{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T19:36:07Z","timestamp":1770320167645,"version":"3.49.0"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:00:00Z","timestamp":1769212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176016, 72274127"],"award-info":[{"award-number":["62176016, 72274127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82330021, 82061160372, 82270771"],"award-info":[{"award-number":["82330021, 82061160372, 82270771"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioData Mining"],"DOI":"10.1186\/s13040-026-00520-w","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T08:16:13Z","timestamp":1769242573000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An online non-radiographic osteoporosis prediction calculator constructed using interpretable machine learning"],"prefix":"10.1186","volume":"19","author":[{"given":"Yuqi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peibiao","family":"Mai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minnan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"issue":"10169","key":"520_CR1","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/S0140-6736(18)32112-3","volume":"393","author":"JE Compston","year":"2019","unstructured":"Compston JE, McClung MR, Leslie WD, Osteoporosis. Lancet. 2019;393(10169):364\u201376.","journal-title":"Lancet"},{"issue":"1","key":"520_CR2","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s41413-023-00306-4","volume":"12","author":"YY Zhang","year":"2024","unstructured":"Zhang YY, Xie N, Sun XD, et al. Insights and implications of sexual dimorphism in osteoporosis. Bone Res. 2024;12(1):8.","journal-title":"Bone Res"},{"issue":"10","key":"520_CR3","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1007\/s00198-022-06454-3","volume":"33","author":"PL Xiao","year":"2022","unstructured":"Xiao PL, Cui AY, Hsu CJ, et al. Global, regional prevalence, and risk factors of osteoporosis according to the world health organization diagnostic criteria: a systematic review and meta-analysis. Osteoporos Int. 2022;33(10):2137\u201353.","journal-title":"Osteoporos Int"},{"key":"520_CR4","doi-asserted-by":"crossref","unstructured":"Papadopoulou SK, Papadimitriou K, Voulgaridou G, et al. Exercise and nutrition impact on osteoporosis and Sarcopenia-the incidence of osteosarcopenia: a narrative review. Nutrients 2021;13(12).","DOI":"10.3390\/nu13124499"},{"key":"520_CR5","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.bone.2016.03.006","volume":"87","author":"EM Curtis","year":"2016","unstructured":"Curtis EM, van der Velde R, Moon RJ, et al. Epidemiology of fractures in the United Kingdom 1988\u20132012: variation with age, sex, geography, ethnicity and socioeconomic status. Bone. 2016;87:19\u201326.","journal-title":"Bone"},{"key":"520_CR6","unstructured":"International Osteoporosis F. Epidemiology of osteoporosis and fragility fractures. 2022."},{"issue":"1","key":"520_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/jbmr.3039","volume":"32","author":"SR Cummings","year":"2017","unstructured":"Cummings SR, Cosman F, Lewiecki EM, et al. Goal-Directed treatment for osteoporosis: A progress report from the ASBMR-NOF working group on Goal-Directed treatment for osteoporosis. J Bone Min Res. 2017;32(1):3\u201310.","journal-title":"J Bone Min Res"},{"key":"520_CR8","doi-asserted-by":"publisher","first-page":"e40179","DOI":"10.2196\/40179","volume":"25","author":"B Suh","year":"2023","unstructured":"Suh B, Yu H, Kim H, et al. Interpretable Deep-Learning approaches for osteoporosis risk screening and individualized feature analysis using large Population-Based data: model development and performance evaluation. J Med Internet Res. 2023;25:e40179.","journal-title":"J Med Internet Res"},{"issue":"Suppl 1","key":"520_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4158\/GL-2020-0524SUPPL","volume":"26","author":"PM Camacho","year":"2020","unstructured":"Camacho PM, Petak SM, Binkley N, et al. American association of clinical Endocrinologists\/American college of endocrinology clinical practice guidelines for the diagnosis and treatment of postmenopausal Osteoporosis-2020 update. Endocr Pract. 