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PloS one2018; 13(7); e0200583; doi: 10.1371/journal.pone.0200583

Use of principle component analysis to quantitatively score the equine metabolic syndrome phenotype in an Arabian horse population.

Abstract: Equine metabolic syndrome (EMS), like human metabolic syndrome, comprises a collection of clinical signs related to obesity, insulin dysregulation and susceptibility to secondary inflammatory disease. Although the secondary conditions resulting from EMS can be life-threatening, diagnosis is not straightforward and often complicated by the presence of other concurrent conditions like pituitary pars intermedia dysfunction (PPID). In order to better characterize EMS, we sought to describe the variation within, and correlations between, typical physical and endocrine parameters for EMS. Utilizing an unsupervised statistical approach, we evaluated a population of Arabian horses using a physical examination including body measurements, as well as blood plasma insulin, leptin, ACTH, glucose, and lipid values. We investigated the relationships among these variables using principle component analysis (PCA), hierarchical clustering, and linear regression. Owner-assigned assessments of body condition were one full score (on a nine-point scale) lower than scores assigned by researchers, indicating differing perception of healthy equine body weight. Rotated PCA defined two factor scores explaining a total of 46.3% of variation within the dataset. Hierarchical clustering using these two factors revealed three groups corresponding well to traditional diagnostic categories of "Healthy", "PPID-suspect", and "EMS-suspect" based on the characteristics of each group. Proxies estimating up to 93.4% of the composite "EMS-suspect" and "PPID-suspect" scores were created using a reduced set of commonly used diagnostic variables, to facilitate application of these quantitative scores to horses of the Arabian breed in the field. Use of breed-specific, comprehensive physical and endocrinological variables combined in a single quantitative score may improve detection of horses at-risk for developing EMS, particularly in those lacking severe clinical signs. Quantification of EMS without the use of predetermined reference ranges provides an advantageous approach for future studies utilizing genomic or metabolomics approaches to improve understanding of the etiology behind this troubling condition.
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Publication Date: 2018-07-12 PubMed ID: 30001422PubMed Central: PMC6042766DOI: 10.1371/journal.pone.0200583Google Scholar: Lookup
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  • Clinical Trial
  • Veterinary
  • Journal Article
  • Research Support
  • N.I.H.
  • Extramural
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article is about a new approach to diagnosing equine metabolic syndrome (EMS) in Arabian horses using statistical analysis and multiple diagnostic variables rather than relying solely on physical examinations.

Objective of the Study

  • The study aimed to better characterize and diagnose EMS, a condition in horses that is similar to human metabolic syndrome.
  • EMS is typically associated with obesity, insulin irregularities and potential for secondary inflammatory diseases, making diagnosis complex due to possible presence of other conditions such as pituitary pars intermedia dysfunction (PPID).

Methodologies and Data Analysis

  • The research used an unsupervised statistical approach, studying a population of Arabian horses with physical exams and blood tests scoring insulin, leptin, ACTH, glucose, and lipid values.
  • Using principal component analysis (PCA), the researchers studied the relationships and correlations between these variables, as well as hierarchical clustering and linear regression.
  • Physical condition assessments provided by horse owners were found to be on average one full point lower than ratings given by researchers, indicating a variance in perceptions of what constitutes a healthy horse weight.

Findings of the Study

  • Using rotated PCA, two factor scores were identified that accounted for 46.3% of variation within the dataset.
  • Through hierarchical clustering with these two factors, three groups were defined, correlating well with the traditional diagnostic categories of “Healthy”, “PPID-suspect”, and “EMS-suspect”.
  • Predictive models were created that could approximate up to 93.4% of the composite “EMS-suspect” and “PPID-suspect” scores using a reduced set of common diagnostic variables. This could make the application of these quantitative scores easier in real-world situations.

Conclusion and Implications for Future Studies

  • The utilization of breed-specific, comprehensive physical and endocrinological variables in a single quantitative score could potentially enhance the detection of Arabian horses at risk for EMS development, particularly for those not displaying severe clinical symptoms.
  • Quantifying EMS without predetermined reference ranges could provide a better approach for future genomic or metabolomic studies aiming to understand the underlying causes of EMS.

Cite This Article

APA
Lewis SL, Holl HM, Long MT, Mallicote MF, Brooks SA. (2018). Use of principle component analysis to quantitatively score the equine metabolic syndrome phenotype in an Arabian horse population. PLoS One, 13(7), e0200583. https://doi.org/10.1371/journal.pone.0200583

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 13
Issue: 7
Pages: e0200583
PII: e0200583

Researcher Affiliations

Lewis, Samantha L
  • Department of Animal Sciences, University of Florida, Gainesville, FL, United States of America.
Holl, Heather M
  • Department of Animal Sciences, University of Florida, Gainesville, FL, United States of America.
Long, Maureen T
  • Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States of America.
Mallicote, Martha F
  • Department of Large Animal Clinical Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, United States of America.
Brooks, Samantha A
  • Department of Animal Sciences, University of Florida, Gainesville, FL, United States of America.

MeSH Terms

  • Animals
  • Blood Glucose / metabolism
  • Body Weight
  • Female
  • Horse Diseases / blood
  • Horses
  • Insulin / blood
  • Leptin / blood
  • Lipids / blood
  • Male
  • Metabolic Syndrome / blood
  • Metabolic Syndrome / pathology
  • Metabolic Syndrome / veterinary
  • Phenotype

Grant Funding

  • U24 DK097209 / NIDDK NIH HHS

Conflict of Interest Statement

The authors have declared that no competing interests exist.

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Citations

This article has been cited 4 times.
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