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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.

References

This article includes 41 references
  1. Johnson PJ, Wiedmeyer CE, LaCarrubba A, Ganjam VK, Messer NT 4th. Laminitis and the equine metabolic syndrome.. Vet Clin North Am Equine Pract 2010 Aug;26(2):239-55.
    doi: 10.1016/j.cveq.2010.04.004pubmed: 20699172google scholar: lookup
  2. Frank N, Geor RJ, Bailey SR, Durham AE, Johnson PJ. Equine metabolic syndrome.. J Vet Intern Med 2010 May-Jun;24(3):467-75.
    doi: 10.1111/j.1939-1676.2010.0503.xpubmed: 20384947google scholar: lookup
  3. Sojka-Kritchevsky JE, Johnson PJ. Current status and future directions: equine pituitary pars intermedia dysfunction and equine metabolic syndrome.. Equine Vet J 2014 Jan;46(1):99-102.
    doi: 10.1111/evj.12194pubmed: 24329586google scholar: lookup
  4. Frank N. Equine metabolic syndrome.. Vet Clin North Am Equine Pract 2011 Apr;27(1):73-92.
    doi: 10.1016/j.cveq.2010.12.004pubmed: 21392655google scholar: lookup
  5. Morgan RA, Keen JA, McGowan CM. Treatment of equine metabolic syndrome: A clinical case series.. Equine Vet J 2016 Jul;48(4):422-6.
    doi: 10.1111/evj.12445pubmed: 25808563google scholar: lookup
  6. Gordon ME, Jerina ML, Raub RH, Davison KA, Young JK, Williamson KK. The effects of dietary manipulation and exercise on weight loss and related indices of health in horses.. Comparative Exercise Physiology 2009;6(01):33–42.
    doi: 10.1017/S1478061509356169google scholar: lookup
  7. Johnson PJ. The equine metabolic syndrome peripheral Cushing's syndrome.. Vet Clin North Am Equine Pract 2002 Aug;18(2):271-93.
    doi: 10.1016/S0749-0739(02)00006-8pubmed: 15635908google scholar: lookup
  8. McFarlane D. Equine pituitary pars intermedia dysfunction.. Vet Clin North Am Equine Pract 2011 Apr;27(1):93-113.
    doi: 10.1016/j.cveq.2010.12.007pubmed: 21392656google scholar: lookup
  9. Karikoski NP, Patterson-Kane JC, Singer ER, McFarlane D, McGowan CM. Lamellar pathology in horses with pituitary pars intermedia dysfunction.. Equine Vet J 2016 Jul;48(4):472-8.
    doi: 10.1111/evj.12450pubmed: 25869529google scholar: lookup
  10. Durham AE. Endocrine Disease in Aged Horses.. Vet Clin North Am Equine Pract 2016 Aug;32(2):301-15.
    doi: 10.1016/j.cveq.2016.04.007pubmed: 27449391google scholar: lookup
  11. Frank N. Equine Metabolic Syndrome.. Journal of Equine Veterinary Science 2009;29(5):259–67.
    doi: 10.1016/j.jevs.2009.04.183google scholar: lookup
  12. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares.. Equine Vet J 1983 Oct;15(4):371-2.
    pubmed: 6641685doi: 10.1111/j.2042-3306.1983.tb01826.xgoogle scholar: lookup
  13. Stephenson HM, Green MJ, Freeman SL. Prevalence of obesity in a population of horses in the UK.. Vet Rec 2011 Feb 5;168(5):131.
    doi: 10.1136/vr.c6281pubmed: 21257596google scholar: lookup
  14. Carter RA, Geor RJ, Burton Staniar W, Cubitt TA, Harris PA. Apparent adiposity assessed by standardised scoring systems and morphometric measurements in horses and ponies.. Vet J 2009 Feb;179(2):204-10.
    doi: 10.1016/j.tvjl.2008.02.029pubmed: 18440844google scholar: lookup
  15. Carter RA, Treiber KH, Geor RJ, Douglass L, Harris PA. Prediction of incipient pasture-associated laminitis from hyperinsulinaemia, hyperleptinaemia and generalised and localised obesity in a cohort of ponies.. Equine Vet J 2009 Feb;41(2):171-8.
    doi: 10.2746/042516408X342975pubmed: 19418747google scholar: lookup
  16. Mccue M, Geor R, Schultz N. Equine Metabolic Syndrome: A Complex Disease Influenced by Genetics and the Environment.. Journal of Equine Veterinary Science 2015;35(5):367–75.
    doi: 10.1016/j.jevs.2015.03.004google scholar: lookup
  17. Menzies-Gow NJ, Harris PA, Elliott J. Prospective cohort study evaluating risk factors for the development of pasture-associated laminitis in the United Kingdom.. Equine Vet J 2017 May;49(3):300-306.
