Abstract Objective ACTH-dependent Cushing's syndrome (CS) causes profound immune dysfunction and severe infections. This study aimed to characterize immune cell phenotypes, develop predictive models for differentiating Cushing's disease (CD) from ectopic ACTH syndrome (EAS) and predicting infection risk, and evaluate immune recovery after surgical remission. Design Retrospective single-center study. Methods We studied 211 patients with ACTH-dependent CS (173 CD, 38 EAS) and aged 11-75 years. Twelve patients were evaluated longitudinally after remission. Lymphocyte subsets were analyzed by flow cytometry, severe infection incidence was documented, and machine learning models were developed for disease differentiation. Results EAS patients showed more severe immune suppression than CD, with reduced CD4+ T cells (224 158.0–390.0 vs. 442 313.0–611.0 cells/μL, p 0.001) and CD3+ T cells (505 342.0–815.0 vs. 930 713.0–1246.0 cells/μL, p 0.001), while elevated percentage of CD19+ B cells (18.2% 13.3–25.5% vs. 9.6% 6.1–14.4%, p 0.001). Machine learning based model integrating immune and non-immune parameters achieved improved balanced accuracy of 75.4% (AUC=0.840, 95% CI: 0.770-0.910) for CD/EAS differentiation. Severe infections occurred in 13.7% of patients. The multivariate model incorporating 24h UFC, serum potassium nadir, CD19+ B cell percentage, and CD4+ T cell count predicted severe infection risk (AUC=0.851, 95% CI: 0.774-0.927; sensitivity 86.2%, specificity 68.7%). Following surgical remission, profound T cell reconstitution occurred after 8 weeks, with CD4+ T cells increasing 3-fold (p 0.001). Conclusions Immune profiling distinguishes CD/EAS and predicts infection risk in ACTH-dependent CS. Surgical remission facilitates immune reconstitution.
Liu et al. (Fri,) studied this question.