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Journal Article Ppublish 31 citations

PubMed: 35569273 DOI: 10.1016/j.jpba.2022.114827

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

J Pharm Biomed Anal 217:114827

Authors: Michele Ciccarelli, Fabrizio Merciai, Albino Carrizzo, Eduardo Sommella, Paola Di Pietro, Vicky Caponigro, Emanuela Salviati, Simona Musella, Veronica di Sarno, Mariarosaria Rusciano, Anna Laura Toni, Paola Iesu, Carmine Izzo, Gabriella Schettino, Valeria Conti, Eleonora Venturini, Carolina Vitale, Giuliana Scarpati, Domenico Bonadies, Antonella Rispoli, Benedetto Polverino, Sergio Poto, Pasquale Pagliano, Ornella Piazza, Danilo Licastro, Carmine Vecchione, Pietro Campiglia

Affiliations

  • Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy.
  • Department of Pharmacy, University of Salerno, Fisciano, SA, Italy; PhD Program in Drug Discovery and Development, University of Salerno, Fisciano, SA, Italy.
  • Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy; IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy.
  • Department of Pharmacy, University of Salerno, Fisciano, SA, Italy.
  • San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy.
  • IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy.
  • AREA Science Park Padriciano, 9934149 Trieste, Italy.
  • Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy; IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy.
  • Department of Pharmacy, University of Salerno, Fisciano, SA, Italy.

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.

MeSH

Blood, Chemistry, Pharmaceutical, Phenotype, Covid-19, Lipidomics

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