Does our microbiome influence oral drug treatment?

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Does our microbiome influence oral drug treatment?

Treatment of Type II Diabetes with Metformin

A review of the literature

Ina Therese Kvakkestad

Project thesis at the faculty of medicine UNIVERSITY OF OSLO

Supervisor: PhD Johannes Espolin Roksund Hov

31.01.2019

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Does our microbiome influence oral drug treatment? Viewing the treatment of Type II Diabetes with metformin

Ina Therese Kvakkestad, [email protected] http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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ABSTRACT

Aim: To get an overview of the literature on how our microbiome impacts the effect metformin has on patients with T2D.

Method: A scoping study with the inclusion criteria of metformin as an intervention and analysis performed on the microbiome. Different populations were included.

Results: There are evidence that metformin directly affects our microbiome and that these changes in turn mediate the effects of metformin, in particular from studies where transfer of metformin treated human microbiota to mice leads to improved glucose tolerance compared with non-treated. There is a discrepancy between microbial changes and their apparent effects in animal and human research. This could be due to small human populations or true

biological differences.

Improved glucose tolerance and a reduced %HbA1c and an increase in GLP-1 are frequent findings. A metformin mediated reduction of B. fragilis changes the composition of bile acids in the intestines which in turn promotes release of GLP-1. Short chain fatty acids, increased because of an increase in SCFA-producing bacteria, are positively associated with GLP-1 in rodents. GLP-1-increase was negatively associated with the Firmicuites to Bacteroidetes ratio, but the association is uncertain. Akkermansia muciniphila was increased in one of the human studies and in several rodent studies. It is associated with a reduced %HbA1c and immunological improvements, but thus far only for rodents.

Conclusion: Metformin directly affects the microbiome, and the microbiome imposes metabolic improvements on us. More research is needed to fully understand this

“cooperation”, and hopefully this understanding will help us to better prevent and treat type II diabetes.

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ACKNOWLEDGEMENTS

I decided early that I wanted to write about the gut microbiota for my project thesis.

From my supervisor I got to know that the microbiome’s potential influence on drugs was an interesting field of research now.

“Drug metabolism by the intestinal microflora may potentially generate new metabolites with distorted bioactivities or properties; however, the role of the gut microbiota and its influences on orally administered drugs are much less understood. Studies of the functional connections

between the gut microbiota and drug pharmacology are even scarcer”

(Feng et al 2015).

As obesity and metabolic disturbances is a prevailing problem in the world, I decided to dig deeper into this, here in terms of the gut microbiota and metformin.

A big thanks to my supervisor Johannes Espolin Roksund Hov,

who introduced me to this exciting field and helped me in the writing process.

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TABLE 1: AN OVERVIEW OF THE LITTERATURE REVIEWED

Study 1 ref (1)

Ma W, Chen J, Meng Y, Yang J, Cui Q, Zhou Y.

Metformin Alters Gut Microbiota of Healthy Mice:

Implication for Its Potential Role in Gut Microbiota Homeostasis.

Front Microbiol.

2018;9:1336.

Study 2 ref (2)

Sun L, Xie C, Wang G, Wu Y, Wu Q, Wang X, et al.

Modulation of gut microbiota by berberine and metformin during the treatment of high-fat diet- induced obesity in rats

Nat Med.

2018;24(12):1919- 29.

Study 3 ref (3)

Lee H, Ko G. Effect of metformin on metabolic improvement and gut microbiota.

Appl Environ Microbiol.

2014;80(19):5935- 43

Study 4 ref (4)

Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, et al.

An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice.

Gut.

2014;63(5):727- 35.

Study 5 ref (5)

Pyra KA, Saha DC, Reimer RA.

Prebiotic Fiber Increases Hepatic Acetyl CoA Carboxylase Phosphorylation and Suppresses Glucose-Dependent Insulinotropic Polypeptide Secretion More Effectively When Used with Metformin in Obese Rats.

The Journal of Nutrition.

2012;142(2):213- 20

Study 6 ref (6)

Cabreiro F, Au C, Leung KY, Vergara- Irigaray N, Cocheme HM, Noori T, et al.

Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism.

Cell.

2013;153(1):228- 39

Study 7 ref (7)

Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannerås-Holm L, et al.

Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.

Nature Medicine.

2017;23:850.

Study 8 ref (8)

Napolitano A, Miller S, Nicholls AW, Baker D, Van Horn S, Thomas E, et al.

Novel gut-based pharmacology of metformin in patients with type 2 diabetes mellitus.

PLoS One.

2014;9(7):e100778

Study 9 ref (9)

Sun L, Xie C, Wang G, Wu Y, Wu Q, Wang X, et al.

Gut microbiota and intestinal FXR mediate the clinical benefits of metformin.

Nat Med.

2018;24(12):1919- 29

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TABLE 2: ABBREVIATIONS

A.

muciniphila

Akkermansia muciniphila GUDCA Glycoursodeoxycholic Acid:

conjugated bile acid

ACC acetyl-coenzyme A carboxylase KEGG KEGG is a database resource for understanding high-level functions and utilities of biological systems.

AMP 5' adenosine monophosphate KO KEGG orthologs

AMPK AMP - activated protein kinase LPS Lipopolysaccharides

AUC Area under the curve ND Normal diet

CA Cholic acid. Primary bile acid NCI-H716 cells

NCI-H716 is a cell line derived from ascites fluid. This cell line is currently the only human model available for studying endocrine cells (10)

CDCA Chenodeoxycholic acid – a primary bile acids in human and mice

MCP-1 Monocyte chemoattractant protein-1

C4 7αC4, C4, is a metabolic

intermediate in the rate limiting step for the synthesis of bile acids from hepatic cholesterol.

PYY Peptide YY

CL Clostridium PPAR Peroxisome proliferator-activated

receptor

CT controll OGTT Oral glucose tolerance test

CYP7A1 Cholesterol 7 alpha-hydrolase: the first rate limiting step in bile acid synthesis.

OFS Oligo fructo saccharides

DPP4 dipeptidyl peptidase 4 OTUs Operational taxonomic units

F/B-ratio Firmicuites/Bacteroidetes-ratio RCT Randomized controlled trial

FGF-19 Fibroblast growth factor 19 T2D Type 2 diabetes mellitus

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FXR Farnesoid X receptor TCA Taurocholic acid: an FXR-antagonist

GIP Gastroinhibitory intestinal peptide TGR5 A G-coupled reseptor specific for bile acids.

GLP-1 Glucolipoproteine-1 TNF Tumor necrosis factor

GLUT-2 GLUT2 is a receptor in liver cells, pancreatic beta cells, and epithelial cells of the intestinal mucosa and kidney. It reacts to small changes in blood glucose.

