Dietary Patterns and Incident Type 2 Diabetes …...was positively associated with incident T2DM in...
Transcript of 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.
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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
2.5.2.1 Factor Analysis
2.5.2.2 Reduced Rank Regression 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
exploratory factor analysis.
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
exploratory factor analysis.
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.
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.
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.
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.
Table 11. Spearman rank correlation coefficients of the relationship between baseline
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.
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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
and Diabetes Project.
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
characteristics and patterns as determined using reduced rank regression analysis
using data from the Sandy Lake Health and Diabetes Project.
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 Lake Health and Diabetes Project.
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 and Diabetes Project.
Table B2. 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.
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
reduced rank regression analysis.
Table B4. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis
using data from the Sandy Lake Health and Diabetes Project.
Table B5. 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 C1. 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 C2. 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.
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
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Table C4. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis
using data from the Sandy Lake Health and Diabetes Project.
Table C5. 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 D1. Correlation Coefficients for Physical Activity and Fitness Measures.
Table D2. – Baseline characteristics of participants the Sandy Lake Health and Diabetes Project
according to diabetes status at follow-up.
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
exploratory factor analysis.
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
exploratory factor analysis.
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.
Table D4. 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 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
Health and Diabetes Project.
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.
Table E2. Pattern loadings for each food as listed on the 34-item FFQ in the Sandy Lake Health
and Diabetes Project.
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.
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.
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.
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.
Table E4. 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 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
Health and Diabetes Project.
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
pattern, determined using reduced rank regression using data from the Sandy Lake
Health and Diabetes Project.
Table H2. 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.
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
determined by reduced rank regression.
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.
Table H4. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis
using data from the Sandy Lake Health and Diabetes Project.
Table H5. 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 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
and Diabetes Project.
Table J1. 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.
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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
.
1.4.2.2 Factor Analysis
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
.
1.4.2.3 Reduced Rank Regression Analysis
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
1.8 References
1. International Diabetes Federation. IDF Diabetes Atlas. Brussels: International Diabetes
Federation; 2009. Available at: http://www.diabetesatlas.org/downloads. Accessed
November 27, 2009.
2. Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario. Determination of prevalence and
incidence using a validated administrative data logarithm. Diabetes Care 2002;25:512-
16.
3. Harris SB, Gittelsohn J, Hanley A, Barnie A, Wolever TMS, Gao J, Logan A, Zinman B.
The prevalence of NIDDM and associated risk factors in Native Canadians. Diabetes
Care 1997;20:185-7.
4. Young TK, Reading J, Elias B, O'Neil JD. Type 2 diabetes mellitus in Canada's First
Nations: status of an epidemic in progress. CMAJ 2000;163:561-6.
5. Delisle, HF, Ekoe , J-M. Prevalence of non-insulin-dependent diabetes mellitus and
impaired glucose tolerance in two Algonquin communities in Quebec. Canadian Medical
Association Journal 1993;148:41-7.
6. 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.
7. Pickup JC, Crook MA. Is type II diabetes a disease of the innate immune system?
Diabetologia. 1998;41:1241-8.
8. Daniel M, Green LW, Marion SA, Gamble D, Herbert CP, Hertzman C, Sheps SB.
Effectiveness of community-directed diabetes prevention and control in a rural
Aboriginal population in British Columbia, Canada. Soc Sci Med. 1999;48:815-32.
9. Ford ES. Body mass index, diabetes, and C-reactive protein among U.S. adults. Diabetes
Care. 1999;22:1971-7.
10. Hotta K, Funahashi T, Arita Y, Takahashi M, Matsuda M, Okamoto Y, Iwahashi H,
Kuriyama H, Ouchi N, Maeda K, Nishida M, Kihara S, Sakai N, Nakajima T, Hasegawa
K, Muraguci M, Ohmoto Y, Nakamura T, Yamashita S, Hanafusa T, Matsuzaw Y.
Plamsa concentrations of a novel, adipose-specific protein, adiponectin, in type 2
diabetic patients. Aterioscler Thromb Vasc Biol. 2000;20:1595-9.
11. Hu FB, van Dam RM, Liu S. Diet and risk of Type 2 diabetes: the role of types of fat
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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
2.5.2.1 Factor 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.
2.5.2.2 Reduced Rank Regression Analysis
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.
