How to Design Studies, Collect Data, and Analyze Data in Nutritional Epidemiology Lu Qi, MD, PhD

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1 How to Design Studies, Collect Data, and Analyze Data in Nutritional Epidemiology Lu Qi, MD, PhD Regents Distinguished Chair and Professor, Tulane University School of Public Health and Tropical Medicine Adjunct Professor, Harvard School of Public Health

2 Nutritional Epidemiology Hippocrates ( BC), the father of Western medicine, put it: If we could give every individual the right amount of nourishment and exercise, not too little and not too much, we would have found the safest way to health. Oral cancer in betel nut chewers in Travancore: its aetiology, pathology and treatment. Lancet 1933 Modern Nutritional Epidemiology Recent 30 years

3 Dr. Walter Willett

4 Nutritional Epidemiology Lifestyle Metabolic Changes Diet Genetic Other factors Human health

5 Study design 1. Descriptive levels of nutrient intakes, temporal trend 2. Observational epidemiology a. Case-control b. Cohort 3. Experimental randomized clinical trials 4. Mechanistic - e.g. biomarkers, metabolomics

6 Exposures in Nutritional Epidemiology 1. Nutrients (vitamins, minerals, amino acids, etc.) 2. Foods and beverages (fruits & vegetables, alcohol, sugary drink) 3. Dietary patterns (combinations of foods and nutrients) 4. Biomarkers (blood, urine, hair, nail, etc.) 5. Others: additives (antioxidants, coloring and stabilizing agents), contaminants, chemicals formed in cooking or processing

7 Diet assessment methods Current diet Usual diet Diet record 24 hr recall Food frequency questionnaire FFQ

8 Diet Assessment Primer

9 24-hour recall Recall and report food and beverage intake in preceding 24 hours Usually performed through in person, phone interview Standard questions, detail information Open-ended response structure Usually takes mins to complete

10 24 hr recall: strengths Easily administered, quick interview, cheap Long-term memory not required High response rate Low literacy skill required Can obtain detailed information Analytic flexibility Does not interfere with eating behavior

11 24 hr recall: limitations Require well trained interviewers to collect information using standard and consistent approach Participant memory may affect accuracy How many needed to represent usual diet? May not adequately account for day-to-day variation Difficulty in recalling quantities

12 24-hr recall: application Describe average diet of a group/population, e.g. The National Health and Nutrition Examination Survey (NHANES) Evaluate the effectiveness of an intervention study to change diet Compare mean intake over time Assess total dietary intake and/or particular aspects of the diet

13 Self-administered tools

14 Food/Diet Record Self-reported record of food and beverage intake over a period of days (3-7 days) Detail description of food, preparation method, and quantities Quantities assessed by: actual weighing, volume measurement, estimation assisted by photographs

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16 Diet Records: strengths Often regarded as gold standard Quantitatively accurate information Less likely to omit food Long-term memory not required

17 Diet Records: Limitations Substantial training of participants Literate and highly motivated participants Participant fatigue and reduced cooperation (maximum 7 consecutive days) May alter usual eating habits Still may not represent usual intake Burdensome coding (may need to contact participants for clarification) High cost, not suitable to large cohorts The Baltimore Longitudinal Study of Aging, a longitudinal study

18 Diet Record: application Clinical and research setting to monitor compliance to dietary intervention Validation study of other dietary assessment methods

19 Food Frequency Questionnaires (FFQ) Conceptual Basis 1. Long-term intake is important 2. It may be better to have crude information over a long period than precise information for a short period 3. It is easier to recall usual intake Structure: a. food list b. frequency information Choice of Foods: Limited vs comprehensive typically ranges from 80 to 120

20 Food list Frequency

21 Administration of FFQ 1. Self-administered (e.g., mail, pre-interview) 2. Personal interview 3. Telephone interview 4. Web-based

22 FFQ: strengths 1. Convenient for large population studies, selfadministered 2. Easy for participants to complete 3. Provides long-term dietary intake data 4. Provides data on foods directly 5. Provide better information on nutrients with large within-person variation than other methods

23 FFQ: limitations 1. Total energy intakes may be not correctly estimated, affected by list of foods 2. Culture-specific 3. Errors are not random- likely biased by current intake

