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

Autor: Ciccarelli M; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Merciai F; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy; PhD Program in Drug Discovery and Development, University of Salerno, Fisciano, SA, Italy., Carrizzo A; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy; IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy., Sommella E; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy., Di Pietro P; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Caponigro V; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy., Salviati E; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy., Musella S; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy., Sarno VD; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy., Rusciano M; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Toni AL; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Iesu P; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Izzo C; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Schettino G; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Conti V; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Venturini E; IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy., Vitale C; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Scarpati G; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Bonadies D; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Rispoli A; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Polverino B; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Poto S; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy., Pagliano P; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Piazza O; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy., Licastro D; AREA Science Park Padriciano, 9934149 Trieste, Italy., Vecchione C; Department of Medicine and Surgery, University of Salerno, Baronissi, SA, Italy; IRCCS Neuromed, Loc. Camerelle, Pozzilli, IS, Italy. Electronic address: cvecchione@unisa.it., Campiglia P; Department of Pharmacy, University of Salerno, Fisciano, SA, Italy. Electronic address: pcampiglia@unisa.it.
Jazyk: angličtina
Zdroj: Journal of pharmaceutical and biomedical analysis [J Pharm Biomed Anal] 2022 Aug 05; Vol. 217, pp. 114827. Date of Electronic Publication: 2022 May 10.
DOI: 10.1016/j.jpba.2022.114827
Abstrakt: 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.
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Databáze: MEDLINE