Influence of maternal overnutrition and gestational diabetes on the programming of metabolic health outcomes in the offspring: experimental evidence
Bibliographic record
Abstract
The incidence of obesity and type 2 diabetes mellitus have risen across the world during the past few decades and has also reached an alarming level among children. In addition, women are currently more likely than ever to enter pregnancy obese. As a result, the incidence of gestational diabetes mellitus is also on the rise. While diet and lifestyle contribute to these trends, population health data show that maternal obesity and diabetes during pregnancy during critical stages of development are major factors that contribute to the development of chronic disease in adolescent and adult offspring. Fetal programming of metabolic function, through physiological and (or) epigenetic mechanisms, may also have an intergenerational effect, and as a result may perpetuate metabolic disorders in the next generation. In this review, we summarize the existing literature that characterizes how maternal obesity and gestational diabetes mellitus contribute to metabolic and cardiovascular disorders in the offspring. In particular, we focus on animal studies that investigate the molecular mechanisms that are programmed by the gestational environment and lead to disease phenotypes in the offspring. We also review interventional studies that prevent disease with a developmental origin in the offspring.
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 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".