{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:00:23Z","timestamp":1785218423596,"version":"3.55.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012669","name":"Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0854"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0854"]}],"id":[{"id":"10.13039\/501100012669","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3605200"],"award-info":[{"award-number":["2023YFC3605200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3605202"],"award-info":[{"award-number":["2023YFC3605202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Intrinsic capacity (IC) is a critical multidimensional indicator of healthy aging; however, current research on IC primarily emphasizes its longitudinal trajectories, with limited recognition of the heterogeneity within older adults and a notable lack of targeted assessment instruments. This study aimed to identify distinct IC profiles among Chinese older adults and explored an interpretable, web-based analytical model to enable rapid profiles classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional survey was conducted across 24 provinces in China, enrolling 4,508 older adults from community and institutional care settings via stratified sampling. IC scores were assessed using the MNA-SF, SPPB, and GDS-15. Latent profile analysis (LPA) was employed to delineate IC subgroups. Feature selection integrated univariate regression, random forest, and LASSO regression. An XGBoost algorithm was subsequently trained (70%) to predict profile membership and validated on an independent test set (30%). Model interpretability was enhanced using SHAP and XGBoost gain values. Furthermore, an interactive web-based platform was developed using Python (version 3.9), JavaScript (version 7), and MySQL (version 8.0).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Based on their five-domain IC scores, two latent profiles were identified: \u201cOverall Low IC\u201d and \u201cOverall High IC\u201d (Entropy = 0.846,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.05). Key predictors selected included self-rated health (SRH) (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.26, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 1.17\u20131.36), age (\n                      <jats:italic>OR<\/jats:italic>\n                      = 0.87, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.80\u20130.95), presence of multiple chronic diseases (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.15, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 1.05\u20131.25), chest tightness and pain (\n                      <jats:italic>OR<\/jats:italic>\n                      = 1.28, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 1.02\u20131.62), and mobility issues (\n                      <jats:italic>OR<\/jats:italic>\n                      = 2.54, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 2.08\u20133.11). The XGBoost model demonstrated robust discriminative performance for the \u201cOverall High IC\u201d profile, achieving an AUC of 0.910 (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.898\u20130.921) and accuracy of 0.874 (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.862\u20130.885) in the training set, and an AUC of 0.853 (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.828\u20130.875) with accuracy of 0.819 (95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 0.800\u20130.838) in the test set. Decision curve analysis (DCA) confirmed a clinical net benefit within a threshold probability range of 4%\u221297%. The deployed web-based platform generates stratified predictions within 3 s and demonstrates clinical interpretability at the individual level.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study delineates two IC profiles among Chinese older adults. The XGBoost-based model was explored for rapid IC profile classification. These findings highlight heterogeneity in IC and support the feasibility of a simplified digital approach, with external validation warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1858286","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:37:37Z","timestamp":1785217057000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent profile analysis and analytical model construction based on heterogeneity of intrinsic capacity in Chinese older adults"],"prefix":"10.3389","volume":"18","author":[{"given":"Dewei","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Zhejiang Financial College","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Leng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Walker College of Business, Appalachian State University, Boone","place":["NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Peking University First Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Peking University First Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University","place":["Chengdu, China"]},{"name":"Shuanghua Digital Health and Intelligent Medicine Research Institute","place":["Chengdu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China","place":["Chengdu, China"]},{"name":"Shuanghua Digital Health and Intelligent Medicine Research Institute","place":["Chengdu, China"]},{"name":"Institute of Intelligent Chinese Medicine, Chongqing University of Chinese Medicine","place":["Chongqing, China"]},{"name":"International Research Center for Complexity Sciences, Hangzhou International Innovation Institute, Beihang University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1186\/s12877-026-07488-61","article-title":"Advancing healthy ageing in Greece: the WHO ICOPE protocol within the JA PreventNCD framework","volume":"26","author":"Adamakidou","year":"2026","journal-title":"BMC Geriatr"},{"key":"B2","doi-asserted-by":"publisher","first-page":"glag057","DOI":"10.1101\/2025.10.05.25337351","article-title":"Associations and interaction effects of socioeconomic, lifestyle, and genetic factors on intrinsic capacity","volume":"81","author":"Beyene","year":"2026","journal-title":"J. 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