2. Ley SH, Harris SB, Connelly PW, Mamakeesick M, Gittelsohn J, Hegele RA,
Retnakaran R, Zinman B, Hanley AJG. Adipokines and incident type 2 diabetes in an
Aboriginal Canadian population: the Sandy Lake Health and Diabetes Project. Diabetes
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.
8. Gittelsohn J, Wolever TMS, Harris SB, Harris-Giraldo R, Hanley AJG, Zinman B.
Specific patterns of food consumption and preparation are associated with diabetes and
obesity in a Native Canadian community. J Nutr. 1998; 128: 541-7.
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.
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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.
19. Hatcher L. A step-by-step approach to using the SAS system for factor analysis and
structural equation modeling. Cary, NC. SAS Institute Inc., 1994.
20. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer
AFH, Boeing H. A dietary pattern protective against type 2 diabetes in the European
Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study cohort.
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.
22. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a
new statistical method to derive dietary patterns in nutrition epidemiology. Am J
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.
28. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin
6, and risk of developing type 2 diabetes mellitus. JAMA. 2001; 286: 327-34.
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.
30. Spranger J, Kroke A, Möhlig M, Hoffmann K, Bergmann MM, Ristow M, Boeing H,
Pfeiffer AFH. Inflammatory cytokines and the risk to develop type 2 diabetes. Diabetes.
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
according to diabetes status at follow-up.
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
factor analysis using data from the Sandy Lake Health and Diabetes Project.
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
Spearman rank correlation coefficients examining the associations between the 3 dietary
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
and Diabetes Project.
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
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.
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
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 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
Waist Circumference (cm) 91.8±12.9 90.0±14.7 87.4±13.0 0.0150
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
HDL Cholesterol (mmol/l)* 1.24±0.27 1.25±0.29 1.27±0.27 0.4922
LDL Cholesterol (mmol/l)* 2.55±0.71 2.50±0.75 2.39±0.76 0.1369
Triglycerides (mmol/l)‡ 1.23 (0.89-1.60) 1.19 (0.90-1.56) 1.05 (0.80-1.61) 0.2044
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
n converters to T2DM † 24 (15.2) 33 (20.8) 28 (17.6) 0.4311
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.
52
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 exploratory factor
analysis.
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.
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
Waist Circumference (cm) 90.8±13.2 91.6±13.7 86.7±13.6 0.0025
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
HDL Cholesterol (mmol/l)* 1.24±0.29 1.25±0.29 1.27±0.25 0.3840
LDL Cholesterol (mmol/l)* 2.50±0.71 2.49±0.74 2.46±0.77 0.8975
Triglycerides (mmol/l)‡ 1.27 (0.91-1.71) 1.17 (0.86-1.53) 1.10 (0.81-1.54) 0.2328
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
n converters to T2DM † 25 (15.9) 39 (24.2) 21 (13.3) 0.0288
53
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.
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
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)
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).
Spearman rank correlation coefficients examining the associations between the 4 dietary
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
rank regression analysis using data from the Sandy Lake Health and Diabetes Project.
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
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; 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.
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
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
HDL Cholesterol (mmol/l)* 1.27±0.27 1.25±0.29 1.24±0.26 0.6730
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
n converters to T2DM † 17 (10.9) 33 (20.6) 35 (22.0) 0.0198
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
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.
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
Waist Circumference (cm) 91.5±13.1 89.2±14.0 88.5±13.7 0.1320
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
HDL Cholesterol (mmol/l)* 1.23±0.28 1.27±0.29 1.26±0.26 0.5093
LDL Cholesterol (mmol/l)* 2.48±0.73 2.51±0.76 2.45±0.74 0.7428
Triglycerides (mmol/l)‡ 1.14 (0.85-1.56) 1.19 (0.86-1.62) 1.20 (0.87-1.60) 0.7522
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
n converters to T2DM † 27 (17.2) 36 (22.6) 21 (13.2) 0.0863
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.
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
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.
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
Waist Circumference (cm) 89.1±14.0 90.6±12.6 89.3±14.3 0.5640
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
HDL Cholesterol (mmol/l)* 1.26±0.29 1.22±0.25 1.29±0.28 0.0520
LDL Cholesterol (mmol/l)* 2.37±0.69 2.49±0.73 2.58±0.79 0.0422
Triglycerides (mmol/l)‡ 1.17 (0.83-1.61) 1.16 (0.90-1.56) 1.21 (0.85-1.60) 0.9670
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
n converters to T2DM † 20 (12.7) 30 (18.8) 35 (22.2) 0.0876
63
Table 11. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis using data
from the Sandy Lake Health and Diabetes Project.
