Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity
Bibliographic record
Abstract
Evidence supports a complex interplay of gut microbiome and host metabolism as regulators of obesity. The metabolic phenotype and microbial metabolism of host diet may also contribute to greater obesity risk in children early in life. This study aimed to identify features that discriminated overweight/obese from normal weight infants by integrating gut microbiome and serum metabolome profiles. This prospective analysis included 50 South Asian children living in Canada, selected from the SouTh Asian biRth cohorT (START). Serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry and the relative abundance of bacterial 16S rRNA gene amplicon sequence variant was evaluated at 1 year. Cumulative body mass index (BMI AUC ) and skinfold thickness (SSF AUC ) scores were calculated from birth to 3 years as the total area under the growth curve (AUC). BMI AUC and/or SSF AUC >85th percentile was used to define overweight/obesity. Data Integration Analysis for Biomarker discovery using Latent cOmponent (DIABLO) was used to identify discriminant features associated with childhood overweight/obesity. The associations between identified features and anthropometric measures were examined using logistic regression. Circulating metabolites including glutamic acid, acetylcarnitine, carnitine, and threonine were positively, whereas γ-aminobutyric acid (GABA), symmetric dimethylarginine (SDMA), and asymmetric dimethylarginine (ADMA) were negatively associated with childhood overweight/obesity. The abundance of the Pseudobutyrivibrio and Lactobacillus genera were positively, and Clostridium sensu stricto 1 and Akkermansia were negatively associated with childhood overweight/obesity. Integrative analysis revealed that Akkermansia was positively whereas Lactobacillus was inversely correlated with GABA and SDMA, and Pseudobutyrivibrio was inversely correlated with GABA. This study provides insights into metabolic and microbial signatures which may regulate satiety, energy metabolism, inflammatory processes, and/or gut barrier function, and therefore, obesity trajectories in childhood. Understanding the functional capacity of these molecular features and potentially modifiable risk factors such as dietary exposures early in life may offer a novel approach for preventing childhood obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".