AIMS
The long-term depressive symptoms trajectories across the transition to diagnosed cardiovascular disease (CVD) remain poorly characterized. We aimed to investigate trajectories of depressive symptoms before and after incident CVD in multinational aging populations.
METHODS AND RESULTS
In this multicohort study, we analyzed longitudinal data from the Health and Retirement Study (HRS, 2002-2018), English Longitudinal Study of Ageing (ELSA, 2002-2019), and China Health and Retirement Longitudinal Study (CHARLS, 2011-2018). Depressive symptoms were measured biennially by the 8-item (HRS and ELSA) and 10-item (CHARLS) Centre for Epidemiological Studies Depression scale and standardized to Z-scores. Incident CVD was ascertained by medical history. Data were analyzed using discontinuous growth curve modeling and pooled using meta-analysis. Among 28 704 included participants (mean [SD] age, 62.3 [9.8] years; 43.4% male), 7363 developed CVD during follow-up. Participants who developed CVD had higher levels of depressive symptoms than those without CVD (pooled β [95% confidence interval]: 0.161 SD [0.086-0.236]). A significant increase in depressive symptoms scores was observed around the time of CVD diagnosis (pooled β: 0.089 SD [0.060-0.118]). Additionally, depressive symptoms scores increased both in the years preceding CVD onset (pooled β: 0.007 SD/year [0.004-0.010]) and following diagnosis (pooled β: 0.009 SD/year [0.004-0.013]).
CONCLUSION
Depressive symptoms show a gradual increase before CVD diagnosis, elevate further around the time of diagnosis, and continue to worsen thereafter. These findings call for the integration of mental health screening and management into CVD care at all stages, from primary prevention to long-term disease management.
Jiao Wang, Zhi-Yuan Wu, Xin-Ye Zou et al.· European Journal of Preventi...· 0 citations
Objectives: Individuals with type 2 diabetes (T2D) remain at high cardiovascular disease (CVD) risk. The pan-immune-inflammation value (PIV) integrates circulating neutrophils, monocytes, platelets, and lymphocytes but does not capture albumin-related nutritional and inflammatory reserve. We investigated associations between the PIV-to-albumin ratio (PIVA) and incident CVD, cardiovascular mortality, and disease progression in individuals with T2D. Methods: This prospective study included 15,355 UK Biobank participants with T2D and no baseline CVD. PIVA was calculated from peripheral blood cell counts and serum albumin and was natural log-transformed. Fine–Gray competing-risk and cause-specific Cox models were used to evaluate incident CVD and cardiovascular mortality. Multi-state models characterized transitions from T2D to CVD and death. We also examined nonlinear associations, renal biomarker mediation, joint associations with the CVD polygenic risk score and triglyceride–glucose index, sensitivity analyses, and external validation of cardiovascular mortality in the National Health and Nutrition Examination Survey. Results: During median follow-ups of 12.94 years for incident CVD and 14.49 years for cardiovascular mortality, 4829 incident CVD events and 514 cardiovascular deaths occurred. Each 1-unit increase in lnPIVA was associated with higher risks of incident CVD (subdistribution hazard ratio [sHR], 1.140; 95% confidence interval [CI], 1.088–1.194) and cardiovascular mortality (sHR, 1.449; 95% CI, 1.247–1.684). Associations were nonlinear. Higher lnPIVA was also associated with transitions from T2D to CVD, from T2D directly to cardiovascular death, and from incident CVD to cardiovascular death. Cystatin C explained a larger proportion of these associations than creatinine. Elevated lnPIVA identified excess cardiovascular risk across strata of genetic susceptibility and insulin resistance, and the findings were generally supported by sensitivity and external validation analyses. Conclusions: lnPIVA was associated with incident CVD, cardiovascular mortality, and adverse cardiovascular transitions in individuals with T2D. As an immune-inflammatory and albumin-based index, PIVA may help identify high-risk individuals beyond conventional cardiometabolic and genetic risk profiles.
Fangkun Liu, Xinghua Yang, Bo Gao et al.· Biomedicines· 0 citations
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