Decoding inflammation: Novel biomarkers illuminate CRP dynamics in peritoneal dialysis patients

To identify factors linked to systemic inflammation and evaluate the predictive value of novel inflammatory biomarkers in peritoneal dialysis (PD) patients.

We enrolled 111 maintenance PD patients, stratified by C-reactive protein (CRP) into a normal group (CRP < 3 mg/L) and an inflammation group (CRP ≥ 3 mg/L). We compared clinical characteristics, laboratory parameters, and novel inflammatory biomarkers. Analyses included group comparisons, Spearman correlations between CRP and clinical parameters, multiple linear regression relating novel biomarkers to CRP, and binary logistic regression with receiver operating characteristic (ROC) analysis for predictive value.

The inflammation group had significantly lower urine volume, hemoglobin (HGB), prealbumin (pre-ALB), potassium (K), magnesium (Mg), and transferrin saturation (TSAT), but higher serum ferritin (SF) and white blood cell count (WBC) (p < 0.05). CRP positively correlated with age, neutrophils, monocytes, WBC, SF, glucose, glycated hemoglobin A1c (HbA1c), triglycerides, β2-microglobulin, and novel indices include systemic immune inflammation index (SII), platelet-lymphocyte ratio (PLR), platelet-albumin ratio (PAR), systemic inflammation response index (SIRI), albumin-prealbumin ratio (APR), and aggregate index of systemic inflammation (AISI) (p < 0.05). Negative correlations existed with HGB, pre-ALB, residual urea clearance index (Kt/V), residual creatinine clearance (CCr), residual glomerular filtration rate (GFR), and electrolytes (Na, Cl, Mg) (p < 0.05). Multiple linear regression identified AISI and APR as independent predictors of CRP levels. ROC analysis showed that the combination of AISI and APR had superior diagnostic utility for systemic inflammation compared to individual indices.

Systemic inflammation in PD patients is associated with impaired nutrition, anemia, and reduced residual renal function. AISI and APR strongly correlate with CRP, and their combination significantly enhances prediction of systemic inflammation, providing a valuable clinical tool for risk stratification.