The relationship among inflammatory biomarkers, hyperuricemia and chronic kidney disease: analysis of the NHANES 2015-2020

Abstract

Background: Inflammation and hyperuricemia are closely associated with chronic kidney disease (CKD). The systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are emerging as novel biomarkers. While, the synergistic effects of these biomarkers with hyperuricemia on CKD remain unclear.

Method: We analyzed 10,226 participants from 2015-2020 National Health and Nutrition Examination Survey (NHANES). The relationships among inflammatory biomarkers (SIRI, SII, MLR, NLR, and PLR), hyperuricemia and CKD were assessed by multivariate logistic regression models. Restricted cubic splines (RCS) and segmented regression models were used to evaluate the nonlinear relationships. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve, and incremental predictive value was further calculated by Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). The interaction analysis was performed to explore the combined effects.

Results: SIRI, SII, MLR, and NLR were significantly linked with CKD. MLR exhibited a threshold effect at 0.22 (p-non-linear < 0.05), with significantly stronger association with CKD above this cutoff. SIRI demonstrated the best diagnostic accuracy among these biomarkers. Significant interactions were observed between hyperuricemia and inflammatory biomarkers (SIRI, SII, MLR, NLR), indicating that the association between inflammatory biomarkers and CKD is more pronounced in the presence of hyperuricemia.

Conclusion: There were significant associations between inflammatory biomarkers (SII, SIRI, NLR, MLR) and CKD, with particularly stronger correlations observed in patients with hyperuricemia.

Keywords: Inflammatory biomarkers; NHANES; chronic kidney disease; hyperuricemia; interaction.

Figure 1.

Flow chart of the screening process for the selection of eligible participants.

Figure 2.

Restricted cubic spline (RCS) analysis of inflammatory biomarkers and CKD prevalence rate. Models were adjusted for age, gender, race, education level, BMI, PIR, smoking status, alcohol consumption, serum uric acid, diabetes, dyslipidemia, CVD and cancer. CKD, chronic kidney disease; hs-CRP, high-sensitive C-reactive protein; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; BMI, body mass index; PIR, poverty income ratio; CVD, cardiovascular disease.

Figure 3.

Receiver operating characteristic (ROC) curve analysis evaluating the diagnostic performance of SIRI, hyperuricemia, and their combination for CKD. CKD, chronic kidney disease; SIRI, systemic inflammation response index.

Figure 5.

Associations between MLR and CKD differ by hyperuricemia status. Models were adjusted for age, gender, race, education level, BMI, PIR, smoking status, alcohol consumption, diabetes, hypertension, dyslipidemia, CVD and cancer. CKD, chronic kidney disease; MLR, monocyte-to-lymphocyte ratio; BMI, body mass index; PIR, poverty income ratio; CVD, cardiovascular disease.

Figure 4.

Associations between inflammatory biomarkers (SII, SIRI, NLR) and CKD differ by hyperuricemia status. Models were adjusted for age, gender, race, education level, BMI, PIR, smoking status, alcohol consumption, diabetes, hypertension, dyslipidemia, CVD and cancer. CKD, chronic kidney disease; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index; NLR, neutrophil-to-lymphocyte ratio; BMI, body mass index; PIR, poverty income ratio; CVD, cardiovascular disease.