Effective biomarkers and model-based approaches for predicting vitiligo recurrence.

Belliappa P R
MBBS, DVL, DNB, MNAMS, Consultant Dermatologist and Cosmetologist
Derma Clear
Bangalore

Vitiligo is an acquired autoimmune condition marked by melanocyte loss in the skin and mucous membranes, leading to patchy depigmentation and affecting 0.5–2% of the global population. While biomarkers for disease activity and treatment response are known, limited research has explored cytokines as predictors of vitiligo recurrence. This study aimed to identify cytokines linked to recurrence and disease activity.

A total of 92 vitiligo patients (55 with non-segmental and 37 with segmental vitiligo) and 40 matched healthy controls were enrolled. To assess recurrence-related inflammatory markers, patients in a stable phase were followed monthly over six months, after blood collection. Plasma samples were analyzed using the Meso Scale Discovery (MSD) platform to quantify cytokines.

The expression of plasma interferon (IFN)-γ, C-X-C motif chemokine ligand (CXCL)-9, CXCL10, CXCL11, and interleukin (IL)-6 and IL-15 were significantly elevated in vitiligo patients compared to controls (all p<0.001), with no differences between segmental and non-segmental subtypes. All cytokines were also higher in both active and stable vitiligo cases versus controls. Notably, only CXCL9 showed a significant association with disease activity, with higher levels in active than stable patients (p=0.027).

Plasma levels of IFN-γ, CXCL9, CXCL10, CXCL11, and IL-6 were significantly higher in patients who experienced recurrence compared to those with persistent stable disease (p=0.001, p=0.003, p<0.001, p=0.002, p=0.026, respectively). Receiver Operating Characteristic analysis revealed CXCL10 as the strongest single predictor of recurrence (area under the curve [AUC]=0.896), followed by IFN-γ (AUC=0.806), CXCL11 (AUC=0.785), CXCL9 (AUC=0.773), and IL-6 (AUC=0.709).

A multivariate logistic regression model identified IFN-γ as an independent predictor of recurrence [odds ratio = 1.051 (95% confidence interval: 1.012–1.116)]. The model achieved 90.5% accuracy in the training set and 88.9% in the test set, supporting its utility in predicting vitiligo recurrence using circulating cytokine levels.

Plasma IFN-γ, CXCL9, CXCL10, CXCL11, and IL-6 may serve as biomarkers for vitiligo recurrence, with CXCL9 also linked to disease activity. IFN-γ was identified as an independent predictor in a logistic model, supporting its use in recurrence risk prediction.

Reference: Liu B, Shen J, Li J, et al. Candidate approaches for predicting vitiligo recurrence: an effective model and biomarkers. Front Immunol. 2025;16:1468665.