mmol/L or 2-hour post

glucose load plasma glucose (2hrPG) ≥11.0 mmol/L (based on OGTT results as per the 1999

WHO criteria16

), or current use of insulin or oral hypoglycemic agents, or a positive response to

the question “Have you ever been diagnosed with diabetes by a nurse (practitioner) or a

doctor?”2. Of 492 study participants at follow-up, blood samples for 383 (77.8%) were

available. T2DM status for the remaining 109 (22.2%) participants was determined based on

self-reported clinical diagnosis of T2DM via a telephone interview2.

2.5 Statistical Analyses

2.5.1 Descriptive Statistics

Means and standard deviations were calculated for all normally distributed continuous baseline

characteristics, stratified by incident diabetes at follow-up. Medians and interquartile ranges

were calculated for non-normally distributed baseline variables, also stratified by diabetes status

at follow-up. Student’s t test was used for normally distributed and log-transformed non-

normally distributed variables, to compare baseline characteristics amongst study participants

who developed T2DM at follow-up and those who remained free of disease. Chi-square test was

used to test differences between the same groups for categorical variables, such as sex, and

presence of hypertension, IFG, and IGT.

Spearman rank correlation coefficients for the relationship between age and baseline biomarkers

(WC, BMI, SBP, HDL-C, FPG, 2hrPG, FI, CRP, IL-6, adiponectin and leptin) were calculated

and the results were used in selecting covariates to be considered in subsequent logistic

regression analyses.

36

2.5.2 Dietary Pattern Analysis


Factor analysis is a multivariate statistical technique used to identify underlying themes or

constructs amongst predictor variables, such as FFQ items, based on the explained common

variance among these variables. It has been used extensively as a dietary pattern analysis

technique in nutritional epidemiological research (see discussion of FA in section 1.5.2.2).

FFQ data were sorted and cleaned, then merged with datasets of baseline characteristics and

follow-up data. Exploratory factor analysis (FA) was conducted, using the FACTOR procedure

in SAS 9.1.3 (SAS Institute Inc. Cary, NC, USA), using the “principal factors” (“method=prin”)

and “priors=smc” options with a promax (oblique) rotation. The number of factors (or patterns)

to retain in the FA was determined using a scree plot and/or interpretability criteria, in addition

to a cut-point of the minimum common variation explained amongst FFQ items18, 19

. A scree

plot is a graphical representation of the eigenvalues for each factor or pattern, which describe

the amount of common variance accounted for by a given factor17

. The number of factors is

plotted on the x-axis against the variance explained on the y-axis. Typically there is a clustering

of eigenvalues on the scree plot, followed by a break, and then another clustering of

eigenvalues, representing the “scree”17

. When using the scree plot to determine the number of

factors (patterns) to retain, the first eigenvalues which appear before the break are retained.17

Interpretability criteria used to determine the number of factors to retain recommend: 1) at least

three significant item loadings (a priori-selected minimum factor loadings) on each

factor/pattern, 2) a shared conceptual meaning amongst the items that load on a particular

factor/pattern, 3) a meaningful difference between the items that load on different

factors/patterns (to ensure that the separation amongst factors/patterns is sensible), and 4) a

simple structure that emerges amongst the rotated factors/patterns (ie. if an item has a high

loading on one pattern, it should have a low loading on another pattern)18, 19

. Additionally, an a

priori-proposed cut-point may be selected for the minimum acceptable percent variation

explained by a retained factor/pattern, such as 15%19

, and adjusted based on the results of the

previously described scree plot and interpretability criteria18

.

Based on a scree plot, interpretability criteria, as well as a cut-point of 15% for the minimum

common variation explained, three factors were retained for the current factor analysis. A

37

pattern loading cut-point of ≥0.30 was used to select factor loadings or FFQ items upon which

names of retained factors or dietary patterns would be assigned. (Factors identified by factor

analysis will henceforth be referred to as “patterns” or “dietary patterns.”) Using the three-factor

FA, pattern scores were calculated for each study participant for each identified pattern using

participants’ frequency of consumption responses to the FFQ multiplied by the pattern loadings

for each FFQ item. Although the dietary patterns were named based on FFQ items with loadings

≥0.30, all foods are associated with a pattern loading in FA, and thus all foods contributed to the

calculation of individual pattern scores.

Two- and 4-factor solutions were also considered; however, they did not meet the employed

criteria as appropriately as the 3-factor solution, including placement on the scree plot,

interpretability criteria, and/or the selected cut-point for the minimum common variation

explained. Further discussion and description of these solutions may be found in section 3.2.1

and Appendix A.

Spearman rank correlation coefficients were calculated for all continuous variables, to examine

the association between pattern scores and baseline characteristics, as well between patterns.

Partial Spearman rank correlation coefficients were calculated in the same manner to adjust for

variables which had significant associations with dietary pattern scores, such as age. To further

examine the relationship between baseline variables and pattern scores, participants were split

into tertiles based on their pattern score for each dietary pattern. For continuous variables,

ANOVA was used to test the null hypothesis that the mean baseline values (log-transformed

means for non-normally distributed variables) did not differ significantly between tertiles;

whereas Chi-square tests were used for categorical variables.


Reduced rank regression (RRR) is a multivariate statistical technique which has recently been

applied to dietary pattern analysis with respect to the use of dietary patterns to predict chronic

disease. Unlike FA, which identifies dietary patterns based on common variance explained

amongst FFQ items, RRR identifies patterns based on common variance explained amongst a

priori-selected intermediate response variables. Typically, intermediate response variables are

either food components or characteristics believed to play a role in predicting disease risk (eg.

38

glycemic index or cereal fibre content), or biochemical markers indicated as being

pathophysiologically relevant to the disease of interest. In the current study, anthropometric

measures of abdominal obesity, as well as traditional and novel biomarkers associated with

T2DM were selected based on their association with the primary outcome, T2DM. Similar

intermediate response variables were employed by Heidemann et al20

, as discussed in section

1.5.2.3.

RRR, using the PLS procedure in SAS 9.1.3 (SAS Institute Inc. Cary, NC, USA) with the

“reduced rank regression” method (“method=rrr”) with the seven intermediate response

variables of WC, HDL-C, FPG, 2-hour post-prandial plasma glucose (2hPG), FI, CRP and

adiponectin, yielded seven factors or patterns. (From this point forward, RRR-derived factors or

patterns will be referred to as “patterns” or “dietary patterns.”) Of the seven identified patterns,

three were carried forth in subsequent analyses based on the percent variation explained (≥1.0)

(amongst the intermediate response variables) by the patterns, and the interpretability criteria

used for retaining FA patterns (with the exception of allowing FFQ items to have high loadings

on more than one pattern). In the literature, only one RRR pattern is retained in most instances

because the first pattern always explains the most variation; however, there have been studies in

which more than one pattern has been retained21

. It is important to note that, unlike in FA, the

number of patterns selected for retention (ie. to be carried forward in subsequent analyses) in

RRR does not change the distribution of the model effect loadings amongst the “retained”

patterns. Therefore, regardless of the number of patterns retained in RRR, the patterns remain

exactly the same. A cut-point of ≥0.2020, 22, 23

for the weights of the loading of individual FFQ

items on the three different patterns (model effect loadings) was employed to select the FFQ

items upon which names of retained patterns would be based. The same methods as described in

the FA section were employed to calculate individual pattern scores for each participant based

on each of the retained patterns (and the same is true that all FFQ items or foods are considered

in the calculation of the scores).

As in the FA method described previously, Spearman rank correlation coefficients were

calculated for all continuous variables, and adjusted for age. Similarly, tertiles were developed

based on participants’ pattern scores on each pattern. ANOVA was used for continuous

variables to test for differences in baseline characteristics amongst the tertiles of scores, and

Chi-square tests were used for categorical variables.

39

2.5.3 Logistic Regression Analysis

2.5.2.3.1 Logistic Regression Based on Factor Analysis

Logistic regression was conducted, considering FA-derived pattern scores for each pattern

separately as the primary exposure and incident T2DM at follow-up as the outcome variable,

using the LOGISTIC procedure in SAS 9.1.3 (SAS Institute Inc. Cary, NC, USA). Covariates to

be considered in the logistic regression were determined based on significant partial correlation

coefficients describing the relationships between pattern scores and baseline variables,

significant differences between baseline variables across tertiles of pattern scores, as well as

covariates known to be associated with incident T2DM. Model 1 was adjusted for age and sex

only, while Model 2 adjusted for age, sex, and waist circumference (WC) (a marker of

abdominal obesity, which is associated with hyperinsulinemia and insulin resistance which are

understood to predict T2DM23-25

). Model 3 adjusted for age, sex, WC, IL-6, (IL-6 is marker of

inflammation associated with IGT, and T2DM27-30

) and adiponectin (which has previously been

shown to be independently significantly associated with incident T2DM in this population2).

Tests for non-linearity of the association of the FA-derived pattern scores with incident T2DM

were conducted using quadratic terms of the pattern scores for each pattern in the unadjusted

logistic regression models. As well, the potential for effect modification by age and gender on

the associations of pattern scores and incident T2DM was assessed; variables in Model 1 in

addition to interaction terms were used for these tests.

Finally, sensitivity analyses were conducted to specifically assess the impact of additional

adjustment for measures of physical activity and smoking status. Results of these analyses are

summarized in Chapter Three (Results).

2.5.2.3.2 Logistic Regression Based on Reduced Rank Regression Analysis

Similarly, logistic regression considering RRR-derived pattern scores for each pattern separately

as the primary exposure and incident T2DM at follow-up as the outcome variable, using the

LOGISTIC procedure, was conducted. Age, sex, WC, adiponectin and leptin were considered as

covariates in the logistic regression analyses, as they were in the logistic regression based on the

FA-derived patterns. WC and adiponectin were used in identifying the RRR-derived patterns;

40

however, correlations between the biomarkers and pattern scores were much lower in magnitude

than expected, indicating that their inclusion as intermediate response variables did not

appreciably account for their variation amongst study participants. Therefore, for consistency,

and based on partial correlation coefficients and differences amongst pattern score tertiles, the

same models employed in logistic regression based on FA were employed in the RRR-derived

logistic regression analyses.

Tests for non-linearity of the RRR-derived pattern scores were conducted using quadratic terms

of the pattern scores for each pattern in the unadjusted logistic regression models. As well, the

potential for effect modification by age and gender on the associations of pattern scores and

incident T2DM was assessed; variables in Model 1 in addition to interaction terms were used for

these tests.

Finally, a series of sensitivity analyses were conducted using differing combinations of

intermediate response variables in deriving RRR dietary patterns to assess the effect on the

identification of dietary patterns. Analyses included using only age as a covariate (Appendix

A), as well as the highly correlated variables, WC and FI (Appendix B), versus the less-

correlated variables, SBP and adiponectin (Appendix C).

41

2.6 References

1. Hanley AJG, Harris SB, Barnie A, Gittelsohn J, Wolever TMS, Logan A, Zinman B.

The Sandy Lake Health and Diabetes Project: design, methods and lessons learned.

Chronic Diseases in Canada. 1995;16:149-56.




Care. 2008;31:1410-5.

3. Harris SB, Gittelsohn J, Hanley A, Barnie A, Wolever TMS, Gao J, Logan A, Zinman B.

Prevalence of NIDDM and associated risk factors in Native Canadians. Diabetes Care

1997;20:185-7.

4. Kriska AM, Knowler WC, LaPorte RE, Drash AL, Wing RR, Blair SN, Bennett PH,

Kuller LH. Development of questionnaire to examine relationship of physical activity

and diabetes in Pima Indians. Diabetes Care. 1990;13:401-11.

5. Kriska AM, Hanley AJG, Harris SB, Zinman B. Physical activity, physical fitness, and

insulin and glucose concentrations in an isolated Native Canadian population

experiencing rapid lifestyle change. Diabetes Care. 2001;24:1787-92.

6. Siconolfi SF, Garber LE, Lasater TM, Carleton RA. A simple step test for estimating

maximal oxygen uptake in epidemiologic studies. Am J Epidemiol. 1985;121:382-90.

7. Wolever TMS, Hamad S, Gittelsohn J, Hanley AJG, Logan A, Harris SB, Zinman B.

Nutrient intake and food use in an Ojibwa-Cree community in northern Ontario assessed

by 24 h dietary recall. Nutr. Res. 1997;17:603-18.




9. Rubiano F, Nunez C, Heymsfield SB. A comparison of body composition techniques.

Annals New York Academy of Sciences. 2000;904:335-8.

10. Tsui EYL, Gao XJ, Zinman B. Bioelectrical impedance analysis (BIA) using bipolar foot

electrodes in the assessment of body composition in type 2 diabetes mellitus. Diabetic

Medicine. 1998;15:125-8.

11. Hanley AJG, Harris SB, Barnie A, Smith J, Logan A, Zinman B. Usefulness of

bioelectrical impedance analysis in a population-based study of diabetes

among Native Canadians. Int J Obesity Relat Met Disord. 1994;18;383.

12. Lipid Research Clinics Program: Manual of Laboratory Operations. Washington D, U.S.

Govt. Printing Office, 1984, p1-81 (NIH publ. no. 75-6282).

13. Hanley AJG, Harris SB, Gao XJ, Kwan J, Zinman B. Serum immunoreactive letpin

concentrations in a Canadian Aboriginal population with high rates of NIDDM. Diabetes

Care. 1997;20:1408-15.

14. Hanley AJG, Connelly PW, Harris SB, Zinman B. Adiponectin in a Native Canadian

population experiencing rapid epidemiological transition. Diabetes Care. 2003; 26:

3219-25.

15. Connelly PW, Hanley AJ, Harris SB, Hegele RA, Zinman B. Relation of waist

circumference and glycemic status to C-reactive protein in the Sandy Lake Oji-Cree.

International Journal of Obesity. 2003;27:347-54.

16. World Health Organization. Diabetes mellitus: report of a WHO study group. Geneva:

WHO, 1985; WHO Technical Report Series No 727.

42

17. Cattell RB. The scree test for the number of factors. Multivariate Behavior Research.

1966;1:245-76.

18. Kim JO, Mueller CW. Factor analysis: statistical methods and practical issues. Beverly

Hills, CA. Sage, 1978.






Diabetologia 2005; 48:1126-34.

21. Weikert C, Hoffmann K, Dierkes J, Zyriax B-C, Klipstein-Grobusch K, Schulze MB,

Jung R, Windler E, Boeing H. A homocysteine metabolism-related dietary pattern and

the risk of coronary heart disease in two independent German study populations. J. Nutr.

2005;135:1981-8.



Epidemiol 2004; 159: 935-44.

23. Hoffmann K, Boeing H, Boffeta P, Nagel G, Orfanos P, Ferrari P, Bamia C. Comparison

of two statistical approaches to predict all-cause mortality by dietary patterns in German

elderly subjects. British Journal of Nutrition. 2005;93:709-16.

24. Abate N. Insulin resistance and obesity: the role of fat distribution pattern. Diabetes

Care. 1996;19:292-4.

25. Boden G. Role of fatty acids in the pathogenesis of insulin resistance and NIDDM.

Diabetes. 1997;46:3-10.

26. Weyer C, Funahashi T, Tanaka S, Hotta K, Matsuzawa Y, Pratley RE, Tataranni PA.

Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin

resistance and hyperinsulinemia. Journal of Clinical Endocrinology and Metabolism.

2001;86:1930-5.

27. Pickup JC, Mattock MB, Chusney GD, Burt D. NIDDM as a disease of the innate

immune system: association of acute-phase reactants and interleukin-6 with metabolic

syndrome X. Diabetologia.1997;40:1286-92.



29. Müller S, Martin S, Koenig W, Hanifi-Moghaddam P, Rathmann W, Haastert B, Giani

G, Illig T, Thorand B, Kolb H. Impaired glucose tolerance is associated with increased

serum concentrations of interleukin-6 and co-regulated acute-phase proteins but not

TNF-alpha or its receptors. Diabetologia. 2002;45:805-12.



2003;52:812-7

43

Chapter 3

Results

3.1 Descriptive Statistics

Table 1 presents baseline characteristics of participants stratified by diabetes status at follow-

up. Seventeen and a half percent (17.5%) of the 492 participants free of type 2 diabetes mellitus

(T2DM) at baseline had developed T2DM at the time of follow-up. Those who converted to

T2DM were, on average, older, with greater body weight, body mass index (BMI), percent body

fat, and waist circumference (WC) (all p <0.0001). In addition, converters had higher systolic

blood pressure (SBP) and diastolic blood pressure (DBP), as well as higher levels of low-density

lipoprotein cholesterol (LDL-C) and serum triglycerides (TG) (all p<0.0001), and lower levels

of high-density lipoprotein cholesterol (HDL-C) (p= 0.02). Those who developed T2DM had

higher fasting plasma glucose (FPG) levels, 2-hour postprandial plasma glucose (2hPG) levels,

and fasting serum insulin (FI) (all p <0.0005). Prevalence rates of hypertension (HTN), impaired

glucose tolerance (IGT) (p<0.0001 for both), and impaired fasting glucose (IFG) (p=0.03) were

also greater amongst those who developed T2DM. Finally, those who developed T2DM had

higher levels of adipokines, including C-reactive protein (CRP), interleukin-6 (IL-6) and leptin,

as well as lower levels of adiponectin (all p<0.05).

To better understand the relationships amongst the baseline biomarkers, Spearman rank

correlation coefficients were calculated and are presented in Table 2. Age was most closely

correlated with measures of adiposity (WC and BMI), SBP, CRP and adiponectin, while SBP,

FPG, FI and adipokines (especially CRP, adiponectin and leptin) were closely correlated with

measures of adiposity. FI was also highly correlated with HDL-C, FPG, 2hPG, CRP and leptin.

44

Table 1. Baseline characteristics of participants the Sandy Lake Health and Diabetes Project


No Diabetes Incident Diabetes p-value

n (%) 406 (82.5) 86 (17.5)

Age (years)* 25.4±13.0 31.5±12.4 <0.0001 Sex, Male/Female† 173/233 (42.6/57.4) 34/52 (39.5/60.5) 0.6005

Anthropometry*

Height (cm) 165.3±10.4 166.81±9.1 0.2012

Weight (kg) 69.8±18.1 82.0±15.9 <0.0001 BMI (kg/m²) 25.4±5.5 29.4±5.3 <0.0001

Percent Body Fat (%) 33.0±13.2 40.1±10.3 <0.0001

Waist Circumference (cm) 87.8±13.2 98.2±12.2 <0.0001

Blood Pressure Systolic (mmHg)‡ 113.0 (103.5-120.0) 118.0 (110.0-130.0) <0.0001

Diastolic (mmHg)* 64.0±11.5 69.9±12.3 <0.0001

MAP (mmHg)‡ 79.4 (73.3-86.3) 83.9 (77.5-96.3) <0.0001 Hypertension †§ 54 (13.3) 29 (33.7) <0.0001

Lipid Profile

HDL Cholesterol (mmol/l)* 1.26±0.28 1.19±0.25 0.0257

LDL Cholesterol (mmol/l)* 2.42±0.74 2.74±0.66 0.0002 Triglycerides (mmol/l)‡ 1.10 (0.81-1.53) 1.48 (1.16-1.82) <0.0001

Glucose Homeostasis

FPG (mmol/l)* 5.3±0.46 5.6±0.58 0.0004

2hrPG (mmol/l)* 5.4±1.62 6.5±2.08 <0.0001 FI (mmol/l)‡ 94.0 (66.0-131) 123.0 (91.0-187.0) <0.0001

IGT †¶ 36 (8.9) 23 (26.7) <0.0001

IFG †|| 22(5.4) 10 (11.6) 0.0339

Adipokines‡ CRP (mg/l) 1.45 (0.40-4.28) 2.82 (1.24-7.48) 0.0012

IL-6 (ng/l) 0.67 (0.33-1.23) 0.83 (0.52-1.38) 0.0237

Adiponectin (μg/l) 14.5 (11.0-19.6) 11.0 (8.01-15.1) <0.0001

Leptin (ng/ml) 10.6 (5.20-19.4) 15.0 (9.40-25.7 <0.0001

n of subjects for each characteristic may vary due to occasional missing values. * Mean ± SD and Welch’s t test.

† n (%) and Chi-square test; ‡ Median (25th-75th percentile) and Welch’s t test on log transformation; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure of >=85 mmHg or

participation in antihypertensive medication therapy; ¶ Impaired glucose tolerance defined as fasting plasma

glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l; || Impaired fasting glucose defined

as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure;

FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin;

3.2 Factor Analysis

Exploratory factor analysis (FA) initially identified a total of 34 factors or dietary patterns using

items from the 34-item FFQ, an expected result since FA, as a first step, identifies an equal

number of factors to the number of predictor variables or FFQ items. Of the 34 factors, three

met the interpretability criteria as described in the Methods section. That is, they each accounted

45

for at least 15% of the common variance amongst the food groups (Table 3), they all appeared

before the plateau on the scree plot (Figure 1), they each had at least three significant loadings

(Tables 3 and 4), there was a common theme amongst their variables (FFQ items), as described

below (Table 3), and they had no shared significant loadings with other factors or patterns

(Tables 3 and 4). The first factor or dietary pattern (henceforth factors or dietary patterns

identified by factor analysis will be referred to as patterns or dietary patterns), characterized by

higher intake of other vegetables (other than carrots, peas and corn, eg. tomatoes, onions, and

lettuce), carrots, peas, corn, whole wheat bread, milk, and macaroni, was named “Balanced

Market Foods.” The second pattern, characterized by higher intake of pop, klik,

cookies/cake/pastry, chocolate/candy, canned fruit, beef, Carnation milk, white bread,

chips/French fries, and lard was named “Beef & Processed Foods.” The third identified dietary

pattern was named “Traditional Foods,” and was characterized by higher intakes of fish, moose,

duck, berries, rabbit, and Indian tea. Table 4 lists the pattern loadings for each of the 34 FFQ

items for each of the 3 retained patterns, illustrating that all FFQ items contribute to each dietary

pattern, but vary in their weight or influence within the patterns. For example, although the

Traditional Foods pattern is most highly influenced by fish, moose, duck, berries, rabbit and

Indian Tea (loadings: 0.57, 0.55, 0.54, 0.44, 0.43, 0.34, respectively), other FFQ items such as

whole wheat bread, macaroni, peas, and corn (loadings: -0.07, -0.04, -0.10, 0.12, respectively),

also load on the Traditional Foods pattern, though their loadings are much less influential.

Pattern scores for each individual study participant were calculated by multiplying the

frequency of consumption of the FFQ items by the weight assigned to each FFQ item as it

relates to the establishment of each dietary pattern.

46

Table 2.Spearman rank correlation coefficients between novel and traditional biomarkers of

participants of the Sandy Lake Health and Diabetes Project at baseline.

Adipo=Adiponectin; * p=<0.0001; † p=<0.001; ‡ p<0.05

Once pattern scores were calculated for each study participant for each dietary pattern (Balanced

Market Foods, Beef & Processed Foods, and Traditional Foods), participants were divided into

tertiles based on their overall pattern score for each dietary pattern. Tables 5a, 5b, and 5c

describe the baseline characteristics stratified by tertile of pattern score for each dietary pattern.

