The diagnostic and prognostic role of novel biomarkers in anti-neutrophil cytoplasmic antibody-associated vasculitis

Abstract

Background: ANCA-associated vasculitis (AAV) is a group of autoimmune diseases characterized by small vessel inflammation, diagnosed primarily through clinical features, histopathology, and ANCA testing. Novel biomarkers derived from routine blood counts, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), may support disease assessment. This study evaluated their utility in distinguishing AAV patients, reflecting disease activity, and predicting prognosis.

Methods: In this retrospective case-control study, 65 AAV patients and 65 age- and sex-matched healthy controls were enrolled. AAV diagnosis adhered to the 2012 Chapel Hill Consensus and the American College of Rheumatology 1990 criteria. NLR, PLR, MLR, SII, and SIRI were calculated from complete blood counts. Disease activity (Birmingham Vasculitis Activity Score, BVAS), extent (Disease Extent Index, DEI), damage (Vasculitis Damage Index, VDI), and prognosis (Five-Factor Score, FFS 2009) were assessed. Statistical analyses included Mann-Whitney U tests, Spearman correlations, and receiver operating characteristic (ROC) curves to evaluate discriminatory and predictive capacities.

Results: AAV patients exhibited significantly higher NLR (6.94 ± 0.76 vs. 1.88 ± 0.08), PLR (242.44 ± 23.09 vs. 125.97 ± 4.34), MLR (0.44 ± 0.03 vs. 0.20 ± 0.01), SII (1813.71 ± 221.85 vs. 446.62 ± 22.40), and SIRI (3.19 ± 0.31 vs. 0.72 ± 0.06) compared to controls (all P < 0.001). ROC analysis showed strong discriminatory power, with SIRI (AUC = 0.902) and NLR (AUC = 0.885) performing best. NLR, PLR, SII, and SIRI correlated positively with BVAS (rs = 0.325-0.356, P < 0.01) and FFS 2009 (rs = 0.358-0.386, P < 0.05), and all markers correlated with DEI (rs = 0.396-0.488, P < 0.01), but not VDI. For predicting active disease (BVAS ≥ 15), SII had the highest AUC (0.726, P = 0.003).

Conclusions: NLR, PLR, MLR, SII, and SIRI effectively distinguish AAV patients from controls and reflect disease activity, extent, and prognosis. While not standalone diagnostic tools, these markers offer valuable support to standard AAV assessment, particularly in challenging cases. Their accessibility suggests potential for enhancing clinical management, pending validation in larger cohorts.

Keywords: disease activity; monocyte-to-lymphocyte ratio; prognosis; systemic immune-inflammation index; systemic inflammation response index.

Figure 1

The significance of NLR, PLR, SII and SIRI in diagnosing AAV ROC analysis showing the ability of NLR (A), PLR (B), MLR (C), SII (D), and SIRI (E) in differentiating AAV patients from healthy controls.

Figure 2

The difference of NLR, PLR, MLR, SII and SIRI in active group and non-active group. (A) The difference of NLR in the active and non-active group; (B) The difference of PLR in the active and non-active group; (C) The difference of MLR in the active and non-active group; (D) The difference of SII in the active and non-active group; (E) The difference of SIRI in the active and non-active group.

Figure 3

The Predictive Ability of NLR, PLR, SII, and SIRI for disease activity ROC analysis showing the ability of NLR (A), PLR (B), SII (C), and SIRI (D) to predict active disease.