2020;26(Suppl 1):1\u201346.","journal-title":"Endocr Pract"},{"key":"520_CR10","doi-asserted-by":"crossref","unstructured":"Li CC, Ou LC, Chang YF, et al. Performance and interventional cutoffs of osteoporosis self-assessment tools in the community: implications for screening and early referral. Osteoporos Int. 2025.","DOI":"10.1007\/s00198-025-07656-1"},{"issue":"1","key":"520_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s11657-020-00871-9","volume":"16","author":"JA Kanis","year":"2021","unstructured":"Kanis JA, Norton N, Harvey NC, et al. SCOPE 2021: a new scorecard for osteoporosis in Europe. Arch Osteoporos. 2021;16(1):82.","journal-title":"Arch Osteoporos"},{"key":"520_CR12","doi-asserted-by":"crossref","unstructured":"Looker AC, Melton LJ 3rd, Harris TB, Borrud LG, Shepherd JA. Prevalence and trends in low femur bone density among older US adults: NHANES 2005\u20132006 compared with NHANES III. J Bone Min Res. 2010;25(1):64\u201371.","DOI":"10.1359\/jbmr.090706"},{"key":"520_CR13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s40842-018-0062-7","volume":"4","author":"P Choksi","year":"2018","unstructured":"Choksi P, Jepsen KJ, Clines GA. The challenges of diagnosing osteoporosis and the limitations of currently available tools. Clin Diabetes Endocrinol. 2018;4:12.","journal-title":"Clin Diabetes Endocrinol"},{"issue":"2","key":"520_CR14","doi-asserted-by":"publisher","first-page":"101561","DOI":"10.1016\/j.jocd.2025.101561","volume":"28","author":"SN Emir","year":"2025","unstructured":"Emir SN, Guner G. Evaluation of lumbar vertebral bone quality using T1-weighted MRI: can it differentiate normal, osteopenia, and osteoporosis? J Clin Densitom. 2025;28(2):101561.","journal-title":"J Clin Densitom"},{"issue":"12","key":"520_CR15","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.1007\/s00198-022-06491-y","volume":"33","author":"J Yang","year":"2022","unstructured":"Yang J, Liao M, Wang Y, et al. Opportunistic osteoporosis screening using chest CT with artificial intelligence. Osteoporos Int. 2022;33(12):2547\u201361.","journal-title":"Osteoporos Int"},{"key":"520_CR16","doi-asserted-by":"publisher","first-page":"101876","DOI":"10.1016\/j.eclinm.2023.101876","volume":"58","author":"GH Li","year":"2023","unstructured":"Li GH, Cheung CL, Tan KC, et al. Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study. EClinicalMedicine. 2023;58:101876.","journal-title":"EClinicalMedicine"},{"issue":"22","key":"520_CR17","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1056\/NEJMcp070341","volume":"356","author":"S Khosla","year":"2007","unstructured":"Khosla S, Melton LJ. 3rd. Clinical practice. Osteopenia. N Engl J Med. 2007;356(22):2293\u2013300.","journal-title":"N Engl J Med"},{"issue":"3","key":"520_CR18","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.afos.2019.09.001","volume":"5","author":"LS Toh","year":"2019","unstructured":"Toh LS, Lai PSM, Wu DB, et al. A comparison of 6 osteoporosis risk assessment tools among postmenopausal women in Kuala Lumpur, Malaysia. Osteoporos Sarcopenia. 2019;5(3):87\u201393.","journal-title":"Osteoporos Sarcopenia"},{"key":"520_CR19","doi-asserted-by":"publisher","first-page":"102584","DOI":"10.1016\/j.eclinm.2024.102584","volume":"71","author":"TG Petersen","year":"2024","unstructured":"Petersen TG, Abrahamsen B, Hoiberg M, et al. Ten-year follow-up of fracture risk in a systematic population-based screening program: the risk-stratified osteoporosis strategy evaluation (ROSE) randomised trial. EClinicalMedicine. 2024;71:102584.","journal-title":"EClinicalMedicine"},{"issue":"1","key":"520_CR20","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","volume":"181","author":"J Goecks","year":"2020","unstructured":"Goecks J, Jalili V, Heiser LM, Gray JW. How machine learning will transform biomedicine. Cell. 2020;181(1):92\u2013101.","journal-title":"Cell"},{"issue":"1","key":"520_CR21","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13040-024-00363-3","volume":"17","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Li S, Wu W, et al. Machine-learning-based models to predict cardiovascular risk using oculomics and clinic variables in KNHANES. BioData Min. 2024;17(1):12.","journal-title":"BioData Min"},{"issue":"1","key":"520_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12911-025-02880-5","volume":"25","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Li S, Mai P, et al. A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR. BMC Med Inf Decis Mak. 2025;25(1):51.