Gut Microbiome-Targeted Treatment for Diabetes: What’s ... · Gut Microbiome-Targeted Treatment for Diabetes: What’s Your Gut Telling You? Amanda K. Kitten, Pharm.D. Master of

GutMicrobiome-TargetedTreatmentforDiabetes:What’sYourGutTellingYou?

AmandaK.Kitten,Pharm.D.MasterofScienceGraduateStudentandPharmacotherapyResident

DivisionofPharmacotherapyTheUniversityofTexasatAustinCollegeofPharmacyPharmacotherapyEducationandResearchCenter

UTHealthSanAntonio

Friday,April13,2018

LearningObjectives1. Identifypotentialmechanismsbywhichthemicrobiomeaffectshumanhealth2. Explainhowthemicrobiomeinfluencesthedevelopmentofdiabetesmellitus3. Describethedifferencesseeningutmicrobiomecompositionbetweenpatientswithdiabetesand

healthysubjects4. Evaluatemicrobiome-targetedtherapiesaspotentialinterventionstopreventandtreattype2

diabetesmellitus(T2DM)

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RoleoftheMicrobiomeinHumanHealth

I. Overviewofthehumanmicrobiomea. Definitions1

i. Microbiota:microbesthatcollectivelyinhabitagivenecosystemii. Microbiome:collectionofallgenomesofmicrobesinanecosystemiii. Dysbiosis:disturbanceorchangeinthecompositionandfunctionofmicrobes

b. Scope2i. Body’sbacteriawouldcircletheEarth2.5timesii. Weighsupto1to2kgiii. Outnumberhumancellsby10:1iv. 95%ofbacterialocatedingastrointestinal(GI)tract

c. Studyingthemicrobiomei. Transitionfromculture-basedmethodstoculture-independentmolecularassaysii. Methodsareusedtodiscernthestructure(i.e.,anatomy)andfunction(i.e.,physiology)

ofthemicrobiota

Table1.ToolsforAnalyzingMicrobiome1

Approach Data Platform16SrRNAgenesequencing Communitycomposition Next-generationsequencingMetagenomics Wholegenomesequencing Next-generationsequencingMetatranscriptomics Geneexpression Next-generationsequencingMetaproteomics Proteinexpression MassspectrometryMetabolomics Metabolicproductivity Massspectrometry

iii. Mostcommonapproachis16SrRNAgenesequencing3

1. 16Sgeneencodesforthe16SrRNAmoleculethatisuniquetobacteriaandarchaea,thusdistinguishesthesecellsfromhumancells

2. 16Sgeneisamplifiedusingpolymerasechainreactionandsequencedusingnext-generationsequencingtechnology

3. Machinelearningisusedtoclustersimilarsequencesandreferencedatabases(e.g.,Greengenes)assistwithassigningtaxonomy

d. Compositioni. Variessubstantiallybybodysite4

1. OuterbodysitespredominatedbyGram-positiveaerobicorganismsfromtheActinobacteriaandFirmicutesphyla

2. Gutmicrobiome(representedbystool)predominatedbyanaerobicGram-positiveandGram-negativebacteria

a. Firmicutes(e.g.,Lachnospiraceae,Ruminococcaceae)b. Bacteroidetes(e.g.,Bacteroidaceae,provetellaceae)c. Actinobacteria(e.g.,Bifidobacteriaceae)

ii. Microbiotaextensivelyconservedathightaxonomiclevels;variationincreasesatprogressivelylowertaxonomiclevels

iii. Largeinter-individualvariabilityinmicrobiotacomposition,butnotecosystemfunction

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Figure1.DominantBacterialTaxabyBodySite4

II. Globalgutmicrobiotafunctions1

a. Matureandtraintheimmunesystemb. Inhibitinvasionbypathogensc. Mediatehost-cellproliferationandvascularizationd. Regulateintestinalendocrinefunctions,neurologicsignaling,andbonedensitye. Provideasourceofenergybiogenesisf. Biosynthesizevitamins,neurotransmitters,andrelatedcompoundsg. Metabolizebilesaltsh. Xenobioticmetabolismandelimination

III. Associationsbetweengutdysbiosisandhumandisease1

a. Endogenousandexogenousfactorsinfluencegutmicrobiotai. Neonatalmodeofdeliveryii. Hostgeneticfeaturesiii. Hostimmuneresponseiv. Dietv. Medications

vi. Environmentalexposuresvii. Ageviii. Physicalactivityix. Smokingx. Alcoholconsumption

b. Disruptionofmicrobialcommunitiesassociatedwithahostofchronicandacutediseases

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Table2:InfluenceofGutMicrobiomeCommunitiesonHealth5

Health Microbialproductsoractivities DiseaseNutrient&energy

supply• SCFAproduction&vitaminsynthesis• Energysupply,guthormones,&satiety• Lipopolysaccharides,inflammation

Obesity&metabolicsyndrome

Cancerprevention • Butyrateproduction,phytochemicalrelease• Toxinandcarcinogeninflammation• Mediatesinflammation

Cancerpromotion

Pathogeninhibition • SCFAproduction,intestinalpH,bacteriocins• Competitionforsubstratesand/orbindingsites• Toxinproduction,tissueinvasion,inflammation

Pathogeninvasion

GIimmunefunction

• Balanceofpro-andanti-inflammatorysignals• Inflammation,immunedisorders

IBD

Gutmotility • Metabolites(SCFAs,gases)fromnon-digestiblecarbohydrates

IBS(constipation,diarrhea,bloating)

Cardiovascularhealth

• Lipid&cholesterolmetabolism Cardiovasculardisease

SCFA=short-chainfattyacid;IBD=inflammatoryboweldisease;IBS=irritablebowelsyndromeGutDysbiosisandDiabetesMellitus

I. Overviewofdiabetesmellitus

a. Diseaseprevalence6,7i. Asof2015,30.3millionAmericans(9.4%ofthepopulation)withdiabetes

1. 23.1millionpeoplediagnosed2. 7.2millionpeopleundiagnosed

ii. 29millionAmericans(9%ofthepopulation)haveT2DMiii. Localprevalence8

1. SanAntonioa. 14.2%ofpopulationdiagnosedwithtype1diabetesmellitus

(T1DM)orT2DMb. T2DMinSanAntonioprevalencevariesbyrace

i. Whites:8%ii. Blacks:12%iii. Hispanics:16%

2. Bexarcounty:prevalence13%3. Texas:prevalence10%4. UnitedStates:prevalence9%

b. Morbidityandmortalityi. Absolutenumberofdeathsduetodiabetesincreasedby93%from1990to20109ii. In2012estimatedannualcostofdiabetes$245billion8

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II. Pathophysiology:EgregiousEleven10-13

Figure2:b-cell-CentricConstruct:EgregiousEleven7

a. Describespathwaysthatcontributetodevelopmentofdiabetesb. Dysfunctionalpathways

i. Pancreaticb-cells:decreasedinsulinproductionii. Muscle:disruptionsininsulinsignaltransductionresultingininsulinresistanceiii. Liver:decreasedinhibitionofhepaticglucoseproduction(HGP)byhyperinsulinemiaiv. Adipose:enlargedfatcellsexhibitinsulinresistance;fat“spill-over”canworsen

insulinresistanceinmuscleandliverv. Decreasedincretineffect

1. Glucagon-likepeptide-1(GLP-1)diminishedindiabetes2. GLP-1aidsinglucosedisposalaswellasinhibitionofHGP

vi. a-cell:overproductionofglucagonindiabetespatients,contributingtoincreasedbasalHGP

vii. Kidney:increasedsodium-glucosecotransporter-2(SGLT2)thresholdviii. Brain:delayedsatietyinresponsetoincreasesininsulinix. Stomach/smallintestine:increasedglucoseabsorptionx. Immunedysregulation/inflammation:macrophageandinterleuin-1(IL-1)

recruitmenttopancreasresultsinb-cellapoptosisxi. Colon/microbiome:influenceshostmetabolisminthreemainwaysthatcanaffect

multipleotherfacetsofEgregiousEleven

Figure 3—b-Cell–centric construct: the egregious eleven. Dysfunction of the b-cells is the final common denominator in DM. A: Eleven currentlyknownmediating pathways of hyperglycemia are shown. Many of these contribute to b-cell dysfunction (liver, muscle, adipose tissue [shown in redto depict additional association with IR], brain, colon/biome, and immune dysregulation/inflammation [shown in blue]), and others result fromb-cell dysfunction through downstream effects (reduced insulin, decreased incretin effect, a-cell defect, stomach/small intestine via reducedamylin, and kidney [shown in green]). B: Current targeted therapies for each of the current mediating pathways of hyperglycemia. GLP-1,glucagon-like peptide 1; QR, quick release.

