{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:22:44Z","timestamp":1792783364193,"version":"4.5.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"1.3.5 project for discipline of excellence, West China Hospital, Sichuan University","award":["2025HXFH053"],"award-info":[{"award-number":["2025HXFH053"]}],"id":[{"id":"https:\/\/ror.org\/011ashp19","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Endocrine"],"DOI":"10.1007\/s12020-026-04799-x","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:49:13Z","timestamp":1791665353000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic transitions from prediabetes to diabetes and incident cardiovascular events in middle-aged and older chinese adults: a nationwide longitudinal study"],"prefix":"10.1007","volume":"91","author":[{"given":"Shanshan","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanmin","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Gan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"4799_CR1","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee., 2. Diagnosis and classification of diabetes: Standards of Care in Diabetes\u20142026. Diabetes Care. 49(Suppl 1), S27\u2013S49, (2026)","DOI":"10.2337\/dc26-S002"},{"key":"4799_CR2","volume-title":"IDF Diabetes Atlas","author":"International Diabetes Federation","year":"2025","unstructured":"International Diabetes Federation, IDF Diabetes Atlas, 11th edn. (International Diabetes Federation, Brussels, Belgium, 2025)","edition":"11"},{"issue":"24","key":"4799_CR3","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1001\/jama.2021.22208","volume":"326","author":"L Wang","year":"2021","unstructured":"L. Wang et al., Prevalence and treatment of diabetes in China, 2013\u20132018. JAMA 326(24), 2498\u20132506 (2021)","journal-title":"JAMA"},{"key":"4799_CR4","doi-asserted-by":"publisher","first-page":"m2297","DOI":"10.1136\/bmj.m2297","volume":"370","author":"X Cai","year":"2020","unstructured":"X. Cai et al., Association between prediabetes and risk of all-cause mortality and cardiovascular disease: updated meta-analysis. BMJ. 370, m2297 (2020)","journal-title":"BMJ"},{"issue":"6","key":"4799_CR5","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1056\/NEJMoa012512","volume":"346","author":"WC Knowler","year":"2002","unstructured":"W.C. Knowler, Diabetes Prevention Program Research Group et al., Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl. J. Med. 346(6), 393\u2013403 (2002)","journal-title":"N Engl. J. Med."},{"issue":"8","key":"4799_CR6","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s00125-019-4895-0","volume":"62","author":"D Vistisen","year":"2019","unstructured":"D. Vistisen et al., Reversion from prediabetes to normoglycaemia and risk of cardiovascular disease and mortality: the Whitehall II cohort study. Diabetologia. 62(8), 1385\u20131390 (2019)","journal-title":"Diabetologia"},{"issue":"3","key":"4799_CR7","doi-asserted-by":"publisher","first-page":"e019045","DOI":"10.1161\/JAHA.120.019045","volume":"10","author":"X Liu","year":"2021","unstructured":"X. Liu et al., Reversion from pre-diabetes mellitus to normoglycemia and risk of cardiovascular disease and all-cause mortality in a Chinese population: a prospective cohort study. J. Am. Heart Assoc. 10(3), e019045 (2021)","journal-title":"J. Am. Heart Assoc."},{"issue":"6","key":"4799_CR8","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1007\/s00125-009-1338-3","volume":"52","author":"CC Lee","year":"2009","unstructured":"C.C. Lee et al., Association of C-reactive protein with type 2 diabetes: prospective analysis and meta-analysis. Diabetologia. 52(6), 1040\u20131047 (2009)","journal-title":"Diabetologia"},{"issue":"9","key":"4799_CR9","doi-asserted-by":"publisher","first-page":"1737","DOI":"10.2337\/dc09-0288","volume":"32","author":"S Kodama","year":"2009","unstructured":"S. Kodama et al., Association between serum uric acid and development of type 2 diabetes. Diabetes Care. 32(9), 1737\u20131742 (2009)","journal-title":"Diabetes Care"},{"issue":"2","key":"4799_CR10","doi-asserted-by":"publisher","first-page":"e56864","DOI":"10.1371\/journal.pone.0056864","volume":"8","author":"Q Lv","year":"2013","unstructured":"Q. Lv et al., High serum uric acid and increased risk of type 2 diabetes: a systemic review and meta-analysis of prospective cohort studies. PLoS One. 8(2), e56864 (2013)","journal-title":"PLoS One"},{"issue":"2","key":"4799_CR11","doi-asserted-by":"publisher","first-page":"e3365","DOI":"10.1002\/dmrr.3365","volume":"37","author":"SK Kunutsor","year":"2021","unstructured":"S.K. Kunutsor et al., Handgrip strength: a risk indicator for type 2 diabetes: systematic review and meta-analysis of observational cohort studies. Diabetes Metab. Res. Rev. 37(2), e3365 (2021)","journal-title":"Diabetes Metab. Res. Rev."