As shown in Table 5a, there were no significant differences in baseline characteristics across

the tertiles of the Balanced Market Foods pattern; however, differences in sex amongst tertiles

approached significance (p=0.06), though the differences did not seem to increase or decrease

consistently corresponding to pattern score. There were significant differences amongst tertiles

of the Beef & Processed Foods pattern with negative trends for age, weight, BMI, percent body

fat, WC, and SBP (all p<0.05), a positive trend for adiponectin across increasing tertiles of

pattern score (p<0.02), and unclear trends for mean arterial pressure (MAP), FPG, and

proportion of participants with HTN (all p<0.05) across increasing tertiles of pattern score

(Table 5b). There was a significant difference between the number of people who converted to

T2DM in each tertile of the Traditional Foods scores (p=0.03), though with no clear trend

corresponding to increasing or decreasing pattern score (Table 5c). There was a negative trend

in height across tertiles of the Traditonal Foods scores, and a positive trend for 2hPG (all

p<0.05). There were also significant differences across tertiles of Traditional Foods scores for

Age

WC

BM

I

SB

P

HD

L-C

FP

G

2hrP

G

FI

CR

P

IL-6

Adip

o

Lep

tin

Age 1.00

WC *0.57 1.00

BMI *0.45 *0.93 1.00

SBP *0.46 *0.51 *0.42 1.00

HDL-C -0.01 *-0.32 *-0.32 -0.08 1.00

FPG *0.19 *0.34 *0.30 *0.25 ‡-0.14 1.00

2hrPG *0.22 *0.27 *0.30 †0.15 *-0.17 *0.31 1.00

FI ‡0.10 *0.55 *0.60 *0.24 *-0.31 *0.42 *0.36 1.00

CRP *0.48 *0.56 *0.57 *0.29 †-0.16 *0.19 *0.36 *0.38 1.00

IL-6 ‡0.14 *0.22 *0.25 †0.15 ‡-0.12 0.06 *0.26 *0.24 *0.43 1.00

Adipo *-0.25 *-0.41 *-0.39 ‡-0.09 *0.38 †-0.16 *-0.24 *-0.27 *-0.30 *-0.18 1.00

Leptin †0.16 *0.49 *0.65 ‡0.12 ‡-0.15 †0.15 *0.58 *0.58 *0.45 *0.32 †-0.17 1.00

47

weight, BMI, WC, MAP, and adiponectin, though there was no clear trend (all p<0.05) across

tertiles of increasing pattern score.

Table 3. Pattern names, FFQ items in each pattern, and percent common variation identified by


Pattern Name FFQ Items in Pattern Percent Common Variance Accounted For

Balanced Market Foods

Other Vegetables

Carrots Peas Corn Whole Wheat Bread Milk Macaroni

46.76

Beef & Processed Foods

Pop Klik Cookies/Cake/Pastry Chocolate/Candy Canned Fruit Beef Canned Milk White Bread Chips/French fries

Lard

19.91

Traditional Foods

Fish Moose

Duck Berries Rabbit Indian Tea

15.75

Three-factor factor analysis solution with oblique rotation; Foods with factor loadings >= 0.30 are shown for

simplicity since those foods were most highly considered when patterns were named.

Spearman rank correlation coefficients examining the associations between the 3 dietary

patterns and baseline characteristics revealed a significant inverse correlation between the Beef

& Processed Foods pattern and age (r=-0.16; p<0.001) (Table 6). As a result, partial Spearman

rank correlation coefficients were calculated for all factors, adjusting for age. Following this

adjustment, significant correlations existed between the Balanced Market Foods pattern and IL-

6 (p<0.05), and the Traditional Foods pattern and weight (p<0.01), BMI, WC, FPG, and 2hPG

(all p<0.05) (Table 6). Adjusting for age attenuated the observed inverse associations between

the Beef & Processed Foods pattern and measures of adiposity (weight, BMI, percent body fat

48

and WC); whereas the same adjustment had little impact on the inverse associations between the

Traditional Foods pattern and those same measures of adiposity.

Figure 1. Scree plot of eigenvalues by factor (pattern) from factor analysis of FFQ data from the

Sandy Lake Health and Diabetes Project.

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

1 5 9 13 17 21 25 29 33

Factors/Patterns

Eig

en

valu

es

Exploratory factor analysis without factor retention specification


patterns themselves were all significant (Balanced Market Foods and Beef & Processed Foods

[r=0.40], Balanced Market Foods and Traditional Foods [r=0.44], Beef & Processed Foods and

Traditional Foods [r=0.25] [all age-adjusted, all p<0.0001]), illustrating that factors or patterns

identified by factor analysis can have a substantial magnitude of correlation when an oblique

rotation is used.

49

Table 4. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health


FFQ Items Balanced Market Foods Beef & Processed Foods Traditional Foods

Fish -1 5 57

Moose 2 2 55

Beef 9 34 -3

Pork 19 19 9

Duck -7 4 54

Rabbit 7 -3 43

Klik -3 42 9 Eggs 10 19 -3

Lard 7 32 -16

Margarine 23 14 -15

Cold Cereal 19 27 4

Hot Cereal 21 -2 19

Beans -8 27 8

White Bread 6 34 -26

Whole Wheat Bread 40 -2 -7

Bannock 4 24 15

Macaroni 32 21 -4

Indian Tea 12 -10 34 Soup 27 11 14

Chips/French Fries 2 33 2

Other Potatoes 28 18 3

Peas 59 -5 -10

Corn 50 3 12

Carrots 59 -8 4

Other Vegetables 61 -9 1

Berries -5 3 44

Fresh Fruit 19 20 10

Canned Fruit 8 38 16

Milk 37 4 4

Canned Milk -17 34 -10 Pop -5 43 -15

Tea 2 21 -28

Cookies/Cakes/Pastries 7 41 9

Chocolate/Candy -1 40 4

Three-factor factor analysis solution with oblique rotation; Eigenvalues (loadings) shown as eigenvalue*100 for

simplicity; Loadings >= 30 bolded

50


according to tertiles of the Balanced Market Foods pattern score as determined by exploratory

factor analysis.

Balanced Market Foods Pattern Score

T1 T2 T3 p-value

n 156 161 158 -

Age (years)* 25.7±14.0 27.3±12.6 27.2±12.8 0.4944

Sex, Male/Female†β 67/89 (43.0/57.1) 78/83 (48.5/51.6) 56/102 (35.4/64.6) 0.0620

Anthropometry*

Height (cm) 165.0±10.8 166.6±9.4 165.4±10.4 0.3567

Weight (kg) 70.4±18.7 72.8±17.1 73.0±19.2 0.3679

BMI (kg/m²) 25.6±5.7 26.1±5.4 26.5±6.0 0.4023

Percent Body Fat (%) 33.3±13.5 33.7±12.5 35.3±12.9 0.3560

Waist Circumference (cm) 88.4±13.9 90.1±13.1 90.5±14.0 0.3626

Blood Pressure

Systolic (mmHg)‡ 112.5 (102.8-120.0) 115.0 (105.0-122.0) 115.0 (105.0-120.0) 0.4114

Diastolic (mmHg)* 64.9±11.4 64.7±12.7 65.9±11.7 0.5926

MAP (mmHg)‡ 79.9 (73.8-86.5) 80.0 (73.3-87.5) 81.0 (73.7-90.8) 0.7000

Hypertension†§ β 20 (12.8) 35 (21.7) 27 (17.1) 0.1098

Lipid Profile

HDL Cholesterol (mmol/l)* 1.25±0.29 1.26±0.28 1.25±0.26 0.9109

LDL Cholesterol (mmol/l)* 2.43±0.72 2.46±0.77 2.56±0.73 0.2779

Triglycerides (mmol/l)‡ 1.17 (0.87-1.58) 1.09 (0.80-1.60) 1.20 (0.91-1.60) 0.1333

Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.49 5.4±0.54 0.2153

2hPG (mmol/l)* 5.4±1.62 5.6±1.72 5.8±1.93 0.1507

FI (mmol/l)‡ 96.0 (62.0-134.0) 94.0 (63.5-129.0) 103.0 (79.0-148.0) 0.0648

IGT†¶ β 15 (9.6) 19 (11.8) 22 (15.2) 0.3145

IFG †|| β 5 (3.2) 15 (9.3) 12 (7.6) 0.0826

Adipokines‡

CRP (mg/l) 1.46 (0.37-5.03) 1.78 (0.51-5.19) 1.78 (0.51-3.67) 0.0603

IL-6 (ng/l) 0.85 (0.38-1.47) 0.69 (0.38-1.14) 0.61 (0.33-1.13) 0.1764

Adiponectin (μg/l) 14.7 (11.0-20.1) 14.1 (10.1-18.1) 12.8 (9.64-18.8) 0.3194

Leptin (ng/ml) 11.1 (4.95-18.6) 10.4 (5.70-19.8) 12.6 (7.00-21.8) 0.3084

n converters to T2DM † 25 (16.0) 29 (18.0) 31 (19.6) 0.7073

Three-factor factor analysis solution with oblique rotation; n of subjects for each characteristic may vary due to

occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for categorical

variables; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure of >=85

mmHg or participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance defined as

fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l;

MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose;

FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-transformed)for

continuous variables, Chi-Square for dichotomous variables.

51


according to tertiles of the Beef & Processed Foods pattern score as determined by exploratory

factor analysis.

Beef & Processed Foods Pattern Score

T1 T2 T3 p-value

n 158 159 159 -

Age (years)* 29.2±13.7 26.8±14.0 24.2±11.0 0.0025

Sex, Male/Female †β 61/97 (38.6/61.4) 79/80 (49.7/50.3) 62/97 (39.0/61.0) 0.0765

Anthropometry*

Height (cm) 165.9±9.3 166.0±9.9 165.3±11.5 0.8068

Weight (kg) 74.9±17.3 72.1±19.2 69.3±18.1 0.0255

BMI (kg/m²) 27.1±5.5 25.9±5.9 25.2±5.5 0.0106

Percent Body Fat (%) 36.9±12.1 32.7±13.0 32.8±13.4 0.0049


Blood Pressure

Systolic (mmHg)‡ 115.0 (105.0-121.5) 117.0 (108.0-122.0) 111.0 (101.0-120.0) 0.0062

Diastolic (mmHg)* 66.3±12.5 65.6±12.1 63.7±11.0 0.1271

MAP (mmHg)‡ 80.7 (75.8-87.7) 81.7 (73.8-90.8) 78.2 (73.2-85.0) 0.0213

Hypertension†§ β 31 (19.6) 34 (21.4) 17 (10.7) 0.0257

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.4±0.47 5.5±0.48 5.3±0.52 0.0380

2hPG (mmol/l)* 5.8±1.71 5.5±1.83 5.5±1.73 0.3997

FI (mmol/l)‡ 97.0 (69.0-134.0) 102.0 (71.0-149.0) 94.0 (67.0-130.0) 0.4067

IGT†¶ β 21 (13.3) 20 (12.6) 17 (10.7) 0.7653

IFG †|| β 6 (3.8) 15 (9.4) 11 (6.9) 0.1333

Adipokines‡

CRP (mg/l) 1.87 (0.63-5.10) 1.70 (0.39-4.91) 1.62 (0.44-4.21) 0.7844

IL-6 (ng/l) 0.87 (0.42-1.42) 0.63 (0.32-1.25) 0.68 (0.34-1.14) 0.0983

Adiponectin (μg/l) 13.5 (9.35-17.7) 13.6 (9.64-18.1) 15.3 (11.1-20.9) 0.0160

Leptin (ng/ml) 13.2 (6.90-21.3) 11.3 (5.20-20.0) 9.90 (5.30-19.0) 0.0999






fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired

fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l;

MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-transformed)for


52


according to tertiles of the Traditional Foods pattern score as determined by exploratory factor

analysis.









Traditional Foods Pattern Score

T1 T2 T3 p-value

n 157 161 158 -

Age (years)* 26.0±10.7 27.7±13.0 26.5±15.2 0.5172

Sex, Male/Female †β 75/82 (47.8/52.2) 69/92 (42.9/57.1) 58/100

(36.7/63.3) 0.1379

Anthropometry*

Height (cm) 167.3±10.0 166.5±10.4 163.3±9.8 0.0012

Weight (kg) 74.2±17.7 74.9±18.0 67.1±18.4 0.0001

BMI (kg/m²) 26.4±5.4 26.9±5.7 25.0±5.8 0.0079

Percent Body Fat (%) 34.1±12.9 35.6±12.5 32.7±13.5 0.1426


Blood Pressure

Systolic (mmHg)‡ 114.0 (105.0-120.0) 117.0 (105.0-122.0) 112.0 (103.0-120.0) 0.0832

Diastolic (mmHg)* 65.2±12.5 66.7±11.7 63.6±11.4 0.0675

MAP (mmHg)‡ 80.7 (73.7-86.3) 81.2 (75.0-90.8) 78.7 (73.0-87.3) 0.0305

Hypertension†§ β 22 (14.0) 37 (23.0) 23 (14.6) 0.0588

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.48 5.4±0.54 0.1087

2hPG (mmol/l)* 5.4±1.68 5.6±1.76 5.9±1.81 0.0300

FI (mmol/l)‡ 95.0 (65.0-134.0) 102.0 (68.0-142.0) 98.0 (68.0-141.0) 0.4075

IGT†¶ β 13 (8.3) 21 (13.0) 24 (15.2) 0.1587

IFG †|| β 7 (4.5) 13 (8.1) 12 (7.6) 0.3783

Adipokines‡

CRP (mg/l) 1.68 (0.45-4.45) 1.85 (0.61-5.12) 1.64 (0.39-4.29) 0.6223

IL-6 (ng/l) 0.76 (0.38-1.24) 0.69 (0.41-1.28) 0.60 (0.31-1.17) 0.4465

Adiponectin (μg/l) 13.8 (9.46-18.7) 12.7 (9.69-17.8) 15.0 (11.2-21.4) 0.0092

Leptin (ng/ml) 10.3 (5.20-19.0) 11.0 (5.70-21.2) 12.3 (6.10-20.3) 0.5658


53


characteristics and dietary patterns as determined using exploratory factor analysis on FFQ data

from the Sandy Lake Health and Diabetes Project.

Three-factor factor analysis solution with oblique rotation; MAP=Mean arterial pressure; FPG=Fasting plasma

glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001;

‡ p<0.05

Balanced Market

Foods

Beef & Processed

Foods Traditional Foods

Crude Age-

Adjusted Crude

Age-

Adjusted Crude

Age-

Adjusted

Age (years) 0.08 - †-0.16 - -0.07 -

Anthropometry*

Height (cm) 0.01 -0.01 -0.02 0.02 †-0.17 ‡-0.14

Weight (kg) 0.03 -0.01 ‡-0.13 -0.05 *-0.19 †-0.17

BMI (kg/m²) 0.04 -0.01 ‡-0.14 -0.07 ‡-0.14 ‡-0.11

Percent Body Fat (%) 0.05 0.02 ‡-0.13 -0.07 -0.06 -0.05

Waist Circumference (cm) 0.04 -0.01 ‡-0.14 -0.05 ‡-0.14 ‡-0.10

Blood Pressure

Systolic (mmHg)‡ 0.03 -0.03 *-0.10 -0.02 -0.07 -0.05

Diastolic (mmHg)* 0.01 -0.04 -0.08 -0.02 -0.07 -0.04

MAP (mmHg)‡ 0.01 -0.05 *-0.11 -0.03 -0.08 -0.05

Lipid Profile

HDL Cholesterol (mmol/l)* 0.01 0.00 0.03 0.03 0.09 0.08

LDL Cholesterol (mmol/l)* 0.08 0.05 ‡-0.12 -0.06 -0.04 -0.03

Triglycerides (mmol/l)‡ 0.06 0.05 -0.08 -0.02 -0.10 -0.08

Glucose Homeostasis

FPG (mmol/l)* 0.04 0.04 -0.05 -0.03 ‡0.09 ‡0.12

2hPG (mmol/l)* 0.06 0.04 -0.06 -0.03 ‡0.14 ‡0.15

FI (mmol/l)‡ 0.08 0.07 -0.03 -0.02 0.04 0.05

Adipokines‡

CRP (mg/l) 0.02 -0.04 -0.08 -0.00 -0.04 -0.01

IL-6 (ng/l) ‡-0.09 ‡-0.13 ‡-0.11 -0.09 -0.06 -0.05

Adiponectin (μg/l) -0.05 -0.03 ‡0.11 0.07 ‡0.11 0.09

Leptin (ng/ml) 0.05 0.02 ‡-0.10 -0.07 0.02 0.02

Patterns

Balanced Market Foods 1.00 1.00 *0.36 *0.40 *0.43 *0.44

Beef & Processed Foods 1.00 1.00 *0.25 *0.25

Traditional Foods 1.00 1.00

54

3.2.1 Associations of Factor Analysis-Derived Pattern Scores with Incident Type 2 Diabetes Mellitus

Logistic regression relating the three dietary patterns identified by factor analysis to incident

T2DM at follow-up indicated modest, non-significant associations with T2DM in the unadjusted

models (Table 7). The same was true when the models were adjusted for age and sex (Model 1),

and additionally WC (Model 2). In Model 2, the odds ratios (ORs) for the Balanced Market

Foods and Traditional Foods patterns were both close to unity; whereas the OR for the Beef &

Processed Foods pattern approached significance, indicating a 34% increased risk of T2DM per

unit increase in the Beef & Processed Foods score (p=0.05). When adjusted for age, sex, WC,

IL-6, and adiponectin, a 1-unit increase in the Beef & Processed Foods pattern score was

associated with a statistically significant 38% increase in risk of developing T2DM at follow-up

(OR=1.38; 95% CI: 1.02, 1.86; p<0.04), whereas the ORs for the Balanced Market Foods and

Traditional Foods scores remained non-significant.

Table 7. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor

dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake Health and

Diabetes Project

Model Balanced Market Foods Beef & Processed


Unadjusted 1.20

(0.91, 1.57)

1.14

(0.87, 1.51)

0.93

(0.70, 1.23)

Model 1 1.18

(0.90, 1.56)

1.28

(0.96, 1.71)

0.90

(0.67, 1.22)

Model 2 1.16

(0.88, 1.54)

1.34

(1.00, 1.80)

1.04

(0.76, 1.43)

Model 3 1.15

(0.86, 1.53) 1.38

(1.02, 1.86)*

1.05

(0.76, 1.45)

Three-factor factor analysis solution with oblique rotation; ORs presented per unit increase in pattern score;

Model 1 – Adjusted for age and sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age, sex,

WC, IL-6, and adiponectin; *p<0.05

A test for non-linearity of the FA-derived pattern scores indicated that there was a linear

association between scores for each identified dietary pattern with risk of incident T2DM (data

not shown).

55

Testing for interaction indicated that there were no significant interactions between age and

pattern score, nor sex and pattern score in Model 1 on the association of dietary patterns with

incident T2DM (data not shown).

Sensitivity analyses were conducted to examine the effect of physical activity, physical fitness

(as measured by VO2max), and smoking on the logistic regression models (Appendix D);

however, greater than 20% of participant data were missing for these measures. Physical activity

and fitness did not differ between individuals who converted to T2DM at follow-up versus those

who did not; however, a significantly lesser proportion of current smokers (at the time of

baseline data collection) converted to T2DM at follow-up (p<0.02). Neither physical activity

nor physical fitness was significantly associated with any of the three FA-derived patterns.

When physical activity was added to the logistic regression model as a covariate, it attenuated

the association between the Beef & Processed Foods pattern and risk of T2DM (OR:1.24, 95%

CIs:0.88, 1.74). Adjustment for physical fitness and current smoking status did not appreciably

change the results.

To test the robustness of the current findings of the 3-factor FA solution, 2-factor and 4-factor

solutions were also considered. The 2-factor solution was rejected because the patterns it

produced appeared to be oversimplified, while the 4-factor solution (Appendix E) was rejected

because the fourth identified pattern did not account for at least 15% of the common variance

amongst the FFQ items. In the 2-factor solution, elements of the Balanced Market Foods pattern

were distributed amongst the two factors, while others fell below the 0.30 pattern loading cut-

point. In the 4-factor solution, a “Proto-Historic/Tea Foods” pattern emerged, borrowing

elements from the Beef & Processed Foods pattern, and incorporating other FFQ items which

fell below the 0.30 pattern loading cut-point in the 3-factor solution. Interestingly, the Proto-

Historic/Tea Foods pattern identified in the 4-factor solution indicated a positive trend across

tertiles of pattern scores for age (p=0.0002), as well as SBP and DBP (both p<0.05), while the

Beef and Processed Foods pattern (in the 4-factor solution) continued to indicate a negative

trend across tertiles of pattern scores for age (p<0.0001) as did weight, BMI, percent body fat,

and WC. Where the 3-factor Beef and Processed Foods pattern indicated a positive trend across

tertiles of pattern scores for fasting glucose, a significant association was not observed in the 4-

factor solution for the Beef & Processed Foods pattern. However, the 4-factor Beef and

56

Processed Foods pattern did indicate a negative trend across tertiles of pattern scores for 2-hour

post-prandial glucose (p<0.05).


patterns of the 4-factor solution and baseline characteristics revealed a significant inverse

correlation between the Beef & Processed Foods pattern and age (r=-0.33; p<0.0001) and a

significant positive association between the Proto-Historic/Tea Foods pattern and age (r=0.23,

p<0.0001). As a result, partial Spearman rank correlation coefficients were calculated for all

factors, adjusting for age. Following this adjustment, a significant negative association existed

between the Beef & Processed Foods pattern and IL-6 (p<0.05), with no significant associations

remaining between either the Beef & Processed Foods or the Proto-Historic/Tea Foods patterns

an other baseline characteristics.

Similar to results using the 3-factor solution pattern scores, logistic regression relating the

dietary patterns identified by the FA-derived 4-factor solution to incident T2DM at follow-up

indicated modest, non-significant associations with T2DM in the unadjusted models. Similarly,

the same was true when the models were adjusted for age and sex (Model 1), and additionally

WC (Model 2). In Model 2, the OR for the Proto-Historic/Tea Foods pattern approached

significance, indicating a 41% increased risk of T2DM per unit increase in the Proto-

Historic/Tea Foods score (p=0.05), whereas the ORs for the other three factors (including the

Beef & Processed Foods pattern) remained non-significant with p-values greater than 0.12.

When adjusted for age, sex, WC, IL-6, and adiponectin, a 1-unit increase in the Proto-

Historic/Tea Foods pattern score was associated with a statistically significant 47% increase in

risk of developing T2DM at follow-up (OR=1.47; 95% CI: 1.03, 2.10; p<0.04), whereas the

ORs for the Beef & Processed Foods, Balanced Market Foods and Traditional Foods scores

remained non-significant.

3.3 Reduced Rank Regression Analysis

Intermediate response variables were selected for the dietary pattern analysis using reduced rank

regression (RRR) based on their physiological relevance to the etiology of T2DM. Traditional

markers of T2DM selected for inclusion included WC, HDL-C, FPG, 2hPG, and FI. Novel

57

biomarkers included CRP and adiponectin, both of which are adipokines that have recently been

linked with obesity and insulin resistance1-3

. RRR using the seven aforementioned biomarkers as

intermediate response variables (their associations amongst one another are presented in Table

2), and the 34 FFQ items as predictor variables, identified seven dietary patterns (a result which

was expected as the number of dietary patterns identified is equal to the number of intermediate

response variables inputted into the analysis). The first three patterns identified by RRR were

utilized for subsequent analyses because they each accounted for at least 1.0% of the total

variance amongst the selected intermediate biomarkers (Table 8). Similar to FA, all FFQ items

load onto each pattern, though with differing weights depending on their level of influence (in

the case of RRR, on the selected intermediate response variables) (Table 9). Also similar to FA,

factor scores were calculated for each study participant by multiplying their frequency of intake

of FFQ items by the loading for each of the dietary patterns.

