Submitted:
06 May 2025
Posted:
08 May 2025
You are already at the latest version
Abstract

Keywords:
1. Introduction
2. Pediatric Asthma Endotypes
3. Metabolomics Endotyping of Asthmatic Children
4. Pediatric SARS-CoV-2
5. Pediatric Asthma and SARS-CoV2
6. Shared Inflammatory and Metabolic Networks Between Asthma and Pediatric SARS-CoV-2
7. From Asthma Endotypes to SARS-CoV-2 Risk Stratification: Integrative Analysis
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Endotype | Immunological Features | Clinical Phenotype | Treatment Response |
|---|---|---|---|
| Th2-high | ↑ IL-4, IL-5, IL-13, eosinophilia, ↑ IgE,↓ IFN-I/III | Cluster 1: mild early-onset atopic asthma (low exacerbations, preserved lung function)Cluster 2: moderate persistent atopic asthma (↓ lung function, severe hyperreactivity)Cluster 3: severe highly atopic asthma (significant symptoms, intensive therapy required, impaired lung function) | Good response to ICS and biologics |
| Th2-lowneutrophilic | ↑ Th17, IL-17, IL-12, TNF-α,neutrophilia,↓ IFN-I/III, ↑ IFN-γ,Th1/Th17 activation | Mild early-onset non-atopic asthma (normal lung function)Severe persistent non-allergic asthma (poor control, obesity-associated, high exacerbations) | Poor response to ICSrequires specialist evaluation |
| Th2-low paucigranulocytic | Absence of eosinophils or neutrophils,low systemic inflammation | Mild or inactive asthma,airway remodeling and bronchial hyperreactivity,persistent airflow limitation | Variable |
| Obesity-related (T2-high) | Eosinophilic inflammation,high BMI, predominant Th2 profile,possible ↓ IFN-I/III | Early-onset asthma with T2 inflammation | Partial, nutritional intervention may beneficial |
| Obesity-related (T2-low) | Neutrophilic inflammation,↑ IL-6, IL-1β, ↑ IL-17, ↑ IFN-γ, ↓ IFN-I/III, Th1/Th17 activation | Late-onset asthma with systemic inflammation | Poor, targeted metabolic interventions required |
| Virus-induced | Neutrophilia (during viral infection), immature IFN-I/III responses in children, impaired antiviral defense | Preschool episodic wheeze | Variable, does not always progress to asthma |
| STRA | ↑ IL-33, ILC2, persistent eosinophilia, sustained Th2 profile, immune dysregulation, ↓ IFN-I/III | Severe,corticosteroid-resistant asthma | Biologics needed, intensive management, poor ICS response due to steroid resistance |
| Samples | Patients | Technique | Key Findings | Clinical Relevance |
|---|---|---|---|---|
| EBC[30] | 42 asthmatic children (8– 17 years): 31 with non severe and 11 with severe asthma. | LC-MS | EBC metabolomic profiling separated severe, non-severe asthma and controlsKey metabolites: retinoic acid, adenosine and vitamin D derivatives | Breathomics profiling may serve as a non-invasive tool to distinguish asthma severity phenotypes and support individualized therapeutic strategies in children |
| EBC[31] | 89 asthmatic children and 20 controls | NMR+ML | 3 distinct clusters outlined in the asthmatic group, with significant clinical and chemical differences (eosinophils, exacerbations and family history) | Global breathomics profiling aided by ML may facilitate endotype discovery in pediatric asthma through non-invasive spectral clustering |
| PLASMA[32] | 215 asthmatic subjects, including 41 with exacerbative asthma | UHPLC-MS | 32 unique cohort-independent metabolites distinguished exacerbation-prone from non-prone asthmatic childrenThe arginine, lysine and methionine pathways were the most affected | Specific plasma metabolites enable identification of exacerbation-prone asthma in children, even under high-dose ICS therapy, supporting a role for metabolomics in early risk stratification |
| PLASMA[33] | 22 children with mild-to-moderate asthma (8 normal weight, 7 overweight and 7 obese) and 35 with severe refractory asthma (15 normal weight, 9 overweight and 11 obese)(9-17 years) | LC-MS | Distinct plasma metabolomic profiles in severe vs. mild-to-moderate asthmaPathways involved included glycine/serine/threonine metabolism and N-acylethanolamine and N-acyltransferase signalling both associated with oxidative stress | Oxidative stress-related metabolic alterations may underlie corticosteroid insensitivity in severe pediatric asthma and represent potential therapeutic targets |
| PLASMA [34] | 64 asthmatic subjects (5-12 years) with mild asthma phenotype(35 normal, 18 overweightand 8 obese) | GC-MS | Linoleic, oleic, erucic, cis-11-eicosenoic and arachidic acids significantly associated with poorer asthma control and lung function (FEV1, FVC, FEV1/FVC, PEF, FEF25-75% e FeNO)in overweight/obese childrenNo associations for arachidonic, α-linolenic, EPA and DHA | Fatty acid profiling could guide personalized nutritional interventions to improve asthma control and therapeutic outcomes in children |
| PLASMA [35] | 380 asmathic children | LC-MS | Specific metabolites correlated with three clinical features associated with disease severity:-AHR correlated with 91 of the 574 metabolites-%FEV1/FVC ratio pre- and post-bronchodilator with 102 and 155 respectively)Key metabolites: thiamine, creatinine, fatty acids (oleic, myristic), carnitine and gammalinolenic acid | Asthma severity metabolome associated with different degrees of asthma severity in children, reflecting measurable biological alterations at the systemic levelPotential of metabolomics to refine asthma phenotyping and guide personalized severity assessment |
