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Published on: November 2, 2013
Association of systemic inflammatory biomarkers with prostate cancer risk: a population-based (NHANES) and clinical
Guoqiang Huang1, Kaiwen Xiao1, Shuangquan Lin1
1Department of Urology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Objective:
To evaluate the associations between systemic inflammatory biomarkers-systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), pan-immune inflammation value (PIV), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR)-and prostate cancer (PCa) risk, and to assess their potential for risk in both general and clinical populations.
Methods:
A dual-cohort study was conducted using data from the National Health and Nutrition Examination Survey (NHANES; 2001-2010; N=7,354 males, 514 were classified as PCa) and a clinical validation cohort from the second affiliated hospital of Nanchang University (N=353, 175 with biopsy-confirmed PCa). Multivariable logistic regression, restricted cubic spline (RCS) analysis, and receiver operating characteristic (ROC) curve analysis were employed to examine linear/nonlinear relationships and predictive performance of the biomarkers. Models were adjusted for demographic, clinical, and laboratory covariates.
Results:
Elevated SII, NLR, PLR, SIRI, and PIV were significantly associated with increased PCa risk in both cohorts, while higher LMR was protective. In the clinical cohort, the highest quartile of SIRI (OR=6.265, 95% CI: 3.130-13.012) and PIV (OR=6.638, 95% CI: 3.343-13.665) showed the strongest risks. RCS analyses revealed nonlinear relationships between biomarkers and PCa risk, total PSA (tPSA), and free PSA (fPSA). Elevated SII, NLR, PLR, SIRI, and PIV were significantly associated with increased PCa risk in both cohorts, while a higher LMR was protective. In the clinical cohort, the highest quartile of SIRI (OR=6.265, 95% CI: 3.130-13.012) and PIV (OR=6.638, 95% CI: 3.343-13.665) exhibited the strongest risks. RCS analyses revealed nonlinear relationships between biomarkers and PCa risk, total PSA (tPSA), and free PSA (fPSA). ROC analysis indicated moderate discriminatory power for PIV (AUC=0.709, 95% CI: 0.655-0.763) and SIRI (AUC=0.704, 95% CI: 0.650-0.759) compared with tPSA in the clinical cohort. However, fPSA and SIRI did not demonstrate a clear advantage, and the DeLong test showed no significant statistical difference.
Conclusion:
Systemic inflammatory biomarkers, particularly composite indices such as SIRI and PIV, are strongly associated with PCa risk and demonstrate nonlinear relationships with PSA parameters. These biomarkers may enhance risk stratification for PCa and serve as non-invasive tools to complement existing diagnostic approaches.
