TY - JOUR
T1 - Metabolomics Markers of COVID-19 Are Dependent on Collection Wave
AU - Lewis, Holly May
AU - Liu, Yufan
AU - Frampas, Cecile F.
AU - Longman, Katie
AU - Spick, Matt
AU - Stewart, Alexander
AU - Sinclair, Emma
AU - Kasar, Nora
AU - Greener, Danni
AU - Whetton, Anthony D.
AU - Barran, Perdita E.
AU - Chen, Tao
AU - Dunn-Walters, Deborah
AU - Skene, Debra J.
AU - Bailey, Melanie J.
N1 - Funding Information:
This research was funded by Biotechnology and Biological Sciences Research Council, grant number BB/V011456/1 and Engineering and Physical Sciences Research Council, grant number EP/R031118/1. Y. Liu’s PhD was supported by a Surrey-Unilever-IPE (Institute of Process Engineering, Chinese Academy of Sciences) PhD studentship.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7/30
Y1 - 2022/7/30
N2 - The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.
AB - The effect of COVID-19 infection on the human metabolome has been widely reported, but to date all such studies have focused on a single wave of infection. COVID-19 has generated numerous waves of disease with different clinical presentations, and therefore it is pertinent to explore whether metabolic disturbance changes accordingly, to gain a better understanding of its impact on host metabolism and enable better treatments. This work used a targeted metabolomics platform (Biocrates Life Sciences) to analyze the serum of 164 hospitalized patients, 123 with confirmed positive COVID-19 RT-PCR tests and 41 providing negative tests, across two waves of infection. Seven COVID-19-positive patients also provided longitudinal samples 2–7 months after infection. Changes to metabolites and lipids between positive and negative patients were found to be dependent on collection wave. A machine learning model identified six metabolites that were robust in diagnosing positive patients across both waves of infection: TG (22:1_32:5), TG (18:0_36:3), glutamic acid (Glu), glycolithocholic acid (GLCA), aspartic acid (Asp) and methionine sulfoxide (Met-SO), with an accuracy of 91%. Although some metabolites (TG (18:0_36:3) and Asp) returned to normal after infection, glutamic acid was still dysregulated in the longitudinal samples. This work demonstrates, for the first time, that metabolic dysregulation has partially changed over the course of the pandemic, reflecting changes in variants, clinical presentation and treatment regimes. It also shows that some metabolic changes are robust across waves, and these can differentiate COVID-19-positive individuals from controls in a hospital setting. This research also supports the hypothesis that some metabolic pathways are disrupted several months after COVID-19 infection.
KW - COVID-19
KW - LC-MS
KW - machine learning
KW - targeted metabolomics
U2 - 10.3390/metabo12080713
DO - 10.3390/metabo12080713
M3 - Article
C2 - 36005585
AN - SCOPUS:85138082169
SN - 2218-1989
VL - 12
JO - Metabolites
JF - Metabolites
IS - 8
M1 - 713
ER -