{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tong X"],"funding":["Shenzhen Science and Technology Innovation Program","Natural Science Foundation of Guangdong Province, China","Outstanding Medical Innovation Talent Program of The Eighth Affiliated Hospital of Sun Yat-sen University","Basic and Applied Basic Research Foundation of Guangdong Province","National Natural Science Foundation of China","Guangdong Provincial Clinical Research Center for Orthopedic Diseases"],"pagination":["1263"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11970002"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["Background
Osteoporosis is a prevalent bone disease that increases frailty. Developing a nomogram prediction model to predict osteoporosis risk at multiple time points using bone mineral densities, behavioral habits, and clinical risk factors would be essential to identify individual risk and guide prevention.Methods
The study population from the UK Biobank was followed from 2014 to December 31st, 2022. The study outcome was identified as the first occurrence of osteoporosis in the UK Biobank during the follow-up period. After rebalancing with the synthetic minority over-sampling technique, a nomogram prediction model was developed using a LASSO Cox regression. Model discrimination between different risk levels was visualised with Kaplan-Meier curves, and model performance "],"journal":["BMC public health"],"pubmed_title":["Developing and validating a nomogram prediction model for osteoporosis risk in the UK biobank: a national prospective cohort."],"pmcid":["PMC11970002"],"funding_grant_id":["YXYXCXRC202101","82103909","JCYJ20220530144403007","2021A1515220132","2023A1515110720","2023B110001"],"pubmed_authors":["Yao XI","Tong X","Cui S","Shen H"],"additional_accession":[]},"is_claimable":false,"name":"Developing and validating a nomogram prediction model for osteoporosis risk in the UK biobank: a national prospective cohort.","description":"Background
Osteoporosis is a prevalent bone disease that increases frailty. Developing a nomogram prediction model to predict osteoporosis risk at multiple time points using bone mineral densities, behavioral habits, and clinical risk factors would be essential to identify individual risk and guide prevention.Methods
The study population from the UK Biobank was followed from 2014 to December 31st, 2022. The study outcome was identified as the first occurrence of osteoporosis in the UK Biobank during the follow-up period. After rebalancing with the synthetic minority over-sampling technique, a nomogram prediction model was developed using a LASSO Cox regression. Model discrimination between different risk levels was visualised with Kaplan-Meier curves, and model performance ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-08T03:13:44.359Z","creation":"2025-07-08T03:13:44.359Z"},"accession":"S-EPMC11970002","cross_references":{"pubmed":["40181326"],"doi":["10.1186/s12889-025-22485-x"]}}