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Objectives <p>To investigate sarcopenia state transitions (non-sarcopenia, possible sarcopenia, and sarcopenia) and their determinants among older Chinese adults, emphasizing the roles of physical activity, cognitive status, and other risk factors. </p> Methods <p>A longitudinal study utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011–2015, integrating Multi-State Markov (MSM) models and Transformer-based deep learning approaches. We examined 5,756 participants (including 3,373 for deep learning) across three waves, with a mean age of 67.9 years (SD = 6.5). MSM models estimated transition intensities and probabilities between sarcopenia states. Deep learning with SHAP analysis identified key determinants of transitions and mortality. Covariates included age, sex, BMI, smoking, physical activity, mild cognitive impairment (MCI), and functional disability.</p> Results <p>MSM models indicated a high transition rate from non-sarcopenia to possible sarcopenia (intensity: 0.383, 95% CI: 0.355–0.411) and a 38.6% five-year recovery probability from possible sarcopenia to non-sarcopenia. Physical activity reduced deterioration risk (HR: 0.916, 95% CI: 0.842–0.997) and mortality in possible sarcopenia (HR: 0.565, 95% CI: 0.339–0.944). MCI increased deterioration risk (HR: 1.724, 95% CI: 1.268–2.346). Age &gt; 80 significantly elevated deterioration (HR: 3.007, 95% CI: 1.992–4.538) and mortality risks (HR: 7.400, 95% CI: 2.542–21.544). Sex, BMI, smoking, and functional disability also influenced transitions.</p> Conclusions <p>Sarcopenia exhibits bidirectional progression, with physical activity serving as a key protective factor. MCI attenuates this benefit, highlighting the need for tailored interventions that address cognitive status and other risk factors in older adults.</p>

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Dynamic sarcopenia transitions in older Chinese: physical activity and cognitive insights

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Yutao Li,
  • Yuxin Tang,
  • Weifeng Pan,
  • Hengguo Song

摘要

Objectives

To investigate sarcopenia state transitions (non-sarcopenia, possible sarcopenia, and sarcopenia) and their determinants among older Chinese adults, emphasizing the roles of physical activity, cognitive status, and other risk factors.

Methods

A longitudinal study utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2011–2015, integrating Multi-State Markov (MSM) models and Transformer-based deep learning approaches. We examined 5,756 participants (including 3,373 for deep learning) across three waves, with a mean age of 67.9 years (SD = 6.5). MSM models estimated transition intensities and probabilities between sarcopenia states. Deep learning with SHAP analysis identified key determinants of transitions and mortality. Covariates included age, sex, BMI, smoking, physical activity, mild cognitive impairment (MCI), and functional disability.

Results

MSM models indicated a high transition rate from non-sarcopenia to possible sarcopenia (intensity: 0.383, 95% CI: 0.355–0.411) and a 38.6% five-year recovery probability from possible sarcopenia to non-sarcopenia. Physical activity reduced deterioration risk (HR: 0.916, 95% CI: 0.842–0.997) and mortality in possible sarcopenia (HR: 0.565, 95% CI: 0.339–0.944). MCI increased deterioration risk (HR: 1.724, 95% CI: 1.268–2.346). Age > 80 significantly elevated deterioration (HR: 3.007, 95% CI: 1.992–4.538) and mortality risks (HR: 7.400, 95% CI: 2.542–21.544). Sex, BMI, smoking, and functional disability also influenced transitions.

Conclusions

Sarcopenia exhibits bidirectional progression, with physical activity serving as a key protective factor. MCI attenuates this benefit, highlighting the need for tailored interventions that address cognitive status and other risk factors in older adults.