182 b-Cell–Centric Classification of Diabetes Diabetes Care Volume 39, February 2016

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Figure3:MicrobiomeandHostMetabolism9

1. Increasedproductionoflipopolysaccharides(LPS)14,15

a. LPSsshedfromGram-negativebacterialcellwalls(i.e.,E.coli)i. Bindtotoll-likereceptor-4(TLR4)/CD14complexii. TLR4activatesinnateimmunesystem,resultinginpro-

inflammatoryresponseiii. Decreaseexpressionoftightjunctionproteinsandincrease

mucosaintegrityb. DecreasedintegrityofintestinalmucosaincreasesreleaseofLPS

intobloodstream14i. HigherplasmaLPSlevelsinDMpatientsthanhealthy

counterparts2. Decreasedshort-chainfattyacids(SCFAs)production15

a. SCFAs(butyrate,acetate,propionate)producedbybacterialfermentationofdietaryfiberandresistantstarches

i. Mainenergysourceforgutepithelium(mainlybutyrate)ii. BindG-proteincoupledreceptors(GPCRs)41and43in

intestinalmucosa,immunecells,liver,andadiposetissues1. Intestinalmucosa:SCFAsbindtoGPCRson

enterohepaticL-cellsincolonàincreaseGLP-1secretion

2. Immunecells:inhibitNF-KBactivation;decreaseTNF-aandIL-6suppressionanddecreasedinflammation

high-fat diet (21) and mice receiving antibiotics exhibited

lower levels of circulating LPSs and TNFa as well as

decreased insulin resistance compared with pair-fed mice

(22). As a part of the immune system, Toll-like receptors

(TLRs) recognise microbial molecules and activate the

innate immune system. LPSs bind to and activate the

TLR4/CD14 complex, which activates pro-inflammatory

pathways. Other TLRs, such as TLR2 and TLR5, have also

been proposed to be part of the signalling pathways

affecting the development of metabolic syndrome as

observed in studies of Tlr2- and Tlr5-deficient mice

(23, 24). Additional evidence of the importance of the

crosstalk among the immune system, inflammation

and metabolism was observed in the development of

non-alcoholic fatty liver disease (NAFLD). Mice without

the inflammasome complexes NLRP3 or NLRP5,

A. Lipopolysaccharide B. Short-chain fatty acids

Dietary fibres ButyrateAcetate

Propionate

C. Bile acids

Primarybile acids

Secondarybile acids

TLR4 Energy source

↑ Inflammation

↑ Lipogenesis↑ Gluconeogenesis

↑ GLP1 and PYY

↓ Inflammation

GPR41 GPR43

↑ GLP1↑ Energy expenditure

TGR5

LPS

Figure 1

Microbes and host metabolism. Microbes may influence host

metabolism through numerous mechanisms, of which three

important mechanisms are depicted. (A) Lipopolysaccharide.

Lipopolysaccharide (LPS) originates from the outer membrane

of Gram-negative bacteria and binds to Toll-like receptor 4

(TLR4), which activates pro-inflammatory signalling pathways

resulting in low-grade inflammation and thus decreased insulin

sensitivity. (B) Short-chain fatty acids. Bacteria in the colon

ferment dietary fibres to short-chain fatty acids (mainly

butyrate, acetate and propionate). Acetate and propionate are

used as substrates for gluconeogenesis and lipogenesis in the

liver, whereas butyrate is an important energy substrate for

colonic mucosa cells. Moreover, short-chain fatty acids bind to

the G protein-coupled receptors GPR41 and GPR43 resulting in

various effects depending on the cellular types affected. In

immune cells, this signalling results in decreased inflammation

and in the enteroendocrine L-cells it results in increased GLP1

and PYY levels together leading to improved insulin sensitivity.

(C) Bile acids. Primary bile acids are produced by the liver and

recirculated to the liver from the gut. However, gut bacteria are

capable of deconjugating primary bile acids hindering their

recirculation. The primary deconjugated bile acids are further

metabolised by gut bacteria to secondary bile acids. Secondary

bile acids bind to the G protein-coupled receptor TGR5, which

results in increased energy expenditure in muscles and GLP1

secretion in the enteroendocrine L-cells, both of which lead

to improved insulin sensitivity.

Eu

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Review K H Allin and others Gut microbiota in T2DM 172 :4 R170

www.eje-online.org

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3. Bileacids16a. Gutbacteriaconvertprimarybileacidstosecondarybileacidsvia

bilesalthydrolasesb. SecondarybileacidsactassignalingmoleculestoinduceGLP-1

secretionfromsmallintestineL-cellsc. Gutmicrobesimplicatedinspecificmechanismsofdysbiosis

i. LPSproductionbyGram-negativebacteria141. E.coli2. Salmonella3. Shigella4. Pseudomonas5. Neisseria6. H.influenza7. Bodetellapertussis8. Vibriocholerae

ii. BeneficialSCFAproducers:17,181. MainlyspeciesintheFirmicutesphyla

a. Roseburiasp.b. Faecalibacteriumprausnitziic. Eubacteriumhalliid. Eubacteriumrectale

iii. Microbiotawithbeneficialbilesalthydrolases161. Lactobacillus2. Bifidobacterium3. Firmicutes4. Enterococcus5. Clostridum6. Bacteroides

III. AmericanDiabetesAssociation(ADA)acknowledgedimportanceoftherelationshipbetween

microbiomeanddiabetes9a. 2014ADAandJDRFResearchSymposium:DiabetesandtheMicrobiome

i. Firstgatheringofexpertsthatfocusedonthelinkbetweenthepathophysiologyofthemicrobiomeofdiabetes

ii. Symposiummadeseveralrecommendationstoguidefuturediabetesandmicrobiomeresearch

TheGutMicrobiomeinPatientswithDiabetes

I. Microbiomestudies:associationswithmetabolic(dys)functiona. Historically,studieshaveyieldeddiverseresults19-21b. SeveralrecentrobuststudiesdemonstrateddifferencesbetweenT2DMpatientsand

controlsaswellascomplexrelationshipsbetweenbacterialtaxa17,18

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MLG:metagenomiclinkagegroup

Figure4:MicrobiotaTrendsinMetabolic(Dys)function17

c. Discordantfindingsduetodifferencesin:12,13,22

i. Dietii. Ageiii. Birth(Caesariansection

versusvaginaldelivery)iv. Hostgenotype

v. Physicalactivityvi. Smokingvii. Alcoholconsumptionviii. Medicationsix. Geographiclocation

Microbiome-TargetedTherapiesforthePreventionandTreatmentofDiabetes

I. Personalizednutrition:23

a. Individualizeddietaryplanbasedonanindividual’sdistinctivecharacteristicsb. Linkbetweenmicrobiomecompositionandpost-prandialglucoseresponse(PPRG)

i. IdentifiedmicrobiomeasintegralcomponentinformulatingapersonalizednutritionplantooptimizePPGR

Figure5.IllustrationofExperimentalDesign23

the previous findings in studies of inflammatory bowel disease andobese patients26. By contrast, control-enriched markers were fre-quently involved in cell motility and metabolism of cofactors andvitamins (P , 0.002; Supplementary Fig. 9).

At the module or pathway level, the gut microbiota of T2D patientswas functionally characterized with our T2D-associated markers andshowed enrichment in membrane transport of sugars, branched-chainamino acid (BCAA) transport, methane metabolism, xenobioticsdegradation and metabolism, and sulphate reduction. By contrast,there was a decrease in the level of bacterial chemotaxis, flagellarassembly, butyrate biosynthesis and metabolism of cofactors andvitamins (Fig. 2b and Supplementary Table 10; see SupplementaryFig. 10 for the detailed information on butyrate-CoA transferase).Some important functions, including butyrate biosynthesis and sul-phate reduction, coincided with the T2D-associated bacteria identifiedin the MLG analysis. The butyrate-producing bacteria seemed to be theprimary contributors to the cell motility functions (SupplementaryTable 11), potentially indicating some functional enrichment mightbe related to the presence of specific species enrichment.

We found that seven of the T2D-enriched KEGG orthologuesmarkers were related to oxidative stress resistance, including catalase(K03781), peroxiredoxin (K03386), Mn-containing catalase (K07217),glutathione reductase (NADPH) (K00383), nitric oxide reductase(K02448), putative iron-dependent peroxidase (K07223), and cyto-chrome c peroxidase (K00428), but none of the identified control-enriched KEGG orthologues markers had similar types of function.

This may indicate that the gut environment of a T2D patient is one thatstimulates bacterial defence mechanisms against oxidative stress(Supplementary Table 10). Similarly, we found 14 KEGG orthologuesmarkers related to drug resistance that were greatly enriched in T2Dpatients, further supporting that T2D patients may have a more hostilegut environment, and the medical histories of these patients may reflectthis (Supplementary Table 10).

T2D-related dysbiosis in gut microbiotaIn light of the above MGWAS result and an additionalPERMANOVA27 (permutational multivariate analysis of variance)analysis that clearly showed that T2D was a significant factor forexplaining the variation in the examined gut microbial samples(Supplementary Table 12), we deduced that the gut microbiota inT2D patients featured dysbiosis, which is a state where the balanceof the normal microbiota has been disturbed. However, the degree ofthis T2D-related dysbiosis was moderate, because only 3.8 6 0.2%(mean 6 s.e.m.; n 5 344) of the gut microbial genes (at the relativeabundance level) were associated with T2D in an individual.Additionally, we did not observe a significant difference in thewithin-sample diversity between T2D and control groups (Fig. 3a).Specifically, the degree of gut microbiota change in T2D was not assubstantial as that seen in inflammatory bowel disease (from theMetaHIT samples8; see Fig. 3a) or enterotypes (Supplementary Fig. 11).A similar result using the eggNOG orthologue groups profile sup-ported the same conclusion (Supplementary Fig. 12).

a

b

Desulfovibrio Desulfovibrio sp. 3_1_syn3sp. 3_1_syn3Desulfovibrio sp. 3_1_syn3

E. coliE. coliE. coli

A. muciniphilaA. muciniphilaA. muciniphila

Con-142Con-142Con-142 Con-180Con-180Con-180

C. bolteaeC. bolteae

Bacteroides Bacteroides sp. 20_3sp. 20_3

C. symbiosumC. symbiosum

T2D-14T2D-14

Clostridium Clostridium sp. HGF2sp. HGF2

T2D-8T2D-8

T2D-2T2D-2C. hathewayiC. hathewayi

T2D-16T2D-16

E. lentaE. lentaT2D-62T2D-62

Clostridium ramosumClostridium ramosum

T2D-12T2D-12

T2D-170T2D-170

T2D-9T2D-9

T2D-93T2D-93T2D-90T2D-90T2D-37T2D-37

T2D-6T2D-6B. intestinalisB. intestinalis

T2D-165T2D-165

T2D-79T2D-79

T2D-73T2D-73T2D-30T2D-30

C. symbiosum

T2D-14

Clostridium sp. HGF2

T2D-8

T2D-2C. hathewayi

E. lentaT2D-62

Clostridium ramosum

T2D-12T2D-9

T2D-79

T2D-73

C. bolteae

Bacteroides sp. 20_3

T2D-16

T2D-170

T2D-93T2D-90T2D-37

T2D-6B. intestinalis

T2D-165

Con-130Con-130

Con-109Con-109

F. prausnitziiF. prausnitziiCon-131Con-131

Con-152Con-152Con-144Con-144

Con-133Con-133

E. rectaleE. rectale

Clostridiales Clostridiales sp. SS3/4sp. SS3/4


H. parainfluenzaeH. parainfluenzae


Con-120Con-120

Con-122Con-122R. intestinalisR. intestinalis

R. inulinivoransR. inulinivorans

T2D-30

Con-130

Con-109

F. prausnitziiCon-131

Con-152Con-144

Con-133

E. rectale

Clostridiales sp. SS3/4

Con-101Con-104

H. parainfluenzae

Con-148Con-155

Con-120

Con-122R. intestinalis

R. inulinivorans

ClostridialesClostridium

Faecalibacterium

Eubacterium Roseburia

Subdoligranulum

Lachnospiraceae Erysipelotrichaceae

Firmicutes

DesulfovibrioEscherichiaHaemophilus

Proteobacteria

BacteroidesAlistipes

Bacteroidales

Parabacteroides

T2D-enriched MLGsControl-enriched MLGs

VerrucomicrobiaAkkermansia

ActinobacteriaEggerthella

Unclassified

b

Butyrate-producing bacteriaCon-343 Con-3380 Con-1831 Con-1697

Butyrate biosynthesis

Akkermansia muciniphilaT2D-317

Mucin degradation

Sulphate-reducing bacteriaT2D-823

H2S biosynthesis

Oxidative stress resistance Drug resistance

Cell motility

Xenobiotics biodegradation and metabolism

CH4 metabolism

Mucin layer integrality

T2D

Gut microbiota Gut environment

Sugar related membrane transport

Metabolism of cofactors and vitamins

BCAA transport

Butyrate

Cofactors

Vitamins

Host tissues

Xenobiotics

Oxidative stress

Mucin layer

BCAA

H2S

CH4

Figure 2 | Taxonomic and functional characterization of gut microbiota inT2D. a, A co-occurrence network was deduced from 47 MLGs that wereidentified from 52,484 gene markers. Nodes depict MLGs with their IDdisplayed in the centre. The size of the nodes indicates gene number within theMLG. The colour of the nodes indicates their taxonomic assignment.Connecting lines represent Spearman correlation coefficient values above 0.4

(blue) or below 20.4 (red). b, A schematic diagram showing the main functionsof the gut microbes that had a predicted T2D association. Red text denotesenriched functions in T2D patients; blue text denotes depleted functions inT2D patients; black text denotes an uncertain functional role relative to T2D.The dashed line arrows point to the inference that was not detected directly butreported by previous studies.

RESEARCH ARTICLE

5 8 | N A T U R E | V O L 4 9 0 | 4 O C T O B E R 2 0 1 2

Macmillan Publishers Limited. All rights reserved©2012

Nuts (456,000)Beef (444,000)

Legumes (420,000)

Fruit (400,000)

Poultry (386,000)

Rice (331,000)

Other (4,010,000)

Baked goods (542,000)Vegetables (548,000)

Sweets (639,000)

Dairy (730,000)

Bread (919,000)

Overall energy documented: 9,807,000 Calories

Glu

cose

(mg/

dl)

Time

Anthropometrics

Blood tests

Gut microbiome16S rRNA

Metagenomics

QuestionnairesFood frequency

LifestyleMedical

Diary (food, sleep, physical activity)

Continuous glucose monitoring

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7

Standardized meals (50g available carbohydrates)

G G F

Bread Bread Bread & butter

Bread & butter

Glucose Glucose Fructose

Per person profiling Computational analysisMain

cohort

800 Participants

Validationcohort

100 Participants

PPGRprediction

26 Participants

Dietaryintervention

A

Glu

cose

(mg/

dl)

DayBMI1 2 3 4 5 6 7

Standardized meal

Lunch

Snack

Dinner

Postprandial glycemic response(PPGR; 2-hour iAUC)

D

5,435 days, 46,898 meals, 9.8M Calories, 2,532 exercises

130K hours, 1.56M glucose measurements

B C

Freq

uenc

y

Freq

uenc

y

HbA1c%

45% 33% 22% 76% 21% 3%

% Protein

% Carbohydrate

% F

at

F

1000

2000

00 20 40 60 80 100

Freq

uenc

y

% of meal

Carbohydrate

Fat

Protein

E

Sleep

PCo1 (10.5%)

PCo2

(5.2

%)

GStudy participants MetaHIT - stoolHMP - stool HMP - oral

PCo1 (27.9%)

PCo2

(2.2

%)

Using smartphone-adjusted website

Using a subcutaneous sensor (iPro2)

Participant 141

HMP - urogenital

Figure 1. Profiling of Postprandial Glycemic Responses, Clinical Data, and Gut Microbiome(A) Illustration of our experimental design.

(B and C) Distribution of BMI and glycated hemoglobin (HbA1c%) in our cohort. Thresholds for overweight (BMI R 25 kg/m2), obese (BMI R 30 kg/m2),

prediabetes (HbA1c% R 5.7%) and TIIDM (R6.5%) are shown.

(legend continued on next page)

1080 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

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c. Studydesign:three-partstudyi. Part1:CreatedPPGpredictionalgorithmbasedonprofilingof800Israeli

participants(54%overweight,22%obese),whichincluded:1. Continuousglucosemonitoring(CGM)2. Realtimediary:food,sleep,physicalactivity3. Gutmicrobiomeanalysis(16SrRNAandmetagenomicanalysis)4. Bloodtests(HbA1c%,lipidlevels)5. Anthropometrics6. Lifestyle,medicalhistory

ii. Part2:AlgorithmvalidationinsecondcohortofIsraeliparticipantsiii. Part3:Dietaryintervention

1. 26newparticipantsrandomizedtoalgorithm-produceddietplan(intervention)ordietician-produceddietplan(comparator)afterweek-longprofilingperiod

a. Two-weekinterventionincludedoneweekof“good”diet(e.g.,foodsassociatedwithlowerPPGR)andoneweekof“bad”diet(e.g.,foodassociatedwithhigherPPGR)

b. PPRGhighlyvariablebetweenindividualsd. Analyzedpartialdependenceplots(PDPs)tobetterunderstandtheroleofvariousfactorsin

thealgorithm’spredictionsi. PDPsillustratehowindividualvariablescontributetomodelpredictionsii. Valuesgreaterthan0indicatepositivecontributionandvalueslessthan0indicate

negativecontributioniii. MealcarbohydrateweightmostsignificantcontributortoPPGRiv. Relativeabundance(RA)ofmultiplebacterialtaxacontributedtoPPGRaswell

Figure6:MicrobiomePDP23

Eubacterium rectalePTR (59)

59

430

63

n.d. 1.1 1.2

PTR

223

0.75 0.8 0.85

Coprococcus catus PTR (53)

158

455

103

n.d. 1.1 1.2

PTR

77

Time fromlast sleep (12)

4971

10947

Time (min)0 400 800 1200

Parti

al d

epen

denc

e(a

.u.)

