Application of NMR-based metabolomics and machine learning for non-invasive disease screening in dogs

Background: Blood-based metabolomics is increasingly recognised as a powerful tool for disease detection in human medicine. However, its application in veterinary science remains limited. Objective: To evaluate the ability of an NMR-based metabolomics platform combined with machine learning to screen dogs for cancer, cardiovascular disease (CVD), and overall health status. Animals: Client-owned dogs were recruited from two sites. Of 156 animals enrolled, 139 remained after exclusions and were used for training and cross-validation of classification models. Methods: Blood samples were obtained from clinically healthy dogs and dogs with a range of diseases. Full blood count was performed, and serum metabolomic and lipoprotein profiling data were generated using NMR spectroscopy. Machine learning classifiers were trained to distinguish healthy from non-healthy dogs, and to further identify cancer and CVD cases. Model performance was evaluated by cross-validation and against null models with permuted class labels. Results: Models showed high discriminative performance for separating healthy from non-healthy animals (ROC AUC 0.916 ± 0.012; accuracy 86.5 ± 3.8%; sensitivity 81.7 ± 6.9%; specificity 87.5 ± 6.0%) and identifying pets with cancer (ROC AUC 0.911 ± 0.008; accuracy 83.5 ± 3.4%; sensitivity 86.5 ± 6.7%; specificity 82.4 ± 6.6%) or CVD (ROC AUC 0.924 ± 0.010; accuracy 90.0 ± 5.8%; sensitivity 85.6 ± 5.1%; specificity 90.6 ± 7.2%) from pets without the disease. Key predictive features included glutamine and creatine concentrations, lymphocyte count and percentage, platelet count and mean platelet volume (MPV), as well as lipoprotein cholesterol levels. Conclusion: This study provides the first evidence that NMR metabolomics combined with machine learning enables accurate, non-invasive, multi-disease screening in dogs, highlighting its potential for translation into routine veterinary practice for diagnosis and health monitoring.