Published: 26 Feb 2018 | Last Updated: 27 Feb 2018 13:34:43

Dogs have long been called Man’s Best Friend, providing both companionship and work to their human owners. But a revolutionary new research platform called VetCompass™ Programme at the Royal Veterinary College (RVC) is now harnessing the power of Big Data Analyses from anonymised veterinary clinical records to take this friendship from dogs to a whole new level. Information from first-opinion veterinary data on millions of dogs now offers possibilities for huge advances in our understanding of many common human diseases.

Humans have experienced large increases in average lifespan over the last 150 years but this often means spending more years of later life with multiple chronic diseases. The closely entwined lives of companion dogs with their owners have suggested the dog a powerful research model to better understand the genetic and environmental determinants of health in humans. But the shorter livespans of dogs mean that lifetime studies can be accomplished in roughly one-seventh of the time in dogs that would be needed for a similar study in humans.  

A ground-breaking new study involving the VetCompass™ Programme at the RVC reports that many chronic conditions that are both common, and also commonly occur together, in human populations (e.g. obesity, arthritis, hypothyroidism and diabetes mellitus) show very similar occurrence patterns in companion dogs. The effects of age on disease risk was also very similar between humans and dogs, especially for neoplastic, congenital, and metabolic causes of death.

This novel research provides strong evidence that the companion dog may be an ideal translational model to study complex questions about human health and paves the way for a whole new arena of research on aging whereby dogs yet again benefit humans.

Hoffman JM, Creevy KE, Franks A, O'Neill DG, Promislow DEL. The companion dog as a model for human aging and mortality. Aging Cell. 2018:  

The full paper is freely available in Open Access at http://onlinelibrary.wiley.com/doi/10.1111/acel.12737/full