Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy)

https://doi.org/10.1016/j.jpba.2022.114827Get rights and content

Highlights

  • An untargeted lipidomics study was performed on 99 Covid-19 patients.

  • A RP-UHPLC-TIMS-MS approach was used to profile plasma lipid signatures.

  • Significant differences were found in lipid levels of severe patients.

  • LPCs, LPC-Os. PC-Os were found highly decreased in severe and deceased patients.

  • A machine learning model was built to predict severity and outcome.

Abstract

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease’s severity and patient’s outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639–0.904) and outcome (AUC= 0.789, CI: 0.658–0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches.

Abbreviations

RT
retention time
CCS
Collision Cross Section
CEs
cholesteryl esters
DGs
diacylglycerols
TGs
triacylglycerols
LPCs
lysophosphatidylcholines
LPC-Os
ether-linked lysophosphatidylcholines
PCs
phosphatidylcholines
PC-Os
ether-linked phosphatidylcholine
PEs
phosphatidylethanolamines
Cer
Ceramides
TIMS-MS
Trapped ion mobilty mass spectrometry

Keywords

COVID-19
Lipidomics
Severity
Trapped ion mobility
Untargeted

Cited by (0)

1

These authors share first authorship

2

These authors share last authorship

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