{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:39:44Z","timestamp":1792467584171,"version":"4.5.0"},"reference-count":49,"publisher":"e-Century Publishing Corporation","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int J Clin Exp Pathol"],"DOI":"10.62347\/anwp5930","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:48:02Z","timestamp":1761889682000},"page":"524-534","source":"Crossref","is-referenced-by-count":0,"title":["Application of machine learning on health examination data for predicting the decrease of bone mineral density"],"prefix":"10.62347","volume":"18","author":[{"given":"Bohan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"48694","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.2174\/0929867327666200330142432","article-title":"Osteoporosis: mechanism, molecular target and current status on drug development","volume":"28","author":"Li","year":"2021","unstructured":"Li H, Xiao Z, Quarles LD and Li W. Osteoporosis: mechanism, molecular target and current status on drug development. Curr Med Chem 2021; 28: 1489-1507.","journal-title":"Curr Med Chem"},{"key":"ref1","doi-asserted-by":"publisher","first-page":"117135","DOI":"10.1016\/j.bone.2024.117135","article-title":"Association of coffee and tea consumption with osteoporosis risk: a prospective study from the UK biobank","volume":"186","author":"Zhang","year":"2024","unstructured":"Zhang S, Wu S, Xia B, He Q, Mi N, Zhao J, Hu L, Wang D, Zheng L, Sheng P, Yuan J, Zhang Z and Wei F. Association of coffee and tea consumption with osteoporosis risk: a prospective study from the UK biobank. Bone 2024; 186: 117135.","journal-title":"Bone"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"117168","DOI":"10.1016\/j.bone.2024.117168","article-title":"Associations of sleep behaviors and genetic risk with risk of incident osteoporosis: a prospective cohort study of 293,164 participants","volume":"186","author":"Zhao","year":"2024","unstructured":"Zhao H, Jia H, Jiang Y, Suo C, Liu Z, Chen X and Xu K. Associations of sleep behaviors and genetic risk with risk of incident osteoporosis: a prospective cohort study of 293,164 participants. Bone 2024; 186: 117168.","journal-title":"Bone"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/S0140-6736(02)08657-9","article-title":"Epidemiology and outcomes of osteoporotic fractures","volume":"359","author":"Cummings","year":"2002","unstructured":"Cummings SR and Melton LJ. Epidemiology and outcomes of osteoporotic fractures. Lancet 2002; 359: 1761-1767.","journal-title":"Lancet"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1007\/s00198-022-06459-y","article-title":"Relationship between prevalence and risk of osteoporosis or osteoporotic fracture with non-alcoholic fatty liver disease: a systematic review and meta-analysis","volume":"33","author":"Pan","year":"2022","unstructured":"Pan B, Cai J, Zhao P, Liu J, Fu S, Jing G, Niu Q and Li Q. Relationship between prevalence and risk of osteoporosis or osteoporotic fracture with non-alcoholic fatty liver disease: a systematic review and meta-analysis. Osteoporos Int 2022; 33: 2275-2286.","journal-title":"Osteoporos Int"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1007\/s00198-009-0925-y","article-title":"Osteoporosis in China","volume":"20","author":"Wang","year":"2009","unstructured":"Wang Y, Tao Y, Hyman ME, Li J and Chen Y. Osteoporosis in China. Osteoporos Int 2009; 20: 1651-1662.","journal-title":"Osteoporos Int"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1002\/jbmr.3757","article-title":"The prevalence of osteoporosis in China, a nationwide, multicenter DXA survey","volume":"34","author":"Zeng","year":"2019","unstructured":"Zeng Q, Li N, Wang Q, Feng J, Sun D, Zhang Q, Huang J, Wen Q, Hu R, Wang L, Ma Y, Fu X, Dong S and Cheng X. The prevalence of osteoporosis in China, a nationwide, multicenter DXA survey. J Bone Miner Res 2019; 34: 1789-1797.","journal-title":"J Bone Miner Res"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s00198-015-3093-2","article-title":"Projection of osteoporosis-related fractures and costs in China: 2010-2050","volume":"26","author":"Si","year":"2015","unstructured":"Si L, Winzenberg TM, Jiang Q, Chen M and Palmer AJ. Projection of osteoporosis-related fractures and costs in China: 2010-2050. Osteoporos Int 2015; 26: 1929-1937.","journal-title":"Osteoporos Int"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s00198-004-1627-0","article-title":"An estimate of the worldwide prevalence, mortality and disability associated