TMP Trimethoprim

GUDCA Glycoursodeoxycholic Acid:

conjugated bile acid

Tregs Foxp3 regulatory T cells

HFD High fat diet TUDCA Tauroursodeoxycholic acid

HFHS High fat, high sucrose diet SAMe S-adenosylmethionine

HOMA-IR Homeostatic Model Assessment of Insulin Resistance

SKIN-1 Protein skinhead-1

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INTRODUCTION

Even if we corrected for height, weight and gender, different people would still react more or less differently to the same drugs. What other components are there to determine the effects on medication? Our genes were suggested. The term pharmacogenomics was coined in 1959 (11). After the human genome sequencing project in 2004, we realized that our genes did not give us the complete answer. The ”genomists” then proposed it could be caused by

epigenetics (12). If that is the case, what are the most important factors to affect our epi- genes? Our environment, our diet, our mood, our level of physical activity... or could it be the community of living organisms residing in our gut?

Researchers have started to look at the microbiota in order to try to explain medical effects that we previously have not been able to understand. Defining interactions between drug therapy, microbiome and host physiology is experimentally challenging given the complex and heterogeneous nature of mammalian gut microbiota (13). Here I will look at the drug that has been uncontested as the primary choice of treatment for diabetes mellitus type II for more than 60 years, metformin (1, 5, 9, 14) .

METFORMIN

Metformin was extracted from a plant, Gallega officinalis (15). This plant was used in medieval Europe as a folk medicine to treat diabetes. Some of its compounds however were toxic. It was thus not until the late 1950s in the UK and 1990s in the US that metformin was extracted and in use the way we know it today (16). It has a persistent anti-hyperglycemic effect, and additionally it gives a modest weight loss, improvement of lipid profiles and lowering of micro- and macrovascular complications (16). Dangerous side-effects are rare but gastrointestinal problems such as bloating and diarrhea are rather common (17). That

information reviles that metformin does influence our gut, somehow.

It has long been held true that the primary way in which metformin lowers the blood glucose is through inhibition of gluconeogenesis (18) through stimulating the AMP-activated protein kinase activity in the liver (9). This theory has recently been contested as studies have shown

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that metformin’s effects are not attenuated when AMPK is knocked out, and that intravenous metformin is less efficient than oral medication (1). This strongly indicate that we must look for other mechanisms, and that they might be found in the gastrointestinal system.

Even pharmacokinetics supports this. 30 % of the oral dose of metformin passes into the colon (19). Plasma concentrations in serum are typically in the low micro molar range but are 30–300 times higher in jejunal samples (19, 20). Lastly, metformin has proven efficient in treating other diseases as well – ranging from endometriosis to cancer. If we could discover a common mediator of the effects of metformin, could we also find other ways to prevent and treat these diseases?

THE MICROBIOME

Mammals, including humans, coexist with intestinal microbes in a relationship that includes elements of commensalism, symbiosis and pathogenesis (21). The microbiota strongly

influences host metabolism (6) for instance through metabolites, including the endotoxin LPS, bile acids and short chain fatty acids (9). It is also involved in our immune response (22).

More number oriented, we have about 10¹⁴ bacteria that reside in our colon, they generate a biomass of more than 1.5 kg and their combined genomes exceed the human genome more than 100-fold (23). There are about 1500 different species, 70 genera (23) and 9 phyla (24) in our intestines. The phyla are: Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, Proteobacteria, Verrucomicrobia, Cyanobacteria, Spirochetes and vadinBE97 (24).

By far the most abundant are Firmicutes and Bacteroidetes constituting around 98 % of the microbiome (24, 25). Considering production of the important bacterial metabolites, short- chain fatty acids, bacteria of the Bacteroidetes phylum mainly produces acetate and

propionate, whereas the Firmicutes have butyrate as their primary metabolic end product (26).

There are several studies indicating associations between different composition of the gut microbiota and diseases. Dysbiosis are significantly associated with obesity, type 2 diabetes (4), metabolic syndromes (27), inflammatory bowel disease, cardiovascular disease and

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autism (3). Hopefully, in the future, knowledge of the microbiome can help us prevent and treat more diseases.

TYPE II DIABETES MELLITUS

T2D consist of insulin resistance and pancreatic overproduction of glucagon (28). Up to a certain level, the pancreas can overcome the insulin resistance by producing more insulin, but when type II diabetes is a factum there will be a partial loss and partial dysfunction of beta- cells (29).

Why does this disease process happen in some people? There is evidence that inflammation and the immune system plays a big role (29, 30). The picture is complicated, but scientists have seen that the generalized immune responses stimulate receptors which promote

cytokines production which in turn destroy β-cells (27). The immunologic reactions might be to chronic exposure to glucolipotoxicity (31).

Patients with T2DM have a markedly blunted incretin secretory response which has been proposed as the cause for an impaired postprandial insulin secretion by up to 60 % (32).

Glucagon-like peptide-1 (GLP-1) and gastroinhibitory intestinal peptide (GIP) constitutes more than 90% of all the incretin function (32) Augmentation of GLP-1 results in

improvement of beta cell health (32). Basal and/or nutrient-stimulated GLP-1 secretion is reduced in obesity (33) and secretion in response to a meal is decreased in T2D patients (34).

As destruction of beta-cells are associated with a high blood glucose (35) , could we avoid type II diabetes by eating more healthily, less sugar? The underlying gene-nutrient

interactions that cause T2DM are not completely understood (36). Interestingly, factors such as epigenetic modification and miRNAs have recently been suggested a likely to have a substantial role in the impact of nutrients in T2DM (36). If this is important, what then is important to the turning on and off our genes?

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METHOD

The question “Can our microbiome influence treatment with oral drugs?” should be read as a conceptual question. According to Booth et al. a conceptual question is suitable for a literature review (37).

Within the literature reviews, a scoping study “aim to rapidly map the key concepts underpinning a research area and the main sources and types of evidence available (…), especially where an area is complex or has not been reviewed comprehensively before” (38).

Further, it can be used in identifying gaps in the existing evidence base followed by conclusions as to the overall state of the research activity in a particular area (38).

This thesis is a scoping study where I try to put forward both the evidence of association between the actions of metformin and the gut microbiome, and also highlight what we need to know more about. The scoping study allows use of different study designs (38).

Changes in the microbiome – or absence of such – during metformin treatment were of interest. My only requirement was that they had analysed the microbiome with the treatment.

I have tried to take into account all the relevant evidence and tried to make reliable judgements about its validity and implications.

CRITERIA FOR CONSIDERING THE STUDIES

SEARCH METHODS FOR IDENTIFICATION OF STUDIES

To define my scope of search I wrote my question in PICO form like this:

TABEL 3: PICO

P (population) Gut microbiome and host. The host could be research animals and humans. The humans could be healthy or have some kind of metabolic problem, HFD, obesity, or T2D

I (intervention) Metformin

C (control) No metformin/ other drugs which have proven to have similar effects/diets*

O (outcome) ** Change in the microbiome and/or its metabolic products

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* Oligofructose

**This is what my “O” should have looked like. Because of a misconception I first though my wanted outcome would be to witness a change in absorption rate of the drug and maybe metabolization.

ELECTRONIC SEARCHES

The electronic searching was limited to PubMed. A librarian helped me make the search as comprehensive as possible. The search strategies included a combination of controlled vocabulary with MeSH and free‐ text terms. The free terms were used in order to include newer studies that might not have been included in the Mesh-register. In order to find all synonyms, I read some of the articles that I found in my pilot searches. I also included the Mesh word as a free term. Boolean operators, OR and AND were used. In the PICO, C was not included in the PubMed search.

