{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T11:43:55Z","timestamp":1777117435516,"version":"3.51.4"},"reference-count":45,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Care Science"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Osteoarthritis (OA), the most prevalent joint disease and a leading cause of disability globally, has its disease burden inadequately captured by body mass index (BMI). As the sole quantified risk factor in current Global Burden of Disease estimates, BMI accounted for only 20% of OA burden. A critical limitation of BMI is its inability to distinguish fat distribution patterns, particularly abdominal adiposity, which is increasingly recognized as a key driver of metabolic and musculoskeletal pathologies. Herein, we hypothesize that anthropometric indicators reflecting central adiposity, such as average sagittal abdominal diameter (ASAD), may outperform BMI in predicting OA risk, especially when considering sex and age differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross\u2010sectional study analyzed 27,791 National Health and Nutrition Examination Survey participants (1999\u20132023) with complete OA diagnosis, anthropometric, and metabolic data. Participants were stratified by sex and age (40\u2010year cutoff). Multivariable logistic regression, adjusted for confounders, estimated predictor\u2010OA associations via standardized odds ratios (sORs), and these associations were evaluated by the area under the receiver operating characteristic curve (AUROC). Data were split into training (70%) and validation (30%) sets, with DeLong's test comparing different predictors against BMI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the overall population, ASAD showed a stronger association with OA (sOR\u2009=\u20091.483) than BMI (sOR\u2009=\u20091.436), with comparable validation AUROC (ASAD: 0.857; BMI: 0.854). Sex\u2010stratified analysis revealed that BMI was the optimal predictor for males (sOR\u2009=\u20091.466; validation AUROC\u2009=\u20090.844), while ASAD outperformed BMI in females (sOR\u2009=\u20091.486 vs. 1.450; validation AUROC\u2009=\u20090.865 vs. 0.863). Further age stratification revealed that in males under 40, both BMI (sOR\u2009=\u20091.261; validation AUROC\u2009=\u20090.750) and ASAD (sOR\u2009=\u20091.194; validation AUROC\u2009=\u20090.889) were the strongest predictors, and that ASAD (sOR\u2009=\u20091.490; validation AUROC\u2009=\u20090.769) and BMI (sOR\u2009=\u20091.482; validation AUROC\u2009=\u20090.736) remained strong for males aged 40 and above. In age\u2010stratified analyses of females, ASAD showed the strongest consistent association with OA risk, both in participants under 40 (sOR\u2009=\u20091.472; validation AUROC\u2009=\u20090.801) and those aged 40 and above (sOR\u2009=\u20091.421; validation AUROC\u2009=\u20090.764).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ASAD emerges as a superior predictor for females and a competitive population\u2010level complement to BMI. BMI remains an optimal OA predictor for males. Within the National Health and Nutrition Examination Survey framework, these findings underscore the necessity of integrating abdominal adiposity metrics, particularly ASAD, into OA risk assessment to improve sex\u2010specific prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.70061","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T01:31:21Z","timestamp":1773365481000},"page":"128-140","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refining Osteoarthritis Risk Prediction: Average Sagittal Abdominal Diameter Complements and Enhances Body Mass Index With Sex\u2010Specific Insights From National Health and Nutrition Examination Survey"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2909-3397","authenticated-orcid":false,"given":"Yuwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering The Chinese University of Hong Kong  Hong Kong China"},{"name":"InnoHK Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science Park  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3895-1384","authenticated-orcid":false,"given":"Xiaoshuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering The Chinese University of Hong Kong  Hong Kong China"},{"name":"Department of Rehabilitation Medicine The Third Affiliated Hospital of Southern Medical University Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3848-0110","authenticated-orcid":false,"given":"Hu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computing The Hong Kong Polytechnic University  Hong Kong 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Kong Science Park  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4460-9286","authenticated-orcid":false,"given":"Michael Tim Yun","family":"Ong","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics and Traumatology Prince of Wales Hospital  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4769-7816","authenticated-orcid":false,"given":"Dongquan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6975-4730","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Sports Injuries Peking University Third Hospital Beijing 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