Knowledge on the genetic epidemiology of disorders in the dog population has implications for both veterinary medicine and sustainable breeding. Limited data on frequencies of genetic disease variants across breeds exists, and the disease heritage of mixed breed dogs remains poorly explored to date. Advances in genetic screening technologies now enable comprehensive investigations of the canine disease heritage, and generate health-related big data that can be turned into action. We pursued population screening of genetic variants implicated in Mendelian disorders in the largest canine study sample examined to date by examining over 83,000 mixed breed and 18,000 purebred dogs representing 330 breeds for 152 known variants using a custom-designed beadchip microarray. We further announce the creation of MyBreedData (www.mybreeddata.com), an online updated inherited disorder prevalence resource with its foundation in the generated data. We identified the most prevalent, and rare, disease susceptibility variants across the general dog population while providing the first extensive snapshot of the mixed breed disease heritage. Approximately two in five dogs carried at least one copy of a tested disease variant. Most disease variants are shared by both mixed breeds and purebreds, while breed- or line-specificity of others is strongly suggested. Mixed breed dogs were more likely to carry a common recessive disease, whereas purebreds were more likely to be genetically affected with one, providing DNA-based evidence for hybrid vigor. We discovered genetic presence of 22 disease variants in at least one additional breed in which they were previously undescribed. Some mutations likely manifest similarly independently of breed background; however, we emphasize the need for follow up investigations in each case and provide a suggested validation protocol for broader consideration. In conclusion, our study provides unique insight into genetic epidemiology of canine disease risk variants, and their relevance for veterinary medicine, breeding programs and animal welfare. Like any human, dogs may suffer from or pass on a variety of inherited disorders. Knowledge of how likely a typical dog is to carry an inherited disorder in its genome, and which disorders are the most common and relevant ones across dog breeds, is valuable for both veterinary care and breeding of healthy dogs. We have explored the largest global dog study sample collected to date, consisting of more than 100,000 mixed breed and purebred dogs, to advance research on this subject. We found that mixed breed dogs and purebred dogs potentially suffer from many of the same inherited disorders, and that around two in five dogs carried at least one of the conditions that we screened for. A dog carrying an inherited disorder is not a “bad dog”–but we humans responsible for breeding selections do need to make sustainable decisions avoiding inbreeding, i.e. mating of dogs that are close relatives. The disease prevalence information we generated during this study is made available online (www.mybreeddata.com), as a free tool for breed and kennel clubs, breeders, as well as the veterinary and scientific community.

As an individual's metabolism reflects health and disease states well, metabolomics holds a vast potential in biomedical applications. However, normal physiological factors, such as age, can also influence metabolism, challenging the establishment of disease-specific metabolic aberrations. Here, we examined how physiological and diet-related factors drive variance in the metabolism of healthy pet dogs. We analysed 2068 serum samples using a canine nuclear magnetic resonance (NMR) spectroscopy-based metabolomics platform. With generalized linear models, we discovered that age, breed, sex, sterilization, diet type and fasting time significantly affected the canine metabolite profiles. Especially, breed and age caused considerable variation in the metabolite concentrations, and breeds with very different body conformations systematically differed in several lipid measurands. Our results enhance the understanding how normal physiological factors influence canine metabolism, aid accurate interpretation of the NMR results, and suggest the NMR platform might be applied in identifying aberrations in nutrient absorption and metabolism.

Background: Metabolomics has been proven to be an invaluable research tool by providing comprehensive insight into systemic metabolism. However, the lack of scalable and quantitative methods with known reference intervals (RIs) and documented reproducibility has prevented the use of metabolomics in the clinical setting.Objective: The objective of this study was to validate the developed quantitative nuclear magnetic resonance (NMR) spectroscopy‐based metabolomics platform for canine serum and plasma samples and determine optimal sample handling conditions for its use.Methods: Altogether, 8247 canine samples were analyzed using a Bruker's 500 MHz NMR spectrometer. Using statistical approaches derived from international guidelines, we studied method precision, measurand stability in various long‐ and short‐term storage conditions, as well as the effect of prolonged contact with red blood cells (RBCs), and differences among blood collection tubes. We also screened interferences with lipemia, hemolysis, and bilirubinemia. The results were compared against routine clinical chemistry methods, and RIs were defined for all measurands.Results: We determined RIs for 123 measurands, most of which were previously unpublished. The reproducibility of the results of the NMR platform appeared generally outstanding, and the integrity of the results can be ensured by following standard blood drawing and processing guidelines.Conclusions: Owing to the advantages of quantitative results, high reproducibility, and scalability, this canine metabolomics platform holds great potential for numerous clinical and research applications to improve canine health and well‐being.

