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.
Highlights
Equine Metabolic Syndrome (EMS) is a condition characterized by regional and abnormal adiposity, hyperinsulinemia, and susceptibility to laminitis [1, 2]
In a comparing obesity measurements with previously described insulin dysregulation estimators, only MIRG and reciprocal inverse square of basal insulin (RISQI) were significantly correlated to all three morphometric measures, heart girth (HG)/H, neck circumference (NC)/H and AVG body condition scoring (BCS) scores (Table 1)
The overall goal of this study was to use unsupervised statistical modelling to describe the variation within the obesity and EMS phenotype in an Arabian horse population, without the constraint of a prior diagnosis
Summary
Equine Metabolic Syndrome (EMS) is a condition characterized by regional and abnormal adiposity, hyperinsulinemia, and susceptibility to laminitis [1, 2]. Overlap of clinical signs for Pituitary Pars Intermedia Dysfunction (PPID), known as Equine Cushing’s disease, creates additional difficulty in diagnosis [7]. Polyuria, abnormal adiposity, and laminitis, which in some PPID horses may be due to endocrinopathic insulin dysregulation [3, 8]. PPID can exist concurrently with EMS, and there is some evidence suggesting that underlying EMS may be the cause of overlapping characteristics of these two conditions, like insulin dysregulation and laminitis, as these findings are not present in all PPID cases [3, 8, 9]
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