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<p>To develop and validate a parsimonious risk model for short-term memory decline in older adults and to evaluate its cross-population transportability between Chinese and Japanese cohorts. The model was developed in 5985 cognitively normal older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015). Seven machine learning algorithms were compared, and a Cox proportional hazards (CoxPH) model was selected for its optimal balance between performance and parsimony. The final model was validated in a temporal CHARLS cohort (2015–2018; <i>n</i> = 1333) and an external Japanese cohort from the Japanese Study of Aging and Retirement (JSTAR, 2007–2009; <i>n</i> = 2798). Performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap-derived confidence intervals. In temporal validation, the model demonstrated good discrimination (C-index = 0.72) with acceptable calibration (slope = 1.40). In the external JSTAR cohort, discriminative performance remained moderate and stable (C-index = 0.68), and calibration was comparable (slope = 0.96) despite differences in baseline incidence and follow-up duration. Decision curve analysis showed net benefit in the temporal cohort and consistent risk stratification in the external cohort. Sensitivity analyses confirmed stable performance across varying follow-up horizons. The six-predictor model consistently stratified short-term memory decline risk across distinct East Asian populations. The findings support its cross-population transportability for relative risk stratification in aging cohorts.</p>

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A parsimonious six-predictor model for memory decline: cross-population validation in Chinese and Japanese aging cohorts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Lihui Tu,
  • Qinge Zhang,
  • Yoshinori Nanjo,
  • Dongmei Gu,
  • Yasuo Kuniyoshi

摘要

To develop and validate a parsimonious risk model for short-term memory decline in older adults and to evaluate its cross-population transportability between Chinese and Japanese cohorts. The model was developed in 5985 cognitively normal older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015). Seven machine learning algorithms were compared, and a Cox proportional hazards (CoxPH) model was selected for its optimal balance between performance and parsimony. The final model was validated in a temporal CHARLS cohort (2015–2018; n = 1333) and an external Japanese cohort from the Japanese Study of Aging and Retirement (JSTAR, 2007–2009; n = 2798). Performance was assessed using discrimination, calibration, decision curve analysis, and bootstrap-derived confidence intervals. In temporal validation, the model demonstrated good discrimination (C-index = 0.72) with acceptable calibration (slope = 1.40). In the external JSTAR cohort, discriminative performance remained moderate and stable (C-index = 0.68), and calibration was comparable (slope = 0.96) despite differences in baseline incidence and follow-up duration. Decision curve analysis showed net benefit in the temporal cohort and consistent risk stratification in the external cohort. Sensitivity analyses confirmed stable performance across varying follow-up horizons. The six-predictor model consistently stratified short-term memory decline risk across distinct East Asian populations. The findings support its cross-population transportability for relative risk stratification in aging cohorts.