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
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; 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)
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
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
1. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. C-reactive protein, interleukin
6, and risk of developing type 2 diabetes mellitus. JAMA. 2001; 286: 327-34.
2. Spranger J, Kroke A, Möhlig M, Hoffmann K, Bergmann MM, Ristow M, Boeing H,
Pfeiffer AFH. Inflammatory cytokines and the risk to develop type 2 diabetes. Diabetes.
2003;52:812-7
3. Ley SH, Harris SB, Connelly PW, Mamakeesick M, Gittelsohn J, Hegele RA,
Retnakaran R, Zinman B, Hanley AJG. Adipokines and incident type 2 diabetes in an
Aboriginal Canadian population: the Sandy Lake Health and Diabetes Project. Diabetes
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
1. van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk
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.
4. Gittelsohn J, Wolever TMS, Harris SB, Harris-Giraldo R, Hanley AJG, Zinman B.
Specific patterns of food consumption and preparation are associated with diabetes and
obesity in a Native Canadian community. J Nutr. 1998; 128: 541-7.
5. Hatcher L. A step-by-step approach to using the SAS system for factor analysis and
structural equation modeling. Cary, NC. SAS Institute Inc., 1994.
6. Hoffmann K, Schulze MB, Schienkiewitz A, Nöthlings U, Boeing H. Application of a
new statistical method to derive dietary patterns in nutrition epidemiology. Am J
Epidemiol 2004; 159: 935-44.
7. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer
AFH, Boeing H. A dietary pattern protective against type 2 diabetes in the European
Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study cohort.
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.
Pattern Name FFQ Items in Pattern Percent Common Variance Accounted For
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
and Diabetes Project.
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
Waist Circumference (cm) 84.4±13.7 89.5±13.1 95.2±12.0 <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
HDL Cholesterol (mmol/l)* 1.28±0.30 1.20±0.25 1.28±0.27 0.0174
LDL Cholesterol (mmol/l)* 2.15±0.65 2.51±0.69 2.78±0.74 <0.0001
Triglycerides (mmol/l)‡ 1.03 (0.76-1.33) 1.21 (0.90-1.62) 1.31 (0.97-1.74) <0.0001
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
n converters to T2DM † 13 (8.3) 39 (24.2) 33 (20.9) 0.0005
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
characteristics and patterns as determined using reduced rank regression analysis using data
from the Sandy Lake Health and Diabetes Project.
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
Lake Health and Diabetes Project.
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
and Diabetes Project.
Pattern Name FFQ Items in Pattern Percent Variance Accounted For
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
reduced rank regression analysis using data from the Sandy Lake Health and Diabetes Project.
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
simplicity; Loadings >= 20 bolded
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
reduced rank regression analysis.
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
Waist Circumference (cm) 83.4±13.6 90.3±13.1 95.3±11.6 <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
HDL Cholesterol (mmol/l)* 1.28±0.29 1.24±0.27 1.24±0.26 0.2877
LDL Cholesterol (mmol/l)* 2.19±0.66 2.51±0.74 2.74±0.71 <0.0001
Triglycerides (mmol/l)‡ 1.02 (0.77-1.33) 1.18 (0.83-1.55) 1.35 (1.04-1.82) <0.0001
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
n converters to T2DM † 17 (10.8) 32 (20.0) 36 (22.6) 0.0160
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
reduced rank regression analysis.
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
Waist Circumference (cm) 90.6±12.8 90.5±14.6 87.9±13.4 0.1333
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
HDL Cholesterol (mmol/l)* 1.26±0.26 1.25±0.28 1.25±0.28 0.8639
LDL Cholesterol (mmol/l)* 2.56±0.76 2.46±0.74 2.42±0.73 0.2219
Triglycerides (mmol/l)‡ 1.18 (0.88-1.50) 1.21 (0.86-1.61) 1.15 (0.86-1.63) 0.8392
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
n converters to T2DM † 31 (19.9) 30 (18.8) 23 (14.5) 0.4128
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
characteristics and patterns as determined using reduced rank regression analysis using data
from the Sandy Lake Health and Diabetes Project.
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
regression-derived dietary pattern scores and incident type 2 diabetes using data from the Sandy
Lake Health and Diabetes Project.
