Unraveling Ruminant Feed Efficiency Through Metabolomics: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Advancements in metabolomic technologies have revolutionized our understanding of feed efficiency (FE) in livestock, offering new pathways to enhance both profitability and sustainability in ruminant production. METHODS: This review offers a critical and systematic evaluation of the metabolomics methods used to measure and assess FE in ruminants. We conducted a comprehensive search of PubMed, Web of Science, and Scopus databases, covering publications from 1971 to 2023. This review synthesizes findings from 71 studies that applied metabolomic approaches to uncover the biological mechanisms driving interindividual variations in FE across cattle, sheep, goats, and buffaloes. RESULTS: Most studies focused on cattle and employed targeted metabolomics to identify key biomarkers, including amino acids, fatty acids, and other metabolites linked to critical pathways such as energy metabolism, nitrogen utilization, and muscle development. Despite promising insights, challenges remain, including small sample sizes, methodological inconsistencies, and a lack of validation studies, particularly for non-cattle species. CONCLUSIONS: By leveraging state-of-the-art metabolomic methods, this review highlights the potential of metabolomics to provide cost-effective, non-invasive molecular markers for FE evaluation, paving the way for more efficient and sustainable livestock management. Future research should prioritize larger, species-specific studies with standardized methods to validate identified biomarkers and enhance practical applications in livestock production systems.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".