Gut Microbiome-Targeted Treatment for Diabetes: What’s Your Gut Telling You? Amanda K. Kitten, Pharm.D. Master of Science Graduate Student and Pharmacotherapy Resident Division of Pharmacotherapy The University of Texas at Austin College of Pharmacy Pharmacotherapy Education and Research Center UT Health San Antonio Friday, April 13, 2018 Learning Objectives 1. Identify potential mechanisms by which the microbiome affects human health 2. Explain how the microbiome influences the development of diabetes mellitus 3. Describe the differences seen in gut microbiome composition between patients with diabetes and healthy subjects 4. Evaluate microbiome-targeted therapies as potential interventions to prevent and treat type 2 diabetes mellitus (T2DM)
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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)
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
vi. a-cell:overproductionofglucagonindiabetespatients,contributingtoincreasedbasalHGP
vii. Kidney:increasedsodium-glucosecotransporter-2(SGLT2)thresholdviii. Brain:delayedsatietyinresponsetoincreasesininsulinix. Stomach/smallintestine:increasedglucoseabsorptionx. Immunedysregulation/inflammation:macrophageandinterleuin-1(IL-1)
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
a. LPSsshedfromGram-negativebacterialcellwalls(i.e.,E.coli)i. Bindtotoll-likereceptor-4(TLR4)/CD14complexii. TLR4activatesinnateimmunesystem,resultinginpro-
i. Firstgatheringofexpertsthatfocusedonthelinkbetweenthepathophysiologyofthemicrobiomeofdiabetes
ii. Symposiummadeseveralrecommendationstoguidefuturediabetesandmicrobiomeresearch
TheGutMicrobiomeinPatientswithDiabetes
I. Microbiomestudies:associationswithmetabolic(dys)functiona. Historically,studieshaveyieldeddiverseresults19-21b. SeveralrecentrobuststudiesdemonstrateddifferencesbetweenT2DMpatientsand
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).
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
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
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lass
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teria
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ily)
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(Gen
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Roseburia inulinivorans
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‘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.
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
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fidob
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riale
s (O
rder
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orio
bact
eria
les
(Ord
er)
Bifid
obac
teria
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(Fam
ily)
Cor
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amily
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fidob
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rium
(Gen
us)
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Col
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Bact
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des
ster
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(Spe
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istip
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s (S
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Bacteria decreasing in ‘good’ diet week Bacteria increasing in ‘good’ diet week
Paric
ipan
ts -
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d’ d
iet w
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Paric
ipan
ts -
‘bad
’ die
t wee
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C Bifidobacterium adolescentis
Day
Fold
cha
nge
(with
resp
ect t
o da
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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.
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
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ae (F
amily
)Bi
fidob
acte
rium
(Gen
us)
Col
linse
lla (G
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aero
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es (G
enus
)D
orea
(Gen
us)
Bifid
obac
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tis (S
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es)
Col
linse
lla a
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(Spe
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bact
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long
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cter
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(Phy
lum
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s (P
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Prot
eoba
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ia (P
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Bact
eroi
dia
(Cla
ss)
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map
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obac
teria
(Cla
ss)
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tapr
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bact
eria
(Cla
ss)
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prot
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lass
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cter
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(Ord
er)
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s (O
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ily)
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ae (F
amily
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us)
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lla (G
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umin
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(Spe
cies
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s (S
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es)
Ros
ebur
ia in
ulin
ivor
ans
(Spe
cies
)Ba
cter
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s vu
lgat
us (S
peci
es)
Bact
eroi
des
ster
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(Spe
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)Al
istip
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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
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P10E9E2E8P6
E11E5E3E7P9
E14E6P2P8E4P4
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P10E9E2E8P6
E11E5E3E1
C Bifidobacterium adolescentis
Day
Fold
cha
nge
(with
resp
ect t
o da
ys 0
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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.
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.
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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
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®)?