FFQs and Dietary Pattern Analysis The road to better
understanding the contribution of diet towards maternal and
offspring health
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Diet and Health Incident of Diabetes, IDF 2013
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Diet and Health Incident of Diabetes, IDF 2013
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Diet and Health kCal per day, 2014
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Diet and Health
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Uncover food patterns associated with increased and reduced
incidence of disease, their biomarkers (e.g., body weight), and/or
their internal regulators (e.g., gene expression). Using: 1. Food
Frequency Questionnaires (FFQs); and 2. Diet pattern analysis using
Principal Component Analysis (PCA). Diet and Health
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Dietary Analysis FFQs are questionnaires used to determine the
food and beverages, and their quantities, consumed by an
individual; For the NutriGen study, FFQs from each of the four
cohorts (ABC, CHILD, FAMILY, and START) have been processed.
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Dietary Analysis FFQs are questionnaires used to determine the
food and beverages, and their quantities, consumed by an
individual; For the NutriGen study, FFQs from each of the four
cohorts (ABC, CHILD, FAMILY, and START) have been processed.
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Dietary Analysis SHARE (ABC, FAMILY, and START) CHILD Origin
McMaster (Kelemen LE, et al., 2003) and the Food Processor nutrient
analysis software Fred Hutchinson Cancer Research Center and
Nutrition Data Systems for Research Items~160 (variation between
ethnicities)~150 Food GroupingNOYES (e.g., doughnuts, pies,
pastries) Ethnic- Specific YES (White European, South Asian,
Chinese, and Aboriginal/First Nation)NO Consumption
FrequencySelf-definedRanged (e.g., 1-2x/week) Serving SizeEqual
between SHARE studiesSome differences with SHARE
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SHARE (ABC, FAMILY, and START) CHILD Origin McMaster (Kelemen
LE, et al., 2003) and the Food Processor nutrient analysis software
Fred Hutchinson Cancer Research Center and Nutrition Data Systems
for Research Items~160 (variation between ethnicities)~150 Food
GroupingNOYES (e.g., doughnuts, pies, pastries) Ethnic- Specific
YES (White European, South Asian, Chinese, and Aboriginal/First
Nation)NO Consumption FrequencySelf-definedRanged (e.g., 1-2x/week)
Serving SizeEqual between SHARE studiesSome differences with SHARE
Dietary Analysis Requires standardization
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Dietary Pattern Analysis 1. Standardize CHILD food portions to
that of the SHARE FFQ. e.g., cup versus 1 cup servings, change from
2/week to 1/week
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Dietary Pattern Analysis 1. Standardize CHILD food portions to
that of the SHARE FFQ. e.g., cup versus 1 cup servings, change from
2/week to 1/week 2. Create standard food groups to reduce number of
variables and ease interpretation of dietary patterns e.g., canned
meat lunch meat, breakfast sausages => processed meat
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Dietary Pattern Analysis *Hu et al AJCN 1998, Fung et al AJCN
2001, Nettleton et al AJCN 2009, Gadgil et al JAND 2013. 1.
Standardize CHILD food portions to that of the SHARE FFQ. e.g., cup
versus 1 cup servings, change from 2/week to 1/week 2. Create
standard food groups to reduce number of variables and ease
interpretation of dietary patterns e.g., canned meat lunch meat,
breakfast sausages => processed meat 3. Built upon food
groupings from previous studies* analyzing dietary pattern analysis
and cardiometabolic conditions, allergies, and indicators (e.g.,
FPG, HOMA-IR, CRP, cholesterol and TG).
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Snacks Sweets Condiments Sweet Drinks Artificial Sweet Tea
Coffee Coolers, Spirits, and Mixed Drinks Full-Fat Dairy Low-Fat
Dairy Fermented Dairy Meats Meat Dishes Organ Meats Processed Meats
Poultry & Waterfowl Eggs Fish & Seafood Leafy Greens
Cruciferous Vegetables Starchy Vegetables Vegetable Medley Other
Vegetables Fresh Seasonings Legumes Tofu Fruits Non-Meat Dishes
Stir-Fried Noodles and Rice Refined Grains Pasta Pizza French Fries
Whole Grains Nuts and Seeds Fats Fried Foods Dietary Pattern
Analysis
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Principal Component Analysis (PCA) Reduces complex data into
fewer dimensions Are there underlying patterns that distinguish
groups of individuals? e.g., dietary pattern Performed in R, using
psych package To uncover that we need to consider three PCA
parameters: 1. Number of dimensions/factors (i.e., number of diet
patterns) 2. Rotation method (i.e., diet patterns) 3. Loading
scores (i.e., foods within each diet) Dietary Pattern Analysis
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Scree plot (breakpoint or breakpoint -1) Arbitrary cutoff
(e.g., eigenvalue of 1.0) Dietary Analysis 1. Number of
Dimensions
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Groups the data in a specified manner, that best tells the
story Oblique - assume that the variables are correlated Orthogonal
- assume that the variables in the analysis are uncorrelated
Multiple choices but varimax is most common dietary analysis Aims
to load food strongly in one dimension only. Dietary Analysis 2.
Rotation Method
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Dietary Analysis 3. Loading Scores How strongly a specific food
item/group contributes to a dimension/dietary pattern Typical
cutoff range from 0.20-0.30. In this case, 0.30 was used as the
cutoff as it provided a clear contrast between dietary patterns
(e.g., prudent and Western)
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ABC WesternPrudent Western: Red meats, processed meats, fried
foods, refined grains, snacks, pasta, pizza, french fries, sweets
and condiments. Prudent: Red meats, seafood, non-red meats,
legumes, leafy greens, fruit and vegetables.
Next Steps Compare loading scores to maternal outcomes such as
GWG, GDM status, FPG, and AUC glucose. If associations uncovered,
does the diet also contribute to the health of the offspring.