0.6

0.3

0

-0.3

M00514TtrS-TtrR TCS (27)

M00496NblS-NblR TCS (28)

M00256Cell div. trans. sys. (30)

Bacteroidesdorei (45)

Alistipesputredinis (48)

Relative abundance

Alanine aminotransferase (ALT)AgeBMISystolic blood pressureNon-fasting total cholesterolGlucose fluctuations (noise, σ/μ)HbA1c%Waist-to-hip ratio

Parti

al d

epen

denc

e(a

.u.)

0.3

0.1

-0.1

Parti

al d

epen

denc

e(a

.u.)

Parti

al d

epen

denc

e(a

.u.)

9630

-3-6

Mealcarbohydrates (2)

Weight (g)

PPG

R (i

AUC

, mg/

dl. h

)

Meal carbohydrates (g)

Participant 49

Participant 145

F

G

2

1

0

-1

Meal sodium (5)Meal

dietary fiber (14)

A B

C D

E

5390

401

79 105

5230

632

164 14

334

44850

424

323

8400

7518

8623

7290

Meal water (21)

Amount (ml)0 300 600

5588

103306968

8697

Weight (mg) Weight (g)

40

30

20

10

0

Meal carbohydrates (g)

Mea

l fat

(g)

Participant 267 Participant 465

Color Scale

P<0.005 P<0.01 P<0.05 n.s. P<0.005P<0.01P<0.05

Positive associationNegative association

Freq

uenc

y

Carbohydrate-PPGR slope

R-difference

Freq

uenc

y40

20

0

PPGR

(iAUC

, mg/dl .h)

0 40 80 120

0 1000 2000 0 3 6 9 12

24-hourdietary fiber (25)

7575

8342

Weight (g)0 20 40

n.d. 10-6 10-5 n.d. 10-6 10-5 n.d. 10-3 n.d. 10-4 10-3 10-2 n.d. 10-4 10-3 10-2

Relative abundance Relative abundance Relative abundance Relative abundance

Parabacteroides distasonis (63)

101

363

332

Relative abundance

n.d. 10-4 10-3 10-2

PhylumBacteroidetes (95)

144

436

217

n.d. 10-0.6 10-0.2

Relative abundance

Ratio mapped togene-set (93)

320

448

24

Ratio mapped

PDP Legend

0.6

0.3

0

-0.3

-0.6

Feature name(Feature rank)

Feature value

85 - # undetected372- # with above-zero contribution

497 - #with below-zero contribution

n.d 10-6 10-5

Feature distribution

Negative trend: Feature is beneficial

Parti

al d

epen

denc

e(a

.u.)

Meal fat / carbohydrates (4)

log2(fat/carbs)

4

2

0

-2

-47611

8303

-2 -1 0 1 2

Above-zero contribution segmentBelow-zero contribution segment

Slope > 095.1%

R difference:0.21

R difference:0.11

87

M00

514

TtrS

-TtrR

TC

S (2

7)M

0051

3 Lu

xQN

/Cqs

S-Lu

xU-L

uxO

TC

S (3

8)M

0047

2 N

arQ

-Nar

P TC

S (4

1)Al

istip

es p

utre

dini

s (4

8)M

0066

4 N

odul

atio

n (4

9)M

0045

3 Q

seC

-Qse

B TC

S (5

0)Sp

. in

genu

s Su

bdol

igra

nulu

mun

(54)

M00

035

Met

hion

ine

degr

adat

ion

(55)

Euba

cter

ium

rect

ale

PTR

(59)

M00

112

Toco

pher

ol b

iosy

nthe

sis

(60)

Stre

ptoc

occu

s sa

livar

ius

PTR

(65)

M00

412

ESC

RT-

III c

ompl

ex (7

0)Eu

bact

eriu

m e

ligen

s PT

R (7

9)M

0006

6 La

ctos

ylce

ram

ide

bios

ynth

. (80

)Ak

kerm

ansi

a m

ucin

iphi

la P

TR (8

2)Al

istip

es fi

nego

ldii

(83)

Bact

eroi

des

xyla

niso

lven

s (8

5)Eu

bact

eriu

m re

ctal

e (8

7)Ak

kerm

ansi

a m

ucin

iphi

la (9

6)M

0015

6 C

ytoc

hrom

e c

oxid

ase

(98)

Phyl

um E

urya

rcha

eota

(99)

Phyl

um C

yano

bact

eria

(107

)

Figure 4. Factors Underlying the Predictionof Postprandial Glycemic Responses(A) Partial dependence plot (PDP) showing the

marginal contribution of the meal’s carbohydrate

content to the predicted PPGR (y axis, arbitrary

units) at each amount of meal carbohydrates

(x axis). Red and green indicate above and below

zero contributions, respectively (number indicate

meals). Boxplots (bottom) indicate the carbohy-

drates content at which different percentiles (10,

25, 50, 75, and 90) of the distribution of all meals

across the cohort are located. See PDP legend.

(B) Histogram of the slope (computed per partici-

pant) of a linear regression between the carbohy-

drate content and the PPGR of all meals. Also

shown is an example of one participant with a low

slope and another with a high slope.

(C) Meal fat/carbohydrate ratio PDP.

(D) Histogram of the difference (computed per

participant) between the Pearson R correlation of

two linear regression models, one between the

PPGR and the meal carbohydrate content and

another when adding fat and carbohydrate*fat

content. Also shown is an example of the carbo-

hydrate and fat content of all meals of one partici-

pant with a relatively low R difference (carb alone

correlates well with PPGR) and another with a

relatively high difference (meals with high fat

content have lower PPGRs). Dot color and size

correspond to the meal’s PPGR.

(E) Additional PDPs.

(F) Microbiome PDPs. The number of participants

in which the microbiome feature was not detected

is indicated (left, n.d.). Boxplots (box, IQR; whiskers

10–90 percentiles) based only on detected values.

(G) Heatmap of statistically significant correlations

(Pearson) between microbiome features termed

beneficial (green) or non-beneficial (red) and

several risk factors and glucose parameters.

See also Figure S5.


Page10

e. Post-interventionmicrobiotaalterationsi. Mostsignificantmicrobiotachangeswereinter-individualpre-andpost-

interventionii. Severalbacterialtaxaweresignificantlychangedduetodiettypeinallparticipants

Figure7:HeatmapofTaxaChangesinRAbetween“Good”and“Bad”DietWeeks23

iii. IncreaseinRoseburiainulinivoransduring“good”dietweekanddecreaseduring

“bad”dietweek1. LowlevelsofRoseburiageneraconsistentlyassociatedwithT2DM

1 2 3 4 5 6 1 2 3 4 5 6‘Bad’ diet week (day) ‘Good’ diet week (day)

Glu

cose

(mg/

dl)

Fold

cha

nge

(w.r.

t day

s 0-

3)

Participant E3

1 2 3 4 5 6 1 2 3 4 5 6‘Good’ diet week (day) ‘Bad’ diet week (day)

Glu

cose

(mg/

dl)

Participant P8

Bifidobacterium (G)Bifidobacterium pseudocatenulatum (S)

Actinobacteria (C)Alistipes putredinis (S)Akkermansia muciniphila (S)

Parabacteroides merdae (S)Streptococcus thermophilus (S)Corpobacter fastidiosus (S)

Lactobacillus ruminis (S)Bifidobacterium (G)Bifidobacterium pseudocatenulatum (S)

A B

Actin

obac

teria

(Phy

lum

)Ac

tinob

acte

ria (C

lass

)Bi

fidob

acte

riale

s (O

rder

)C

orio

bact

eria

les

(Ord

er)

Bifid

obac

teria

ceae

(Fam

ily)

Cor

ioba

cter

iace

ae (F

amily

)Bi

fidob

acte

rium

(Gen

us)

Col

linse

lla (G

enus

)An

aero

stip

es (G

enus

)D

orea

(Gen

us)

Bifid

obac

teriu

m a

dole

scen

tis (S

peci

es)

Col

linse

lla a

erof

acie

ns (S

peci

es)

