Based on the Gut Microbiome A journey to better health with - - PDF document

6/12/2019 1 A journey to better health with Microbiome Solutions.

Personalized Dietary Treatment Based on the Gut Microbiome Conflict of Interest Disclosure Statement

Susan Yake is a paid consultant for DayTwo

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Objectives

Objective 1: Discuss the degree of variation in personal glycemic response to meals among different individuals Objective 2: Describe the role the gut microbiome plays in the development and management of diabetes and

• ther diseases

Objective 3: Explain that machine learning algorithms can be used to personalize diets to normalize blood glucose levels in people with diabetes and pre-diabetes

“If you think you are too small to make a difference, try sleeping in a closed room with a mosquito.” (African Proverb)

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Andromeda Galaxy Compared to Microbes in the Gut

There are 100 Billion Stars in the Andromeda Galaxy There are 390 x as many microbes in the human body - 39 Trillion This is 1.3 x more than the 30 Trillion human cells in the human body It is estimated that 3 to 4 lbs. of our weight is from bacteria

Sender R, Fuchs S, Milo R. Are We Really Vastly Outnumbered? Revisiting the Ratio of Bacterial to Host Cells in Humans. Cell 2016;164:337–40. pmid:26824647 https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002533

Intervent ervention ion Study dy

Ice Cream Coffee & Chocolate te Corn & Nuts ts Marzipan Sushi Muesli Noodles Edama mame me Pita & Hummus mmus Eggs gs & Bread

Can you guess ess the good d diet et?

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Intervent ervention ion Study dy

Ice Cream Noodles Edama mame me Pita & Hummus mmus Eggs gs & Bread Coffee & Chocolate te Corn & Nuts ts Marzipan Sushi Muesli

Can you guess ess the good d diet et?

Initial tial Intervention ervention Result ults

200 200 150 150 100 100 50 50

Spiking Diet Non-Spiking Diet (mg/dl)

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Weizmann Institute Research

• Prof. Eran Segal, Ph.D

Eran Elinav, M.D. Ph.D

What is the best diet for humans?

An apple a day keeps the doctor away?

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Post-Prandial Glucose Response as a Measure of Healthy Nutrition The Personalized Nutrition Project: Clinical and Microbiome Data Collected

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People e Have e a Widel ely y Diff fferent erent Glucose se Respo pons nse e to the Same Food

Carl Carl

1 2 40 160 120 80 1 2 40 160 120 80 1 2 40 160 120 80 1 2 40 160 120 80

Nancy Phil Phil Jill

Glucose levels (mg/dl) Time (hours) Time (hours) Time (hours) Time (hours)

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People e Have e a Widel ely y Diff fferent erent Glucose se Respo pons nse e to the Same Food

Population Responses to Standardized Meals Opposite Responses to Bananas and Cookies

0.01 0.03 0.02 0.00 10 20 30 40 50 60 70 80 Standardized meal PPGR (iAUC, mg/dl∙h) Participants (density) 8 22 22 29 29 33 33 Glucose Bread Bread & butter Fructose 85 100 115 85 100 115 30 60 90 120 Time (Min) Blood glucose (mg/dl) Participant nt 445 Participant nt 644 Banana Cookie 30 60 90 120

Zeevi et al., Cell, in press

Thera rape peutic utic Potentia ential l of The Microbio

• biome

me

Microbiome was important in explaining the difference in meal glucose responses Glucose responses were modifiable by changing what was eaten This lead to exploring if the microbiome could accurately predict glucose response for a food

• r meal

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How do we use trends to make predictions?

Inputs

Measure personal features

AI & ML

Design personalized diet to lower glycemic responses

Output

Predict personal glycemic responses Microbiome Blood tests and CGMS Questionnaires / Lifestyles Anthropometrics Food diary

Personalized Nutrition Predictor

Machin ine e Learn rnin ing Proces ess

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Cell Volume 163, Issue 5, Pages 1079-1094 (November 2015)

DOI: 10.1016/j.cell.2015.11.001

David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan, Jotham Suez, Jemal Ali Mahdi, Elad Matot, Gal Malka, Noa Kosower, Michal Rein, Gili Zilberman-Schapira, Lenka Dohnalová, Meirav Pevsner-Fischer, Rony Bikovsky, Zamir Halpern, Eran Elinav, Eran Segal

• Bifidobacterium adolescentis

decreases following the ‘good’ diet week

• Low levels associate with greater

weight loss (Santacruz et al., 2009)

• Roseburia inulinivorans increases

following the ‘good’ diet week

• Low levels associate with TIIDM

(Qin et al., 2012)

Zeevi et al., Cell, 2015

Dietary interventions targeting post-meal glucose responses induce consistent changes in microbiota

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Glycem emic ic Index ex vs Glycem emic ic Respo pons nse

Glycemic Index is an Average

Original study was done with 10 people Score is 0 to 100 Score of 55 or lower is good Score based on single food item Amount of food scored is set Glycemic response varies by an average

