Metformin could promote healthy aging based on genetics

A research team from the School of Public Health, LKS Faculty of Medicine of the University of Hong Kong (HKUMed), provides genetic evidence that metformin might promote healthy ageing using a cohort study of more than 300,000 participants of European descent (UK Biobank).

This proof-of-concept work supports further clinical research into the drug repositioning of metformin in healthy longevity. The findings are now published in The Lancet Healthy Longevity, a leading peer-reviewed, international journal in the field of geriatrics and gerontology.

Background
Metformin is a first-line medication for type 2 diabetes. Increasing evidence suggests metformin’s benefits extend far beyond diabetes and may promote healthy ageing. However, earlier observational studies can be biased, whilst clinical trials of metformin in longevity are underway and some genetic studies suggested metformin may have protective effects against other ageing-related diseases such as cancer and Alzheimer’s disease.1,To address the role of metformin in healthy longevity, the research team set out to investigate this research question by exploring the target-specific effect of metformin on biomarkers of ageing using genetics (i.e. drug-target Mendelian randomisation) in a large cohort study. Since genetic variants are randomly allocated at conception, this provides a potentially less biased assessment in whether metformin may promote healthy longevity in comparison to conventional pharmacoepidemiologic studies.

Research methods and findings
The study included 321,412 white British participants from the UK Biobank with valid genomic and phenotypic data. The researchers derived ageing metrics of interest, including phenotypic age derived from chronological age and nine clinical markers, and leukocyte telomere length (LTL). To assess the target-specific effect of metformin in biomarkers of ageing, the researchers identified variants in the protein-encoding genes related to metformin using data from the Genotype-Tissue Expression (GTEx) project and UK Biobank, with relevant statistical approaches (i.e. Mendelian randomisation and colocalisation). The researchers also used a conventional observational design to compare biomarkers of ageing by metformin users only with users of other antidiabetic drugs via propensity score matching in UK Biobank.

The research team found that glycated hemoglobin (HbA1c) lowering induced by the metformin target GPD13 were associated with younger phenotypic age and longer LTL, whilst AMPKγ2 (PRKAG2)4 was associated with younger phenotypic age only. Such effects might be in part due to the glycaemic property of metformin. These findings from genetic analyses were corroborated by the propensity score matching analyses.

Significance of the study
Metformin is a highly affordable medicine with a known safety profile and has long been on the WHO Model Lists of Essential Medicines. This drug-target Mendelian randomisation provides genetic evidence that encourages further exploration of this safe and affordable medication to be repurposed for the promotion of healthy ageing. ‘Increasing evidence suggests metformin may also exert its effect via glycaemic-independent pathways. Better understanding of mechanisms of metformin action using big data approaches and different omics is warranted and improve evaluation of its repositioning potential,’ said Dr Luo Shan, Research Assistant Professor, School of Public Health, HKUMed.

The findings may foreshadow results from the TAME (Targeting Ageing with Metformin) trial, the first-ever anti-ageing study approved by the U.S. Food and Drug Administration, to evaluate the role of metformin in longevity, which is in its preparatory stage. ‘Our work has demonstrated the utility of using large-scale epidemiologic studies and genomic data in evaluating drug reposition opportunities. Genetic validation studies, such as this study, shall help improve the success rate of subsequent clinical trials,’ said Dr Ryan Au Yeung Shiu-lun, Assistant Professor, School of Public Health, HKUMed.

About the research team
The study was led by Dr Ryan Au Yeung Shiu-lun, Assistant Professor, and Dr Luo Shan, Research Assistant Professor of the School of Public Health, HKUMed. The research team also included Professor Ian Wong Chi-kei, Lo Shiu Kwan Kan Po Ling Professor in Pharmacy, Head of the Department of Pharmacology and Pharmacy, Director of Centre for Safe Medication Practice and Research, HKUMed, and Lead Scientist of the Laboratory of Data Discovery for Health (D24H); Dr Celine Chui Sze-ling, Assistant Professor of School of Nursing and School of Public Health, HKUMed, and Co-Principal Investigator of D24H; Professor Zheng Jie, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China; Huang Yuan, Director of Technology Transfer of Hong Kong Quantum AI Lab; and Dr Mary Schooling, Honorary Associate Professor and Cluster Leader (Non-communicable Diseases in Global Health), School of Public Health, HKUMed.


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