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"13","key":"520_CR23","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1056\/NEJMra2302038","volume":"388","author":"CJ Haug","year":"2023","unstructured":"Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical Medicine, 2023. N Engl J Med. 2023;388(13):1201\u20138.","journal-title":"N Engl J Med"},{"issue":"1","key":"520_CR24","first-page":"58","volume":"16","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Yu M, Tong C, Zhao Y, Han J. CA-UNet segmentation makes a good ischemic stroke risk prediction. Interdisciplinary Sciences: Comput Life Sci. 2024;16(1):58\u201372.","journal-title":"Interdisciplinary Sciences: Comput Life Sci"},{"issue":"1","key":"520_CR25","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1093\/ije\/dyt228","volume":"43","author":"S Kweon","year":"2014","unstructured":"Kweon S, Kim Y, Jang MJ, et al. Data resource profile: the Korea National health and nutrition examination survey (KNHANES). Int J Epidemiol. 2014;43(1):69\u201377.","journal-title":"Int J Epidemiol"},{"issue":"11","key":"520_CR26","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1093\/pm\/pnab085","volume":"22","author":"TK Yoo","year":"2021","unstructured":"Yoo TK, Oh E. Association between dry eye syndrome and osteoarthritis severity: A nationwide Cross-Sectional study (KNHANES V). Pain Med. 2021;22(11):2525\u201332.","journal-title":"Pain Med"},{"issue":"2","key":"520_CR27","first-page":"42","volume":"18","author":"JS Lee","year":"2013","unstructured":"Lee JS, Jang S. A study on reference values and prevalence of osteoporosis in korea: the Korea National health and nutrition examination survey 2008\u20132011. J Korean Official Stat. 2013;18(2):42\u201365.","journal-title":"J Korean Official Stat"},{"issue":"3","key":"520_CR28","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/s001980050281","volume":"11","author":"JA Kanis","year":"2000","unstructured":"Kanis JA, Gluer CC. An update on the diagnosis and assessment of osteoporosis with densitometry. Committee of scientific Advisors, international osteoporosis foundation. Osteoporos Int. 2000;11(3):192\u2013202.","journal-title":"Osteoporos Int"},{"key":"520_CR29","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.jad.2021.03.001","volume":"286","author":"E Choi","year":"2021","unstructured":"Choi E, Choi KW, Jeong HG, et al. Long working hours and depressive symptoms: moderation by gender, income, and job status. J Affect Disord. 2021;286:99\u2013107.","journal-title":"J Affect Disord"},{"key":"520_CR30","doi-asserted-by":"crossref","unstructured":"Oh H, Kim J, Huh Y, Kim SH, Jang SI. Association of household income level with vitamin and mineral intake. Nutrients 2021;14(1).","DOI":"10.3390\/nu14010038"},{"issue":"Pt A","key":"520_CR31","doi-asserted-by":"publisher","first-page":"113080","DOI":"10.1016\/j.envres.2022.113080","volume":"212","author":"J Oh","year":"2022","unstructured":"Oh J, Ye S, Kang DH, Ha E. Association between exposure to fine particulate matter and kidney function: results from the Korea National health and nutrition examination survey. Environ Res. 2022;212(Pt A):113080.","journal-title":"Environ Res"},{"issue":"17","key":"520_CR32","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1016\/j.jacc.2022.02.040","volume":"79","author":"HH Lee","year":"2022","unstructured":"Lee HH, Lee H, Townsend RR, Kim DW, Park S, Kim HC. Cardiovascular implications of the 2021 KDIGO blood pressure guideline for adults with chronic kidney disease. J Am Coll Cardiol. 2022;79(17):1675\u201386.","journal-title":"J Am Coll Cardiol"},{"key":"520_CR33","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30."