Evidence map›Paper›PMID 40201075›Full record

ReviewFrontiers in veterinary science2025

Animal health and nutrition: metabolic disorders in cattle and improvement strategies.

Danju Kang, Shera Elizabeth Lungu, Felix Danso, Chrystella Fernanda Dzou, Yanjun Chen, Xinyu Zheng, Fanghong Nie, Hongying Lin, Jinjun Chen, Guangxian Zhou

Abstract readReview
In one paragraph

Review in Frontiers in veterinary science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

10 authors.

Danju Kang *Department of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Shera Elizabeth Lungu *Department of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Felix DansoDepartment of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Chrystella Fernanda DzouDepartment of Economics, College of Applied Economics, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Yanjun ChenDepartment of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Xinyu ZhengDepartment of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Fanghong NieCollege of Food Science and Technology, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Hongying LinDepartment of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Jinjun ChenDepartment of Veterinary Medicine, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.
Guangxian ZhouDepartment of Animal Science, College of Coastal Agriculture, Guangdong Ocean University, Zhanjiang, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The health and productivity of cattle are significantly compromised by metabolic diseases on a global scale. These disorders disrupt normal metabolic processes, leading to substantial economic losses for the livestock industry. Metabolic disorders can arise from defective biochemical pathways, deficiencies in enzymes, coenzymes, or cofactors, and may be either inherited or acquired. Dairy cows are particularly susceptible during the transition period from late lactation to early management, facing conditions such as ketosis, milk fever, and hepatic lipidosis. This susceptibility is primarily due to reduced dry matter intake caused by fetal development and a decline in rumen capacity. The negative energy balance (NEB) during this phase, characterized by elevated blood concentrations of non-esterified fatty acids (NEFAs) due to increased energy mobilization, is closely linked to the onset of these diseases. Providing high-energy-density diets during this period is critical to mitigating the effects of NEB. Metabolic disorders represent a major health challenge in cattle, adversely affecting animal welfare and agricultural output. A comprehensive understanding of their etiology, clinical manifestations, diagnostic approaches, and management strategies is essential for effective prevention and treatment. Ongoing research and the adoption of preventive measures are vital to reducing the economic and health impacts of these diseases. Early diagnosis and proactive management strategies are crucial to mitigating their impact on dairy cattle health and productivity. Early identification enables timely interventions, preventing disease progression and reducing adverse effects on animal health. Proactive measures, such as optimizing nutritional programs, implementing precision farming technologies, and ensuring timely veterinary care, are essential for enhancing the overall wellbeing of dairy cows. This review serves as a valuable resource for veterinarians, researchers, and dairy farmers, offering in-depth insights into the etiology, clinical signs, diagnostics, and management of prevalent metabolic disorders in dairy cattle. By equipping stakeholders with this knowledge, it aims to support informed decision-making and improve herd management practices. The focus on early diagnosis and proactive strategies underscores the potential to significantly reduce the economic and health burdens imposed by metabolic diseases on the livestock industry.

Indexed as

hepatic lipidosisketosismetabolic disordersmilk fevernegative energy balance

Identifiers

PMID40201075
PMCPMC11977490

What OpenQuestion holds

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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 OpenQuestion graph.