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
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
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
reduced rank regression analysis using data from the Sandy Lake Health and Diabetes Project.
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
simplicity; Loadings >= 20 bolded
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
reduced rank regression analysis.
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
Waist Circumference (cm) 84.0±13.2 89.8±12.7 95.4±12.6 <0.0001
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
HDL Cholesterol (mmol/l)* 1.29±0.28 1.23±0.27 1.24±0.27 0.2143
LDL Cholesterol (mmol/l)* 2.14±0.66 2.55±0.67 2.75±0.76 <0.0001
Triglycerides (mmol/l)‡ 1.01 (0.72-1.31) 1.20 (0.90-1.69) 1.34 (1.01-1.80) <0.0001
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
n converters to T2DM † 18 (11.5) 33 (20.6) 34 (21.4) 0.0404
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;
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.
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
Waist Circumference (cm) 90.6±14.2 90.0±12.8 88.7±13.9 0.4611
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
HDL Cholesterol (mmol/l)* 1.22±0.26 1.27±0.27 1.28±0.29 0.1196
LDL Cholesterol (mmol/l)* 2.48±0.72 2.47±0.70 2.49±0.80 0.9677
Triglycerides (mmol/l)‡ 1.19 (0.83-1.63) 1.15 (0.89-1.54) 1.20 (0.85-1.62) 0.8289
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
n converters to T2DM † 22 (14.0) 34 (21.3) 28 (17.7) 0.2403
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;
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.
101
Table C4. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis using data
from the Sandy Lake Health and Diabetes Project.
Regular Tea, Low Junk
Foods Proto-Historic
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
Proto-Historic 1.00 1.00
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
regression-derived dietary pattern scores and incident type 2 diabetes using data from the Sandy
Lake Health and Diabetes Project.
Model Regular Tea, Low Junk
Foods Proto-Historic
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
according to diabetes status at follow-up.
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
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
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
Waist Circumference (cm) 88.4±13.9 90.1±13.1 90.5±14.0 0.3626
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
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) 0.2118
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
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.
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
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
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
Waist Circumference (cm) 91.8±12.9 90.0±14.7 87.4±13.0 0.0150
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
HDL Cholesterol (mmol/l)* 1.24±0.27 1.25±0.29 1.27±0.27 0.4922
LDL Cholesterol (mmol/l)* 2.55±0.71 2.50±0.75 2.39±0.76 0.1369
Triglycerides (mmol/l)‡ 1.23 (0.89-1.60) 1.19 (0.90-1.56) 1.05 (0.80-1.61) 0.2044
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
n converters to T2DM † 24 (15.2) 33 (20.8) 28 (17.6) 0.4311
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;
107
FI=Fasting serum insulin; p-values calculated using ANOVA (non-normally distributed were log-transformed)for
continuous variables, Chi-Square for dichotomous variables.
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.
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
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
Waist Circumference (cm) 90.8±13.2 91.6±13.7 86.7±13.6 0.0025
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
HDL Cholesterol (mmol/l)* 1.24±0.29 1.25±0.29 1.27±0.25 0.3840
LDL Cholesterol (mmol/l)* 2.50±0.71 2.49±0.74 2.46±0.77 0.8975
Triglycerides (mmol/l)‡ 1.27 (0.91-1.71) 1.17 (0.86-1.53) 1.10 (0.81-1.54) 0.2328
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
n converters to T2DM † 25 (15.9) 39 (24.2) 21 (13.3) 0.0288
108
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.
Table D4. 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.
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 -0.02 -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
dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake Health and
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 1.11
(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)
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; 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.
Pattern Name FFQ Items in Pattern Percent Common Variance Accounted For
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
and Diabetes Project.
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
Waist Circumference (cm) 88.4±13.8 90.6±13.1 90.1±14.0 0.3287
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
HDL Cholesterol (mmol/l)* 1.25±0.29 1.26±0.28 1.25±0.25 0.9551
LDL Cholesterol (mmol/l)* 2.43±0.72 2.46±0.77 2.55±0.73 0.3528
Triglycerides (mmol/l)‡ 1.16 (0.86-1.56) 1.09 (0.80-1.59) 1.20 (0.90-1.61) 0.1468
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
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.