},{"issue":"1","key":"520_CR34","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, et al. From local explanations to global Understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56\u2013672522.","journal-title":"Nat Mach Intell"},{"key":"520_CR35","doi-asserted-by":"publisher","first-page":"e077169","DOI":"10.1136\/bmj-2023-077169","volume":"384","author":"S Gupta","year":"2024","unstructured":"Gupta S, Glezerman IG, Hirsch JS, et al. Derivation and external validation of a simple risk score for predicting severe acute kidney injury after intravenous cisplatin: cohort study. BMJ. 2024;384:e077169.","journal-title":"BMJ"},{"issue":"12","key":"520_CR36","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1016\/S2213-8587(18)30288-2","volume":"6","author":"K Bhaskaran","year":"2018","unstructured":"Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6(12):944\u201353.","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"12","key":"520_CR37","doi-asserted-by":"publisher","first-page":"2457","DOI":"10.1016\/j.clnu.2023.10.013","volume":"42","author":"Y Liu","year":"2023","unstructured":"Liu Y, Liu Y, Huang Y, et al. The effect of overweight or obesity on osteoporosis: A systematic review and meta-analysis. Clin Nutr. 2023;42(12):2457\u201367.","journal-title":"Clin Nutr"},{"key":"520_CR38","doi-asserted-by":"crossref","unstructured":"Lin YJ, Liang WM, Chiou JS, et al. Genetic predisposition to bone mineral density and their health conditions in East Asians. J Bone Min Res. 2024.","DOI":"10.1093\/jbmr\/zjae078"},{"issue":"2","key":"520_CR39","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11914-024-00864-4","volume":"22","author":"E Gruneisen","year":"2024","unstructured":"Gruneisen E, Kremer R, Duque G. Fat as a friend or foe of the bone. Curr Osteoporos Rep. 2024;22(2):245\u201356.","journal-title":"Curr Osteoporos Rep"},{"key":"520_CR40","doi-asserted-by":"publisher","first-page":"k2575","DOI":"10.1136\/bmj.k2575","volume":"362","author":"DH Lee","year":"2018","unstructured":"Lee DH, Keum N, Hu FB, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575.","journal-title":"BMJ"},{"issue":"4","key":"520_CR41","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1111\/jcpe.13248","volume":"47","author":"M Romandini","year":"2020","unstructured":"Romandini M, Shin HS, Romandini P, Lafori A, Cordaro M. Hormone-related events and periodontitis in women. J Clin Periodontol. 2020;47(4):429\u201341.","journal-title":"J Clin Periodontol"},{"issue":"11","key":"520_CR42","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1001\/jama.2023.0180","volume":"329","author":"S Glynne","year":"2023","unstructured":"Glynne S, Newson L, Reisel D. Hormone therapy for the prevention of chronic conditions in postmenopausal persons. JAMA. 2023;329(11):940\u20131.","journal-title":"JAMA"},{"issue":"1","key":"520_CR43","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/s11657-022-01061-5","volume":"17","author":"CL Gregson","year":"2022","unstructured":"Gregson CL, Armstrong DJ, Bowden J, et al. UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos. 2022;17(1):58.","journal-title":"Arch Osteoporos"},{"key":"520_CR44","first-page":"422","volume":"38","author":"H Ji","year":"2024","unstructured":"Ji H, Shen G, Liu H, et al. Biodegradable Zn-2Cu-0.5Zr alloy promotes the bone repair of senile osteoporotic fractures via the immune-modulation of macrophages. Bioact Mater. 2024;38:422\u201337.","journal-title":"Bioact Mater"},{"issue":"2","key":"520_CR45","doi-asserted-by":"publisher","first-page":"224","DOI":"10.7326\/M22-1034","volume":"176","author":"A Qaseem","year":"2023","unstructured":"Qaseem A, Hicks LA, Etxeandia-Ikobaltzeta I, et al. Pharmacologic treatment of primary osteoporosis or low bone mass to prevent fractures in adults: A living clinical guideline from the American college of physicians. Ann Intern Med. 2023;176(2):224\u201338.","journal-title":"Ann Intern Med"},{"key":"520_CR46","doi-asserted-by":"publisher","first-page":"110289","DOI":"10.1016\/j.compbiomed.2025.110289","volume":"192","author":"FR Carvalho","year":"2025","unstructured":"Carvalho FR, Gavaia PJ. Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data. Comput Biol Med. 2025;192:110289.","journal-title":"Comput Biol Med"},{"issue":"Pt A","key":"520_CR47","doi-asserted-by":"publisher","first-page":"110711","DOI":"10.1016\/j.compbiomed.2025.110711","volume":"196","author":"FR Carvalho","year":"2025","unstructured":"Carvalho FR, Gavaia PJ. Letter to the editor: robustness of osteoporosis risk prediction models with enhanced statistical analyses. Comput Biol Med. 2025;196(Pt A):110711.","journal-title":"Comput Biol Med"},{"issue":"1","key":"520_CR48","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1038\/s41392-024-01929-7","volume":"9","author":"Y Shi","year":"2024","unstructured":"Shi Y, Ma J, Li S, et al. Sex difference in human diseases: mechanistic insights and clinical implications. Signal Transduct Target Ther. 2024;9(1):238.","journal-title":"Signal Transduct Target Ther"},{"issue":"5","key":"520_CR49","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1161\/ATVBAHA.116.307301","volume":"37","author":"AP Arnold","year":"2017","unstructured":"Arnold AP, Cassis LA, Eghbali M, Reue K, Sandberg K. Sex hormones and sex chromosomes cause sex differences in the development of cardiovascular diseases. Arterioscler Thromb Vasc Biol. 2017;37(5):746\u201356.","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"12","key":"520_CR50","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1038\/nrendo.2013.179","volume":"9","author":"SC Manolagas","year":"2013","unstructured":"Manolagas SC, O\u2019Brien CA, Almeida M. The role of Estrogen and androgen receptors in bone health and disease. Nat Rev Endocrinol. 2013;9(12):699\u2013712.","journal-title":"Nat Rev Endocrinol"},{"issue":"4","key":"520_CR51","doi-asserted-by":"publisher","first-page":"e245423","DOI":"10.1001\/jamanetworkopen.2024.5423","volume":"7","author":"TXM Tran","year":"2024","unstructured":"Tran TXM, Chang Y, Choi HR, et al. Adiposity, body composition Measures, and breast cancer risk in Korean premenopausal women. JAMA Netw Open. 2024;7(4):e245423.","journal-title":"JAMA Netw Open"},{"issue":"24","key":"520_CR52","doi-asserted-by":"publisher","first-page":"2145","DOI":"10.1093\/eurheartj\/ehae206","volume":"45","author":"Y Lv","year":"2024","unstructured":"Lv Y, Zhang Y, Li X, et al. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study. Eur Heart J. 2024;45(24):2145\u201354.","journal-title":"Eur Heart J"},{"issue":"6","key":"520_CR53","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1111\/j.1525-139X.2007.00349.x","volume":"20","author":"DS Schmidt","year":"2007","unstructured":"Schmidt DS, Salahudeen AK. Obesity-survival paradox-still a controversy? Semin Dial. 2007;20(6):486\u201392.","journal-title":"Semin Dial"},{"issue":"1","key":"520_CR54","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1186\/s12889-023-16711-7","volume":"23","author":"M Dai","year":"2023","unstructured":"Dai M, Xia B, Xu J, Zhao W, Chen D, Wang X. Association of waist-calf circumference ratio, waist circumference, calf circumference, and body mass index with all-cause and cause-specific mortality in older adults: a cohort study. BMC Public Health. 2023;23(1):1777.","journal-title":"BMC Public Health"},{"issue":"3","key":"520_CR55","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1210\/endrev\/bnac031","volume":"44","author":"M Schini","year":"2023","unstructured":"Schini M, Vilaca T, Gossiel F, Salam S, Eastell R. Bone turnover markers: basic biology to clinical applications. Endocr Rev. 2023;44(3):417\u201373.","journal-title":"Endocr Rev"},{"key":"520_CR56","doi-asserted-by":"crossref","unstructured":"Zhang D, Wang X, Sun K, et al. Onion (Allium Cepa L.) flavonoid extract ameliorates osteoporosis in rats facilitating osteoblast proliferation and differentiation in MG-63 cells and inhibiting RANKL-induced osteoclastogenesis in RAW 264.7 cells. Int J Mol Sci. 2024;25(12).","DOI":"10.3390\/ijms25126754"},{"key":"520_CR57","doi-asserted-by":"publisher","first-page":"117424","DOI":"10.1016\/j.atherosclerosis.2023.117424","volume":"388","author":"H Deng","year":"2024","unstructured":"Deng H, Li H, Liu Z, et al. Pro-osteogenic role of interleukin-22 in calcific aortic valve disease. Atherosclerosis. 2024;388:117424.","journal-title":"Atherosclerosis"},{"key":"520_CR58","doi-asserted-by":"crossref","unstructured":"Duan JY, You RX, Zhou Y, et al. Assessment of causal association between the socio-economic status and osteoporosis and fractures: a bidirectional mendelian randomization study in European population. J Bone Min Res. 2024.","DOI":"10.1093\/jbmr\/zjae060"},{"key":"520_CR59","doi-asserted-by":"publisher","first-page":"e46791","DOI":"10.2196\/46791","volume":"6","author":"Q Yang","year":"2023","unstructured":"Yang Q, Cheng H, Qin J, et al. A machine Learning-Based preclinical osteoporosis screening tool (POST): model development and validation study. JMIR Aging. 2023;6:e46791.","journal-title":"JMIR Aging"},{"issue":"3","key":"520_CR60","doi-asserted-by":"publisher","first-page":"391","DOI":"10.15288\/jsad.2018.79.391","volume":"79","author":"JW LaBrie","year":"2018","unstructured":"LaBrie JW, Boyle S, Earle A, Almstedt HC. Heavy episodic drinking is associated with poorer bone health in adolescent and young adult women. J Stud Alcohol Drugs. 2018;79(3):391\u20138.","journal-title":"J Stud Alcohol Drugs"},{"issue":"21","key":"520_CR61","doi-asserted-by":"publisher","first-page":"e162","DOI":"10.3346\/jkms.2023.38.e162","volume":"38","author":"X Wu","year":"2023","unstructured":"Wu X, Park S. A prediction model for osteoporosis risk using a Machine-Learning approach and its validation in a large cohort. J Korean Med Sci. 2023;38(21):e162.","journal-title":"J Korean Med Sci"},{"issue":"1","key":"520_CR62","doi-asserted-by":"publisher","first-page":"55","DOI":"10.7326\/M14-0697","volume":"162","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55\u201363.","journal-title":"Ann Intern Med"},{"issue":"8","key":"520_CR63","doi-asserted-by":"publisher","first-page":"e2227779","DOI":"10.1001\/jamanetworkopen.2022.27779","volume":"5","author":"JH Lu","year":"2022","unstructured":"Lu JH, Callahan A, Patel BS, et al. Assessment of adherence to reporting guidelines by commonly used clinical prediction models from a single vendor: A systematic review. JAMA Netw Open. 2022;5(8):e2227779.","journal-title":"JAMA Netw Open"},{"key":"520_CR64","doi-asserted-by":"crossref","unstructured":"Kamran F, Tjandra D, Heiler A, et al. Evaluation of sepsis prediction models before onset of treatment. NEJM AI 2024: AIoa2300032%@ 2836\u20139386.","DOI":"10.1056\/AIoa2300032"},{"issue":"4","key":"520_CR65","doi-asserted-by":"publisher","first-page":"e249640","DOI":"10.1001\/jamanetworkopen.2024.9640","volume":"7","author":"SD Fihn","year":"2024","unstructured":"Fihn SD, Berlin JA, Haneuse S, Rivara FP. Prediction models and clinical Outcomes-A call for papers. JAMA Netw Open. 2024;7(4):e249640.","journal-title":"JAMA Netw Open"}],"container-title":["BioData Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13040-026-00520-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00520-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13040-026-00520-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:03:33Z","timestamp":1770271413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13040-026-00520-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,24]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["520"],"URL":"https:\/\/doi.org\/10.1186\/s13040-026-00520-w","relation":{},"ISSN":["1756-0381"],"issn-type":[{"value":"1756-0381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,24]]},"assertion":[{"value":"31 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2026","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 study protocol was approved by the Institutional Review Board of the Korean Center for Disease Control and Prevention (No. 2008\u201304EXP-01-C, 2009\u201301CON-03-C, 2010\u201302CON-21-C and 2011\u201302CON-06-C). The raw data sets are publicly available through the KNHANES website, and data collection from the KNHANES dataset was approved by the Institutional Review Board of the Korean National Institute for Bioethics Policy, which waived the requirement for informed consent for this study. The study adhered to the tenets of the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have read and agreed to the published version of the manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}