ArticleMetabolites2026
Metabolomic Subtyping and Machine Learning-Based Diagnosis Reveal Clinical Heterogeneity in Silicosis.
Article in Metabolites, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
BACKGROUND/
objectivesSilicosis 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.
methodsWe 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.
resultsA total of 860 differentially abundant metabolites, including elevated pathogen-associated compounds, were identified in silicosis patients. Unsupervised NMF clustering revealed two distinct metabolic subtypes with different clinical features. Patients in the NMF2 subgroup had a 5.3-fold greater risk of pulmonary infections (
conclusionsThese findings highlight metabolic heterogeneity in silicosis and suggest clinically relevant subtypes, providing a foundation for improved stratification, early detection, and targeted interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.