The microbiome and personalized nutrition. Eran Elinav, M.D., Ph.D. Immunology Department Weizmann Institute of Science, Israel

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1 The microbiome and personalized nutrition Eran Elinav, M.D., Ph.D. Immunology Department Weizmann Institute of Science, Israel AICR meeting November, 2016

2 The microbiome is a signaling hub Genetics Immunity

3 Studying host-microbiome interactions Circadian rhythmicity Thaiss et al, Cell 2014 Thaiss et al, Cell 2014, in press Nutrition Suez et al, Nature 2014 Zeevy et al, Cell 2015 Immunity Wladarska et al, Cell 2014 Levy et al, Cell 2015 Gury et al, Cell 2016 Eran Segal Relapsing obesity Thaiss et al, Nature 2016, in press Microbial Drivers of disease Korem et al, Science 2015

4 The obesity pandemic No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30% 37.5% 78 million 17% 12.5 million Adults Children

5 Prevalence of Type II Diabetes worldwide

The obesity paradox Weight change (%) 2.0 0.0-2.0-4.

6 The obesity paradox Weight change (%) Intervention Males (average -7.0%) Weight Females change (average females -5.3%) Williams et al., Obesity Reviews, 2015

7 Our dieting failure Time following weight loss Months Exercise Meal replacement Very low energy diet Diet Exercise+diet Orlistat (pharma) Average weight loss Kilograms Source: McKinsey Global Institute November 2014

8 Current paradigm: defining diets by the food Low GI

9 Current paradigm: defining diets by the food High GI Low GI

10 Current paradigm: defining diets by the food

People have widely different glucose responses to the same food Percent of population 20 15 10 5 0

11 People have widely different glucose responses to the same food Percent of population Response to Glucose Glucose response (mg/dl) Glucose rise (mg/dl) Time (hours)

People have widely different glucose responses to the same food Percent of population 25 20 15 10 5 0

12 People have widely different glucose responses to the same food Percent of population Response to Bread Glucose response (mg/dl) Glucose rise (mg/dl) Time (hours)

13 one size fits all diet cannot be effective

14 Diet must be tailored to the individual

15 Factors driving personalized dietary responses can be measured Genetics Microbes Lifestyle

16 A novel sciencebased approach to nutrition: The Personalized Nutrition Project CRC Zeevi et al, Cell, 2015

17 The Personalized Nutrition Project: Experimental Setup Register online Log activities & food Analyze your bacteria Measure glucose response to food g Today Study Week Start During Study Week Study Week End View Bacteria Personal Diet Genetics Glucose Microbes Blood tests Questionnaires Connection meeting Study week analysis Plan personal diet

The Personalized Nutrition Project: Understanding personal glucose responses 1000 people profiled 50,000 meals measured 9,000,000 Kcal 12,000 Kg

18 The Personalized Nutrition Project: Understanding personal glucose responses 1000 people profiled 50,000 meals measured 9,000,000 Kcal 12,000 Kg 5,000 standardized meals 2,000,000 glucose measures 150,000 hours 10 Billion metagenome reads 5,000 more people registered

19 The Personalized Nutrition Project: Cohort statistics Age BMI years of age ~50% overweight ~20% obese ~25% pre-diabetic HbA1C%

20 Microbiome features overlap with HMP & MetaHIT

(mg/dl) 350 300 250 200 150 100 50 30 R=0.

21 Postprandial (post-meal) response is correlated with multiple clinical markers Maximal post-meal response (mg/dl) R=0.34 BMI Participants (sorted)

2 0 100 200 300 400 500 600 700 Participants (sorted) Maximal post-meal response (mg/dl) 350 300

22 Postprandial (post-meal) response is correlated with multiple clinical markers Maximal post-meal response (mg/dl) R=0.56 HbA1c% Participants (sorted) Maximal post-meal response (mg/dl) R=0.65 Wakeup glucose (mg/dl) Participants (sorted)

Postprandial (post-meal) response is correlated with multiple clinical markers Maximal post-meal response (mg/dl) 350 300 250 200 150 100 50 60 55 50 R=0.