• f 20 percent

Glycemic Response is Personal

First study has 1000+ individuals Score is 1 to 9.9 Score of 7 or higher is good Score can be based on single food or a combination of foods Score changes with amount for meal or snack Results of score reproducible The American Journal of Clinical Nutrition, Volume 104, Issue 4, 1 October 2016, Pages 1004–1013

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Mayo Clinic ic Study

Different glycemic response from Participants after eating a bagel and cream cheese Carbohydrate sensitivity is measured as the correlation between carbohydrates (in grams) in the meal consumed and the computed postprandial glycemic response

Intervent ervention

• n Impact

act on Time-In In-Range….Interim Results

Pre-Diabetes RCT in Weizmann Algori rithm hm Diet

60% 60% avg reduction of time spent >140 mg/dl

Mediter erra ranea ean Diet et

10% % avg reduction of time spent >140 mg/dl

N=93 participants; Based on CGM Data

*** P<0.0001

Months 1 2 3 4 5 6

• 70
• 60
• 50
• 40
• 30
• 20
• 10

Time >140 (% reduction, CGM-based) Algorithm Standard of Care

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HbA1c 1c aver erage e reduction of 0.62% 2% in 3 months hs and 0.92% % in 6 months hs

• 3%
• 2%

2%

• 1%

1% 2% 3% Reduction in HbA1C

Intervention Impact on HbA1c….Interim Results

Algorithm

• rithm diet

t reduc duces s averag age e glucos ucose e levels ls

Placebo, Metformin, Lifestyle from Diabetes Prevention Program, NEJM 2002 CGM-based HbA1c% estimate from Nathan et al., Diabetes Care 2008

*** P<0.0001

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Microbiome Test Samples Ready for Full Spectrum Sequencing

US Studi dies es of the Microbiom biome e Usin ing g Machin hine e Learnin arning

Most Common Microbial Phyla

Average HE Average Ulcerative Colitis Average LS Colonic Crohn’s Disease Average Ileal Crohn’s Disease

Most Common Microbial Phyla

Major State Shifts in Microbial Ecology Phyla Between Healthy and Three Forms of IBD

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The Adult Healthy Gut Microbiome Is Remarkably Stable Over Time Source: Eric Alm, MIT

• Average of 200 species in the human gut
• Between 300 and 1000 species
• Most estimate there are 500

The Twins Study was the first study of its kind to compare molecular profiles of identical twin astronauts with one in space and another on Earth The Twins Study is a supplemental study built upon the framework of the One-Year Mission research investigations The Twins Study explores space through you by using omics (DNA, Gene Expression, Microbiome) to look more closely at individual health

NASA’s Human Research Program

• DR. MICHAEL SCHMIDT

CHRISTOPHER E. MASON, PH.D.

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Northwest Biologist with a Ph.D. in chemistry Founder of the Institute for Functional Medicine

Leads the Personalized LifeStyle Medicine Institute

First member of the Board of Trustees of Bastyr University

Over 40 years researching nutrition at the cell level and 35 years as a recognized international leader in nutritional medicine

Hyperinsulinemia and Obesity

• High insulin levels are associated

with increased risk of obesity

• Hyperinsulinemia increases the

risk of weight regain after weight loss

• Higher glucose variability and

insulin levels can result in increased hunger level making weight loss difficult

Karel A. Erion and Barabara E Corkey Hyperinsulinemia: a Cause of Obesity Current Obesity Reports 2017 May 2 6(2): 178-186 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5487935/

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• 1/2 of pts with HTN are insulin

resistant and have hyperinsulinemia

• High insulin levels linked to very-low-

density lipoprotein synthesis and plasma triglyceride levels.

• Atherosclerotic Heart Disease and

Rise in CHF is associated with high insulin levels

. Garg A. Insulin resistance in the pathogenesis of dyslipidemia. Diabetes Care. 1996;19:387–9.

Hyperinsulinemia and Heart Disease

Sources: International Diabetes Federation. IDF Diabetes Atlas, 8th edition. International Diabetes Federation, 2017. http://www.diabetesatlas.org

Hyperinsulinemia

Diabetes doubles the risk

• f developing cancers of

the liver, pancreas and endometrium Clear but smaller increase in risk for colon and breast cancers in people who have diabetes

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Hyperinsulinemia & Alzheimer’s Disease

After Insulin has lowered the glucose level, it must be degraded by the insulin-degrading (IDE) enzyme to prevent hypoglycemia IDE also degrades amyloid, the protein fragment found in synapse-destroying plaques in Alzheimer’s Disease IDE cannot degrade amyloid when the insulin is being degraded.

Hyperinsulinemia increased the risk of Alzheimer’s Disease

Gut Biome me DNA Seque uencing cing

2

Lab Kit/ Provi vide de Samp mple le

The Clini nica cal l Experienc erience e

1

Rece ceive ve Gut Inventory Glyce cemic c Result ults

3

Build ld Personali alized d Glyce cemic c Controlle lled d Meal l Plan(s)

4

Adherence ce & Engag agement

5

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Questions ions? Susan an.Ya .Yake@gmail.com ke@gmail.com

Thank nk you!