((((((((("Biological Availability"[Mesh]) OR "Biological availability") OR Bioavailability) OR Metabolism) OR Metabolization) OR uptake)) AND ((((((("Pharmaceutical

Preparations"[Mesh]) OR "Pharmaceutical preparation") OR "Pharmaceutical preparations") OR drugs) OR agents) OR medication) OR medicines)) AND (((((((("Gastrointestinal Microbiome"[Mesh]) OR "gastrointestinal microbiome") OR "intestinal microbiomes") OR microbiome) OR microbiota) OR "gastrointestinal flora") OR "Enteric bacteria") OR "Gut flora")) AND "Metformin"[Mesh].

ARTICLE INCLUSION CRITERIA

The study must have analysed the gut microbiota and/or their metabolites, and metformin should be an intervention. I allowed studies performed on animals, insects and humans. I chose not to have restriction on date, but I did restrict my search to English articles. I had no requirements when it comes to geography even though it is likely that differences in the microbial makeup exist across the world as diet and environmental factors probably plays a big role.

CRITERIA OF EXCLUSION

Metformin used in treatment of diseases not within the range of metabolic problems: obesity, metabolic syndrome or T2D.

PEER REVIEWED

All articles were published in peer reviewed journals.

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I looked for all types of studies, except for single case studies and review articles. I did not have a limit of age or gender despite the fact that the microbiome changes with age. To some extent age limited in itself in T2D in humans.

DATA COLLECTION AND ANALYSIS

SELECTION OF STUDIES

I scanned all titles and abstracts from the initial search to exclude trials that did not meet the inclusion criteria. 26 articles appeared and 9 were relevant based on the preset criteria.

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RESULTS

Underneath are compressed versions of the results in the articles. The language of the author was tried preserved. At times I chose to leave out methods, and statistical analyses. This, I did to highlight the results and tendencies. On the studies where results are put forward for

metformin and an additional intervention, I have focused more at the results related to metformin. Five of the studies were performed on rodents, one on C. elegans and three were performed on humans.

STUDIES ON RODENTS

1 METFORMIN ALTERS GUT MICROBIOTA OF HEALTHY MICE: IMPLICATION FOR ITS POTENTIAL ROLE IN GUT MICROBIOTA HOMEOSTASIS (2018)

Aim: To fully understand the mechanism of action of metformin in treating diseases other than diabetes.

Method: Healthy mice were treated with metformin for 30 days. The gut microbes were observed using 16sRNA-based microbiome profiling technique. They also compared the altered microbiome profile with the profiles under various disease conditions. 19 healthy mice were separated into two groups: 9 were controls and 10 were included in the metformin- treated group. Until 8 weeks of age, mice were maintained on a chow diet. Then, mice in the metformin treated group were given 300 mg metformin/kg of body weight once daily via intragastric administration. Mice in the control group (C) were treated with an equivalent amount of saline via intragastric administration for 30 days. Fecal samples were obtained from 19 mice under sterile conditions.

Results: After the 30 days: 46 significantly changed gut microbes: 22 enriched and 24 depleted. At the class level, Verrucomicrobiaceae, Prevotellaceae, Porphyromonadaceae, Rikenellaceae were enriched, while Lachnospiraceae, Rhodobacteraceae were reduced. The group treated with metformin could clearly be separated from control. Six pathways were significantly enriched: ribosome, purine metabolism and biosynthesis of amino acids, lipopolysaccharide (LPS), folate, aminoacyl-tRNA.

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They found a high similarity between the gut flora of prediabetic and diabetic mice. 5 diseases exhibit significant microbiome profile similarity with metformin treated T2D. T2D had the strongest negative similarity to metformin treated-T2D. Metformin treatment of healthy mice exhibits a negative correlation with multiple inflammatory diseases and have negative

similarity with multiple inflammatory diseases such as diarrhea irritable bowel syndrome, necrotizing enterocolitis, systemic inflammatory response syndrome and rheumatoid arthritis.

2 MODULATION OF GUT MICROBIOTA BY BERBERINE AND METFORMIN DURING THE TREATMENT OF HIGH-FAT DIET-INDUCED OBESITY IN RATS (2015)

Aim: To compare the modulations of gut microbiota with berberine and metformin treatment.

Method: High throughput 454 pyrosequencing produced 421,930 high quality sequences from 150 fecal samples collected at week 0, 8 and 18 of metformin and berberine. 50 rats were randomly divided into 2 groups: (1) 10 ND group, (2) 40 HFD group. After 10 weeks of feeding, the HFD rats were randomly divided into 4 groups, with 10 rats in each group: (1) HFD group; (2) HFD + Berberine200 (200 mg/kg body weight); (3) HFD + Berberine100 (100 mg/kg body weight); (4) HFD + metformin group (200 mg/kg body weight metformin).

From the 10th week, all the animals including those in ND, HFD and treatment groups were intragastrically given drugs or placebo. Throughout the duration of the trial, the body weight of each rat was monitored twice weekly and stool samples were collected at 0th, 8th, and 18th weeks. At the end of the trial, after 12 h of food deprivation, fasting body weight was

precisely examined. Tissues, including peripheral fat, epididymal fat, liver and cecum (both full and empty) were excised, weighed, and frozen for further analysis. Adiposity index was then calculated.

Results: Metformin and berberine similarly shifted the overall structure of the gut microbiota in rats; there is an enrichment of SCFA-producing bacteria, but diversity is significantly reduced. These are reverting effects on the HFD-induced structural changes.

Metformin treatment: Among 134 key operational taxonomic unites (OTUs), 70 were decreased and 56 were increased. Seven OTUs belonging to Blautia, Bacteroides,

Butyricoccus, Phascolarctobacterium, and Parasutterella, were significantly increased. The

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total relative abundance of these 7 OTUs reached a median of 9.3% in HFD + metformin group and 1.7% in HFD group. Taxon-based analysis also revealed that a total of 8 in 13 phyla, 17 in 22 classes, 16 in 30 orders, 29 in 51 families, and 63 in 110 genera were significantly different in the mice on and off metformin.

Proteobacteria were increased by both metformin and berberine. Firmicutes and Bacteroidetes showed no difference after treatment of metformin. In addition, metformin demonstrated enrichment on Verrucomicrobia (A. muciniphila) and Pevotella. At the genus level, both drugs showed enriching effects on Allobaculum, Bacteroides, Blautia, Butyricicoccus, Lactobacillus, Phascolarctobacterium, Parasutterella and Klebsiella, and inhibiting effects on Clostridium XlVa, Flavonifractor, Lachnospiracea incertae sedis, Roseburia, Clostridium XI, etc.

Treatments attenuated the increase of body weight despite a HFD, and inhibited the

accumulation of body fat when compared with the HFD control. Through association analysis they saw that adiposity index and body weight were well predicted by the gut microbiome data (goodness of prediction of 0.76 and 0.40, respectively). The predicting performance retained using only the identified 134 key OTUs. This achieved for predicting the adiposity index and body weight, respectively.