Table 8. Pattern names, FFQ items in each pattern, and percent total variation explained by each

pattern, determined using reduced rank regression using data from the Sandy Lake Health and

Diabetes Project.

Pattern Name FFQ Items in Pattern Percent Variance Accounted For

Tea & Fibre

Tea Hot Cereal Peas (Pop)

(Chips/French Fries) (Chocolate/Candy) (Canned Fruit) (Beef)

5.81

Traditional

Duck Berries Soup Rabbit Moose Fish (Pop) (Chips/French Fries)

(White Bread)

1.71

Proto-Historic

Bannock

Canned Milk (Cold Cereal) (Other Vegetables) (Moose) (Pop)

1.16

Intermediate response variables: waist circumference, high-density lipoprotein cholesterol, fasting plasma glucose,

2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; Foods with factor

loadings >= 0.20 are shown for simplicity since those foods were considered when patterns were named. ( ) denotes

negative factor loadings

58

As with FA, once pattern scores were calculated for each study participant for each dietary

pattern (Tea & Fibre, Traditional, and Proto-Historic), participants were divided into tertiles

based on their overall pattern score for each dietary pattern. Tables 10a, 10b, and 10c describe

the baseline characteristics stratified by tertile of pattern score for each dietary pattern. As

shown in Table 10a, there were significant differences indicating a positive trend amongst

tertiles of the Fibre & Tea pattern for the proportion of people who converted to T2DM

(p=0.02), as well as those with HTN, and IGT (both p<0.0001). Similarly, there were significant

differences amongst tertiles of the Tea & Fibre pattern scores for weight, BMI, percent body fat,

WC, SBP, DBP, MAP, LDL-C, TG, FPG, 2hPG, FI, CRP and leptin (all p<0.05), indicating a

positive trend. The significant difference across tertiles of the Tea & Fibre pattern score for

adiponectin (p<0.0001) indicated a negative trend. There was a significant difference amongst

tertiles of the Traditional pattern for sex (p=0.001), indicating that the proportion of women in

each tertile increased with increasing Traditional pattern scores (Table 10b). A negative trend

amongst significantly different tertiles of the Traditional pattern was seen for height and weight

(both p<0.02), and a positive trend across tertiles for adiponectin (p<0.001). Interestingly, a

positive tend amongst significantly different tertiles of the Traditional pattern was seen for

2hPG, and consequently, proportion with IGT (both p<0.0001). The significant differences

across tertiles of the Proto-Historic pattern scores for age and LDL-C (both p<0.05) indicate a

positive trend with increasing pattern score; however, those across score tertiles for FPG and

consequently, proportion with IFG (p<0.02) indicate a negative trend (Table 10c). There was a

significant difference amongst tertiles of Proto-Historic pattern scores for adiponectin (p<0.05);

however, there was no clear trend with increasing or decreasing pattern score. In review of the

results of Tables 10a-c, it is clear that the Tea & Fibre pattern tracks the most with the selected

intermediate biomarkers, which is logical since the Tea & Fibre pattern accounted for the most

common variation amongst the intermediate biomarkers. However, the Tea & Fibre pattern also

tracks closely with age, and age is closely correlated with the intermediate biomarkers employed

in the RRR analysis.

Spearman rank correlation coefficients examining the correlations between the RRR dietary

patterns and baseline characteristics revealed a significant positive correlation between age and

both the Tea & Fibre (r=0.52; p<0.0001) and Proto-Historic patterns (r=0.11; p<0.5) (Table 11).

As a result, partial Spearman rank correlation coefficients were calculated for all factors,

59

adjusting for age. Following this adjustment, significant positive associations existed between

the Tea & Fibre pattern and BMI, percent body fat, LDL-C, TG, 2hPG, FI, CRP and leptin (all

p<0.05), and a significant age-adjusted inverse association between the Tea & Fibre pattern and

adiponectin (p<0.05). There were significant age-adjusted positive associations between the

Traditional pattern and FPG, 2hPG and adiponectin (all p<0.001), and significant age-adjusted

inverse associations between the Traditional pattern and height, weight and WC (all p<0.05).

Finally, significant age-adjusted inverse associations existed between the Proto-Historic pattern

and height FPG (all p<0.05). Interestingly, the age-adjustment had a large impact on the Tea &

Fibre pattern, though little impact on either the Traditional or Proto-Historic patterns.

Table 9. Pattern loadings for each food as listed on the 34-item FFQ, as determined by reduced


FFQ Items Tea & Fibre Traditional Proto-Historic

Fish 8 21 19 Moose -11 27 -28 Beef -20 2 -12 Pork -6 0 -13 Duck -6 40 9 Rabbit -18 28 -15 Klik -11 -5 4 Eggs 16 -7 5 Lard 7 -2 -5

Margarine 2 8 13 Cold Cereal -4 19 -40 Hot Cereal 31 18 -2 Beans 19 -7 10 White Bread -5 -22 -19 Whole Wheat Bread 14 -9 -19 Bannock -6 17 36 Macaroni -14 1 12

Indian Tea -7 15 -2 Soup 13 30 -2 Chips/French Fries -35 -22 -10 Other Potatoes 9 12 -11 Peas 22 -14 13 Corn -1 8 -3 Carrots 10 8 -11 Other Vegetables 17 14 -37

Berries -10 34 -12 Fresh Fruit -0 16 10 Canned Fruit -22 -1 -15 Milk 9 4 -7 Canned Milk -1 8 25 Pop -36 -23 -27 Tea 41 -11 -15 Cookies/Cakes/Pastries -14 7 -8

Chocolate/Candy -25 16 -4


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; Loadings shown as

loading*100 for simplicity; Loadings >= 20 bolded

60

Table 10a. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Tea & Fibre pattern as determined by reduced rank

regression.


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; n of subjects for each characteristic may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th

percentile); β Chi-Square test for categorical variables; § Hypertension defined as systolic blood pressure >=130

mmHg or diastolic blood pressure of >=85 mmHg or participation in antihypertensive medication therapy; ¶

IGT=Impaired glucose tolerance defined as fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8

mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and

2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-

prandial plasma glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed

were log-transformed) for continuous variables, Chi-Square for dichotomous variables.

Tea & Fibre Pattern Score

T1 T2 T3 p-value

n 156 160 159 -

Age (years)* 19.4±8.0 26.5±12.8 34.3±13.4 <0.0001

Sex, Male/Female †β 66/90 (42.3/57.7) 74/86 (46.3/53.8) 61/98 (38.4/61.6) 0.3621

Anthropometry*

Height (cm) 164.7±11.5 165.5±9.8 167.0±9.2 0.1181

Weight (kg) 66.3±19.3 72.2±17.3 77.8±16.5 <0.0001

BMI (kg/m²) 24.2±5.8 26.2±5.3 27.8±5.3 <0.0001

Percent Body Fat (%) 30.5±13.8 34.4±12.8 37.5±11.2 <0.0001

Waist Circumference (cm) 84.0±13.4 90.7±13.0 94.5±12.4 <0.0001

Blood Pressure

Systolic (mmHg)‡ 111.3 (100.5-119.3) 112.0 (104.0-102.0) 118.0 (110.0-128.0) <0.0001

Diastolic (mmHg)* 62.1±10.3 63.8±11.2 69.7±12.8 <0.0001

MAP (mmHg)‡ 76.7 (72.8-83.2) 79.3 (73.3-86.1) 85.0 (78.0-93.7) <0.0001

Hypertension†§ β 14 (9.0) 23 (14.4) 45 (28.3) <0.0001

Lipid Profile


LDL Cholesterol (mmol/l)* 2.19±0.67 2.53±0.75 2.72±0.71 <0.0001

Triglycerides (mmol/l)‡ 1.03 (0.75-1.35) 1.19 (0.87-1.61) 1.29 (1.04-1.79) <0.0001

Glucose Homeostasis

FPG (mmol/l)* 5.3±0.45 5.4±0.51 5.5±0.51 0.0010

2hPG (mmol/l)* 5.2±1.53 5.5±1.65 6.1±1.97 <0.0001

FI (mmol/l)‡ 85.0 (56.0-125.5) 102.0 (74.0-140.0) 108.0 (75.0-149.0) 0.0056

IGT†¶ β 10 (6.4) 12 (7.5) 36 (22.6) <0.0001

IFG †|| β 9 (5.8) 11 (6.9) 12 (7.6) 0.8173

Adipokines‡

CRP (mg/l) 0.68 (0.22-2.74) 1.62 (0.53-5.03) 3.05 (1.41-6.81) <0.0001

IL-6 (ng/l) 0.63 (0.33-1.16) 0.63 (0.34-1.17) 0.87 (0.39-1.42) 0.1671

Adiponectin (μg/l) 16.2 (11.8-22.1) 13.6 (10.1-17.2) 12.6 (8.74-16.7) <0.0001

Leptin (ng/ml) 9.50 (4.25-16.9) 10.5 (4.85-20.4) 14.6 (7.90-23.7) 0.0003


61

Table 10b. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Traditional pattern as determined by reduced rank

regression.

Intermediate response variables: waist circumference, high-density lipoprotein cholesterol, fasting plasma glucose, 2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; n of subjects for

each characteristic may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th





2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-

prandial plasma glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed


Traditional Pattern Score

T1 T2 T3 p-value

n 157 159 159 -

Age (years)* 25.3±9.3 27.1±12.9 27.8±16.1 0.2230

Sex, Male/Female †β 85/72 (54.1/45.9) 61/98 (38.4/61.6) 56/103 (35.2/64.8) 0.0013

Anthropometry*

Height (cm) 168.0±10.9 165.4±10.2 163.8±9.1 0.0010

Weight (kg) 75.3±17.5 71.5±19.3 69.6±17.9 0.0197

BMI (kg/m²) 26.6±5.5 25.9±5.9 25.7±5.7 0.3892

Percent Body Fat (%) 33.7±12.8 34.2±13.4 34.5±12.8 0.8601


Blood Pressure

Systolic (mmHg)‡ 114.0 (106.0-120.0) 113.0 (102.5-120.0) 116.0 (104.0-122.5) 0.2084

Diastolic (mmHg)* 64.4±11.3 66.3±11.8 64.9±12.7 0.3446

MAP (mmHg)‡ 80.0 (73.7-85.3) 79.3 (74.3-88.7) 80.8 (73.3-91.0) 0.7320

Hypertension†§ β 20 (12.7) 28 (17.6) 34 (21.4) 0.1253

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.45 5.4±0.48 5.5±0.52 0.0008

2hPG (mmol/l)* 5.1±1.65 5.7±1.61 6.1±1.89 <0.0001

FI (mmol/l)‡ 93.0 (69.0-125.0) 94.0 (60.0-144.5) 107.0 (77.0-148.0) 0.0765

IGT†¶ β 7 (4.5) 18 (11.3) 33 (20.8) <0.0001

IFG †|| β 8 (5.1) 8 (5.0) 16 (10.1) 0.1219

Adipokines‡

CRP (mg/l) 1.63 (0.49-4.21) 1.80 (0.46-5.02) 1.77 (0.46-4.69) 0.7618

IL-6 (ng/l) 0.66 (0.35-1.22) 0.75 (0.37-1.35) 0.68 (0.33-1.21) 0.5058

Adiponectin (μg/l) 12.3 (8.81-17.7) 14.1 (11.0-18.6) 15.1 (11.0-21.4) 0.0005

Leptin (ng/ml) 9.40 (4.40-19.0) 13.0 (5.90-20.7) 11.8 (6.80-21.1) 0.0676


62

Table 10c. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Proto-Historic pattern as determined by reduced

rank regression.


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; n of subjects for

each characteristic may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th





2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed


Proto-Historic Pattern Score

T1 T2 T3 p-value

n 157 160 158 -

Age (years)* 24.9±12.0 26.9±12.5 28.4±14.5 0.0555

Sex, Male/Female †β 73/84 (46.5/53.5) 68/92 (42.5/57.5) 60/98 (38.0/62.0) 0.3094

Anthropometry*

Height (cm) 166.7±9.5 165.9±10.8 164.5±10.2 0.1474

Weight (kg) 72.5±18.5 72.8±17.0 70.9±19.5 0.6123

BMI (kg/m²) 25.9±5.6 26.3±5.4 25.9±6.0 0.7661

Percent Body Fat (%) 33.4±12.4 34.9±12.8 34.1±13.8 0.5957


Blood Pressure

Systolic (mmHg)‡ 112.5 (104.0-120.0) 116.3 (107.0-121.0) 114.0 (102.0-120.0) 0.6131

Diastolic (mmHg)* 64.0±12.1 66.1±11.7 65.5±12.0 0.3034

MAP (mmHg)‡ 80.0 (73.0-86.0) 80.5 (74.7-88.3) 80.0 (73.7-89.3) 0.2981

Hypertension†§ β 22 (14.0) 27 (16.9) 33 (20.9) 0.2685

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.5±0.51 5.4±0.47 5.3±0.48 0.0083

2hPG (mmol/l)* 5.5±1.76 5.6±1.68 5.8±1.84 0.2428

FI (mmol/l)‡ 102.0 (72.0-146.0) 99.0 (69.5-133.0) 94.0 (65.0-136.0) 0.4037

IGT†¶ β 16 (10.2) 16 (10.0) 26 (16.5) 0.1365

IFG †|| β 18 (11.5) 9 (5.6) 5 (3.2) 0.0105

Adipokines‡

CRP (mg/l) 1.31 (0.41-3.45) 1.88 (0.54-4.11) 2.34 (0.46-7.19) 0.1244

IL-6 (ng/l) 0.57 (0.34-1.06) 0.75 (0.33-1.26) 0.77 (0.39-1.46) 0.1386

Adiponectin (μg/l) 14.0 (9.80-18.6) 13.2 (9.83-18.1) 14.2 (10.8-20.9) 0.0465

Leptin (ng/ml) 9.90 (5.40-18.0) 12.2 (6.30-19.5) 12.5 (5.30-21.8) 0.1568


63


characteristics and patterns as determined using reduced rank regression analysis using data


Tea & Fibre Traditional Proto-Historic

Crude Age-

Adjusted Crude

Age-

Adjusted Crude

Age-

Adjusted

Age (years) *0.52 -0.03 ‡0.11

Anthropometry

Height (cm) ‡0.10 -0.08 *-0.20 *-0.22 ‡-0.10 ‡-0.10

Weight (kg) *0.31 0.04 †-0.16 †-0.17 -0.02 -0.07

BMI (kg/m²) *0.33 ‡0.09 -0.08 -0.08 0.01 -0.04

Percent Body Fat (%) *0.25 ‡0.10 0.01 0.03 0.06 0.01

Waist Circumference (cm) *0.37 0.08 ‡-0.11 ‡-0.11 0.02 -0.04

Blood Pressure

Systolic (mmHg) *0.27 0.02 -0.02 -0.01 0.04 0.01

Diastolic (mmHg) ‡0.28 0.05 -0.02 -0.01 0.06 0.03

MAP (mmHg) *0.32 0.05 -0.01 0.00 0.06 0.03

Lipid Profile

HDL Cholesterol (mmol/l) -0.08 -0.09 0.05 0.04 0.05 0.05

LDL Cholesterol (mmol/l) *0.35 ‡0.11 -0.04 -0.03 ‡0.12 0.07

Triglycerides (mmol/l) *0.29 ‡0.14 0.00 0.01 0.00 -0.03

Glucose Homeostasis

FPG (mmol/l) *0.18 0.07 †0.16 †0.17 ‡-0.13 ‡-0.15

2hPG (mmol/l) *0.22 ‡0.14 *0.24 *0.26 0.08 0.05

FI (mmol/l) *0.20 †0.17 0.08 0.08 -0.05 -0.07

Adipokines

CRP (mg/l) *0.36 †0.16 -0.03 -0.01 ‡0.14 0.09

IL-6 (ng/l) ‡0.13 0.06 -0.01 0.00 ‡0.12 0.09

Adiponectin (μg/l) *-0.26 ‡-0.14 †0.16 †0.16 0.05 0.08

Leptin (ng/ml) *0.20 ‡0.14 0.07 0.09 ‡0.11 0.07

Patterns

Tea & Fibre 1.00 1.00 0.04 0.07 0.02 -0.03

Traditional 1.00 1.00 0.00 0.01

Proto-Historic 1.00 1.00


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; MAP=Mean

arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001; ‡ p<0.05

64

3.3.1 Associations of Reduced Rank Regression-Derived Pattern Scores with Incident Type 2 Diabetes Mellitus

Logistic regression relating the three dietary patterns identified by RRR to incident T2DM at

follow-up indicated a significant association between the Tea & Fibre pattern and incident

T2DM in the unadjusted model (OR=1.31, 95% CI: 1.03, 1.67). However, once adjusted for age

and sex, the relationship was attenuated and ceased to maintain statistical significance. The

Traditional and Proto-Historic patterns did not predict T2DM at follow-up in either the

unadjusted or adjusted models.

Table 12. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived dietary pattern scores and incident type 2 diabetes using data from the Sandy

Lake Health and Diabetes Project.

Model Tea & Fibre Traditional Proto-Historic

Unadjusted 1.31

(1.03, 1.67)*

0.88 (0.70, 1.10)

1.28 (0.95, 1.71)

Model 1 1.08

(0.82, 1.42)

0.81

(0.64, 1.03)

1.19

(0.88, 1.62)

Model 2 0.93

(0.70, 1.25)

0.91

(0.70, 1.17)

1.24

(0.90, 1.70)

Model 3 0.89

(0.66, 1.21)

0.93

(0.72, 1.21)

1.23

(0.88, 1.71)


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; ORs presented per

unit increase in pattern score; Model 1 – Adjusted for age and sex; Model 2 – Adjusted for age, sex, and WC

Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin; *p<0.05

A test for non-linearity of the RRR-derived pattern scores indicated that there was a linear

association between pattern scores for the Tea & Fibre and Proto-Historic patterns with risk of

incident T2DM in the unadjusted model (data not shown). However, non-linearity was indicated

(p=0.0305 for quadratic term) in the association of the Traditional pattern with incident T2DM.

To examine the non-linearity of the Traditional pattern further, logistic regression using tertiles

of Traditional pattern score as the primary exposure, and incident T2DM at follow-up as the

primary outcome in the unadjusted model was conducted. The OR for tertile 2 (n=159) versus

tertile 1 (reference category) (n=157) was 1.38 (95% CIs: 0.63, 3.00), and the OR for tertile 3

65

(highest pattern score) (n=159) versus tertile 1 was 0.70 (95% CIs: 0.18, 2.67). These findings

illustrate the non-linear nature of the relationship between increasing Traditional pattern score

and incident T2DM at follow-up in the Sandy Lake Health and Diabetes Project.

Testing for interaction indicated that there were no significant interactions between sex and

pattern score in Model 1 on incident T2DM (data not shown). Similarly, there was no significant

interaction between age and the Proto-Historic pattern score. Interestingly, there was a

significant interaction between age and the Tea & Fibre and Traditional pattern scores (both

p<0.03). To further examine this result, the sample was divided at the median age (23.5 years)

and logistic regression was repeated, relating the Tea & Fibre and Traditional patterns to risk of

T2DM (Appendix F and G, respectively). Although there was an appreciable difference

between the age groups for risk of T2DM in the unadjusted model for the Tea & Fibre pattern,

the difference was attenuated in adjustment for age in the subsequent models. There was very

little difference between the ORs calculated for the two age groups predicting risk of T2DM

using Traditional pattern score.

As mentioned previously (section 3.2.1), sensitivity analyses were conducted to examine the

effect of physical activity, physical fitness and current smoking status on the logistic regression

models (Appendix D). There were significant differences across tertiles of the Tea & Fibre

pattern scores for physical fitness (with indication of a positive trend) and current smoking

status (with indication of a negative trend) (both p<0.03), and across tertiles of the Traditional

pattern scores for physical fitness (with indication of a negative trend) and current smoking

status (with indication of a negative trend) (both p<0.02). Interestingly, there was an age-

adjusted negative association between physical fitness and the Tea & Fibre pattern score when

Spearman rank correlation coefficients were calculated (in addition to an age-adjusted negative

association between physical fitness and the Traditional pattern). When physical activity was

added to the logistic regression model as a covariate, the association between the Tea & Fibre

pattern and risk of T2DM was further attenuated (OR: 0.81, 95% CIs: 0.57, 1.15); however, the

association between the Proto-Historic pattern and risk of T2DM was strengthened (OR: 1.40,

95% CIs: 0.94, 2.08), though it remained non-significant. Adjustment for physical activity and

current smoking status did not appreciably affect the results.

66

Finally, sensitivity analyses examining the outcome of RRR when using log-transformed non-

normally distributed intermediate response variables (WC, HDL, FPG, 2hPG, log[FI],

log[CRP], log[adiponectin]) was conducted, and is presented in Appendix H. The result was the

identification of 3 similar patterns to those identified using untransformed intermediate response

variables. The first pattern identified was similar to the Tea & Fibre pattern, though it had

higher loadings for vegetables and eggs, while the loadings for chocolate/candy, canned fruit,

and beef became less influential. The second pattern was similar to the Traditional pattern, but

also had a high loading for hot cereal, and had no negative loadings ≥-0.20 (before rounding).

The third pattern examined was similar to the Proto-Historic pattern, though without such high

loadings for canned milk, pop and moose, but higher loadings for eggs, margarine, and duck (as

well as a large negative loading for milk). Both the first (Hot Market Foods & Vegetables) and

third (Modified Proto-Historic) patterns were significantly associated with age (r=0.49, r=0.22,

respectively; both p<0.0001) similar to the Tea & Fibre and Traditional patterns. Age-

adjustment of the Spearman rank correlation coefficients had a greater effect on the Hot Market

Foods & Vegetables pattern, similar to the effect observed on the Tea & Fibre pattern. In the

logistic regression models, the Hot Market Foods & Vegetables pattern had a slightly larger

significant OR (OR: 1.35, 95% CIs: 1.06, 1.72) than was observed in relating the Tea & Fibre

pattern to risk of T2DM. Additionally, the Modified Proto-Historic pattern had a stronger and

significant OR than observed in relating the Proto-Historic pattern to risk of T2DM (OR: 1.36,

95% CIs: 1.05, 1.76). In the adjusted models, the Modified Proto-Historic pattern continued to

produce ORs greater than those seen in the analyses using the Proto-Historic pattern scores as

the exposure variable, though the ORs remained non-significant (Model 3: OR: 1.32, 95% CIs:

0.99, 1.76, p-value=0.06). The ORs in the adjusted models using the Hot Market Foods &

Vegetables and Traditional Foods & Hot Cereal pattern scores as exposure variables remained

non-significant, and similar in magnitude to those observed for the Tea & Fibre and Traditional

patterns.