Introduction: Reproduction causes major hormonal and physiological changes to the female body. However, the metabolic changes occurring during canine reproduction are scarcely studied.Methods: In this cross-sectional study, we assessed the metabolic effects of canine reproductive status using a ¹H NMR metabolomics platform optimized and validated for canine use. The study population consisted of a total of 837 healthy, intact female dogs in breeding age, of which 663 dogs were in anestrus, 78 in heat, 43 were pseudopregnant, 15 were pregnant, and 38 were lactating. The differences in metabolite profiles between these states were studied by the Kruskal-Wallis test with post-hoc tests performed using the Dunn's test, and visualized by box plots and a heatmap. The ability of the metabolite profile to differentiate pregnant dogs from non-pregnant ones was assessed by creating a multivariate Firth logistic regression model using forward stepwise selection.Results: Lactation, pregnancy and heat all were associated with distinct metabolic changes; pregnancy caused major changes in the concentrations of glycoprotein acetyls, albumin and creatinine, and smaller changes in several lipids, citrate, glutamine, and alanine. Pseudopregnancy, on the other hand, metabolically largely resembled anestrus. Lactation caused major changes in amino acid concentrations and smaller changes in several lipids, albumin, citrate, creatinine, and glycoprotein acetyls. Heat, referring to proestrus and estrus, affected cholesterol and LDL metabolism, and increased HDL particle size. Albumin and glycoprotein acetyls were the metabolites included in the final multivariate model for pregnancy detection, and could differentiate pregnant dogs from non-pregnant ones with excellent sensitivity and specificity.Discussion: These results increase our understanding of the metabolic consequences of canine reproduction, with the possibility of improving maternal health and ensuring reproductive success. The identified metabolites could be used for confirming canine pregnancy.

Background: Metabolic profiles differ between healthy humans and those with inflammatory bowel disease. Few studies have examined metabolic profiles in dogs with chronic enteropathy (CE). Hypothesis: Serum metabolic profiles of dogs with CE are significantly different from those of healthy dogs. Animals: Fifty-five dogs with CE and 204 healthy controls. Methods: A cross-sectional study. The serum concentrations of 99 metabolites measured using a canine-specific proton nuclear magnetic resonance spectroscopy platform were studied. A 2-sample unpaired t-test was used to compare the 2 study samples. The threshold for significance was set at P < .05 with a Bonferroni correction for each metabolite group. Results: Nineteen metabolites and 18 indices of lipoprotein composition were significantly different between the CE and healthy dogs. Four metabolites were significantly higher in dogs with CE, including phenylalanine (mean and SD) (healthy: 0.0417 mmol/L; [SD] 0.0100; CE: 0.0480 mmol/L; SD: 0.0125; P value: <.001) and lactate (healthy: 1.8751 mmol/L; SD: 0.7808; CE: 2.4827 mmol/L; SD CE: 1.4166; P value: .003). Fifteen metabolites were significantly lower in dogs with CE, including total fatty acids, and glycine (healthy: 0.2273 mmol/L; SD: 0.0794; CE: 0.1828 mmol/L; SD CE: 0.0517; P value: <.001). Conclusions and Clinical Importance: The metabolic profile of dogs with CE is significantly different from that of healthy dogs, this opens novel research avenues to develop better diagnostic and prognostic approaches as well as therapeutic trials.

Introduction: Phenobarbital is a commonly used anticonvulsant for the treatment of canine epileptic seizures. In addition to its central nervous system (CNS) depressing effects, long-term phenobarbital administration affects liver function. However, broader metabolic consequences of phenobarbital treatment are poorly characterized.Objectives: To identify metabolic changes in the sera of phenobarbital-treated dogs and to investigate the relationship between serum phenobarbital concentration and metabolite levels.Methods: Leftovers of clinical samples were used: 58 cases with phenobarbital concentrations ranging from 7.8 µg/mL to 50.8 µg/mL, and 25 controls. The study design was cross-sectional. The samples were analyzed by a canine-specific¹H NMR metabolomics platform. Differences between the case and control groups were evaluated by logistic regression. The linear relationship between metabolite and phenobarbital concentrations was evaluated using linear regression.Results: Increasing concentrations of glycoprotein acetyls, LDL particle size, palmitic acid, and saturated fatty acids, and decreasing concentrations of albumin, glutamine, histidine, LDL particle concentration, multiple HDL measures, and polyunsaturated fatty acids increased the odds of the sample belonging to the phenobarbital-treated group, having ap-value < .0033, and area under the curve (AUC) > .7. Albumin and glycoprotein acetyls had the best discriminative ability between the groups (AUC: .94). No linear associations between phenobarbital and metabolite concentrations were observed.Conclusion: The identified metabolites are known to associate with, for example, liver and CNS function, inflammatory processes and drug binding. The lack of a linear association to phenobarbital concentration suggests that other factors than the blood phenobarbital concentration contribute to the magnitude of metabolic changes.