Publication date: Dec 31, 2024
Background: Sarcoidosis is a granulomatous disease affecting multiple organ systems and poses a diagnostic challenge due to its diverse clinical manifestations and absence of specific diagnostic tests. Currently, blood biomarkers such as ACE, sIL-2R, CD163, CCL18, serum amyloid A, and CRP are employed to aid in the diagnosis and monitoring of sarcoidosis. Metabolomics holds promise for identifying highly sensitive and specific biomarkers. This study aimed to leverage metabolomics for the early diagnosis of sarcoidosis and to identify metabolic phenotypes associated with disease progression. Methods: Serum samples from patients with sarcoidosis (n = 40, including stage 1 to stage 4), were analyzed for metabolite levels by semi-untargeted liquid chromatography-mass spectrometry (LC-MS). Metabolomics data from patients with sarcoidosis were compared with those from patients with COVID-19 and healthy controls to identify distinguishing metabolic biosignatures. Univariate and multivariate analyses were applied to obtain diagnostic and prognostic metabolic phenotypes. Results: Significant changes in metabolic profiles distinguished stage 1 sarcoidosis from healthy controls, with potential biomarkers including azelaic acid, itaconate, and glutarate. Distinct metabolic phenotypes were observed across the stages of sarcoidosis, with stage 2 exhibiting greater heterogeneity compared with stages 1, 3, and 4. Conclusions: we explored immunometabolic phenotypes by comparing patients with sarcoidosis with patients with COVID-19 and healthy controls, revealing potential metabolic pathways associated with acute and chronic inflammation across the stages of sarcoidosis.
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| Concepts | Keywords |
|---|---|
| Amyloid | early diagnosis |
| Biomarkers | immunometabolic |
| Cd163 | metabolic phenotype |
| Healthy | sarcoidosis |
| Organ |