ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026
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.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients 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.
methodsThe 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.
resultsIn 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.
conclusionThis 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.
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