{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T19:38:40Z","timestamp":1793907520457,"version":"4.8.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-21831-8","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T13:13:44Z","timestamp":1761830024000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and validation of a type 2 diabetes mellitus prediction tool using a large Japanese regional insurance claims database"],"prefix":"10.1038","volume":"15","author":[{"given":"Tatsunori","family":"Satoh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eiji","family":"Nakatani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Ariyasu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chihiro","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuri","family":"Oshiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emi","family":"Ohata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Itoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaori","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Usui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"21831_CR1","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.diabres.2018.02.023","volume":"138","author":"N Cho","year":"2018","unstructured":"Cho, N. et al. IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 138, 271\u2013281 (2018).","journal-title":"Diabetes Res. Clin. Pract."},{"key":"21831_CR2","doi-asserted-by":"publisher","first-page":"491","DOI":"10.2217\/fca-2018-0045","volume":"14","author":"RJ Henning","year":"2018","unstructured":"Henning, R. J. Type-2 diabetes mellitus and cardiovascular disease. Future Cardiol. 14, 491\u2013509 (2018).","journal-title":"Future Cardiol."},{"key":"21831_CR3","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1001\/jama.287.19.2570","volume":"287","author":"JA Beckman","year":"2002","unstructured":"Beckman, J. A., Creager, M. A. & Libby, P. Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. JAMA 287, 2570\u20132581 (2002).","journal-title":"JAMA"},{"key":"21831_CR4","doi-asserted-by":"publisher","first-page":"e0134321","DOI":"10.1371\/journal.pone.0134321","volume":"10","author":"S Song","year":"2015","unstructured":"Song, S. et al. Long-Term diabetes mellitus is associated with an increased risk of pancreatic cancer: A Meta-Analysis. PLoS One. 10, e0134321 (2015).","journal-title":"PLoS One"},{"key":"21831_CR5","doi-asserted-by":"publisher","first-page":"963","DOI":"10.2337\/dc17-1962","volume":"41","author":"C Bommer","year":"2018","unstructured":"Bommer, C. et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 41, 963\u2013970 (2018).","journal-title":"Diabetes Care"},{"key":"21831_CR6","doi-asserted-by":"publisher","first-page":"440","DOI":"10.2337\/dci17-0005","volume":"40","author":"RR Kalyani","year":"2017","unstructured":"Kalyani, R. R., Golden, S. H. & Cefalu, W. T. Diabetes and aging: unique considerations and goals of care. Diabetes Care. 40, 440\u2013443 (2017).","journal-title":"Diabetes Care"},{"key":"21831_CR7","doi-asserted-by":"publisher","first-page":"e0142779","DOI":"10.1371\/journal.pone.0142779","volume":"10","author":"A Nanri","year":"2015","unstructured":"Nanri, A. et al. Development of risk score for predicting 3-Year incidence of type 2 diabetes: Japan epidemiology collaboration on occupational health study. PLoS One. 10, e0142779 (2015).","journal-title":"PLoS One"},{"key":"21831_CR8","unstructured":"The National Health and Nutrition Survey Japan. Ministry of Health, Labour and Walfare. (2019). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes\u00a0(2019)."},{"key":"21831_CR9","doi-asserted-by":"publisher","first-page":"11","DOI":"10.2337\/cd18-0105","volume":"37","author":"American Diabetes Association","year":"2019","unstructured":"American Diabetes Association. Standards of medical care in Diabetes-2019 abridged for primary care providers. Clin. Diabetes. 37, 11\u201334 (2019).","journal-title":"Clin. Diabetes"},{"key":"21831_CR10","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.2337\/dc20-0717","volume":"43","author":"J Leal","year":"2020","unstructured":"Leal, J. et al. Benchmarking the Cost-Effectiveness of interventions delaying diabetes: A simulation study based on NAVIGATOR data. Diabetes Care. 43, 2485\u20132492 (2020).","journal-title":"Diabetes Care"},{"key":"21831_CR11","doi-asserted-by":"publisher","first-page":"S73","DOI":"10.1016\/j.jval.2018.04.490","volume":"21","author":"NH Bhanpuri","year":"2018","unstructured":"Bhanpuri, N. H. et al. Estimated reduction in medication cost during first year of a continuous care intervention for treatment of type 2 diabetes. Value Health. 