{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T23:37:43Z","timestamp":1793144263203,"version":"4.5.0"},"reference-count":31,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82404373"],"award-info":[{"award-number":["82404373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3603000"],"award-info":[{"award-number":["2022YFC3603000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Psychogeriatrics"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Sarcopenia is a prominent issue among aging populations and associated with poor health outcomes. This study aimed to examine the predictive value of questionnaire and biomarker data for sarcopenia, and to further develop a user\u2010friendly calculator for community\u2010dwelling middle\u2010aged and older adults.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used two waves (2011 and 2013) of the China Health and Retirement Longitudinal Study (CHARLS) to predict sarcopenia, defined by the Asian Working Group for Sarcopenia 2019 criteria. We restricted the analytical sample to adults aged 45 or above (<jats:italic>N<\/jats:italic>\u2009=\u20092934). Five machine learning models were used to construct Q\u2010based (only questionnaire variables), Bio\u2010based (only biomarker variables), and combined (questionnaire plus biomarker variables) models. Area under the receiver operating characteristic curve (AUROC) was used for performance assessment. Temporal external validation was performed based on two datasets from CHARLS. Important predictors were identified by Shapley values and coefficients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Extreme gradient boosting (XGBoost), considering both questionnaire and biomarker characteristics, emerged as the optimal model, and its AUROC was 0.759 (95% CI: 0.747\u20130.771) at a decision threshold of 0.20 on the test set. Models also performed well on the external datasets. We found that cognitive function was the most important predictor in both Q\u2010based and combined models, and blood urea nitrogen was the most important predictor in the Bio\u2010based model. Other key predictors included education, haematocrit, total cholesterol, drinking, number of chronic diseases, and instrumental activities of daily living score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our findings offer a potential for early screening and targeted prevention of sarcopenia among middle\u2010aged and older adults in the community setting.<\/jats:p><\/jats:sec>","DOI":"10.1111\/psyg.13205","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T06:34:44Z","timestamp":1729751684000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning\u2010based prediction of sarcopenia in community\u2010dwelling middle\u2010aged and older adults: findings from the <scp>CHARLS<\/scp>"],"prefix":"10.1111","volume":"25","author":[{"given":"Zongjie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3937-0263","authenticated-orcid":false,"given":"Yafei","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"School of Nursing The Hong Kong Polytechnic University  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junmin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province Xiamen University  Xiamen China"},{"name":"National Institute for Data Science in Health and Medicine Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(19)31138-9"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afz046"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnesp.2021.07.031"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2019.12.012"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2014.04.021"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnesp.2022.09.012"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2020.05.041"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2018.04.001"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.1078304"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.755705"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-023-30379-x"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/metabo13040565"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/cm9.0000000000002633"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11212881"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41580-021-00407-0"},{"key":"e_1_2_12_17_1","volume-title":"Paper\/Poster 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