The Microbiome and Diabetes: ADA/JDRF Research Symposium Summary Robert E. Ratner, MD, FACP, FACE

Friday, March 4, 2016 4:30 p.m. – 5:15 p.m.

The microbiome—the population of bacteria, viruses, fungi and archaea that live in our gut and on our skin—is a complex ecosystem with a high degree of inter-individual variability. It provides benefits to the host, including nutrient harvest from food and protection against pathogens. The microbiome is dynamically regulated by both genes and environment, and in turn, critically influences both physiology and lifelong health.

Characteristics of the microbiome, such as diversity, have been correlated to environmental factors, including diet and medications; metabolic functions; and immune system activities. Relationships in some cases appear to be associated with type 1 and type 2 diabetes and obesity. For example, the composition of gut microorganisms in animals and humans with obesity or diabetes is distinct from those who are lean. The composition also appears to be different between children who develop type 1 diabetes and those who do not. In addition, patients who have undergone bariatric surgery experience dramatic changes in the composition of their microbes, which may be either influence, or be associated, with the improved metabolism and blood glucose control that is often observed following the procedure. These differences suggest that something about obesity and diabetes may alter the microbiome, or, alternatively, that microbiome composition may predispose individuals to these diseases.

While there appears to be a strong association between the composition of the microbiome and changes in the host’s metabolism, the mechanisms behind these changes remain relatively unclear. It has been hypothesized that the microbiome may function by influencing fatty acid or carbohydrate metabolism, gut hormone concentrations, or inflammation; however, there are not enough data at present to propose a unifying model for these relationships. Once clear mechanisms for how these microorganisms exert their influence are understood, it may be possible to utilize this knowledge to intentionally change human metabolism. Importantly, many of the approaches that could potentially be derived from an understanding of the microbiome, such as probiotics and nutritional therapies, are relatively inexpensive and may be readily accessible to broad populations.

This presentation will summarize the current understanding of the microbiome as it relates to diabetes, highlighting the relationship between the microbiome and metabolism and key recommendations for pivotal research questions, as well as resource and policy needs to address these questions.

References:

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The Microbiome and Diabetes:ADA/JDRF Symposium Summary

Robert E. Ratner, MDChief Scientific and Medical Officer

American Diabetes Association

100 trillion microorganisms ; 10 times the number of human cells in our body (Savage 1977)

Predominantly not yet cultured to date (~70% of dominant species)

Central to Food-Microbiota-Host interactions (microbiome and human genome crosstalks in immune, neural and endocrine functions)

Mutualistic association derived from a long co-evolution

The microbiome can be modulated (unlike the human genome)

The human intestinal microbiota

Hartstra/nieuwdorp, Diabetes Care 2014.

pH dictates bacterial survival and gutmicrobiota composition

Reference gene catalogs:3.3 million bacterial genes/ 124 subjects10 million bacterial genes/1267 subjects

Li et al, Nature Biotech 2014

500,000 bacterial genes/individual

Comparable gene catalog for Europeans, Americans, Japanese, Chinese

50% bacterial genes of each microbiomeshared by >50% individuals: 

metagenomic core

Qin et al, Nature 2010

Arumugam et al, Nature 2011

57 Common species 

Human microbiomes differ by gut bacterial genes, species and ecology

‘Density plots’ for ~400 individuals

- ecological landscape -

Data density (Fraction of data close to a central point )

Scheffer, Nature 2001

Bacteroides

Prevotella

RuminococcusMethanobrevibacter

Human microbiomes differ by bacterial gene counts

Low HighGene count

n=277

Each column is an individualEach row is a gene, 50 are

displayed per MetaGenomic Species (MGS)

Colors reflect gene abundance

less more

Knownspecies

n=10

UnknownMGSn=58

Low gene count (low bacterial richness) individuals (23%) have less healthy metabolic &

inflammatory traits.Low gene count is a predictor of relapse rate in

IBD, aggravation in chronic conditions, non-response to a calory-restricted diet in obesity,…

Microbiome: a source of biomarkers for stratification and monitoring

Le Chatelier, Nature 2013 ; Cotillard, Nature 2013

Type 1 Diabetes Is Accelerating at a Rate that Appears Tied to the Environment (Versus Genetics)

Atkinson, Michels, Eisenbarth

( Lancet, 2014)

Type 1 Diabetes Is Increasing in Populations That Do Not Carry Classic High Risk Genes

Steck, Diabetes, 2011Markel et al, Science, 2013

Female NODMale NOD provided female microbiota

Male NOD

Studies of Type 1 Diabetes Using ”Biobreeding” (BB) BB-DP and BB-DR Rats

BB-DP rats -spontaneously develop diabetes at about 70 days of age, ~80%

BB-DR rats - a similar genetic strain (~<1% T1D), that requires extra stimuli such as viral infection to induce diabetes

A Single Bacteria (Lactobacillus johnsonii N6.2) Modulates Type 1 Diabetes in BB Rats

Valladares, Plos One, 2011

Microbiome analysis (16S RNA) suggests Lactobacillus johnsonii present in BBDR rats while infrequent or absent in BBDP rats; Lactobacillus Reuteri the opposite

WT BBDP

BBDP LR

BBDP LJ 6.1

*

Bacterial Diversity is Higher in Healthy Children than Those Who Develop Type 1 Diabetes

Giongo, ISME J, 2010

*

*

*

**

*

In DIPP, The Bacteroidetes:Firmicutes Ratio Differs in Early Life as a Function of Disease Development

Giongo et al. 2010, ISME J.