67

3.4 References





2003;52:812-7




Care. 2008;31:1410-5

68

Chapter 4

Discussion

4.1 Summary of Findings

Exploratory factor analysis (FA) based on a food frequency questionnaire (FFQ) administered at

baseline identified three prominent dietary patterns: Balanced Market Foods, Beef & Processed

Foods, and Traditional Foods. Younger individuals were more likely to consume the Beef &

Processed Foods pattern, which included pop, klik, cookies/cake/pastry, chocolate/candy,

canned fruit, beef, canned milk, white bread, chips and lard. Once adjusted for age, the Balanced

Market Foods pattern was negatively correlated with interleukin-6 (IL-6) levels. The Traditional

Foods pattern was negatively correlated with weight, body mass index (BMI), and waist

circumference (WC), and positively correlated with fasting plasma glucose (FPG), and 2-hour

post-prandial plasma glucose (2hPG) at baseline (indicating that those who consumed this

pattern were lighter, with a lower BMI and WC, but in contrast, were more likely to have

elevated glucose levels). Logistic regression using dietary pattern scores at baseline to predict

incident type 2 diabetes (T2DM) at follow-up showed a significant association between

increased scores on the Beef & Processed Food pattern and incident T2DM when adjusted for

age, sex, WC, IL-6, and adiponectin. Interestingly, while the odds ratio (OR) for the Beef &

Processed Foods pattern in the unadjusted model was not statistically significant (OR 1.14, 95%

CIs: 0.87, 1.51), the relationship between the dietary pattern and incident T2DM strengthened

with each stepwise adjustment, with a multivariate adjusted OR of 1.38 (95% CIs: 1.02, 1.86).

This result indicates that age negatively confounds the relationship between the Processed

Foods pattern and incident T2DM, and that sex, WC, and IL-6 may have a similar effect on the

relationship; whereas adiponectin may be a positive confounder. Therefore, despite a

participant’s age, sex, WC, IL-6 and adiponectin levels, eating a diet high in foods which were

dominant in the Beef & Processed Foods pattern increased their risk of developing T2DM by

approximately 38%.

Reduced rank regression (RRR) based on the same FFQ from baseline, and using WC, HDL-C,

FPG, 2hPG, fasting serum insulin (FI), C-reactive protein (CRP) and adiponectin as

intermediate response variables, yielded seven dietary patterns. Only three of the seven

identified patterns were utilized in the analysis: the Tea & Fibre pattern, the Traditional, and the

69

Proto-Historic. Older individuals were more likely to consume foods from the Tea & Fibre and

Proto-Historic patterns than their younger counterparts. Following adjustment for age, the Tea

& Fibre pattern was significantly positively correlated with BMI, percent body fat, low-density

lipoprotein cholesterol (LDL-C), triglycerides (TG), 2hPG, FI, CRP, and leptin, and negatively

correlated with adiponectin. Correlations with 2hPG, FI, CRP, and adiponectin were expected

since the pattern was developed in consideration of these biomarkers (as well as WC, FPG, and

high-density lipoprotein cholesterol [HDL-C]). These correlations indicate that those who ate

foods emphasized by the Tea & Fibre pattern, paradoxically, tended to have higher BMI,

percent body fat, LDL-C, TG, 2hPG, FI, CRP and leptin levels, with lower adiponectin levels

even when age was taken into account. The Traditional pattern was significantly negatively

correlated with height, weight and WC, and positively correlated with FPG, 2hPG and

adiponectin. Correlations between the pattern and WC, FPG, 2hPG, and adiponectin were

expected; however, this also indicates that lighter (and shorter) participants were more likely to

consume foods emphasized by the Traditional pattern. There was a significant negative

correlation between the Proto-Historic pattern and height and FPG, indicating that those who ate

foods emphasized in the Proto-Historic pattern were more likely to be shorter in stature with

lower FPG. Logistic regression using RRR-driven dietary pattern scores at baseline to predict

incident T2DM at follow-up produced a significant odds ratio (OR=1.31 95% CI: 1.03, 1.67) for

the Tea & Fibre pattern in the unadjusted model. However, once adjusted for age and sex, the

OR was substantially attenuated and significance was lost. This result indicates that those who

ate foods emphasized in the Tea & Fibre pattern were approximately 31% more likely to

develop diabetes by the time of follow-up; however, age in particular, as well as sex explained a

substantial proportion of this association.

4.2 Results in the Context of Previous Literature

FA has been the most commonly used a posteriori method in the dietary pattern analysis

literature, and has included studies which have investigated the relationships between dietary

patterns and various chronic diseases. The majority of studies have identified both a “western”-

type diet characterized by red and processed meat and high fat, low fibre food, and a “prudent”-

type diet characterized by fruits, vegetables, and whole grains1-3

. Where the “western”-type diet

70

is typically associated with an increased risk of T2DM, the “prudent”-type diet tends to protect

against T2DM, though does not consistently reach significance in multivariate adjusted models1-

3.

The results of this study are consistent with those seen in the existing FA and diabetes

literature, as the Beef & Processed Foods pattern, which was characterized by red and processed

meat, and high-fat, low-fibre foods, was associated with a statistically significant 38% increase

in risk of T2DM in the fully-adjusted model. However, the current study extends this literature

because it used data from a geographically isolated community with a known genetic risk for

T2DM in which no other study has investigated dietary patterns and incident T2DM. More

importantly, this study is the first to examine dietary patterns prospectively in a First Nations

community in Canada. As well, the current study considered a wide age range of participants;

whereas most studies using FA to predict T2DM risk consider only adult participants at least 30

years of age1-3

.

In 1998, Gittelsohn et al4 published the results of exploratory factor analysis using this dataset,

and related factor scores, cross-sectionally, to prevalent T2DM. Methods for the factor analysis

were similar with the exception of using the “PRIORS=ONE” statement in the FACTOR

procedure of SAS (SAS Institute Inc. Cary, NC, USA), using pattern loadings of |0.40| as a cut-

point for naming food patterns, and retaining 7 factors based on a scree plot and proportion of

variance criteria4. (The “PRIORS=ONE” option is used in principal components analysis (PCA)

and its purpose is to set the cumulative correlations amongst the identified factors to a value of

1.05. As such, PCA optimally weights observed variables, such as FFQ items, rather than

allowing the actual observed common variance to be considered, as is the case in FA5). As well,

Gittlelsohn et al4 used data from only adults (≥20 years); whereas individuals aged 10-79 were

used in the current analysis. Despite these differences, some of the patterns identified by

Gittelsohn et al4 were similar to those in the current analysis (Table 13). Although the logistic

regression performed by Gittelsohn et al4 was done cross-sectionally, relating dietary patterns

(identified using exploratory FA) to prevalence of T2DM at baseline, a similar result to this

prospective analysis was observed, as both the Junk or Beef & Processed Foods patterns seem to

predict T2DM (OR for Q4 versus Q1 in Gittelsohn et al4 = 2.40, 95% CIs: 1.13, 5.10, adjusted

for age and sex; versus OR for all Beef & Processed Foods scores in the current analysis = 1.38,

95% CIs: 1.02, 1.86, adjusted for age, sex, WC, IL-6 and adiponectin). However the current

71

study extends the findings of Gittelsohn et al4 because it relates dietary patterns as identified

using FA prospectively to incident T2DM rather than cross-sectionally at baseline. As a

prospective analysis, the current study reduces the likelihood of recall bias that may have

occurred cross-sectionally where individuals who had T2DM may have eaten a dietary pattern

which was different from the diet they would have eaten had they not been diagnosed with

T2DM.

Table 13. Comparison of dietary patterns identified by factor analysis in current study to those

identified by Gittelsohn et al4.

Current Study Gittelsohn et al

4

“Balanced Market Foods”

Other vegetables

Carrots Peas

Corn

Whole wheat bread Milk

Macaroni

OR: 1.15†

95% CIs: 0.86, 1.53

“Vegetables”

Peas

Carrots Corn

Other Vegetables

OR: 0.86‡*

95% CIs: 0.42, 1.75

“Beef & Processed Foods”

Pop Klik

Cookies/cake/pastry

Chocolate/candy Canned fruit

Beef

Canned milk

White bread Chips

Lard

OR: 1.38†

95% CIs: 1.02, 1.86

“Junk Foods”

Chips Chocolate/candy

Cookies/cake/pastry

Pop Klik

Canned fruit

OR: 2.40‡*

95% CIs: 1.13, 5.10

“Traditional Foods” Fish

Moose

Duck

Berries Rabbit

Indian tea

OR: 1.05† 95% CIs: 0.76, 1.45

“Bush Foods” Rabbit

Duck

Fish

Moose Indian Tea

OR: 2.40‡* 95% CIs: 0.29, 1.21

† OR calculated based on logistic regression relating dietary pattern scores to incident diabetes at follow-up,

presented per unit increase, adjusted for age, sex, waist circumference, interleukin-6 and adiponectin; ‡ OR

calculated based on logistic regression relating dietary pattern scores to prevalent cases of diabetes at baseline,

adjusted for age and sex; *4th quintile (highest pattern score) versus 1st quintile (lowest pattern score)

72

RRR has recently emerged as a technique that is being applied to dietary pattern analysis. In

2004, Hoffmann et al6 published a study investigating the link between dietary patterns

identified by RRR (using type of fat, fibre, magnesium, and alcohol intake as intermediate

response variables) and incident T2DM in a German nested case-control analysis using data

from the EPIC-Potsdam study. In 2005, Heidemann et al7 published a similar study which also

used data from the EPIC-Potsdam study; however, Heidemann et al7 used diabetes-relevant

biomarkers as intermediate response variables (glycosylated hemoglobin (HbA1c), HDL-C,

CRP, and adiponectin). The single pattern retained by Heidemann et al7 was high in fresh fruit,

and low in high-caloric soft drinks, beer, red meat, processed meat, poultry, legumes, and bread

(excluding wholegrain bread) and was negatively associated with T2DM (OR for Q5 vs. Q1 :

0.27; 95% CIs:0.13, 0.64) in the fully-adjusted multivariate model. While this pattern bears

some resemblance to the Tea & Fibre pattern identified in the current analysis, the two patterns

not only differ in their food composition, but also the intermediate biomarkers selected, and

their ability to predict T2DM (Table 14). Other studies using RRR to calculate dietary pattern

scores to predict T2DM have used inflammatory markers alone8, measures of insulin

resistance9, and coagulation and fibrinolytic factors

10 as intermediate response variables.

Similarities exist amongst the results of these studies in that they all identified a pattern which

was significantly associated with risk of T2DM. However, the pattern identified in the study

which used coagulation and fibrinolytic factors as intermediate response variables10

was quite

different from the patterns identified in the studies by Schulze et al8 and McNaughton et al

9

(Table 15). As evidenced by this brief comparison, it is difficult to compare the results of the

current analysis to studies which used different intermediate response variables since RRR

seems to rely upon patho-physiologically relevant biomarkers for identifying dietary patterns.

73

Table 14. Comparison of dietary patterns identified by reduced rank regression analysis in

current study to those identified by Heidemann et al7.

Current Study Heidemann et al

7

Tea & Fibre pattern

Intermediate

Biomarkers

Odds Ratios

(95% CIs) Factor 1 pattern

Intermediate

Biomarkers

Odds Ratios

(95% CIs)

High Tea

Hot cereal

Peas

Low

Pop

Chips/fries Chocolate/candy

Canned Fruit

Beef

WC

HDL-C

FPG 2hPG

FI

CRP Adiponectin

0.89†

(0.66, 1.21)

High Fresh fruit

Low

Soft drinks Beer

Red meat

Processed meat Poultry

Legumes

Bread

HbA1c

HDL-C

CRP Adiponectin

0.27‡

(0.13, 0.64)

WC=Waist circumference; HDL-C=High-density lipoprotein cholesterol; FPG=Fasting plasma glucose; 2hPG=2-

hour postprandial plasma glucose; FI=Fasting serum insulin; CRP=C-reactive protein; HbA1c=Glycosylated

hemoglobin; † Model 3 - Adjusted for age, sex, WC, IL-6, and adiponectin; ‡ Q5 vs. Q1 in fully adjusted,

multivariate model

Unfortunately, the current analysis has not produced evidence for the strong associations seen in

the literature between RRR-derived dietary patterns and incident T2DM. The available sample

size of 492 is much smaller than the samples typically used in the previously mentioned studies

of 1000 to 28 000 participants7-10

, a difference that is likely to impact the width of the

confidence intervals of the ORs, as well as limiting the ability to conduct subgroup analyses for

age and other covariates. Limited availability of pathophysiologically-relevant intermediate

response variables likely affected the identification of dietary patterns in the current study since

RRR relies upon the common variation amongst the intermediate response variables when

identifying patterns. Patterns identified using inflammatory biomarkers could differ from

patterns identified using measures of insulin resistance, and even more so from those using food

characteristics and nutrients as intermediate biomarkers. Therefore, the selection of intermediate

biomarkers can greatly influence the outcome of the RRR and consequently, the relationship of

the identified pattern with incident T2DM. Liese et al10

chose to simplify the pattern scores by

retaining only the prominent foods (those with high factor loadings) when calculating pattern

scores in order to reduce the influence of other, less prominent foods on the final pattern scores.

This sort of pattern score simplification was not employed in the current study, which is another

consideration which may have affected the results; however in the study by Liese et al10

, there is

74

no explanation of how pattern simplification affected the results. Log-transformations of non-

normally distributed intermediate response variables for RRR has been used in some studies8,

and as illustrated in Appendix H, may appreciably affect the results of the analysis. However,

while reducing the skewness of the non-normally distributed variables, log-transformation

eliminates some of the observed variation that occurs naturally, and there does not appear to be

a consensus in the literature as to whether the non-normally distributed intermediate response

variables should be log-transformed for RRR. In addition to cultural differences, at the time of

FFQ administration there was a relatively limited quality and variety of foods available to study

participants, such as whole grains, variety of fresh fruit and vegetables, and affordable low-fat

dairy products. Therefore food groups described in other studies may represent a much different

quality and variety of food from what was available to Sandy Lake residents who participated in

this study. The age range of participants in the Sandy Lake Health and Diabetes Project is much

wider than that seen in the previously published dietary pattern analysis literature focusing on

T2DM. This may be an important issue when considering the results of the current study, as

there are notable associations between biomarkers of T2DM and age in the current analysis.

Additionally, there are considerable associations between age and the foods consumed, as seen

in both the results of the current FA and RRR analysis. As such, in the current RRR analysis

which relied upon biomarkers of T2DM to identify dietary patterns, age had an effect on the

foods consumed, the levels of biomarkers, as well as the risk of incident T2DM, and these

strong age effects may have overwhelmed the RRR diet pattern associations that have been

observed in other studies. Finally, the participants in this study are from a population with a

strong predisposition for T2DM. As such, the effect of food choices and dietary patterns may

have a more limited effect on incident T2DM when compared to other populations with a lesser

genetic predisposition and impact of non-dietary risk factors.

75

Table 15. Comparison of dietary patterns positively associated with an outcome of type 2

diabetes mellitus, identified by reduced rank regression analysis.

Intermediate Response Variables Predictability Schulze et al

8

High

Sugar-sweetened soft drinks

Refined grains

Diet soft drinks

Processed Meat

Low

Wine

Coffee

Cruciferous vegetables

Yellow vegetables

sTNFR2

IL-6

CRP

E-selectin

sICAM-1

sVCAM-1

3.09 (1.99, 4.79)*

McNaughton et al9

High

Low calorie/diet soft drinks

Onions

Sugar-sweetened beverages

Burgers and sausages

Crisps and other snacks

White bread

Low

Medim-/high-fiber breakfast cereals Jam

French dressing/vinaigrette

Wholemeal bread

HOMA-IR 1.51 (1.10, 2.09)†

Liese et al10

High

Red meat

Low-fiber bread and cereal

Dried beans

Fried potatoes

Tomato vegetables

Eggs

Cheese Cottage Cheese

Low

Wine

Fibrinogen

PAI-1 4.51 (1.60, 12.69)*

sTNFR2= Soluble tumour necrosis factor-alpha receptor 2; IL-6=Interleukin-6; CRP= C-reactive protein; sICAM-1= Soluble intracellular cell adhesion molecule 1; sVCAM-1= Soluble vascular cell adhesion molecule 1;

HOMA-IR=Homeostasis model assessment of insulin resistance; PAI-1= Plasminogen activation inhibitor-

1;*Multivariate-adjusted odds ratio comparing extreme quintiles; † Multivariate-adjusted hazard ratio comparing

extreme quintiles;

A few studies have attempted to compare RRR to other dietary pattern analysis techniques,

including papers by Hoffmann et al11

, Nettleton et al12

, DiBello et al13

, and Vujkovic et al14

;

76

however, all of these papers have compared RRR to principal components analysis (PCA) (as

well as partial least squares [PLS] in the paper by DiBello et al13

). The comparison by

Hoffmann et al11

used nutrients as intermediate response variables in the RRR analysis,

including percent energy from: saturated fat, monounsaturated fat, polyunsaturated fat, protein,

and carbohydrate to predict all-cause mortality. It was concluded that RRR was more

appropriate than PCA for that study because the primary RRR-derived pattern was associated

with all-cause mortality in all models, whereas the primary PCA-derived pattern was only

associated with the outcome in the minimally adjusted model11

. Nettleton et al12

used CRP, IL-6,

fibrinogen, and homocysteine as intermediate response variables to predict high internal and

common carotid intima media thickness, and coronary artery calcium. The RRR pattern was

significantly positively associated with only coronary artery calcium in the adjusted model,

while the PCA pattern was not significantly associated with any of the outcomes in any of the

models (both comparing highest category of pattern score to the reference category)12

. Vujkovic

et al14

used maternal biomarkers (total plasma homocysteine, serum folate, red blood cell folate,

whole blood vitamin B6, and serum vitamin B12) to predict spina bifida in offspring, and found

that PCA and RRR derived a similar primary pattern, which was named the “Mediterranean”

diet. Low scores for both the PCA- and RRR-derived Mediterranean diets were associated with

a significantly increased risk of spina bifida in the offspring compared to higher quartiles of

Mediterranean diet scores in the unadjusted models; however, only the RRR-derived pattern

predicted the outcome in the multivariate-adjusted model14

. DiBello et al13

compared PCA, RRR

and PLS, using adipose tissue levels of α-linolenic and trans fatty acids and intake of saturated

fat, fibre, and folate as intermediate response variables in the RRR and PLS analyses. All three

of the DPA techniques identified primary patterns that had significant negative associations with

myocardial infarction; however, of the five patterns identified by each of the techniques, it was

reported that more of the patterns identified by PCA and PLS had significant associations with

the outcome than those identified by RRR13

.

As such, this is the first known study to present results from both RRR and exploratory FA with

regard to dietary patterns and their ability to predict chronic disease, such as T2DM. In light of

the considerations, including age, which have limited the usefulness of RRR in the Sandy Lake

group, a preliminary conclusion might be that FA is a preferable dietary pattern analysis

technique in this very high-risk population.

77

4.3 Dietary Pattern Analysis: Methodological Considerations

FA is the most widely used data-reduction technique in dietary pattern analysis. It identifies

patterns based on the explained common variance amongst food groups (often items or

categories on FFQs). The strength of this method is that it identifies patterns of foods which are

commonly consumed together. Using an example from the current analysis, it may be said that

those who eat carrots, also tend to eat peas, corn, other vegetables, whole wheat bread, milk, and

macaroni (as described by the Balanced Market Foods pattern). However, since FA does not

consider outside factors such as biomarkers of disease pathologies, it is quite possible that

patterns derived by exploratory FA may not be associated with disease risk. Alternatively, RRR

identifies dietary patterns based on the explained variation between the food groups and a

priori-selected intermediate response variables (often biomarkers associated with disease

pathologies). As such, RRR dietary patterns are expected to have stronger associations with

diseases associated with the selected intermediate response variables. However, since RRR

patterns are not based on the variation amongst the food groups, the foods which load most

heavily upon the RRR dietary patterns may not represent a common dietary pattern (ie. a dietary

pattern may be identified which is not typically consumed by any, or many people in the given

population group).

Recently, Imamura et al15

published a paper examining the generalizability of dietary patterns

identified using RRR to predict T2DM. They used dietary data from the Framingham Offspring

Study (FOS) cohort and developed RRR-derived patterns using the diet patterns identified in

three previous studies by Schulze et al8 (NHS), Heidemann et al

7 (EPIC), and McNaughton et

al9 (Whitehall II Study). Imamura et al

15 found that the NHS dietary pattern score was as

predictive of T2DM as the newly-calculated dietary pattern score from the FOS data; however,

the dietary pattern scores derived from the EPIC7 and Whitehall II

9 studies were significantly

less predictive. This result indicates that RRR-derived dietary patterns may not be generalizable

across population groups – a logical conclusion since the biomarkers used as intermediate

response variables may also vary in their ability to predict chronic disease across ethnicities,

age, sex, and other demographic variables.

Interestingly, a recent study by DiBello et al13

which compared principal components analysis

(PCA) to partial least squares (PLS) and RRR, found that PCA and PLS derived more dietary

78

patterns significantly associated with the selected outcome (first myocardial infarction) than

RRR. One of the possible explanations for this finding is that RRR may be superior to PCA and

related methods (ie. FA) when diet and a specific disease pathology or nutrients are being

investigated simultaneously for their effect on disease outcome; however, when the disease

pathway or key nutrients are unclear or their measures unavailable, RRR may in fact limit the

ability of dietary patterns to predict disease outcomes13

. As such, in addition to the influence of

age affecting the usefulness of RRR in the current analysis, the intermediate biomarkers

employed may not have been optimal in their ability to identify dietary patterns and food groups

which are most influential in triggering T2DM. Perhaps use of biomarkers of endothelial

dysfunction such as adhesion molecules, or coagulation and fibrinolytic factors would have

produced more significant RRR-derived results. However, since T2DM risk (and presumably its

biomarkers) increases with age, it is likely that regardless of the intermediate response variables

selected, age would have explained a large proportion of the pattern of results seen amongst the

varied age range of Sandy Lake Health and Diabetes Project participants.

4.4 Potential Mechanisms

In the current analysis, the Beef & Processed Foods pattern identified by FA was associated

with a 38% increased risk of incident T2DM at follow-up (in the fully-adjusted, multivariate

model), and was characterized by market foods which are high in sugar, and/or fat (especially

saturated and trans fats), and low in fibre. Consumption of pop, which was the highest loading

FFQ item on the Beef & Processed Foods pattern, has been associated with increased weight

gain, risk of obesity, and T2DM16, 17

. As well, foods with a high glycemic index (GI), such as

pop, cake, cookies, pastry, chocolate, candy, canned fruit, white bread, and chips, contribute to a

high glycemic load (GL) which has been linked with insulin resistance in animal models18

, and

T2DM in humans (especially among diets low in cereal fibre19, 20

). Additionally, the fat quality

of foods may impact risk of T2DM18

as Vessby et al21

observed a 10% decrease in insulin

sensitivity in individuals consuming a diet high in saturated fat versus a diet high in

monounsaturated fat (and low in saturated fat). Therefore, the saturated fat provided by

prominent foods in the Beef & Processed Foods pattern may play a role in the pattern’s positive

association with incident T2DM. Finally, the low fibre and whole grain content of the Beef &

79

Processed Foods pattern may contribute to the pattern’s positive association with T2DM, since

diets high in whole grains and fibre (especially cereal fibre), appear to protect against T2DM18,

22, 23.

