S

FFQs and Dietary Pattern AnalysisThe 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 Grouping NO YES (e.g., doughnuts, pies, pastries)

Ethnic- Specific

YES (White European, South Asian, Chinese, and Aboriginal/First Nation) NO

Consumption

Frequency Self-defined Ranged (e.g., 1-2x/week)

Serving Size Equal between ‘SHARE’ studies Some 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 Grouping NO YES (e.g., doughnuts, pies, pastries)

Ethnic- Specific

YES (White European, South Asian, Chinese, and Aboriginal/First Nation) NO

Consumption

Frequency Self-defined Ranged (e.g., 1-2x/week)

Serving Size Equal between ‘SHARE’ studies Some 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 2013.

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

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 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)

ABC

Wes

ter

n Prud

ent

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

Wes

ter

nPrud

ent

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

Wes

ter

nPrud

ent

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

Wes

ter

nPrud

ent

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

-pe

scet

aria

n

Wes

ter

nPrud

ent

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 = 0.988

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