Delivery mode-associated gut microbiota in the first 3 months of life in a country with high obesity rates
Bibliographic record
Abstract
Delivery methods during childbirth and their related gut microbiota profiles have important impacts on health later in life, they can contribute to the development of diseases such as obesity, whose highest prevalence rate is found among the Mexican child population. Coincidentally, Mexico has one of the highest global average annual rate increase in cesarean births (C-section). Since Mexico leads the world in childhood obesity, studying the relationship between childbirth delivery methods and gut microbiota profiles in this vulnerable population may be used to identify early risk factors for obesity in other developed and developing countries. The objective of this study is to determine the association between child delivery method and gut microbiota profiles in healthy Mexican newborns.Fecal samples of 57 term infants who participated in a randomized clinical trial in 2013 to study the safety of Agave fructans in newborns, were used in this study. DNA samples were extracted and used to characterize the microbiota composition using high-throughput 16S rRNA gene sequencing. The samples were further divided based on childbirth delivery method, as well as early diet. Gut microbiota profiles were determined and analyzed using cluster analysis followed by multiple correspondence analysis.An unusual high abundance of Proteobacteria was found in the gut microbiota of all Mexican infants studied, regardless of delivery method. Feces from infants born by C-section had low levels of Bacteroidetes, high levels of Firmicutes, especially Clostridium and Enterococcus, and a strikingly high ratio of Firmicutes/Bacteroidetes (F:B). Profiles enriched in Bacteroidetes and low F:B ratios, were strongly associated with vaginal delivery.The profile of gut microbiota associated with feces from Mexican infants born by C-section, may be added to the list of boosting factors for the worrying obesity epidemic in Mexico.
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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 | 0.000 |
| 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".