Creevy Kate E, Austad Steven N, Hoffman Jessica M, O'Neill Dan G, Promislow Daniel E L
Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, Georgia 30602.
Department of Biology, University of Alabama at Birmingham, Birmingham, Alabama 35294.
Cold Spring Harb Perspect Med. 2016 Jan 4;6(1):a026633. doi: 10.1101/cshperspect.a026633.
The companion dog is the most phenotypically diverse species on the planet. This enormous variability between breeds extends not only to morphology and behavior but also to longevity and the disorders that affect dogs. There are remarkable overlaps and similarities between the human and canine species. Dogs closely share our human environment, including its many risk factors, and the veterinary infrastructure to manage health in dogs is second only to the medical infrastructure for humans. Distinct breed-based health profiles, along with their well-developed health record system and high overlap with the human environment, make the companion dog an exceptional model to improve understanding of the physiological, social, and economic impacts of the longevity dividend (LD). In this review, we describe what is already known about age-specific patterns of morbidity and mortality in companion dogs, and then explore whether this existing evidence supports the LD. We also discuss some potential limitations to using dogs as models of aging, including the fact that many dogs are euthanized before they have lived out their natural life span. Overall, we conclude that the companion dog offers high potential as a model system that will enable deeper research into the LD than is otherwise possible.
伴侣犬是地球上表型差异最大的物种。不同犬种之间这种巨大的变异性不仅体现在形态和行为上，还体现在寿命以及影响犬类的疾病方面。人类和犬类物种之间存在着显著的重叠和相似之处。犬类与我们人类的环境密切共享，包括其中许多风险因素，而且管理犬类健康的兽医基础设施仅次于人类的医疗基础设施。基于不同犬种的独特健康概况，以及其完善的健康记录系统和与人类环境的高度重叠，使得伴侣犬成为增进对长寿红利（LD）的生理、社会和经济影响理解的卓越模型。在本综述中，我们描述了关于伴侣犬特定年龄发病率和死亡率模式的已知情况，然后探讨现有证据是否支持LD。我们还讨论了将犬类用作衰老模型的一些潜在局限性，包括许多犬在其自然寿命结束前就被安乐死这一事实。总体而言，我们得出结论，伴侣犬作为一个模型系统具有很高的潜力，能够比其他方式更深入地研究LD。
