Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: Results from a prospective cohort study

doi:10.21203/rs.3.rs-3910457/v1

Download PDF

Research Article

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: Results from a prospective cohort study

https://doi.org/10.21203/rs.3.rs-3910457/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

The microbiota's impact on hypoglycemic agents is increasingly evident. However, the influence of metformin, a primary anti-diabetes drug, on gut microbiota remains unclear.

Research design and methods

This prospective cohort study aimed to investigate the longitudinal effects of metformin on the gut microbiota in 25 newly diagnosed, treatment-naïve diabetes patients receiving 1500 mg daily doses. Microbiota compositions were analyzed at baseline, 1, 3, and 6 months using 16S rRNA gene se-quencing.

Results

Metformin showed clinical improvements, but no significant dissimilarity in microbiome composition structure (Adonis: P = 0.077). Species richness and intestinal flora diversity declined, and α diversity significantly decreased after 3 months. The Firmicutes to Bacteroidetes ratio (ANOVA: P = 0.037) markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), C-reactive protein(CRP), triglyceride(TG) (p < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group.

Conclusions

Metformin's role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

gut microbiota

type 2 diabetes mellitus

metformin

Type 2 diabetes mellitus (T2DM) is a prevalent chronic disease associated with life-threatening complications, disabilities, and substantial financial burdens, concurrently reducing life expectancy [1]. This disorder, marked by insulin resistance and β-cell dysfunction, is linked to inflammation induced by overnutrition and environmental factors [2]. International Diabetes Federation (IDF) projects a 46% increase in T2DM cases, with approximately 783 million adults affected by 2045 [3]. The gut microbiome is recognized as a modifiable "novel organ," intricately shaping T2DM's metabolic and immunological functions [4]. The complex interaction of the intestinal microbiota is theorized to play a pivotal role in the pathophysiology of obesity, T2DM, and associated metabolic disorders. Changes in the gut microbiome's composition and functionality, termed intestinal dysbiosis, may diminish short-chain fatty acid (SCFA) synthesis [5], potentially compromising gut barrier integrity, impacting pancreatic β-cell proliferation, and attenuating insulin biosynthesis, thereby accelerating T2DM's progression toward insulin resistance.

The interaction between gut microbes and commonly used drugs in T2DM patients is intricate and bidirectional [6]. Metformin, the first-line drug for T2DM, is chosen for its cost-effectiveness, safety, and protective impact on cardiovascular disease mortality [7]. Metformin enhances intestinal glucose uptake, increases lactate production, elevates glucagon-like peptide 1 (GLP-1) concentrations, influences the bile acid pool, and induces microbiota alterations [8]. The gut serves as humans' primary reservoir for metformin, with distinctive actions observed in the lower bowel.

Metformin, with approximately 50% bioavailability, results in nearly equal exposure in both the intestine and plasma; however, metformin accumulates in the intestine at a rate 300 times greater than in the plasma [9]. In contrast to oral dosing, intravenously administered metformin does not improve glucose metabolism [10], suggesting that the gastrointestinal tract may be this drug's principal site of action. A delayed-release oral metformin targeting the ileum shows comparable or superior glucose-lowering effects with reduced systemic exposure compared to immediate or extended-release formulations [11]. Moreover, concurrent oral administration of a broad-spectrum antibiotic with metformin in mice nullifies its glucose-lowering effect, emphasizing the probable origin of metformin's efficacy in the lower bowel [12]. This underscores the possibility that the glucose-lowering efficacy of metformin may originate in the lower bowel.

Although a substantial body of literature exists on the gut microbiota and metformin, existing research spans treatment durations of 24h to 12 months, most of which were within six months with conflicting findings [13]. Noel T. Mueller et al. [14] conducted a randomized trial to examine the effects of metformin treatment and behavioral weight loss on gut microbiota composition over 12 months in overweight and obese adults. At the same time, the changes of bacteria were not observed dynamically. Standard study designs include cross-sectional investigations or case-control trials [4, 15–21], with noted disparities in basic clinical data between experimental and control groups. Due to significant individual differences in gut microbiota composition, attributing observed variations to individual specificity is challenging. Addressing these limitations necessitates establishing a longitudinal cohort study with metformin-naïve T2DM patients as their controls. This study aims to investigate gut microbiota changes during different treatment stages (1 month, 3 months, and 6 months) of metformin therapy and their correlation with glycemic and lipid metabolism indicators. Our research provides novel insights into longer-term effects induced by metformin, leveraging longitudinal data.

Study subjects and sample collection

This prospective, longitudinal study was performed at Peking Union Medical College Hospital (PUMCH, Beijing, China). We prospectively recruited participants from the outpatient department of the endocrinology center of PUMCH between April 2021 and October 2023. Twenty-five patients were included. The detailed flow chart of the study design is shown in Fig. 1.

Inclusion criteria were: (1) Clinical diagnosis of T2DM based on the 2020 American Diabetes Association (ADA) criteria, with no antidiabetic medication in the past three months. (2) Age between 18 and 70 years, inclusive, with no gender restrictions. Exclusion criteria included: (1) Other types of diabetes. (2) Acute diabetic complications in the last 6 months, such as diabetic ketoacidosis, lactic acidosis, and hyperosmolar hyperglycemic state. (3) Severe chronic diabetes complications, e.g., foot lesions, chronic ulcers, end-stage renal disease. (4) History of infectious diseases or recent occurrences within the last 6 months. (5) Significant abnormalities in liver function or impaired kidney function. (6) History of gastrointestinal diseases or surgery. (7) History of dental or oral diseases. (8) Mental disorders, tumors, alcohol dependence, substance abuse history, lactating, or participation in other clinical studies in the preceding three months. (9) Cardiovascular diseases, including coronary heart disease, cerebrovascular diseases, intermittent claudication, stroke, primary myocarditis, and other diagnosed cardiovascular diseases. (10) History of autoimmune or genetic diseases. (11) Long-term antacids, anti-inflammatory drugs, or proton pump inhibitors use. (12) Recent use of antibiotics or supplements with prebiotics, probiotics, and live bacteria in the last three months. (13) Allergic disposition or known allergy to multiple drugs, as deemed unsuitable by the investigator.

