Metabolomic Subtyping and Machine Learning-Based Diagnosis Reveal Clinical Heterogeneity in Silicosis.

ABSTRACT:

Background/objectives

Silicosis remains a major occupational health concern worldwide and is characterized by notable clinical heterogeneity in terms of disease progression and complications. However, the underlying metabolic mechanisms contributing to this heterogeneity remain poorly understood.

Methods

We conducted a case-control study involving 156 silicosis patients and 132 silica-exposed controls. The plasma samples were analyzed via untargeted metabolomics based on liquid chromatography-mass spectrometry (LC-MS/MS). To explore disease subtypes and potential biomarkers, we applied non-negative matrix factorization (NMF) clustering, weighted gene co-expression network analysis (WGCNA), and machine learning approaches.

Results

A total of 860 differentially abundant

SUBMITTER: Si J 

PROVIDER: S-EPMC12843767 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature