Variation in biochemistry test results between annual wellness visits in apparently healthy Golden Retrievers.

BACKGROUND: Annual wellness testing is widely recommended for apparently healthy dogs, but there is little data to assist with distinguishing normal variation from clinically important changes. OBJECTIVES: To define variability in biochemistry analytes between annual wellness tests in healthy Golden Retrievers. ANIMALS: Four hundred thirty-four Golden Retrievers undergoing annual health assessments by their primary care veterinarians as part of a prospective cohort study. METHODS: Changes in 23 biochemistry analytes were calculated between year 1 and year 2 health checks for 196 dogs classified as healthy for ≥3 consecutive years. Using a direct nonparametric method, annual change intervals were constructed to define normal variability. A validation cohort of 238 dogs without a diagnosis of systemic disease for ≥3 consecutive years were compared with the reference and annual change intervals, and the proportions of dogs outside annual change intervals and a population-based reference interval were compared by using a McNemar test. RESULTS: Annual change intervals were calculated based on 190 dogs after outlier removal. For all 23 analytes, >90% of dogs in the validation cohort were within the annual change interval. There were no significant differences in the classification by reference versus annual change intervals. CONCLUSIONS AND CLINICAL IMPORTANCE: The annual change intervals met performance requirements for classification of dogs that did not develop systemic disease in the year following wellness testing as normal.

Population characteristics of golden retriever lifetime study enrollees.

BACKGROUND: Studying cancer and other diseases poses a problem due to their protracted and multifactorial nature. Prospective studies are useful to investigate chronic disease processes since collection of lifestyle information, exposure data and co-incident health issues are collected before the condition manifests. The Golden Retriever Lifetime Study is one of the first prospective studies following privately-owned dogs throughout life to investigate the incidence and risk factors for disease outcomes, especially cancer.Owners of golden retrievers in the contiguous United States volunteered their dogs in early life. Owners and veterinarians complete online questionnaires about health status and lifestyle; dogs undergo a physical examination and collection of biological samples annually. The data presented summarize the initial study visits and the corresponding questionnaires for 3044 dogs in the cohort. RESULTS: The median age of dogs at enrollment was 14.0 months (interquartile range (IQR): 8-20 months). Approximately half of the population had undergone gonadectomy by their initial study visit. Medical conditions reported at enrollment consisted primarily of integumentary, gastrointestinal and urinary dysfunction. A large majority of the dogs have a record of having received preventive care (vaccines, parasiticides, flea and heartworm prevention) by the time of the initial study visit. Clinical pathology data were unremarkable. CONCLUSIONS: This study represents one of the first lifetime observational investigations in veterinary medicine. The population characteristics reported here indicate a healthy cohort of golden retrievers cared for by owners committed to their dogs' health. Data acquired over the study period will provide valuable information about genetic, dietary and environmental risk factors associated with disease in golden retrievers and a framework for future prospective studies in veterinary medicine.

Enriched biodiversity data as a resource and service.

BACKGROUND: Recent years have seen a surge in projects that produce large volumes of structured, machine-readable biodiversity data. To make these data amenable to processing by generic, open source "data enrichment" workflows, they are increasingly being represented in a variety of standards-compliant interchange formats. Here, we report on an initiative in which software developers and taxonomists came together to address the challenges and highlight the opportunities in the enrichment of such biodiversity data by engaging in intensive, collaborative software development: The Biodiversity Data Enrichment Hackathon. RESULTS: The hackathon brought together 37 participants (including developers and taxonomists, i.e. scientific professionals that gather, identify, name and classify species) from 10 countries: Belgium, Bulgaria, Canada, Finland, Germany, Italy, the Netherlands, New Zealand, the UK, and the US. The participants brought expertise in processing structured data, text mining, development of ontologies, digital identification keys, geographic information systems, niche modeling, natural language processing, provenance annotation, semantic integration, taxonomic name resolution, web service interfaces, workflow tools and visualisation. Most use cases and exemplar data were provided by taxonomists. One goal of the meeting was to facilitate re-use and enhancement of biodiversity knowledge by a broad range of stakeholders, such as taxonomists, systematists, ecologists, niche modelers, informaticians and ontologists. The suggested use cases resulted in nine breakout groups addressing three main themes: i) mobilising heritage biodiversity knowledge; ii) formalising and linking concepts; and iii) addressing interoperability between service platforms. Another goal was to further foster a community of experts in biodiversity informatics and to build human links between research projects and institutions, in response to recent calls to further such integration in this research domain. CONCLUSIONS: Beyond deriving prototype solutions for each use case, areas of inadequacy were discussed and are being pursued further. It was striking how many possible applications for biodiversity data there were and how quickly solutions could be put together when the normal constraints to collaboration were broken down for a week. Conversely, mobilising biodiversity knowledge from their silos in heritage literature and natural history collections will continue to require formalisation of the concepts (and the links between them) that define the research domain, as well as increased interoperability between the software platforms that operate on these concepts.