Fecal sampling

Participants received a fecal sampling kit, guided by outpatient medical staff, comprising sterile sampling paper, a collection tube, sterile gloves, sample labels, and a collection bag. Following instructions, participants collected approximately 5g of fecal material from various points on the surface, middle, and depth of their personal specimens at home. Samples were coded as follows: EJSG0 – pre-metformin treatment, EJSG1–1 month post-first metformin, EJSG2–3 months into therapy, and EJSG3–6 months post-treatment. The collected sample was placed in the collection tube, securely capped, inverted for thorough mixing, and promptly delivered to the hospital on the same day. Within 24 hours, participants stored the samples in a -80℃ ultra-low-temperature freezer.

Clinical data collection

We collected the baseline information of patients, including age, BMI, duration of diabetes, complications, family history, and health status. Demographic and clinical information for these patients was collected by an experienced neurologist from the patient review and/or medical records.

Participants underwent clinical examination four times during the trial: at baseline (visit 1), 1 month (visit 2), 3 months (visit 3), 6 months (visit 4) after metformin. Gastrointestinal symptoms, diet, and physical activity were collected at all four visits. Participants were examined with height, weight, and blood pressure at baseline. At visits 1, 3and 4, respectively, blood samples were collected after participants had fasted for 10 h overnight. Fasting plasma glucose (FPG), Uric Acid (UA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), hypersensitive C-reactive protein (hs-CRP), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein Cholesterol (LDL-C), urine albumin-to-creatinine ratio (ACR), free fatty acid (FFA), Apolipoprotein B (ApoB) were analyzed by full-automatic blood biochemical analyzer. Fasting insulin (FINS) and C-Peptide (C-P) were measured using chemiluminescence immunoassay. Hemoglobin A1c (HbA1c) was measured using liquid chromatography. We calculated homeostasis model assessment indices (HOMA) of β-cell function (HOMA-β) and of insulin resistance (HOMA-IR) as follows: HOMA-IR = FPG (mmol/L)*FINS (mIU/L)/22.5. HOMA-β = 20*FINS (mIU/L)/(FPG (mmol/L)-3.5). BMI = weight(kg)/height(m)².

Bacterial DNA Extraction and 16S rRNA gene sequencing

DNA extraction utilized the DNeasy® PowerSoil® Pro Kit (QIAGEN, Germany) following the manufacturer's protocols. The V3–V4 region of the 16S rRNA gene was PCR-amplified, and Illumina HiSeq 2500 platform sequenced the PCR products. Paired-end reads were merged using FLASH [22]. Quality filtering, performed by the fastq quality filter in the FASTX Toolkit 0.0.14, retained only high-quality reads (Q value ≥ 25) with a base ratio of ≥ 90%. Removal of chimeric reads was carried out using USEARCH 64-bit v8.0.1517. Read normalization, based on the smallest sample size, was achieved by random subtraction.

Bioinformatics analysis of sequencing data

Operational taxonomic units (OTUs) were aligned via the UCLUST algorithm with a 97% identity and taxonomically classified using the SILVA 16S rRNA database v128[23]. Microbial alpha diversity was assessed using the Chao1 index for species richness and the Shannon index for overall richness and evenness. Beta diversity was computed using weighted UniFrac distance metrics, visualized through principal coordinate analysis (PCoA), and group comparisons were conducted using the Adonis method.

Statistical analysis

Data was presented as mean ± standard error of the mean. The unpaired two-tailed Student t-test assessed differences in mean values for parametric variables between two groups. ANOVA with Tukey post hoc test was employed for four groups, while the Kruskal–Wallis test evaluated differences for nonparametric variables. Linear discriminant analysis effect size (LefSE) identified taxonomic bacterial biomarkers, setting an LDA log score cutoff of 2[24]. The DESeq2 package calculated differentially expressed OTUs and genes [25]. Spearman correlation analyses assessed associations between taxa abundance and glycemic/inflammatory traits. GraphPad Prism 7 and R software constructed images. Significance was considered at P < 0.05. Fuzzy c-means clustering, using the Mfuzz R package, classified gut microbiota trajectories [26]. PICRUSt2 evaluated differences in community functional attributes, identifying significantly differential KOs through LEfSe. Metabolic pathways were verified in the MetaCyc database.

Characteristic of the study subjects

This study comprised 25 newly diagnosed T2DM patients, including 15 males and 10 females, with an average age of 46.80 ± 11.42 years (range: 25 to 69 years). The median duration of diabetes was 12 months (1,120). 19 individuals underwent an 8-week intervention involving diet and exercise control, and 17 patients had a family history of diabetes. Regarding complications, 3 had diabetic nephropathy, and 2 presented with diabetic retinopathy. The overall rate of intervention-related side effects was 8%. Two patients experienced gastrointestinal adverse reactions, persisting in one individual but resolving in the other after one month of medication. No participants withdrew due to severe side effects. The clinical characteristics of the four groups are presented in Table 1.