Anae

rost

ipes

had

rus

(Spe

cies

)Eu

bact

eriu

m h

allii

(Spe

cies

)D

orea

long

icat

ena

(Spe

cies

)Ba

cter

oide

tes

(Phy

lum

)Vi

ruse

s (P

hylu

m)

Prot

eoba

cter

ia (P

hylu

m)

Bact

eroi

dia

(Cla

ss)

Gam

map

rote

obac

teria

(Cla

ss)

Del

tapr

oteo

bact

eria

(Cla

ss)

Beta

prot

eoba

cter

ia (C

lass

)Ba

cter

oida

les

(Ord

er)

Ente

roba

cter

iale

s (O

rder

)Bu

rkho

lder

iale

s (O

rder

)Vi

ruse

s, n

onam

e (O

rder

)D

esul

fovi

brio

nale

s (O

rder

)Pr

evot

ella

ceae

(Fam

ily)

Bact

eroi

dace

ae (F

amily

)Su

ttere

llace

ae (F

amily

)Pr

evot

ella

(Gen

us)

Bact

eroi

des

(Gen

us)

Barn

esie

lla (G

enus

)R

umin

ococ

cus

lact

aris

(Spe

cies

)Eu

bact

eriu

m e

ligen

s (S

peci

es)

Ros

ebur

ia in

ulin

ivor

ans

(Spe

cies

)Ba

cter

oide

s vu

lgat

us (S

peci

es)

Bact

eroi

des

ster

coris

(Spe

cies

)Al

istip

es p

utre

dini

s (S

peci

es)

Bacteria decreasing in ‘good’ diet week Bacteria increasing in ‘good’ diet week

Paric

ipan

ts -

‘goo

d’ d

iet w

eek

Paric

ipan

ts -

‘bad

’ die

t wee

k

P9E14E6P2P8E4

E12P1

P10E9E2E8P6

E11E5E3E7P9

E14E6P2P8E4P4

E12P1

P10E9E2E8P6

E11E5E3E1

C Bifidobacterium adolescentis

Day

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Day

Roseburia inulinivorans

D

E

‘Good’ diet week

‘Bad’ diet week


‘Bad’ diet week

Fold change (days 4-7 vs. days 0-3)

-0.5 -0.25 0 0.25 0.5

Statistically significantdecrease (P<0.05)

Statistically significantinecrease (P<0.05)

Fold

cha

nge

(w.r.

t day

s 0-

3)

Figure 6. Dietary Interventions Induce Consistent Alterations to the Gut Microbiota Composition(A) Top: Continuous glucose measurements of a participant from the expert arm for both the ‘‘bad’’ diet (left) and ‘‘good’’ diet (right) week. Bottom: Fold change

between the relative abundance (RA) of taxa in each day of the ‘‘bad’’ (left) or ‘‘good’’ (right) weeks and days 0–3 of the sameweek. Shown are only taxa that exhibit

statistically significant changes with respect to a null hypothesis of no change derived from changes in the first profiling week (no intervention) of all participants.

(B) As in (A) for a participant from the predictor arm. See also Figure S7 for changes in all participants.

(C) Heatmap of taxa with opposite trends of change in RA between ‘‘good’’ and ‘‘bad’’ intervention weeks that was consistent across participant and statistically

significant (Mann-Whitney U-test between changes in the ‘‘good’’ and ‘‘bad’’ weeks, p < 0.05, FDR corrected). Left and right column blocks shows bacteria

increasing and decreasing in their RA following the ‘‘good’’ diet, respectively, and conversely for the ‘‘bad’’ diet. Colored entries represent the (log) fold change

between the RA of a taxon (x axis) between days 4–7 and 0–3 within each participant (y axis). Asterisks indicate a statistically significant fold change.

See also Figure S7 for all changes.



Page11

Figure8:FoldChangeinRoseburiainulinivoransDuring“Good”and“Bad”DietWeeks23

iv. Rapidmicrobialresponsetodietarychange

Figure9:SignificantChangesinRAofTaxaandCGMDuring“Good”and“Bad”DietWeeks23

f. Conclusions

i. GutmicrobiomeindependentlycontributestoPPGRii. Changesinthegutmicrobiomeinresponsetodietindicatearoleforthe

microbiomeinmediatingmetabolismandglycemiccontroliii. Furtherresearchneededtobettercharacterizeroleofdifferentmicrobialtaxato

identifyaspecificmicrobiomecompositiontooptimizemetabolichealth

II. Metforming. Mechanismofaction

i. Activatesadenosinemonophosphate-activatedproteinkinase(AMPK)intheliver1. Reduceshepaticglucoseproduction(HPG)2. Insulinsensitivity(IS)improvement

ii. Reacheshighconcentrationsinintestinewhenadministeredorallyiii. ActivatesAMPKinintestinalmucosa,aidsinmaintainingbarrierintegrity24,25


Glu

cose

(mg/

dl)

Fold

cha

nge

(w.r.

t day

s 0-

3)

Participant E3


Glu

cose

(mg/

dl)

Participant P8





A B

Actin

obac

teria

(Phy

lum

)Ac

tinob

acte

ria (C

lass

)Bi

fidob

acte

riale

s (O

rder

)C

orio

bact

eria

les

(Ord

er)

Bifid

obac

teria

ceae

(Fam

ily)

Cor

ioba

cter

iace

ae (F

amily

)Bi

fidob

acte

rium

(Gen

us)

Col

linse

lla (G

enus

)An

aero

stip

es (G

enus

)D

orea

(Gen

us)

Bifid

obac

teriu

m a

dole

scen

tis (S

peci

es)

Col

linse

lla a

erof

acie

ns (S

peci

es)

Anae

rost

ipes

had

rus

(Spe

cies

)Eu

bact

eriu

m h

allii

(Spe

cies

)D

orea

long

icat

ena

(Spe

cies

)Ba

cter

oide

tes

(Phy

lum

)Vi

ruse

s (P

hylu

m)

Prot

eoba

cter

ia (P

hylu

m)

Bact

eroi

dia

(Cla

ss)

Gam

map

rote

obac

teria

(Cla

ss)

Del

tapr

oteo

bact

eria

(Cla

ss)

Beta

prot

eoba

cter

ia (C

lass

)Ba

cter

oida

les

(Ord

er)

Ente

roba

cter

iale

s (O

rder

)Bu

rkho

lder

iale

s (O

rder

)Vi

ruse

s, n

onam

e (O

rder

)D

esul

fovi

brio

nale

s (O

rder

)Pr

evot

ella

ceae

(Fam

ily)

Bact

eroi

dace

ae (F

amily

)Su

ttere

llace

ae (F

amily

)Pr

evot

ella

(Gen

us)

Bact

eroi

des

(Gen

us)

Barn

esie

lla (G

enus

)R

umin

ococ

cus

lact

aris

(Spe

cies

)Eu

bact

eriu

m e

ligen

s (S

peci

es)

Ros

ebur

ia in

ulin

ivor

ans

(Spe

cies

)Ba

cter

oide

s vu

lgat

us (S

peci

es)

Bact

eroi

des

ster

coris

(Spe

cies

)Al

istip

es p

utre

dini

s (S

peci

es)


Paric

ipan

ts -

‘goo

d’ d

iet w

eek

Paric

ipan

ts -

‘bad

’ die

t wee

k

P9E14E6P2P8E4

E12P1

P10E9E2E8P6

E11E5E3E7P9

E14E6P2P8E4P4

E12P1

P10E9E2E8P6

E11E5E3E1


Day

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Day


D

E


‘Bad’ diet week


‘Bad’ diet week


-0.5 -0.25 0 0.25 0.5



Fold

cha

nge

(w.r.

t day

s 0-

3)













Glu

cose

(mg/

dl)

Fold

cha

nge

(w.r.

t day

s 0-

3)

Participant E3


Glu

cose

(mg/

dl)

Participant P8





A B

Actin

obac

teria

(Phy

lum

)Ac

tinob

acte

ria (C

lass

)Bi

fidob

acte

riale

s (O

rder

)C

orio

bact

eria

les

(Ord

er)

Bifid

obac

teria

ceae

(Fam

ily)

Cor

ioba

cter

iace

ae (F

amily

)Bi

fidob

acte

rium

(Gen

us)

Col

linse

lla (G

enus

)An

aero

stip

es (G

enus

)D

orea

(Gen

us)

Bifid

obac

teriu

m a

dole

scen

tis (S

peci

es)

Col

linse

lla a

erof

acie

ns (S

peci

es)

Anae

rost

ipes

had

rus

(Spe

cies

)Eu

bact

eriu

m h

allii

(Spe

cies

)D

orea

long

icat

ena

(Spe

cies

)Ba

cter

oide

tes

(Phy

lum

)Vi

ruse

s (P

hylu

m)

Prot

eoba

cter

ia (P

hylu

m)

Bact

eroi

dia

(Cla

ss)