Surprisingly, non-significant odds ratios were observed in the current study amongst the FA-

derived Balanced Market Foods and Traditional Foods patterns identified by factor analysis.

The Balanced Market Foods pattern was expected to exert some protective effects with regard to

risk of T2DM because of its high vegetable content (other vegetables, carrots, peas, and corn).

Similarly, the Traditional Foods pattern was expected to protect against T2DM because many of

the foods with the greatest loadings on the pattern (fish, moose, duck, berries, rabbit) were not

processed, and were typically foods associated with hunting and gathering, and thus physical

activity. However, it was noted by Gittelsohn et al4, that the absence of a negative association

between a traditional-type pattern and risk of T2DM may be due in part to the setting in which

traditional foods are consumed, which is typically at feasts. As such, those who are consuming

the traditional foods may not necessarily be the same individuals who are hunting and gathering.

Low frequency of consumption of these foods could also attenuate the protective effect that

these traditional foods may have on risk of T2DM and associated risk factors. Finally, it is

important to note the significant associations amongst the identified patterns themselves because

of the oblique rotation which was employed in the current analysis. As such, foods, as well as

entire patterns, which may be positively associated with risk of T2DM (such as the Beef &

Processed Foods pattern) may have exerted effects on the Balanced Market Foods and

Traditional Foods patterns (which may have otherwise been demonstrated to protect against risk

of T2DM).

In the RRR analysis, the Tea & Fibre pattern was associated with a 31% increased risk of

T2DM in the unadjusted model. However, once adjusted for age (and sex), the point estimate

decreased from 1.31 to 1.08, and significance was lost. As such, it seems that age explains much

of the association between the Tea & Fibre pattern and T2DM risk. Interestingly, tea, peas and

hot cereal, which had the most prominent positive loadings on the Tea & Fibre pattern, were

significantly positively associated with age (data not shown), which is a well-known risk factor

for T2DM. In this particular sample, participants ranged in age from 10 to 79 years, which

represents a much greater age range than what is usually seen in studies of incident T2DM.

Most studies examining T2DM prospectively are based on samples ranging in age from 30 to 40

80

years up to 55 to 75 years; therefore, in most studies, the influence of age may not be as great as

seen in this particular study. The strong influence of age on T2DM risk, as well as the

biomarkers used as intermediate response variables, in this study sample is evident when

examining the difference in the odds ratios between the unadjusted model and the age and sex-

adjusted model, particularly when using the RRR-driven pattern scores. The strong influence of

age in the RRR-driven patterns over the FA-driven patterns may be attributed to the age effect

on both the intermediate biomarker variables and the FFQ items, whereas the FA-driven pattern

would only be affected by an age effect on the FFQ items. The Tea & Fibre pattern identified by

RRR was also characterized by foods which are higher in cereal fibre and lower in energy

density compared to the foods which were prominent in the Traditional and Proto-Historic

patterns; however, paradoxically, the Tea & Fibre pattern was significantly associated with an

increased risk of incident T2DM at follow-up. Interestingly, the tea which was most prominent

in the Tea & Fibre pattern was highly positively correlated with white bread, canned milk, as

well as lard (data not shown). Therefore the tea’s association with high GI and high-saturated

fat-containing foods may have also contributed to the pattern’s positive association with T2DM

risk.

4.5 Strengths and Limitations

This study has a number of strengths and limitations that should be considered when

interpreting the findings.

4.5.1 Strengths

The Sandy Lake Health and Diabetes Project is a 10-year prospective study examining incident

T2DM, based on a well-characterized, isolated Aboriginal Canadian population. Since the study

was designed to examine T2DM, the available anthropometric and biochemical measures,

including OGTTs to determine T2DM status, for use in these analyses are relevant to T2DM.

There was a high participation rate of 72% at baseline and excellent return rate of 89% in a

challenging environment where loss to follow-up can be quite burdensome. Ethnographic

interviews and pilot-testing were used in developing the FFQ, thus ensuring that it was

81

culturally appropriate and included both traditional and store-bought food items available in the

community. Oral glucose tolerance tests (OGTTs), which are the gold-standard in identifying

cases of T2DM in epidemiologic studies, were used to identify cases of T2DM at both baseline

and follow-up. A variety of biochemical measures related to T2DM risk were available for use

as intermediate response variables in the RRR analysis. Finally, the isolation of the study

population may be seen as a strength because isolation contributes to a lesser variety of foods

consumed compared to more urban populations, thus contributing to the identification of well-

defined dietary patterns, particularly by FA.

4.5.2 Limitations

The use of an FFQ with a short food list (34 items) may be considered a limitation in this study,

as a meta-analysis of FFQ validation studies published in 2007 concluded that FFQs with more

items (ie. longer FFQs) are better equipped for ranking study participants by their dietary

intake24

. Since the premise of the current study was to rank participants by their dietary intake in

the context of identified dietary patterns, the length of the employed FFQ may have resulted in

misclassification of dietary exposure. As well, the manner in which food items were grouped in

the FFQ (eg. where the FFQ item described as “pork” may include lean pork loin as well as pan-

fried bacon) could have had an impact on the identification of the dietary patterns in addition to

the ranking of individuals based on their intake and adherence to the patterns (eg. where a study

participant consuming lean pork regularly may have a similar pattern score to a study participant

consuming pan-fried bacon regularly despite considerable differences in the saturated fat

content of their diets) . Additionally, although the employed FFQ was developed using

ethnographic interviews and was pilot-tested in the studied population group, the FFQ was not

validated; therefore the accuracy, validity, and reliability of the FFQ in this research setting are

not known. The non-quantitative nature of the 34-item FFQ limited the ability to adjust for

covariates in the logistic regression analysis of the association between dietary pattern scores

and incident T2DM, since energy and other dietary characteristics (such as dietary fibre,

saturated fat, and glycemic index) could not be considered. As mentioned in the description of

the strengths of the study, the isolated nature of the community may also be considered a

limitation, since it limits the variety of foods available. A limited availability of high fibre, low-

fat foods in the community may have resulted in a limited ability to identify dietary patterns that

82

are negatively associated with T2DM as that seen in an analysis by Heidemann et al7. As well,

the limited availability of biomarkers believed to play a relevant role in the patho-physiology of

T2DM may have limited the usefulness of RRR in this study, since the success of RRR in

identifying relevant patterns appears to rely heavily upon the selection of appropriate

intermediate response variables (often biomarkers of disease). Measures of adhesion molecules

such as ICAM and VCAM, or coagulation and fibrinolytic factors such as fibrinogen and PAI-1

may have provided more predictive intermediate response variables for RRR analysis. Finally,

the limited sample size restricted the ability to stratify the sample by age intervals or categories

of pattern scores to calculate ORs for ordered categories of age or diet pattern scores and

incident T2DM.

4.6 Future Directions

These analyses based on the Sandy Lake Health and Diabetes Project data provided insight into

the dietary patterns consumed by this isolated Aboriginal Canadian community at baseline, and

related the observed dietary patterns to outcomes of T2DM at follow-up, ten years later.

Additionally, this study employed two a posteriori approaches to dietary pattern analysis: FA

and RRR. Although the employed techniques were not compared empirically, interpretation of

the findings of each analysis technique has lead to some qualitative comparison. Future analyses

in the same population may include repeating the current analysis using more definitive diet

measures and considering other intermediate biomarkers such as markers of coagulation and

fibrinoytic factors as utilized by Liese et al10

. As well, other DPA techniques may be employed

and compared, including cluster analysis, PCA, and partial least squares (PLS) analysis.

RRR should be studied further to better understand its strengths and limitations in predicting

risk of various chronic diseases in a variety of populations using a variety of intermediate

response variables.

83

4.7 Conclusion

Analysis of FFQ data from the Sandy Lake Health and Diabetes Project identified two different

sets of dietary patterns using FA and RRR. Patterns identified by both dietary pattern analysis

techniques were associated with risk of T2DM risk at follow-up; however, only the pattern

identified by FA had a significant OR when adjusted for covariates. In this setting, where age is

very influential due to its wide range and impact on exposures, covariates and outcomes, FA

may be better choice than RRR for identifying dietary patterns related to incident T2DM. FA

identified a dietary pattern characterized by beef and processed foods which was associated with

a 38% increased risk of T2DM after adjusting for age, sex, WC, and novel biomarkers

associated with T2DM (IL-6 and adiponectin). Dietary patterns identified using RRR did not

produce significant ORs with the exception of the Tea & Fibre pattern, which was associated

with a 31% increased risk of T2DM in the unadjusted model. However, once adjusted for age

(and other covariates), the association was attenuated and lost significance due to the strong

influence of age on disease risk and the pattern itself. In conclusion, a dietary pattern

characterized by pop, klik, cake/cookies/pastry, chocolate/candy, canned fruit, canned milk,

beef, white bread, chips/fries, and lard was significantly associated with incident T2DM. FA

may be more appropriate than RRR in this setting where available diet, exposure measures, and

incident of T2DM is so greatly influenced by age, although additional study of this issue is

warranted.

84

4.8 References

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for type 2 diabetes mellitus in U.S. men. Ann Intern Med 2002; 136:201-9.

2. Fung TT, Schulze M, Manson JE, Willett WC, Hu FB. Dietary patterns, meat intake and

the risk of type 2 diabetes in women. Arch Intern Med. 2004;164:2235-40.

3. Montonen J, Järvinen R, Heliövaara M, Reunanen A, Aromaa A, Knekt P. Food

consumption and the incidence of type II diabetes mellitus. European Journal of Clinical

Nutrition. 2005; 59: 441-8.








Epidemiol 2004; 159: 935-44.




Diabetologia 2005; 48:1126-34.

8. Schulze MB, Hoffmann K, Manson JE, Willett WC, Meigs JB, Weikert C, Heidemann

C, Colditz GA, Hu FB. Dietary pattern, inflammation, and incidence of type 2 diabetes

in women. American Journal of Clinical Nutrition. 2005;82:675-84.

9. McNaughton SA, Mishra GD, Brunner EJ. Dietary patterns, insulin resistance, and

incidence of type 2 diabetes in the Whitehall II study. Diabetes Care. 2008;31:1343-8.

10. Liese AD, Weis KE, Schulz M, Tooze JA. Food intake patterns associated with incident

type 2 diabetes. Diabetes Care. 2009;32:263-8.

11. Hoffmann K, Boeing H, Boffetta P, Nagel G, Orfanos P, Ferrari P, Bamia C.

Comparison of two statistical approaches to predict all cause mortality by dietary

patterns in German elderly subjects. British Journal of Nutrition. 2005;93:709-16.

12. Nettleton JA, Steffen LM, Schulze MB, Jenny NS, Barr RG, Bertoni AG, Jacobs DR Jr.

Associations between markers of subclinical atherosclerosis and dietary patterns derived

by principal components analysis and reduced rank regression in the Multi-Ethnic Study

of Atherosclerosis (MESA). Am J Clin Nutr. 2007;85:1615-25.

13. DiBello JR, Kraft P, McGarvey ST, Goldberg R, Campos H, Baylin A. Comparison of 3

methods for identifying dietary patterns associated with risk of disease. American

Journal of Epidemiology. 2008;168:1433-43.

14. Vujkovic M, Steegers EA, Looman CW, Ocké MC, Steegers-Theunissen RP. The

maternal Mediterranean dietary pattern is associated with a reduced risk of spina bifida

in the offspring. 2008;116:408-15.

15. Imamura F, Lichtenstein AH, Dallal GE, Meigs JB, Jacques PF. Generalizability of

dietary patterns associated with incidence of type 2 diabetes mellitus. Am J Clin Nutr.

2009;90:1075-83.

16. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: a

systematic review. Am J Clin Nutr. 2006;84:274-88.

85

17. Schulze MB, Manson JE, Ludwig DS, Colditz GA, Stampfer MJ, Willett WC, Hu FB.

Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and

middle-aged women. JAMA. 2004;292:927-34.

18. Hu FB, van Dam RM, Liu S. Diet and risk of Type 2 diabetes: the role of types of fat

and carbohydrate. Diabetologia. 2001; 44: 805-17.

19. Salmeron J, Ascherio A, Rimm, EB, Colditz GA, Spiegelman D, Jenkins DJ, Stampfer

MJ, Wing AL, Willett WC. Dietary fiber, glycemic load, and risk of NIDDM in men.

Diabetes Care. 1997;20:545-50.

20. Salmeron J, Manson JE, Stampfer MJ, Colditz GA, Wing AL, Willett WC. Dietary fiber,

glycemic load, and risk of non-insulin-dependent diabetes mellitus in women. JAMA

1997;277:472-7.

21. Vessby B, Uusitupa M, Hermansen K, Riccardi G, Rivellese AA, Tapsell LC, Nälsén C,

Berglund L, Louheranta A, Rasmussen BM, Calvert GD, Maffetone A, Pederson E,

Gustafsson I-B, Storlien LH. Substituting dietary saturated for monounsaturated fat

impairs insulin sensitivity in healthy mean and women: The KANWU study.

Diabetologia 2001; 44: 312-9.

22. Meyer KA, Kushi LH, Jacobs DR Jr, Slavin J, Sellers TA, Folsom AR. Carbohydates,

dietary fiber, and incident Type II diabetes in older women. Am J Clin Nutr.

2000;71:951-30.

23. Liu S, Manson JE, Stampfer MJ, Hu FB, Giovannucci E, Colditz GA, Hennekens CH,

Willett WC. A prospective study of whole-grain intake and risk of Type II diabetes

mellitus in US women. Am J Public Health 2000;90:1409-15.

24. Molag ML, de Vries JHM, Ocké MC, Dagnelie PC, van den Brandt PA, Jansen MCJF,

van Staveren WA, van’t Veer P. Design characteristics of food frequency questionnaires

in relation to their validity. Am J Epidemiol. 2007;166:1468-78.

86

Appendix A

Reduced Rank Regression with Only Age as an Intermediate Response Variable

Table A1. Pattern name, FFQ items in the pattern, and percent common variation identified by

reduced rank regression analysis using data from the Sandy Lake Health and Diabetes Project.


Tea & Hot Cereal

Tea Hot Cereal (Pop) (Chips/French Fries) (Chocolate/Candy) (Cold Cereal)

40.58

Intermediate response variable: age; Foods with factor loadings >= 0.20 are shown for simplicity since those foods

were considered when pattern was named. ( ) denotes negative factor loadings

87

Table A2. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health


FFQ Items Tea & Hot Cereal

Fish 13

Moose 2

Beef -13 Pork 7

Duck 1

Rabbit -14

Klik -13

Eggs 8

Lard 16

Margarine 4

Cold Cereal -26

Hot Cereal 27

Beans 11

White Bread -3

Whole Wheat Bread 2 Bannock 12

Macaroni -2

Indian Tea -2

Soup 13

Chips/French Fries -37

Other Potatoes 17

Peas 15

Corn -3

Carrots 7

Other Vegetables 4

Berries 3 Fresh Fruit -12

Canned Fruit -9

Milk -16

Canned Milk 8

Pop -45

Tea 33

Cookies/Cakes/Pastries -12

Chocolate/Candy -36

Intermediate response variable: age; Loadings shown as loading*100 for simplicity; Loadings >= 20 bolded

88

Table A3. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Tea & Hot Cereal pattern score as determined by reduced

rank regression analysis.

Tea & Hot Cereal Pattern Score

T1 T2 T3 p-value

n 156 161 158 -

Age (years)* 18.7±6.7 24.8±10.3 36.8±14.0 <0.0001

Sex, Male/Female†β 70/86 (44.9/55.1) 63/98 (39.1/60.9) 68/90 (43.0/57.0) 0.5711

Anthropometry*

Height (cm) 165.2±11.3 165.0±10.6 167.1±8.4 0.1234

Weight (kg) 67.6±20.1

71.5±17.6 77.2±16.0 <0.0001

BMI (kg/m²) 24.5±5.9 26.1±5.7 27.6±5.1 <0.0001

Percent Body Fat (%) 30.5±13.4 35.3±13.6 36.6±11.1 <0.0001


Blood Pressure

Systolic (mmHg)‡ 111.0 (101.0-119.0) 113.0 (102.5-120.0) 118.3 (110.0-129.0) <0.0001

Diastolic (mmHg)* 60.8±10.2 65.4±11.2 69.4±12.8 <0.0001

MAP (mmHg)‡ 76.3 (72.0-81.3) 80.7 (74.0-86.7) 85.1 (78.0-94.2) <0.0001

Hypertension†§ β 13 (8.3) 24 (14.9) 45 (28.5) <0.0001

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.47 5.4±0.48 5.5±0.52 0.0246

2hPG (mmol/l)* 5.2±1.51 5.6±1.71 6.08±1.93 <0.0001

FI (mmol/l)‡ 92.5 (57.5-125.0) 95.0 (69.0-150.0) 110.0 (73.0-146.0) 0.0358

IGT†¶ β 7 (4.5) 16 (9.9) 35 (22.2) <0.0001

IFG †|| β 9 (5.8) 11 (6.8) 12 (7.6) 0.8106

Adipokines‡

CRP (mg/l) 0.81 (0.23-2.94) 1.81 (0.50-5.05) 2.62 (1.22-6.11) <0.0001

IL-6 (ng/l) 0.56 (0.32-0.98) 0.75 (0.38-1.38) 0.82 (0.39-1.44) 0.0202

Adiponectin (μg/l) 16.1 (11.7-21.5) 12.7 (10.2-18.1) 13.2 (9.39-17.6) 0.0036

Leptin (ng/ml) 8.60 (4.05-16.9) 13.3 (5.70-21.1) 12.7 (7.40-21.3) 0.0008


Intermediate response variables: age; n of subjects for each characteristic may vary due to occasional missing

values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for categorical variables; §

Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure of >=85 mmHg or

participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance defined as fasting plasma

glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial

pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-

values calculated using ANOVA (non-normally distributed were log-transformed) for continuous variables, Chi-

Square for dichotomous variables.

89

Table A4. Spearman rank correlation coefficients of the relationship between baseline



Tea & Hot Cereal

Crude Age-

Adjusted

Age (years) *0.63 -

Anthropometry

Height (cm) 0.08 ‡-0.13

Weight (kg) *0.25 ‡-0.11

BMI (kg/m²) *0.26 -0.05

Percent Body Fat (%) *0.20 0.00

Waist Circumference (cm) *0.34 -0.04

Blood Pressure

Systolic (mmHg) *0.28 -0.03

Diastolic (mmHg) *0.30 0.05

MAP (mmHg) *0.34 0.04

Lipid Profile

HDL Cholesterol (mmol/l) 0.00 0.01

LDL Cholesterol (mmol/l) *0.39 0.09

Triglycerides (mmol/l) *0.26 0.05

Glucose Homeostasis

FPG (mmol/l) ‡0.14 0.01

2hPG (mmol/l) *0.20 0.08

FI (mmol/l) ‡0.13 0.07

Adipokines

CRP (mg/l) *0.30 0.01

IL-6 (ng/l) ‡0.13 0.05

Adiponectin (μg/l) †-0.17 -0.00

Leptin (ng/ml) †0.16 0.06

Intermediate response variables: age; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour

post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001; ‡ p<0.05

90

Table A5. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived dietary pattern score and incident type 2 diabetes using data from the Sandy


Model Tea & Hot Cereal

Unadjusted 1.33

(1.08, 1.65)*

Model 1 1.06

(0.81, 1.39)

Model 2 1.33

(1.08, 1.64)*

Model 3 1.05

(0.80, 1.39)

Intermediate response variables: age; ORs presented per unit increase in pattern score; Model 1 – Adjusted for age

and sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin;

*p<0.05

91

Appendix B

Reduced Rank Regression Analysis with Highly Correlated Variables (Waist Circumference and Fasting Serum Insulin) as

Intermediate Response Variables

Table B1. Pattern names, FFQ items in each pattern, and percent total variation explained by

each pattern, determined using reduced rank regression using data from the Sandy Lake Health



Tea, Hot Cereal & Peas

Tea Hot Cereal Peas (Chocolate/Candy)

(Chips/French Fries) (Beef) (Pop) (Berries) (Rabbit)

10.44

Cereal, Soup & Chocolate

Hot Cereal Cold Cereal

Milk Berries Soup Chocolate/Candy Corn (Macaroni) (Whole Wheat Bread)

2.70

Intermediate response variables: waist circumference, fasting serum insulin; Foods with factor loadings >= 0.20 are

shown for simplicity since those foods were considered when patterns were named. ( ) denotes negative factor

loadings

92

Table B2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by


FFQ Items Tea, Hot Cereal & Peas Cereal, Soup & Chocolate

Fish -0.03 0.07

Moose -0.17 0.09

Beef -0.24 0.18

Pork -0.04 0.01

Duck -0.17 0.05

Rabbit -0.22 0.17

Klik -0.15 0.05

Eggs 0.15 -0.19

Lard 0.12 0.04

Margarine -0.00 -0.18 Cold Cereal -0.16 0.34

Hot Cereal 0.26 0.38

Beans 0.20 0.07

White Bread 0.09 -0.03

Whole Wheat Bread 0.12 -0.22

Bannock -0.06 -0.07

Macaroni -0.13 -0.27

Indian Tea -0.10 -0.17

Soup 0.13 0.24

Chips/French Fries -0.30 0.00

Other Potatoes 0.06 0.14

Peas 0.24 -0.14 Corn -0.05 0.21

Carrots 0.05 -0.03

Other Vegetables 0.14 0.12

Berries -0.22 0.24

Fresh Fruit -0.06 0.17

Canned Fruit -0.13 0.19

Milk -0.04 0.29 Canned Milk 0.03 -0.04

Pop -0.24 -0.07

Tea 0.37 0.00

Cookies/Cakes/Pastries -0.16 0.03 Chocolate/Candy -0.33 0.23

Intermediate response variables: waist circumference, fasting serum insulin; Loadings shown as loading*100 for


93

Table B3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Tea, Hot Cereal & Peas pattern score as determined by


Tea, Hot Cereal & Peas Pattern Score

T1 T2 T3 p-value

n 157 160 159 -

Age (years)* 19.4±9.4 26.4±12.0 34.3±13.1 <0.0001

Sex, Male/Female†β 63/94 (40.1/59.9) 66/94 (41.3/58.8) 73/86 (45.9/54.1) 0.5431

Anthropometry*

Height (cm) 163.6±10.7 165.3±11.1 168.2±8.3 0.0003

Weight (kg) 65.2±19.5 72.2±17.6 78.8±15.3 <0.0001

BMI (kg/m²) 24.0±5.8 26.3±5.7 27.8±4.9 <0.0001

Percent Body Fat (%) 30.4±13.7 35.2±13.5 36.8±10.6 <0.0001


Blood Pressure

Systolic (mmHg)‡ 111.0 (101.0-119.0) 112.0 (103.3-120.0) 118.0 (111.0-126.0) <0.0001

Diastolic (mmHg)* 61.9±10.1 64.4±11.5 69.2±12.8 <0.0001

MAP (mmHg)‡ 76.7 (72.5-83.7) 79.4 (73.7-87.3) 84.7 (78.0-93.0) <0.0001

Hypertension†§ β 15 (9.6) 25 (15.6) 42 (26.4) 0.0003

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.49 5.3±0.48 5.5±0.49 0.0386

2hPG (mmol/l)* 5.4±1.49 5.6±1.82 5.9±1.91 0.0563

FI (mmol/l)‡ 89.0 (57.0-130.0) 99.0 (71.0-133.0) 108.0 (75.0-154.0) 0.0119

IGT†¶ β 11 (7.0) 17 (10.6) 30 (18.9) 0.0042

IFG †|| β 11 (7.0) 7 (4.4) 14 (8.8) 0.2828

Adipokines‡

CRP (mg/l) 0.76 (0.21-2.47) 1.86 (0.52-5.54) 2.84 (1.34-5.64) <0.0001

IL-6 (ng/l) 0.61 (0.31-1.04) 0.70 (0.37-1.33) 0.82 (0.38-1.38) 0.01530

Adiponectin (μg/l) 16.2 (11.9-22.1) 13.9 (10.5-18.0) 12.3 (8.74-16.2) <0.0001

Leptin (ng/ml) 9.50 (4.20-17.1) 11.7 (5.95-20.6) 13.0 (7.00-23.1) 0.0032


Intermediate response variables: waist circumference, fasting serum insulin; n of subjects for each characteristic

may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square

test for categorical variables; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood

pressure of >=85 mmHg or participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance

defined as fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose

<7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma

glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-

transformed) for continuous variables, Chi-Square for dichotomous variables.

