| Literature DB >> 34330463 |
Dimitris Tsoukalas1, Evangelia Sarandi2, Spyridoula Georgaki3.
Abstract
COVID-19 has re-established the significance of analyzing the organism through a metabolic perspective to uncover the dynamic interconnections within the biological systems. The role of micronutrient status and metabolic health emerge as pivotal in COVID-19 pathogenesis and the immune system's response. Metabolic disruption, proceeding from modifiable factors, has been proposed as a significant risk factor accounting for infection susceptibility, disease severity and risk for post-COVID complications. Metabolomics, the comprehensive study and quantification of intermediates and products of metabolism, is a rapidly evolving field and a novel tool in biomarker discovery. In this article, we propose that leveraging insulin resistance biomarkers along with biomarkers of micronutrient deficiencies, will allow for a diagnostic window and provide functional therapeutic targets. Specifically, metabolomics can be applied as: a. At-home test to assess the risk of infection and propose nutritional support, b. A screening tool for high-risk COVID-19 patients to develop serious illness during hospital admission and prioritize medical support, c(i). A tool to match nutritional support with specific nutrient requirements for mildly ill patients to reduce the risk for hospitalization, and c(ii). for critically ill patients to reduce recovery time and risk of post-COVID complications, d. At-home test to monitor metabolic health and reduce post-COVID symptomatology. Metabolic rewiring offers potential virtues towards disease prevention, dissection of high-risk patients, taking actionable therapeutic measures, as well as shielding against post-COVID syndrome.Entities:
Keywords: COVID-19; Infection susceptibility; Insulin resistance; Metabolic health; Metabolomics; Micronutrient deficiencies; Post-COVID
Mesh:
Substances:
Year: 2021 PMID: 34330463 PMCID: PMC8234252 DOI: 10.1016/j.clnesp.2021.06.011
Source DB: PubMed Journal: Clin Nutr ESPEN ISSN: 2405-4577
Fig. 1The catabolic pathway of BCAAs along with the extracted diagnostic biomarkers for insulin resistance and deficiency of B complex vitamins, zinc (Zn) and magnesium (Mg). BCAT: Branched-chain amino acid aminotransferase, BCKADC: Branched-chain ketoacid dehydrogenase complex, 3-MCC: 3-methylcrotonyl-CoA carboxylase, PCC: Propionyl-CoA carboxylase, MCM: Methylmalonyl-CoA mutase. Vitamins acting as coenzymes in the reactions are marked with yellow.
Summary of the latest clinical studies in metabolomics for the extraction of COVID-19 diagnostic biomarkers.
| Author(s), Year | Population | Group allocation | Specimen | Method | Metabolic profile in CP | Pathways | Conclusions |
|---|---|---|---|---|---|---|---|
| Shen at al. (2020) [ | N = 102 | 53 H, 49 CP | serum | Targeted, UPLC-MS/MS | Lipid metabolism, arginine metabolism | Severe cases can be classified using a machine learning method based on the proteomic and metabolomic profiling. | |
| Thomas et al. (2020) [ | N = 49 | 16 H, 33 CP | serum | Untargeted, Targeted UHPLC-MS | Tryptophan metabolism, glycolysis, pentose phosphate metabolism, fatty acid metabolism | The obtained serum metabolome reflects IL-6 function, providing promising therapeutic targets. | |
| Song et al. (2020) [ | N = 76 | 26 H, 50 CP | plasma | Untargeted, Targeted UPLC-MS/MS | Lipid metabolism, β-oxidation, TCA cycle, amino acid metabolism | Exosomes might be associated with COVID-19 pathogenesis. | |
| Kimhofer et al. (2020) [ | N = 42 | 25 H, 19 CP | plasma | Untargeted NMR, LCMS | Amino acid metabolism, gluconeogenesis, lipid metabolism | The metabolic imbalances indicate multi-organ associations, independently of the respiratory symptoms. | |
| Blasco et al. (2020) [ | N = 100 | 45 H, 55 CP | plasma | Targeted LC-HRMS | Major discriminators: Cytosine, indole-3-acetic acid, 2-aminophenol, isoleucine, asparagine, 1-NH2-cyclopropane-1-carboxylate, leucine, urate, xanthine | BCAAs metabolism, biotin metabolism, purine metabolism, nicotinate nicotinamide metabolism | Involvement of cytosine metabolism and tryptophan-nicotinamide pathway in COVID-19 infection. |
