Diagnosis of Bovine Respiratory Disease in feedlot cattle using blood 1H NMR metabolomics

Abstract

Current diagnosis methods for Bovine Respiratory Disease (BRD) in feedlots have a low diagnostic accuracy. The current study aimed to search for blood biomarkers of BRD using ¹H NMR metabolomics and determine their accuracy in diagnosing BRD. Animals with visual signs of BRD (n = 149) and visually healthy (non-BRD; n = 148) were sampled for blood metabolomics analysis. Lung lesions indicative of BRD were scored at slaughter. Non-targeted ¹H NMR metabolomics was used to develop predictive algorithms for disease classification using classification and regression trees. In the absence of a gold standard for BRD diagnosis, six reference diagnosis methods were used to define an animal as BRD or non-BRD. Sensitivity (Se) and specificity (Sp) were used to estimate diagnostic accuracy (Acc). Blood metabolomics demonstrated a high accuracy at diagnosing BRD when using visual signs of BRD (Acc = 0.85), however was less accurate at diagnosing BRD using rectal temperature (Acc = 0.65), lung auscultation score (Acc = 0.61) and lung lesions at slaughter as reference diagnosis methods (Acc = 0.71). Phenylalanine, lactate, hydroxybutyrate, tyrosine, citrate and leucine were identified as metabolites of importance in classifying animals as BRD or non-BRD. The blood metabolome classified BRD and non-BRD animals with high accuracy and shows potential for use as a BRD diagnosis tool.

Figure 1

NMR spectrum showing 12 identified metabolites and unknown components that were selected in the classification and regression tree analysis as metabolites of importance in differentiating BRD and non-BRD animals. The region in the box is magnified for clarity.

Figure 2

Principal Component Analysis score plot showing the discrimination between BRD animals (blue dots, n = 149) and non-BRD animals (red dots, n = 148) as defined by the visual diagnosis (VD).

Figure 3

Principal Component Analysis loading plot showing the correlation between metabolites and principal components 1 and 2. All 34 identified metabolites as well as four unknown components that were selected in the classification and regression tree analysis are displayed in the plot. Citrate appears twice as the CART analysis selected two different citrate peaks as components of importance in differentiating BRD and non-BRD animals for two different diagnosis methods.