24 Comparing dietary assessment instruments

25 Up and coming technology in diet assessment

26 Analysis of nutrition data Nutrients Dietary patterns Foods/ beverages

27 Analysis of nutrients Ley SH, et al. Lancet. 2014

28 Analysis of foods and beverages Ley SH, et al. Lancet. 2014

29 Curr Opin Lipidol. 2002

30 Ley SH, et al. Lancet. 2014

31 Healthy Eating Index. The Healthy Eating Index (HEI) is a measure of diet quality that assesses conformance to the Dietary Guidelines for Americans. USDA's primary use of the HEI is to monitor the diet quality of the U.S. population and the low-income subpopulation.

32 High component scores indicate intakes close to the recommended ranges or amounts; low component scores indicate less compliance with the recommended ranges or amounts

33 Schwingshackl L, J Acad Nutr Diet. 2015

34 Change-change analysis

35 Etiological findings from epidemiological studies usually use just one exposure measure Dietary intakes change over time, at different age Dietary intakes are affected by disease conditions, such obesity

36 2011 NHS, n=121,701 HPFS, n=51,529 NHS 2, n=116,686

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39 Diet/lifestyle Body weight

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41 Temporal change of SSB consumption NEJM. 2009

42 Nature 2012

43 Sugar sweetened beverage (SSB) as the #1 source for diet added sugar NHANES,

44 Epidemiology evidence Weight gain Obesity

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46 Decrease intake of less healthful foods containing high amounts of fat and/or sugar High calcium and protein contents of yogurt influence appetite and energy intake Yogurt increases the circulating concentration of the anorectic peptides glucagon-like peptide (GLP)-1 and peptide YY (PYY)

47 Certain yogurts contain probiotics that are good bacterial strains that may affect gut microbiota and benefit a person's health

48 Gene-diet interactions

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50 26,000 big mac!

51 Temporal change of obesity in the US 60% remains not obese! 30% remains lean!

52 Foods Health

53 Genetics loads the gun and environment pulls the trigger. Dr. Francis Collins

54 Genetic effect on obesity Nat. Genet genetic loci identified

55 Linked to food intake and energy expenditure

56 March 18, 2014

57 Why fried foods

58 Prospective cohort study of 9850 men and women, followed for 6 ys Average yearly change in body weight, and incidence of overweight/obesity

59 Foods become crunchy, aromatic, palatable, and rich in fat and calories after frying Chicken 1 leg or ~4 ounce Roasted Calories 181 Fried Calories 284 Potato ~3 ounce Baked Calories 170 Fried Calories 271

60 DietGen Consortium NHS: Nurses Health Study; 121,700 women; followed from women with GWAS HPFS: Health Professionals Follow-up Study; 51,529 men; followed from men with GWAS WGHS: Women Genome Health Study; 25,000 women; followed from ,421 women with GWAS

61 Fried food intakes Participants were asked to report their average weekly frequency of fried food consumption at home and away from home with 4 to 5 possible responses (ranging from never to daily). In the current analysis, three categories of frequency of fried food consumption were coded consistently across questionnaires from all cohorts (<1/wk, 1-3/wk, and 4/wk).

62 Genetic susceptibility to obesity NUDT3 ETV5 ZNF608 GIPR FTO FLJ35779 MC4R TFAP2B PRKD1 MTIF3 GRS genetic risk score MTCH2 POMC BDNF GPC5B NRXN3 PCSK1 TMEM160 TMEM18 SH2B1 32 obesity genes 1 point = 1 risk allele FAIM2 LRRN6C GNPDA2 MAP2K5 RPL27A

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64 Genetic risk score and BMI by fried food consumption

65 Individual SNP-fried food consumption interactions FTO SEC16B NEGR1 GNPDA2 MC4R

66 Omics and novel risk factors

67 Traditional risk factors

68 Omics and novel risk factors

69 Metabolomics

70 2011 The Framingham Offspring Study Amino acids The Malmö Diet and Cancer study Liquid chromatography-tandem mass spectrometry (LC-MS) 48 metabolites

71 Predict T2D during 12 years of follow-up Aromatic amino acids (AAA) phenylalanine, tyrosine, and tryptophan Associated with insulin resistance

