Biomarkers in Sarcoidosis: From Traditional Markers to Precision Medicine.

Sarcoidosis remains a diagnostic and therapeutic challenge due to its heterogeneous clinical presentation and lack of pathognomonic features. Despite five decades of biomarker research, no single marker has achieved sufficient accuracy for a standalone diagnosis. Traditional biomarkers retain clinical utility when used strategically: High-sensitivity markers (soluble interleukin-2 receptor [sIL-2R], serum amyloid A [SAA], chitotriosidase) excel at confirming disease, while high-specificity markers (lysozyme) better exclude sarcoidosis. Chitotriosidase has emerged as superior to angiotensin-converting enzyme (ACE) for disease monitoring, and sIL-2R remains invaluable for detecting extrapulmonary involvement. However, their limitations necessitate multibiomarker approaches tailored to specific clinical phenotypes. Recent advances address critical unmet needs. High-sensitivity troponin T provides crucial prognostic information in cardiac sarcoidosis, with levels >14 ng/L predicting adverse outcomes. Novel fibrosis markers, including alveolar nitric oxide, heat shock protein 90α (HSP90α), and advanced Krebs von den Lungen-6 (KL-6) measurement, enable better assessment of disease progression. Prediagnostic inflammatory proteins elevated years before clinical manifestation suggest opportunities for early intervention. Revolutionary omics technologies are transforming biomarker discovery. Extracellular vesicle proteomics identifies treatment-responsive signatures, retrotrans-genomics reveals viral element activation in pathogenesis, and Mendelian randomization distinguishes causal from associative proteins. Integration of multiomics data through machine learning algorithms promises personalized diagnostic and therapeutic strategies. The future of sarcoidosis management lies in intelligent biomarker integration rather than reliance on single tests. Success will be measured by improved patient outcomes through earlier diagnosis, accurate risk stratification, and personalized treatment selection. This paradigm shift from empirical to precision medicine requires continued collaboration between researchers, clinicians, and patients to translate biomarker discoveries into clinical practice.