Evidence of altered fatty acid metabolism in dogs with naturally occurring valvular heart disease and congestive heart failure.

INTRODUCTION: Myxomatous mitral valve disease (MMVD) is the most common cardiac condition in adult dogs. The disease progresses over several years and affected dogs may develop congestive heart failure (HF). Research has shown that myocardial metabolism is altered in cardiac disease, leading to a reduction in ß-oxidation of fatty acids and an increased dependence upon glycolysis. OBJECTIVES: This study aimed to evaluate whether a shift in substrate use occurs in canine patients with MMVD; a naturally occurring model of human disease. METHODS: Client-owned dogs were longitudinally evaluated at a research clinic in London, UK and paired serum samples were selected from visits when patients were in ACVIM stage B1: asymptomatic disease without cardiomegaly, and stage C: HF. Samples were processed using ultra-performance liquid chromatography mass spectrometry and lipid profiles were compared using mixed effects models with false discovery rate adjustment. The effect of disease stage was evaluated with patient breed entered as a confounder. Features that significantly differed were screened for selection for annotation efforts using reference databases. RESULTS: Dogs in HF had altered concentrations of lipid species belonging to several classes previously associated with cardiovascular disease. Concentrations of certain acylcarnitines, phospholipids and sphingomyelins were increased after individuals had developed HF, whilst some ceramides and lysophosphatidylcholines decreased. CONCLUSIONS: The canine metabolome appears to change as MMVD progresses. Findings from this study suggest that in HF myocardial metabolism may be characterised by reduced ß-oxidation. This proposed explanation warrants further research.

Accuracy of history, physical examination, cardiac biomarkers, and biochemical variables in identifying dogs with stage B2 degenerative mitral valve disease.

BACKGROUND: Treatment is indicated in dogs with preclinical degenerative mitral valve disease (DMVD) and cardiomegaly (stage B2). This is best diagnosed using echocardiography; however, relying upon this limits access to accurate diagnosis. OBJECTIVES: To evaluate whether cardiac biomarker concentrations can be used alongside other clinical data to identify stage B2 dogs. ANIMALS: Client-owned dogs (n = 1887) with preclinical DMVD prospectively sampled in Germany, the United Kingdom, and the United States. METHODS: Dogs that met inclusion criteria and were not receiving pimobendan (n = 1245) were used for model development. Explanatory (multivariable logistic regression) and predictive models were developed using clinical observations, biochemistry, and cardiac biomarker concentrations, with echocardiographically confirmed stage B2 disease as the outcome. Receiver operating characteristic curves assessed the ability to identify stage B2 dogs. RESULTS: Age, appetite, serum alanine aminotransferase activity, body condition, serum creatinine concentration, murmur intensity, and plasma N-terminal propeptide of B-type natriuretic peptide (NT-proBNP) concentration were independently associated with the likelihood of being stage B2. The discriminatory ability of this explanatory model (area under curve [AUC], 0.84; 95% confidence interval [CI], 0.82-0.87) was superior to NT-proBNP (AUC, 0.77; 95% CI, 0.74-0.80) or the vertebral heart score alone (AUC, 0.76; 95% CI, 0.69-0.83). A predictive logistic regression model could identify the probability of being stage B2 (AUC test set, 0.86; 95% CI, 0.81-0.91). CONCLUSION AND CLINICAL IMPORTANCE: Our findings indicate accessible measurements could be used to screen dogs with preclinical DMVD. Encouraging at-risk dogs to seek further evaluation could result in a greater proportion of cases being appropriately managed.

The gut microbiome in dogs with congestive heart failure: a pilot study.

Compromised gut health and dysbiosis in people with heart failure has received a great deal of attention over the last decade. Whether dogs with heart failure have a similar dysbiosis pattern to what is described in people is currently unknown. We hypothesised that dogs with congestive heart failure have quantifiable dysbiosis compared to healthy dogs that are similar in sex and age. A total of 50 dogs (15 healthy dogs and 35 dogs with congestive heart failure) were prospectively recruited, and their faecal gut microbiome was assessed using 16S rRNA sequencing (Illumina MiSeq platform). There was no significant change in the microbial diversity and richness in dogs with congestive heart failure. However, there was an increase in abundance of Proteobacteria in the congestive heart failure group (p = 0.014), particularly due to an increase in the family Enterobacteriaceae (p = 0.002) and Escherichia coli (p = 0.033). We conclude that dogs with congestive heart failure have dysbiosis, and we show additional trends in our data suggesting that dogs may have a similar pattern to that described in people. The results of this study provide useful preliminary information and raise the possibility that dogs represent a clinically relevant animal model of dysbiosis in people with heart failure.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity.

During the development of new drugs or compounds there is a requirement for preclinical trials, commonly involving animal tests, to ascertain the safety of the compound prior to human trials. Machine learning techniques could provide an in-silico alternative to animal models for assessing drug toxicity, thus reducing expensive and invasive animal testing during clinical trials, for drugs that are most likely to fail safety tests. Here we present a machine learning model to predict kidney dysfunction, as a proxy for drug induced renal toxicity, in rats. To achieve this, we use inexpensive transcriptomic profiles derived from human cell lines after chemical compound treatment to train our models combined with compound chemical structure information. Genomics data due to its sparse, high-dimensional and noisy nature presents significant challenges in building trustworthy and transparent machine learning models. Here we address these issues by judiciously building feature sets from heterogenous sources and coupling them with measures of model uncertainty achieved through Gaussian Process based Bayesian models. We combine the use of insight into the feature-wise contributions to our predictions with the use of predictive uncertainties recovered from the Gaussian Process to improve the transparency and trustworthiness of the model.