Evidence map›Paper›PMID 42073289›Full record

ReviewFoods (Basel, Switzerland)2026

Applications and Prospects of Metabolomics and Lipidomics Technologies in the Study of Livestock and Poultry Meat and Egg Quality.

Keyu Li, Ying Lu, Dan Yue, Yuwei Qian, Huaijing Liu, Zhengmei Sheng, Jinpeng Shi, Yang Yang, Jiao Wu, Dongmei Xi and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Keyu LiYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0002-1870-9424
Ying LuYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0002-6056-3246
Dan YueYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Yuwei QianYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Huaijing LiuYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Zhengmei ShengYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Jinpeng ShiYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Yang YangYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Jiao WuYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Dongmei XiYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Yuqing ChongYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.

Funding

Major Science and Technology Projects in Yunnan Province 202202AE090005Major Science and Technology Projects in Yunnan Province 202302AE090015the National Nature Science Foundation of China 32302707"Xingdian Talent" Industry Innovation Talent Program in Yunnan Province XDYC-CYCX-2022-0029Yunnan Province Basic Research Program Project 202301AU070117
6 · The paper itself

Abstract

As essential branches of systems biology, metabolomics and lipidomics systematically reveal the composition, dynamic changes, and biological functions of small-molecule metabolites and lipids using high-throughput analytical techniques. This review examines the application of these omics technologies in evaluating livestock and poultry meat and egg quality, focusing on their roles in elucidating the molecular mechanisms behind key traits such as flavor, tenderness, and nutritional value. By identifying key metabolic markers-including glutamic acid, inosine monophosphate, and specific triglycerides-the intrinsic links between these markers and intramuscular fat deposition, flavor precursor formation, and antioxidant capacity are highlighted. Furthermore, this paper emphasizes the transformative impact of integrating multi-omics data with artificial intelligence (AI). AI-driven analytical frameworks are overcoming the limitations of traditional high-dimensional data processing, enabling robust biomarker discovery, predictive modeling for product quality, and reverse design for genetic improvement. Ultimately, the synergistic application of metabolomics, lipidomics, and AI will drive the development of modern animal husbandry toward intelligent, predictable, and precision-based production.

Indexed as

artificial intelligence (AI)egg qualitylipidomicsmeat qualitymetabolomics

Identifiers

PMID42073289
PMCPMC13114456

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.