Table 1

Clinical characteristics for the 25 individuals with T2D enrolled in this study
	EJSG0	EJSG1	EJSG2	EJSG3	P01	P02	P03
weight(kg)	82.12 ± 16.40	80.45 ± 15.87	78.49 ± 15.63	78.10 ± 16.46	<0.0001	<0.0001	<0.0001
BMI(kg/m²)	28.10 ± 4.41	27.53 ± 4.27	26.87 ± 4.25	26.71 ± 4.51	<0.0001	<0.0001	<0.0001
FPG(mmol/L)	8.64 ± 2.15	6.35 ± 0.89	6.07 ± 0.60	6.70 ± 0.96	<0.0001	<0.0001	<0.0001
2h FPG(mmol/L)	13.82 ± 4.46	7.72 ± 1.51	8.12 ± 1.50	7.50 ± 0.93
HbA1c(%)	7.48 ± 1.18		6.59 ± 1.08	6.07 ± 0.56		<0.0001	<0.0001
FINS(µIU/ml)	15.50 ± 9.18		12.51 ± 6.31	12.37 ± 8.37		0.4282	0.4021
C-P(ng/ml)	1.74 ± 0.62		1.55 ± 0.54	1.96 ± 0.78		0.6551	0.5303
TC(mmol/L)	5.04 ± 1.03		4.69 ± 1.14	4.79 ± 0.89		0.4510	0.6714
TG(mmol/L)	2.54 ± 2.21		1.76 ± 1.05	2.12 ± 1.43		0.2303	0.6582
HDL-c(mmol/L)	1.11 ± 0.30		1.14 ± 0.31	1.17 ± 0.32		0.9200	0.7973
LDL-c(mmol/L)	2.88 ± 0.85		2.83 ± 0.85	2.81 ± 0.71		0.9674	0.94645
hsCRP (mg/L)	1.43 ± 1.26		2.73 ± 4.08	1.75 ± 1.68		0.9779	0.6502
FFA(mmol/L)	582.15 ± 172.62		531.05 ± 135.45	502.48 ± 214.82		0.6376	0.3311
apoB(mmol/L)	1.00 ± 0.233		0.97 ± 0.26	0.97 ± 0.20		0.8775	0.8458
HOMA-IR	6.29 ± 4.40		3.93 ± 2.64	3.81 ± 2.92		0.0505	0.0480
HOMA-β	66.10 ± 39.98		83.57 ± 30.60	79.60 ± 53.62		0.4985	0.5598
ALT(U/L)	34.08 ± 18.66		25.96 ± 13.89	25.13 ± 12.88		0.1640	0.1183
Cr(µmol/L)	67.80 ± 14.79		65.21 ± 12.94	66.39 ± 18.30		0.8275	0.9466
UA(µmol/L)	385.60 ± 75.00		406.42 ± 95.32	398.57 ± 81.20		0.6639	0.8554
Urine ACR(mg/g Cr)	23.48 ± 46.05		17.74 ± 52.39	12.14 ± 22.27		0.8885	0.6461
¹ ALT, alanine transaminase; hsCRP, hypersensitive C-reactive protein; FPG; Fasting plasma glucose; UA, Uric Acid; AST, aspartate aminotransferase; TC, total cholesterol; TG, triglyceride, HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein Cholesterol; ACR, albumin-to-creatinine ratio; FFA, free fatty acid; ApoB, Apolipoprotein B; FINS, fasting serum lisulin; C-P, C-Peptide; HbA1c, Hemoglobin A1c; HOMA, homeostasis model assessment indices. Wilcoxon signed–rank test; data are shown as means ± s.e.m. P01: EJSG0 versus EJSG1, P02: EJSG0 versus EJSG2, P03*: EJSG0 versus EJSG3

Differences in microbial diversity

This study compared the differences in α diversity indices among different groups, including the Chaol index, which focuses on assessing species richness; Shannon and Simpson indices, emphasizing a comprehensive reflection of richness and evenness within species; and the observed species index, reflecting the number of observed OTU. According to the results of the inter-group diversity index analysis (Fig. 2a), after treatment with metformin, there was an overall decrease in species richness and diversity of the intestinal microbiota. Specifically, the observed species(P = 0.005), Shannon index (P = 0.004), and Simpson index (P = 0.043) of three-month post-treatment were significantly lower than pre-treatment, indicating statistically significant differences. β-diversity in subjects before metformin treatment and at 1 month,3 months, and 6 months post-treatment shows no significant separation trends along the PCI and PC2 directions (Fig. 2b, ANOSIM analysis, P = 0.066). PC1 explains 26.47% of the observed differences, while PC2 accounts for 24.58% of the variation. We observed no significant differences in β diversity estimates among the four groups.

Changes in taxonomic profile induced by metformin treatment

In total, 100 stool samples were obtained from 25 T2DM individuals. A total of 43582493 DNA sequencing reads were left after demultiplexing and quality control filtering. A rarefaction depth of 435825 reads per sample was selected, and 25712 OTUs were identified across 100 samples. We next tested for differences in the number of observed OTUs across the groups of participants.

The seven dominant bacterial groups accounted for ∼75% of the total bacterial count at phylum and genus level, respectively (Fig. 1c and d). From the boxplot, we can see that the fecal microbiome of metformin-treated patients showed a trend for a decrease in the ratio of phyla Firmicutes to Bacteriodetes (F/B ratio), and there was a statistically significant difference between the paired two groups (P = 0.037). To observe in-depth changes in the gut microbiome composition, we used DESeq2 and evaluated the statistical significance of differential absolute abundance of OTU groups between time points. Metformin treatment for 1, 3, and 6 months resulted in significant alterations in the abundance of 6, 13, and 19 bacterial strains, respectively, with a false-discovery rate (FDR < 0.05). At the genera level, at one month, when compared with pre-treatment, a significant increase in Citrobacter (Proteobacteria| Enterobacteriaceae) was observed (adj. P < 0.05, Fig. 3a). A significant decrease in Romboutsia (Firmicutes| Peptostreptococcaceae) and Clostridium sensu stricto (Firmicutes| Clostridiaceae) was found. The Romboutsia difference remained significant the most in 3 months compared with the group before metformin (Fig. 3b). Pseudomonas (Proteobacteria| Pseudomonadaceae) and Escherichia-Shigella (Proteobacteria| Enterobacteriaceae) increased significantly. Alloprevotella (Bacteroidetes| Prevotellaceae) increased after three months of treatment. Ruminiclostridium (Firmicutes| Ruminococcaceae) decreased most, and Tyzzerella 3 (Firmicutes| Lachnospiraceae) increased most in 6-month patient subgroups compared with pre-treatment, respectively (adj. P < 0.05, Fig. 3c).

In addition, for graphic representation of differentially abundant taxa as well as their effect sizes and phylogenetic relationship, the LEfSe method was performed (Fig. 3d-f). This method detected 17 differentially abundant taxonomic clades, which mainly matched those found with edgeR analysis. At the phylum level, we found that at the final point, subjects from the 6-month group had a higher abundance of Bacteroidetes (LDA>3), as well as its family Bacteroidaceae (LDA>4) and its genus Bacteroides (LDA>4), while subjects in the pre-treatment group presented a higher abundance of Firmicutes (LDA>4), and its family Ruminococcaceae (LDA>2), Peptostreptococcaceae (LDA>3) and its genus Ruminiclostridium (LDA>2), Faecalibacterium (LDA>3), and Romboutsia (LDA>2). Integrating both analysis results into a single table allows for a more intuitive observation of microbial variations across specific bacterial species (Supplementary Materials, Table S1).

In order to verify the findings from previous publications reporting that metformin increased the abundance of Akkermansia (Verrucomicrobia| Akkermansiaceae) Verrucomicrobia (P = 0.757), Prevotella (Bacteroidetes| Prevotellaceae) (P = 0.551), and Fusobacterium (Fusobacteria| Fusobacteriaceae) (P = 0.270), a continuous growth trend was noted in these bacterial species, but these changes were not statistically significant. This particular microbiota can produce short-chain fatty acids (SCFAs). SCFAs are directly utilized as an energy source by the intestinal mucosal cells or transferred to the systemic circulation to generate an important source of energy for the host and have the ability to behave as signaling molecules[28].

Metformin treatment led to distinct changes in different gut microbiota subgroups over time

To identify the different longitudinal trajectories of the gut microbiota in patients treated with metformin, we calculated the within-cluster sum of squares for different cluster numbers based on the mean relative abundance of the microbial genera. Three clusters of longitudinal trajectories for microbial fluctuation were identified via the “elbow” method (Supplementary Figure S1). Then, the longitudinal trajectories of the three clusters were constructed using fuzzy c-means clustering, including (i) genera temporarily changing from pre-treatment to 6 months (Fig. 4a); (ii) genera continuing to decrease from pre-treatment to 6 months (Fig. 4b), and (iii) genera continuing to increase from pre-treatment to 6 months (Fig. 4c). Detailed information on the gut microbiota of each cluster is shown in Figure S2. Genera in clusters 1 and 2 were selected to construct multivariate linear regression models to verify the correlation between gut microbiota and time. After adjusting for BMI, 6 genera were found to have changed significantly over time. The abundance of the Lachnospiraceae ND3007 group (P = 0.006), [Eubacterium] xylanophilum group (P = 0.014), Romboutsia (P = 0.013), Faecalibacterium (P = 0.003), and Ruminococcaceae UCG-014 (P = 0.008) decreased significantly over time. Conversely, the abundance of Bacteroides (P = 0.049) increased over time.

Clinical parameters correlated with the gut microbiota

Metformin treatment was associated with significantly improved treatment efficacy and change of 15 clinical metabolic parameters. We further investigated whether these improvements correlate with the change in microbiota compositions and clinical metabolic parameters by using a correlation matrix based on the Spearman correlation distance test. Heatmaps showing correlations between clinical factors and gut microbiota at baseline and after metformin treatment (Fig. 4a and b showed the correlation at phylum and genus level, respectively) Phylum Firmicutes positively correlated with HbA1c and FPG, whereas Bacteroidetes exhibited the opposite correlation. At the genus level, Bacteroides and Escherichia_Shigella positively associated with decreased HbA1c and FPG; Ruminococcus, Lachnospira, Blautia, and Lachnospiraceae UCG_008 were negatively correlated with elevated HbA1c, FINS, and HOMA; Paraprevotella was positively correlated with elevated LDL-C, while Parasutterella was negative with it. Lachnospiraceae UCG_008 was correlated with elevated FPG, while Escherichia_Shigella exhibited the opposite correlation with the change of FPG. However, we noticed that these correlations were not statistically significant.

