High-coverage lipidomics analysis reveals biomarkers for diagnosis of acute exacerbation of chronic obstructive pulmonary disease

脂类学 慢性阻塞性肺病 生物标志物 恶化 内科学 慢性阻塞性肺疾病急性加重期 医学 肺病 胃肠病学 病理 化学 生物化学
作者
Xinguang Liu,Hailong Zhang,Yimei Si,Yan Du,Jinyan Wu,Jiansheng Li
出处
期刊:Journal of Chromatography B [Elsevier BV]
卷期号:1201-1202: 123278-123278 被引量:17
标识
DOI:10.1016/j.jchromb.2022.123278
摘要

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is the leading cause of morbidity and mortality in COPD management. However, detecting the progression from the stable stage to acute exacerbation mainly depends on doctors' judgment of clinical symptoms, and there is no biomarker that can be used for auxiliary clinical diagnosis. In this work, serum samples from COPD patients (n = 82) and healthy subjects (n = 29) were collected and analyzed. Patients with COPD were divided into stable COPD (SCOPD) and AECOPD groups, with the latter comprising subtypes 1 and 2. High-coverage lipidomics profiling of 913 lipids belonging to 19 subclasses was carried out by liquid chromatography-Q-Exactive orbitrap mass spectrometry. We performed 4 cross-comparisons to characterize metabolic disturbances associated with the progression of stable COPD to AECOPD-ie, SCOPD vs healthy subjects, AECOPD vs SCOPD, AECOPD subtype 1 vs SCOPD, and AECOPD subtype 2 vs SCOPD. We tentatively identified 86 lipids with differential abundance among groups, lipids that were altered from the stable stage of disease to AECOPD included sphingolipids, ether-containing glycerophospholipids, phosphatidylglycerols, and glycerol lipids. Three panels of lipid biomarkers specific to AECOPD, AECOPD subtypes 1 and 2 vs SCOPD yielded areas under the receiver operating characteristic curve of 0.788, 0.921 and 0.920, respectively, with sensitivity of 77.5%, 80.7% and 91.3%, respectively, and specificity of 75.8%, 97.0% and 87.9%, respectively. The result indicated differences in lipid metabolism may underlie AECOPD and its 2 subtypes and can serve as biomarkers for early diagnosis, and high-coverage lipidomics proved to be an accurate approach to profile the lipid metabolism in biological samples.
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