Gam

map

rote

obac

teria

(Cla

ss)

Del

tapr

oteo

bact

eria

(Cla

ss)

Beta

prot

eoba

cter

ia (C

lass

)Ba

cter

oida

les

(Ord

er)

Ente

roba

cter

iale

s (O

rder

)Bu

rkho

lder

iale

s (O

rder

)Vi

ruse

s, n

onam

e (O

rder

)D

esul

fovi

brio

nale

s (O

rder

)Pr

evot

ella

ceae

(Fam

ily)

Bact

eroi

dace

ae (F

amily

)Su

ttere

llace

ae (F

amily

)Pr

evot

ella

(Gen

us)

Bact

eroi

des

(Gen

us)

Barn

esie

lla (G

enus

)R

umin

ococ

cus

lact

aris

(Spe

cies

)Eu

bact

eriu

m e

ligen

s (S

peci

es)

Ros

ebur

ia in

ulin

ivor

ans

(Spe

cies

)Ba

cter

oide

s vu

lgat

us (S

peci

es)

Bact

eroi

des

ster

coris

(Spe

cies

)Al

istip

es p

utre

dini

s (S

peci

es)


Paric

ipan

ts -

‘goo

d’ d

iet w

eek

Paric

ipan

ts -

‘bad

’ die

t wee

k

P9E14E6P2P8E4

E12P1

P10E9E2E8P6

E11E5E3E7P9

E14E6P2P8E4P4

E12P1

P10E9E2E8P6

E11E5E3E1


Day

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Fold

cha

nge

(with

resp

ect t

o da

ys 0

-3)

Day


D

E


‘Bad’ diet week


‘Bad’ diet week


-0.5 -0.25 0 0.25 0.5



Fold

cha

nge

(w.r.

t day

s 0-

3)












Page12

1. DecreasedleakageofLPSfromgutresultinginimprovedISanddecreasedinflammation

Figure10:MetforminMediationofLPS24

h. Impactongutmicrobiome26,27

i. Metformintreatmentresultsinmicrobiomesimilartonon-diabeticsubjectsii. IncreaseinEscherichiaandIntestinibactergeneraiii. IncreaseinBifidobacteriumiv. IncreasedRAofA.muciniphilav. DirectlypromotesgrowthofBifidobacteriumadolescentis

Figure11.GutmicrobiotainpatientswithT2DMwithandwithoutmetforminversushealthy

controls27

the development of insulin resistance, as evident in

mice with NF-kB (p50) knockout (Gao et al. 2009). This

mouse model exhibited increased insulin sensitivity in the

liver and produced significantly less glucose in a hyper-

insulinemic–euglycemic clamp. Furthermore, inhibition

of the NF-kB pathway improved insulin resistance in

db/db mice (Kim et al. 2013). Of particular interest is the

finding that the activation of AMPK by AICAR inhibited

the NF-kB pathway (Cacicedo et al. 2004). Aligning with

this, metformin-mediated AMPK activation attenuates

the activation of the NF-kB pathway (Hattori et al. 2006,

Huang et al. 2009). Therefore, the inhibition of the NF-kB

pathway by metformin-mediated AMPK activation

would lead to an improvement in hepatic insulin

signaling (Fig. 3).

Phosphatase and tensin homolog (PTEN), a tumor

suppressor, can reverse PI3K (Phosphatidylinositol-4, 5-

bisphosphate 3-kinase) function by dephosphorylating

the PI(3,4,5)P3 to PI(4,5)P2, therefore, suppressing the

PI3K-PKB/AKT pathway (Myers et al. 1998, Stiles et al.

2004). Intriguingly, LPS can induce the expression of

PTEN (Okamura et al. 2007), and metformin can suppress

PTEN expression in pre-adipocyte 3T3 cells (Okamura et al.

2007, Lee et al. 2011). This metformin action is AMPK

dependent, as the metformin effect is lost in cells treated

with Compound C (an AMPK inhibitor) or with AMPK

depletion by shRNA. This report showed that PTEN is a

downstream regulator of AMPK and that the AMPK–PTEN

pathway plays a critical role in regulating inflammatory

response (Fig. 3). However, further studies will be needed

to demonstrate conclusively how metformin’s effect on

PTEN occurs in the liver as well as muscle and determines

how activated AMPK suppresses PTEN expression.

Perspective

Since the maximum metformin dose prescribed to

patients with diabetes is w2.5 g/day, this high therapeutic

dose might affect multiple targets. As an oral agent,

metformin can change the composition of gut microbiota

(Shin et al. 2014) and activate mucosal AMPK (Duca

et al. 2015) that will maintain intestinal barrier integrity

(Peng et al. 2009, Elamin et al. 2013). Together, these

metformin effects will decrease LPS levels in the

Microbiota(intestine)

Permeability(enterocyte)

Insulin signaling(hepatocyte)

LPSLPS

AMPK

AMPK

NF-κB

PTENActivation Inhibition

ACC

Figure 3

Metformin improves insulin signaling in the liver. Metformin can alter the

microbiota in the intestine, resulting in a reduction in LPS production and

translocation across the intestinal barrier. Activation of AMPK by

metformin also blocks LPS-mediated activation of the NF-kB signaling

pathway and PTEN induction.

Jou

rnal

of

En

do

crin

olo

gy

Review H AN AND L HE Current understanding ofmetformin effect

228 :3 R103

http://joe.endocrinology-journals.org ! 2016 Society for EndocrinologyDOI: 10.1530/JOE-15-0447 Printed in Great Britain

Published by Bioscientifica Ltd.

T2D is associated with a decrease ingenera producing the short-chain fattyacid butyrate (Roseburia spp., Subdoli-granulum spp., Clostridiales spp.). At thefunctional level, Forslund et al. (2015)observe increases in the antioxidantgene catalase and in genes involved inribose, glycine, and tryptophan degrada-tion. Conversely, decreases in threonineand arginine degradation and in pyruvatesynthase capacity were also detected.The consequences of these functional mi-crobial changes in the regulation of hostphysiology in the context of T2D are diffi-cult to predict fully. However, it is plau-sible that changes in catalase levels area direct consequence of alterations in

the gut environment as a way to detoxifyincreased hydrogen peroxide that mightresult from inflammation, a conditioncharacterizing T2D (Figure 1). In order tomake their interpretation more robustand distinguish T2D-microbial featuresfrom alterations in glycaemia, Forslundet al. (2015) compare gut microbial pro-files of T2D and T1D patients, who alsosuffer from abnormal sugar levels. Asobserved in previous studies, while T1Dpatients display enhanced microbialgene richness when compared to non-diabetic patients, T2D patients show theopposite trend. Concomitantly, none ofthe enriched functional changes observedin the T2D analysis were present in T1D

patients (Forslund et al., 2015). Alto-gether, the data lead Forslund et al.(2015) to suggest that changes in microbi-al taxonomy and function are indepen-dent of glycaemic levels and due to otherT2D-associated disease phenotypes.Importantly, Forslund et al. (2015) could

not retrieve signatures associated withuntreated T2D from the taxonomic infor-mation. On the other hand, metformintreatment status or drug treatment-blinded T2D samples could be separated,implying that T2D metagenomic data areconfounded by metformin treatment. Inorder to investigate this further, T2Dmetformin-treated patients (n = 93) werecompared to T2D-untreated patients (n =106). Univariate tests show a statisticallysignificant decrease in Intestinibacterspp. in all cohorts, and an increase inEscherichia spp. in two out of the threecohorts (interestingly, the Chinese cohorthas elevated Escherichia spp. in all pa-tients when compared to Sweden and/orDenmark cohorts). These differencesremain significant when normalized byseveral parameters (gender, body massindex, etc.) and correlated with fastingserum concentrations of metformin (For-slund et al., 2015). While changes in themicrobiota have now been observed inmany studies (Karlsson et al., 2013; Na-politano et al., 2014; Shin et al., 2014),the underlying causes remain to be deter-mined. Is metformin changing the micro-biota by: (1) altering ratios of sensitive/resistant strains caused by direct actionof the drug on bacterial metabolism (Cab-reiro et al., 2013), (2) modifying the physi-ology of the host caused by impairing theprogression of T2D, or (3) a combinationof both? Testing the effects of metformintreatment on the gut microbiota of healthyhumans could untangle the specific ef-fects of metformin on the microbiotawith the potential to regulate host physi-ology. So how do changes in the micro-biota induced by metformin improveT2D? A study performed in mice chal-lenged with a high-fat diet (Shin et al.,2014) showed that metformin maintainsthe abundance of Akkermansia mucini-phila, a gut microbe linked with intestinalfitness and improved glycaemic control(Delzenne et al., 2015). Interestingly, For-slund et al. (2015) did not observe any sig-nificant changes in A. muciniphila acrossany of the cohorts, suggesting that thesemodifications might be rodent specific

Figure 1. Schematic Illustration of the Interactions between the Anti-diabetic DrugMetformin and theMicrobiota of Type 2Diabetic Patients in Cohorts fromDenmark, Sweden,and ChinaType 2 diabetic patients display a dysbiotic and dysfunctional microbiota, which contributes to impairedglucose homeostasis. Metformin treatment of type 2 diabetic patients leads to positive taxonomic andfunctional changes in the microbiota. Microbial-associated changes possibly contribute to improvedglycaemia but are also responsible for the side effects of the drug.

