References
-
Ludvigsson, J. F. Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr. 109, 1088–1095 (2020).
-
Girona-Alarcon, M. et al. The different manifestations of COVID-19 in adults and children: a cohort study in an intensive care unit. BMC Infect. Dis. 21, 87 (2021).
-
Garazzino, S. et al. Epidemiology, clinical features and prognostic factors of pediatric SARS-CoV-2 infection: results from an Italian multicenter study. Front. Pediatr. 9, 649358 (2021).
-
Rhedin, S. et al. Risk factors for multisystem inflammatory syndrome in children-a population-based cohort study of over 2 million children. Lancet Reg. Health Eur. 19, 100443 (2022).
-
Stephenson, T. et al. Physical and mental health 3 months after SARS-CoV-2 infection (long COVID) among adolescents in England (CLoCk): a national matched cohort study. Lancet Child Adolesc. Health 6, 230–239 (2022).
-
Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59–72 (2020).
-
Thaker, S. K., Ch’ng, J. & Christofk, H. R. Viral hijacking of cellular metabolism. BMC Biol. 17, 59 (2019).
-
Allen, C. N. S., Arjona, S. P., Santerre, M. & Sawaya, B. E. Hallmarks of metabolic reprogramming and their role in viral pathogenesis. Viruses 14, 602 (2022).
-
Hoxha, M. What about COVID-19 and arachidonic acid pathway? Eur. J. Clin. Pharm. 76, 1501–1504 (2020).
-
Blasco, H. et al. The specific metabolome profiling of patients infected by SARS-COV-2 supports the key role of tryptophan-nicotinamide pathway and cytosine metabolism. Sci. Rep. 10, 16824 (2020).
-
San Juan, I. et al. Abnormal concentration of porphyrins in serum from COVID-19 patients. Br. J. Haematol. 190, e265–e267 (2020).
-
Pang, Z., Chong, J., Li, S. & Xia, J. MetaboAnalystR 3.0: toward an optimized workflow for global metabolomics. Metabolites 10, 186 (2020).
-
Wang, C. et al. Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19. Theranostics 11, 8008–8026 (2021).
-
Centers for Disease Control and Prevention. Emergency Preparedness and Response: Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with Coronavirus Disease 2019 (COVID-19) https://emergency.cdc.gov/han/2020/han00432.asp (2020).
-
Troisi, J. et al. Metabolomic signature of endometrial cancer. J. Proteome Res. 17, 804–812 (2018).
-
Wold, S., Sjöström, M. & Eriksson, L. Partial least squares projections to latent structures (PLS) in chemistry. in (eds von Ragué Schleyer, P. et al.) Encyclopedia of Computational Chemistry (John & Wiley Sons, 1998).
-
Sysi-Aho, M. et al. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinform. 8, 93 (2007).
-
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
-
Szymańska, E., Saccenti, E., Smilde, A. K. & Westerhuis, J. A. Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics 8, 3–16 (2012).
-
Domingos, P. MetaCost: a general method for making classifiers cost-sensitive. In Proc. Fifth ACM SIGKDD International Conference Knowledge Discovery and Data Mining 155–164 (ACM, 1999).
-
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics 3, 211–221 (2007).
-
Xia, J. & Wishart, D. S. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).
-
Pellegrino, R., Chiappini, E., Licari, A., Galli, L. & Marseglia, G. L. Prevalence and clinical presentation of long COVID in children: a systematic review. Eur. J. Pediatr. 181, 3995–4009 (2022).
-
Chiu, C. Y. et al. Metabolomics reveals dynamic metabolic changes associated with age in early childhood. PLoS ONE 11, e0149823 (2016).
-
Pang, Z., Zhou, G., Chong, J. & Xia, J. Comprehensive meta-analysis of COVID-19 global metabolomics datasets. Metabolites 11, 44 (2021).
-
García, L. F. Immune response, inflammation, and the clinical spectrum of COVID-19. Front. Immunol. 11, 1441 (2020).
-
Firpo, M. R. et al. Targeting polyamines inhibits coronavirus infection by reducing cellular attachment and entry. ACS Infect. Dis. 7, 1423–1432 (2021).
