Review: Micronutrient supply, developmental programming, and strategic supplementation in ruminant livestock
Bibliographic record
Abstract
Developmental programming, also known as fetal programming, is the idea that changes in offspring development with both immediate and longer-term consequences can arise from in utero stress, including compromised maternal nutrition. Large animal models of demonstrate that perturbed maternal nutrition, including macro- and micronutrient supply, (amino acids, vitamins, and trace elements) can alter development during gestational and postnatal offspring outcomes. Strategic supplementation of micronutrients (methionine, arginine, selenium, folate, vitamin B12, choline, cobalt, sulfur and others) also alters placental function and therefore, fetal nutrient supply. Specifically, in the offspring, multiple visceral tissues, metabolism, growth, and reproduction are impacted by compromised nutrition and these effects are potentially mitigated by strategic supplementation. Furthermore, compromised maternal nutrition and strategic supplementation alter gene expression, metabolomic patterns, and biochemical pathways in the offspring. Developmental programming is mechanistically driven, at least in part, by epigenetic mechanism and one carbon-metabolism and associated specific micronutrients. The concept of developmental programming is strongly supported by data from ruminant animal models, wherein compromised maternal nutrition is a stressor driving programming events. Changes in the offspring’s transcriptome and metabolome can be influenced by changes in maternal nutrition during development. Evidence suggests that strategic supplementation of micronutrients potentially mitigates the compromised development. Future research needs include efforts focused on: mechanistic investigations, livestock production outcomes, animal health implications, and host-microbiome interrelationships associated with maternal nutrition, developmental programming and strategic supplementation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".