Personalized Nutrition by Prediction of Glycemic Responses. Tal Korem Lab of Prof. Eran Segal Weizmann Institute of Science

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1 Personalized Nutrition by Prediction of Glycemic Responses Tal Korem Lab of Prof. Eran Segal Weizmann Institute of Science

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

3 General recommendations in nutrition Source: USDA

4 Consumption of artificial sweeteners

5 Increase in artificial sweetener consumption is a major recent change in our nutrition 86% of Americans use diet products Consumers spend $21B per year on diet drinks Sylvetsky et al., Am J Clinical Nutrition 2012

6 Artificial sweeteners are recommended for weight loss and for assisting in blood glucose control REPLACING SUGARY FOODS AND DRINKS WITH SUGAR-FREE OPTIONS CONTAINING NON-NUTRITIVE SWEETENERS IS ONE WAY TO LIMIT CALORIES AND ACHIEVE OR MAINTAIN A HEALTHY WEIGHT. WHEN USED TO REPLACE FOODS AND DRINKS WITH ADDED SUGARS, IT CAN HELP PEOPLE WITH DIABETES MANAGE BLOOD GLUCOSE LEVELS

7 Artificial sweeteners induce glucose intolerance by altering the gut microbiota Jotham Suez Eran Elinav Suez et al., Nature 2014

8 What is the effect of artificial sweeteners on mice? Suez et al., Nature 2014

9 Artificial sweeteners induce glucose intolerance in mice Lean mice Obese mice Different formulations Different doses Different mouse strains

10 Do artificial sweeteners interact with the microbiome? Genetics Lifestyle Microbes

11 What is the effect of artificial sweeteners on mice? Suez et al., Nature 2014

12 Antibiotics reverse the effect of artificial sweeteners A, Ciprofloxacin & Metronidazole (targets Gram-) B, Vancomycin (targets Gram+) Suez et al., Nature 2014

13 Transferring the microbiota of mice that consume artificial sweeteners transfers the glucose intolerance Suez et al., Nature 2014

14 Transferring the microbiota grown in the presence of artificial sweeteners transfers the glucose intolerance Suez et al., Nature 2014

15 Artificial sweeteners drive glucose intolerance in mice by altering the gut microbiota but what about people?

16 Suez et al., Nature 2014 What happens to humans after just five days of consuming artificial sweeteners?

17 Artificial sweeteners induce glucose intolerance in most but not all individuals Responders Non-responders Before massive consumption After massive consumption Suez et al., Nature 2014

18 Transferring the microbiota of responders to artificial sweeteners transfers the glucose intolerance phenotype Responders Non-Responders Suez et al., Nature 2014

19 Validation studies (2015) Diabetologia DOI /s LETTER Positive association between artificially sweetened beverage consumption and incidence of diabetes Allison C. Sylvetsky Meni 1,2 &Susan E. Swither s 3 &Kristina I.Rother 1 Received: 15 May 2015 /Accepted: 30 June 2015 # Springer-Verlag (outside theusa) 2015 Keywords Artificial sweeteners. Artificiallysweetened beverages. Diabetes. Diet soda. Non-nutritivesweeteners. Obesity. Soda. Sugar-sweetened beverages Abbreviations ASB Artificially sweetened beverages SSB Sugar-sweetened beverages When corrected for BMI and waist circumferen nificant association between sugar-sweetened soft type 2 diabetes persisted whereas the relationsh ASB intake and type 2 diabetes was attenuated, obesity asan important link between ASB and type In fact, the prevalence of obesity was strikingly h ASB cohort than among the SSB consumers, desp that their energy intakes were similar (ASB consu 27.2±4.2 kg/m 2, obesity prevalence 20.3%, ene 7,899±2,046 kj/day; SSB consumers: BMI 26.2±

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

21 What is healthy nutrition?

22 How can we take a science-based approach to nutrition? David Zeevi

23 What should a marker of healthy nutrition satisfy? Relevant for weight management Relevant for metabolic disease Easily measurable quantitatively

24 Blood sugar levels Postprandial (post-meal) glucose response as a measure of healthy nutrition Directly affects fat storage, weight gain and hunger Strongly associated with disease Easily measurable Diabetes Obesity Cardiovascular disease Hunger Chronic metabolic disorders Time Bonora et al., Diabetologia 2001; Cavalot et al., Diabetes Care 2011; Wang et al., Diabetes Care 2004; Temelkova-Kurktschiev et al., Diabetes Care 2000; O'Keefe et al., Am J Cardiol 2007

25 Maintaining normal blood glucose levels is key to fighting the rise in disease

26 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

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

28 What could affect our response to food? Genetics Nutrition Lifestyle Microbes

29 התזונה כגורם מרכזי המשפיע על הרכב המיקרוביום The microbiome affects our response to food Koeth et al., Nature Medicine 2013

30 Insulin sensitivity Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome Healthy donor Recipient w/ Metabolic Syndrome Recipient w/ Metabolic Syndrome Vrieze et al, Gastroenterology 2012

31 What could affect our response to food? Genetics Nutrition Lifestyle Microbes

32 The Personalized Nutrition Project: Clinical and microbiome data collected Profiling 800 people

33 Zeevi et al., Cell 2015 Continuous glucose monitoring

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

35 Cohort bacterial composition comparable to other international cohorts

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

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

38 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

39 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

40 Different people have opposite responses to different standardized meals Zeevi et al., Cell 2015

41 Meal response Glucose Different people have widely different post-meal responses to the same real-life meals Meal carbohydrates

42 General recommendations in nutrition Source: USDA

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

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

45 Variability in post-meal glucose response across people associates with microbiota composition and function Positive association with PPGR to glucose + bread High levels associate with a high-fat low-fiber diet (Wu et al., 2011)

46 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

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

48 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)

49 Zeevi et al., Cell 2015 Prediction scheme

50 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

51 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

52 Zeevi et al., Cell 2015 Features contributing to prediction

53 Features contributing to prediction Meal response

54 Zeevi et al., Cell 2015 Features contributing to prediction

55 Zeevi et al., Cell 2015 Features contributing to prediction

56 Can personally tailored dietary interventions improve post-prandial glucose responses?

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

58 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

59 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

60 A good meal for one person can be a bad meal for another Participants Zeevi et al., Cell 2015

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

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

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

64 Dietary interventions targeting post-prandial glucose responses induce consistent changes in microbiota Bifidobacterium adolescentis decreases during good week. Low levels associated with greater weight loss (Santacruz et al., 2009)

65 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

66 Summary Artificial sweeteners induce glucose intolerance driven by gut microbial changes High interpersonal variability in post-meal glucose observed in an 800-person cohort Using personal and microbiome features enables accurate glucose response prediction Short-term personalized dietary interventions successfully lower post-meal glucose

67 Segal Lab Elinav Lab David Zeevi Adina Weinberger Daphna Rothschild Nastya Godneva Tali Avnit-Sagi Maya Pompan-Lotan Elad Matot Dar Lador Michal Rein Orly Ben Yaakov Rony Bikovsky Noa Kossower Gal Malka Niv Zmora Jotham Suez Jamel Abu-Mahdi Gili Zilberman-Schapira Lenka Dohnalova Merav Pevsner-Fischer Christoph Thaiss Kfar Shaul Medical Center Dr. David Israeli Tel-Aviv Sourasky Medical Center Prof. Zamir Halpern