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BACKGROUND: Identifying inflammatory states is vital in veterinary diagnostics, yet reliable markers for chronic, low-grade inflammation are lacking. This study aims to characterize metabolic alterations in diverse inflammatory conditions and evaluate a novel multi-marker model. METHODS: Plasma from 175 healthy and 207 diseased dogs (representing infectious, neoplastic, and degenerative pathologies) was analyzed via canine-validated 1H NMR spectroscopy. Logistic regression was employed to identify both general and disease-specific metabolic alterations. A multivariable model was developed using forward stepwise selection, and its diagnostic performance was compared against that of C-reactive protein (CRP), albumin, glycoprotein acetyls (GlycA), and a combination of GlycA and albumin. RESULTS: Metabolic changes were observed across all the conditions studied, with some alterations specific to individual diseases and others common across conditions. The final six-metabolite model (albumin, GlycA, pyruvate, alanine, glucose, and phenylalanine/tyrosine ratio) showed excellent performance (AUC=0.82). It significantly outperformed CRP in detecting all chronic diseases (0.68–0.89 vs. 0.46–0.60) and acute gastroenteritis ( 0.86 vs. 0.71). CONCLUSIONS: Metabolic profiling provides a superior diagnostic approach to CRP, particularly for chronic inflammatory conditions, by capturing systemic signatures that traditional markers miss.
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Elo et al. (2026) studied this question.
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