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However, it is unclear whether these equations achieve high predictive accuracy and fairness in patients with type 2 diabetes (T2D), and whether a T2D-specific risk equation is needed. Accordingly, we developed a Weibull Accelerated Failure Time (AFT) survival model for predicting the 3-year CVD risk in 23,795 patients with T2D from the All of Us dataset, using sociodemographic information, physical measurements, medication, and CVD history. Among patients without CVD history, our Weibull AFT (vs. PREVENT) achieved a greater C-index (0.646 vs. 0.465), greater Concordance Fractions (0.610\u20130.674 vs. 0.541\u20130.600), and comparable Concordance Imparity (0.006 vs. 0.002) across sex and race\/ethnicity (0.065 vs. 0.058) subgroups. Our findings highlight the need for a T2D-specific CVD risk equation and demonstrate the value of diverse datasets for developing fair and accurate predictive models.<\/jats:p>","DOI":"10.1038\/s41598-026-45129-5","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:36:44Z","timestamp":1774967804000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and evaluation of cardiovascular disease risk prediction models for patients with type 2 diabetes"],"prefix":"10.1038","volume":"16","author":[{"given":"Yang","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Che-Yi","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun Ju","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esmaeil","family":"Keyvanshokooh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mary Beth","family":"Weber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco J.","family":"Pasquel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gian-Gabriel P.","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"issue":"4","key":"45129_CR1","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1016\/j.amepre.2023.11.009","volume":"66","author":"RC Woodruff","year":"2024","unstructured":"Woodruff, R. 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This study analyzes data from the All of Us Research Program, which uses a centralized, electronic informed consent (eConsent) process. It is modular (Primary Consent, HIPAA Authorization, and Genomics Consent) and uses multimedia tools like short videos to ensure participant understanding. Most importantly, it includes a \u201cteach-back\u201d quiz that participants must pass to confirm they understand the voluntary nature of the study. The details of this consent process are provided on their web page (\n                      \n                      ).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"This manuscript has been read and its submission approved by all authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"FJP reported receiving grants through the institution from Insulet, Tandem Diabetes Care, Ideal Medical Technologies, Novo Nordisk, and Dexcom; receiving consulting fees from Dexcom; receiving consulting fees to the institution from Insulet. All other authors have no competing interests to declare.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15574"}}