94

Table B3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Cereal, Soup & Chocolate pattern score as determined by


Cereal, Soup & Chocolate Pattern Score

T1 T2 T3 p-value

n 156 160 159 -

Age (years)* 28.0±11.8 26.6±13.1 25.8±14.3 0.3287

Sex, Male/Female†β 73/83 (46.8/53.2) 70/90 (43.8/56.3) 58/101 (36.5/63.5) 0.1623

Anthropometry*

Height (cm) 167.5±9.7 165.4±10.3 164.2±10.4 0.0148

Weight (kg) 74.1±18.0 73.0±19.1 69.2±17.5 0.0437

BMI (kg/m²) 26.2±5.5 26.5±6.1 25.5±5.4 0.2665

Percent Body Fat (%) 33.6±13.0 34.7±13.6 34.1±12.4 0.7527


Blood Pressure

Systolic (mmHg)‡ 115.0 (106.5-121.3) 114.8 (103.8-121.3) 113.0 (103.5-120.0) 0.5709

Diastolic (mmHg)* 66.4±12.1 65.0±11.5 64.1±12.2 0.2478

MAP (mmHg)‡ 81.3 (74.2-88.3) 79.4 (74.0-87.9) 79.2 (73.3-85.3) 0.2626

Hypertension†§ β 31 (19.9) 32 (20.0) 19 (12.0) 0.0942

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.48 5.4±0.51 5.4±0.49 0.3726

2hPG (mmol/l)* 5.6±1.73 5.5±1.79 5.8±1.75 0.2665

FI (mmol/l)‡ 84.0 (62.0-125.0) 102.0 (65.0-140.0) 106.5 (82.0-160.0) 0.0002

IGT†¶ β 17 (10.9) 18 (11.3) 23 (14.5) 0.5647

IFG †|| β 7 (4.5) 14 (8.8) 11 (6.9) 0.3171

Adipokines‡

CRP (mg/l) 1.88 (0.59-5.05) 1.72 (0.44-4.51) 1.63 (0.36-4.45) 0.8496

IL-6 (ng/l) 0.72 (0.38-1.35) 0.65 (0.32-1.17) 0.70 (0.38-1.29) 0.7481

Adiponectin (μg/l) 13.9 (10.6-18.8) 13.5 (9.80-18.1) 14.2 (10.9-19.8) 0.4387

Leptin (ng/ml) 10.2 (4.20-19.8) 11.3 (6.60-20.3) 11.2 (6.20-20.7) 0.4819


Intermediate response variables: waist circumference, fasting serum insulin; n of subjects for each characteristic

may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square

test for categorical variables; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood

pressure of >=85 mmHg or participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance

defined as fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose

<7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma

glucose; FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-

transformed) for continuous variables, Chi-Square for dichotomous variables.

95

Table B4. Spearman rank correlation coefficients of the relationship between baseline



Tea, Hot Cereal & Peas Cereal, Soup & Chocolate

Crude Age-

Adjusted Crude

Age-

Adjusted

Age (years) *0.55 ‡-0.12

Anthropometry

Height (cm) *0.18 0.02 †-0.16 †-0.16

Weight (kg) *0.36 0.09 ‡-0.10 -0.06

BMI (kg/m²) *0.34 0.09 -0.03 0.03

Percent Body Fat (%) *0.23 0.06 0.04 ‡0.10

Waist Circumference (cm) *0.40 0.09 -0.07 -0.00

Blood Pressure

Systolic (mmHg) *0.30 0.05 -0.04 0.01

Diastolic (mmHg) *0.29 0.05 -0.07 -0.02

MAP (mmHg) *0.33 0.06 -0.06 -0.01

Lipid Profile

HDL Cholesterol (mmol/l) -0.06 -0.06 -0.06 -0.06

LDL Cholesterol (mmol/l) *0.36 ‡0.11 -0.07 -0.01

Triglycerides (mmol/l) *0.28 ‡0.12 0.04 0.08

Glucose Homeostasis

FPG (mmol/l) ‡0.12 -0.01 0.08 ‡0.10

2hPG (mmol/l) ‡0.13 0.01 0.07 ‡0.10

FI (mmol/l) ‡0.17 ‡0.13 *0.20 *0.23

Adipokines

CRP (mg/l) *0.36 0.13 -0.04 0.03

IL-6 (ng/l) ‡0.10 0.02 -0.01 0.02

Adiponectin (μg/l) *-0.25 ‡-0.12 0.01 -0.02

Leptin (ng/ml) ‡0.17 0.09 0.08 ‡0.12

Patterns

Tea & Fibre 1.000 1.00 0.01 0.09

Traditional 1.00 1.00

Intermediate response variables: waist circumference, fasting serum insulin; MAP=Mean arterial pressure;

FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001;

† p=<0.001; ‡ p<0.05

96

Table B5. Odds ratios and 95% confidence intervals for the association between reduced rank



Model Tea, Hot Cereal & Peas Cereal, Soup & Chocolate

Unadjusted 1.40

(1.11, 1.78)*

0.86

(0.67, 1.10)

Model 1 1.19

(0.91, 1.55)

0.86

(0.67, 1.10)

Model 2 1.01

(0.76, 1.35)

0.88

(0.68, 1.14)

Model 3 0.99

(0.73, 1.33)

0.90

(0.69, 1.17)

Intermediate response variables: waist circumference, fasting serum insulin; ORs presented per unit increase in pattern score; Model 1 – Adjusted for age and sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted

for age, sex, WC, IL-6, and adiponectin; *p<0.05

97

Appendix C

Reduced Rank Regression Analysis with Uncorrelated Variables (Systolic Blood Pressure and Adiponectin) as Intermediate

Response Variables

Table C1. Pattern names, FFQ items in each pattern, and percent total variation explained by




Regular Tea, Low Junk Foods

Tea (Chips/French Fries) (Chocolate/Candy)

(Pop) (Indian Tea)

10.00

Proto-Historic

Bannock Canned Milk Rabbit Soup

(Chips/French Fries) (Cold Cereal) (Milk)

4.39

Intermediate response variables: systolic blood pressure, adiponectin; Foods with factor loadings >= 0.20 are

shown for simplicity since those foods were considered when patterns were named. ( ) denotes negative factor loadings

98

Table C2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by


FFQ Items

Regular Tea, Low Junk

Foods Proto-Historic

Fish 2 5

Moose -9 2

Beef -16 -7

Pork -5 8

Duck -11 16

Rabbit -18 22

Klik -18 -19

Eggs 18 -7 Lard 13 12

Margarine -7 -7

Cold Cereal -15 -22

Hot Cereal 18 -4

Beans 7 -14

White Bread 9 16

Whole Wheat Bread 13 -17

Bannock 1 50

Macaroni -14 -4

Indian Tea -21 -1

Soup -3 22 Chips/French Fries -38 -31

Other Potatoes 12 20

Peas 20 -6

Corn -9 -17

Carrots 6 -3

Other Vegetables 18 -2

Berries -17 -4

Fresh Fruit -13 2

Canned Fruit -12 15

Milk 8 -21

Canned Milk 7 41

Pop -25 -3 Tea 45 -6

Cookies/Cakes/Pastries -16 -5

Chocolate/Candy -28 -10

Intermediate response variables: systolic blood pressure, adiponectin; Loadings shown as loading*100 for


99

Table C3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Regular Tea, Low Junk Foods pattern score as determined by


Regular Tea, Low Junk Foods Pattern Score

T1 T2 T3 p-value

n 156 160 159 -

Age (years)* 19.9±9.4 26.8±12.0 33.5±13.8 <0.0001

Sex, Male/Female†β 61/95 (39.1/60.9) 66/94 (41.3/58.8) 74/85 (46.5/53.5) 0.3873

Anthropometry*

Height (cm) 164.0±10.9 165.8±9.8 167.4±9.6 0.0118

Weight (kg) 65.8±18.6 72.0±17.8 78.6±16.2 <0.0001

BMI (kg/m²) 24.3±5.8 26.0±5.5 28.0±5.2 <0.0001

Percent Body Fat (%) 31.1±13.6 34.7±13.2 36.8±11.2 0.0005


Blood Pressure

Systolic (mmHg)‡ 109.0 (100.0-117.5) 113.0 (104.5-120.0) 119.0 (111.0-129.0) <0.0001

Diastolic (mmHg)* 60.8±9.5 65.7±11.5 69.1±13.0 <0.0001

MAP (mmHg)‡ 75.5 (71.8-80.6) 81.0 (73.8-86.8) 85.0 (78.0-93.3) <0.0001

Hypertension†§ β 10 (6.4) 29 (18.1) 43 (27.0) <0.0001

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.48 5.4±0.50 5.4±0.50 0.2768

2hPG (mmol/l)* 5.3±1.52 5.6±1.84 5.9±1.84 0.0035

FI (mmol/l)‡ 89.5 (57.0-125.5) 97.0 (69.0-145.0) 111.5 (80.0-152.0) 0.0032

IGT†¶ β 10 (6.4) 21 (13.1) 27 (17.0) 0.0150

IFG †|| β 13 (8.3) 7 (4.4) 12 (7.6) 0.3296

Adipokines‡

CRP (mg/l) 0.82 (0.22-3.07) 1.83 (0.49-5.05) 2.65 (1.27-2.48) <0.0001

IL-6 (ng/l) 0.59 (0.32-1.11) 0.71 (0.36-1.24) 0.82 (0.42-1.42) 0.1506

Adiponectin (μg/l) 16.5 (12.2-22.3) 12.9 (9.91-17.5) 12.6 (8.67-16.2) <0.0001

Leptin (ng/ml) 9.90 (4.15-17.3) 11.4 (5.40-21.3) 13.2 (7.00-21.0) 0.0172


Intermediate response variables: systolic blood pressure, adiponectin; n of subjects for each characteristic may vary

due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for

categorical variables; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure

of >=85 mmHg or participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance defined

as fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l;


FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-transformed) for


100

Table C3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Proto-Historic pattern score as determined by reduced rank

regression analysis.

Proto-Historic Pattern Score

T1 T2 T3 p-value

n 157 160 158 -

Age (years)* 25.3±11.0 26.6±12.4 28.4±15.5 0.1164

Sex, Male/Female†β 62/95 (39.5/60.5) 68/92 (42.5/57.5) 72/86 (45.6/54.4) 0.5513

Anthropometry*

Height (cm) 165.4±10.5 166.2±10.2 165.5±10.1 0.7674

Weight (kg) 73.6±20.0 72.3±17.3 70.5±17.6 0.3048

BMI (kg/m²) 26.7±6.1 26.1±5.5 25.5±5.4 0.2124

Percent Body Fat (%) 35.1±12.9 34.5±13.4 32.7±12.7 0.2281


Blood Pressure

Systolic (mmHg)‡ 112.5 (104.0-120.0) 112.5 (104.0-120.0) 118.0 (104.0-124.5) 0.0016

Diastolic (mmHg)* 64.3±10.6 65.8±12.7 65.4±12.5 0.4866

MAP (mmHg)‡ 80.0 (74.0-86.0) 79.2 (73.3-87.4) 82.4 (74.0-90.7) 0.1164

Hypertension†§ β 18 (11.5) 28 (17.5) 36 (22.8) 0.0291

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.4±0.50 5.4±0.48 5.3±0.49 0.5669

2hPG (mmol/l)* 5.6±1.77 5.5±1.78 5.7±1.74 0.5856

FI (mmol/l)‡ 96.0 (71.0-133.0) 102.0 (64.0-152.0) 96.5 (67.0-135.0) 0.6719

IGT†¶ β 18 (11.5) 19 (11.9) 21 (13.3) 0.8736

IFG †|| β 12 (7.6) 11 (6.9) 9 (5.7) 0.7856

Adipokines‡

CRP (mg/l) 1.75 (0.51-4.71) 1.88 (0.55-5.26) 1.59 (0.39-4.00) 0.3743

IL-6 (ng/l) 0.73 (0.38-1.20) 0.69 (0.36-1.27) 0.66 (0.30-1.29) 0.6357

Adiponectin (μg/l) 12.2 (8.28-16.2) 13.9 (10.6-19.8) 15.5 (11.7-21.0) <0.0001

Leptin (ng/ml) 11.3 (5.95-21.6) 10.9 (5.30-19.8) 10.8 (5.30-20.0) 0.6133


Intermediate response variables: systolic blood pressure, adiponectin; n of subjects for each characteristic may vary

due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for

categorical variables; § Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure

of >=85 mmHg or participation in antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance defined

as fasting plasma glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l;


FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-transformed) for


101

Table C4. Spearman rank correlation coefficients of the relationship between baseline



Regular Tea, Low Junk


Crude Age-

Adjusted Crude

Age-

Adjusted

Age (years) *0.51 - 0.05 -

Anthropometry

Height (cm) †0.16 -0.00 -0.03 -0.03

Weight (kg) *0.32 0.07 -0.06 ‡-0.09

BMI (kg/m²) *0.31 0.08 -0.06 -0.08

Percent Body Fat (%) *0.20 0.05 -0.05 -0.06

Waist Circumference (cm) *0.37 0.08 -0.05 -0.08

Blood Pressure

Systolic (mmHg) *0.36 †0.16 ‡0.14 ‡0.15

Diastolic (mmHg) *0.31 ‡0.10 0.04 0.02

MAP (mmHg) *0.37 ‡0,15 0.09 0.08

Lipid Profile

HDL Cholesterol (mmol/l) -0.07 -0.08 0.08 0.06

LDL Cholesterol (mmol/l) *0.35 ‡0.12 0.01 -0.03

Triglycerides (mmol/l) *0.29 ‡0.14 -0.01 -0.04

Glucose Homeostasis

FPG (mmol/l) ‡0.11 -0.00 -0.04 -0.05

2hPG (mmol/l) †0.16 0.06 0.05 0.02

FI (mmol/l) *0.18 ‡0.15 0.00 -0.01

Adipokines

CRP (mg/l) *0.30 0.09 -0.04 -0.08

IL-6 (ng/l) ‡0.10 0.03 -0.02 -0.04

Adiponectin (μg/l) *-0.26 ‡-0.14 *0.23 *0.25

Leptin (ng/ml) ‡0.13 0.06 -0.03 -0.05

Patterns

Regular Tea, Low Junk Foods 1.000 1.00 0.03 -0.01


Intermediate response variables: systolic blood pressure, adiponectin; MAP=Mean arterial pressure; FPG=Fasting

plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001;

‡ p<0.05

102

Table C5. Odds ratios and 95% confidence intervals for the association between reduced rank



Model Regular Tea, Low Junk


Unadjusted 1.31

(1.03, 1.65)*

1.15

(0.88, 1.51)

Model 1 1.09

(0.83, 1.42)

1.08

(0.81, 1.42)

Model 2 0.96

(0.72, 1.27)

1.20

(0.90, 1.62)

Model 3 0.94

(0.70, 1.25)

1.32

(0.96, 1.81)

Intermediate response variables: systolic blood pressure, adiponectin; ORs presented per unit increase in pattern score; Model 1 – Adjusted for age and sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age,

sex, WC, IL-6, and adiponectin; *p<0.05

103

Appendix D

Sensitivity Analyses Considering Physical Activity, Physical Fitness, and Current Smoking Status as Covariates

Table D1. Correlation Coefficients for Physical Activity and Fitness Measures

PASTT PMET VO2MAX

PASTT 1.00 0.97 0.32

PMET 1.00 0.36

VO2MAX 1.00

104

Table D2. – Baseline characteristics of participants the Sandy Lake Health and Diabetes Project


No Diabetes Incident Diabetes p-value

n (%) 406 (82.5) 86 (17.5)

Age (years)* 25.4±13.0 31.5±12.4 <0.0001

Sex, Male/Female† 173/233 (42.6/57.4) 34/52 (39.5/60.5) 0.6005 Current Smoker † 258 (84.3) 54 (72.0) 0.0131

Anthropometry*

Height (cm) 165.3±10.4 166.81±9.1 0.2012

Weight (kg) 69.8±18.1 82.0±15.9 <0.0001 BMI (kg/m²) 25.4±5.5 29.4±5.3 <0.0001

Percent Body Fat (%) 33.0±13.2 40.1±10.3 <0.0001

Waist Circumference (cm) 87.8±13.2 98.2±12.2 <0.0001

Physical Activity & Fitness PMET‡ 106.5 (56.6-163.3) 107.5 (45.0-175.8) 0.9496

V02Max‡ 2.48 (2.11-3.43) 2.30 (2.15-3.36) 0.9816

Blood Pressure

Systolic (mmHg)‡ 113.0 (103.5-120.0) 118.0 (110.0-130.0) <0.0001 Diastolic (mmHg)* 64.0±11.5 69.9±12.3 <0.0001

MAP (mmHg)‡ 79.4 (73.3-86.3) 83.9 (77.5-96.3) <0.0001

Hypertension †§ 54 (13.3) 29 (33.7) <0.0001

Lipid Profile HDL Cholesterol (mmol/l)* 1.26±0.28 1.19±0.25 0.0257

LDL Cholesterol (mmol/l)* 2.42±0.74 2.74±0.66 0.0002

Triglycerides (mmol/l)‡ 1.10 (0.81-1.53) 1.48 (1.16-1.82) <0.0001

Glucose Homeostasis FPG (mmol/l)* 5.3±0.46 5.6±0.58 0.0004

2hrPG (mmol/l)* 5.4±1.62 6.5±2.08 <0.0001

FI (mmol/l)‡ 94.0 (66.0-131) 123.0 (91.0-187.0) <0.0001 IGT †¶ 36 (8.9) 23 (26.7) <0.0001

IFG †|| 22(5.4) 10 (11.6) 0.0339

Adipokines‡

CRP (mg/l) 1.45 (0.40-4.28) 2.82 (1.24-7.48) 0.0012 IL-6 (ng/l) 0.67 (0.33-1.23) 0.83 (0.52-1.38) 0.0237

Adiponectin (μg/l) 14.5 (11.0-19.6) 11.0 (8.01-15.1) <0.0001

Leptin (ng/ml) 10.6 (5.20-19.4) 15.0 (9.40-25.7 <0.0001

n of subjects for each characteristic may vary due to occasional missing values. * Mean ± SD and Welch’s t test; † n (%) and Chi-square test; ‡ Median (25th-75th percentile) and Welch’s t test on log transformation;

§ Hypertension defined as systolic blood pressure >=130 mmHg or diastolic blood pressure of >=85 mmHg or

participation in antihypertensive medication therapy; ¶ Impaired glucose tolerance defined as fasting plasma

glucose <7.0 mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l; || Impaired fasting glucose defined

as fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure;

FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting plasma insulin.;

nPMET=70; nVO2Max=45 in T2DM group; nPMET=267; nVO2Max=214 in T2DM-free group.

105

Table D3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Balanced Market Foods pattern score as determined by


Balanced Market Foods Pattern Score

T1 T2 T3 p-value

n 156 161 158 -

Age (years)* 25.7±14.0 27.3±12.6 27.2±12.8 0.4944

Sex, Male/Female†β 67/89 (43.0/57.1) 78/83 (48.5/51.6) 56/102 (35.4/64.6) 0.0620

Smoker † 99 (32.4) 109 (35.6) 98 (32.0) 0.2609

Anthropometry*

Height (cm) 165.0±10.8 166.6±9.4 165.4±10.4 0.3567

Weight (kg) 70.4±18.7 72.8±17.1 73.0±19.2 0.3679

BMI (kg/m²) 25.6±5.7 26.1±5.4 26.5±6.0 0.4023

Percent Body Fat (%) 33.3±13.5 33.7±12.5 35.3±12.9 0.3560


PA & Fitness PMET‡ 113.8 (55.9-184.6) 112.9 (64.4-168.8) 101.8 (48.4-152.8) 0.6421

V02Max‡ 2.56 (2.10-3.46) 2.61 (2.12-3.48) 2.30 (2.10-3.37) 0.6490

Blood Pressure

Systolic (mmHg)‡ 112.5 (102.8-120.0) 115.0 (105.0-122.0) 115.0 (105.0-120.0) 0.4114

Diastolic (mmHg)* 64.9±11.4 64.7±12.7 65.9±11.7 0.5926

MAP (mmHg)‡ 79.9 (73.8-86.5) 80.0 (73.3-87.5) 81.0 (73.7-90.8) 0.7000

Hypertension†§ β 20 (12.8) 35 (21.7) 27 (17.1) 0.1098

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.49 5.4±0.54 0.2153

2hPG (mmol/l)* 5.4±1.62 5.6±1.72 5.8±1.93 0.1507

FI (mmol/l)‡ 96.0 (62.0-134.0) 94.0 (63.5-129.0) 103.0 (79.0-148.0) 0.2118

IGT†¶ β 15 (9.6) 19 (11.8) 22 (15.2) 0.3145

IFG †|| β 5 (3.2) 15 (9.3) 12 (7.6) 0.0826

Adipokines‡

CRP (mg/l) 1.46 (0.37-5.03) 1.78 (0.51-5.19) 1.78 (0.51-3.67) 0.0603

IL-6 (ng/l) 0.85 (0.38-1.47) 0.69 (0.38-1.14) 0.61 (0.33-1.13) 0.1764

Adiponectin (μg/l) 14.7 (11.0-20.1) 14.1 (10.1-18.1) 12.8 (9.64-18.8) 0.3194

Leptin (ng/ml) 11.1 (4.95-18.6) 10.4 (5.70-19.8) 12.6 (7.00-21.8) 0.3084


Three-factor factor analysis solution with oblique rotation; n of subjects for each characteristic may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for categorical








106

Table D3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Beef & Processed Foods pattern score as determined by


Beef & Processed Foods Pattern Score

T1 T2 T3 p-value

n 158 159 159 -

Age (years)* 29.2±13.7 26.8±14.0 24.2±11.0 0.0025

Sex, Male/Female †β 61/97 (38.6/61.4) 79/80 (49.7/50.3) 62/97 (39.0/61.0) 0.0765

Current Smoker † 109 (35.5) 91 (29.6) 107 (34.9) 0.3459

Anthropometry*

Height (cm) 165.9±9.3 166.0±9.9 165.3±11.5 0.8068

Weight (kg) 74.9±17.3 72.1±19.2 69.3±18.1 0.0255

BMI (kg/m²) 27.1±5.5 25.9±5.9 25.2±5.5 0.0106

Percent Body Fat (%) 36.9±12.1 32.7±13.0 32.8±13.4 0.0049


PA & Fitness

PMET‡ 96.1 (32.8-153.7) 113.4 (56.6-193.9) 110.8 (68.5-168.5) 0.0877

V02Max‡ 2.32 (2.06-3.34) 2.91 (2.12-3.57) 2.34 (2.13-3.33) 0.3051

Blood Pressure

Systolic (mmHg)‡ 115.0 (105.0-121.5) 117.0 (108.0-122.0) 111.0 (101.0-120.0) 0.0062

Diastolic (mmHg)* 66.3±12.5 65.6±12.1 63.7±11.0 0.1271

MAP (mmHg)‡ 80.7 (75.8-87.7) 81.7 (73.8-90.8) 78.2 (73.2-85.0) 0.0213

Hypertension†§ β 31 (19.6) 34 (21.4) 17 (10.7) 0.0257

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.4±0.47 5.5±0.48 5.3±0.52 0.0380

2hPG (mmol/l)* 5.8±1.71 5.5±1.83 5.5±1.73 0.3997

FI (mmol/l)‡ 97.0 (69.0-134.0) 102.0 (71.0-149.0) 94.0 (67.0-130.0) 0.4067

IGT†¶ β 21 (13.3) 20 (12.6) 17 (10.7) 0.7653

IFG †|| β 6 (3.8) 15 (9.4) 11 (6.9) 0.1333

Adipokines‡

CRP (mg/l) 1.87 (0.63-5.10) 1.70 (0.39-4.91) 1.62 (0.44-4.21) 0.7844

IL-6 (ng/l) 0.87 (0.42-1.42) 0.63 (0.32-1.25) 0.68 (0.34-1.14) 0.0983

Adiponectin (μg/l) 13.5 (9.35-17.7) 13.6 (9.64-18.1) 15.3 (11.1-20.9) 0.0160

Leptin (ng/ml) 13.2 (6.90-21.3) 11.3 (5.20-20.0) 9.90 (5.30-19.0) 0.0999


Three-factor factor analysis solution with oblique rotation; n of subjects for each characteristic may vary due to occasional missing values; * Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for categorical






107



Table D3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Traditional Foods pattern score as determined by exploratory

factor analysis.






