#AI reads Urine# 1H-NMR urine metabolomic fingerprint for severity discrimination in pulmonary sarcoidosis

This study, titled "¹H-NMR urine metabolomic fingerprint for severity discrimination in pulmonary sarcoidosis," conducted by Florence Jeny and colleagues, aimed to identify urinary metabolite biomarkers for distinguishing the severity of pulmonary sarcoidosis. A consecutive cohort of 37 well-phenotyped sarcoidosis patients (excluding those with recent use of specific medications, diabetes, urinary tract infections, etc.) was enrolled, with their first-morning urine samples collected after controlling for diet and exercise interference. Using ¹H-nuclear magnetic resonance (¹H-NMR) spectroscopy combined with multivariate statistical analyses like principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), the researchers identified a urinary metabolomic signature predictive of severe pulmonary sarcoidosis (defined by a composite physiologic index > 40). Specifically, severe cases showed decreased levels of taurine, hippurate, serine, and creatinine, along with increased 3-hydroxyisovalerate, a profile linked to activated inflammatory pathways. The PLS model exhibited good predictive performance (AUC of 0.89 in the training set and correct prediction of all 8 test samples). While the study demonstrates the potential of urinary ¹H-NMR metabolomics in discriminating sarcoidosis severity and identifying prognostic biomarkers, it emphasizes the need for further validation in larger, multicenter cohorts across different disease stages to confirm its utility in patient follow-up and treatment monitoring.

ERJ Open Res. 2025 Aug 26;11(4):00763-2024. doi: 10.1183/23120541.00763-2024.

Youhe Gao

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For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/