Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

Study information

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study design

time series / longitudinal observational

Citation

PMID PubMed identifier for scientific articles.

34329692

DOI Digital object identifier for electronic documents.

10.1016/j.diabres.2021.108985

URI

Authors

Lee Y, Kim AH, Kim E, Lee S, Yu KS, Jang IJ, Chung JY, Cho JY

Journal

Diabetes research and clinical practice

Year

2021

Keywords:

Gut microbiome, Hypoglycemic effect, Metabolomics, Metformin

AIMS: Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach. METHODS: An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin. RESULTS: The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect. CONCLUSIONS: We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome.

Experiment 1

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Reviewed Marked as Reviewed by Svetlana up on 2025-2-7

Curated date: 2025/01/30

Curator: Aleru Divine

Revision editor(s): Aleru Divine, WikiWorks

Subjects

Location of subjects: South Korea

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Response to metformin Response to metformin,response to metformin

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Baseline

Group 1 name Corresponds to the case (exposed) group for case-control studies: Postmetformin

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Samples collected from participants on day 4 after the last metformin dose.

Group 0 sample size Number of subjects in the control (unexposed) group: 20

Group 1 sample size Number of subjects in the case (exposed) group: 20

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Statistical test: LEfSe

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: Yes

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 2.0

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased

Signature 1

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Curated date: 2025/01/30

Curator: Aleru Divine

Revision editor(s): Aleru Divine, WikiWorks

Source: Figure 1D and Supplementary Table 2

Description: Bacteria differentially represented between baseline and postmetformin identified by linear discriminant analysis coupled with effect size (LEfSe).

Abundance in Group 1: increased abundance in Postmetformin

NCBI	Quality Control	Links
Escherichia

Revision editor(s): Aleru Divine, WikiWorks

Signature 2

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Reviewed Marked as Reviewed by Svetlana up on 2025-2-7

Curated date: 2025/01/30

Curator: Aleru Divine

Revision editor(s): Aleru Divine, WikiWorks

Source: Figure 1D and Supplementary Table 2

Description: Bacteria differentially represented between baseline and postmetformin identified by linear discriminant analysis coupled with effect size (LEfSe).

Abundance in Group 1: decreased abundance in Postmetformin

NCBI	Quality Control	Links
Intestinibacter
Clostridium
Romboutsia

Revision editor(s): Aleru Divine, WikiWorks