Xue Li1,2, Stefano Celotto3, Damiano Pizzol4, Danijela Gasevic2,5, Meng-Meng Ji6, Tommaso Barnini7, Marco Solmi8,9, Brendon Stubbs10, Lee Smith11, Guillermo F López Sánchez12, Gabriella Pesolillo13, Zengli Yu14, Ioanna Tzoulaki15,16, Evropi Theodoratou2,17, John P A Ioannidis18,19,20,21, Nicola Veronese9, Jacopo Demurtas22,23. 1. School of Public Health, Zhejiang University, Hangzhou, China. 2. Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK. 3. MD, Primary Care Department, AAS3 Alto Friuli e Collinare e Medio Friuli, Udine, Italy. 4. Italian Agency for Development Cooperation, Khartoum, Sudan. 5. School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic., Australia. 6. School of Public Health, Nanjing Medical University, Nanjing, China. 7. Primary Care Service, AUSL Toscana Centro, Firenze, Italy. 8. Neurosciences Department, University of Padua, Padua, Italy. 9. Padua Neuroscience Center, University of Padua, Padua, Italy. 10. Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. 11. The Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge, UK. 12. Faculty of Health, Education, Medicine and Social Care, School of Medicine, Vision and Eye Research Institute, Anglia Ruskin University-Cambridge Campus, Cambridge, UK. 13. Primary Care Service, ASL 02, Chieti, Italy. 14. School of Public Health, Zhengzhou University, Zhengzhou, China. 15. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. 16. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 17. Cancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK. 18. Department of Medicine, Stanford University, Stanford, CA, USA. 19. Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. 20. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. 21. Department of Statistics, Stanford University, Stanford, CA, USA. 22. Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy. 23. Primary Care Department USL Toscana Sud-Est, Grosseto, Italy.
Abstract
BACKGROUND: The objective was to capture the breadth of outcomes that have been associated with metformin use and to systematically assess the quality, strength and credibility of these associations using the umbrella review methodology. METHODS: Four major databases were searched until 31 May 2020. Meta-analyses of observational studies and meta-analyses of randomized controlled trials (RCTs) (including active and placebo control arms) were included. RESULTS: From 175 eligible publications, we identified 427 different meta-analyses, including 167 meta-analyses of observational studies, 147 meta-analyses of RCTs for metformin vs placebo/no treatment and 113 meta-analyses of RCTs for metformin vs active medications. There was no association classified as convincing or highly suggestive from meta-analyses of observational studies, but some suggestive/weak associations of metformin use with a lower mortality risk of CVD and cancer. In meta-analyses of RCTs, metformin was associated with a lower incidence of diabetes in people with prediabetes or no diabetes at baseline; lower ovarian hyperstimulation syndrome incidence (in women in controlled ovarian stimulation); higher success for clinical pregnancy rate in poly-cystic ovary syndrome (PCOS); and significant reduction in body mass index in people with type 1 diabetes mellitus, in women who have obesity/overweight with PCOS and in obese/overweight women. Of 175 publications, 166 scored as low or critically low quality per AMSTAR 2 criteria. CONCLUSIONS: Observational evidence on metformin seems largely unreliable. Randomized evidence shows benefits for preventing diabetes and in some gynaecological and obstetrical settings. However, almost all meta-analyses are of low or critically low quality according to AMSTAR 2 criteria.
BACKGROUND: The objective was to capture the breadth of outcomes that have been associated with metformin use and to systematically assess the quality, strength and credibility of these associations using the umbrella review methodology. METHODS: Four major databases were searched until 31 May 2020. Meta-analyses of observational studies and meta-analyses of randomized controlled trials (RCTs) (including active and placebo control arms) were included. RESULTS: From 175 eligible publications, we identified 427 different meta-analyses, including 167 meta-analyses of observational studies, 147 meta-analyses of RCTs for metformin vs placebo/no treatment and 113 meta-analyses of RCTs for metformin vs active medications. There was no association classified as convincing or highly suggestive from meta-analyses of observational studies, but some suggestive/weak associations of metformin use with a lower mortality risk of CVD and cancer. In meta-analyses of RCTs, metformin was associated with a lower incidence of diabetes in people with prediabetes or no diabetes at baseline; lower ovarian hyperstimulation syndrome incidence (in women in controlled ovarian stimulation); higher success for clinical pregnancy rate in poly-cystic ovary syndrome (PCOS); and significant reduction in body mass index in people with type 1 diabetes mellitus, in women who have obesity/overweight with PCOS and in obese/overweight women. Of 175 publications, 166 scored as low or critically low quality per AMSTAR 2 criteria. CONCLUSIONS: Observational evidence on metformin seems largely unreliable. Randomized evidence shows benefits for preventing diabetes and in some gynaecological and obstetrical settings. However, almost all meta-analyses are of low or critically low quality according to AMSTAR 2 criteria.