Predictive value of pan-immune-inflammation value for all-cause and cause-specific mortality in depressed individuals: a cohort study

To assess the predictive value of the pan-immune-inflammation value(PIV) for all-cause and cause-specific mortality among individuals with depressive symptoms.

This prospective cohort study utilized data from the US National Health and Nutrition Examination Survey(NHANES, 2005 to 2018) and the National Death Index(NDI, 2005 to 2019). A total of 8567 American patients with depression were enrolled. Participants were grouped by PIV quartiles(Q1, Q2, Q3, Q4) or dichotomized(low group, high group). Cox proportional hazards models were employed to estimate the association between PIV and all-cause, cardiovascular, and cancer mortality in patients with depression. Smooth curve fitting was applied to explore potential non-linear dose-response relationships. Threshold effect analysis was conducted to identify an inflection point, upon which a two-piecewise regression model was constructed. Kaplan-Meier survival curves were plotted to examine the association between PIV levels and survival status. Subgroup analyses were performed to evaluate the robustness of the observed associations.

Cox regression showed that higher PIV levels were significantly associated with increased risks of all-cause mortality(HR=1.05, 95%CI: 1.04 to 1.07, P<0.001), cardiovascular mortality(HR=1.07, 95%CI: 1.05 to 1.10, P<0.001), and cancer mortality(HR=1.05, 95%CI: 1.01 to 1.08, P<0.001). Smooth curve fitting revealed a U-shaped relationship between PIV and all-cause mortality, with an inflection point at 80.97(P<0.05). Survival analysis indicated that when PIV>80.97, compared with the low group, the high group had increased all-cause mortality(HR=1.29, 95%CI: 1.12 to 1.48, P<0.001); compared with the Q1 group, the Q4 group also showed increased allcause mortality(HR=1.53, 95%CI: 1.25 to 1.87, P<0.001). Subgroup analyses revealed stronger associations in specific populations: among females, for all-cause mortality(HR=1.08, 95%CI: 1.05 to 1.10, P=0.038) and cardiovascular mortality(HR=1.11, 95%CI: 1.07 to 1.16, P=0.031); In individuals with diabetes and those of other races(excluding Mexican American, other Hispanic, non-Hispanic White, and non-Hispanic Black), stronger associations were found between PIV and all-cause mortality(diabetes: HR=1.09, 95%CI: 1.06 to 1.10, P=0.023; other races: HR=1.22, 95%CI: 1.11 to 1.35, P=0.032) and cancer mortality(diabetes: HR=1.12, 95%CI: 1.06 to 1.18, P=0.006; other races: HR=2.00, 95%CI: 1.79 to 2.24, P=0.002).

Elevated PIV serves as an independent risk factor for multiple mortality outcomes in individuals with depressive symptoms. This study underscores the potential of PIV as a biomarker for predicting mortality risk. Utilizing PIV could aid in the early identification and targeted management of highrisk individuals, contributing to a comprehensive strategy for reducing the disease burden.