Functional analysis

To investigate the functional role of the gut microbiota in immunotherapy, we further annotated the metagenomic sequencing data in the KEGG database and identified 28 KOs enriched in the EJSG0 group, 4 KOs enriched in the after-treatment group by LEfSe (LDA > 2, p < 0.05) (Fig. 5a). The most abundant KOs in the pre-treatment group and post-treatment group were energy metabolism pathways and amino acid metabolism pathways, respectively (Fig. 5b). At the pathway level, the EJSG2 group was significantly enriched with KOs that played essential functions in several KEGG pathways, including cationic antimicrobial peptide (CAMP) resistance (map01503), pertussis(map05133), betalain biosynthesis (map00965), peroxisome (map04146) and longevity regulating pathway(map04211). Moreover, the MAPK signaling pathway - fly (map04013) was enriched in the EJSG1 group (Supplemental Figure S3). Several after-treatment group-associated KOs, Bacteroides, Achromobacter, and Parabacteroides, which belong to the Bacteroidetes and Proteobacteria phylum, were found to be the dominant species enriched (Fig. 5c and d). Furthermore, comparisons of differential metabolic pathways in the KEGG database showed that amino acid metabolism pathways, such as isoleucine biosynthesis, predominated in the post-treatment group. In the MetaCyc database, we found that metformin significantly altered 17 functional pathways (Supplementary Figure S4-5), including ten pathways of biosynthesis, four pathways of L-arginine, vanillate or ornithine degradation, a polymyxin resistance pathway, and two pathways in metabolism: superpathway of chorismate metabolism and ketogluconate metabolism.

Our study, the first of its kind, employed a longitudinal design to examine the long-term effects of metformin therapy on treatment-naive patients. Analyzing 100 samples collected at pre-treatment and 1, 3, and 6 months post-metformin treatment, we aimed to understand the temporal dynamics of the gut microbiota. Unlike single-time-point studies, longitudinal designs provide crucial insights into periodic patterns, interdependencies, and temporal variations within the microbiome[29].

Results indicated a decrease in intestinal microbiota species diversity three months post-metformin treatment compared to pre-treatment, while β-diversity remained unchanged. This reduction in diversity may stem from the drug's direct impact on microbial species or dietary control initiated by patients post-T2DM diagnosis, leading to a more uniform dietary structure. Similar findings have been observed in healthy individuals taking metformin, with some studies noting a significant reduction in microbial community diversity within 24 hours of metformin administration [30]. The prevailing perspective suggests lower species diversity in the gut microbiota of individuals with disease states compared to healthy individuals [31]. However, conflicting findings exist; a recent systematic review summarizing 13 clinical studies revealed varying impacts of metformin on microbial diversity, with 8 studies reporting no significant effect, 3 showing an increasing trend, and 2 indicating an apparent decrease in α-diversity [13].

In terms of species composition at the phylum level, Firmicutes, Bacteroidetes, and Proteobacteria were the top three dominant bacterial phyla. Metformin administration led to a gradual increase in Bacteroidetes, a decline in Firmicutes, and a significant decrease in the Firmicutes/Bacteroidetes (F/B) ratio. Our study, alongside previous research on metformin's impact on the gut microbiota in T2DM patients, suggests a significant decrease in the F/B ratio after metformin intake. A study by Carbajal et al. in 2020 found a nearly 50% decrease in the relative abundance of Firmicutes in patients with T2DM who had been orally administered metformin for at least a year [32]. Additionally, a study in an Estonian population of 38 individuals suggested that a reduction in certain anaerobic bacteria in the gut, such as Bacteroidetes, could predict hyperglycemia [33]. The F/B ratio's association with increased plasma glucose following an oral glucose load aligns with previous studies, although conflicting trends exist [34, 35].

Our study identified three longitudinal trajectories for the altered gut microbiota. Clusters 2 and 3 exhibited a relatively stable change trend. Escherichia-Shigella and Bacteroides were enriched in patients after metformin treatment, both strongly linked to notable improvements in glucose metabolism. While previous studies reported a significant decrease in Bacteroides after metformin treatment, correlated with reduced HbA1c[6], our findings align with Sun et al.'s discovery of short-term metformin treatment associated with a lowered abundance of Bacteroides fragilis, resulting in improved glucose tolerance[19]. Despite our 16S rRNA gene-based amplicon sequencing not perfectly assigning individual species among the genus Bacteroides, inconsistent results were observed. Clinical studies reported a decrease in the abundance of Bacteroides [36, 37], as well as in C57BL/6 mice fed with a high-fat diet for 24 weeks of metformin [34]. Additionally, we observed a gradual increase in Escherichia-Shigella abundance over time following medication, consistent with previous studies [16, 38, 39]. This increase is commonly attributed to gastrointestinal side effects associated with metformin intake, with reports indicating a positive correlation between its abundance and the reduction in BMI after metformin administration [36, 40]. Simultaneously, an increase in Escherichia has been linked to increases in serum acetate, correlating with decreases in fasting insulin, anti-inflammation, and insulin resistance in individuals with T2DM [14, 41]. Furthermore, metformin increased the trend in the abundance of Akkermansia and Prevotella, both known for producing SCFAs to improve metabolic dysfunction, including hyperglycemia.