with hip fracture","volume":"15","author":"Johnell","year":"2004","unstructured":"Johnell O and Kanis JA. An estimate of the worldwide prevalence, mortality and disability associated with hip fracture. Osteoporos Int 2004; 15: 897-902.","journal-title":"Osteoporos Int"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s00198-019-04931-w","article-title":"Opportunistic screening for osteoporosis using thoraco-abdomino-pelvic CT-scan assessing the vertebral density in rheumatoid arthritis patients","volume":"30","author":"Perrier-Cornet","year":"2019","unstructured":"Perrier-Cornet J, Omorou AY, Fauny M, Loeuille D and Chary-Valckenaere I. Opportunistic screening for osteoporosis using thoraco-abdomino-pelvic CT-scan assessing the vertebral density in rheumatoid arthritis patients. Osteoporos Int 2019; 30: 1215-1222.","journal-title":"Osteoporos Int"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1292\/jvms.17-0428","article-title":"Quantitative computed tomographic evaluation of bone mineral density in beagle dogs: comparison with dual-energy x-ray absorptiometry as a gold standard","volume":"80","author":"Kwon","year":"2018","unstructured":"Kwon D, Kim J, Lee H, Kim B, Han H, Oh H, Kim M, Yoon H, Lee B and Eom K. Quantitative computed tomographic evaluation of bone mineral density in beagle dogs: comparison with dual-energy x-ray absorptiometry as a gold standard. J Vet Med Sci 2018; 80: 620-628.","journal-title":"J Vet Med Sci"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.jocd.2017.06.017","article-title":"Quantitative computed tomography-current status and new developments","volume":"20","author":"Engelke","year":"2017","unstructured":"Engelke K. Quantitative computed tomography-current status and new developments. J Clin Densitom 2017; 20: 309-321.","journal-title":"J Clin Densitom"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"Kanis JA. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. WHO Study Group. Osteoporos Int 1994; 4: 368-381.","DOI":"10.1007\/BF01622200"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.1007\/s00198-021-05900-y","article-title":"The clinician&#x2019;s guide to prevention and treatment of osteoporosis","volume":"33","author":"LeBoff","year":"2022","unstructured":"LeBoff MS, Greenspan SL, Insogna KL, Lewiecki EM, Saag KG, Singer AJ and Siris ES. The clinician&#x2019;s guide to prevention and treatment of osteoporosis. Osteoporos Int 2022; 33: 2049-2102.","journal-title":"Osteoporos Int"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s001980170070","article-title":"A simple tool to identify asian women at increased risk of osteoporosis","volume":"12","author":"LK","year":"2001","unstructured":"Koh LK, Sedrine WB, Torralba TP, Kung A, Fujiwara S, Chan SP, Huang QR, Rajatanavin R, Tsai KS, Park HM and Reginster JY; Osteoporosis Self-Assessment Tool for Asians (OSTA) Research Group. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int 2001; 12: 699-705.","journal-title":"Osteoporos Int"},{"key":"ref15","unstructured":"Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF and Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 2020; 9: 14."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1080\/13645706.2019.1575882","article-title":"Introduction to artificial intelligence in medicine","volume":"28","author":"Mintz","year":"2019","unstructured":"Mintz Y and Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28: 73-81.","journal-title":"Minim Invasive Ther Allied Technol"},{"key":"ref17","doi-asserted-by":"publisher","unstructured":"Pickhardt PJ, Nguyen T, Perez AA, Graffy PM, Jang S, Summers RM and Garrett JW. Improved CT-based osteoporosis assessment with a fully automated deep learning tool. Radiol Artif Intell 2022; 4: e220042.","DOI":"10.1148\/ryai.220042"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.1007\/s00330-023-09421-6","article-title":"Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography","volume":"33","author":"Chen","year":"2023","unstructured":"Chen YC, Li YT, Kuo PC, Cheng SJ, Chung YH, Kuo DP and Chen CY. Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography. Eur Radiol 2023; 33: 5097-5106.","journal-title":"Eur Radiol"},{"key":"ref19","doi-asserted-by":"publisher","unstructured":"Yang Q, Cheng H, Qin J, Loke AY, Ngai FW, Chong KC, Zhang D, Gao Y, Wang HH, Liu Z, Hao C and Xie YJ. A machine learning-based Preclinical Osteoporosis Screening Tool (POST): model development and validation study. JMIR Aging 2023; 6: e46791.","DOI":"10.2196\/46791"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"14066","DOI":"10.18632\/aging.103358","article-title":"The achievement of comprehensive control targets among type 2 diabetes mellitus patients of different ages","volume":"12","author":"Yu","year":"2020","unstructured":"Yu Y, Xie K, Lou Q, Xia H, Wu D, Dai L, Hu C, Wang K, Shan S, Hu Y and Tang W. The achievement of comprehensive control targets among type 2 diabetes mellitus patients of different ages. Aging (Albany NY) 2020; 12: 14066-14079.","journal-title":"Aging (Albany NY)"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"606","DOI":"10.3390\/biom13040616","article-title":"Correlation between blood lipid level and osteoporosis in older adults with type 2 diabetes mellitus-a retrospective study based on inpatients in Beijing, China","volume":"13","author":"Zhao","year":"2023","unstructured":"Zhao X, Sun J, Xin S and Zhang X. Correlation between blood lipid level and osteoporosis in older adults with type 2 diabetes mellitus-a retrospective study based on inpatients in Beijing, China. Biomolecules 2023; 13: 606.","journal-title":"Biomolecules"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.biopha.2017.09.122","article-title":"Ameliorative effect of borneol, a natural bicyclic monoterpene against hyperglycemia, hyperlipidemia and oxidative stress in streptozotocin-induced diabetic Wistar rats","volume":"96","author":"Madhuri","year":"2017","unstructured":"Madhuri K and Naik PR. Ameliorative effect of borneol, a natural bicyclic monoterpene against hyperglycemia, hyperlipidemia and oxidative stress in streptozotocin-induced diabetic Wistar rats. Biomed Pharmacother 2017; 96: 336-347.","journal-title":"Biomed Pharmacother"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"898305","DOI":"10.3389\/fcvm.2022.898305","article-title":"Plasma homocysteine level is independently associated with conventional atherogenic lipid profile and remnant cholesterol in adults","volume":"9","author":"Zhou","year":"2022","unstructured":"Zhou L, Liu J, An Y, Wang Y and Wang G. Plasma homocysteine level is independently associated with conventional atherogenic lipid profile and remnant cholesterol in adults. Front Cardiovasc Med 2022; 9: 898305.","journal-title":"Front Cardiovasc Med"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1080\/10641963.2017.1281940","article-title":"A comparative research on obesity hypertension by the comparisons and associations between waist circumference, body mass index with systolic and diastolic blood pressure, and the clinical laboratory data between four special Chinese adult groups","volume":"40","author":"Wu","year":"2018","unstructured":"Wu O, Leng JH, Yang FF, Yang HM, Zhang H, Li ZF, Zhang XY, Yuan CD, Li JJ, Pan Q, Liu W, Ren YJ, Liu B, Liu QM and Cao CJ. A comparative research on obesity hypertension by the comparisons and associations between waist circumference, body mass index with systolic and diastolic blood pressure, and the clinical laboratory data between four special Chinese adult groups. Clin Exp Hypertens 2018; 40: 16-21.","journal-title":"Clin Exp Hypertens"},{"key":"ref25","doi-asserted-by":"publisher","unstructured":"Guan L, Zhang X, Tian H, Jin X, Fan H, Wang N, Sun J, Li D, Li J, Wang X, Zeng Z and Li Y. Prevalence and risk factors of metabolic-associated fatty liver disease during 2014-2018 from three cities of Liaoning province: an epidemiological survey. BMJ Open 2022; 12: e047588.","DOI":"10.1136\/bmjopen-2020-047588"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"4247782","DOI":"10.1155\/2022\/4247782","article-title":"The correlation between serum sclerostin level and arterial stiffness in peritoneal dialysis patients","volume":"2022","author":"Liu","year":"2022","unstructured":"Liu F, Ma H, Ma Y, Zhou W, Wang C and Xiong Y. The correlation between serum sclerostin level and arterial stiffness in peritoneal dialysis patients. Evid Based Complement Alternat Med 2022; 2022: 4247782.","journal-title":"Evid Based Complement Alternat Med"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"18050","DOI":"10.1038\/s41598-020-75073-x","article-title":"Homocysteine level is positively and independently associated with serum creatinine and urea nitrogen levels in old male patients with hypertension","volume":"10","author":"Yang","year":"2020","unstructured":"Yang Q, Lu Y, Deng Y, Xu J and Zhang X. Homocysteine level is positively and independently associated with serum creatinine and urea nitrogen levels in old male patients with hypertension. Sci