TY  - JOUR
AU  - Sakal, Collin
AU  - Li, Tingyou
AU  - Li, Juan
AU  - Li, Xinyue
PY  - 2024
DA  - 2024/3/22
TI  - Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study
JO  - JMIR Aging
SP  - e53240
VL  - 7
KW  - cognitive impairment
KW  - China
KW  - prediction
KW  - predictions
KW  - predict
KW  - predictor
KW  - predictors
KW  - risk
KW  - risks
KW  - population
KW  - demographic
KW  - demographics
KW  - gerontology
KW  - geriatric
KW  - geriatrics
KW  - older adult
KW  - older adults
KW  - elder
KW  - elderly
KW  - older person
KW  - older people
KW  - ageing
KW  - aging
KW  - MCI
KW  - cognitive
KW  - cognition
KW  - machine learning
KW  - variable
KW  - variables
KW  - model
KW  - models
KW  - mild cognitive impairment
AB  - Background: The societal burden of cognitive impairment in China has prompted researchers to develop clinical prediction models aimed at making risk assessments that enable preventative interventions. However, it is unclear what types of risk factors best predict future cognitive impairment, if known risk factors make equally accurate predictions across different socioeconomic groups, and if existing prediction models are equally accurate across different subpopulations. Objective: This paper aimed to identify which domain of health information best predicts future cognitive impairment among Chinese older adults and to examine if discrepancies exist in predictive ability across different population subsets. Methods: Using data from the Chinese Longitudinal Healthy Longevity Survey, we quantified the ability of demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors and hobbies, psychological factors, diet, exercise and sleep, chronic diseases, and 3 recently published logistic regression–based prediction models to predict 3-year risk of cognitive impairment in the general Chinese population and among male, female, rural-dwelling, urban-dwelling, educated, and not formally educated older adults. Predictive ability was quantified using the area under the receiver operating characteristic curve (AUC) and sensitivity-specificity curves through 20 repeats of 10-fold cross-validation. Results: A total of 4047 participants were included in the study, of which 337 (8.3%) developed cognitive impairment 3 years after baseline data collection. The risk factor groups with the best predictive ability in the general population were demographics (AUC 0.78, 95% CI 0.77-0.78), cognitive tests (AUC 0.72, 95% CI 0.72-0.73), and instrumental activities of daily living (AUC 0.71, 95% CI 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all 3 recreated prediction models had significantly higher AUCs when making predictions among female older adults compared to male older adults and among older adults with no formal education compared to those with some education. Conclusions: This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among Chinese older adults. However, the most predictive risk factors and existing models have lower predictive power among male, urban-dwelling, and educated older adults. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2024/1/e53240
UR  - https://doi.org/10.2196/53240
DO  - 10.2196/53240
ID  - info:doi/10.2196/53240
ER  -