Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection

Abstract

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management.

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

“…Furthermore, our data underline that within the entire lipidome signature, not only LPCs, but specifically ether linked lipids such as LPC-Os and PC-Os owns the highest accuracy in discriminating both severity and outcome. Previous Targeted-MS based lipidomics studies were conducted to distinguish between asymptomatic COVID-19 and healthy subjects, or in diagnostics, to discriminate symptomatic COVID-19 and healthy patients, as a new tool to augment RT-PCR strategy [ 31 ] . In other studies, with comparable numbers of patients, different proteomic and metabolomic profiles were demonstrated to be associated with clinical phenotypes of COVID-19, from mild to severe [ 32 ] .…”

“…In other studies, with comparable numbers of patients, different proteomic and metabolomic profiles were demonstrated to be associated with clinical phenotypes of COVID-19, from mild to severe [ 32 ] . While previous lipidomics studies reported the observation of relative fold-changes of detected lipids between different study groups, the comparison of lipid concentration, is essential to support inter study comparison [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] . Nevertheless, only few studies have reported concentration data (as nmol/mL), and hence comparison is not possible.…”

Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum

“…SARS-CoV-2-(+) patients could be effectively distinguished from healthy controls modelling untargeted 1 H NMR data (Figure 1A). Phenoconversion from a healthy into an infected stated is associated with changes in the concentrations of a wide range of metabolic entities (lipoproteins, glycoproteins, amino acids, lipids and other metabolites) that can be derived from NMR spectroscopic and mass spectrometric data [26][27][28] , and which result in distinctive embedded biomarker features including some of those previously observed in diabetes 29,30 , cardiovascular disease [31][32][33][34][35] , liver dysfunction 36,37 , neurological disruption 38 and inflammation 39,40 .Many of these pathological changes can be observed using a variety of NMR experiments on blood plasma and these changes are robust to sample handling with standardised protocols 41,42 and were also observed by others 22,[43][44][45] .…”

Metabolomic Profiling of Plasma Reveals Differential Disease Severity Markers in COVID-19 Patients

“…Recent studies have revealed critical metabolic dysregulations occurring in COVID-19 cases (Song et al, 2020 ; Wu et al, 2020a ; Jimenez et al, 2021 ). In addition to mass spectrometry, other metabolomics techniques, such as nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry lipid profiling, have been employed (Kimhofer et al, 2020 ; Gray et al, 2021 ; Lorente et al, 2021 ; Meoni et al, 2021 ). In this context, an in-depth evaluation of metabolites, particularly those associated with different COVID-19 clinical presentations, is needed (Akarachantachote et al, 2014 ) and could further improve disease diagnosis, prognosis, or both, thus, potentially leading to personalized management strategies in the future (Blasco et al, 2020 ).…”