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<p>Multimorbidity, the presence of two or more conditions, is associated with a higher risk of death as individuals age. However, modeling multimorbidity in laboratory animals is difficult, if not impossible, because specific conditions are seldom individually diagnosed and treated in these settings. Because of their shared environment, physiology, and genetic diversity, and because they are medically managed as individuals, companion dogs have potential to serve as a translational multimorbidity model. Yet it is unknown how diagnoses accumulate over time, how these diagnoses are associated with mortality, and which multimorbid condition combinations are the most prevalent and hazardous. Utilizing owner-reported data from over 50,000 dogs in the Dog Aging Project (DAP), we assessed how multimorbidities develop, learned which are the most prevalent, and evaluated their impact on survival. Like in humans, we show that the accumulation of conditions is associated with an increased risk of death in dogs. We also present data suggesting that future reported condition rates vary depending on the conditions a dog’s owner already has reported. Not surprisingly, our analysis reveals that the overall canine aging process appears to be the driving factor behind disease development, more so than other covariates. Through classifying multimorbidities, we found that almost all either exhibit a synergistic effect or are driven by a single, predominant condition. In both cases, osteoarthritis, overweight, and cancer were highly prevalent throughout. This analysis expands on previously conducted DAP work and further highlights the usefulness of the companion dog as a translational model for human aging.</p>

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Multimorbidity as a predictor of mortality in companion dogs

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Eric R. Foreman,
  • Laura Corlin,
  • Kate E. Creevy,
  • Jessica M. Hoffman,
  • Joshua M. Akey,
  • Rozalyn M. Anderson,
  • Elhanan Borenstein,
  • Marta G. Castelhano,
  • Amanda E. Coleman,
  • Matthew D. Dunbar,
  • Virginia R. Fajt,
  • Erica C Jonlin,
  • Matt Kaeberlein,
  • Elinor K. Karlsson,
  • Kathleen F. Kerr,
  • Jing Ma,
  • Evan L. MacLean,
  • Stephanie McGrath,
  • Natasha J Olby,
  • Daniel E.L. Promislow,
  • May J Reed,
  • Audrey Ruple,
  • Stephen M. Schwartz,
  • Sandi Shrager,
  • Noah Snyder-Mackler,
  • M. Katherine Tolbert

摘要

Multimorbidity, the presence of two or more conditions, is associated with a higher risk of death as individuals age. However, modeling multimorbidity in laboratory animals is difficult, if not impossible, because specific conditions are seldom individually diagnosed and treated in these settings. Because of their shared environment, physiology, and genetic diversity, and because they are medically managed as individuals, companion dogs have potential to serve as a translational multimorbidity model. Yet it is unknown how diagnoses accumulate over time, how these diagnoses are associated with mortality, and which multimorbid condition combinations are the most prevalent and hazardous. Utilizing owner-reported data from over 50,000 dogs in the Dog Aging Project (DAP), we assessed how multimorbidities develop, learned which are the most prevalent, and evaluated their impact on survival. Like in humans, we show that the accumulation of conditions is associated with an increased risk of death in dogs. We also present data suggesting that future reported condition rates vary depending on the conditions a dog’s owner already has reported. Not surprisingly, our analysis reveals that the overall canine aging process appears to be the driving factor behind disease development, more so than other covariates. Through classifying multimorbidities, we found that almost all either exhibit a synergistic effect or are driven by a single, predominant condition. In both cases, osteoarthritis, overweight, and cancer were highly prevalent throughout. This analysis expands on previously conducted DAP work and further highlights the usefulness of the companion dog as a translational model for human aging.