23 Postprandial (post-meal) response is correlated with multiple clinical markers Maximal post-meal response (mg/dl) R=0.45 Age Participants (sorted) Maximal post-meal response (mg/dl) R=-0.22 HDL Cholesterol (mg/dl) Participants (sorted)

24 Testing the cohort response to standardized meals 800 x

25 Testing the cohort response to standardized meals

26 Major inter-personal variability in the response to test foods

27 Major inter-personal variability in the response to real-life foods

28 Prediction scheme: current state of the art Measured iauc (mg/dl h) r=0.37 Meal carbohydrates (g)

29 New prediction scheme

30 Prediction scheme Model Features R

31 Prediction scheme Model Features Meal features Nutrient weights, water R

32 Prediction scheme Model Features Meal features Nutrient weights, water R Logged features Time of day, sleep, exercise

33 Prediction scheme Model Features Meal features Nutrient weights, water R Logged features Time of day, sleep, exercise Microbiome features Gene levels, bacteria levels, 16S

34 Prediction scheme Model Features Meal features Nutrient weights, water R Logged features Time of day, sleep, exercise Microbiome features Gene levels, bacteria levels, 16S Personal features Blood tests, questionnaires

35 Accurate predictions of personalized glucose responses State of the art Our prediction 800 participants Prediction validation 100 participants Measured PPGR (iauc; mg/dl*h) Meal carbohydrates (g) Predicted PPGR (iauc; mg/dl*h) Predicted PPGR (iauc; mg/dl*h)

36 Microbiome features affect the post-meal glucose response

Constructing personally tailored diets that achieve normal

predicted responses Food diary Continuous glucose monitoring

Diet: High predicted responses Food diary One week profiling

37 Constructing personally tailored diets that achieve normal post-prandial glucose responses Expert (n=14) Good Diet: Low predicted responses Food diary Continuous glucose monitoring Daily microbiota profiling 6 x Predict Days Bad Diet: High predicted responses Food diary One week profiling Algorithm (n=12) Continuous glucose monitoring Daily microbiota profiling Days

38 Foods that appear in the good diet of one person may appear in the bad diet of another

39 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

40 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

Personally tailored diets reduce the post-prandial glucose response Glucose levels (mg/dl) 180

41 Personally tailored diets reduce the post-prandial glucose response Glucose levels (mg/dl) Days Good Bad Glucose rise (mg/dl) 30 P< Time (hr)

42 Can you distinguish between the good and bad menus? Bad Good? Menu Menu Bad Good? Menu Menu Breakfast Lunch Snack Dinner Night snack Orange juice Schnitzel Peach Bread with butter Grapes Croissant Goulash with rice Halva Hummus Red wine

43 Can you distinguish between the good and bad menus? Bad Menu Good Menu Breakfast Lunch Snack Dinner Night snack Orange juice Schnitzel Peach Bread with butter Grapes Croissant Goulash with rice Halva Hummus Red wine

44 Personally tailored diets reduce the post-prandial glucose response Glucose levels (mg/dl) Days Good bad Glucose rise (mg/dl) 30 P< Time (hr)

45 Personally tailored diets reduce the post-prandial glucose response Glucose levels (mg/dl) Good bad Days Glucose rise (mg/dl) 30 P< Time (hr)

46 Personally tailored diets reduce the post-prandial glucose response

47 Personalized dietary interventions induce consistent changes in microbiota

48 The diet- microbiome- metabolism axis can be diagnostically and therapeutically exploited

Thanks Elinav lab Meirav Pevsner-Fischer Hagit Shapiro Mally Bachash-Dori

Sara Federici Chagai rott Maayan Levy Christoph Thaiss Jotham Suez Gili

Evgeny Tatirovsky Leviel Fluhr Sofia Braverman Jemal Ali Mahdi Daniel

Elise Taieb Rina Gudinski Career Development chair Segal Lab David Zeevi

Pompan-Lotan Dar Lador Michal Rein Orly Ben Yaakov Noa Kossower Gal Malka

49 Thanks Elinav lab Meirav Pevsner-Fischer Hagit Shapiro Mally Bachash-Dori Nira Truzman Eran Blacher Karina Golberg Stavros Bashiardes Niv Zmora Sara Federici Chagai rott Maayan Levy Christoph Thaiss Jotham Suez Gili Zilberman-Shapira Timur Tuganbaev Ori Maza Shlomic Itav Shachar Rozin Evgeny Tatirovsky Leviel Fluhr Sofia Braverman Jemal Ali Mahdi Daniel Halstuch Maya Elazar Uria Mor Lenka Dohnalova Keren Uritzky Yotam Harnik Elise Taieb Rina Gudinski Career Development chair Segal Lab David Zeevi Tal Korem Adina Weinberger Daphna Rothschild Tali Avnit-Sagi Maya Pompan-Lotan Dar Lador Michal Rein Orly Ben Yaakov Noa Kossower Gal Malka Izhak Levy Hadas Elisar Anastasia Godneva Neta Levkovich Prof. Zamir Halpern Prof. Alon Harmelin

Personalized Nutrition by Prediction of Glycemic Responses

A science-based approach to nutrition: Personalized Nutrition by Prediction of Glycemic Responses Eran Segal Weizmann Institute of Science The metabolic disease epidemic Changes in our nutrition greatly