21, S73 (2018).","journal-title":"Value Health"},{"key":"21831_CR12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12902-019-0436-6","volume":"19","author":"H Lai","year":"2019","unstructured":"Lai, H., Huang, H., Keshavjee, K., Guergachi, A. & Gao, X. Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr. Disord. 19, 101 (2019).","journal-title":"BMC Endocr. Disord"},{"key":"21831_CR13","doi-asserted-by":"publisher","first-page":"e001223","DOI":"10.1136\/bmjdrc-2020-001223","volume":"8","author":"M Vettoretti","year":"2020","unstructured":"Vettoretti, M. et al. Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions. BMJ Open. Diabetes Res. Care 8, e001223 (2020).","journal-title":"BMJ Open. Diabetes Res. Care"},{"key":"21831_CR14","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1016\/j.jdiacomp.2017.01.005","volume":"31","author":"N Yokota","year":"2017","unstructured":"Yokota, N. et al. Predictive models for conversion of prediabetes to diabetes. J. Diabetes Complications. 31, 1266\u20131271 (2017).","journal-title":"J. Diabetes Complications"},{"key":"21831_CR15","first-page":"e920880","volume":"26","author":"H Wang","year":"2020","unstructured":"Wang, H. et al. A retrospective population study to develop a predictive model of prediabetes and incident type 2 diabetes mellitus from a hospital database in Japan between 2004 and 2015. Med. Sci. Monit. 26, e920880 (2020).","journal-title":"Med. Sci. Monit."},{"key":"21831_CR16","doi-asserted-by":"publisher","first-page":"170","DOI":"10.2188\/jea.JE20220329","volume":"34","author":"J Xu","year":"2024","unstructured":"Xu, J. et al. Development and validation of prediction models for the 5-year risk of type 2 diabetes in a Japanese population: Japan public health Center-based prospective (JPHC) diabetes study. J. Epidemiol. 34, 170\u2013179 (2024).","journal-title":"J. Epidemiol."},{"key":"21831_CR17","doi-asserted-by":"publisher","DOI":"10.2188\/jea.JE20200480","author":"E Nakatani","year":"2021","unstructured":"Nakatani, E., Tabara, Y., Sato, Y., Tsuchiya, A. & Miyachi, Y. Data resource profile of shizuoka kokuho database (SKDB) using integrated health- and care-insurance claims and health checkups: the shizuoka study. J. Epidemiol. advpub https:\/\/doi.org\/10.2188\/jea.JE20200480 (2021).","journal-title":"J. Epidemiol. advpub"},{"key":"21831_CR18","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1111\/hae.14793","volume":"29","author":"A Shoji-Asahina","year":"2023","unstructured":"Shoji-Asahina, A. et al. Risk factors, treatment and survival rates of late-onset acquired haemophilia A: A cohort study from the Shizuoka Kokuho database. Haemophilia 29, 799\u2013808 (2023).","journal-title":"Haemophilia"},{"key":"21831_CR19","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jdin.2023.04.007","volume":"12","author":"H Hashizume","year":"2023","unstructured":"Hashizume, H., Nakatani, E., Sasaki, H. & Miyachi, Y. Hydrochlorothiazide increases risk of nonmelanoma skin cancer in an elderly Japanese cohort with hypertension: the Shizuoka study. JAAD Int. 12, 49\u201357 (2023).","journal-title":"JAAD Int."},{"key":"21831_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jdin.2022.12.002","volume":"11","author":"N Ubukata","year":"2023","unstructured":"Ubukata, N., Nakatani, E., Hashizume, H., Sasaki, H. & Miyachi, Y. Risk factors and drugs that trigger the onset of Stevens\u2013Johnson syndrome and toxic epidermal necrolysis: A population-based cohort study using the Shizuoka Kokuho database. JAAD Int. 11, 24\u201332 (2023).","journal-title":"JAAD Int."},{"key":"21831_CR21","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","volume":"43","author":"H Quan","year":"2005","unstructured":"Quan, H. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care. 43, 1130\u20131139 (2005).","journal-title":"Med. Care"},{"key":"21831_CR22","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1097\/00005650-199801000-00004","volume":"36","author":"A Elixhauser","year":"1998","unstructured":"Elixhauser, A., Steiner, C., Harris, D. R. & Coffey, R. M. Comorbidity measures for use with administrative data. Med. Care. 36, 8\u201327 (1998).","journal-title":"Med. Care"},{"key":"21831_CR23","doi-asserted-by":"publisher","first-page":"367","DOI":"10.2147\/CLEP.S245555","volume":"12","author":"Y Ono","year":"2020","unstructured":"Ono, Y., Taneda, Y., Takeshima, T., Iwasaki, K. & Yasui, A. Validity of claims diagnosis codes for cardiovascular diseases in diabetes patients in Japanese administrative database. Clin. Epidemiol. 