Cell Host & Microbe

Previews

2 Cell Host & Microbe 19, January 13, 2016 ª2016 Elsevier Inc.

T2D is associated with a decrease ingenera producing the short-chain fattyacid butyrate (Roseburia spp., Subdoli-granulum spp., Clostridiales spp.). At thefunctional level, Forslund et al. (2015)observe increases in the antioxidantgene catalase and in genes involved inribose, glycine, and tryptophan degrada-tion. Conversely, decreases in threonineand arginine degradation and in pyruvatesynthase capacity were also detected.The consequences of these functional mi-crobial changes in the regulation of hostphysiology in the context of T2D are diffi-cult to predict fully. However, it is plau-sible that changes in catalase levels area direct consequence of alterations in

the gut environment as a way to detoxifyincreased hydrogen peroxide that mightresult from inflammation, a conditioncharacterizing T2D (Figure 1). In order tomake their interpretation more robustand distinguish T2D-microbial featuresfrom alterations in glycaemia, Forslundet al. (2015) compare gut microbial pro-files of T2D and T1D patients, who alsosuffer from abnormal sugar levels. Asobserved in previous studies, while T1Dpatients display enhanced microbialgene richness when compared to non-diabetic patients, T2D patients show theopposite trend. Concomitantly, none ofthe enriched functional changes observedin the T2D analysis were present in T1D

patients (Forslund et al., 2015). Alto-gether, the data lead Forslund et al.(2015) to suggest that changes in microbi-al taxonomy and function are indepen-dent of glycaemic levels and due to otherT2D-associated disease phenotypes.Importantly, Forslund et al. (2015) could

not retrieve signatures associated withuntreated T2D from the taxonomic infor-mation. On the other hand, metformintreatment status or drug treatment-blinded T2D samples could be separated,implying that T2D metagenomic data areconfounded by metformin treatment. Inorder to investigate this further, T2Dmetformin-treated patients (n = 93) werecompared to T2D-untreated patients (n =106). Univariate tests show a statisticallysignificant decrease in Intestinibacterspp. in all cohorts, and an increase inEscherichia spp. in two out of the threecohorts (interestingly, the Chinese cohorthas elevated Escherichia spp. in all pa-tients when compared to Sweden and/orDenmark cohorts). These differencesremain significant when normalized byseveral parameters (gender, body massindex, etc.) and correlated with fastingserum concentrations of metformin (For-slund et al., 2015). While changes in themicrobiota have now been observed inmany studies (Karlsson et al., 2013; Na-politano et al., 2014; Shin et al., 2014),the underlying causes remain to be deter-mined. Is metformin changing the micro-biota by: (1) altering ratios of sensitive/resistant strains caused by direct actionof the drug on bacterial metabolism (Cab-reiro et al., 2013), (2) modifying the physi-ology of the host caused by impairing theprogression of T2D, or (3) a combinationof both? Testing the effects of metformintreatment on the gut microbiota of healthyhumans could untangle the specific ef-fects of metformin on the microbiotawith the potential to regulate host physi-ology. So how do changes in the micro-biota induced by metformin improveT2D? A study performed in mice chal-lenged with a high-fat diet (Shin et al.,2014) showed that metformin maintainsthe abundance of Akkermansia mucini-phila, a gut microbe linked with intestinalfitness and improved glycaemic control(Delzenne et al., 2015). Interestingly, For-slund et al. (2015) did not observe any sig-nificant changes in A. muciniphila acrossany of the cohorts, suggesting that thesemodifications might be rodent specific

Figure 1. Schematic Illustration of the Interactions between the Anti-diabetic DrugMetformin and theMicrobiota of Type 2Diabetic Patients in Cohorts fromDenmark, Sweden,and ChinaType 2 diabetic patients display a dysbiotic and dysfunctional microbiota, which contributes to impairedglucose homeostasis. Metformin treatment of type 2 diabetic patients leads to positive taxonomic andfunctional changes in the microbiota. Microbial-associated changes possibly contribute to improvedglycaemia but are also responsible for the side effects of the drug.

Cell Host & Microbe

Previews

2 Cell Host & Microbe 19, January 13, 2016 ª2016 Elsevier Inc.

Page13

i. Aremetformin’seffectsonmicrobiomearesultofinfluenceonbloodglucoseordirecteffectsonthemicrobiome?

Figure12.Metformin’sCyclicalMechanism

III. Fecalmicrobiotatransplantation(FMT)a. Wuetal.transferredfecalsamplesfromT2DMpatientsbefore(M0)and4monthsafter

(M4)initiationofmetformintreatmenttogerm-freemice26i. MicewhoreceivedM4fecaltransplantsdemonstratedbetterglucosetolerance

comparedtoM0recipients1. Glycemiccontrolbymetforminpartlyduetochangesinmicrobiome

b. Vriezeetal.investigatedshort-termsafetyandefficacyofFMTfromleandonorstotreatmetabolicsyndrome28

i. Prospective,blinded,randomizedcontrolledstudy

Table3:StudySubjects

Recipients DonorsInclusioncriteria:• Caucasianmaleswithtreatmentnaïve

metabolicsyndromeExclusioncriteria:• Useofanymedication,probiotics,and/or

antibioticsinprevious3months

• LeanCaucasianmales(BMI<23kg/m2)• Matchedbyagetorecipients

ii. Randomizedtoreceiveallogenicorautologousmicrobiotainfusioniii. Outcomes

1. Primary:changeininsulinsensitivitywithleandonorFMT2. Secondary:changeinspecificsmallandlargegutmicrobiotaandSCFAs3. Differencesbetweentreatmentgroupsat6weekswithp-valuescorrected

formultiplecomparisonsiv. Results

1. Baselinecharacteristicssimilarbetweenautologousandallogeneicinfusiongroups(Appendix1)

Healthymicrobiome

Glycemiccontrol

Metformin(?)

Metformin(?)

Page14

Table4:InsulinSensitivityinRecipients

Basalstate Clamp(step1) Clamp(step2) Characteristics Allogenic Autologous Allogenic Autologous Allogenic AutologousBaseline EGPa 10.0 10.0 3.8 4.6 * *Day60 EGP 9.8 10.3 3.8 4.8 * *Baseline Rda ---- --- 11.6 10.5 26.2 18.9Day60 Rda ---- --- 13.6 10.3 45.3 19.5

EGP:endogenousglucoseproduction,Rd:rateofglucosedisposal,aunits:mcmol/kg/min,*EGPcompletelysuppressed

Table5:InsulinSensitivityinDonors

Basalstate Clamp(step1) Clamp(step2)EGPa 13.0 2.2 *Rda --- 22.5 65.0

EGP:endogenousglucoseproduction,Rd:rateofglucosedisposal,aunits:mcmol/kg/min,*EGPcompletelysuppressed

1. Nosignificantchangesingutmicrobiotaofautologousinfusionsubjects:184±71speciesbeforetransplantationcomparedto211±50speciesafter

2. Significantgutmicrobiotachangesinallogenicinfusionsubjectsa. 178±62speciesbeforetransplantationcomparedto234±40species

after(P<0.05)b. SpecifictaxacloselyrelatedtoRoseburiasp.,indicatingarolein

butyrateproduction

Table6:SpecificBacteriaTaxaChanges

Phylum BacterialtaxaFoldchangeafter/before

transplantationq-value

Firmicutes DoreaFormicigenerans 1.92 0.02Firmicutes Clostridiumsphenoides 1.95 0.02Firmicutes Coprobacilluscateneformis 1.65 0.02Firmicutes Ruminococcuslactaris 2.47 0.02Firmicutes Clostridiumnexile 2.09 0.03

Preoteobacteria Oxalobacterformigenes 1.70 0.02

v. Conclusions1. ResultsindicatepossibleroleforFMTinpreventionand/ortreatmentof

metabolicdisorders(e.g.,diabetes)2. Long-termfollow-upneededtoassesstreatmentlongevityandeffectson

weight,HbA1c,bloodpressure,lipids,andothermarkersofmetabolichealth3. FuturestudiesexaminingFMTspecificallyinDMneeded

