{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:31:16Z","timestamp":1788849076064,"version":"build-2803163510"},"reference-count":76,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Blood-based metabolomics is increasingly recognised as a powerful tool for disease detection in human medicine. However, its application in veterinary science remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Animals<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Models showed high discriminative performance for separating healthy from non-healthy animals (ROC AUC 0.916 \u00b1 0.012; accuracy 86.5 \u00b1 3.8%; sensitivity 81.7 \u00b1 6.9%; specificity 87.5 \u00b1 6.0%) and identifying pets with cancer (ROC AUC 0.911 \u00b1 0.008; accuracy 83.5 \u00b1 3.4%; sensitivity 86.5 \u00b1 6.7%; specificity 82.4 \u00b1 6.6%) or CVD (ROC AUC 0.924 \u00b1 0.010; accuracy 90.0 \u00b1 5.8%; sensitivity 85.6 \u00b1 5.1%; specificity 90.6 \u00b1 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fvets.2026.1830153","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:24:51Z","timestamp":1782793491000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of NMR-based metabolomics and machine learning for non-invasive disease screening in dogs"],"prefix":"10.3389","volume":"13","author":[{"given":"Riccardo","family":"Finotello","sequence":"first","affiliation":[{"name":"Department of Human Sciences, Link University","place":["Rome, Italy"]},{"name":"Ospedale Veterinario I Portoni Rossi\u2014AniCura Holding Italy S.r.l.","place":["Zola Predosa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao Thing","family":"Teoh","sequence":"additional","affiliation":[{"name":"LatusPet Limited","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Nili","sequence":"additional","affiliation":[{"name":"LatusPet Limited","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Sepehri","sequence":"additional","affiliation":[{"name":"LatusPet Limited","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ylenia","family":"Cuzzup\u00e8","sequence":"additional","affiliation":[{"name":"Ospedale Veterinario I Portoni Rossi\u2014AniCura Holding Italy S.r.l.","place":["Zola Predosa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Scoccianti","sequence":"additional","affiliation":[{"name":"LatusPet Limited","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Procoli","sequence":"additional","affiliation":[{"name":"Ospedale Veterinario I Portoni Rossi\u2014AniCura Holding Italy S.r.l.","place":["Zola Predosa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C.","family":"Anthony","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, Medical Sciences Division, University of Oxford","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Livia","family":"Benigni","sequence":"additional","affiliation":[{"name":"YouLiv4 Veterinary Imaging Referrals","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mara Vittoria","family":"Alonzo","sequence":"additional","affiliation":[{"name":"Ospedale Veterinario I Portoni Rossi\u2014AniCura Holding Italy S.r.l.","place":["Zola Predosa, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Islom B.","family":"Nazarov","sequence":"additional","affiliation":[{"name":"LatusPet Limited","place":["Oxford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1111\/1467-9566.13017","article-title":"Animals, veterinarians and the sociology of diagnosis","volume":"42","author":"Hobson-West","year":"2019","journal-title":"Sociol Health Illn"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1056440","DOI":"10.3389\/fvets.2022.1056440","article-title":"Point-of-care testing in companion and food animal disease diagnostics","volume":"9","author":"Velayudhan","year":"2022","journal-title":"Front Vet Sci"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1242\/dmm.004358","article-title":"Leading the way: canine models of genomics and disease","volume":"3","author":"Shearin","year":"2010","journal-title":"Dis Model Mech"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/1751-0147-46-105","article-title":"Mortality in over 350,000 insured Swedish dogs from 1995-2000: I. 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