FFQs and Dietary Pattern Analysis

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

2. Diet and Health Incident of Diabetes, IDF 2013

3. Diet and Health Incident of Diabetes, IDF 2013

4. Diet and Health kCal per day, 2014

5. Diet and Health

6. 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: • Food Frequency Questionnaires (FFQs); and • Diet pattern analysis using Principal Component Analysis (PCA).

7. 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.

8. 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.

9. Dietary Analysis

10. Dietary Analysis Requires standardization

11. Dietary Pattern Analysis • 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

12. Dietary Pattern Analysis • 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 • 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

13. Dietary Pattern Analysis • 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 • 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 • 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). *Hu et al AJCN 1998, Fung et al AJCN 2001, Nettleton et al AJCN 2009, Gadgil et al JAND 2013.

14. Dietary Pattern Analysis • Meats • Meat Dishes • Organ Meats • Processed Meats • Poultry & Waterfowl • Eggs • Fish & Seafood • Full-Fat Dairy • Low-Fat Dairy • Fermented Dairy • Fats • Fried Foods • Snacks • Sweets • Condiments • Sweet Drinks • Artificial Sweet • 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 • Tea • Coffee • Coolers, Spirits, and Mixed Drinks • Whole Grains • Nuts and Seeds

15. 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: • Number of dimensions/factors (i.e., number of diet patterns) • Rotation method (i.e., diet patterns) • Loading scores (i.e., foods within each diet)

16. Dietary Analysis1. Number of Dimensions • Scree plot (“breakpoint” or “breakpoint” -1) • Arbitrary cutoff (e.g., eigenvalue of 1.0)

17. Dietary Analysis 2. Rotation Method • 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.

18. 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)

19. ABC Western Prudent 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.

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

21. FAMILY Western Prudent 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.

22. START Western Prudent 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.

23. NutriGen Pollo-pescetarian Western Prudent 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.

24. 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.

26. K-means (2 clusters)

27. K-means (2 clusters) PCA Scores vs K-means Classification AUC = 0.988