3 EFFECT OF METFORMIN ON METABOLIC IMPROVEMENT AND GUT MICROBIOTA (2014)

Aim: To investigate the effect of diet and metformin treatment on the gut microbiome. What is the difference between a normal and a HFD under metformin treatment?

Method: The composition of the gut microbiota was investigated using a mouse model of HFD-induced obesity with and without metformin treatment. 6-week-old mice were on an HFD for 28 weeks. Metformin was administered every day during the HFD for 10 weeks. As controls, mice that receive HFD without treatment, a dietary change from an HFD to an ND (HFD-ND), an ND without metformin treatment (ND), and metformin treatment on an ND (ND-metformin) were included in all procedures.

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Results: Treatment with metformin improved markers of metabolic disorders, including serum glucose levels, body weight, and total cholesterol levels. Moreover, A. muciniphila and Cl. cocleatum abundances increased significantly after metformin treatment on the HFD group. A total of 18 KEGG metabolic pathways were significantly upregulated:

Lipopolysaccharide biosynthesis, pentose and glucoronate interconversions, sphingolipid, propionate, and fructose and mannose metabolism were significantly enriched. These effects were not seen in the ND group.

They also found that metformin treatment in HFD mice significantly reduced the level of TNF-α expression in female mice and increased the level of monocyte chemoattractant protein 1 (MCP-1) expression in male mice.

The composition of Bacteroidetes in the HFD group (43 %) was significantly lower than that in the ND group (79 %). In contrast, the composition of Firmicutes was significantly higher in the HFD group (51 %). When metformin was administered to the HFD group, the

composition of Bacteroidetes increased to 77 % - similar to that in the ND group.

Additionally, the composition of the phylum Verrucomicrobia in the HFD-metformin group significantly increased, unlike the effect of a dietary change from and HFD to an ND.

In the HFD-metformin group, the abundances of the families Bacteroidaceae,

Verrucomicrobiaceae, Clostridiales family XIII and incertae sedis, as well as the species A.

muciniphila and Cl. cocleatum changed significantly compared with those in the HFD and HFD-ND groups. Metformin treatment also affected the composition of the gut microbiota in mice fed a ND. The families Rikenellaceae, Ruminococcaceae, and Verrucomicrobiaceae, as well as Aslistipes spp, Akkermansia spp, and Clostridium spp, were more abundant in the ND- metformin group than the ND group.

A. muciniphila was negatively correlated with serum glucose levels. Cl. orbiscindens showed a negative correlation with body weight and PPAR-α and GLUT2 levels and a positive correlation with TNF-α, mucin levels. PPAR-α and GLUT2 levels were negatively correlated with Blautia producta and Allobaculum sp. Stain Cl. cocleatum was positively correlated with AMPK-α 1 and total cholesterol levels. There was no difference in the inflammation scores between the HFD-ND and HFD-metformin groups.

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4 AN INCREASE IN THE AKKERMANSIA SPP. POPULATION INDUCED BY METFORMIN TREATMENT IMPROVES GLUCOSE HOMEOSTASIS IN DIET-INDUCED OBESE MICE (2014)

Aim: To determine whether the antidiabetic effect of metformin is related to alterations of intestinal microbial composition.

Method: 24 normal mice (4-week-old) were fed either a normal diet or a HFD

(fat/proteins/carbohydrates: 3/1/1). After 8 weeks, the mice were divided into four groups of six and fed the following diets: (1) ND-CT, (2) ND-metformin, (3) HFD-CT or (4) HFD- metformin. The two groups of metformin treated mice received 300 mg/kg/day of metformin by oral gavage for a period of 6 weeks. Body weight was monitored once a week. The effect on the composition of the gut microbiota was assessed by analyzing 16S rRNA gene

sequences with 454 pyrosequencing. Adipose tissue inflammation was examined by flow cytometry analysis of the immune cells present in visceral adipose tissue (VAT).

Results: Compared with HFD-CT mice, HFD-metformin mice showed a significant

improvement in glucose tolerance, fasting blood glucose levels and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index. In ND mice metformin did not affect fasting blood neither of these factors. 6 weeks of metformin treatment had no effect on body weight or fat pad weight in ND- or HFD-fed mice.

HFD-CT mice had a greater abundance of Firmicutes and a lower abundance of Bacteroidetes than ND-CT mice. In addition, HFD-CT mice showed a significantly lower abundance of the phylum Verrucomicrobia than ND-CT mice.

Metformin resulted in a profound shift in the fecal microbial community profiles of the gut microbiota in diabetic mice. The tendency toward phyla-wide changes after treatment with metformin was similar in ND- and HFD-fed mice; however, the changes were more marked in the HFD-fed group. There were significant differences in the abundance of Firmicutes and Bacteroidetes between HFD-CT mice and HFD-metformin mice, but there was no significant difference in the abundance of these two phyla between ND-metformin mice and ND-CT mice.

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The abundance of Verrucomicrobia was significantly increased in the HFD-metformin, whereas no significant change was seen in ND-metformin. Furthermore, HFD-metformin mice formed a cluster that was distinct from that of HFD-CT mice and not even intermediate between those of the control HFD and ND mice. The microbial communities of ND on and off metformin were, however, closely clustered.

Changes in the abundance of 29 genera which belonged to six phyla, accounted for the differences in the gut microbial communities seen in metformin treatment. The relative abundance of Anaerotruncus, Lactococcus, Akkermansia, Parabacteroides, Odoribacter, Alistipes, Lawsonia, Blautia and Lactonifactor was altered by a HFD. Metformin rescued these HFD-induced changes and restored the levels to those seen in ND-fed mice.

Despite the low relative abundance (0.3–2.9%) of sequences assigned to A. muciniphila, this genus made a large contribution to the observed differences in the composition of the gut microbial communities in HFD-CT and metformin treated HFD mice. A. muciniphila was responsible for the greater abundance of Verrucomicrobia in HFD-metformin mice compared with HFD-CT mice.

Metformin treatment significantly improved the glycemic profile of HFD mice. They had a higher abundance of the mucin-degrading bacterium A. muciniphila than HFD-CT. In addition, the number of mucin-producing goblet cells was significantly increased with

metformin treatment in both ND and HFD mice. To make sure that the increase in goblet cells was caused by A. muciniphila, they supplementation with these bacteria alone and saw indeed that both number (per villus) and density (per unit surface area) of goblet cells in HFD-fed mice was restored. Furthermore, HFD-A. muciniphila mice showed a significant

improvement in glucose tolerance, similar to that seen in HFD-metformin mice. Treatment with the same dose of dead cells did not ameliorate the impaired glucose tolerance. Adipose tissue inflammation was attenuated by inducing Foxp3 regulatory T cells (Tregs) in visceral adipose tissue.

5 PREBIOTIC FIBER INCREASES HEPATIC ACETYL COA CARBOXYLASE PHOSPHORYLATION AND SUPPRESSES GLUCOSE-DEPENDENT INSULINOTROPIC POLYPEPTIDE SECRETION MORE EFFECTIVELY WHEN USED WITH METFORMIN IN OBESE RATS (2012)

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Objective: To determine whether using oligo fructo saccharides (OFS) as an adjunct with metformin augments GLP-1 secretion in obese rats.

