Equine obesity can cause life-threatening secondary chronic conditions, similar to those in humans and other animal species. Equine metabolic syndrome (EMS), primarily characterized by hyperinsulinemia, is often present in obese horses and ponies. Due to clinical similarities to conditions such as pituitary pars intermedia dysfunction (formerly equine Cushing's disease), conclusive diagnosis of EMS often proves challenging. Aside from changes in diet and exercise, few targeted treatments are available for EMS, emphasizing the need for early identification of at-risk individuals to enable implementation of preventative measures. A genomewide association study (GWAS) using Arabian horses with a history of severe laminitis secondary to EMS revealed significant genetic markers near a single candidate gene () that may play a role in cholesterol homeostasis. The best marker, BIEC2-263524 (chr14:69276814 T > C), was correlated with elevated insulin values and increased frequency of laminitis ( = 0.0024 and = 9.663 × 10, respectively). In a second population of Arabian horses, the BIEC2-263524 marker maintained its associations with higher modified insulin-to-glucose ratio (MIRG) values ( = 0.0056) and BCS ( = 0.0063). Screening of the predicted coding regions by sequencing identified a polymorphic guanine homopolymer and 5 haplotypes in the 3' untranslated region (UTR). An 11 guanine (11-G) allele at was correlated with elevated insulin values in the GWAS population ( = 0.0008) and, in the second population, elevated MIRG and increased BCS > 6.5 ( = 0.0055 and = 0.0162, respectively). The BIEC2-263524-C and the 3' UTR -11(G) polymorphisms were correlated at a 98% frequency, indicating strong linkage disequilibrium across this 150-kb haplotype. Assays for these markers could diagnose horses with a genetic predisposition to develop obesity. Additionally, discovery of FAM174A function may improve our understanding of the etiology of this troubling illness in the horse and warrants investigation of this locus for a role in metabolic- and obesity-related disorders of other species.

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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