Divya Joshi1, Talha Rafiq1, Marie Pigeyre2, Renee de Mutsert3, Femke Rutters4, David Campbell5, Jean-Pierre Despres6, Andre C. Carpentier7, Joris Hoeks8, Patrick Schrauwen3,9, Parminder Raina1,10,11
1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; 2Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 3Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands; 4Department of Epidemiology and Data Science, Amsterdam University Medical Centre, Location Vrije Universiteit Amsterdam, Amsterdam, North Holland, The Netherlands; 5Cumming School of Medicine and Depts. of Medicine, Community Health Sciences & Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada; 6Department of Kinesiology, Laval University, Laval, Quebec, Canada; 7Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada; 8Department of Nutrition and Movement Sciences, NUTRIM Institute of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Limburg, The Netherlands; 9Institute for Clinical Diabetology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany; 10McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada; 11Labarge Centre for Mobility in Aging, McMaster University, Hamilton, Ontario, Canada
Background: Various metabolic processes display circadian rhythmicity, yet limited studies have explored how circulating metabolites that display circadian or diurnal variations contribute to type 2 diabetes (T2D) risk. This study examines the association between rhythmic metabolites and T2D risk and investigates causality using Mendelian randomization (MR).
Methods: We analyzed baseline and three-year follow-up data from 9,992 adults participating in the Canadian Longitudinal Study on Aging, for whom untargeted plasma metabolomics data were available. A total of 98 metabolites, previously reported to exhibit rhythmic variation, were assessed. Multivariable regression was used to examine the associations between chronotype polygenic risk scores and rhythmic metabolites, and between rhythmic metabolites and incident T2D. We performed pathway enrichment analysis to explore biological relevance and two-sample MR to evaluate the causal effects of rhythmic metabolites on T2D risk.
Results: Altogether, 20 rhythmic metabolites were associated with incident T2D. Key pathways included leucine, isoleucine and valine biosynthesis and degradation, and glycine, serine, and threonine metabolism. Genetic predisposition to chronotype was associated with several T2D-associated metabolites. MR analyses revealed causal associations between higher levels of mannose (OR=1.29; 95% CI=1.22-1.37; p<0.001), valine (OR=1.17; 95% CI=1.02-1.34; p=0.023), isoleucine (OR=1.17; 95%
CI=1.02-1.33; p=0.023), and sphingomyelin (d18:0/18:0, d19:0/17:0) (OR=1.14; 95% CI=1.02-1.28; p=0.025) and higher T2D risk, whereas glycine (OR=0.97; 95% CI=0.94-0.99; p=0.026) and 1-linoleoyl-GPC (18:2) (OR=0.91; 95% CI=0.84-0.99; p=0.020) were protective. Directional discordance was noted for some metabolites, including creatine, threonine, and certain glycerophosphatidylethanolamines, which showed inverse causal effects compared to observational estimates.
Discussion/Conclusion: Rhythmic metabolites, particularly amino acids and phospholipids are implicated in T2D pathophysiology. Aligning circadian rhythms through behavioral or clinical interventions may offer novel strategies for diabetes prevention.