| Zhao et al. (2020) [ | N = 6 | 2H, 4CP | Colostrum (breastmilk) | Untargeted LC-MS/MS, | aminoacyl-tRNA biosynthesis, aromatic amino acid metabolism, tryptophan metabolism | Breastfeeding with deficiencies in immune-related compounds may not provide adequate defenses to infants. | |
| Thomas et al. (2020) [ | N = 52 | 23 H, 29 CP | serum | Targeted UHPLC-MS | Glycolysis, Lipid metabolism, protein degradation, ferroptosis, cyclic AMP-AMPK, energy metabolism | Altered RBC's membrane homeostasis suggests that their respond to oxidative stress is compromised. | |
| Chen et al. (2020) [ | N = 83 | 17 H, 66 CP | plasma | Untargeted, NMR | Lipid metabolism, glycolysis, TCA cycle | Identification of biomarkers to assist COVID-19 prognosis and stratification | |
| Su et al. (2020) [ | N = 266 | 133 H, 133 CP | plasma | Untargeted UHPLC-MS/MS | Nucleotide, Amino acid, Carbohydrate, lipid metabolism | Mild to severe: Overactivation of pro-inflammatory mechanisms is accompanied by downregulation of key metabolic pathways and possible hepatic failure. | |
| Barberis et al. (2020) [ | N = 161 | 26 H, 32 CN with symptoms, 103 CP | plasma | Untargeted UPLC-MS/MS, GCxGC-MS | Amino acid metabolism, TCA cycle, aminoacyl tRNA degradation, arachidonic acid metabolism | Host's response to the virus involves lipid deregulation and metabolic dysfunction. | |
| Smet et al. (2020) [ | N = 186 | 186 CP | serum | LC-MS | 25-OH-Vitamin D | Vitamin D levels are associated with disease stage. | |
| Grassin Delyle et al. (2020) [ | N = 40 | 12 ARDS CN, 28CP | EBC | PTR-MS | COVID-19 entails a metabolic breathprint. | ||
| Dogan et al. (2020) [ | N = 85 | 41 H, 44 CP | serum | Untargeted LC/Q-TOF/MS | purine, glutamine, leukotriene D4, glutathione metabolisms | Antioxidant balance and purine metabolism are targets in COVID-19 therapeutics | |
| Lodge et al. (2021) [ | N = 84 | 34 H, 15 CP, 35 CN with symptoms | plasma | Untargeted NMR | Lipoprotein metabolism, amino acid metabolism | NMR data indicate systemic disease. | |
| Ampudia et al. (2020) [ | N = 53 | 16 H, 19 recovered, 18 severe CP | serum | Untargeted RP-LC-QTOF-MS | Lipid metabolism | The phenotype of recovered patients was not similar to that of CN patients, with particular deregulation of unsaturated FAs. | |
| Wu et al. (2021) [ | N = 97 | 48 H, 28 CP, 21 CN | plasma | Targeted LC-MS/MS | BCAAs metabolism | Diabetic complications may accelerate metabolic dysfunction and susceptibility to infection. | |
| Lv et al. (2021) [ | N = 103 | 47 H, 56 CP | serum, stool | Untargeted GC–MS | phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl-tRNA | COVID-19 fecal analysis indicates malnutrition, dysbiosis and inflammation. | |
| Bai et al. (2021) [ | N = 19 | 5 H, 6 Cured severe/elderly | plasma | Untargeted UPLC-HRMS | Lipid metabolism | Hepatic dysfunction is present in recovery, indicating systemic response to infection and the need for assistive treatment. | |
| Sindelar et al. (2021) [ | N = 341 | 67 CN, 274 CP (145 non severe CP, 129 severe CP) | plasma | Untargeted LC-MS/MS | Lipid metabolism | A machine learning model can predict disease severity to guide treatment at early stages. | |
| Shi et al. (2021) [ | N = 187 | 78 H, 79 CP, 30 with symptoms | serum | Untargeted GC–MS | TCA cycle, glycolysis, | Distinctive and predictive serum metabolome of COVID-19 reflects systemic implications. | |
| Paez-Franco et al. (2021) [ | N = 92 | 27 H, 65 CP (19 mild CP, 46 severe CP) | serum | Untargeted GC–MS | BCAA, glutamate and phenylalanine metabolism, Warburg effect | Lung damage and hypoxia induce deregulated amino acid metabolism, suggesting the potentials of amino acid supplementation. | |
| Xu et al. (2021) [ | N = 130 | 27 H, 103 CP recovered | plasma | Untargeted LC-MS | Amino acid metabolism glycerophospholipid metabolism | Metabolomic signature of lung impairments in recovery as a potential therapeutic strategy. | |
