Objective Diabetes mellitus (DM) and cognitive impairment (CoI) are correlated, but the combined impact of depressive symptoms on CoI risk in older adults remains unclear. Methods This study included 1,200 U.S. adults aged ≥60 years (National Health and Nutrition Examination Survey, NHANES) and 1,500 Chinese adults (China Health and Retirement Longitudinal Study, CHARLS). DM was defined by laboratory measures or self-report; depressive symptoms were assessed via the Patient Health Questionnaire-9 (PHQ-9) (NHANES) and CES-D-10 (CHARLS); CoI was measured using the CERAD battery (NHANES) and situational memory/mental integrity scores (CHARLS). Multivariate logistic regression estimated associations of DM and depressive symptoms with CoI. Additive interaction was evaluated by the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S). Restricted cubic spline (RCS) analyses examined nonlinear DM–depressive symptoms interactions. Results Both DM and depressive symptoms were independently and significantly associated with CoI. After multivariate adjustment, diabetic patients exhibited significantly increased CoI risk; depressive symptoms similarly elevated CoI risk. Interaction analysis revealed an additive effect when DM co-occurred with depressive symptoms in CHARLS but not in NHANES. Mediation analyses further suggest bidirectional associations: in CHARLS, diabetes and depressive symptoms appear to mediate each other’s effects on CoI. Additionally, RCS analysis in NHANES indicated a nonlinear interaction between depressive symptom severity (PHQ-9) and DM on CoI (p<0.05). Conclusion The observed associations varied across cohorts, with a significant additive interaction between diabetes and depressive symptoms found only in the CHARLS cohort. Integrating metabolic and mental health screening and interventions may optimize cognitive outcomes in high-risk older adult populations.
Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single‐subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi‐feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual‐level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph‐theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT‐based networks exhibited reduced MC primarily within and between higher‐order networks involving the default mode and frontoparietal networks, whereas CV‐based networks showed increased MC predominantly within the default mode network. By contrast, both SA‐ and SD‐based networks demonstrated enhanced MC mainly within and between lower‐order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first‐episode, drug‐naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate‐to‐good classification performance in distinguishing patients from controls. Overall, our findings of individual‐level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.
Xuetian Sun, Yuhao Shen, Xiao Chen et al.· Human Brain Mapping· 0 citations
