Screening of Serum Biomarkers of Coal Workers’ Pneumoconiosis by Metabolomics Combined with Machine Learning Strategy

Abstract

Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers’ pneumoconiosis (CWP) by high-throughput metabolomics, combining with machine learning strategy for precision screening. A case–control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as benzamide, terazosin, propylparaben and N-methyl-2-pyrrolidone. However, after adjusting for the influence of confounding factors, including age, smoking, drinking and chronic diseases, only one metabolite, propylparaben, was significantly correlated with CWP. The more severe CWP was, the higher the content of propylparaben in serum. Moreover, the receiver operating characteristic curve (ROC) of propylparaben showed good sensitivity and specificity as a biomarker of CWP. Therefore, it was demonstrated that the serum metabolite profiles in CWP patients changed significantly and that the serum metabolites represented by propylparaben were good biomarkers of CWP.

Serum Osteopontin, KL-6, and Syndecan-4 as Potential Biomarkers in the Diagnosis of Coal Workers’ Pneumoconiosis: A Case–Control Study

“…Because the disease is progressive and incurable, early diagnosis or early prevention is especially important to control this disease. Previous studies showed elevated levels of serum markers such as IL-6, IL-8, propylparaben, TGF-β1 in patients with pneumoconiosis [7][8][9] and indicated the potential role of serum biomarkers for the early diagnosis. However, although these biomarkers show promise, they have not yet been applied in clinical settings.…”

Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms

“…However, no currently available clinical indicator or system can provide sufficiently accurate predictions for disease progression in CWP patients of the early phase 16 . Therefore, developing and validating sensitive and specific clinical indicators to effectively predict the progression of CWP in the early phase is essential.…”

“…However, no currently available clinical indicator or system can provide sufficiently accurate predictions for disease progression in CWP patients of the early phase. 16 Therefore, developing and validating sensitive and specific clinical indicators to effectively predict the progression of CWP in the early phase is essential. The objective of this research was the development of a computational tool for predicting the risk of CWP with early stage in dust–exposed workers from large amounts of clinical indicators, which have shown that there were differences between patients confirmed CWP and dust–exposed workers, including arterial blood gas analysis, pulmonary function test, blood cell analysis, inflammatory markers, blood biochemical parameters, coagulation function, and serum tumor markers.…”