Rep 2020; 10: 18050.","journal-title":"Sci Rep"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"619","DOI":"10.2147\/DMSO.S167293","article-title":"Effects of Caralluma russeliana stem extract on some physiological parameters in streptozotocin-induced diabetic male rats","volume":"11","author":"Zari","year":"2018","unstructured":"Zari TA and Al-Thebaiti MA. Effects of Caralluma russeliana stem extract on some physiological parameters in streptozotocin-induced diabetic male rats. Diabetes Metab Syndr Obes 2018; 11: 619-631.","journal-title":"Diabetes Metab Syndr Obes"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.htct.2022.09.1277","article-title":"Lipids profile in children and adolescents with &#x3B2;-thalassemia major","volume":"45","author":"Jabbar","year":"2023","unstructured":"Jabbar HK, Hassan MK and Al-Naama LM. Lipids profile in children and adolescents with &#x3B2;-thalassemia major. Hematol Transfus Cell Ther 2023; 45: 467-472.","journal-title":"Hematol Transfus Cell Ther"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"1355287","DOI":"10.3389\/frai.2024.1355287","article-title":"Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction","volume":"7","author":"Qiu","year":"2024","unstructured":"Qiu C, Su K, Luo Z, Tian Q, Zhao L, Wu L, Deng H and Shen H. Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction. Front Artif Intell 2024; 7: 1355287.","journal-title":"Front Artif Intell"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1111\/jmwh.12954","article-title":"Clinical update on osteoporosis","volume":"64","author":"Anthamatten","year":"2019","unstructured":"Anthamatten A and Parish A. Clinical update on osteoporosis. J Midwifery Womens Health 2019; 64: 265-275.","journal-title":"J Midwifery Womens Health"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/gye.16.3.245.250","article-title":"Development and assessment of the Osteoporosis Index of Risk (OSIRIS) to facilitate selection of women for bone densitometry","volume":"16","author":"Sedrine","year":"2002","unstructured":"Sedrine WB, Chevallier T, Zegels B, Kvasz A, Micheletti MC, Gelas B and Reginster JY. Development and assessment of the Osteoporosis Index of Risk (OSIRIS) to facilitate selection of women for bone densitometry. Gynecol Endocrinol 2002; 16: 245-250.","journal-title":"Gynecol Endocrinol"},{"key":"ref33","unstructured":"Lydick E, Cook K, Turpin J, Melton M, Stine R and Byrnes C. Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Manag Care 1998; 4: 37-48."},{"key":"ref34","unstructured":"Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA and Tu JV. Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. CMAJ 2000; 162: 1289-1294."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1002\/cpt.1796","article-title":"An Introduction to machine learning","volume":"107","author":"Badillo","year":"2020","unstructured":"Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam-Thong T, Siebourg-Polster J, Steiert B and Zhang JD. An Introduction to machine learning. Clin Pharmacol Ther 2020; 107: 871-885.","journal-title":"Clin Pharmacol Ther"},{"key":"ref36","doi-asserted-by":"publisher","unstructured":"Kim SK, Yoo TK, Oh E and Kim DW. Osteoporosis risk prediction using machine learning and conventional methods. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013: 188-191.","DOI":"10.1109\/EMBC.2013.6609469"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11657-020-00802-8","article-title":"Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women","volume":"15","author":"Shim","year":"2020","unstructured":"Shim JG, Kim DW, Ryu KH, Cho EA, Ahn JH, Kim JI and Lee SH. Application of machine learning approaches for osteoporosis risk prediction in postmenopausal women. Arch Osteoporos 2020; 15: 169.","journal-title":"Arch Osteoporos"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"3088","DOI":"10.1177\/0300060519850648","article-title":"Artificial neural network optimizes self-examination of osteoporosis risk in women","volume":"47","author":"Meng","year":"2019","unstructured":"Meng J, Sun N, Chen Y, Li Z, Cui X, Fan J, Cao H, Zheng W, Jin Q, Jiang L and Zhu W. Artificial neural network optimizes self-examination of osteoporosis risk in women. J Int Med Res 2019; 47: 3088-3098.","journal-title":"J Int Med Res"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"7635","DOI":"10.3390\/ijerph18147635","article-title":"Development of machine learning models for prediction of osteoporosis from clinical health examination