Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues

Authors: BOCHER, OZVAN - Non ARS Employee; ARRUDA, ANA - Non ARS Employee; YOSHIJI, SATOSHI - Broad Institute Of Mit/harvard; ZHAO, CHI - University Of Massachusetts, Amherst; HUERTA-CHAGOYA, ALICIA - Broad Institute Of Mit/harvard; SU, CHEN - McGill University - Canada; YIN, XIANYONG - Nanjing University; CAMMANN, DAVIS - University Of Nevada Las Vegas, Las Vegas, Nv; TAYLOR, HENRY - National Institutes Of Health (NIH); CHEN, JINGCHUN - University Of Nevada Las Vegas, Las Vegas, Nv; SUZUKI, KEN - University Of Tokyo; MANDLA, RAVI - Broad Institute Of Mit/harvard; YANG, TA - Kyoto University; MATSUDA, FUMIHIKO - Kyoto University; MERCADER, JOSEP - Broad Institute Of Mit/harvard; FLANNICK, JASON - Broad Institute Of Mit/harvard; MEIGS, JAMES - Broad Institute Of Mit/harvard; WOOD, ALEXIS - Children'S Nutrition Research Center (CNRC); VUJKOVIC, MARIJANA - University Of Pennsylvania; VOIGHT, BENJAMIN - University Of Pennsylvania; SPRACKLEN, CASSANDRA - University Of Massachusetts, Amherst; ROTTER, JEROME - Harbor-Ucla Medical Center; MORRIS, ANDREW - University Of Manchester; ZEGGINI, ELEFTHERIA - Technical University Of Munich

Submitted to: Nature Metabolism
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/8/2025
Publication Date: 1/27/2026
Citation: Bocher, O., Arruda, A.L., Yoshiji, S., Zhao, C., Huerta-Chagoya, A., Su, C.Y., Yin, X., Cammann, D., Taylor, H.J., Chen, J., Suzuki, K., Mandla, R., Yang, T.Y., Matsuda, F., Mercader, J.M., Flannick, J., Meigs, J.B., Wood, A.C., Vujkovic, M., Voight, B.F., Spracklen, C.N., Rotter, J.I., Morris, A.P., Zeggini, E. 2026. Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues. Nature Metabolism. https://doi.org/10.1038/s42255-025-01444-1.
DOI: https://doi.org/10.1038/s42255-025-01444-1

Interpretive Summary: Type 2 diabetes (T2D) affects hundreds of millions of people worldwide and arises from complex interactions between genes, environment, and metabolism. This large international study investigated how differences in gene and protein activity contribute to T2D across diverse global populations and tissues. Using genetic data from over 2.5 million people of European, African, East Asian, and American ancestry, researchers applied a statistical approach called Mendelian randomization to determine which genes and proteins may cause rather than merely correlate with diabetes. They found over 900 genes and 46 proteins whose levels directly influence diabetes risk, with many effects consistent across ancestries but differing by tissue—such as opposite effects of the same gene in fat versus pancreas. The study highlights that understanding tissue-specific and ancestry-specific biology is crucial for identifying drug targets and tailoring diabetes prevention and treatment strategies. These results are highly relevant to scientists, clinicians, and policy makers aiming to develop equitable, precision approaches to reduce the global burden of diabetes.

Technical Abstract: Type 2 diabetes (T2D) is a prevalent disease arising from complex molecular mechanisms. Here we leverage T2D genetic associations to identify causal molecular mechanisms in an ancestry-aware and tissue-aware manner. Using two-sample Mendelian randomization corroborated by colocalization across four global ancestries, we analyse 20,307 gene and 1,630 protein expression levels using blood-derived cis-quantitative trait loci (QTLs). We detect causal effects of genetically predicted levels of 335 genes and 46 proteins on T2D risk, with 16.4% and 50% replication in independent cohorts, respectively. Using gene expression cis-QTLs derived from seven T2D-relevant tissues, we identify causal links between the expression of 676 genes and T2D risk, refining known associations such as BAK1 and describing additional ones like CPXM1. Causal effects are mostly shared across ancestries but are highly heterogeneous across tissues. Our findings provide insights into cross-ancestry and tissue-informed multi-omics causal inference approaches and demonstrate their power in uncovering molecular processes driving T2D.