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GutMicrobiome-TargetedTreatmentforDiabetes:What’sYourGutTellingYou?
AmandaK.Kitten,Pharm.D.MasterofScienceGraduateStudentandPharmacotherapyResident
DivisionofPharmacotherapyTheUniversityofTexasatAustinCollegeofPharmacyPharmacotherapyEducationandResearchCenter
UTHealthSanAntonio
Friday,April13,2018
LearningObjectives1. Identifypotentialmechanismsbywhichthemicrobiomeaffectshumanhealth2. Explainhowthemicrobiomeinfluencesthedevelopmentofdiabetesmellitus3. Describethedifferencesseeningutmicrobiomecompositionbetweenpatientswithdiabetesand
healthysubjects4. Evaluatemicrobiome-targetedtherapiesaspotentialinterventionstopreventandtreattype2
diabetesmellitus(T2DM)
Page2
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
Page3
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
Page4
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
Page5
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
Page6
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
rop
ean
Jou
rnal
of
En
do
crin
olo
gy
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
Page8
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
Con-101Con-101Con-104Con-104
H. parainfluenzaeH. parainfluenzae
Con-148Con-148Con-155Con-155
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.
Page9
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.
1086 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.
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
‘Good’ 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.
(legend continued on next page)
1090 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.
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
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
‘Good’ 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.
(legend continued on next page)
1090 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.
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
‘Good’ 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.
(legend continued on next page)
1090 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.
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.
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17. QinJ,LiY,CaiZ,etal.Ametagenome-wideassociationstudyofgutmicrobiotaintype2diabetes.Nature.2012;490(7418):55-60.
18. KarlssonFH,TremaroliV,NookaewI,etal.GutmetagenomeinEuropeanwomenwithnormal,impairedanddiabeticglucosecontrol.Nature.2013;498(7452):99-103.
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24. AnH,HeL.Currentunderstandingofmetformineffectonthecontrolofhyperglycemiaindiabetes.JEndocrinol.2016;228(3):R97-106.
25. ForslundK,HildebrandF,NielsenT,etal.Disentanglingtype2diabetesandmetformintreatmentsignaturesinthehumangutmicrobiota.Nature.2015;528(7581):262-266.
26. WuH,EsteveE,TremaroliV,etal.Metforminaltersthegutmicrobiomeofindividualswithtreatment-naivetype2diabetes,contributingtothetherapeuticeffectsofthedrug.NatMed.2017;23(7):850-858.
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Page19
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
Recommended