| PLASMA [36] | 1.165 asthmatic subjects(aged 6 and 14 years) from 2 different cohorts | LC-MS+SNF and spectral clustering | Detection of 5 metaboendotypes with significant phenotypic differences, including pre and post bronchodilator FEV1/FVCKey metabolites: cholesterol esters, triglycerides and fatty acids | Existence of reproducible, clinically meaningful metabo-endotypes in asthma, supporting metabolomics as a tool for precision medicine |
| PLASMA [38] | Asthmatics children 6-17 years (257 lean, 99 overweight, 138 obese) | UPLC-TSQ MS | Specific markers of systemic inflammation identified in obese children (↑leptin, CRP and certain amino acid metabolites associated with glutathione synthesis and oxidative stress)↓ concentrations of arginine-related metabolites in uncontrolled obese asthma patients than in obese controlled asthma at 12 months | Persistent asthma symptoms and systemic metabolic-inflammation in obesity-related asthmaSupports amino acid-based biomarker identification for stratified management |
| SERUM[39] | 158 adolescents (39 obese asthmatics, 39 healthy-weight asthmatics, 38 obese controls and 42 healthy-weight controls) | HPLCGC | Obese asthmatic children showed ↓ total carotenoid levels and ↑ n-6/n-3 PUFA ratioBoth markers were significantly associated with decreased FEV1 and increased insulin resistance | Diet modification may reduce asthma burden:protective role for carotenoids and n-3 PUFA in pulmonary function and metabolic regulation in obese pediatric asthma |
| SERUM[40] | 89 asthmatic children 7-11 years (49, healthy-weight, 40 obese) | Multi-omics integration with SNF (metabolomics WITH NMR, transcriptomics, epigenomics) | Anthropometric, metabolic, nutritional and immune factors contribute interdependently to the obese asthma phenotypeWHR and metabolic markers (↑ HOMA-IR, ↑ leptin, ↓ adiponectin) showed the strongest associations with reduced lung function, though none predicted symptom-based severity or control | Highlights truncal adiposity as a key driver of the asthma endotype and supports integrated metabolic–immune profiling to define pediatric obesity-related asthma |
| SERUM [41] | 602 asthmatic children, 593 controls without asthma. | NMR | ↓ levels of citrate, ketone bodies, histidine and glutamine in asthma cases compared to controlsLipid metabolites lost significance after controlling for obesity with the exception of FC% in mVLDL and SFA% | Nutrient deficiencies may contribute to the pathophysiology of asthma and represent therapeutic targets for nutritional supplementation strategies |. |
| SERUM[42] | 55 children (27 with asthma and 28 control) | NMR + shotgun metagenomics | Significant microbe–metabolite associations in asthmatic children: ↓ Prevotella sp. oral taxon 306 and ↓ DMG (dimethylglycine), ↓ dimethylamine, ↓ glucose, ↓ pyridoxine, and ↑ proline-glutamate chimera, ↑ serine, ↑ lactateSeveral control-enriched species inversely correlated with total and allergen-specific IgE levels | Integration of metagenomics and metabolomics reveals host–microbiome interactions in mite-sensitized childhood asthma, with diagnostic implications |
| SERUM[21] | 53 children, aged 3-5 years,(15 lowly sensitized non-atopic asthma, 13 highly sensitized atopic asthma, 25 healthy controls) | NMR+16S rRNA sequencing | Four metabolites (tyrosine, isovalerate, glycine, and histidine) associated with lowly sensitized asthmaAcetic acid strongly linked to highly sensitized asthma and showed a robust correlation with airway microbiota | Distinct metabolomic signatures linked to IgE sensitization profiles and microbiota–metabolite axis in the pathogenesis of childhood asthma |
| FECES[43] | 110 asthmatic subjects aged 3-5 years | UHPLC-MS/MS+16S rRNA sequencing | ↑ Veillonella and histidine metabolites (carnosine, methyl-histidine, β-alanyl-methyl-histidine) in high wheeze group↑ sphingolipids (sphinganine, sphingosine, ceramides) in ICS-treated non-responders | Gut microbiome–metabolome signatures linked to wheeze severity and ICS responseFecal histidine and sphingolipid metabolites as potential biomarkers in pediatric asthma |
| FECES/ PLASMA [44] | 46 asthmatic children, 4-13 years-old (13 normal-weight, 8 overweight, 25 obese) | NMRGC-MS+16S rRNA sequencing | ↑ Leptin ↓ plasma acetate in obese allergic asthma phenotypeChildren with worse asthma outcomes show ↑ fecal D-lactate, ↑ D/L lactate ratio, and ↑ plasma creatinine.Persistent asthma associated with ↓ plasma citrate and ↓ dimethylsulfone (DMSO₂) | Distinct metabolic alterations linked to obesity and asthma severity in childrenPlasma and fecal metabolites as potential biomarkers for endotype-specific profiling |
| URINE[45] | 30 asthmatic children 6-17 years(15 corticosteroid respondent, 15 CS-nonrespondent) | LC coupled with FTMS | Five urinary metabolites (3,6-dihydronicotinic acid, 3-methoxy-4-hydroxyphenyl(ethylene)glycol, 3,4-dihydroxyphenylalanine, γ-glutamylcysteine, cysteinylglycine) associated with corticosteroid resistance in children with severe asthmaKey pathways: tyrosine metabolism, degradation of aromatic compounds and glutathione metabolism | Urine metabolomics identifies non-invasive biomarkers of corticosteroid resistancePathway-specific profiling to guide personalized treatment in severe pediatric asthma |
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