Latent profile analysis and analytical model construction based on heterogeneity of intrinsic capacity in Chinese older adults.
Background: 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....
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 15 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196086848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196086848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2026 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 196086848 196086848 196086848 10.3389/fnagi.2026.1858286 196086848 ppf: 1 ppct: 14 formats: tig: atl: Latent profile analysis and analytical model construction based on heterogeneity of intrinsic capacity in Chinese older adults. aug: au: Xu, Dewei Jiang, Yanyu Liu, Hao Leng, Yanlin Xiong, Jason Wang, Hui Wang, Zhaoxia Wang, Junfeng Tang, Yong affil: College of Computer Science, Sichuan University, Chengdu, China sug: subj: Chinese Persons Psychosocial Factors Mental Health Models, Statistical Health Status Human Funding Source China Male Female Residential Facilities Nursing Homes Cross Sectional Studies Community Health Centers Nonexperimental Studies Surveys Stratified Random Sample Middle Age Aged Aged, 80 and Over Multi-Stage Cluster Random Forest Regression ROC Curve Descriptive Statistics Data Analysis Software Chi Square Test Mann-Whitney U Test Odds Ratio Confidence Intervals Healthy Aging Age Factors Comorbidity Chest Pain Physical Mobility Scales Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: 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. Methods: 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). Results: Based on their five-domain IC scores, two latent profiles were identified: "Overall Low IC" and "Overall High IC" (Entropy = 0.846, P < 0.05). Key predictors selected included self-rated health (SRH) (OR = 1.26, 95% CI : 1.17–1.36), age (OR = 0.87, 95% CI : 0.80–0.95), presence of multiple chronic diseases (OR = 1.15, 95% CI : 1.05–1.25), chest tightness and pain (OR = 1.28, 95% CI : 1.02–1.62), and mobility issues (OR = 2.54, 95% CI : 2.08–3.11). The XGBoost model demonstrated robust discriminative performance for the "Overall High IC" profile, achieving an AUC of 0.910 (95% CI : 0.898–0.921) and accuracy of 0.874 (95% CI : 0.862–0.885) in the training set, and an AUC of 0.853 (95% CI : 0.828–0.875) with accuracy of 0.819 (95% CI : 0.800–0.838) in the test set. Decision curve analysis (DCA) confirmed a clinical net benefit within a threshold probability range of 4%−97%. The deployed web-based platform generates stratified predictions within 3 s and demonstrates clinical interpretability at the individual level. Conclusion: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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