Personalized Nutrition by Prediction of Glycemic Responses

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2 A science-based approach to nutrition: Personalized Nutrition by Prediction of Glycemic Responses Eran Segal Weizmann Institute of Science

3 The metabolic disease epidemic

4 Changes in our nutrition greatly contributed to the recent metabolic syndrome epidemic

5 Changes in our nutrition greatly contributed to the recent metabolic syndrome epidemic Reduced fat consumption and increased carbohydrates consumption Increased consumption of added sugar Increased consumption of food additives and artificial sweeteners Changed meal times and introduced shift working

6 If nutritional changes drove the metabolic syndrome epidemic, can it be treated by restoring healthy nutrition?

7 Studying the link between nutrition, lifestyle, genetics, and microbiome

8 How can we take a science-based approach to nutrition? Tal Korem Dudi Zeevi

9 Science based approach to nutrition: What should a marker of healthy nutrition satisfy? Relevant for weight management Relevant for metabolic disease Easily measurable quantitatively

10 Postprandial (Post-meal) glucose response as a measure of healthy nutrition Directly affects fat storage, weight gain, and hunger Strongly associated with obesity, diabetes, CVD Easily measurable quantitatively

11 Glucose levels (mg/dl) People have widely different glucose responses to the same food Carl Jill Nancy Phil Time (hours) Adapted from Vega-López et al. Diabetes Care 2007

12 Diets that maintain normal blood glucose levels must be personally tailored

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14 The Personalized Nutrition Project: Understanding personal glucose responses Niv Zmora Adina Weinberger Zeevi et al., Cell 2015

15 Zeevi et al., Cell 2015 Continuous glucose monitoring

16 Frequency The Personalized Nutrition Project: Cohort statistics years of age 55% overweight 22% obese 21% pre-diabetic Age Zeevi et al., Cell 2015

17 What is the variability across people in the response to the same food?

18 Testing the cohort response to standardized meals 800 x Zeevi et al., Cell 2015

19 Replicate 2 (mg/dl) Replicate 2 (mg/dl) Replicate 2 (mg/dl) The same person has a highly similar post-meal response to the same standardized meal across different days 150 Bread & Butter R= R=0.70 Bread Replicate 1 (mg/dl) Glucose R= Replicate 1 (mg/dl) Replicate 1 (mg/dl) Zeevi et al., Cell 2015

20 Different people have widely different post-meal responses to the same standardized meal Population Responses to Standardized Meals Four Individual Responses to Bread Zeevi et al., Cell 2015

21 Different people have widely different post-meal responses to the same standardized meal Zeevi et al., Cell 2015

22 What explains the variability in people s response to the same food?

23 Variability in post-meal glucose response across people associates with microbiota composition and function Zeevi et al., Cell 2015

24 Positive association between ABC transporters and post-meal glucose response to all standardized meals Positive association with TIIDM (Karlsson et al., 2013) Positive association with western high-fat/high-sugar diet (Turnbaugh et al., 2009) Zeevi et al., Cell 2015

25 Can we predict the personal post-prandial glucose response to any complex meal?

26 Measured PPGR (iauc; mg/dl*h) Meal Carbohydrates: State of the art in predicting post-meal glucose responses State of the art Zeevi et al., Cell 2015 Meal carbohydrates (g)

27 Zeevi et al., Cell 2015 Prediction scheme

28 Model features 800 People X MetaPhlAn abundances KEGG abundances 16S OTUs Growth dynamics 46,898 Meals X 200 Nutrients Including fatty acids, vitamins and minerals 5,417 Days X Multiple recorded features Meal times, sleep, exercise, stress, hunger, medication 800 People X 30 Blood parameters 100 Questions 100 FFQ features Zeevi et al., Cell 2015

29 Measured PPGR (iauc; mg/dl*h) Accurate predictions of personalized glucose responses State of the art Our prediction 800 participants Prediction validation 100 participants Meal carbohydrates (g) Predicted PPGR (iauc; mg/dl*h) Predicted PPGR (iauc; mg/dl*h) Zeevi et al., Cell 2015

30 Zeevi et al., Cell 2015 Meal and lifestyle factors affect the post-meal glucose response

31 Zeevi et al., Cell 2015 Microbiome features affect the post-meal glucose response

32 Can personally tailored dietary interventions improve post-prandial glucose responses? Daphna Rothschild Orly Ben-Yaacov Michal Rein

33 Constructing personally tailored diets that achieve normal post-prandial glucose responses Zeevi et al., Cell 2015

34 Can you distinguish between the good and bad menus? Bad Good? Bad Good? Diet Diet Diet Diet Breakfast Lunch Snack Dinner Night snack Muesli Sushi Marzipan Corn and nuts Toblerone and coffee Egg with bread and coffee Hummus and pita Edamame Vegetable noodles with tofu Ice cream Zeevi et al., Cell 2015

35 Can you distinguish between the good and bad menus? Bad Diet Good Diet Breakfast Lunch Snack Dinner Night snack Muesli Sushi Marzipan Corn and nuts Toblerone and coffee Egg with bread and coffee Hummus and pita Edamame Vegetable noodles with tofu Ice cream Zeevi et al., Cell 2015

