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基于代谢组学联合机器学习策略的煤工尘肺血清生物标志物筛查。

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

机构信息

Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China.

Department of Occupational Disease, Peking University Third Hospital, Beijing 100191, China.

出版信息

Int J Environ Res Public Health. 2022 Jun 9;19(12):7051. doi: 10.3390/ijerph19127051.

DOI:10.3390/ijerph19127051
PMID:35742299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9222502/
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.

摘要

尘肺仍然是全球最严重的职业疾病之一。然而,目前缺乏有效的治疗方法,早期检测对于疾病预防至关重要。本研究旨在通过高通量代谢组学结合机器学习策略,探索职业性煤工尘肺(CWP)的血清生物标志物,进行精准筛查。本研究在中国北京进行了一项病例对照研究,共纳入 150 例不同分期的尘肺患者和 120 例健康对照。代谢组学共发现 CWP 组与对照组之间存在 68 种差异代谢物。然后,通过三种机器学习方法从这些差异代谢物中筛选出 CWP 的潜在生物标志物。四个最重要的差异代谢物被鉴定为苯甲酰胺、特拉唑嗪、丙基对羟基苯甲酸酯和 N-甲基-2-吡咯烷酮。然而,在调整了年龄、吸烟、饮酒和慢性疾病等混杂因素的影响后,只有丙基对羟基苯甲酸酯与 CWP 显著相关。CWP 越严重,血清中丙基对羟基苯甲酸酯的含量越高。此外,丙基对羟基苯甲酸酯的受试者工作特征曲线(ROC)作为 CWP 的生物标志物具有良好的敏感性和特异性。因此,本研究表明 CWP 患者的血清代谢谱发生了显著变化,丙基对羟基苯甲酸酯等血清代谢物是 CWP 的良好生物标志物。

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