Page15

IV. PotentialmicrobiometargetedDMtreatmentsa. Probiotics29

i. Livingbacteriaorfungithatconferahealthbenefitforthehostii. Modesofaction:antimicrobialactivity,improvedintestinalintegrity,

immunomodulationiii. Example:Lactobacillusplantarum

b. Prebiotics29i. Nondigestiblecompoundsthatleadtofavorablechangesinintestinalmicrobiota

whichstimulategrowthofselectiveandbeneficialgutbacteriaii. Oftendesignedtoincreaseabundanceoflactobacilliandbifidobacteriaiii. Compoundsindevelopment:transgalactooligosaccharides,inulin,oligofructose,

xylooligosaccharidec. Physicalactivity30-33

i. Animalstudiesshowaerobicexercisecontributestoimprovedintestinalintegrity,increasedmicrobialdiversity,andreducedinflammation

ii. Onlyobservationalhumanstudies

V. Futuredirections&researchgapsa. Developmentoftreatmentmodalitiestargetingthegutmicrobiomewilldependonfurther

datacollectioninordertodefinetheoptimalmicrobiomecompositionb. NeedforRCTsassessingdiet,prebiotics,physicalactivity,andmicrobiotareplacement

therapiesfordiabetestreatmentandpreventionc. ADAandJDFRrecommendfurtherresearchontheroleofthegutmicrobiomeinDM9

i. Needtodefinethedistinctionbetweenthemicrobiomesofmetabolicallyhealthyobeseindividualsandobeseindividualswhodevelopdiabetes

ResearchProject:The

Page16

ResearchProject:TheMicrobiomeasaPotentialMediatorofDiabetesHealthDisparitiesMicrobiomeasaPotentialmediatorofDiabetesHealthDisparities

ResearchQuestion

IsMexicanAmericanethnicityapredictorofgutmicrobiomecompositionamongpatientswithdiabetesindependentofotherfactorscommonlyassociatedwithhealthdisparitiesinthispopulation?

MethodsDesign ProspectivestudyofvolunteersfromSanAntonio,TXfromJune1,2017toMay31,2018Population Subjectstoenroll:50

InclusionCriteria Exclusioncriteria• ≥18yearsofage• Self-identifyas

MexicanAmerican

• Priorgastrointestinalsurgerythathasalteredtheanatomyoftheesophagus,stomach,orsmall/largeintestine

• Chronicdailyuseofanymedicationsthatcouldaltergastrointestinalsecretoryormotorfunction(e.g.,prokineticagents,narcoticanalgesics,laxatives,anticholinergics,anti-diarrheals)

• Useofantibiotics,gastric-acidsuppressingmedications,probiotics,withintwomonthsofthestoolsamplecollection

Protocol • Subjectstobedividedintotwogroups:T2DMversusnoT2DMo T2DMdefinedashavingbeendiagnosedandcurrentlyreceivingtreatment

• Subjectrecruitmentandpre-screening(SeeAppendix1)• Datacollection

o Surveyitems:countryofbirth,countryofbirthofparentsandgrandparents,age,sex,socioeconomicstatus,heightandweight,chroniccomorbidities,medicationuse

o Fooddiarytobecompletedoverfirstthreestudydayso Stoolcollectionkittobeusedonstudydayfour

• Sampleprocessingandsequencingo DNAextractionwithMoBiopowerlyzerkito Amplify16SrDNAV4regiono 16SrRNAsequencingwithIlluminaMiSeqmachine

• Microbiomeanalysiso Processsequenceswithsoftwareo Classifysequencesintooperationaltaxanomicunits(OTUs)usingMothur’sBayesian

ClassifierandreferencedtoGreengenesdatabaseofDiabetesSummary

• Gutmicrobiomeplaysamajorroleinhumanhealth,especiallywithrespecttometabolichealth• Haveidentifiedmultiplemechanismsthroughwhichgutmicrobiomeplaysaroleindiabetes

pathophysiology• Variousindividualtaxaimplicatedingutdysbiosiscontributingtodiabetes,butconflictingstudy

resultsindicatecomplexrelationshipexistsbetweengutmicrobiota,diet,age,physicalactivity,genotype,age,andmedicationusage

• Noveltreatmentmodalitiestargetinggutmicrobiota,includingpersonalizeddietaryalgorithmandleandonorFMT,associatedwithbeneficialeffectsonglycemiccontrolandmetabolicsyndrome

• Randomizedcontroltrialsofmicrobiome-targetedtherapiesneededaswellasfurthermicrobiomestudiestodistinguishhealthyfromdysbioticgutmicrobiomes

Page17

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8. CityofSanAntonioMetropolitanHealthDistrict.Type2diabetesinBexarCounty.Availableat:http://www.sanantonio.gov/Portals/0/Files/health/HealthyLiving/FactSheet-Diabetes.pdf.AccessedApril11,2018.

9. SemenkovichCF,DanskaJ,DarsowT,etal.AmericanDiabetesAssociationandJDRFResearchSymposium:DiabetesandtheMicrobiome.Diabetes.2015;64(12):3967-3977.

10. SchwartzSS,EpsteinS,CorkeyBE,GrantSF,GavinJR,3rd,AguilarRB.Thetimeisrightforanewclassificationsystemfordiabetes:rationaleandimplicationsofthebeta-cell-centricclassificationschema.DiabetesCare.2016;39(2):179-186.

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16. Chavez-TalaveraO,TailleuxA,LefebvreP,StaelsB.Bileacidcontrolofmetabolismandinflammationinobesity,type2diabetes,dyslipidemia,andnonalcoholicfattyliverdisease.Gastroenterology.2017;152(7):1679-1694e1673.

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Appendix1:BaselineCharacteristics28

Appendix2.PrescreeningQuestionsforPotentialSubjects

Supplementary Figure 1. Overview of study scheme.

Supplementary Table 1. Characteristics of Study Subjects at Baseline and After 6 Weeks

Allogenic group (N ! 9) Autologic group (N ! 9)

Baseline 6 weeks Baseline 6 weeks

Age, y 47 " 4 53 " 3Length, cm 185 " 2 178 " 2Weight, kg 123 " 6 122 " 6 113 " 7 113 " 7Body mass index, kg/m2 35.7 " 1.5 35.6 " 1.4 35.6 " 1.5 35.7 " 1.6Body fat mass, % 40 " 1 40 " 1 39 " 2 39 " 1Fasting plasma glucose, mmol/L 5.7 " 0.2 5.7 " 0.2 5.7 " 0.2 5.7 " 0.2Glycated hemoglobin, mmol/mol 39 " 1.1 38 " 1.2 40 " 1.5 39 " 3Cholesterol, mmol/L 4.5 " 0.4 4.6 " 0.4 4.8 " 0.3 4.8 " 0.2

HDLc 1.0 " 0.1 1.0 " 0.1 1.0 " 0.1 0.9 " 0.1LDLc 3.1 " 0.4 3.0 " 0.3 2.9 " 0.2 2.9 " 0.2TG 1.4 " 0.3 1.5 " 0.4 1.6 " 0.3 1.8 " 0.4

Plasma free fatty acid, mmol/L 0.5 " 0.1 0.5 " 0.1 0.7 " 0.2 0.5 " 0.1Systolic blood pressure, mm Hg 138 " 3 132 " 6 140 " 2 142 " 8Diastolic blood pressure, mm Hg 85 " 2 83 " 5 84 " 2 86 " 6

NOTE. Values are expressed as mean " standard error of the mean. The body mass index is the weight in kilograms divided by the square ofthe height in meters. No significant differences in clinical variables were found between baseline and 6 weeks in both treatment groups.HDLc, high-density lipoprotein cholesterol; LDLc, low-density lipoprotein cholesterol; TG, triglycerides.

October 2012 INTESTINAL MICROBIOTA TRANSFER 916.e5

Question NO YESAreyouatleast18yearsofage? Exclude DoyouconsideryourselfMexicanAmerican? Exclude Doyouhaveahistoryofmajorgastrointestinalsurgery? ExcludeDoyouhaveanyuseinthepasttwomonthsofanyofthefollowingmedications:Acidrefluxmedications(e.g.,Tums®,Zantac®,Prilosec®,Nexium®,Prevacid®)?

Exclude

Antibiotics(e.g.,Keflex®,Bactrim®,minocycline,amoxicillin) ExcludeProbiotics(exceptdietaryprobiotics,likeyogurt) ExcludeAnti-diarrheamedications(e.g.,Imodium) ExcludeLaxatives(e.g.,Ex-Lax) ExcludeAnti-depressants(e.g.,Zoloft®,Celexa®,Effexor®) ExcludeAnti-anxietymedications(e.g.,Xanax®,Ativan®,Klonopin®) ExcludeNarcoticpainmedications(e.g.,hydrocodone,codeine,morphine) ExcludeHaveyoubeendiagnosedwithType2Diabetesandareyoucurrentlyusingadiabetesmedication?

Non-T2Dgroup

T2Dgroup

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