Supplementary Material for: Pan-Immune Inflammatory Value as a Systemic Inflammation Marker Associated with Cardiovascular–Kidney–Metabolic Syndrome: Evidence from a Cross-Sectional Study of 751 Patients

Introduction: Cardiovascular-kidney-metabolic syndrome (CKM) is associated with a high risk of renal insufficiency and cardiovascular events, with chronic inflammation being a critical factor in its pathogenesis. The pan-immune inflammatory value (PIV), a novel systemic inflammatory marker that synthesizes data from various immune cell counts, and its relationship with CKM has not yet been elucidated.
Methods: A total of 751 patients were recruited. The relationship between PIV and CKM was investigated. The trends in CKM prevalence across PIV quartiles were assessed using trend tests. To identify associations within specific subpopulations, subgroup analysis and interaction tests were performed. To construct a prediction model, feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression. A nomogram was constructed for visualization. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. The study was designed, executed, and reported in strict accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. A fully completed STROBE checklist, with line-by-line cross-references to the corresponding sections of the manuscript, is provided as supplementary material 3 to ensure maximal transparency and reproducibility.
Results: The median PIV was significantly higher in the CKM Stage 4 group than in the Stage 2-3 group (449.91 vs. 210.66, P < 0.001). Subsequent to full adjustments, each 1-unit increase in PIV associated with a 26% higher odds of stage 4 CKM (OR = 1.26, 95% CI: 1.17-1.36, P < 0.001) with a dose-response relationship (P for trend < 0.001). Subgroup analysis demonstrated that the association was stronger among patients younger than 60 years compared with those aged ≥60 years (OR = 1.50 vs. 1.19), suggesting that PIV may have greater predictive value in younger individuals. Based on LASSO regression for feature selection, a multivariable CKM prediction model was developed, incorporating PIV as the principal independent predictor, and achieved an area under the curve (AUC) of 0.797 (95% CI: 0.760-0.833). The model exhibited good discrimination and calibration performance.
Conclusion: This study indicates that elevated PIV levels are independently associated with a higher likelihood of advanced (stage 4) CKM. The predictive model demonstrates that PIV, when combined with other readily available clinical variables, is strongly associated with CKM severity, suggesting that PIV may serve as a valuable marker for risk stratification in patients with CKM.