Method: Male, diet-induced obese rats were randomized to: 1) high-fat/-sucrose diet (HFHS CT); 2) HFHS+10% OFS (OFS); 3) HFHS + metformin [300 mg/kg/d] (metformin); 4) HFHS+10% OFS+metformin (OFS + metformin). Body composition, glycaemia, satiety hormones, and mechanisms related to dipeptidyl peptidase 4 (DPP4) activity in plasma, hepatic AMP-activated protein kinase (AMPK), and gut microbiota were examined. Direct effects of metformin and SCFA were examined in human entero-endocrine cells.

Results: Both OFS and metformin reduced food and energy intake. Body weight over the course of the 7 weeks was affected by the interactions of diet × time, drug × time, and diet × drug × time. Body weight was lower for rats in the OFS+metformin and metformin groups compared to those in the CT and OFS groups.

At every time point during the oral glucose tolerance test (OGTT), leptin was highest in control. Leptin tAUC was lower in OFS compared to CT and lower in OFS+metformin compared to OFS and CT. Leptin AUC was correlated with fat mass. Ghrelin concentrations were reduced by OFS. OFS + metformin affected peptide YY (PYY) secretion over the course of the OGTT. Similarly, the PYY AUC was increased by OFS and showed a decreasing trend with metformin. GIP concentrations were affected by diet, drug, and their interaction. The OFS+metformin group had a lower GIP AUC than all other groups. The metformin group and the OFS group also had a lower GIP AUC than CT. Both diet and drug affected plasma GLP-1: OFS increased and metformin decreased GLP-1. Diet and drug also affected the GLP-1 AUC in a similar manner.

pAMPK and ACC levels were affected by diet, drug and their interaction: Hepatic pAMPK level was lowest in CT; AMPKα1 mRNA level was upregulated with OFS; OFS+metformin had the highest AMPKα2 mRNA level; Hepatic pACC level was highest in the

OFS+metformin; There were no effects of OFS or metformin on total AMPK or ACC level.

Rats fed OFS had higher total bacteria than those not fed OFS. There was a marked decrease in C. leptum with OFS. OFS increased, and metformin decreased Bifidobacterium spp.

Metformin increased Enterobacteriaceae. Bacteroides/Prevotella were negatively correlated

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with HOMA-IR and liver triglycerides. Bifidobacterium spp. were negatively associated with glucose and ghrelin AUC. C. leptum was positively associated with glucose AUC and plasma DPP4 activity. Total bacteria were negatively associated with fat mass, body weight, ghrelin AUC, HOMA-IR, and liver triglycerides.

STUDIES ON WORMS

6 METFORMIN RETARDS AGING IN C. ELEGANS BY ALTERING MICROBIAL FOLATE AND METHIONINE METABOLISM (2013)

Aim: Defining interactions between drug therapy, microbiome and host physiology. C.

elegans.

Method: Where indicated molten agar was supplemented with phenformin (1.5, 3, 4.5 mM), metformin (25, 50, 100 mM), carbenicillin (120 μM), trimethoprim (TMP 0.1, 0.2, 0.5, 1 μg/ml) or d-glucose (0.25, 1%). Trials were initiated by transfer of L4-stage worms (day 0) on plates supplemented with 15 μM FUdR. Statistical significance of effects on lifespan was estimated using the log rank test, performed using JMP. The effect of biguanide compounds on C. elegans’ physiology was monitored from the rate at which 50% of the E. coli food suspension was consumed, as a read out for C. elegans’ growth, survival, or fecundity.

Result: Metformin at 25, 50, and 100 mM increased mean lifespan by 18%, 36%, and 3%.

Metformin reduced the exponential age increase in mortality rate; it slows aging, rather than reducing risk of death. Life expansion was not increased under E. coli deprivation, but rather decreased

Metformin treated E. coli transplanted to C. elegans also robustly extended worm lifespan.

Metformin induced a dose-dependent inhibition of E. coli proliferation and an alteration in bacterial lawn morphology. It had bacteriostatic effects. Lifespan on metformin treatment was increased to a similar degree in the absence or presence of carbenicillin. UV-irradiation of E.

coli impaired bacterial viability and extended worm lifespan (6) and even under these conditions, metformin still shortened lifespan. Only if the E. coli was resistant, metformin shortened worm lifespan.

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Culture of C. elegans with Bacillus subtilis increases lifespan of C. elegans which make it likely that B. subtilis is less pathogenic than C. elegans. Metformin further increased lifespan of worms cultured on B. subtilis. Respiration rate was transiently reduced but increased with long-term exposure. Lifespan were extended differently in worms with different type of E.

coli and hence different types of LPS on metformin treatment. The variation, however, did not correlate with LPS type. If the E. coli was resistant, metformin treatment shortened worm lifespan.

Metformin markedly changed the folate composition. It increased levels of 5-methyl-THF, 5,10-methylene-THF, and DHF, whereas levels of the remaining folates were decreased. It also increased folate polyglutamylation. Treating with a combination of TRM and metformin they found only a small additional effect with metformin. This imply a shared mechanism of action, suggesting that altered bacterial folate metabolism by metformin increases worm lifespan.

Disruption of microbial folate metabolism has little effect on host folate levels. Microbes are the main source of dietary methionine. In E. coli metformin increases SAMe and 5-methyl- THF. By contrast, in C. elegans it decreases SAMe and the SAMe/SAH ratio without affecting 5-methyl-THF levels.

Metformin did not increase plasma AMPK levels. Lifespan in aak-2 mutants was not increased by metformin especially due to higher initial mortality rates. Metformin cause AMPK-dependent activation of protein skinhead-1, SKN-1. This has previously shown to induce detoxification gene expression. aak-2 and skn-1 protect worms against biguanide toxicity. Extension of lifespan by blocking folate metabolism with TRM or by a folate- deficient mutant E. coli also appeared to be partially aak-2-dependent.

Longevity induced by sams-1 RNAi is AMPK-dependent. This suggests that metformin increases lifespan at least in part via the AMPK-activating effects of reduced SAMe levels.

Glucose supplementation suppressed metformin-induced inhibition of bacterial growth. This might relieve the need of glucogenic amino acids (e.g., methionine) as a source of carbon.

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STUDIES ON HUMANS

7 METFORMIN ALTERS THE GUT MICROBIOME OF INDIVIDUALS WITH TREATMENT - NAIVE TYPE 2 DIABETES, CONTRIBUTING TO THE THERAPEUTIC EFFECTS OF THE DRUG (2017)

Aim: To investigate the effect of metformin on the composition and function of the gut microbiota and to investigate interactions between metformin and microbiota directly.

Method: A randomized, placebo-controlled, double-blind study. Treatment-naive individuals with recently diagnosed T2D were randomized to receive either placebo (n = 18) or

metformin (1,700 mg/d) (n = 22) for 4 months. A subset of the placebo group switched to metformin 6 months after the start of the study. These results were verified in a subset of the placebo group that switched to metformin 6 months. A whole-genome shotgun sequencing was perfumed on 131 fecal samples. A co-abundance network analysis was undertaken to investigate bacterial interactions. To see microbial growth, they mapped whole-genome shotgun reads to the genomes of common strains in the human gut to determine the ratio between DNA copy number near the replication origin and DNA copy number near the terminus of bacterial genomes.