Traditional Foods Pattern Score

T1 T2 T3 p-value

n 157 161 158 -

Age (years)* 26.0±10.7 27.7±13.0 26.5±15.2 0.5172

Sex, Male/Female †β 75/82 (47.8/52.2) 69/92 (42.9/57.1) 58/100

(36.7/63.3) 0.1379

Current Smoker † 114 (37.1) 105 (34.2) 88 (28.7) 0.0672

Anthropometry*

Height (cm) 167.3±10.0 166.5±10.4 163.3±9.8 0.0012

Weight (kg) 74.2±17.7 74.9±18.0 67.1±18.4 0.0001

BMI (kg/m²) 26.4±5.4 26.9±5.7 25.0±5.8 0.0079

Percent Body Fat (%) 34.1±12.9 35.6±12.5 32.7±13.5 0.1426


PA & Fitness

PMET‡ 114.2 (55.9-168.8) 105.2 (54.5-161.4) 102.8 (61.9-167.7) 0.9085

V02Max‡ 2.68 (2.15-3.49) 2.43 (2.06-3.41) 2.27 (2.11-3.25) 0.3239

Blood Pressure

Systolic (mmHg)‡ 114.0 (105.0-120.0) 117.0 (105.0-122.0) 112.0 (103.0-120.0) 0.0832

Diastolic (mmHg)* 65.2±12.5 66.7±11.7 63.6±11.4 0.0675

MAP (mmHg)‡ 80.7 (73.7-86.3) 81.2 (75.0-90.8) 78.7 (73.0-87.3) 0.0305

Hypertension†§ β 22 (14.0) 37 (23.0) 23 (14.6) 0.0588

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.48 5.4±0.54 0.1087

2hPG (mmol/l)* 5.4±1.68 5.6±1.76 5.9±1.81 0.0300

FI (mmol/l)‡ 95.0 (65.0-134.0) 102.0 (68.0-142.0) 98.0 (68.0-141.0) 0.4075

IGT†¶ β 13 (8.3) 21 (13.0) 24 (15.2) 0.1587

IFG †|| β 7 (4.5) 13 (8.1) 12 (7.6) 0.3783

Adipokines‡

CRP (mg/l) 1.68 (0.45-4.45) 1.85 (0.61-5.12) 1.64 (0.39-4.29) 0.6223

IL-6 (ng/l) 0.76 (0.38-1.24) 0.69 (0.41-1.28) 0.60 (0.31-1.17) 0.4465

Adiponectin (μg/l) 13.8 (9.46-18.7) 12.7 (9.69-17.8) 15.0 (11.2-21.4) 0.0092

Leptin (ng/ml) 10.3 (5.20-19.0) 11.0 (5.70-21.2) 12.3 (6.10-20.3) 0.5658


108





Table D4. Spearman rank correlation coefficients of the relationship between baseline



Three-factor factor analysis solution with oblique rotation; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001;

‡ p<0.05

Balanced Market

Foods

Beef & Processed


Crude Age-

Adjusted Crude

Age-

Adjusted Crude

Age-

Adjusted

Age (years) 0.08 - †-0.16 - -0.07 -

Anthropometry*

Height (cm) 0.01 -0.02 -0.02 -0.04 †-0.17 -0.06

Weight (kg) 0.03 0.08 ‡-0.13 -0.06 *-0.19 -0.06

BMI (kg/m²) 0.04 0.08 ‡-0.14 -0.05 ‡-0.14 -0.03

Percent Body Fat (%) 0.05 0.06 ‡-0.13 -0.05 -0.06 -0.00

Waist Circumference (cm) 0.04 0.11 ‡-0.14 -0.04 ‡-0.14 -0.02

PA & Fitness

PMET‡ -0.07 -0.02 0.10 0.08 -0.01 0.04

V02Max‡ -0.04 -0.02 0.01 -0.01 -0.12 -0.09

Blood Pressure

Systolic (mmHg)‡ 0.03 -0.01 *-0.10 0.01 -0.07 -0.09

Diastolic (mmHg)* 0.01 -0.03 -0.08 0.00 -0.07 -0.04

MAP (mmHg)‡ 0.01 -0.03 *-0.11 -0.00 -0.08 -0.06

Lipid Profile

HDL Cholesterol (mmol/l)* 0.01 -0.03 0.03 0.05 0.09 0.07

LDL Cholesterol (mmol/l)* 0.08 0.05 ‡-0.12 -0.08 -0.04 -0.06

Triglycerides (mmol/l)‡ 0.06 0.04 -0.08 -0.04 -0.10 -0.12

Glucose Homeostasis

FPG (mmol/l)* 0.04 0.09 -0.05 -0.02 ‡0.09 ‡0.14

2hPG (mmol/l)* 0.06 0.06 -0.06 0.04 ‡0.14 0.08

FI (mmol/l)‡ 0.08 ‡0.13 -0.03 0.05 0.04 0.04

Adipokines‡

CRP (mg/l) 0.02 -0.04 -0.08 0.10 -0.04 0.03

IL-6 (ng/l) ‡-0.09 -0.12 ‡-0.11 -0.03 -0.06 -0.05

Adiponectin (μg/l) -0.05 -0.11 ‡0.11 0.07 ‡0.11 -0.01

Leptin (ng/ml) 0.05 0.05 ‡-0.10 -0.03 0.02 0.04

Patterns

Balanced Market Foods 1.00 1.00 *0.36 *0.38 *0.43 *0.41

Beef & Processed Foods 1.00 1.00 *0.25 *0.28

Traditional Foods 1.00 1.00

109

Table D5. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor


Diabetes Project.

Model Balanced Market

Foods

Beef & Processed


Unadjusted 1.20

(0.91, 1.57)

1.14

(0.87, 1.51)

0.93

(0.70, 1.23)

Model 1 1.18

(0.90, 1.56)

1.28

(0.96, 1.71)

0.90

(0.67, 1.22)

Model 2 1.16

(0.88, 1.54)

1.34

(1.00, 1.80)

1.04

(0.76, 1.43)

Model 3 1.15

(0.86, 1.53) 1.38

(1.02, 1.86)*

1.05

(0.76, 1.45)

Model 4 1.11

(0.80, 1.54)

1.24

(0.88, 1.74)

1.08

(0.75, 1.57)

Model 5 1.08

(0.73, 1.62) 1.23

(0.82, 1.84) 1.08

(0.69, 1.69)

Model 6 1.12

(0.74, 1.69) 1.21

(0.80, 1.85) 1.10

(0.69, 1.75)

Model 7 1.12

(0.80, 1.58) 1.19

(0.83, 1.69) 1.04

(0.70, 1.53)



WC, IL-6, and adiponectin; Model 4 - Adjusted for age, sex, WC, IL-6, adiponectin and PMET; Model 5 -

Adjusted for age, sex, WC, IL-6, adiponectin and VO2Max; Model 6 - Adjusted for age, sex, WC, IL-6,

adiponectin, PMET and VO2Max; Model 7 - Adjusted for age, sex, WC, IL-6, adiponectin, current smoker status;

*p<0.05

110

Appendix E

Four-Factor Factor Analysis Solution

Table E1. Pattern names, FFQ items in each pattern, and percent common variation identified

by factor analysis using data from the Sandy Lake Health and Diabetes Project.


Balanced Market

Other Vegetables Carrots Peas Corn Whole Wheat Bread Milk

Macaroni

46.76

Traditional

Fish Duck

Moose Berries Rabbit Indian Tea

19.91

Beef & Processed

Pop Chocolate/Candy Chips/Fries Cookies/Cakes/Pastries Klik Canned Fruit Cold Cereal

Beef

15.75

Tea/Proto-Historic

Tea Lard Canned Milk

White Bread Bannock Other Potatoes

13.70

Four-factor factor analysis solution with oblique rotation; Foods with factor loadings >= 0.30 are shown for

simplicity since those foods were most highly considered when factors were named.

111

Table E2. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health


FFQ Items Balanced Market Beef & Processed Traditional

Tea/Proto-Historic

Fish -4 60 1 2

Moose -1 56 3 -6

Beef 7 -2 30 12 Pork 17 9 18 5

Duck -10 56 3 -2

Rabbit 5 43 1 -9 Klik -5 7 38 13

Eggs 8 2 6 21

Lard 3 -6 5 44

Margarine 22 -12 5 17 Cold Cereal 18 2 32 -1

Hot Cereal 18 26 -16 18

Beans -9 9 22 10 White Bread 3 -18 11 39

Whole Wheat Bread 40 -7 -2 2

Bannock -1 26 1 35 Macaroni 30 -2 17 12

Indian Tea 11 31 -5 -9

Soup 24 21 -3 21

Chips/French Fries 3 -5 48 -13 Other Potatoes 25 12 -1 30

Peas 59 -9 -6 5

Corn 49 11 8 -4 Carrots 59 3 -1 -7

Other Vegetables 61 -2 -1 -8

Berries -7 44 5 -5

Fresh Fruit 19 9 23 0 Canned Fruit 5 17 36 10

Milk 37 2 11 -6

Canned Milk -21 0 8 41 Pop -4 -20 51 -1

Tea -1 -15 -12 50

Cookies/Cakes/Pastries 5 8 42 7 Chocolate/Candy -1 -1 50 -5

Four-factor factor analysis solution with oblique rotation; Eigenvalues (loadings) shown as eigenvalue*100 for simplicity; Loadings >= 30 bolded

112

Table E3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Balanced Market pattern score as determined by exploratory

factor analysis.

Balanced Market Pattern Score

T1 T2 T3 p-value

n 158 (33.2) 159 (33.4) 159 (33.4)

Age (years)* 25.7±13.9 27.4±12.6 27.2±12.8 0.4502

Sex, Male/Female†β 69/89 (43.7/56.3) 78/81 (49.1/50.9) 55/104 (34.6/65.4) 0.0308

Anthropometry*

Height (cm) 165.0±10.8 166.7±9.4 165.4±10.4 0.2977

Weight (kg) 70.4±18.7 73.3±17.1 72.6±19.1 0.3206

BMI (kg/m²) 25.6±5.7 26.3±5.4 26.4±6.0 0.4245

Percent Body Fat (%) 33.3±13.4 33.8±12.6 35.2±12.9 0.3867


Blood Pressure

Systolic (mmHg)‡ 112.5 (102.5-120.0) 115.0 (105.0-122.5) 115.0 (104.0-120.0) 0.3174

Diastolic (mmHg)* 64.8±11.4 65.0±12.5 65.7±11.9 0.7745

MAP (mmHg)‡ 79.9 (73.7-86.3) 80.0 (74.0-88.3) 80.7 (73.3-89.2) 0.7274

Hypertension†§ β 20 (12.7) 35 (22.0) 27 (17.0) 0.0875

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.49 5.4±0.54 0.2047

2hPG (mmol/l)* 5.4±1.62 5.6±1.70 5.8±1.93 0.1690

FI (mmol/l)‡ 99.0 (62.0-134.0) 94.5 (66.0-128.0) 102.5 (76.0-152.0) 0.1002

IGT†¶ β 15 (9.5) 18 (11.3) 25 (15.7) 0.2186

IFG †|| β 5 (3.2) 15 (9.4) 12 (7.6) 0.0733

Adipokines‡

CRP (mg/l) 1.45 (0.37-5.02) 1.92 (0.53-5.19) 1.70 (0.48-3.67) 0.0444

IL-6 (ng/l) 0.84 (0.38-1.46) 0.69 (0.38-1.14) 0.61 (0.32-1.15) 0.1562

Adiponectin (μg/l) 14.6 (11.0-20.1) 14.0 (10.1-18.1) 12.9 (9.64-18.5) 0.3336

Leptin (ng/ml) 11.1 (5.00-18.5) 10.5 (5.50-19.8) 12.6 (7.00-21.8) 0.3353

n converters to T2DM † 26 (165) 27 (17.0) 32 (20.1) 06529

Four-factor factor analysis solution with oblique rotation; n of subjects for each characteristic may vary due to








113

Table E3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Beef & Processed pattern score as determined by exploratory

factor analysis.

Beef & Processed Score

T1 T2 T3 p-value

n 158 (33.2) 159 (33.4) 159 (33.4)

Age (years)* 24.8±9.8 27.9±13.7 27.5±15.1 0.0655

Sex, Male/Female†β 75/83 (47.5/52.5) 68/91 (42.8/57.2) 59/100 (37.1/62.9) 0.1743

Anthropometry*

Height (cm) 167.2±10.1 166.4±10.5 163.5±9.8 0.0035

Weight (kg) 74.7±18.2 73.5±17.5 68.1±18.8 0.0026

BMI (kg/m²) 26.6±5.5 26.4±5.3 25.3±6.1 0.0925

Percent Body Fat (%) 34.4±13.1 34.8±11.9 33.1±13.9 0.4466


Blood Pressure

Systolic (mmHg)‡ 114.0 (105.0-120.0) 116.5 (104.0-122.0) 112.5 (104.0-120.0) 0.9633

Diastolic (mmHg)* 65.3±12.4 66.2±11.4 64.1±11.9 0.3079

MAP (mmHg)‡ 80.0 (73.7-86.3) 81.3 (74.7-88.3) 78.8 (73.3-89.7) 0.4967

Hypertension†§ β 20 (12.7) 35 (22.0) 27 (17.0) 0.0875

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.44 5.4±0.49 5.4±0.54 0.1636

2hPG (mmol/l)* 5.4±1.67 5.5±1.68 6.0±1.88 0.0037

FI (mmol/l)‡ 94.0 (67.0-133.0) 102.0 (68.0-136.0) 99.0 (70.0-145.0) 0.9825

IGT†¶ β 14 (8.9) 14 (8.8) 30 (18.9) 0.0069

IFG †|| β 8 (5.1) 10 (6.3) 14 (8.8) 0.3983

Adipokines‡

CRP (mg/l) 1.61 (0.46-4.45) 1.81 (0.55-5.17) 1.75 (0.43-4.34) 0.8358

IL-6 (ng/l) 0.77 (0.40-1.22) 0.69 (0.37-1.29) 0.59 (0.32-1.26) 0.3197

Adiponectin (μg/l) 13.7 (9.46-18.1) 13.4 (9.80-18.6) 14.8 (11.1-21.2) 0.0110

Leptin (ng/ml) 10.5 (5.00-19.0) 11.0 (5.80-20.7) 12.0 (5.90-20.7) 0.2560










114

Table E3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Traditional pattern score as determined by exploratory factor

analysis.

Traditional Score

T1 T2 T3 p-value

n 158 (33.2) 159 (33.4) 159 (33.4)

Age (years)* 32.8±14.7 25.1±12.3 22.3±9.40 <0.0001

Sex, Male/Female†β 69/89 (43.7/56.3) 68/91 (42.8/57.2) 65/94 (40.9/59.1) 0.8767

Anthropometry*

Height (cm) 166.8±9.10 165.0±9.89 165.3±11.5 0.2291

Weight (kg) 76.5±16.7 71.1±18.5 68.7±18.9 0.0004

BMI (kg/m²) 27.4±5.2 25.9±5.7 24.9±5.9 0.0005

Percent Body Fat (%) 36.5±11.7 34.3±12.9 31.7±13.8 0.0052


Blood Pressure

Systolic (mmHg)‡ 117.5 (10.0-124.0) 114.5 (105.5-120.0) 111.0 (101.0-119.0) <0.0001

Diastolic (mmHg)* 67.5±12.8 65.4±11.6 62.7±10.9 0.0012

MAP (mmHg)‡ 82.3 (76.7-91.7) 80.3 (73.7-88.3) 76.3 (72.3-84.0) <0.0001

Hypertension†§ β 39 (24.7) 27 (17.0) 16 (10.1) 0.0026

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.4±0.49 5.3±0.48 5.4±0.51 0.1561

2hPG (mmol/l)* 5.9±1.73 5.5±1.88 5.5±1.64 0.0472

FI (mmol/l)‡ 102.0 (70.0-138.0) 100.0 (72.0-147.0) 93.0 (64.0-130.0) 0.1769

IGT†¶ β 26 (16.5) 19 (12.0) 13 (18.2) 0.0785

IFG †|| β 11 (7.0) 11 (6.9) 10 (6.3) 0.9648

Adipokines‡

CRP (mg/l) 2.15 (0.77-5.18) 1.74 (0.48-5.19) 1.26 (0.31-3.65) 0.1608

IL-6 (ng/l) 0.88 (0.45-1.44) 0.67 (0.32-1.18) 0.63 (0.32-1.14) 0.0131

Adiponectin (μg/l) 13.4 (9.04-17.5) 13.8 (10.3-18.8) 14.9 (11.1-20.4) 0.0066

Leptin (ng/ml) 12.4 (6.90-21.0) 12.6 (6.20-20.7) 8.90 (4.20-18.5) 0.0064










115

Table E3iv. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of the Tea/Proto-Historic pattern score as determined by exploratory

factor analysis.

Tea/Proto-Historic Score

T1 T2 T3 p-value

n 161 (32.8) 161 (33.9) 158 (33.3)

Age (years)* 23.5±11.9 27.1±12.2 29.6±14.4 0.0002

Sex, Male/Female†β 66/90 (42.3/5776) 76/85 (47.2/52.8) 60/98 (38.0/62.0) 0.2485

Anthropometry*

Height (cm) 164.7±10.1 166.6±10.1 165.8±10.5 0.2835

Weight (kg) 70.4±19.0 72.4±18.5 73.5±17.5 0.3277

BMI (kg/m²) 25.7±5.8 25.9±5.7 26.6±5.5 0.3607

Percent Body Fat (%) 33.8±13.4 33.2±12.9 35.4±12.6 0.3066


Blood Pressure

Systolic (mmHg)‡ 112.5 (104.0-120.0) 113.5 (104.0-121.0) 116.0 (105.5-122.0) 00183

Diastolic (mmHg)* 63.6±11.8 64.9±12.4 67.0±11.4 0.0376

MAP (mmHg)‡ 79.2 (73.2-85.9) 79.3 (73.7-87.2) 82.4 (74.7-91.0) 0.0075

Hypertension†§ β 19 (12.2) 29 (18.0) 34 (21.5) 0.0867

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.48 5.4±0.47 5.4±0.53 0.1647

2hPG (mmol/l)* 5.4±1.52 5.6±1.87 5.8±1.86 0.1725

FI (mmol/l)‡ 95.0 (64.0-133.0) 101.0 (71.0-132.0) 99.5 (69.0-147.0) 0.7023

IGT†¶ β 10 (6.4) 21 (13.0) 27 (17.1) 0.0142

IFG †|| β 5 (3.2) 15 (9.3) 12 (7.6) 0.0826

Adipokines‡

CRP (mg/l) 1.41 (0.36-3.73) 1.56 (0.52-3.98) 2.51 (0.51-5.64) 0.0376

IL-6 (ng/l) 0.67 (0.37-1.33) 0.69 (0.32-1.17) 0.69 (0.35-1.37) 0.3511

Adiponectin (μg/l) 14.4 (10.4-18.8) 13.2 (9.35-18.1) 14.1 (11.0-18.8) 0.2198

Leptin (ng/ml) 11.9 (5.40-19.9) 10.4 (5.10-19.7) 11.7 (6.70-21.8) 0.1436










116

Table E4. Spearman rank correlation coefficients of the relationship between baseline



Four-factor factor analysis solution with oblique rotation; MAP=Mean arterial pressure; FPG=Fasting plasma

glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; Tea/Proto-Hist.=Tea/Proto-Historic pattern* p=<0.0001; † p=<0.001; ‡ p<0.05

Balanced Market

Beef &

Processed Traditional Tea/Proto-Historic

Crude Age-

Adjusted Crude

Age-Adjusted

Crude Age-

Adjusted Crude

Age-Adjusted

Age (years) 0.01 -0.00 †-0.16 ‡-0.15 -0.05 0.04 0.05 -0.01

Anthropometry* 0.04 -0.01 †-0.16 †-0.18 *-0.20 0.03 0.09 -0.02

Height (cm) 0.04 -0.01 ‡-0.11 ‡-0.12 *-0.22 -0.07 0.09 -0.02

Weight (kg) 0.05 0.02 -0.04 -0.06 *-0.18 -0.08 0.05 -0.01

BMI (kg/m²) 0.04 -0.00 ‡-0.10 ‡-0.11 *-0.23 -0.04 ‡0.12 -0.01

Percent Body Fat

(%)

Waist

Circumference (cm)

0.03 -0.03 -0.02 -0.04 *-0.20 -0.05 ‡0.13 0.03

Blood Pressure 0.00 -0.04 -0.03 -0.04 *-0.18 -0.05 ‡0.12 0.02

Systolic

(mmHg)‡

0.01 -0.05 -0.04 -0.05 *-0.22 -0.07 ‡0.14 0.03

Diastolic

(mmHg)*

MAP (mmHg)‡ 0.01 0.00 0.08 0.08 0.05 0.04 -0.01 -0.01

Lipid Profile 0.08 0.05 0.00 -0.03 *-0.22 -0.07 ‡0.12 -0.02

HDL Cholesterol

(mmol/l)*

0.06 0.05 -0.06 -0.07 ‡-0.16 -0.04 ‡0.14 0.06

LDL Cholesterol

(mmol/l)*

Triglycerides

(mmol/l)‡

0.04 0.04 ‡0.11 ‡0.11 -0.08 0.01 -0.00 -0.06

Glucose

Homeostasis

0.06 0.04 †-0.16 ‡0.15 ‡-0.11 -0.03 0.06 0.01

FPG (mmol/l)* 0.01 -0.00 †-0.16 ‡-0.15 -0.05 0.04 0.05 -0.01

2hPG (mmol/l)* 0.04 -0.01 †-0.16 †-0.18 *-0.20 0.03 0.09 -0.02

FI (mmol/l)‡ 0.07 0.07 0.06 0.06 -0.08 -0.05 0.05 0.01

Adipokines‡

CRP (mg/l) 0.01 -0.04 -0.00 -0.01 †-0.18 -0.02 ‡0.12 0.02

IL-6 (ng/l) ‡-0.10 ‡-0.13 -0.04 -0.06 ‡-0.15 ‡-0.10 -0.00 -0.04

Adiponectin

(μg/l)

-0.05 -0.03 ‡0.10 ‡0.10 ‡0.13 0.04 0.00 0.06

Leptin (ng/ml) 0.05 0.03 0.04 0.02 ‡0.15 -0.09 0.03 -0.01

Patterns

Balanced Market 1.00 1.00 *0.45 *0.46 *0.35 *0.42 *0.20 *0.19

Beef & Processed 1.00 1.00 *0.26 *0.30 ‡0.13 ‡0.12

Traditional 1.00 1.00 *0.28 *0.39

Tea/ Proto-Hist. 1.00 1.00

117

Table E5. Odds ratios and 95% confidence intervals (CIs) for association between 4-factor


Diabetes Project.

