 FFQs and Dietary Pattern Analysis The road to better understanding the contribution of diet towards maternal and offspring health

Diet and Health Incident of Diabetes, IDF 2013

Diet and Health Incident of Diabetes, IDF 2013

Diet and Health kCal per day, 2014

Diet and Health

 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

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.

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.

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’

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

Dietary Pattern Analysis 1. Standardize CHILD food portions to that of the SHARE FFQ. e.g., ½ cup versus 1 cup servings, change from 2/week to 1/week

Dietary Pattern Analysis 1. Standardize CHILD food portions to that of the SHARE FFQ. e.g., ½ 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

Dietary Pattern Analysis *Hu et al AJCN 1998, Fung et al AJCN 2001, Nettleton et al AJCN 2009, Gadgil et al JAND Standardize CHILD food portions to that of the SHARE FFQ. e.g., ½ 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).

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

 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

 Scree plot (“breakpoint” or “breakpoint” -1)  Arbitrary cutoff (e.g., eigenvalue of 1.0) Dietary Analysis 1. Number of Dimensions

 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

Dietary Analysis 3. Loading Scores  How strongly a specific food item/group contributes to a dimension/dietary pattern  Typical cutoff range from  In this case, 0.30 was used as the cutoff as it provided a clear contrast between dietary patterns (e.g., prudent and Western)

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.

CHILD WesternPrudent Prudent: Non-red meats, legumes, leafy greens, fruit, vegetables, non- meat dishes. Western: Fats, processed meats, fried foods, refined grains,, pasta, pizza, french fries, snacks, sweets and condiments.

FAMILY WesternPrudent Prudent: Fermented dairy, non-red meats, legumes, leafy greens, fruit, vegetables, whole grains, non-meat dishes. Western: Fats, red-meat, processed meats, fried foods, refined grains, pasta, pizza, french fries, snacks, sweets and condiments.

START WesternPrudent Prudent: Low-fat dairy, fermented dairy, legumes, fruit, vegetables, non- meat dishes. Western: Full fat dairy, red-meat, processed meats, fried foods, refined grains, snacks, sweets and condiments.

NutriGen Pollo-pescetarian WesternPrudent Prudent: Fermented dairy, legumes, fruit, vegetables, non-meat dishes. Western: Full-fat dairy, red-meat, processed meats, starchy vegetables, refined grains, pasta, pizza, french fries, snacks, sweets and condiments. Pollo-pescetarian: Eggs, fish, poultry, leafy greens, fruit, vegetables, stir-fried dishes, nuts and seeds.

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.

K-means (2 clusters)

AUC = K-means (2 clusters) PCA Scores vs K-means Classification