The bacterial genera Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium, and Ruminococcaceae UCG-014 exhibited a decreasing trend over time, with only Romboutsia showing a significant change. [Eubacterium] xylanophilum ferments complex phytochemicals to produce SCFAs, including butyrate, which has been found to be antiobesogenic in human studies [42, 43]. The [Eubacterium] xylanophilum group, known for fermenting complex phytochemicals to produce short-chain fatty acids (SCFAs), including butyrate, showed potential antiobesogenic effects in human studies [44]. Ruminococcaceae UCG-014 can increase nitric oxide (NO) synthase activity by regulating the MyD88 pathway, thereby upregulating NO levels and inhibiting inflammatory responses [45]. Faecalibacterium is a fiber-degrading bacteria capable of fermenting fiber and producing SCFAs [46]. Interestingly, the bacteria mentioned above, all producers of SCFA, showed a notable increase in abundance at 6 months, despite a decrease observed in 1 and 3 months. It is noteworthy that most clinical studies reveal that metformin enhances the bacteria-producing SCFAs, mediating therapeutic effects through improvement in SCFA production, bile acid (BA) pool composition alteration, or reduction in lipopolysaccharide (LPS) production [5]. SCFAs exhibit various beneficial effects on animal performance and intestinal health [47], though some studies suggest their effects may not be sustained through 12 months, potentially due to compensatory use during prolonged negative energy balance [14]. Prevotella and Bifidobacterium, also SCFA-producing bacteria, exhibited a continuous but statistically nonsignificant increase over time. Above results indicated that not all SCFA-producing bacteria consistently rise during metformin treatment. The cause of the waning effect on SCFAs and its relevance to health require further research, and our study will enhance SCFA examination to provide more clarity in the future.

Conversely, Romboutsia, Ruminococcus, and Clostridium sensu stricto, positively correlated with body weight and serum lipids, gradually decreased with improved weight and blood lipid profiles due to metformin treatment. The mechanism of action for the Lachnospiraceae_ND3007_group remains unclear. GMrepo, a database linking human phenotypes and gut microbiota[48], associates Lachnospiraceae with Crohn's disease, colitis, and obesity. Given its positive correlation with obesity-related parameters, this partly explains the reduction in this bacterium with metformin treatment.

Pseudomonas exhibited a notable decline after three months of medication, aligning with improved blood glucose levels. However, its overall trend showed an increase, particularly in non-controlled blood glucose. This Gram-negative opportunistic pathogen might contribute to type 2 diabetes pathogenesis through endotoxin production inducing inflammatory factors[49]. Pseudomonas remained more prominent in non-controlled blood glucose levels, indicating its potential relevance to disease management.

Function analysis showed that comparisons of differential metabolic pathways in the KEGG database showed that amino acid metabolism pathways, such as isoleucine biosynthesis, predominated in the post-treatment group. In the MetaCyc database, we found that metformin significantly altered 17 functional pathways, including pathways of biosynthesis, amino acid degradation, and metabolism. These findings suggested that the underlying mechanisms of the gut microbiome influencing metformin's benefit in glycometabolism might be driven by specific bacterial species involved in different metabolic pathways.

The strengths of our investigation are rooted in its longitudinal design, focusing on the enduring impacts of metformin therapy. Moreover, our cohort comprised individuals recently diagnosed with T2DM, minimizing confounding variables by excluding those not on other T2DM medications, with only a minority using statins or antihypertensive therapy. Nevertheless, it is essential to acknowledge specific limitations. Firstly, from a technological perspective, the study's reliance on 16S rRNA gene amplicon sequencing is acknowledged; though suitable for genus-level signals, it has limitations at the species level. Secondly, the investigation did not evaluate individual dietary habits or consider the potential influence of diet on the gut microbiota. Thirdly, a long-term study inherently carries the unavoidable risk of the impact of common environmental factors that permanently shape gut microbiota composition. Fourthly, there was a selection bias as study participants were recruited exclusively from the Han Chinese population, raising uncertainties regarding the generalizability of our findings to other ethnic groups. Last, but not least, all participants in the study exhibited satisfactorily controlled glucose levels, hindering the extrapolation of results to individuals with more severe forms of diabetes.

In conclusion, our prospective cohort study contributes valuable insights into the longitudinal dynamics of metformin's impact on the gut microbiota. Consistent with shorter-term trials, metformin-induced alterations in microbiota composition, including increased Escherichia-Shigella and decreased Romboutsia. The link between microbial changes and long-term clinical outcomes remains unknown and warrants investigation in more extensive studies. Nevertheless, our study sheds light on the biological mechanisms through which metformin influences health outcomes and identifies potential microbial targets for preventing metabolic disorders or gastrointestinal symptoms affecting drug tolerability. Future well-designed trials, encompassing larger groups of T2DM patients, multi-omics data, and placebo control, are essential to unravel the intricacies of drug-microbiota interactions, comprehend the role of baseline gut microbiota, and advance microbiome-based applications in diabetes treatment.

Funding

This research received no external funding.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of PUMCH (JS-2579/202009.22). Informed consent was obtained from all subjects involved in the study.

Consent for publication

The authors affirm that all individual participants provided informed consent for the publication of the data. Additional informed consent was obtained from all individual participants for whom identifying information was included in this article. Written informed consent has been obtained from the patients to publish this paper.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Dissemination to participants and related patients and public communities

After publication, the findings of this review will be disseminated to appropriate audiences such as academia, clinicians, policy makers, and the general public, through various channels including blogs, press releases, and social media.

Author Details

¹Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Street, Dongcheng District, Beijing, 0086 100730,China.

Collaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023;402(10397):203–34.doi:10.1016/S0140-6736(23)01301-6
Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020;21(17).doi:10.3390/ijms21176275
Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183(109119.doi:10.1016/j.diabres.2021.109119
Tilg H, Moschen AR. Microbiota and diabetes: an evolving relationship. Gut. 2014;63(9):1513–21.doi:10.1136/gutjnl-2014-306928
Cunningham AL, Stephens JW, Harris DA. Gut microbiota influence in type 2 diabetes mellitus (T2DM). Gut Pathog. 2021;13(1):50.doi:10.1186/s13099-021-00446-0
Li R, Shokri F, Rincon AL, Rivadeneira F, Medina-Gomez C, Ahmadizar F. Bi-Directional Interactions between Glucose-Lowering Medications and Gut Microbiome in Patients with Type 2 Diabetes Mellitus: A Systematic Review. Genes (Basel). 2023;14(8).doi:10.3390/genes14081572
Foretz M, Guigas B, Viollet B. Metformin: update on mechanisms of action and repurposing potential. Nat Rev Endocrinol. 2023;19(8):460–76.doi:10.1038/s41574-023-00833-4
McCreight LJ, Bailey CJ, Pearson ER. Metformin and the gastrointestinal tract. Diabetologia. 2016;59(3):426–35.doi:10.1007/s00125-015-3844-9
Bailey CJ, Wilcock C, Scarpello JH. Metformin and the intestine. Diabetologia. 2008;51(8):1552–3.doi:10.1007/s00125-008-1053-5
Bonora E, Cigolini M, Bosello O, Zancanaro C, Capretti L, Zavaroni I, et al. Lack of effect of intravenous metformin on plasma concentrations of glucose, insulin, C-peptide, glucagon and growth hormone in non-diabetic subjects. Curr Med Res Opin. 1984;9(1):47–51.doi:10.1185/03007998409109558
Buse JB, DeFronzo RA, Rosenstock J, Kim T, Burns C, Skare S, et al. The Primary Glucose-Lowering Effect of Metformin Resides in the Gut, Not the Circulation: Results From Short-term Pharmacokinetic and 12-Week Dose-Ranging Studies. Diabetes Care. 2016;39(2):198–205.doi:10.2337/dc15-0488
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.doi:10.1136/gutjnl-2012-303839
Pavlo P, Kamyshna I, Kamyshnyi A. Effects of metformin on the gut microbiota: A systematic review. Mol Metab. 2023;77(101805.doi:10.1016/j.molmet.2023.101805
Mueller NT, Differding MK, Zhang M, Maruthur NM, Juraschek SP, Miller ER, 3rd, et al. Metformin Affects Gut Microbiome Composition and Function and Circulating Short-Chain Fatty Acids: A Randomized Trial. Diabetes Care. 2021;44(7):1462–71.doi:10.2337/dc20-2257
de la Cuesta-Zuluaga J, Mueller NT, Corrales-Agudelo V, Velasquez-Mejia EP, Carmona JA, Abad JM, et al. Metformin Is Associated With Higher Relative Abundance of Mucin-Degrading Akkermansia muciniphila and Several Short-Chain Fatty Acid-Producing Microbiota in the Gut. Diabetes Care. 2017;40(1):54–62.doi:10.2337/dc16-1324
Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528(7581):262–6.doi:10.1038/nature15766
Zhang F, Wang M, Yang J, Xu Q, Liang C, Chen B, et al. Response of gut microbiota in type 2 diabetes to hypoglycemic agents. Endocrine. 2019;66(3):485–93.doi:10.1007/s12020-019-02041-5
Elbere I, Silamikelis I, Dindune, II, Kalnina I, Ustinova M, Zaharenko L, et al. Baseline gut microbiome composition predicts metformin therapy short-term efficacy in newly diagnosed type 2 diabetes patients. PLoS One. 2020;15(10):e0241338.doi:10.1371/journal.pone.0241338
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.doi:10.1038/s41591-018-0222-4
Nakajima H, Takewaki F, Hashimoto Y, Kajiyama S, Majima S, Okada H, et al. The Effects of Metformin on the Gut Microbiota of Patients with Type 2 Diabetes: A Two-Center, Quasi-Experimental Study. Life (Basel). 2020;10(9).doi:10.3390/life10090195
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.doi:10.1371/journal.pone.0100778
Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957–63.doi:10.1093/bioinformatics/btr507
Dennison CA, Eslinger AJ, Reimer RA. Preconception Prebiotic and Sitagliptin Treatment in Obese Rats Affects Pregnancy Outcomes and Offspring Microbiota, Adiposity, and Glycemia. Front Endocrinol (Lausanne). 2017;8(301.doi:10.3389/fendo.2017.00301
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.doi:10.1186/gb-2011-12-6-r60
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.doi:10.1186/s13059-014-0550-8
Cannon RL, Dave JV, Bezdek JC. Efficient Implementation of the Fuzzy c-Means Clustering Algorithms. IEEE Trans Pattern Anal Mach Intell. 1986;8(2):248–55.doi:10.1109/tpami.1986.4767778
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–8.doi:10.1038/s41587-020-0548-6
Koh A, De Vadder F, Kovatcheva-Datchary P, Backhed F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell. 2016;165(6):1332–45.doi:10.1016/j.cell.2016.05.041
Park SY, Ufondu A, Lee K, Jayaraman A. Emerging computational tools and models for studying gut microbiota composition and function. Curr Opin Biotechnol. 2020;66(301 – 11.doi:10.1016/j.copbio.2020.10.005
Elbere I, Kalnina I, Silamikelis I, Konrade I, Zaharenko L, Sekace K, et al. Association of metformin administration with gut microbiome dysbiosis in healthy volunteers. PLoS One. 2018;13(9):e0204317.doi:10.1371/journal.pone.0204317
