First-Year IgG Dynamics in IgG4-Related Disease: A Critical Window for Predicting Long-Term Outcomes

To investigate longitudinal serum IgG dynamics in IgG4-related disease (IgG4-RD) after treatment and assess their prognostic value for relapse.

A retrospective cohort of 274 newly treated patients with IgG4-RD was stratified into elevated IgG (n = 186) and normal IgG (n = 88) groups. Treatment responses were evaluated by covariance analysis adjusted for baseline IgG. Longitudinal IgG trends and relapse risk were analyzed using Kaplan-Meier curves and Cox regression.

Elevated baseline IgG level was associated with more severe phenotypes, including male predominance, older age, higher responder index scores, more internal organ involvement, hypocomplementemia, and elevated erythrocyte sedimentation rate/C-reactive protein level. IgG1 and IgG4 mainly contributed to IgG elevation. After treatment, 79.6% of patients with elevated IgG levels achieved normalization, with the greatest decline in the first year. Glucocorticoid (GC)-based regimens reduced IgG levels more effectively than GC-sparing therapies and achieved higher normalization rates (85.2% vs 66.7%). Twelve-month IgG normalization strongly predicted reduced relapse risk. Patients achieving IgG normalization had lower relapse rates (17.9% vs 44.1%; P = 0.001) and superior relapse-free survival (mean 33.20 [95% CI 32.21-34.19] vs 27.71 [95% CI 24.29-31.13] months; log-rank P < 0.001). Multivariate Cox regression confirmed failure of 12-month IgG normalization (hazard ratio [HR] 2.67 [95% CI 1.43-4.99], P = 0.002) and treatment intensity (GCs + weak immunosuppressants [IMs]: HR 0.36, P = 0.012; GCs + potent IMs: HR 0.40, P = 0.020, vs GC-sparing) as independent relapse determinants.

Baseline IgG elevation marks more severe IgG4-RD phenotypes. The first treatment year achieving IgG normalization represents a critical prognostic biomarker. Failure to normalize IgG within 12 months markedly increases relapse risk. Longitudinal IgG monitoring supports risk stratification and treatment optimization.