Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
Dynamic interplay between depressive symptoms and cognitive function in Chinese middle-aged and older adults: a cross-lagged panel network analysis before and after cancer diagnosis.
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Ma Hongfei, Wei Feiran, Zhao Meng, Shen Xiaobing
Аннотация
BACKGROUND: Patients diagnosed with cancer are more prone to depressive symptoms and cognitive dysfunction. This study aimed to explore the dynamic relationship between depressive symptoms and cognitive function in middle-aged and older adults before and after a cancer diagnosis.
METHODS: The data were derived from Waves 1 to 5 of the China Health and Retirement Longitudinal Study (CHARLS). The cognitive functioning and depressive symptoms were measured using the validated Chinese version of the Mini-Mental State Examination (MMSE) and the Center for Epidemiologic Studies Depression Scale-10 (CESD-10), respectively. Cross-sectional network analysis was utilized for constructing the contemporaneous network, and cross-lagged panel network analysis was subsequently employed for longitudinal analysis.
RESULTS: In the temporal network, greater "Hope" before cancer diagnosis was associated with improved "Recall" after diagnosis (β = 0.112) between cognitive function and depressive symptoms. "Attention" (predictability = 0.797) exhibited the highest in-prediction values among all nodes. In contrast, "Drawing" (influence = 1.333) exerted the strongest out-prediction on other symptoms in the cross-lagged network.
CONCLUSION: This study utilized the CHARLS database and employed cross-lagged network analysis to elucidate the dynamic mechanisms of influence between depressive symptoms and cognitive function before and after a cancer diagnosis. This study identified the strongest predictive edges in the temporal network, providing new targets for clinical interventions regarding depressive symptoms and cognitive function before and after a cancer diagnosis. Furthermore, we identified the node with the strongest out-prediction in the temporal network spanning from pre-diagnosis to post-diagnosis, which is critical for developing targeted intervention strategies.
METHODS: The data were derived from Waves 1 to 5 of the China Health and Retirement Longitudinal Study (CHARLS). The cognitive functioning and depressive symptoms were measured using the validated Chinese version of the Mini-Mental State Examination (MMSE) and the Center for Epidemiologic Studies Depression Scale-10 (CESD-10), respectively. Cross-sectional network analysis was utilized for constructing the contemporaneous network, and cross-lagged panel network analysis was subsequently employed for longitudinal analysis.
RESULTS: In the temporal network, greater "Hope" before cancer diagnosis was associated with improved "Recall" after diagnosis (β = 0.112) between cognitive function and depressive symptoms. "Attention" (predictability = 0.797) exhibited the highest in-prediction values among all nodes. In contrast, "Drawing" (influence = 1.333) exerted the strongest out-prediction on other symptoms in the cross-lagged network.
CONCLUSION: This study utilized the CHARLS database and employed cross-lagged network analysis to elucidate the dynamic mechanisms of influence between depressive symptoms and cognitive function before and after a cancer diagnosis. This study identified the strongest predictive edges in the temporal network, providing new targets for clinical interventions regarding depressive symptoms and cognitive function before and after a cancer diagnosis. Furthermore, we identified the node with the strongest out-prediction in the temporal network spanning from pre-diagnosis to post-diagnosis, which is critical for developing targeted intervention strategies.