    doi: 10.1111/evj.12606pubmed: 27363591google scholar: lookup
  18. Kronfeld DS, Treiber KH, Hess TM, Splan RK, Byrd BM, Staniar WB, White NW. 10.1093/jn/136.7.2090S.. J Nutr 2006 Jul;136(7 Suppl):2090S-2093S.
    pubmed: 16772508doi: 10.1093/jn/136.7.2090sgoogle scholar: lookup
  19. Treiber KH, Kronfeld DS, Hess TM, Byrd BM, Splan RK, Staniar WB. Evaluation of genetic and metabolic predispositions and nutritional risk factors for pasture-associated laminitis in ponies.. J Am Vet Med Assoc 2006 May 15;228(10):1538-45.
    doi: 10.2460/javma.228.10.1538pubmed: 16677122google scholar: lookup
  20. Couëtil L, Paradis MR, Knoll J. Plasma adrenocorticotropin concentration in healthy horses and in horses with clinical signs of hyperadrenocorticism.. J Vet Intern Med 1996 Jan-Feb;10(1):1-6.
    doi: 10.1111/j.1939-1676.1996.tb02016.xpubmed: 8965262google scholar: lookup
  21. McGowan TW, Pinchbeck GP, McGowan CM. Prevalence, risk factors and clinical signs predictive for equine pituitary pars intermedia dysfunction in aged horses.. Equine Vet J 2013 Jan;45(1):74-9.
    doi: 10.1111/j.2042-3306.2012.00578.xpubmed: 22594955google scholar: lookup
  22. Treiber KH, Kronfeld DS, Hess TM, Boston RC, Harris PA. Use of proxies and reference quintiles obtained from minimal model analysis for determination of insulin sensitivity and pancreatic beta-cell responsiveness in horses.. Am J Vet Res 2005 Dec;66(12):2114-21.
    doi: 10.2460/ajvr.2005.66.2114pubmed: 16379656google scholar: lookup
  23. Bian SZ, Jin J, Zhang JH, Li QN, Yu J, Yu SY, Chen JF, Yu XJ, Qin J, Huang L. Principal Component Analysis and Risk Factors for Acute Mountain Sickness upon Acute Exposure at 3700 m.. PLoS One 2015;10(11):e0142375.
    doi: 10.1371/journal.pone.0142375pmc: PMC4640520pubmed: 26554385google scholar: lookup
  24. Ventura J, Nuechterlein KH, Subotnik KL, Gutkind D, Gilbert EA. Symptom dimensions in recent-onset schizophrenia and mania: a principal components analysis of the 24-item Brief Psychiatric Rating Scale.. Psychiatry Res 2000 Dec 27;97(2-3):129-35.
    doi: 10.1016/S0165-1781(00)00228-6pubmed: 11166085google scholar: lookup
  25. Agarwal S, Jacobs DR Jr, Vaidya D, Sibley CT, Jorgensen NW, Rotter JI, Chen YD, Liu Y, Andrews JS, Kritchevsky S, Goodpaster B, Kanaya A, Newman AB, Simonsick EM, Herrington DM. Metabolic Syndrome Derived from Principal Component Analysis and Incident Cardiovascular Events: The Multi Ethnic Study of Atherosclerosis (MESA) and Health, Aging, and Body Composition (Health ABC).. Cardiol Res Pract 2012;2012:919425.
    doi: 10.1155/2012/919425pmc: PMC3318892pubmed: 22536533google scholar: lookup
  26. Lewis SL, Holl HM, Streeter C, Posbergh C, Schanbacher BJ, Place NJ, Mallicote MF, Long MT, Brooks SA. Genomewide association study reveals a risk locus for equine metabolic syndrome in the Arabian horse.. J Anim Sci 2017 Mar;95(3):1071-1079.
    doi: 10.2527/jas.2016.1221pubmed: 28380523google scholar: lookup
  27. Elzinga S, Wood P, Adams AA. Plasma Lipidomic and Inflammatory Cytokine Profiles of Horses With Equine Metabolic Syndrome.. Journal of Equine Veterinary Science 2016;40:49–55.
    doi: 10.1016/j.jevs.2016.01.013google scholar: lookup
  28. Perkins GA, Lamb S, Erb HN, Schanbacher B, Nydam DV, Divers TJ. Plasma adrenocorticotropin (ACTH) concentrations and clinical response in horses treated for equine Cushing's disease with cyproheptadine or pergolide.. Equine Vet J 2002 Nov;34(7):679-85.
    doi: 10.2746/042516402776250333pubmed: 12455838google scholar: lookup
  29. NRC. Nutrient Requirements of Horses 6th revised ed.. Washington, DC, USA: National Academy Press; 2007.
  30. SAS II. JMP® 12 Multivariate MethodsJMP® 12 Multivariate Methods. Cary, NC: SAS Institute Inc.; 2015.
  31. Wyse CA, McNie KA, Tannahill VJ, Murray JK, Love S. Prevalence of obesity in riding horses in Scotland.. Vet Rec 2008 May 3;162(18):590-1.