72 Amino acids, old nutrients In 1806, French chemists Louis Nicolas Vauquelin and Pierre Jean Robiquet isolated a compound in asparagus that was subsequently named asparagine About 500 amino acids are known 22 proteinogenic ("protein-building") AA Nine of the 22 standard amino acids are called essential for humans

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74 1969

75 Branched chain amino acids (BCAAs) BCAAs are among the nine essential amino acids for humans, accounting for 35% of the essential amino acids in muscle proteins Many foods, eg. Egg, turkey Pork, wheat germ, milk Cheese, nuts, wheat germ

76 Diabetes

77 Study design

78 Study design Exposure: dietary BCAA intakes Outcome: incident T2D Statistical model: Cox proportional hazards models Covariates: demographic factors, lifestyle factors, family history of diabetes, baseline disease status, total energy intake, and diabetes diet score Exclusion criteria: baseline CVD, cancer or diabetes patients, implausible energy intake, missing dietary intake information

79 Results Table 1. Baseline age-adjusted characteristics of participants in the NHS (1980), NHS II (1991) and HPFS (1986) Characteristics NHS (N=66125) NHS II (N=86630) HPFS (N=39017) Q1 Q3 Q5 Q1 Q3 Q5 Q1 Q3 Q5 N Age, years BMI, kg/m Physical activity, METs/wk Current smoking, % Alcohol intake, g/d Postmenopausal, % Zheng Y, Qi L. Int J Epidemiology 2016

80 Associations between Dietary Intakes and Plasma Levels of Total BCAA P for correlation <0.03 Zheng Y, Qi L. Int J Epidemiology 2016

81 Relative Risk, 95% CI Diet BCAAs and type 2 diabetes in the NHS Q1 Q2 Q3 Q4 Q5 Leucine Zheng Y, Qi L. Int J Epidemiology 2016

82 Relative Risk, 95% CI Diet BCAAs and type 2 diabetes in the HPFS Q1 Q2 Q3 Q4 Q5 Leucine Zheng Y, Qi L. Int J Epidemiology 2016

83 Relative Risk, 95% CI Diet BCAAs and type 2 diabetes in the NHS, NHS 2 and HPFS Q1 Q2 Q3 Q4 Q5 BCAAs (Leucine + isoleucine + valine) Zheng Y, Qi L. Int J Epidemiology 2016

84 Diabetes

85 The Pounds Lost Trial The Preventing Overweight Using Novel Dietary Strategies trial A 2-year intervention 811 overweight or obese adults Four diets varying in macronutrients Sacks et al, NEJM, 2009

86 Diet intervention groups Low fat Low protein Diet Group Fat (%) Protein (%) Carbohydrate (%) High fat High protein

87 Metabolomic measurement of amino acids Baseline 6 months 24 months

88 Correlation heat map for changes of amino acids in POUNDS LOST 6 months 2 years Zheng Y, Qi L. AJCN 2016

89 Tyr Ala Pro Leu/Ile Val Gly OH-Prol Phe Glu Aba Sarc Met Arg PiPA Ser MeHis Trp Lys Tau Cit Orn His Asp Gln Thr -log10(p) Weight loss diets on changes in amino acids in POUNDS LOST AAA β Level1 coeff BCAA 2.5 Significant threshold p= Zheng Y, Qi L. AJCN 2016

90 The DIRECT trial The Dietary Intervention Randomized Control Trial A 2-year Intervention trial 322 overweight or obese subjects Three diets: Low-fat (high carbohydrate) Low-carbohydrate (high fat) Mediterranean Shai et al, NEJM, 2008 (Ben-Gurion University of the Negev, Israel)

91 Comparison of the two trials DIRECT Pounds Lost Participants Overweight or obese Overweight or obese Intervention 2 Years 2 Years Diets Low vs high CHO/fat Low vs high CHO/fat Maximum weight loss 6 months 6 months Weight regain 6 m to 2 y 6 m to 2 y

92 Weight loss diets on changes in amino acids in DIRECT trial AAA BCAA BCAA Zheng Y, Qi L. AJCN 2016

93 Systems Nutritional Epidemiology From genomics to other -omics Epigenome