Mrozinska S, Radkowski P, Gosiewski T, Szopa M, Bulanda M, Ludwig-Galezowska AH, et al. Qualitative Parameters of the Colonic Flora in Patients with HNF1A-MODY Are Different from Those Observed in Type 2 Diabetes Mellitus. J Diabetes Res. 2016;2016(3876764.doi:10.1155/2016/3876764
Chavez-Carbajal A, Pizano-Zarate ML, Hernandez-Quiroz F, Ortiz-Luna GF, Morales-Hernandez RM, De Sales-Millan A, et al. Characterization of the Gut Microbiota of Individuals at Different T2D Stages Reveals a Complex Relationship with the Host. Microorganisms. 2020;8(1).doi:10.3390/microorganisms8010094
Sepp E, Kolk H, Loivukene K, Mikelsaar M. Higher blood glucose level associated with body mass index and gut microbiota in elderly people. Microb Ecol Health Dis. 2014;25(doi:10.3402/mehd.v25.22857
Ryan PM, Patterson E, Carafa I, Mandal R, Wishart DS, Dinan TG, et al. Metformin and Dipeptidyl Peptidase-4 Inhibitor Differentially Modulate the Intestinal Microbiota and Plasma Metabolome of Metabolically Dysfunctional Mice. Can J Diabetes. 2020;44(2):146 – 55 e2.doi:10.1016/j.jcjd.2019.05.008
Larsen N, Vogensen FK, van den Berg FW, Nielsen DS, Andreasen AS, Pedersen BK, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 2010;5(2):e9085.doi:10.1371/journal.pone.0009085
Ejtahed HS, Tito RY, Siadat SD, Hasani-Ranjbar S, Hoseini-Tavassol Z, Rymenans L, et al. Metformin induces weight loss associated with gut microbiota alteration in non-diabetic obese women: a randomized double-blind clinical trial. Eur J Endocrinol. 2019;180(3):165–76.doi:10.1530/EJE-18-0826
Niu X, Lu P, Huang L, Sun Y, Jin M, Liu J, et al. The effect of metformin combined with liraglutide on gut microbiota of Chinese patients with type 2 diabetes. Int Microbiol. 2023.doi:10.1007/s10123-023-00380-y
Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Manneras-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. Nat Med. 2017;23(7):850–8.doi:10.1038/nm.4345
Zhang Q, Xiao X, Zheng J, Li M, Yu M, Ping F, et al. Featured article: Structure moderation of gut microbiota in liraglutide-treated diabetic male rats. Exp Biol Med (Maywood). 2018;243(1):34–44.doi:10.1177/1535370217743765
Million M, Angelakis E, Maraninchi M, Henry M, Giorgi R, Valero R, et al. Correlation between body mass index and gut concentrations of Lactobacillus reuteri, Bifidobacterium animalis, Methanobrevibacter smithii and Escherichia coli. Int J Obes (Lond). 2013;37(11):1460–6.doi:10.1038/ijo.2013.20
Pedersen C, Ijaz UZ, Gallagher E, Horton F, Ellis RJ, Jaiyeola E, et al. Fecal Enterobacteriales enrichment is associated with increased in vivo intestinal permeability in humans. Physiol Rep. 2018;6(7):e13649.doi:10.14814/phy2.13649
Crovesy L, Masterson D, Rosado EL. Profile of the gut microbiota of adults with obesity: a systematic review. Eur J Clin Nutr. 2020;74(9):1251–62.doi:10.1038/s41430-020-0607-6
Chakraborti CK. New-found link between microbiota and obesity. World J Gastrointest Pathophysiol. 2015;6(4):110–9.doi:10.4291/wjgp.v6.i4.110
Tavella T, Rampelli S, Guidarelli G, Bazzocchi A, Gasperini C, Pujos-Guillot E, et al. Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly. Gut Microbes. 2021;13(1):1–19.doi:10.1080/19490976.2021.1880221
Kubinak JL, Petersen C, Stephens WZ, Soto R, Bake E, O'Connell RM, et al. MyD88 signaling in T cells directs IgA-mediated control of the microbiota to promote health. Cell Host Microbe. 2015;17(2):153–63.doi:10.1016/j.chom.2014.12.009
Zeng Q, Li D, He Y, Li Y, Yang Z, Zhao X, et al. Discrepant gut microbiota markers for the classification of obesity-related metabolic abnormalities. Sci Rep. 2019;9(1):13424.doi:10.1038/s41598-019-49462-w
Guilloteau P, Martin L, Eeckhaut V, Ducatelle R, Zabielski R, Van Immerseel F. From the gut to the peripheral tissues: the multiple effects of butyrate. Nutr Res Rev. 2010;23(2):366–84.doi:10.1017/S0954422410000247
Wu S, Sun C, Li Y, Wang T, Jia L, Lai S, et al. GMrepo: a database of curated and consistently annotated human gut metagenomes. Nucleic Acids Res. 2020;48(D1):D545-D53.doi:10.1093/nar/gkz764
Tong M, Li X, Wegener Parfrey L, Roth B, Ippoliti A, Wei B, et al. A modular organization of the human intestinal mucosal microbiota and its association with inflammatory bowel disease. PLoS One. 2013;8(11):e80702.doi:10.1371/journal.pone.0080702

No competing interests reported.

supplementalmaterials.docx

Download PDF

Editorial decision: Revision requested
02 Mar, 2024
Reviews received at journal
29 Feb, 2024
Reviewers agreed at journal
05 Feb, 2024
Reviewers invited by journal
04 Feb, 2024
Submission checks completed at journal
31 Jan, 2024
Editor assigned by journal
31 Jan, 2024
First submitted to journal
30 Jan, 2024

You are reading this latest preprint version

Metformin-induced changes of the gut microbiota in patients with type 2 diabetes mellitus: Results from a prospective cohort study

Status:

Version 1

Abstract

Background

Research design and methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Statistical analysis

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1