Metabolic changes induced by oral glucose tests in horses and their diagnostic use

Abstract

Background: Little is known about the implications of hyperinsulinemia on energy metabolism, and such knowledge might help understand the pathophysiology of insulin dysregulation.

Objectives: Describe differences in the metabolic response to an oral glucose test, depending on the magnitude of the insulin response.

Animals: Twelve Icelandic horses in various metabolic states.

Methods: Horses were subjected to 3 oral glucose tests (OGT; 0.5 g/kg body weight glucose). Basal, 120 and 180 minutes samples were analyzed using a combined liquid chromatography tandem mass spectrometry and flow injection analysis tandem mass spectrometry metabolomic assay. Insulin concentrations were measured using an ELISA. Analysis was performed using linear models and partial least-squares regression.

Results: The kynurenine : tryptophan ratio increased over time during the OGT (adjusted P-value = .001). A high insulin response was associated with lower arginine (adjusted P-value = .02) and carnitine (adjusted P-value = .03) concentrations. A predictive model using only baseline samples performed well with as few as 7 distinct metabolites (sensitivity, 86%; 95% confidence interval [CI], 81%-90%; specificity, 88%; 95% CI, 84%-92%).

Conclusions and clinical importance: Our results suggest induction of low-grade inflammation during the OGT. Plasma arginine and carnitine concentrations were lower in horses with high insulin response and could constitute potential therapeutic targets. Development of screening tools to identify insulin-dysregulated horses using only baseline blood sample appears promising.

Keywords: EMS; biomarker; insulin dysregulation; metabolomics; oral glucose test.

FIGURE 1

Heatmap of the relative metabolite concentrations for the metabolites significantly associated with (A) time during the oral glucose test (OGT) and (B) area under the insulin curve over time (AUC_ins). Each column of the heatmap represents a sample and each row a metabolite. In A, the samples are grouped by time point, whereas in B they are ordered by AUC_ins in ascending order. Metabolite names are displayed on the right side with associated fold change and adjusted P‐values. In the case of numeric predictors like “Time” or “AUC_ins,” the log₂ fold change (logFC) given by the “limma” package represents the slope of the regression line. For each unit of the predictor (eg, time in minutes), the log₂‐transformed normalized metabolite concentrations thus increase by log₂ FC. Note that all lysophosphatidylcholines decreased over time—as on average the colored tiles are darker at 0 than 180 minutes—whereas phosphatidylcholines increased. The associations between metabolites and AUC_ins were less apparent, because there was more individual variability

FIGURE 2

Dumbbell plot of the scaled Variable Importance in Projection (VIP) scores of the top 10 metabolites from the baseline and 120 minutes partial least‐squares discriminant analysis (PLS‐DA) models. The scaling of the scores allows for a better comparability between models. As there is some overlap between the 10 metabolites in each model, the combination of both rankings results in the 15 metabolites displayed here. The dark segments between pairs of points represent the difference in relative importance of the metabolites. Large differences indicate that although the metabolite is very helpful in distinguishing horses with a high area under the insulin curve over time (AUC_ins) from horses with a low 1in‐ model, the difference between both groups regarding this metabolite is less striking at the other time point

FIGURE 3

Model performance estimates on the baseline samples obtained by bootstrap cross‐validation depending on the number of metabolites included. Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were obtained using abovementioned formulas and the mean of previously reported prevalence of hyperinsulinemia. ²⁸ , ²⁹ , ³⁰ The 95% confidence interval is shown as a shaded area behind each estimate. Overall, best model performance is reached with the top 7 and top 20 metabolites as determined by the baseline partial least‐squares discriminant analysis (PLS‐DA) model including all metabolites