    pubmed: 18453379doi: 10.1136/vr.162.18.590google scholar: lookup
  32. Courcier EA, O'Higgins R, Mellor DJ, Yam PS. Prevalence and risk factors for feline obesity in a first opinion practice in Glasgow, Scotland.. J Feline Med Surg 2010 Oct;12(10):746-53.
    doi: 10.1016/j.jfms.2010.05.011pubmed: 20685143google scholar: lookup
  33. Thatcher CD, Pleasant RS, Geor RJ, Elvinger F. Prevalence of overconditioning in mature horses in southwest Virginia during the summer.. J Vet Intern Med 2012 Nov-Dec;26(6):1413-8.
    doi: 10.1111/j.1939-1676.2012.00995.xpubmed: 22946995google scholar: lookup
  34. Carroll CL, Huntington PJ. Body condition scoring and weight estimation of horses.. Equine Vet J 1988 Jan;20(1):41-5.
    doi: 10.1111/j.2042-3306.1988.tb01451.xpubmed: 3366105google scholar: lookup
  35. Dugdale AH, Grove-White D, Curtis GC, Harris PA, Argo CM. Body condition scoring as a predictor of body fat in horses and ponies.. Vet J 2012 Nov;194(2):173-8.
    doi: 10.1016/j.tvjl.2012.03.024pubmed: 22578691google scholar: lookup
  36. Dugdale AH, Curtis GC, Cripps P, Harris PA, Argo CM. Effect of dietary restriction on body condition, composition and welfare of overweight and obese pony mares.. Equine Vet J 2010 Oct;42(7):600-10.
    doi: 10.1111/j.2042-3306.2010.00110.xpubmed: 20840575google scholar: lookup
  37. Walsh DM, McGowan CM, McGowan T, Lamb SV, Schanbacher BJ, Place NJ. Correlation of Plasma Insulin Concentration with Laminitis Score in a Field Study of Equine Cushing's Disease and Equine Metabolic Syndrome.. Journal of Equine Veterinary Science 2009;29(2):87–94.
    doi: 10.1016/j.jevs.2008.12.006google scholar: lookup
  38. Bailey SR, Habershon-Butcher JL, Ransom KJ, Elliott J, Menzies-Gow NJ. Hypertension and insulin resistance in a mixed-breed population of ponies predisposed to laminitis.. Am J Vet Res 2008 Jan;69(1):122-9.
    doi: 10.2460/ajvr.69.1.122pubmed: 18167097google scholar: lookup
  39. Beech J, Boston RC, McFarlane D, Lindborg S. Evaluation of plasma ACTH, alpha-melanocyte-stimulating hormone, and insulin concentrations during various photoperiods in clinically normal horses and ponies and those with pituitary pars intermedia dysfunction.. J Am Vet Med Assoc 2009 Sep 15;235(6):715-22.
    doi: 10.2460/javma.235.6.715pubmed: 19751169google scholar: lookup
  40. Donaldson MT, McDonnell SM, Schanbacher BJ, Lamb SV, McFarlane D, Beech J. Variation in plasma adrenocorticotropic hormone concentration and dexamethasone suppression test results with season, age, and sex in healthy ponies and horses.. J Vet Intern Med 2005 Mar-Apr;19(2):217-22.
    doi: 10.1111/j.1939-1676.2005.tb02685.xpubmed: 15822567google scholar: lookup
  41. Frank N, Elliott SB, Brandt LE, Keisler DH. Physical characteristics, blood hormone concentrations, and plasma lipid concentrations in obese horses with insulin resistance.. J Am Vet Med Assoc 2006 May 1;228(9):1383-90.
    doi: 10.2460/javma.228.9.1383pubmed: 16649943google scholar: lookup

Citations

This article has been cited 4 times.
  1. Yao Y, Zhao J, Hu J, Song H, Wang S, Wang Y. Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis. Biomed Res Int 2022;2022:5217885.
    doi: 10.1155/2022/5217885pubmed: 35198634google scholar: lookup
  2. Patterson Rosa L, Mallicote MF, Long MT, Brooks SA. Metabogenomics reveals four candidate regions involved in the pathophysiology of Equine Metabolic Syndrome. Mol Cell Probes 2020 Oct;53:101620.
    doi: 10.1016/j.mcp.2020.101620pubmed: 32659253google scholar: lookup
  3. Fitzgerald DM, Anderson ST, Sillence MN, de Laat MA. The cresty neck score is an independent predictor of insulin dysregulation in ponies. PLoS One 2019;14(7):e0220203.
    doi: 10.1371/journal.pone.0220203pubmed: 31339945google scholar: lookup
  4. Lindsay-McGee V, Massey C, Li YT, Clark EL, Psifidi A, Piercy RJ. Characterisation of phenotypic patterns in equine exercise-associated myopathies. Equine Vet J 2025 Mar;57(2):347-361.
    doi: 10.1111/evj.14128pubmed: 38965932google scholar: lookup
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