Association of gut microbiome with fasting triglycerides, fasting insulin and obesity status in Mexican children
Bibliographic record
Abstract
Summary Background The association of gut microbiota with obesity and its cardio‐metabolic complications in paediatric populations is still controversial. Objective We investigated the association of obesity and cardio‐metabolic traits with gut microbiota on 167 and 163 children with normal weight and obesity from Mexico City and Oaxaca, Mexico. Methods Anthropometric and biochemical traits were measured. The microbial communities were determined by high‐throughput sequencing of bacterial 16S rRNA gene v3‐v4 region. Results The gut microbial community structure was associated with obesity and fasting plasma insulin (FPI) in Mexico City (PObesity= 0.012, PFPI= 0.0003) and Oaxaca (PObesity= 0.034, PFPI= 0.016), and with triglycerides (TG) in Oaxaca (P= .0002). The Firmicutes/Bacteroidetes ratio was positively associated with TG in Oaxaca (P= .003). Firmicutes and Bacteroidetes phyla were positively and negatively associated with obesity (Mexico City: PFirmicutes= 0.013, PBacteroidetes= 0.009) and TG (Oaxaca: PFirmicutes= 0.002, PBacteroidetes= 0.004). In Oaxaca, Verrucomicrobia was negatively associated with obesity (P= .004). In Mexico City, the bacterial genusFusicatenibacter,Romboutsia, Ruminococcaceae,Ruminiclostridium, Blautia,Clostridium,AnaerostipesandIntestinibacterwere associated with obesity and FPI, while in Oaxaca,Bacteroides,AlistipesandClostridiumwere associated with TG. Conclusion The gut microbial community structure in children is associated with obesity and FPI in Mexico City, and with obesity, FPI and TG in Oaxaca.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".