In DIPP, Specific Bacteria Positively Correlated with Autoimmunity

p-value < 0.01

Giongo, ISME J, 2010

In DIPP, Specific Bacteria Negatively Correlated with Autoimmunity

p-value < 0.01

Giongo et al. 2010, ISME J.

Blaser, Nature, 2011

Increased Antibiotic Use is One Potential Contributor…

One of many examples of an activity that has “changed” over this time period

Mode of Delivery

?

Fecal microbiota and obesity/type 2 diabetes mellitus; associations!

Qin, Nature 2012

Le Chatelier, Nature 2013

Karlsson, Nature 2013

Ridaura, Science 2013

Bacterial Dynamics and Disease Association

Korem T, et al. Science 349:1109, 2015

Diversity: Differential Effects of Weight and Weight Loss

Turnbagh et al, Nature, 2009, 

154 humans

Weight loss vs. macronutrient effects on microbiome

Ley et al, Nature, 2006

Humans: Weight Loss vs. Gain (12 lean, 9 obese, males)

Jumpertz et al, AJCN, 2011

% of weight maintenance calories in diet

% c

hang

e in

abu

ndan

ce fr

om w

eigh

t mai

nten

ance

Underfed

Overfed

Transfer of “obese” microbiota & signallingd = 2

OP

OR

•Obesity altered gut microbiota : not solely a function of obesogenic diet• altered symbiosis => altered peripheral and central molecular signaling machinery responsible for regulating energy metabolism, intestinal nutrient sensing, and inflammation Duca FA, Covasa M. Diabetes 2014

Obese-prone rats have differing

microbiota compared to obese-resistant

rats when bothmaintained on the

same high-fat diet Transfer of microbiota to germ-free mice replicated the obese phenotype and changes in metabolic signaling pathways of the intestine, adipose tissue, liver, and CNS

The intestinal microbial habitat

Mechanisms of Microbiota Regulation of Body Fat

• Accepted hypothesis: Enhanced harvest of ingested energy

• Support: Intestinal bacteria, unlike their host, have the capacity to metabolize complex plant polysaccharides to generate absorbable, energy-rich short chain fatty acids (SCFAs)

• Challenges to this model:• “Lean” microbiota often capable of generating more SCFAs

• “Lean” microbiota not associated with greater loss of calories in stool

• Enhanced calorie ingestion does not induce substantially increased fat deposition

• Increased weight gain and adiposity after colonization with “obese” microbiota not associated with increased gross or net calorie ingestion

• Alternative model: Microbiota-stimulated signaling influences host regulation of energy balance

• Appetite and food intake

• Energy expenditure

Cross-talks between gut microbesand host ?

Obesity

Diabetes

Inflammation

What? Why?

How ?

CVD’s

Gut microbiota derivedcompounds acting as a

triggering factors?

Cani et al Diabetes 2007

BacterialLPS

Metabolic endotoxemia

Gut Microbiota triggers Metabolic Endototoxemiaand Metabolic Inflammation

Cani et al Diabetes 2007

Adapted from

Akkermansia muciniphilaControls gut barrier function

Mucus layer thickness Antimicrobial peptide Reg3g Specific bioactive lipids (2-OG,

2-AG, 2-PG)

Oxidation Fat mass Inflammation

Insulin sensitivity Glucose production

Metabolic endotoxemia

Plasma cholesterol

Everard et al  PNAS 2013 

SCFA production increased in low bacteroides/high firmicuites

Kimura et al, Front. Endocrinol., 2014; Inoue et al, Front. Endocrinol. , 2014

Koch’s postulatesfor causality

• The microorganism must be identified/isolated from a diseased organ(ism).

• The microorganism should be associated with disease (association/intervention).

• The cultured microorganism should reproduce fenotype when introduced into an organism (inoculation).

