Tobias, D. K. et al. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat. Med. 29, 2438–2457 (2023).
Misra, S. et al. Precision subclassification of type 2 diabetes: a systematic review. Commun. Med. (Lond.) 3, 138 (2023).
Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).
Kim, H. et al. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 66, 495–507 (2023).
Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).
Suzuki, K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature https://doi.org/10.1038/s41586-024-07019-6 (2024).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).
Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).
Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).
BasuRay, S., Wang, Y., Smagris, E., Cohen, J. C. & Hobbs, H. H. Accumulation of PNPLA3 on lipid droplets is the basis of associated hepatic steatosis. Proc. Natl Acad. Sci. USA 116, 9521–9526 (2019).
Lee, S. M., Muratalla, J., Sierra-Cruz, M. & Cordoba-Chacon, J. Role of hepatic peroxisome proliferator-activated receptor γ in non-alcoholic fatty liver disease. J. Endocrinol. 257, e220155 (2023).
Getz, G. S. & Reardon, C. A. Apoprotein E and reverse cholesterol transport. Int. J. Mol. Sci. 19, 3479 (2018).
Zhang, K. et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001 (2021).
Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Caleyachetty, R. et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol. 9, 419–426 (2021).
Yaghootkar, H., Whitcher, B., Bell, J. D. & Thomas, E. L. Ethnic differences in adiposity and diabetes risk—insights from genetic studies. J. Intern. Med. 288, 271–283 (2020).
Ntuk, U. E., Gill, J. M. R., Mackay, D. F., Sattar, N. & Pell, J. P. Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK Biobank participants. Diabetes Care 37, 2500–2507 (2014).
Hsu, W. C., Araneta, M. R. G., Kanaya, A. M., Chiang, J. L. & Fujimoto, W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care 38, 150–158 (2015).
Rodriguez, L. A. et al. Examining if the relationship between BMI and incident type 2 diabetes among middle-older aged adults varies by race/ethnicity: evidence from the Multi-Ethnic Study of Atherosclerosis (MESA). Diabet. Med. 38, e14377 (2021).
Aggarwal, R. et al. Diabetes screening by race and ethnicity in the United States: equivalent body mass index and age thresholds. Ann. Intern. Med. 175, 765–773 (2022).
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157–163 (2004).
Inker, L. A. et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).
Zhao, W. et al. Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat. Genet. 49, 1450–1457 (2017).
Goodarzi, M. O. & Rotter, J. I. Genetics insights in the relationship between type 2 diabetes and coronary heart disease. Circ. Res. 126, 1526–1548 (2020).
Sattar, N. et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375, 735–742 (2010).
González-Lleó, A. M., Sánchez-Hernández, R. M., Boronat, M. & Wägner, A. M. Diabetes and familial hypercholesterolemia: interplay between lipid and glucose metabolism. Nutrients 14, 1503 (2022).
Wei, Y. et al. Associations between serum total bilirubin, obesity and type 2 diabetes. Diabetol. Metab. Syndr. 13, 143 (2021).
Hansen, M. et al. Bile acid sequestrants for glycemic control in patients with type 2 diabetes: a systematic review with meta-analysis of randomized controlled trials. J. Diabetes Complications 31, 918–927 (2017).
Glunk, V. et al. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat. Metab. 5, 861–879 (2023).
Fathzadeh, M. et al. FAM13A affects body fat distribution and adipocyte function. Nat. Commun. 11, 1465 (2020).
Li, B.-T. et al. Disruption of the ERLIN–TM6SF2–APOB complex destabilizes APOB and contributes to non-alcoholic fatty liver disease. PLoS Genet. 16, e1008955 (2020).
ElSayed, N. A. et al. 2. Classification and diagnosis of diabetes: Standards of Care in Diabetes—2023. Diabetes Care 46, S19–S40 (2023).
Vyas, D. A., Eisenstein, L. G. & Jones, D. S. Hidden in plain sight—reconsidering the use of race correction in clinical algorithms. N. Engl. J. Med. 383, 874–882 (2020).
Narayan, K. M. V. et al. Incidence and pathophysiology of diabetes in South Asian adults living in India and Pakistan compared with US blacks and whites. BMJ Open Diabetes Res. Care 9, e001927 (2021).
Narayan, K. M. V. & Kanaya, A. M. Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 63, 1103–1109 (2020).
All of Us Research Program Investigators et al. The ‘All of Us’ Research Program. N. Engl. J. Med. 381, 668–676 (2019).
Castro, V. M. et al. The mass general brigham biobank portal: an i2b2-based data repository linking disparate and high-dimensional patient data to support multimodal analytics. J. Am. Med. Inform. Assoc. 29, 643–651 (2022).
Kho, A. N. et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J. Am. Med. Inform. Assoc. 19, 212–218 (2012).
Szczerbinski, L. et al. Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores—a new resource for diabetes precision medicine. Preprint at bioRxiv https://doi.org/10.1101/2023.09.05.23295061 (2023).
Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).
Crawford, S. L. Correlation and regression. Circulation 114, 2083–2088 (2006).
DiCorpo, D. et al. Type 2 diabetes partitioned polygenic scores associate with disease outcomes in 454,193 individuals across 13 cohorts. Diabetes Care 45, 674–683 (2022).
Patel, A. P. et al. Association of rare pathogenic DNA variants for familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and lynch syndrome with disease risk in adults according to family history. JAMA Netw. Open 3, e203959 (2020).
Magudia, K. et al. Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves. Radiology 298, 319–329 (2021).
Bridge, C. P. et al. A fully automated deep learning pipeline for multi-vertebral level quantification and characterization of muscle and adipose tissue on chest CT scans. Radio. Artif. Intell. 4, e210080 (2022).