Results: When fecal samples from before (CT) and after 4 months of metformin treatment were transferred to germ-free mice, glucose tolerance was improved in mice that received metformin-altered microbiota. Significantly decreased HbA1c and fasting blood glucose were only observed with metformin treatment, even though the CT group lost weight. %HbA1c and fasting blood glucose were also significantly reduced when metformin treatment was started after 6 weeks.

Metformin treatment for 2 and 4 months resulted in significant alterations in the relative abundance of 81 and 86 bacterial strains, whereas only one was changed in placebo. Most of the metformin changed species belonged to γ-proteobacteria and Firmicutes; there was an increase of Escherichia (γ-p), A. muciniphila and Bifidobacterium and a reduction of

Intestinibacter (F). 626 and 473 KEGG orthologys (KOs) were increased, whereas 130 and 69 KOs were decreased after 2 and 4 months of metformin. There was especially enrichment of genes for bacterial environmental responses, drug resistance, central carbohydrate

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metabolism, amino acid metabolism, and lipopolysaccharide (LPS) biosynthesis. In the control group, only 2 KOs were changed.

There was a negative correlation between B. adolescentis and %HbA1c, but not for A.

muciniphila. In vitro analysis: metformin directly promoted the growth of B. adolescentis, A.

muciniphila, but not of E. coli in pure cultures. All three of the recipients of metformin treated microbiota had reduction in %HbA1c and an increased glucose tolerance. They did not

observe any differences in body weight, body fat, or fasting insulin.

No substantial changes in fecal bile acids were detected. However, there were significantly larger increases in plasma bile acid concentrations. An increased abundance of bsh, genes encoding bile salt hydrolases, after 2 months on metformin. A significant negative correlation between the concentrations of unconjugated bile acids and %HbA1c.

A. muciniphila was the only taxon that increased in both DNA and RNA abundance, and it was also the taxon that increased the most in abundance. 10% of the protein-coding genes in A. muciniphila were significantly regulated by metformin; of these, 65% were downregulated.

Of particular interest, 81 of the 108 metformin-regulated annotated genes encoded

metalloprotein or metal transporters in addition to several other cofactors and coenzymes, as well as transferase, hydrolase, ligase, and protein components of ribosomes.

There were significantly larger increases in fecal propionate and butyrate concentrations in men, but no differences were observed when combined the results for men and women.

8 NOVEL GUT-BASED PHARMACOLOGY OF METFORMIN IN PATIENTS WITH TYPE 2 DIABETES MELLITUS (2014)

Hypothesis: The pharmacology of metformin includes alteration of bile acid recirculation and gut microbiota resulting in enhanced enteroendocrine hormone secretion.

Aim: To characterize in more detail the gut-based pharmacology of metformin in patients with T2D by relating glycemic control to bile acid excretion and microbiota changes.

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Method: Evaluated T2D subjects on and off metformin. Subjects were studied at 4 time points: at baseline on metformin (Visit), 7 days after stopping metformin (Visit 2), when fasting blood glucose had risen by 25 % after stopping metformin (Visit 3), and when fasting capillary blood glucose returned to baseline levels after restarting the metformin (Visit 4). At these time points they profiled glucose, insulin, gut hormones, peptide tyrosine-tyrosine and glucose-dependent insulinotrophic peptide and bale acids in blood, as well as duodenal and fecal BA and gut microbiota.

Results: The 12-hour mean venous plasma glucose levels increased by ∼15% from Visit 1-3.

The plasma glucose concentrations were decreased by ∼21% from Visit 3 to Visit 4.

Metformin withdrawal was associated with a reduction of active and total GLP-1 and

elevation of serum bile acids, especially cholic acid and its conjugates. These effects reversed when metformin was restarted.

Effects on circulating PYY were more modest, while GIP changes were negligible.

Microbiota abundance of the phylum Firmicutes was positively correlated with changes in cholic acid and conjugates, while Bacteroidetes abundance was negatively correlated.

Firmicutes and Bacteroidetes representation were also correlated with levels of serum PYY.

At Visit 1 total bile acids had increased approximately 2-fold in serum and 3.3-fold in intestines and decreased approximately 1.5-fold in feces. At Visit 3 there were still increases in bile acids but of smaller magnitudes. At Visit 5 bile acids in serum and feces had returned to levels similar to baseline. This pattern was seen for both primary and secondary bile acids, but the changes were larger for the primary ones.

Mean serum concentrations of active GLP-1 were decreased approximately five-fold from Visit 1 to Visit 3. In contrast, increases of active GLP-1 of similar magnitude were seen at Visit 4. PYY showed a somewhat similar profile, but with smaller changes. Mean GIP concentrations changed less than 12% between any visits.

Relative abundances of four genera were significantly different between On- and Off-

metformin, though these differences were not significant after false discovery rate correction for the total number of genera tested in the dataset. Cholic acid and conjugates in patient serum was significantly correlated with Firmicutes and Bacteroidetes abundances.

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Additionally, these two phyla were significantly correlated with circulating concentrations of PYY in patient sera.

9 GUT MICROBIOTA AND INTESTINAL FXR MEDIATE THE CLINICAL BENEFITS OF METFORMIN (2018)

Aim: To find the important microbial-derived metabolites and determine their host targets rather than focusing only on changes in specific gut microbiota. Investigate the impact of the gut microbiota, bile acid metabolism and related host targets on the regulation of glucose metabolism in individuals with T2D and obese.

Method: Serum and stool samples from 22 individuals with T2D were collected before and after being naively treated with 1,000 mg metformin twice daily for 3d. A whole-genome shotgun sequencing of stool samples was performed, and association studies of metagenomics profiling and clinical indicators provided in-depth insight into the function of specific gut microbiota on host metabolism. They further used ultra-performance liquid chromatography–

coupled time-of-flight mass spectrometry metabolite profiling to quantitate bile acids in the serum and stool.

Results: Whole-gene counts were not changed, but the microbiota diversity was slightly decreased, and the composition was substantially reshaped after metformin treatment. The genus Bacteroides showed the largest reduction in abundance. The decrease in level of B.

fragilis exhibited the most striking change. Blautia obeum and B. fragilis were the top changed bacterial species on the pathway of secondary bile acid metabolism regulated by metformin. The levels of Glycoursodeoxycholic and tauroursodeoxycholic acid (GUDCA and TUDCA respectively), conjugated secondary bile acids, were predominantly elevated after metformin treatment. Total level of bile acids remained unchanged, whereas noticeable elevation in ratios of conjugated to unconjugated bile acids were observed.

After metformin treatment FGF19 levels in serum of individuals with T2D were dramatically decreased, and C4 levels were markedly increased suggesting that intestinal FXR signaling was suppressed and hepatic CYP7A1 activity was increased. Both GUDCA and TUDCA

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markedly inhibited (chenodeoxycholic acid) CDCA-induced FXR transcriptional activity.

