Metabotypes are linked to uncontrolled childhood asthma, gut microbiota, and systemic inflammation
Bibliographic record
Abstract
BACKGROUND: Childhood asthma has been linked to distinct metabolomic profiles. OBJECTIVE: We sought to identify phenotypes (metabotypes) in children with moderate to severe asthma through integrative fecal and serum metabolome analysis. METHODS: Children from the Systems Pharmacology Approach to Uncontrolled Pediatric Asthma cohort with Global Initiative for Asthma treatment step 3 or higher were recruited. Asthma control was defined by the Asthma Control Test and annual exacerbation history. Targeted metabolomic profiling of feces and serum was performed using liquid chromatography and flow injection electrospray ionization-triple quadrupole mass spectrometry. Similarity network fusion integrated fecal and serum metabolome profiles, followed by spectral clustering. Clusters were analyzed for differences in asthma characteristics, food diaries, fecal microbiota composition, and levels of serum inflammatory markers and blood cells. RESULTS: Integrative fecal and serum metabolome analysis of 92 children with moderate to severe asthma (median age, 11.5 years, 34% female) revealed 3 metabotypes. Metabotype 1 had the lowest percentage of allergic rhinitis, with elevated serum ceramides and triglycerides. Metabotype 2 had higher odds of asthma control, the highest percentage of children with 4 or more months of breast-feeding, reduced sugar intake, lowest levels of blood neutrophils and serum inflammatory markers, and elevated serum acylcarnitines and ω-3 fatty acids. Metabotype 3 included the highest percentage of uncontrolled asthma patients, with decreased serum cholesteryl esters, phosphatidylcholines, and sphingomyelins, elevated fecal amino acids, and reduced fecal microbiota diversity. CONCLUSIONS: Metabotypes in children with moderate to severe asthma are linked to asthma control, distinct fecal microbiota, and systemic inflammatory patterns. The findings suggest that metabotyping can be valuable in precision medicine approaches for asthma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".