data","volume":"18","author":"Ou Yang","year":"2021","unstructured":"Ou Yang WY, Lai CC, Tsou MT and Hwang LC. Development of machine learning models for prediction of osteoporosis from clinical health examination data. Int J Environ Res Public Health 2021; 18: 7635.","journal-title":"Int J Environ Res Public Health"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1080\/13697137.2021.1951206","article-title":"Osteoporosis and fractures in women: the burden of disease","volume":"25","author":"Lorentzon","year":"2022","unstructured":"Lorentzon M, Johansson H, Harvey NC, Liu E, Vandenput L, McCloskey EV and Kanis JA. Osteoporosis and fractures in women: the burden of disease. Climacteric 2022; 25: 4-10.","journal-title":"Climacteric"},{"key":"ref41","doi-asserted-by":"publisher","unstructured":"Proietto J. Obesity and bone. F1000Res 2020; 9: F1000 Faculty Rev-1111.","DOI":"10.12688\/f1000research.20875.1"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"8509","DOI":"10.1038\/s41598-024-59159-4","article-title":"The association between body mass index and osteoporosis in a Taiwanese population: a cross-sectional and longitudinal study","volume":"14","author":"Chiu","year":"2024","unstructured":"Chiu CT, Lee JI, Lu CC, Huang SP, Chen SC and Geng JH. The association between body mass index and osteoporosis in a Taiwanese population: a cross-sectional and longitudinal study. Sci Rep 2024; 14: 8509.","journal-title":"Sci Rep"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.jhep.2022.02.016","article-title":"Multi-parametric analysis of human livers reveals variation in intrahepatic inflammation across phases of chronic hepatitis B infection","volume":"77","author":"NR","year":"2022","unstructured":"Montanari NR, Ram&#xED;rez R, Aggarwal A, van Buuren N, Doukas M, Moon C, Turner S, Diehl L, Li L, Debes JD, Feierbach B and Boonstra A. Multi-parametric analysis of human livers reveals variation in intrahepatic inflammation across phases of chronic hepatitis B infection. J Hepatol 2022; 77: 332-343.","journal-title":"J Hepatol"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/s11154-023-09812-6","article-title":"Targeting aging with the healthy skeletal system: the endocrine role of bone","volume":"24","author":"Zheng","year":"2023","unstructured":"Zheng XQ, Lin JL, Huang J, Wu T and Song CL. Targeting aging with the healthy skeletal system: the endocrine role of bone. Rev Endocr Metab Disord 2023; 24: 695-711.","journal-title":"Rev Endocr Metab Disord"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1302\/0301-620X.75B2.8444951","article-title":"Plasma bone-specific alkaline phosphatase as an indicator of osteoblastic activity","volume":"75","author":"Leung","year":"1993","unstructured":"Leung KS, Fung KP, Sher AH, Li CK and Lee KM. Plasma bone-specific alkaline phosphatase as an indicator of osteoblastic activity. J Bone Joint Surg Br 1993; 75: 288-292.","journal-title":"J Bone Joint Surg Br"},{"key":"ref46","doi-asserted-by":"publisher","unstructured":"Huh JH, Choi SI, Lim JS, Chung CH, Shin JY and Lee MY. Lower serum creatinine is associated with low bone mineral density in subjects without overt nephropathy. PLoS One 2015; 10: e0133062.","DOI":"10.1371\/journal.pone.0133062"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1038\/aps.2017.165","article-title":"Association of serum uric acid levels with osteoporosis and bone turnover markers in a Chinese population","volume":"39","author":"Yan","year":"2018","unstructured":"Yan DD, Wang J, Hou XH, Bao YQ, Zhang ZL, Hu C and Jia WP. Association of serum uric acid levels with osteoporosis and bone turnover markers in a Chinese population. Acta Pharmacol Sin 2018; 39: 626-632.","journal-title":"Acta Pharmacol Sin"},{"key":"ref48","unstructured":"Lian XL, Zhang YP, Li X, Jing LD, Cairang ZM and Gou JQ. Exploration on the relationship between the elderly osteoporosis and cardiovascular disease risk factors. Eur Rev Med Pharmacol Sci 2017; 21: 4386-4390."}],"container-title":["International Journal of Clinical and Experimental Pathology"],"original-title":[],"link":[{"URL":"https:\/\/e-century.us\/files\/ijcep\/18\/10\/ijcep0165437.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:05:38Z","timestamp":1792465538000},"score":1,"resource":{"primary":{"URL":"https:\/\/e-century.us\/files\/ijcep\/18\/10\/ijcep0165437.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.62347\/anwp5930","relation":{},"ISSN":["1936-2625"],"issn-type":[{"value":"1936-2625","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}