| Danlos et al. (2021) [ | N = 99 | 27 CN, 72 CP | Serum/plasma | Untargeted GC–MS, Targeted UHPLC-MS | Glycolysis, sugars metabolism, amino acid metabolism | Metabolic signs of immunosuppression and tryptophan metabolism as therapeutic targets. | |
| Schwarz et al. (2021) [ | N = 57 | 19 H, 18 CP non-ICU, 20 CP ICU | serum | Targeted, LC-MS | Lipid metabolism | LMs regulate inflammation that reflects disease onset and progression. | |
| Delafiori et al. (2021) [ | N = 815 | 350 H, 442 CP, 23 CP suspicious | plasma | Untargeted, HESI-Q Orbitrap MS | Lipid metabolism | Mass spectrometry-machine learning provides prognostic markers and treatment targets for COVID-19 with high specificity and sensitivity. | |
| Xiao et al. (2021) [ | N = 84 | 17 H, 14 mild CP, 23 severe CP, 20 CN URTI, 7 mild CP independent cohort | serum | Untargeted, UHPLC quadrupole TOF MS/MS | Bile acid biosynthesis, TCA cycle, nicotinate and nicotinamide metabolism, arginine metabolism, nucleic acid metabolism | Cytokine release syndrome is tightly correlated with metabolic regulation. |
H: healthy, CP: COVID-10 positive, CN: COVID-19 negative, MS: Mass Spectrometry, HPLC: High Performance Liquid Chromatography, UPLC: Ultraperformance Liquid Chromatography, DAGs: diglycerides, TAGs: triglycerides, NMR: Nuclear Magnetic Resonance, HDL: high-density lipoprotein, LDL: low-density lipoprotein, VLDL: very low-density lipoprotein, IDL: Intermediate-density lipoprotein, HRMS: High Resolution Mass Spectrometry, PGM2L1: Phosphoglucomutase 2 Like 1, GAPDH: Glyceraldehyde 3-phosphate dehydrogenase, DPG: Dipropylene glycol, NADH: nicotinamide adenine dinucleotide, GSSG: Glutathione disulfide, AMP: Adenosine monophosphate, AMPK: AMP-activated protein kinase, RBCs: red blood cells, EBC: exhaled breath condensate, PTR-MS: Proton-transfer-reaction mass spectrometry, Q-TOF-MS: Quadrupole Time of Flight Mass Spectrometry, LTD4: Leukotriene D4, RP: reverse phase, AAs: amino acids, LMS: Laser Mass Spectrometry, HESI: Heated Electrospray Ionization.
Potential metabolic biomarkers for insulin resistance and micronutrient deficiency assessment.
| Insulin resistance panel | ||
|---|---|---|
| Metabolite | Metabolic pathway/Enzyme | Correlation with insulin resistance |
| Myristoleic/Myristic | Dehydrogenation of SFA/Δ9-desaturase | – |
| Palmitoleic/Palmitic | Dehydrogenation of SFA/Δ9-desaturase | – |
| Oleic/Stearic | Dehydrogenation of SFA/Δ9-desaturase | – |
| AA/DGLA | Omega 6 metabolism/Δ5-desaturase | – |
| GLA/LA | Omega 6 metabolism/Δ6-desaturase | – |
| AA/EPA | Omega 6 and 3 metabolism/Δ5-desaturase | + |
| Leucine/2-ketoisocaproate | BCAA metabolism/BCAT | + |
| Valine/2-ketoisovalerate | BCAA metabolism/BCAT | + |
| Isoleucine/2-keto-3methylvalerate | BCAA metabolism/BCAT | + |
| Circulating BCAA | BCAA metabolism | + |
| Micronutrient status panel | ||
| Metabolite | Metabolic pathway | Micronutrient |
| 8-Oxo-2′-deoxyguanosine | Lipid peroxidation | Vitamin A, Vitamin E, Vitamin C |
| 4-hydroxyphenylpyruvic | Tyrosine metabolism | Vitamin C |
| 4-hydroxyphenyllactate | Tyrosine metabolism | Vitamin C |
| adipic acid | Fatty acids oxidation/carnitine metabolism | Vitamin C |
| Quinolinic acid | Kynurenine pathway | Vitamin E |
| 25-hydroxy-vitamin D | Vitamin D metabolism | Vitamin D |
| Citrate | TCA cycle | Zinc, Magnesium |
| Succinate | TCA cycle | Zinc, Magnesium |
| Pyruvate | TCA cycle | Zinc |
| Methylmalonate | Valine metabolism | Vitamin B12 |
| 2-ketoisocaproic acid | Leucine metabolism | Vitamin B1, B3, B5 |
| 2-ketoisovaleric acid | Valine metabolism | Vitamin B1, B3, B6 |
| 2-keto3-methylvaleric acid | Isoleucine metabolism | Vitamin B1, B3, B7 |
| 3-methylcrotonic acid | Leucine metabolism | Vitamin B7 (dysbiosis) |
| 3-hydroxyisovaleric acid | Leucine metabolism | Vitamin B7 (dysbiosis) |
DGLA: dihomo-gamma linolenic acid, AA: Arachidonic acid, UFA: Unsaturated fatty acids, LA: Linolenic acid, GLA: gamma-linolenic acid, EPA: Eptadecanoic acid, BCAA: Branch chain aminoacids, BCAT: Branched-chain amino acid aminotransferase, TCA: Tricarboxylic acid.