This review demonstrates improved disease monitoring in sarcoidosis through biomarkers, suggesting precision medicine enhances prognostic information and personalized treatment.
. 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 standalone diagnosis. Traditional biomarkers retain clinical utility when used strategically: high-sensitivity markers (sIL-2R, SAA, chitotriosidase) excel at confirming disease, while high-specificity markers (lysozyme) better exclude sarcoidosis. Chitotriosidase has emerged as superior to ACE for disease monitoring, and sIL-2R remains invaluable for detecting extrapulmonary involvement. However, their limitations necessitate multi-biomarker 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, HSP90α, and advanced KL-6 measurement enable better assessment of disease progression. Pre-diagnostic 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 multi-omics 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.
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Pascual et al. (2025) studied this question.
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