Published January 12, 2021 | Version v1
Dataset Open

Lipid profile and differential lipids in serum related to severity of community-acquired pneumonia--a pilot study

  • 1. Department of Respiratory & Critical Care Medicine, Peking University People's Hospital
  • 2. Department of Respiratory, Critical Care & Sleep Medicine, Xiang'an Hospital of Xiamen University

Description

This study aimed to characterize the lipidomic responses to community-acquired pneumonia (CAP) and provide new insight into the underlying mechanisms of pathogenesis and potential avenues for diagnostic and therapeutic treatments.  Lipidomic profiles were generated using ultra high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) platform. Spearman’s rank correlation test and multiple linear regression analysis were applied to explore the lipids correlation with clinical parameters. Kaplan–Meier methods were used to build 30-day survival curves. From UHPLC-MS/MS a total of 509 and 195 lipid species were detected in the positive and negative ionisation mode respectively. Positive ionisation covered six lipid classes (glycerol-phospholipids, glycerolipids, sphingolipids, sterol-lipids, prenol-lipids, and fatty acid), whilst negative ionisation covered three (glycerol-phospholipids, sphingolipids, fatty acid). Four lipids were selected as target: PC (16:0_18:1), PC (18:2_20:4), PC (36:4), and PC (38:6). Areas under the curve for all four lipids were superior to pneumonia severity index and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥65 years old) for discriminating severe CAP from CAP. PC (18:2_20:4), PC (38:6), and PC (36:4) were negatively related to fraction of inspiration O2; PC (16:0_18:1) and PC (18:2_20:4) had significantly linear relationship with the procalcitonin. Patients with an elevated level of PC (16:0_18:1) had significantly increased hospital stays. As the relative abundance of PC (18:2_20:4), PC (36:4), and PC (38:6) decreased, the length of hospitalization days and 30-day mortality rate increased significantly (all log-rank p<0.05). Therefore, the UHPLC-MS/MS platform’s serum lipidomics approach can help reveal lipid changes during CAP and establish lipid profiles related to disease severity.

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