Metabolomics of World Trade Center-Lung Injury: a machine learning approach.
Metabolomics of World Trade Center-Lung Injury: a machine learning approach.
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DOI：
10.1136/bmjresp-2017-000274
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发表时间：
2018
影响因子：
4.1
通讯作者：
Nolan A
中科院分区：
文献类型：
--
作者：
Crowley G;Kwon S;Haider SH;Caraher EJ;Lam R;St-Jules DE;Liu M;Prezant DJ;Nolan A
世贸中心（WTC）暴露后代谢综合征生物标志物的表达可预测世贸中心肺损伤（WTC- li）的发展。代谢组仍然是一个未开发的资源，具有全面表征WTC-LI许多方面的潜力。本病例对照研究通过全面的高维代谢分析和整合机器学习技术，确定了WTC-LI的一个临床相关的、健壮的代谢贡献者子集。不吸烟、男性、暴露于世贸中心的消防员在9/11之前肺功能正常，而在9/11之后肺功能正常。WTC-LI（15秒用力呼气量<正常下限，n=15）和对照组（n=15）从以前的队列中确定。对9/11后6个月内抽取的血清代谢组进行定量分析。机器学习用于降维，以识别与WTC-LI相关的代谢物。580种代谢物符合随机森林（RF）分析的条件，以确定产生最大类别分离的精制代谢物谱。改进剖面的RF正确分类受试者，估计成功率为93.3%。在精炼的剖面中出现了5个代谢物簇。突出的亚通路包括已知的肺部疾病介质，如鞘脂（在WTC-LI病例中升高）和支链氨基酸（在WTC-LI病例中降低）。精细化剖面的主成分分析解释了五个成分中68.3%的方差，证明了类分离。对暴露于世贸中心的9/11救援人员的代谢组分析已经确定了与肺功能丧失相关的生物学途径。由于代谢物是疾病过程的近端标记物，代谢物可以捕捉过去暴露的复杂性，从而更好地为治疗提供信息。这些途径需要进一步的机制研究。
Biomarkers of metabolic syndrome expressed soon after World Trade Center (WTC) exposure predict development of WTC Lung Injury (WTC-LI). The metabolome remains an untapped resource with potential to comprehensively characterise many aspects of WTC-LI. This case–control study identified a clinically relevant, robust subset of metabolic contributors of WTC-LI through comprehensive high-dimensional metabolic profiling and integration of machine learning techniques. Never-smoking, male, WTC-exposed firefighters with normal pre-9/11 lung function were segregated by post-9/11 lung function. Cases of WTC-LI (forced expiratory volume in 1s <lower limit of normal, n=15) and controls (n=15) were identified from previous cohorts. The metabolome of serum drawn within 6 months of 9/11 was quantified. Machine learning was used for dimension reduction to identify metabolites associated with WTC-LI. 580 metabolites qualified for random forests (RF) analysis to identify a refined metabolite profile that yielded maximal class separation. RF of the refined profile correctly classified subjects with a 93.3% estimated success rate. 5 clusters of metabolites emerged within the refined profile. Prominent subpathways include known mediators of lung disease such as sphingolipids (elevated in cases of WTC-LI), and branched-chain amino acids (reduced in cases of WTC-LI). Principal component analysis of the refined profile explained 68.3% of variance in five components, demonstrating class separation. Analysis of the metabolome of WTC-exposed 9/11 rescue workers has identified biologically plausible pathways associated with loss of lung function. Since metabolites are proximal markers of disease processes, metabolites could capture the complexity of past exposures and better inform treatment. These pathways warrant further mechanistic research.