Clustering and Correlations amongst NEFA, Selected Adipokines and Morphological Traits—New Insights into Equine Metabolic Syndrome

SIMPLE SUMMARY: While adipokines’ involvement as biomarkers is better established in human pathology, little data are available on horses. This study aimed to investigate the possible association and relationship between selected metabolic parameters and morphological traits in equine metabolic syndrome. Spearman correlation, univariate linear regression, and hierarchical clustering were used to analyze the correlation between total cholesterol, insulin, non-esterified fatty acids (NEFA), adipokines and age, bodyweight, and general and local adiposity scores. NEFA correlated positively with low significance with bodyweight and adiposity scores, total cholesterol with bodyweight, and omentin with adiposity score. Cluster analysis supported these results and provided further insight into the relationships between studied variables within and between the four groups. These findings highlight NEFA, chemerin, and omentin as valuable biomarkers that could be further analyzed in other horse breeds for a better understanding of equine metabolic pathology. ABSTRACT: Obesity is a common feature in horses suffering from metabolic syndrome. While adipokines involvement as biomarkers is better established in human pathology, little data are available on horses. This study aimed to investigate the possible association and relationship between selected metabolic parameters and morphological traits in equine metabolic syndrome. Adiposity was evaluated using body condition score (BCS) and cresty neck score (CNS). Plasma levels of total cholesterol, insulin, NEFA, and adipokines (omentin and chemerin) were determined using enzyme-linked immunosorbent assays. Spearman correlation, univariate linear regression analysis and hierarchical clustering were performed. Significant positive correlations were observed between NEFA and bodyweight (r = 0.322; p = 0.006), BCS (r = 0.295; p = 0.013), and CNS (r = 0.267; p = 0.024), total cholesterol and bodyweight (r = 0.262; p = 0.027), and omentin and CNS (r = 0.234; p = 0.049). Cluster analysis supported these results and provided more details on the relationships between studied variables within and between the four resulting groups. These findings highlight NEFA, chemerin, and omentin as valuable biomarkers that could be further analyzed in other horse breeds for a better understanding of equine metabolic pathology.