Network-Informed Health-Burden Profiles Among Older Adults with Symptomatic Knee Osteoarthritis Phenotype: A Latent Class Analysis.
- 1School of Nursing, Guangxi University of Chinese Medicine, Nanning, Guangxi, People's Republic of China.
- 2Department of Joint and Sports Medicine, The Second Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, People's Republic of China.
- 3Affiliated Traditional Chinese Medicine School of Guangxi University of Chinese Medicine (Guangxi Traditional Chinese Medicine School), Nanning, Guangxi, People's Republic of China.
Abstract
Purpose
Older adults with symptomatic knee osteoarthritis (KOA) may exhibit heterogeneous combinations of functional limitation, psychological burden, perceived health, comorbidity burden, and multisite pain. This study aimed to identify network-informed key indicators and derive clinically interpretable, management-oriented health-burden profiles to support multidimensional assessment and future preference-sensitive care research.Patients and methods
In this cross-sectional secondary analysis, we analyzed 2011 baseline data from the China Health and Retirement Longitudinal Study (CHARLS) among adults aged 60 years or older with physician-diagnosed arthritis or rheumatism and knee pain. Eighteen indicators were modeled using an extended Bayesian information criterion graphical lasso network based on mixed-scale correlations. Three structural metrics were integrated to prioritize indicators, and the prioritized indicators were entered into latent class analysis (LCA). Multinomial logistic regression examined profile correlates. Robustness was assessed across five multiply imputed datasets, after removing diabetes from the LCA, and in an exploratory 2018 later-wave reproducibility analysis.Results
Among 749 participants, depressive symptoms, poor self-rated health, instrumental and basic activities of daily living limitations, diabetes, and non-knee pain burden ranked highest. Although the two-class model had the lowest Bayesian information criterion (BIC), the three-class model had the lowest sample-size-adjusted BIC (aBIC), acceptable classification quality, adequate minimum class size, and greater clinical interpretability. Profiles reflected lower overall health burden (n=93, 12.4%), psychological-perceived health burden (n=312, 41.7%), and multidimensional high-burden functional limitation (n=344, 45.9%). Rural residence, greater comorbidity burden, and sleep disturbance were associated with higher-burden profiles. The 2018 analysis (n=2280) yielded three broadly analogous qualitative profiles, although class proportions, some item-response patterns, and classification certainty differed from 2011.Conclusion
Network-informed LCA identified three multidimensional health-burden profiles, with partial later-wave reproducibility. These profiles may inform multidimensional assessment and future stratified, preference-sensitive care research but require confirmation in independent clinical cohorts with direct measures of preference, treatment acceptability, and adherence.
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BibTeX
@article{jiang2026networkinformed,
title = {{Network-Informed Health-Burden Profiles Among Older Adults with Symptomatic Knee Osteoarthritis Phenotype: A Latent Class Analysis.}},
author = {Ruizhu Jiang and Yelin Zhang and Xianqun Li and Meng Wei and Yan Yao and Xilin Liao},
journal = {Patient preference and adherence},
year = {2026},
month = oct,
doi = {10.2147/ppa.s631987},
url = {https://europepmc.org/article/MED/42960188},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Network-Informed Health-Burden Profiles Among Older Adults with Symptomatic Knee Osteoarthritis Phenotype: A Latent Class Analysis.
AU - Ruizhu Jiang
AU - Yelin Zhang
AU - Xianqun Li
AU - Meng Wei
AU - Yan Yao
AU - Xilin Liao
PY - 2026
DA - 2026/10/25
JO - Patient preference and adherence
DO - 10.2147/ppa.s631987
UR - https://europepmc.org/article/MED/42960188
AB - Purpose Older adults with symptomatic knee osteoarthritis (KOA) may exhibit heterogeneous combinations of functional limitation, psychological burden, perceived health, comorbidity burden, and multisite pain. This study aimed to identify network-informed key indicators and derive clinically interpretable, management-oriented health-burden profiles to support multidimensional assessment and future preference-sensitive care research.Patients and methods In this cross-sectional secondary analysis, we analyzed 2011 baseline data from the China Health and Retirement Longitudinal Study (CHARLS) among adults aged 60 years or older with physician-diagnosed arthritis or rheumatism and knee pain. Eighteen indicators were modeled using an extended Bayesian information criterion graphical lasso network based on mixed-scale correlations. Three structural metrics were integrated to prioritize indicators, and the prioritized indicators were entered into latent class analysis (LCA). Multinomial logistic regression examined profile correlates. Robustness was assessed across five multiply imputed datasets, after removing diabetes from the LCA, and in an exploratory 2018 later-wave reproducibility analysis.Results Among 749 participants, depressive symptoms, poor self-rated health, instrumental and basic activities of daily living limitations, diabetes, and non-knee pain burden ranked highest. Although the two-class model had the lowest Bayesian information criterion (BIC), the three-class model had the lowest sample-size-adjusted BIC (aBIC), acceptable classification quality, adequate minimum class size, and greater clinical interpretability. Profiles reflected lower overall health burden (n=93, 12.4%), psychological-perceived health burden (n=312, 41.7%), and multidimensional high-burden functional limitation (n=344, 45.9%). Rural residence, greater comorbidity burden, and sleep disturbance were associated with higher-burden profiles. The 2018 analysis (n=2280) yielded three broadly analogous qualitative profiles, although class proportions, some item-response patterns, and classification certainty differed from 2011.Conclusion Network-informed LCA identified three multidimensional health-burden profiles, with partial later-wave reproducibility. These profiles may inform multidimensional assessment and future stratified, preference-sensitive care research but require confirmation in independent clinical cohorts with direct measures of preference, treatment acceptability, and adherence.
SN - 1177-889X
ER -