Diagnostic Potential of the Plasma Lipidome in Infectious Disease: Application to Acute SARS-CoV-2 Infection
Nicola Gray,
Nathan G. Lawler,
Annie Xu Zeng,
Monique Ryan,
Sze How Bong,
Berin A. Boughton,
Maider Bizkarguenaga,
Chiara Bruzzone,
Nieves Embade,
Julien Wist,
Elaine Holmes,
Oscar Millet,
Jeremy K. Nicholson,
Luke Whiley
Affiliations
Nicola Gray
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Nathan G. Lawler
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Annie Xu Zeng
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Monique Ryan
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Sze How Bong
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Berin A. Boughton
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Maider Bizkarguenaga
Centro de Investigación Cooperativa en Biociencias—CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain
Chiara Bruzzone
Centro de Investigación Cooperativa en Biociencias—CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain
Nieves Embade
Centro de Investigación Cooperativa en Biociencias—CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain
Julien Wist
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Elaine Holmes
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Oscar Millet
Centro de Investigación Cooperativa en Biociencias—CIC bioGUNE, Precision Medicine and Metabolism Laboratory, Basque Research and Technology Alliance, Bizkaia Science and Technology Park, Building 800, 48160 Derio, Spain
Jeremy K. Nicholson
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
Luke Whiley
Australian National Phenome Centre, Health Futures Institute, Harry Perkins Institute, Murdoch University, 5 Robin Warren Drive, Perth, WA 6150, Australia
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.