12, 367\u2013375 (2020).","journal-title":"Clin. Epidemiol."},{"key":"21831_CR24","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1007\/s13300-021-01029-7","volume":"12","author":"Y Nagai","year":"2021","unstructured":"Nagai, Y., Kazumori, K., Takeshima, T., Iwasaki, K. & Tanaka, Y. A claims database analysis of dose-dependency of Metformin and incidence of lactic acidosis in Japanese patients with type 2 diabetes. Diabetes Ther. 12, 1129\u20131141 (2021).","journal-title":"Diabetes Ther."},{"key":"21831_CR25","doi-asserted-by":"publisher","first-page":"e0318791","DOI":"10.1371\/journal.pone.0318791","volume":"20","author":"S Sato","year":"2025","unstructured":"Sato, S. et al. High mean corpuscular volume as a predictor of esophageal cancer: A cohort study based on the Japanese Shizuoka Kokuho database. PLoS One. 20, e0318791 (2025).","journal-title":"PLoS One"},{"key":"21831_CR26","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","volume":"15","author":"FE Harrell Jr","year":"1996","unstructured":"Harrell, F. E. Jr, Lee, K. L. & Mark, D. B. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15, 361\u2013387 (1996).","journal-title":"Stat. Med."},{"key":"21831_CR27","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/bmt.2012.244","volume":"48","author":"Y Kanda","year":"2012","unstructured":"Kanda, Y. Investigation of the freely available easy-to-use software \u2018EZR\u2019 for medical statistics. Bone Marrow Transpl. 48, 452\u2013458 (2012).","journal-title":"Bone Marrow Transpl."},{"key":"21831_CR28","doi-asserted-by":"publisher","first-page":"S205","DOI":"10.1016\/S0002-8703(99)70311-X","volume":"138","author":"WB Kannel","year":"1999","unstructured":"Kannel, W. B. Historic perspectives on the relative contributions of diastolic and systolic blood pressure elevation to cardiovascular risk profile. Am. Heart J. 138, S205\u2013S210 (1999).","journal-title":"Am. Heart J."},{"key":"21831_CR29","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1097\/HJH.0000000000003626","volume":"42","author":"Y Sasagawa","year":"2024","unstructured":"Sasagawa, Y. et al. Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia. J. Hypertens. 42, 506\u2013514 (2024).","journal-title":"J. Hypertens."},{"key":"21831_CR30","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/1758-5996-6-14","volume":"6","author":"HR Ahn","year":"2014","unstructured":"Ahn, H. R. et al. The association between liver enzymes and risk of type 2 diabetes: the Namwon study. Diabetol. Metab. Syndr. 6, 14 (2014).","journal-title":"Diabetol. Metab. Syndr."},{"key":"21831_CR31","first-page":"357","volume":"22","author":"A Ruban","year":"2020","unstructured":"Ruban, A. et al. Liver enzymes and risk of stroke: the atherosclerosis risk in communities (ARIC) study. J. Stroke Cerebrovasc. Dis. 22, 357\u2013368 (2020).","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"21831_CR32","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s00592-022-01949-1","volume":"59","author":"J Li","year":"2022","unstructured":"Li, J. et al. Liver enzymes, alcohol consumption and the risk of diabetes: the suita study. Acta Diabetol. 59, 1531\u20131537 (2022).","journal-title":"Acta Diabetol."},{"key":"21831_CR33","doi-asserted-by":"publisher","first-page":"6","DOI":"10.21037\/jlpm-20-93","volume":"6","author":"G Ndrepepa","year":"2021","unstructured":"Ndrepepa, G. Aspartate aminotransferase and cardiovascular disease\u2014a narrative review. J. Lab. Precis Med. 6, 6\u20136 (2021).","journal-title":"J. Lab. Precis Med."},{"key":"21831_CR34","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1186\/1741-7015-9-103","volume":"9","author":"GS Collins","year":"2011","unstructured":"Collins, G. S., Mallett, S., Omar, O. & Yu, L. M. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 9, 103 (2011).","journal-title":"BMC Med."},{"key":"21831_CR35","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.2337\/dc05-2141","volume":"29","author":"W Aekplakorn","year":"2006","unstructured":"Aekplakorn, W. et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care. 29, 1872\u20131877 (2006).","journal-title":"Diabetes Care"},{"key":"21831_CR36","doi-asserted-by":"publisher","first-page":"2116","DOI":"10.2337\/dc09-0197","volume":"32","author":"MB Schulze","year":"2009","unstructured":"Schulze, M. B. et al. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam study. Diabetes Care. 32, 2116\u20132119 (2009).","journal-title":"Diabetes Care"},{"key":"21831_CR37","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1111\/j.1464-5491.2011.03376.x","volume":"29","author":"Y Doi","year":"2012","unstructured":"Doi, Y. et al. Two risk score models for predicting incident type 2 diabetes in Japan. Diabet. Med. 29, 107\u2013114 (2012).","journal-title":"Diabet. Med."},{"key":"21831_CR38","doi-asserted-by":"publisher","first-page":"3213","DOI":"10.1007\/s00125-012-2712-0","volume":"55","author":"Y Heianza","year":"2012","unstructured":"Heianza, Y. et al. Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in japan: the Toranomon hospital health management center study 6 (TOPICS 6). Diabetologia 55, 3213\u20133223 (2012).","journal-title":"Diabetologia"},{"key":"21831_CR39","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1056\/NEJM200003303421301","volume":"342","author":"TW Gress","year":"2000","unstructured":"Gress, T. W., Nieto, F. J., Shahar, E., Wofford, M. R. & Brancati, F. L. Hypertension and antihypertensive therapy as risk factors for type 2 diabetes mellitus. Atherosclerosis risk in communities study. N Engl. J. Med. 342, 905\u2013912 (2000).","journal-title":"N Engl. J. Med."},{"key":"21831_CR40","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1111\/jdi.13306","volume":"11","author":"E Araki","year":"2020","unstructured":"Araki, E. et al. Japanese clinical practice guideline for diabetes 2019. J. Diabetes Investig. 11, 1020\u20131076 (2020).","journal-title":"J. Diabetes Investig"},{"key":"21831_CR41","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.amjms.2016.01.008","volume":"351","author":"RJ Walker","year":"2016","unstructured":"Walker, R. J., Williams, S., Egede, L. E. & J. & Influence of Race, ethnicity and social determinants of health on diabetes outcomes. Am. J. Med. Sci. 351, 366\u2013373 (2016).","journal-title":"Am. J. Med. Sci."},{"key":"21831_CR42","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3349\/ymj.2019.60.2.191","volume":"60","author":"BG Choi","year":"2019","unstructured":"Choi, B. G. et al. Machine learning for the prediction of New-Onset diabetes mellitus during 5-Year Follow-up in Non-Diabetic patients with cardiovascular risks. Yonsei Med. J. 60, 191\u2013199 (2019).","journal-title":"Yonsei Med. J."},{"key":"21831_CR43","doi-asserted-by":"publisher","first-page":"624","DOI":"10.3389\/fendo.2019.00624","volume":"10","author":"B Farran","year":"2019","unstructured":"Farran, B. et al. Use of Non-invasive parameters and Machine-Learning algorithms for predicting future risk of type 2 diabetes: A retrospective cohort study of health data from Kuwait. Front. Endocrinol. 10, 624 (2019).","journal-title":"Front. Endocrinol."},{"key":"21831_CR44","doi-asserted-by":"publisher","first-page":"461","DOI":"10.5694\/j.1326-5377.2007.tb00998.x","volume":"186","author":"SM Twigg","year":"2007","unstructured":"Twigg, S. M. et al. Prediabetes: a position statement from the Australian diabetes society and Australian diabetes educators association. Med. J. Aust. 186, 461\u2013465 (2007).","journal-title":"Med. J. Aust"},{"key":"21831_CR45","doi-asserted-by":"publisher","first-page":"345","DOI":"10.4239\/wjd.v6.i2.345","volume":"6","author":"O Twito","year":"2015","unstructured":"Twito, O., Frankel, M. & Nabriski, D. Impact of glucose level on morbidity and mortality in elderly with diabetes and pre-diabetes. World J. Diabetes. 6, 345\u2013351 (2015).","journal-title":"World J. Diabetes"},{"key":"21831_CR46","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.jamda.2015.04.008","volume":"16","author":"E Garc\u00eda-Esquinas","year":"2015","unstructured":"Garc\u00eda-Esquinas, E. et al. Diabetes and risk of frailty and its potential mechanisms: A prospective cohort study of older adults. J. Am. Med. Dir. Assoc. 16, 748\u2013754 (2015).","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"21831_CR47","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1111\/dme.13132","volume":"34","author":"A Forbes","year":"2017","unstructured":"Forbes, A., Murrells, T. & Sinclair, A. J. Examining factors associated with excess mortality in older people (age\u2009\u2265\u200970 years) with diabetes - a 10-year cohort study of older people with and without diabetes. Diabet. Med. 34, 387\u2013395 (2017).","journal-title":"Diabet. Med."},{"key":"21831_CR48","doi-asserted-by":"publisher","first-page":"e074819","DOI":"10.1136\/bmj-2023-074819","volume":"384","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ 384, e074819 (2024).","journal-title":"BMJ"},{"key":"21831_CR49","doi-asserted-by":"publisher","first-page":"111287","DOI":"10.1016\/j.jclinepi.2024.111287","volume":"169","author":"P Dhiman","year":"2024","unstructured":"Dhiman, P. et al. Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting. J Clin. Epidemiol 169, 111287 (2024).","journal-title":"J Clin. Epidemiol"},{"key":"21831_CR50","doi-asserted-by":"publisher","first-page":"100857","DOI":"10.1016\/j.landig.2025.01.013","volume":"7","author":"RD Riley","year":"2025","unstructured":"Riley, R. D. et al. Importance of sample size on the quality and utility of AI-based prediction models for healthcare. Lancet Digit. Health. 7, 100857 (2025).","journal-title":"Lancet Digit. Health"},{"key":"21831_CR51","doi-asserted-by":"crossref","unstructured":"Steyerberg, E. W. Clinical Prediction Models: A Practical Approach To Development, Validation, and Updating,\u00a0Springer Nature, (2019).","DOI":"10.1007\/978-3-030-16399-0"},{"key":"21831_CR52","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1093\/biomet\/asw049","volume":"103","author":"FH Yong","year":"2016","unstructured":"Yong, F. H., Tian, L., Yu, S., Cai, T. & Wei, L. J. Optimal stratification in outcome prediction using baseline information. Biometrika 103, 817\u2013828 (2016).","journal-title":"Biometrika"},{"key":"21831_CR53","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1186\/2193-1801-3-224","volume":"3","author":"A Bianco","year":"2014","unstructured":"Bianco, A. et al. The surprising influence of family history to type 2 diabetes on anaerobic performance of young male \u00e9lite athletes. Springerplus 3, 224 (2014).","journal-title":"Springerplus"},{"key":"21831_CR54","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.dsx.2012.05.016","volume":"6","author":"DS Prasad","year":"2012","unstructured":"Prasad, D. S., Kabir, Z., Dash, A. K. & Das, B. C. Prevalence and risk factors for diabetes and impaired glucose tolerance in Asian indians: a community survey from urban Eastern India. Diabetes Metab. Syndr. 6, 96\u2013101 (2012).","journal-title":"Diabetes Metab. Syndr."},{"key":"21831_CR55","first-page":"379","volume":"97","author":"F Pomara","year":"2006","unstructured":"Pomara, F., Russo, G. & Gravante, G. Influence of family history to type 2 diabetes on the body composition and homeostasis model assessment: a comparison between young active and sedentary men. Minerva Med. 97, 379\u2013383 (2006).","journal-title":"Minerva Med."},{"key":"21831_CR56","doi-asserted-by":"publisher","first-page":"102","DOI":"10.3109\/03014460.2011.645507","volume":"39","author":"I Zamora-Ginez","year":"2012","unstructured":"Zamora-Ginez, I. et al. Risk factors for diabetes, but not for cardiovascular disease, are associated with family history of type 2 diabetes in subjects from central Mexico. Ann. Hum. Biol. 39, 102\u2013107 (2012).","journal-title":"Ann. Hum. Biol."},{"key":"21831_CR57","doi-asserted-by":"publisher","first-page":"114","DOI":"10.4239\/wjd.v4.i4.114","volume":"4","author":"O Ali","year":"2013","unstructured":"Ali, O. Genetics of type 2 diabetes. World J. Diabetes. 4, 114\u2013123 (2013).","journal-title":"World J. Diabetes"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21831-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21831-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21831-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T14:03:33Z","timestamp":1761833013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-21831-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,30]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["21831"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-21831-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,30]]},"assertion":[{"value":"13 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The study protocol received approval from the ethics committee at Shizuoka Graduate University of Public Health (#SGUPH2021_001_065 \u2212 2), ensuring compliance with all applicable ethical standards. Furthermore, we adhered strictly to the guidelines and regulations approved for this research. Due to the retrospective nature of the study, the ethics committee at Shizuoka Graduate University of Public Health (#SGUPH2021_001_065 \u2212 2) waived the need of obtaining informed consent. Before our access, all patient data were thoroughly anonymized, ensuring the privacy and confidentiality of the subjects\n                      1\n                      .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors declare no conflicts of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"37968"}}