Model Balanced Market Beef &

Processed Traditional

Tea/Proto-

Historic

Unadjusted 1.18

(0.90, 1.55) 1.00

(0.76, 1.32) 0.98

(0.74, 1.30) 1.55

(1.11, 2.14)

Model 1 1.17

(0.89, 1.55)

0.94

(0.70, 1.26)

1.23

(0.91, 1.67)

1.37

(0.98, 1.92)

Model 2 1.15

(0.87, 1.52)

1.09

(0.80, 1.48)

1.28

(0.94, 1.75)

1.41

(1.00, 2.00) *NS

Model 3 1.13

(0.85, 1.51)

1.10

(0.80, 1.51)

1.30

(0.95, 1.79) 1.47

(1.03, 2.10)

Four-factor factor analysis solution with oblique rotation; ORs presented per unit increase in pattern score;


WC, IL-6, and adiponectin; *p<0.05

118

Appendix F

Subgroup Logistic Regression by Age for the Reduced Rank Regression-Driven Tea & Fibre Pattern

Table F1. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived Tea & Fibre pattern scores and incident type 2 diabetes using data from the

Sandy Lake Health and Diabetes Project, sub-grouped by age.

Model Tea & Fibre

(n=493)

Tea & Fibre Older

(Age >23.4 years)

(n=247)

Tea & Fibre Younger

(Age <23.5 years)

(n=246)

Unadjusted 1.31

(1.03, 1.67)*

0.94

(0.66, 1.32)

1.25

(0.78, 2.01)

Model 1 1.08

(0.82, 1.42) 0.91

(0.64, 1.30) 1.18

(0.73, 1.91)

Model 2 0.93

(0.70, 1.25)

0.78

(0.54, 1.15)

1.12

(0.68, 1.85)

Model 3 0.89

(0.66, 1.21)

0.77

(0.52, 1.15)

1.04

(0.62, 1.75)

Stratified by median age (Median age=23.5);Intermediate response variables: waist circumference, high-density

lipoprotein cholesterol, fasting plasma glucose, 2-hour post-prandial plasma glucose, fasting serum insulin, C-

reactive protein, and adiponectin; ORs presented per unit increase in pattern score; Model 1 – Adjusted for age and

sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin;

*p<0.05

119

Appendix G

Subgroup Logistic Regression by Age for the Reduced Rank Regression-Driven Traditional Pattern

Table G1. Odds ratios and 95% confidence intervals for the association between reduced rank

regression-derived Traditional pattern scores and incident type 2 diabetes using data from the

Sandy Lake Health and Diabetes Project, sub-grouped by age.

Model Tea & Fibre

(n=493)

Tea & Fibre Older

(Age >23.4 years)

(n=247)

Tea & Fibre Younger

(Age <23.5 years)

(n=246)

Unadjusted 0.88

(0.70, 1.10)

0.88

(0.66, 1.18)

0.90

(0.61, 1.33)

Model 1 0.81

(0.64, 1.03)

0.83

(0.60, 1.14)

0.96

(0.63, 1.46)

Model 2 0.91

(0.70, 1.17)

0.92

(0.66, 1.28)

0.97

(0.63, 1.50)

Model 3 0.93

(0.72, 1.21)

0.88

(0.63, 1.23)

1.08

(0.69, 1.68)

Stratified by median age (Median age=23.5);Intermediate response variables: waist circumference, high-density

lipoprotein cholesterol, fasting plasma glucose, 2-hour post-prandial plasma glucose, fasting serum insulin, C-

reactive protein, and adiponectin; ORs presented per unit increase in pattern score; Model 1 – Adjusted for age and

sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin;

*p<0.05

120

Appendix H

Reduced Rank Regression Analysis Using Log-Transformed Non-Normally Distributed Intermediate Response Variables

Table H1. Pattern names, FFQ items in each pattern, and percent total variation explained by




Hot Market Foods & Vegetables

Hot Cereal Tea Eggs Peas Other Vegetables

Carrots (Pop) (Chips/French Fries)

5.64

Traditional Foods & Hot Cereal

Duck

Soup Berries Rabbit Moose Hot Cereal Fish

2.31

Modified Proto-Historic

Bannock

Eggs Margarine Duck (Cold Cereal) (Milk) (Beef)

1.19


2-hour post-prandial plasma glucose, fasting serum insulin (log-transformed), C-reactive protein (log-transformed), and adiponectin (log-transformed); Foods with factor loadings >= 0.20 are shown for simplicity since those foods

were considered when patterns were named. ( ) denotes negative factor loadings

121

Table H2. Pattern loadings for each food as listed on the 34-item FFQ, as determined by


FFQ Items

Hot Market Foods &

Vegetables

Traditional Foods & Hot

Cereal Modified Proto-Historic

Fish 11 23 11

Moose -10 26 -0.03

Beef -11 -5 -24

Pork -1 6 -6

Duck -11 37 20

Rabbit -12 29 -7

Klik -9 -3 3

Eggs 29 -11 25 Lard 17 -5 15

Margarine 3 6 23

Cold Cereal 2 16 -48

Hot Cereal 41 25 -8

Beans 16 -7 14

White Bread 0 -17 -5

Whole Wheat Bread 17 -10 0

Bannock 0 20 33

Macaroni -7 -5 23

Indian Tea -7 12 10

Soup 20 36 13 Chips/French Fries -29 -20 -12

Other Potatoes 11 18 9

Peas 26 -10 19

Corn 0 14 6

Carrots 21 7 -6

Other Vegetables 23 17 -12

Berries -6 29 -14

Fresh Fruit -1 15 -9

Canned Fruit -14 9 0

Milk 7 5 -38

Canned Milk -3 8 19

Pop -33 -20 -11 Tea 32 -5 -10

Cookies/Cakes/Pastries -14 10 -3

Chocolate/Candy -18 10 -3

Intermediate response variables: waist circumference, high-density lipoprotein cholesterol, fasting plasma glucose, 2-hour post-prandial plasma glucose, fasting serum insulin (log-transformed), C-reactive protein (log-transformed),

and adiponectin (log-transformed); Loadings shown as loading*100 for simplicity; Loadings >= 20 bolded

122

Table H3i. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Hot Market Foods & Vegetables pattern as



2-hour post-prandial plasma glucose, fasting serum insulin (log-transformed), C-reactive protein (log-transformed), and adiponectin (log-transformed); n of subjects for each characteristic may vary due to occasional missing values;

* Mean ± SD; † n (%); ‡ Median (25th-75th percentile); β Chi-Square test for categorical variables; § Hypertension

defined as systolic blood pressure >=130 mmHg or diastolic blood pressure of >=85 mmHg or participation in

antihypertensive medication therapy; ¶ IGT=Impaired glucose tolerance defined as fasting plasma glucose <7.0

mmol/l and 2-hr postload glucose >=7.8 mmol/l and <11.1 mmol/l ; || IFG=Impaired fasting glucose defined as

fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure;

FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-values

calculated using ANOVA (non-normally distributed were log-transformed) for continuous variables, Chi-Square

for dichotomous variables.

Hot Market Foods & Vegetables Pattern Score

T1 T2 T3 p-value

N 157 160 158 -

Age (years)* 20.1±8.6 26.4±12.3 33.8±14.1 <0.0001

Sex, Male/Female †β 66/91 (42.0/58.0) 71/89 (44.4/55.6) 64/94 (40.5/59.5) 0.7808

Anthropometry*

Height (cm) 164.4±10.9 166.5±10.2 166.2±9.5 0.1586

Weight (kg) 66.5±19.0 73.1±17.7 76.9±16.8 <0.0001

BMI (kg/m²) 24.3±5.6 26.3±5.8 27.7±5.2 <0.0001

Percent Body Fat (%) 31.0±13.4 34.6±13.5 37.0±11.1 0.0002


Blood Pressure

Systolic (mmHg)‡ 111.0 (101.0-120.0) 113.3 (104.0-120.0) 118.0 (109.0-126.0) <0.0001

Diastolic (mmHg)* 62.4±10.7 64.8±11.4 68.5±12.9 <0.0001

MAP (mmHg)‡ 76.7 (73.0-83.0) 80.2 (74.1-86.1) 85.0 (76.3-93.0) <0.0001

Hypertension†§ β 16 (10.2) 25 (15.6) 41 (26.0) 0.0008

Lipid Profile




Glucose Homeostasis

FPG (mmol/l)* 5.3±0.46 5.4±0.48 5.5±0.52 0.0187

2hPG (mmol/l)* 5.2±1.50 5.5±1.73 6.1±1.94 <0.0001

FI (mmol/l)‡ 92.0 (56.5-124.5) 98.5 (71.0-140.0) 110.5 (77.5-150.5) 0.0076

IGT†¶ β 9 (5.7) 15 (9.4) 34 (21.5) <0.0001

IFG †|| β 10 (6.4) 8 (5.0) 14 (8.9) 0.3798

Adipokines‡

CRP (mg/l) 0.77 (0.25-3.02) 1.57 (0.45-4.73) 3.01 (1.35-6.44) <0.0001

IL-6 (ng/l) 0.63 (0.31-1.17) 0.61 (0.34-1.07) 0.90 (0.44-1.44) 0.0375

Adiponectin (μg/l) 16.2 (12.2-22.4) 13.5 (10.1-17.4) 12.4 (8.74-17.9) <0.0001

Leptin (ng/ml) 9.50 (4.90-17.2) 10.4 (4.45-20.3) 14.3 (7.50-23.2) 0.0009


123

Table H3ii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Traditional Foods & Hot Cereal pattern as


Intermediate response variables: waist circumference, high-density lipoprotein cholesterol, fasting plasma glucose, 2-hour post-prandial plasma glucose, fasting serum insulin (log-transformed), C-reactive protein (log-transformed),

and adiponectin (log-transformed); n of subjects for each characteristic may vary due to occasional missing values;





fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure;

FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-values



Traditional Foods & Hot Cereal Pattern Score

T1 T2 T3 p-value

n 157 160 158 -

Age (years)* 24.6±9.1 27.5±12.7 28.1±16.4 0.0401

Sex, Male/Female †β 86/71 (54.8/45.2) 58/102 (36.2/63.8) 58/100 (36.7/63.3) 0.0007

Anthropometry*

Height (cm) 166.8±11.4 166.5±9.8 163.9±9.2 0.0261

Weight (kg) 73.3±18.9 73.9±18.5 69.1±17.4 0.0383

BMI (kg/m²) 26.1±5.7 26.5±6.0 25.5±5.3 0.2484

Percent Body Fat (%) 32.8±13.3 35.5±13.0 34.0±12.6 0.1889


Blood Pressure

Systolic (mmHg)‡ 114.0 (105.0-120.0) 113.8 (104.0-120.0) 115.8 (102.5-122.5) 0.4188

Diastolic (mmHg)* 65.2±11.9 65.4±11.5 65.0±12.5 0.9422

MAP (mmHg)‡ 80.0 (73.8-86.3) 79.2 (74.3-87.5) 80.8 (73.3-91.0) 0.9507

Hypertension†§ β 22 (14.0) 27 (16.9) 33 (20.9) 0.2685

Lipid Profile


LDL Cholesterol (mmol/l)* 2.49±0.76 2.47±0.73 2.48±.74 0.9763


Glucose Homeostasis

FPG (mmol/l)* 5.3±0.46 5.4±.45 5.5±0.55 0.0008

2hPG (mmol/l)* 5.2±1.68 5.7±1.61 6.0±1.91 0.0006

FI (mmol/l)‡ 87.5 (62.0-125.0) 100.0 (67.0-139.0) 104.0 (77.0-148.0) 00262

IGT†¶ β 9 (5.7) 17 (10.6) 32 (20.3) 0.0003

IFG †|| β 9 (5.7) 6 (3.8) 17 (10.8) 0.0370

Adipokines‡

CRP (mg/l) 1.63 (0.40-4.19) 1.66 (0.53-4.77) 1.81 (0.46-4.69) 0.6569

IL-6 (ng/l) 0.67 (0.37-1.24) 0.74 (0.37-1.27) 0.65 (0.33-1.23) 0.6817

Adiponectin (μg/l) 12.6 (8.74-18.5) 13.9 (10.8-17.6) 15.2 (11.1-21.2) 0.0021

Leptin (ng/ml) 9.45 (4.30-17.7) 12.5 (7.00-21.3) 12.3 (6.40-20.5) 0.0209

n converters to T2DM † 24 (15.3) 36 (22.5) 24 (15.2) 0.1463s

124

Table H3iii. Baseline characteristics of participants in the Sandy Lake Health and Diabetes

Project according to tertiles of scores for the Modified Proto-Historic pattern as determined by

reduced rank regression.


2-hour post-prandial plasma glucose, fasting serum insulin (log-transformed), C-reactive protein (log-transformed),

and adiponectin (log-transformed); n of subjects for each characteristic may vary due to occasional missing values;





fasting plasma glucose 6.1-6.9 mmol/l and 2-hr postload glucose <7.8mmol/l; MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; p-values



Modified Proto-Historic Pattern Score

T1 T2 T3 p-value

n 157 159 159 -

Age (years)* 24.1±12.6 26.3±116 30.0±14.4 0.0003

Sex, Male/Female †β 62/95 (39.5/60.5) 68/91 (42.8/57.2) 71/88 (44.6/55.4) 0.6431

Anthropometry*

Height (cm) 164.7±10.9 165.9±10.1 166.6±9.6 0.2811

Weight (kg) 70.1±18.4 73.6±19.4 72.7±17.1 0.2146

BMI (kg/m²) 25.6±5.6 26.5±6.2 26.1±5.2 0.3555

Percent Body Fat (%) 33.6±12.6 34.9±14.2 33.8±12.1 0.6343


Blood Pressure

Systolic (mmHg)‡ 112.5 (103.5-120.0) 113.0 (104.0-121.0) 116.5 (107.0-122.0) 0.1016

Diastolic (mmHg)* 63.8±11.9 65.9±10.8 65.9±13.0 0.1824

MAP (mmHg)‡ 79.2 (73.3-85.0) 80.0 (74.0-87.7) 82.0 (73.7-90.8) 0.1349

Hypertension†§ β 20 (12.7) 25 (15.7) 37 (23.3) 0.0382

Lipid Profile



Triglycerides (mmol/l)‡ 1.13 (0.86-1.62) 1.21 (0.88, 1.59) 1.19 (0.85, 1.59) 0.9097

Glucose Homeostasis

FPG (mmol/l)* 5.4±0.48 5.4±0.50 5.3±0.49 0.1221

2hPG (mmol/l)* 5.5±1.77 5.5±1.70 5.8±1.82 0.3930

FI (mmol/l)‡ 103.0 (76.0-149.0) 98.5 (68.0-131.0) 92.0 (61.0-136.0) 0.0374

IGT†¶ β 17 (10.8) 15 (9.4) 26 (16.4) 0.1375

IFG †|| β 12 (7.6) 12 (7.6) 8 (5.0) 0.5748

Adipokines‡

CRP (mg/l) 1.09 (0.31-3.52) 1.87 (0.61, 5.06) 2.00 (0.51-5.10) 0.0422

IL-6 (ng/l) 0.70 (0.38-1.29) 0.63 (0.31-1.15) 0.75 (0.35-1.43) 0.2194

Adiponectin (μg/l) 14.2 (10.2-18.6) 13.1 (9.75-19.4) 14.2 (10.7-18.7) 0.3608

Leptin (ng/ml) 10.6 (5.50-19.5) 11.9 (5.40-21.0) 11.3 (5.40-21.0) 0.7851


125

Table H4. Spearman rank correlation coefficients of the relationship between baseline



Hot Market Foods &

Vegetables

Traditional Foods & Hot

Cereal Modified Proto-Historic

Crude Age-

Adjusted Crude

Age-

Adjusted Crude

Age-

Adjusted

Age (years) *0.49 -0.01 *0.22

Anthropometry

Height (cm) 0.09 -0.08 †-0.17 *-0.19 0.05 0.02

Weight (kg) *0.30 0.04 ‡-0.14 †-0.17 0.07 -0.05

BMI (kg/m²) *0.32 ‡0.10 -0.08 -0.08 0.05 -0.07

Percent Body Fat (%) *0.24 ‡0.09 0.01 0.02 0.03 -0.05

Waist Circumference (cm) *0.36 0.08 ‡-0.10 ‡-0.12 0.08 -0.05

Blood Pressure

Systolic (mmHg) *0.25 0.01 0.01 -0.00 ‡0.10 0.00

Diastolic (mmHg) *0.26 0.04 -0.03 -0.03 ‡0.10 0.02

MAP (mmHg) *0.29 0.04 -0.01 -0.01 ‡0.11 0.01

Lipid Profile

HDL Cholesterol (mmol/l) -0.06 -0.07 0.05 0.04 ‡0.13 ‡0.13

LDL Cholesterol (mmol/l) *0.34 ‡0.13 -0.02 -0.03 †0.15 0.05

Triglycerides (mmol/l) *0.28 ‡0.14 0.00 -0.00 0.01 -0.08

Glucose Homeostasis

FPG (mmol/l) ‡0.14 0.03 †0.17 †0.17 -0.08 ‡-0.13

2hPG (mmol/l) *0.20 ‡0.12 *0.21 *0.21 0.06 0.01

FI (mmol/l) *0.18 †0.16 ‡0.13 ‡0.12 ‡-0.11 †-0.16

Adipokines

CRP (mg/l) *0.33 ‡0.14 -0.04 -0.03 ‡0.13 0.02

IL-6 (ng/l) ‡0.12 0.05 -0.02 -0.01 0.02 -0.02

Adiponectin (μg/l) *-0.24 ‡-0.12 †0.18 †0.18 0.06 ‡0.11

Leptin (ng/ml) *0.19 ‡0.13 0.08 0.09 0.05 -0.01

Patterns

Tea & Fibre 1.00 1.00 0.00 0.01 0.03 -0.08

Traditional 1.00 1.00 -0.01 -0.00




and adiponectin (log-transformed); MAP=Mean arterial pressure; FPG=Fasting plasma glucose; 2hPG=2-hour post-prandial plasma glucose; FI=Fasting serum insulin; * p=<0.0001; † p=<0.001; ‡ p<0.05

126

Table H5. Odds ratios and 95% confidence intervals for the association between reduced rank



Model Hot Market Foods & Vegetables

Traditional Foods & Hot Cereal Modified Proto-Historic

Unadjusted 1.35

(1.06, 1.72)*

0.94

(0.75, 1.16) 1.36

(1.05, 1.76)*

Model 1 1.13

(0.86, 1.48)

0.86

(0.68, 1.09)

1.25

(0.96, 1.62)

Model 2 0.98

(0.73, 1.31)

0.97

(0.76, 1.24)

1.29

(0.98, 1.71)

Model 3 0.95

(0.70, 1.28)

1.02

(0.79, 1.31)

1.32

(0.99, 1.76)



and adiponectin (log-transformed); ORs presented per unit increase in pattern score; Model 1 – Adjusted for age

and sex; Model 2 – Adjusted for age, sex, and WC; Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin;

*p<0.05

127

Appendix I

Logistic Regression, Adjusted for Dietary Patterns Derived by Factor Analysis

Table I1. Odds ratios and 95% confidence intervals (CIs) for association between 3-factor


Diabetes Project.

Model Balanced Market Foods Beef & Processed Foods Traditional Foods

Unadjusted 1.20

(0.91, 1.57)

1.14

(0.87, 1.51)

0.93

(0.70, 1.23)

Model 1 1.18

(0.90, 1.56)

1.28

(0.96, 1.71)

0.90

(0.67, 1.22)

Model 2 1.16

(0.88, 1.54)

1.34

(1.00, 1.80)

1.04

(0.76, 1.43)

Model 3 1.15

(0.86, 1.53) 1.38

(1.02, 1.86)*

1.05

(0.76, 1.45)

Model 4 1.02

(0.72, 1.45)

1.40

(0.99, 1.97)

0.93

(0.64, 1.34)



WC, IL-6, and adiponectin; Model 4 – Adjusted for age, sex, WC, IL-6, adiponectin, and other factor analysis-

derived dietary patterns *p<0.05

128

Appendix J

Logistic Regression, Adjusted for Dietary Patterns Derived by Reduced Rank Regression Analysis

Table J1. Odds ratios and 95% confidence intervals for the association between reduced rank



Model Tea & Fibre Traditional Proto-Historic

Unadjusted 1.31

(1.03, 1.67)*

0.88

(0.70, 1.10)

1.28

(0.95, 1.71)

Model 1 1.08

(0.82, 1.42)

0.81

(0.64, 1.03)

1.19

(0.88, 1.62)

Model 2 0.93

(0.70, 1.25)

0.91

(0.70, 1.17)

1.24

(0.90, 1.70)

Model 3 0.89

(0.66, 1.21)

0.93

(0.72, 1.21)

1.23

(0.88, 1.71)

Model 4 0.90

(0.66, 1.22)

0.94

(0.72, 1.23)

1.22

(0.88, 1.71)


2-hour post-prandial plasma glucose, fasting serum insulin, C-reactive protein, and adiponectin; ORs presented per

unit increase in pattern score; Model 1 – Adjusted for age and sex; Model 2 – Adjusted for age, sex, and WC

Model 3 – Adjusted for age, sex, WC, IL-6, and adiponectin; Model 4 – Adjusted for age, sex, WC, IL-6,

adiponectin, and other reduced rank regression-derived dietary patterns; *p<0.05

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