Ralstonia• Phylum: Proteobacteria

• Gram negative Rod

• Species: 3 in humans R.mannitolylitica, R.pickettii and R.insidiosa 

• Human infections (via drinking water: in immunocompromised subjects eg. after kidney transplantation)

Adley et al 2013, Ralston et

Ralstonia picketii 4 weeks daily gavage on weight in male DIO

mice

Udayappan/,Kovatcheva, submitted

Udayappan/,Kovatcheva, submitted

Ralstonia picketii 4 weeks daily gavage on OGTT in male DIO

mice

Effect prevaccination with Ralstonia picketii on insulin

resistance

Udayappan/,Kovatcheva, submitted

Impact of Metformin on Gut Microbiome and Metabolism

Forslund K et al. Nature 528:262, 2015

Richness & diversity increase after GBP

Kong AJCN 2013

11 bacterial taxa are significantly modulated (16S rDNA). 50% of the modulations are linked to change in calorie supply

Gastric bypass

M0

M3

M6

microbiota is re-structured

RYGB Microbiota Not Associated with Decreased Food Intake

Liou et al., Science Transl Med 2013

Weight Loss in RYGB-R Mice Despite Increased Food Intake

Liou et al., Science Transl Med 2013

Doubly Labeled WaterEnergy Expenditure (8 days)

RYGB-R SHAM-R GF0

102030405060708090

100110120

kj/d

ay

N=4 N=6 N=4

Energy Expenditure in RYGB Microbiota Recipients

Days 1-8 After Colonization

Liou et al., unpublished

*

*p<0.05

RYGB Alters SCFA Balance Toward Propionate

Liou et al., Science Transl Med 2013

Treated Animals

Microbiota Recipient Animals

Luminal Propionate Stimulates Energy Expenditure

Control Propionate

Pai et al., unpublished

Prebiotics: Affect microbiome and are affected by it

• Non-digestible carbs which are fermented (glucans, galactans, etc.,) to SCFA’s.

• Low counts of some bacteria associated with that fermentation (e.g., Bifidobacteria) are also associated with obesity).

• Decreases adiposity in ob/ob and DIO mice.• Increase PYY and GLP-1 and decrease ghrelin in mice.• Decreases obesity in obese adults and lessens weight

gain in lean adolescents.• Decreases appetite, increases satiation

Delzenne et al, Br J Nutr, 2013; Cani and Delzenne, Current Pharmaceut Des, 2009

Glucose & Lipidmetabolism

Energyhomeostasis

Fat mass Muscle mass Body weight Food intake Leptin sensitivity

Prebiotic-induced microbiota modulation

glucose tolerance Insulin sensitivity Hepatic steatosis Plasma lipids

Inflammation& immunity

Plasma LPS Gut barrier Inflammation Reg3g Intectin

2009

2004

2004

2006

1996!!!1995!!!

Fiordaliso et et al Lipids 1995, Daubioul et al J Nutr 2000, Cani et al Br J Nutr 2004

2007

Effects on appetite (including GLP-1, PYY), plasma lipids, steatosis, LPS, inflammation and glycemia demonstrated in

humans

Fecal transplant - History• 4th Century BC: Chinese medicine, food

poisoning and diarrhea1,3

• 16th Century AD: Li Shizhen “yellow soup”, gastro-intestinal illness2,3

• 1958: Eiseman, antibiotics-induced chronic diarrhea

1. Ge Hong (Dongjin Dynasty) (2000) Zhou Hou Bei Ji Fang. Tianjijn Science & Technology Press: Tianjin, 2. Li S (Ming Dynasty) (2011) Ben Cao Gang Mu. Huaxia Press: Beijin3. Faming Zhang et al, , American College of Gastroenterology 2012: 1755

2. Fecal transplant

Past

Present

Borody, Nature Gastro, 2011

De Vrieze, Science 2013

• Screening donors (blooddonation protocol):- Questionnaire (bowel

habits, travel history, medication, etc)

- Screeening feces + Bloodborn viruses (Hepatitis, HIV, HTLV, CMV, EBV)

In Summary

Increased gut microbial diversity is healthy!Altered gut micobiota are associated with both autoimmunity (type 1) and obesity (type 2)Identification of specific pathogenic or protective organisms is in its infancyPassive (fecal transplant) and active (RYGB and ? prebiotics) microbiome modification alters autoimmunity, energy metabolism and weightControlled trials meeting Koch’s Postulates are required to prove causation

Van Nood, NEJM 2013

Effects of fecal transplantations in clostridium difficile diarroea

Effect donor faeces on periferal insulin sensitivity

A.Vrieze, Gastroenterology 2012

Muscle (periferal) insulin resistance

How can we achieve a deeper understanding of the therapeutic potential of the gut microbiome:

fecal microbial transplant (FMT)• at AMC >250 FMT’s since 2006,

predominantly in RCT due to large placebo effect : Long term (S)AE not observed yet (registry)

• At AMC ongoing/finished RCT’s with single/ multiple FMT using accepted clinical endpoints for:- IBD (Colitis ulcerosa, TURN trial)- vascular inflammation- insulin resistance/Dm2- NAFLD/NASH- Type 1 diabetes- VRE/ESBL

• Causality to find involved bacteria and unravel important metabolites

Smits/Nieuwdorp, Gastroenterology 2013 [; van Nood/Nieuwdorp, NEJM 2013

The Microbiota and Drug Therapy- Metabolomics meet Receptology

ADA Research Symposium – Diabetes and the Microbiome - Oct 2014

The highly complexgut microbiome 

The equally highlycomplex host 

Metabolites TargetsReceptorsSCFAs

Aromaticmetabolites

SecondaryBile acids