GUDCA had no effects on the activity of the bile acid receptor, TGR5.

Antibiotic-treated, microbiota-depleted mice were given an FXR agonist (TCA) combined with CDCA, GUDCA and TUDCA. They found that CDCA further elevated FXR signaling, whereas GUDCA and TUDCA attenuated it, indicating that GUDCA and TUDCA are FXR antagonists.

Metformin treatment upregulated hepatic Cyp7a1 mRNA levels. This resulted from downregulation of the intestinal FXR–FGF19 axis. They saw no evidence of AMPKα activation in the intestine of metformin treatment normal mice, and also that intestinal FXR signaling was attenuated in intestine-specific AMPKα1 knockout mice.

Neither a further inhibition of FXR signaling nor an upregulation of TUDCA were observed when the HFD-mice were treated with both metformin and antibiotics compared to antibiotics only.

In the microbiota-depleted mice, metformin did not inhibit TCA-activated intestinal FXR signaling. These results revealed that inhibition of intestinal FXR signaling by metformin depends on gut microbiota but not intestinal AMPK.

B. fragilis is correlated with changes in bile acid metabolites and FXR signaling: GUDCA and TUDCA levels negatively correlated with B. fragilis. B. fragilis: positively correlated with s FGF19 levels B and correlated negatively with s C4 levels. B. fragilis increased deconjugation of GUDCA and TUDCA in vitro. Metformin treated B. fragilis had its 5-methyl-THF

increased and methionine level decreased. Supplementation of methionine restored metformin inhibition of B. fragilis growth in culture. Metformin treatment significantly reduced B.

fragilis bile salt hydrolyze gene copy number and bile salt hydrolyze activity. Metformin given with a bile salt hydrolyze inhibitor gave no change in deconjugation of GUDCA in B.

fragilis

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Trimethoprim (TMP) which stops bacteria from turning dihydrofolate to the active form tetrahydrofolate, was found to inhibit B. fragilis–mediated hydrolysis of GUDCA. In the mice fed an HFD, supplementation with TMP mimicked metformin effects on bile acid metabolism and intestinal FXR signaling.

Stool obtained from four individuals with T2D before and after metformin treatment was transplanted to microbiota-depleted mice fed an HFD. The abundance of B. fragilis in the mice transplanted feces from the metformin treated was much lower than transplanted from the untreated: glucose intolerance and insulin resistance were substantially improved, and there was a slight loss in body weight; the levels of TβMCA and TUDCA were higher, and intestinal FXR signaling was substantially suppressed.

Colonization of B. fragilis in mice on an HFD gave: a higher body weight gain, impaired glucose tolerance, lower insulin sensitivity, substantially reduced TβMCA and TUDCA levels in the ileum and high activation of FXR signaling. Expression of thermogenic gene mRNAs was markedly downregulated in subcutaneous white adipose tissue after B. fragilis treatment.

When metformin treated mice additionally were given B. fragilis, the reduced body weight was increased, the enhanced energy expenditure was substantially diminished, and glucose tolerance and insulin sensitivity were decreased. B. fragilis-administration markedly abrogated up-regulation of TβMCA and TUDCA levels and decrease of intestinal FXR signaling in metformin treated mice.

Intestinal FXR is essential for metformin-induced metabolic improvements: Fxr-knockout (Fxr∆IE) mice on HFD and metformin for 12 weeks stopped all the metformin mediated effects that were seen in control. GUDCA treatment substantially restored glucose intolerance and insulin resistance and attenuated body weight gain in mice on an HFD, but not in Fxr∆IE mice. Furthermore, GUDCA treatment had therapeutic effects in reversing metabolic

disorders in established obese mice. The levels of serum alanine and aspartate

aminotransferase were lower after GUDCA supplementation. These metabolic improvements were consistent with an elevated metabolic rate. In addition, GUDCA substantially elevated active GLP1 production.

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DISCUSSION:

A quick look at the results and it seems like metformin works in many and mysterious ways.

Are they all separate primary effects or could they be secondary effects mediated by one primary effect, like a metformin affected microbiome?

GUT DYSBIOSIS

Gut microbiota dysbiosis is a condition related with the pathogenesis of intestinal and extra- intestinal illnesses (39). The nine studies reviewed here all report a change in gut microbiota.

Is this a direct consequence of metformin?

Study 1 argues that a change in the microbiota of healthy mice with metformin is evidence of a primary effect on the microbiome (1). Metformin does not lower blood glucose when glucose is already normal, and therefore environmental factors cannot be blamed. Metformin could, however, easily change other factors in our body important for the microbiota. The argument is also weakened by the fact that study 3 does not show any microbial changes in mice on a normal diet (3). Both studies are on mice. Study 1 has a duration of 30 days, whilst study 3 lasts for 28 weeks. The mice were almost the same age when the study was started, 6 and 8 weeks, and they were given the same dose of metformin (300 mg/kg of body weight).

Study 4, also on mice and with the same treatment and conditions, observed like study 1 that the tendency toward phyla-wide changes after metformin were similar in normal and high fat diet mice, but that the changes were more marked in the high fat diet group (4).

Almost the same argument can be made with Study 2. Weight reduction is the same for the two medications metformin and berberine, but the change in the microbiome is different (2).

Therefore, it cannot be weight reduction that induces the changes in the gut flora. Berberine exhibits poor water solubility and presumably is difficult for intestinal epithelial cells to absorb (40). The finding that decrease in diversity and increase in SCFAs was more pronounced for berberine than metformin, would thus be difficult to explain without the influence of the microbiome (2).

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That the microbiome influences, is strengthened by the finding from a study from 2015.

Berberine was detected in nearly all major organs after administration and that it was most likely due to a bacterial enzyme making it more absorbable (41). A study that looked at why metformin and berberine work better together than separately, saw that the maximal plasma concentration and area under the curve for metformin was actually decreased with the addition of berberine (42). If it were important to the effect of metformin how much of the drug that was in circulation, it would be difficult to explain why a reduced absorption increase its effect. The fact that berberine has an effect in itself reduces the strength of this argument though. This same goes for OFS in study 5. They cannot be absorbed directly but are negatively associated with %HbA1c (5).

In vitro trials are good to show direct effects. With metformin to the petri dish, study 3 and 7 found enrichment of A. muciniphila in pure culture (3, 7). In study 4 mice on a HFD and treated solely with a broad spectrum of antibiotics showed a significantly improved glucose tolerance compared to control mice (4). Study 6 also reports bacteriostatic effects of

metformin (6). Also, metformin did not affect the glucose tolerance further, when the mice had been depleted of their gut microbiota with antibiotics (4). The direct effect is inevitable.

Could it be though, that the manner in which metformin helps us via the gut microbiome, is simply by diminishing it? Before looking at the “how-question”, the evidences that metformin directly affects the human microbiome must be mentioned.

