The impact of early life gut colonization on metabolic and obesogenic outcomes: what have animal models shown us?
Bibliographic record
Abstract
The rise in the occurrence of obesity to epidemic proportions has made it a global concern. Great difficulty has been experienced in efforts to control this growing problem with lifestyle interventions. Thus, attention has been directed to understanding the events of one of the most critical periods of development, perinatal life. Early life adversity driven by maternal obesity has been associated with an increased risk of metabolic disease and obesity in the offspring later in life. Although a mechanistic link explaining the relationship between maternal and offspring obesity is still under investigation, the gut microbiota has come forth as a new factor that may play a role modulating metabolic function of both the mother and the offspring. Emerging evidence suggests that the gut microbiota plays a much larger role in mediating the risk of developing non-communicable disease, including obesity and metabolic dysfunction in adulthood. With the observation that the early life colonization of the neonatal and postnatal gut is mediated by the perinatal environment, the number of studies investigating early life gut microbial establishment continues to grow. This paper will review early life gut colonization in experimental animal models, concentrating on the role of the early life environment in offspring gut colonization and the ability of the gut microbiota to dictate risk of disease later in life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".