36 Personally tailored diets reduce the post-prandial glucose response Day Zeevi et al., Cell 2015

37 Can you distinguish between the good and bad menus? Bad Good? Diet Diet Bad Good? Menu Diet Breakfast Orange juice Croissant Lunch Snack Dinner Night snack Schnitzel Peach Bread with butter Grapes Goulash with rice Halva Hummus Red wine Zeevi et al., Cell 2015

38 Can you distinguish between the good and bad menus? Bad Diet Good Diet Breakfast Orange juice Croissant Lunch Snack Dinner Night snack Schnitzel Peach Bread with butter Grapes Goulash with rice Halva Hummus Red wine Zeevi et al., Cell 2015

39 Personally tailored diets reduce the post-prandial glucose response Day Zeevi et al., Cell 2015

40 Foods that appear in the good diet of one person may appear in the bad diet of another Zeevi et al., Cell 2015

41 Personally tailored diets improve post-meal responses Zeevi et al., Cell 2015

42 Dietary interventions targeting post-meal glucose responses induce consistent changes in microbiota Zeevi et al., Cell 2015

43 Dietary interventions targeting post-meal glucose responses induce consistent changes in microbiota Roseburia inulinivorans increases following the good diet week Low levels associate with TIIDM (Qin et al., 2012) Zeevi et al., Cell 2015

44 Personalized nutrition project summary High interpersonal variability in post-meal glucose response to identical meals Personal and microbiome features enables accurate glucose response prediction Short term personalized dietary interventions successfully lower post-meal glucose

45 Why can t we maintain our weight after dieting? Christoph Thaiss Shmulik Motola Daphna Rothschild

46 People tend to regain their weight after a successful diet

47 A mouse model of recurring obesity

48 Is there a memory of previous obesity?

49 Is there a memory of previous obesity?

50 Some differences in 16S microbiome composition are retained after weight loss

51 Does the microbiome have a causal role in enhanced weight regain?

52 Antibiotic treatment abolishes effect of previous obesity

53 Microbiome transfer to germ free mice transmits enhanced weight regain phenotype

54 Can we predict weight regain using only microbiome composition?

55 A microbiome-based predictor accurately predicts the degree of future weight gain a 16S-based microbiota profiling b Prediction of obesity history Controls Ex-obese mice Does microbiome come from ex-obese mouse? True postiive rate ROC score How much weight gain upon HFD feeding? False positive rate c 16S-based prediction of weight gain d Two-step prediction of weight gain sured sured

56 Priority feature importance Measured Measured Multiple microbiome 0.4 factors contribute to predictions 0.2 R = R = Predicted Predicted e 10 0 Features contributing to prediction f Top 10 contributing OTUs R = OTUs Weeksellaceae (F) Lactobacillus (G) Mollicutes (C) Actinobacteria (P) Tenericutes (P) Bacteroides uniformis (S) Bacteroides acidifaciens (S) Tissierellaceae (F) Bacteroides (G) Gammaproteobacteria (C) Priority feature importance

57 Can we treat relapsing obesity by targeting the microbiome?

58 Fecal Microbiome Transplantation (FMT) abolishes microbiome-driven metabolic memory HFD NC ecal transplantation HFD HFD NC NC ecal transplantation D + NC FMT + cychfd FMT b a PC2 (12.23%) d e cose (mg/dl) ) HFD NC NC HFD NC NC fecal transplantation HFD HFD NC NC -0.2 HFD NC -0.3 cychfd Donors -0.3 Time cychfd + NC FMT cychfd + cychfd FMT fecal transplantation PC1 (23.50%) NC primhfd cychfd + NC FMT cychfd + cychfd FMT HFD * * Recipients b PC2 (12.23%) e ucose (AUC) c Weight (g) cychfd + NC FMT 45 cychfd + cychfd FMT Weight (g) Groups NC primhfd cychfd + NC FMT cychfd + cychfd FMT HFD 20 PC1 (23.50%) on HFD Time 15 (weeks) Groups on HFD * NC primhfd c Recipients cychfd + NC FMT 45 cychfd + cychfd FMT HFD 40 NC cychfd * Donors f Time (weeks) FMT f * FMT *

59 Post-biotic therapy abolishes microbiome-driven metabolic memory XN1 XN2

60 Weight (g) c Row max Post-biotic therapy abolishes microbiome-driven cychfd/nc metabolic during HFD memory ringenin l primhfd PrimHFD cychfd CycHFD + Vehicle vehicle cychfd CycHFD + +XN1/XN2 apigenin/naringenin HFD 35 * apigenin/ naringenin uring besity After obesity 20 Groups on HFD Time (weeks)

61 Relapsing obesity summary

62 Segal Lab Elinav Lab David Zeevi Tal Korem Adina Weinberger Dafna Rothschild Nastya Godneva Tali Avnit-Sagi Maya Pompan-Lotan Elad Matot Dar Lador Michal Rein Orly Ben Yaakov Rony Bikovsky Izhak Levi Hadas Elisar Neta Levkovich Noa Kossower Gal Malka Martin Mikl Shira Gabbay Eran Kotler Leeat Keren Ronit Nir Yaara Unger Iris Kalka Niv Zmora Jotham Suez Jamel Abu-Mahdi Gili Zilberman-Schapira Lenka Dohnalova Merav Pevsner-Fischer Christoph Thaiss Hagit Shapiro Shmulik Motola Maayan Levy Kfar Shaul Medical Center Dr. David Israeli Tel-Aviv Sourasky Medical Center Prof. Zamir Halpern