Maternal Macronutrient Consumption and the Developmental Origins of Metabolic Disease in the Offspring
Bibliographic record
Abstract
Recent research aimed at understanding the rise in obesity and cardiometabolic disease in children suggests that suboptimal maternal nutrition conditions organ systems and physiological responses in the offspring contributing to disease development. Understanding the mechanisms by which the macronutrient composition of the maternal diet during pregnancy or lactation affects health outcomes in the offspring may lead to new maternal nutrition recommendations, disease prevention strategies and therapies that reduce the increasing incidence of cardiometabolic disease in children. Recent mechanistic animal model research has identified how excess fats and sugars in the maternal diet alter offspring glucose tolerance, insulin signaling and metabolism. Maternal nutrition appears to influence epigenetic alterations in the offspring and the programming of gene expression in key metabolic pathways. This review is focused on experimental studies in animal models that have investigated mechanisms of how maternal consumption of macronutrients affects cardiometabolic disease development in the offspring. Future research using "-omic" technologies is essential to elucidate the mechanisms of how altered maternal macronutrient consumption influences the development of disease 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".