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
Waist Circumference (cm) 90.9±13.4 90.7±13.4 87.6±14.0 0.0494
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
HDL Cholesterol (mmol/l)* 1.23±0.29 1.26±0.28 1.27±0.25 0.3121
LDL Cholesterol (mmol/l)* 2.48±0.71 2.47±0.74 2.49±0.78 0.9840
Triglycerides (mmol/l)‡ 1.23 (0.88-1.64) 1.17 (0.88-1.54) 1.10 (0.81-1.58) 0.3799
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
n converters to T2DM † 26 (16.6) 36 (22.6) 23 (14.5) 0.1422
Four-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.
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
Waist Circumference (cm) 93.6±12.8 89.2±13.6 86.4±13.6 <0.0001
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
HDL Cholesterol (mmol/l)* 1.25±0.26 1.23±0.28 1.28±0.28 0.2579
LDL Cholesterol (mmol/l)* 2.67±0.72 2.46±0.73 2.31±0.74 <0.0001
Triglycerides (mmol/l)‡ 1.29 (1.02-1.62) 1.14 (0.86-1.56) 1.07 (0.79-1.50) 0.0007
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
n converters to T2DM † 29 (18.4) 26 (16.4) 30 (18.9) 0.8258
Four-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.
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
Waist Circumference (cm) 87.9±13.8 89.9±14.1 91.3±12.9 0.0894
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
HDL Cholesterol (mmol/l)* 1.25±0.28 1.26±0.27 1.26±0.27 0.8970
LDL Cholesterol (mmol/l)* 2.39±0.73 2.52±0.77 2.53±0.72 0.2095
Triglycerides (mmol/l)‡ 1.09 (0.81-1.49) 1.21 (0.83-1.56) 1.22 (0.94-1.67) 0.0643
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
n converters to T2DM † 15 (9.6) 36 (22.4) 34 (21.5) 0.0043
Four-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.
116
Table E4. 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.
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
dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake Health and
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;
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
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
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
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
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); 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
reduced rank regression analysis using data from the Sandy Lake Health and Diabetes Project.
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
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 (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
Waist Circumference (cm) 84.1±13.2 90.8±13.1 94.3±12.7 <0.0001
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
HDL Cholesterol (mmol/l)* 1.28±0.27 1.23±0.30 1.25±0.26 0.1950
LDL Cholesterol (mmol/l)* 2.19±0.69 2.52±0.71 2.73±0.73 <0.0001
Triglycerides (mmol/l)‡ 1.03 (0.74-1.33) 1.20 (0.89-1.62) 1.33 (0.99-1.75) <0.0001
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
n converters to T2DM † 17 (10.8) 33 (20.6) 35 (22.2) 0.0174
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
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 (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.
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
Waist Circumference (cm) 90.2±13.8 90.8±13.8 88.1±13.3 0.1937
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
HDL Cholesterol (mmol/l)* 1.24±0.27 1.26±0.28 1.26±0.27 0.7154
LDL Cholesterol (mmol/l)* 2.49±0.76 2.47±0.73 2.48±.74 0.9763
Triglycerides (mmol/l)‡ 1.14 (0.88-1.54) 1.18 (0.86-1.62) 1.20 (0.84-1.60) 0.8434
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.
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;
* 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.
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
Waist Circumference (cm) 88.0±14.0 90.5±14.0 90.6±12.8 0.1603
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
HDL Cholesterol (mmol/l)* 1.23±0.27 1.24±0.28 1.30±0.27 0.0345
LDL Cholesterol (mmol/l)* 2.34±0.66 2.52±0.76 2.59±0.78 0.0081
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
n converters to T2DM † 20 (12.7) 31 (19.5) 34 (21.4) 0.1088
125
Table H4. Spearman rank correlation coefficients of the relationship between baseline
characteristics and patterns as determined using reduced rank regression analysis using data
from the Sandy Lake Health and Diabetes Project.
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
Proto-Historic 1.00 1.00
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); 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
regression-derived dietary pattern scores and incident type 2 diabetes using data from the Sandy
Lake Health and Diabetes Project.
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)
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); 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
dietary pattern scores and incident type 2 diabetes using data from the Sandy Lake Health and
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)
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; 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
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)
Model 4 0.90
(0.66, 1.22)
0.94
(0.72, 1.23)
1.22
(0.88, 1.71)
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; Model 4 – Adjusted for age, sex, WC, IL-6,
adiponectin, and other reduced rank regression-derived dietary patterns; *p<0.05