The human randomized controlled trial by Wu & Esteve (2017), study 7, provide two

evidence that the bacterial change is indeed caused directly by the drug and not by metabolic improvements (7). First, they observe that germ free mice transplanted with gut bacteria from metformin treated T2D patients show improvements in glucose tolerance (7), proving that the microbiome can solely mediate the effects. Secondly, two human microbiomes were treated directly in a gut simulator and direct effects on the microbiota were found (7). They profiled the microbiomes by whole- genome shotgun sequencing at both the DNA and RNA level and found that metformin exposure significantly altered the DNA and RNA abundance several bacterial strains, in one of the donors more than in the other (7).

Study 8 saw a change in relative abundances of four genera that was significantly different between On- and Off-metformin conditions. This though, was not significant after false discovery rate correction for the total number of genera tested in the dataset. This the authors

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assume to be due to the small number of participants of 12 patients. In study 9 they argue that the fact that they see changes in the microbiome in newly T2D-diagnosed patients, not yet metformin treated before the trial and treated only 3 days before analyses, that the effects observed cannot be mediated by subsequent metabolic improvements (9). The effect was a significant reduction of B. fragilis (9).

What are the most important bacterial changes? Several studies point to an increase in SCFA- producing bacteria, others to an increase in A. muciniphila, one article points to the

importance of a decreasing B. fragilis, and yet some to the importance of the ratio of Bacteroidetes and Firmicuites. Other than that, the consensus is not striking. All microbial changes observed are therefore listed in APPENDIX 1.

We will also discuss whether and how “the effects of metformin” are mediated through the microbiome, but first do we need AMPK-activation to get the wanted effects of metformin?

AMPK-ACTIVATION

An increased AMP kinase (AMPK) activity in the liver will alter mitochondrial function (43) in a way that inhibits energy requiring processes: liver gluconeogenesis, cholesterol synthesis, lipogenesis and triglyceride synthesis and adipocyte lipogenesis; and stimulates energy creating processes: fatty acid oxidation, ketogenesis, skeletal muscle fatty acid oxidation and glucose uptake, activation of adipocyte lipolysis, and modulation of insulin secretion by pancreatic beta-cells (44). Therefore, when metformin was found to increase hepatic AMPK-activation (18), no wonder they expected to have solved the riddle of metformin.

Study 3, though, found metabolic improvement by metformin without the activation of hepatic AMPK; AMPKα1, one of two isoform of AMPK, was actually decreased (3).

Previous studies have shown that AMPK-activation might not be as important for the effects we see by metformin: First, the glucose-lowering effect can only partially be explained by a reduction in endogenous glucose production (27); Second, studies in T2D patients with a loss- of-function variant of a gene which renders them unable to take up metformin in the liver, get the same %HbA1c-lowering effect (15); Third, a delayed-release metformin that is largely

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retained in the gut, with minimal systemic absorption, is as efficient at lowering blood glucose as the standard immediate-release formulation in individuals with type 2 diabetes (15).

On the other hand, in study 5 the authors claim that they are the first to have observed an increase in hepatic AMPK after treatment with oligofructose (OFS) (5). OFS, are unique soluble and fermentable non-digestible carbohydrates with low energy value (45). What is extracted from them are the by-products from the fermentation process by the gut bacteria (46). It is therefore intriguing to hypothesize that it might be these end products that activate AMPK.

The hepatic AMPKα1 mRNA level was upregulated with OFS, whereas AMPKα2 expression was affected by OFS and its interaction with metformin (5). Whilst study 3 saw a decrease in AMPK α1 with metformin, study 5 did not report any changes in that isoform. Why do they not find the same? Study 3 looks at mice whereas study 5 looks at rats. There could be differences in their metabolism. Also, in study 5 the rats are fed a high fat/high sucrose diet, whereas the mice in study 3 are fed 60 % lipid. What the remaining 40 % consist of is not described and it is therefore difficult to say whether their foods could influence.

Study 5 put forward evidence from liver-specific AMPKα2(−/−) knockout mice that show the AMPK-α2 subunit is physiologically critical for blood glucose regulation (5).

Before study 6 was performed, Onken & Driscol had reported that metformin-induced

longevity in C. elegans requires functioning AMPK (47). Study 6 show that AMPK-activation inhibits the toxic effects of metformin in the nematode (6) and that the additional mechanism for how the gut microbiome induces longevity is too AMPK-dependent (6). They hypothesize that this is due to inhibition of bacterial folate metabolism (6), and thus the fact that

trimethoprim activity is AMPK-dependent strengthens the fact that metformin is as well (6).

In study 9 they found that using intestinal ampkα1-knockout mice did not stand in the way of achieving the effects of metformin, at least not by the measurements they used, FXR-

activation, GLP-1 secretion and serum glucose level (9). They say that metformin suppresses intestinal FXR-signalling and that this increases GLP-1 and thus improves blood glucose regulation (9). The FXR-inhibition does not depend upon intestinal AMPK-activation.

Importantly, they did find it to be microbiota-dependent (9).

AMPK- α1 was showed either to decrease or not change at al. But could it be AMPK-α2 that is crucial. Study 5 tells us exactly this and with support: “short term activation in the liver by

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adenovirus-mediated expression of AMPKα2 is found sufficient for controlling

hyperglycaemia in murine models of diabetes” (32); and liver-specific AMPKα2−/− mice, which exhibited hyperglycaemia and glucose intolerance, presented increased fasted hepatic glucose production (48). Study 6 is undertaken on an organism with only one gut bacteria and no liver or circulatory system, and therefore the results might not be important to

understanding human physiology. Study 9 looks specifically at intestinal AMPK-activation.

Would they see the same effect if they used hepatic AMPK-knockout mice? A mouse study from Shaw et al. suggest we are right to question this: In mice without hepatic AMPK- activation the antidiabetic drug metformin no longer normalized blood glucose levels (49).

Could the effect of metformin be partly AMPK dependent and -independent? For instance an AMPK-independent mechanism for increasing GLP-1, whilst an AMPK-dependent regulation of gluconeogenesis? Short-chain fatty acids (SCFAs) have shown to increase AMPK-activity (50). If it is true that metformin works through AMPK-activation, could even that mechanism could be microbiota-mediated?

SHORT-CHAIN FATTY ACIDS

SCFAs are butyrate, acetate, and propionate, and they are produced by some classes of gut bacteria (Bacteroides, Bifidobacterium, Propionibacterium, Eubacterium, Lactobacillus, Clostridium, Roseburia, and Prevotella) during fermentation of non-digestible carbohydrates (51) (52). Previous studies have shown that a reduced level of SCFA-producing bacteria is more common in people with metabolic diseases, including T2D (53). Increased production in response to metformin has been predicted (1), and three of the studies here agree.

In study 2 metformin and berberine both increased SCFAs in obese HFD rats (2). The increase was more marked for berberine than metformin (2). Study 5 show that the human L cell responds directly to the presence of metformin and SCFA by increasing GLP-1 secretion in NCI-H716 cells in vitro (5). Previous studies have also found that SCFAs, more

specifically butyrate and propionate, have this effect on GLP1-release (51).

This sounds to be in contradiction to what they say in study 9 where they find that the microbiota is necessary for an increased GLP1-release mediated through FXR-inhibition (9).

Does our microbiome influence oral drug treatment?