ArticleBMC veterinary research2025
Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome.
Article in BMC veterinary research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Exercise-specific plasma proteomic signatures in racehorses: Candidates for training adaptation and peak load monitoring.Equine veterinary journal · 2026Article
- Using Gut Microbiota and Gut Metabolites as Biomarkers for Diseases in Monogastric Farm Animals.Animals : an open access journal from MDPI · 2026Review
- Abnormal Fetuin-A levels in obese horses are associated with activated TLR4/NF-ƙB/MAPK axis and depleted FBXW7 E3 ubiquitin ligase.Veterinary research communications · 2025Article
- Correction: Quantitative proteomics unveils potential plasma biomarkers and provides insights into the pathophysiological mechanisms underlying equine metabolic syndrome.BMC veterinary research · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEquine Metabolic Syndrome (EMS) is a multifactorial endocrine disorder characterized by obesity, insulin dysregulation (ID), and an increase in the risk of laminitis, a painful condition that can lead to euthanasia in severe cases. Diagnosing EMS is challenging and often relies on clinical history including obesity, difficulty in losing weight, and recurring episodes of laminitis. The gold standard for laboratory support of an EMS diagnosis is the identification of ID, with basal insulin being the simplest and most accessible method, especially in a field setting. However, various factors such as diet, age, stress, season, medications administered, and testing protocols can influence results. Dynamic tests like the oral sugar test (OST) are preferred but present limitations due to low sensitivity and poor repeatability. These diagnostic challenges make EMS difficult to detect in veterinary medicine highlighting the need for an effective method of the early detection of EMS to prevent laminitis and its associated complications.
resultsMass spectrometry-based proteomics represents a powerful tool to identify biomarkers and explore molecular pathways related to the underlying pathology. In the current study we established an integrated proteomics pipeline to identify plasma biomarkers for EMS diagnosis. We compared plasma proteomes from healthy horses, non-ID obese horses and animals diagnosed with EMS. This comparison revealed 76 proteins with significant changes (1% FDR) between groups.
conclusionsOur study demonstrates that the complement system, the coagulation cascade and extracellular matrix remodelling pathways are altered in EMS. These findings offer new insights into the molecular basis of the development of EMS and led to the nomination of several proteins as potential biomarkers for its early detection.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.