Evidence map›Paper›PMID 39858256›Full record

ArticleAnimals : an open access journal from MDPI2025

Lipid Metabolism Alterations in Hyperlipidemic Dogs with Biliary Tract or Endocrine Diseases.

Tae-Woo Kim, Min-Hee Kang, Hee-Myung Park

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Observational
  3. 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

3 authors.

Tae-Woo KimDepartment of Veterinary Internal Medicine, College of Veterinary Medicine, Konkuk University, Seoul 05029, Republic of Korea.
Min-Hee KangDepartment of Bio-Animal Health, Jangan University, Hwaseong 18331, Republic of Korea.
Hee-Myung ParkDepartment of Veterinary Internal Medicine, College of Veterinary Medicine, Konkuk University, Seoul 05029, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fasting hyperlipidemia results from lipid metabolism defects associated with alterations in specific lipoprotein classes. These changes may originate from genetic predispositions or underlying metabolic disorders, including cholestasis and endocrine diseases. This retrospective study aimed to analyze variations in lipoprotein electrophoresis (LPE) profiles in hyperlipidemic dogs and investigate the associations between biliary tract diseases, endocrine disorders, and lipid metabolism. Sixty-five dogs that underwent LPE were classified into non-hyperlipidemic (NHL) and hyperlipidemic (HL) groups, with further subdivisions based on the presence of biliary tract disease, endocrine disorders, or breed predisposition (Miniature Schnauzers). Dogs in the HL group with biliary tract disease and those with underlying metabolic disorders exhibited significantly higher pre-beta fractions and cholesterol concentrations compared to the NHL group. Additionally, significant linear relationships were observed between serum gamma-glutamyl transpeptidase (GGT) levels and both pre-beta fractions and cholesterol concentrations. The Schnauzer group showed lower alpha fractions and higher triglyceride and cholesterol levels than the NHL group. These findings suggest that LPE is a valuable and useful tool for detecting lipid metabolism abnormalities. Clinicians should consider the potential for altered lipid metabolism when evaluating patients with biliary tract or endocrine diseases in small animal practice.

Indexed as

biliary tract diseaseendocrine diseasehyperlipidemialipid metabolismlipoprotein electrophoresis

Identifiers

PMID39858256
PMCPMC11758620

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Registered trials

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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.