Intrauterine programming of obesity and type 2 diabetes
Bibliographic record
Abstract
The type 2 diabetes epidemic and one of its predisposing factors, obesity, are major influences on global health and economic burden. It is accepted that genetics and the current environment contribute to this epidemic; however, in the last two decades, both human and animal studies have consolidated considerable evidence supporting the 'developmental programming' of these conditions, specifically by the intrauterine environment. Here, we review the various in utero exposures that are linked to offspring obesity and diabetes in later life, including epidemiological insights gained from natural historical events, such as the Dutch Hunger Winter, the Chinese famine and the more recent Quebec Ice Storm. We also describe the effects of gestational exposure to endocrine disruptors, maternal infection and smoking to the fetus in relation to metabolic programming. Causal evidence from animal studies, motivated by human observations, is also discussed, as well as some of the proposed underlying molecular mechanisms for developmental programming of obesity and type 2 diabetes, including epigenetics (e.g. DNA methylation and histone modifications) and microRNA interactions. Finally, we examine the effects of non-pharmacological interventions, such as improving maternal dietary habits and/or increasing physical activity, on the offspring epigenome and metabolic outcomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".