Yujin Lee1, Andrew HyoungJin Kim2, Eunwoo Kim3, SeungHwan Lee4, Kyung-Sang Yu5, In-Jin Jang6, Jae-Yong Chung7, Joo-Youn Cho8. 1. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea. Electronic address: yoojinlee@snu.ac.kr. 2. Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA. Electronic address: andrewhyoungjinkim@wustl.edu. 3. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea. Electronic address: eunoo1212@snu.ac.kr. 4. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea. Electronic address: leejh413@snu.ac.kr. 5. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea. Electronic address: ksyu@snu.ac.kr. 6. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea. Electronic address: ijjang@snu.ac.kr. 7. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea; Clinical Trials Center, Seoul National University Bundang Hospital, Seongnam, South Korea. Electronic address: jychung@snubh.org. 8. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, South Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea. Electronic address: joocho@snu.ac.kr.
Abstract
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.
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.