},{"issue":"1","key":"4799_CR12","doi-asserted-by":"publisher","first-page":"e001865","DOI":"10.1136\/bmjdrc-2020-001865","volume":"9","author":"J Boonpor","year":"2021","unstructured":"J. Boonpor et al., Associations between grip strength and incident type 2 diabetes: findings from the UK Biobank prospective cohort study. BMJ Open. Diabetes Res. Care. 9(1), e001865 (2021)","journal-title":"BMJ Open. Diabetes Res. Care"},{"key":"4799_CR13","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"G.S. Collins et al., TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 385, e078378 (2024)","journal-title":"BMJ"},{"issue":"1","key":"4799_CR14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/ije\/dys203","volume":"43","author":"Y Zhao","year":"2014","unstructured":"Y. Zhao et al., Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int. J. Epidemiol. 43(1), 61\u201368 (2014)","journal-title":"Int. J. Epidemiol."},{"issue":"9682","key":"4799_CR15","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1016\/S0140-6736(09)60619-X","volume":"373","author":"AG Tab\u00e1k","year":"2009","unstructured":"A.G. Tab\u00e1k et al., Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet. 373(9682), 2215\u20132221 (2009)","journal-title":"Lancet"},{"issue":"4","key":"4799_CR16","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1001\/jamainternmed.2020.8774","volume":"181","author":"MR Rooney","year":"2021","unstructured":"M.R. Rooney et al., Risk of progression to diabetes among older adults with prediabetes. JAMA Intern. Med. 181(4), 511\u2013519 (2021)","journal-title":"JAMA Intern. Med."},{"issue":"9833","key":"4799_CR17","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1016\/S0140-6736(12)60525-X","volume":"379","author":"L Perreault","year":"2012","unstructured":"L. Perreault, Diabetes Prevention Program Research Group et al., Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet. 379(9833), 2243\u20132251 (2012)","journal-title":"Lancet"},{"key":"4799_CR18","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","volume":"110","author":"E Christodoulou","year":"2019","unstructured":"E. Christodoulou et al., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 12\u201322 (2019)","journal-title":"J. Clin. Epidemiol."},{"issue":"1","key":"4799_CR19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"R.F. Wolff, PROBAST Group et al., A tool to assess the risk of bias and applicability of prediction model studies. Ann. Intern. Med. 170(1), 51\u201358 (2019)","journal-title":"Ann. Intern. Med."},{"issue":"6","key":"4799_CR20","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"A.J. Vickers et al., Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26(6), 565\u2013574 (2006)","journal-title":"Med. Decis. Mak."},{"issue":"1","key":"4799_CR21","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","volume":"17","author":"Ben","year":"2019","unstructured":"Ben et al., Calibration: the Achilles heel of predictive analytics. BMC Med. 17(1), 230 (2019)","journal-title":"BMC Med."},{"key":"4799_CR22","first-page":"i3140","volume":"353","author":"D Richard","year":"2016","unstructured":"D. Richard et al., External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 353, i3140 (2016)","journal-title":"BMJ"}],"container-title":["Endocrine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12020-026-04799-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12020-026-04799-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12020-026-04799-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:03:06Z","timestamp":1791676986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12020-026-04799-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,10]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4799"],"URL":"https:\/\/doi.org\/10.1007\/s12020-026-04799-x","relation":{},"ISSN":["1559-0100"],"issn-type":[{"value":"1559-0100","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,10]]},"assertion":[{"value":"3 July 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was based on publicly available and de-identified data from the China Health and Retirement Longitudinal Study (CHARLS). Ethical approval for all CHARLS waves was granted by the Institutional Review Board of Peking University. The approval number for the main household survey, including anthropometric measurements, was IRB00001052-11015, and the approval number for biomarker collection was IRB00001052-11014. Written informed consent was obtained from all participants before participation in the CHARLS survey. As the present study was a secondary analysis of anonymized public data, no additional ethical approval was required.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"342"}}