-
Sacco, K. et al. Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19. Nat. Med. 28, 1050–1062 (2022).
-
Teoh, S. T. et al. Combined plasma and urinary metabolomics uncover metabolic perturbations associated with severe respiratory syncytial viral infection and future development of asthma in infant patients. Metabolites 12, 178 (2022).
-
Bozzetto, S. et al. Metabolomic profile of children with recurrent respiratory infections. Pharm. Res. 115, 162–167 (2017).
-
Wang, Y. et al. Blood transcriptome responses in patients correlate with severity of COVID-19 disease. Front. Immunol. 13, 1043219 (2023).
-
Nagao-Kitamoto, H. & Kamada, N. Host-microbial cross-talk in inflammatory bowel disease. Immune Netw. 17, 1–12 (2017).
-
Storr, M., Vogel, H. J. & Schicho, R. Metabolomics: is it useful for inflammatory bowel diseases? Curr. Opin. Gastroenterol. 29, 378–383 (2013).
-
Lin, H. M., Helsby, N. A., Rowan, D. D. & Ferguson, L. R. Using metabolomic analysis to understand inflammatory bowel diseases. Inflamm. Bowel Dis. 17, 1021–1029 (2011).
-
Sperisen, P., Cominetti, O. & Martin, F. P. Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research. Front. Mol. Biosci. 2, 44 (2015).
-
Carroll, M. W. et al. The impact of inflammatory bowel disease in Canada 2018: children and adolescents with IBD. J. Can. Assoc. Gastroenterol. 2, S49–S67 (2019).
-
Sindelar, M. et al. Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity. Cell Rep. Med. 2, 100369 (2021).
-
Maeda, R. et al. Amino acid catabolite markers for early prognostication of pneumonia in patients with COVID-19. Nat. Commun. 14, 8469 (2023).
-
Pratomo, I. P. et al. Xanthine oxidase-induced inflammatory responses in respiratory epithelial cells: a review in immunopathology of COVID-19. Int J. Inflamm. 2021, 1653392 (2021).
-
Pol, K., Puhlmann, M. L. & Mars, M. Efficacy of L-arabinose in lowering glycemic and insulinemic responses: the modifying effect of starch and fat. Foods 11, 157 (2022).
-
Ahmed, I., Roy, B. C., Khan, S. A., Septer, S. & Umar, S. Microbiome, metabolome and inflammatory bowel disease. Microorganisms 4, 20 (2016).
-
Ursell, L. K. et al. The intestinal metabolome: an intersection between microbiota and host. Gastroenterology 146, 1470–1476 (2014).
-
Teufel, R. et al. Bacterial phenylalanine and phenylacetate catabolic pathway revealed. Proc. Natl. Acad. Sci. USA 107, 14390–14395 (2010).
-
Sperling, O. Human purine metabolism. in (eds De Jong, J.W.) Myocardial Energy Metabolism, Vol 91 (Springer, 1988).
-
Farley S. E. et al. A global lipid map reveals host dependency factors conserved across SARS-CoV-2 variants. Nat. Commun. 13, 3487 (2022).
-
Van Wyngene, L., Vandewalle, J. & Libert, C. Reprogramming of basic metabolic pathways in microbial sepsis: therapeutic targets at last? EMBO Mol. Med. 10, e8712 (2018).
-
Niu, Z. et al. Circulating glycerolipids, fatty liver index, and incidence of type 2 diabetes: a prospective study among Chinese. J. Clin. Endocrinol. Metab. 106, 2010–2020 (2021).
-
Prentki, M. & Madiraju, S. R. Glycerolipid metabolism and signaling in health and disease. Endocr. Rev. 29, 647–676 (2008).
-
Hofer, S. J. et al. Mechanisms of spermidine-induced autophagy and geroprotection. Nat. Aging 2, 1112–1129 (2022).
-
Danlos et al. Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers. Cell Death Dis. 12, 258 (2021).
-
Korbecki, J. & Bajdak-Rusinek, K. The effect of palmitic acid on inflammatory response in macrophages: an overview of molecular mechanisms. Inflamm. Res. 68, 915–932 (2019).
