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. 2025 Aug 14;21(8):e1012728.
doi: 10.1371/journal.pcbi.1012728. eCollection 2025 Aug.

Constructing the first comorbidity networks in companion dogs in the Dog Aging Project

Affiliations

Constructing the first comorbidity networks in companion dogs in the Dog Aging Project

Antoinette Fang et al. PLoS Comput Biol. .

Abstract

Comorbidity and its association with age are of great interest in geroscience. However, there are few model organisms that are well-suited to study comorbidities that will have high relevance to humans. In this light, we turn our attention to the companion dog. The companion dog shares many morbidities with humans. Thus, a better understanding of canine comorbidity relationships could benefit both humans and dogs. We present an analysis of canine comorbidity networks from the Dog Aging Project, a large epidemiological cohort study of companion dogs in the United States. We included owner-reported health conditions that occurred in at least 60 dogs (n = 160) and included only dogs that had at least one of those health conditions (n = 26,614). We constructed an undirected comorbidity network using a Poisson binomial test, adjusting for age, sex, sterilization status, breed background (i.e., purebred vs. mixed-breed), and weight. The comorbidity network reveals well-documented comorbidities, such as diabetes with cataracts and blindness, and hypertension with chronic kidney disease (CKD). In addition, this network also supports less well-studied comorbidity relationships, such as proteinuria with anemia. A directed comorbidity network accounting for time of reported condition onset suggests that diabetes precedes cataracts, elbow/hip dysplasia before osteoarthritis, and keratoconjunctivitis sicca before corneal ulcer, which are consistent with the canine literature. Analysis of age-stratified networks reveals that global centrality measures increase with age and are the highest in the Senior group compared to the Young Adult and Mature Adult groups. Only the Senior group identified the association between hypertension and CKD. Our results suggest that comorbidity network analysis is a promising method to enhance clinical knowledge and canine healthcare management.

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Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: DP is a paid consultant of WndrHLTH Club, Inc. and Infinity Research Labs.

Figures

Fig 1
Fig 1. Summary of health condition distribution and medical history characteristics.
(a) Boxplot comparing each dog’s age at last reported health condition with the total span of their medical history (i.e., time between first and last reported condition). (b) Histogram representing number of dogs with different numbers of conditions. (c) Bar chart displaying the 20 most frequently occurring health conditions in the study population.
Fig 2
Fig 2. Associations between demographic factors and health conditions, stratified by condition category.
Coefficients from logistic regression models are shown for each health condition, with age, weight, breed background, and sex/reproductive status as predictors. Statistical significance is denoted by asterisks (* p < 0.05, ** p < 0.01, *** p < 0.001).
Fig 3
Fig 3. Undirected comorbidity network.
Nodes are health conditions with node size proportional to condition prevalence, and edges are statistically significant comorbidity connections (at a Bonferroni adjusted P-value < 0.001). Colors indicate health condition categories. (a) Full network view with Bone/Orthopedic, Ear/Nose/Throat, Eye, Gastrointestinal, Infection/Parasites, Mouth/Dental/Oral, Skin, and Respiratory categories collapsed into single aggregate nodes. (b) Zoomed-in view of the Skin conditions, including their first-degree neighbors to ear infection.
Fig 4
Fig 4. Age-stratified comorbidity networks.
Undirected comorbidity networks in Young adult (a), Mature adult (b) and Senior (c). In each network, nodes represent health conditions with node size proportional to condition prevalence, and edges represent statistically significant comorbidity associations (at a Bonferroni-adjusted P-value < 0.01). Colors indicate health condition categories. No network is shown for the Puppy stratum as no significant comorbidities were detected in this age group. Conditions in the Skin and Infection/Parasites categories are presented as aggregate nodes in (b) and (c) to minimize cluttering. (d) Heatmap displaying edge overlap between disease networks across age groups. Numbers indicate shared edges between a pair of networks, with diagonal values showing total number of edges per network.
Fig 5
Fig 5. Time-directed network using a window size of 12 months.
Nodes are health conditions, and edges are statistically significant comorbidity connections (at a Bonferroni-adjusted P-value < 0.01). Colors indicate health condition categories. Arrowheads point from the health condition that occurs earlier in time to the health condition that occurs later. (a) Full network view with Skin and Infection/Parasites categories collapsed into single aggregate nodes. (b) Zoomed-in view of the Skin and Infection/Parasites conditions, including their first-degree neighbors.

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