How To Help Itching Dogs

Hello Summarians!

The interaction between genetic risk and environment is always interesting to observe—a true examination of nature versus nurture.

We can’t do much to change our genetics, but we can modify our surroundings if we are aware of the best way to manage that relationship.

Our first study helps bring some data to help understand how that might be done …

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Environment And Atopy In Dogs

Canine atopic dermatitis and other allergic diseases have increased markedly in recent decades, paralleling trends seen in people. Because genetic risk has not changed substantially over this time, growing evidence points to environmental and lifestyle factors as major drivers. Dogs now share much of the human exposome—the cumulative impact of environmental exposures across life—including urban living, indoor housing, altered diets, reduced physical activity, higher stress, and increased exposure to pollutants and chemicals. These factors interact with genetic predisposition and can push susceptible individuals past a threshold where clinical disease develops.

Epidemiological studies consistently show that rural living, contact with other animals, outdoor activity, and less sanitized environments are protective against canine atopic dermatitis, whereas urban living and highly clean indoor environments are associated with higher risk. These findings support biodiversity-based hypotheses, which emphasize the importance of diverse microbial and environmental exposures in shaping immune tolerance. Although atopic dogs show reduced skin microbiome diversity, current evidence suggests this dysbiosis is largely a consequence of inflammation and skin barrier dysfunction rather than a primary cause.

Lifestyle changes linked to urbanization may further contribute. Reduced outdoor exercise and rising obesity—known risk factors for atopic dermatitis in people—may promote low-grade systemic inflammation and allergic immune responses in dogs. Diet has also shifted substantially, with many dogs now fed commercial diets consistently rather than varied home-prepared foods. Several studies associate early-life exposure to less processed or noncommercial diets with reduced allergy risk, while ultraprocessed, carbohydrate-rich diets are sometimes linked to increased risk. However, findings are inconsistent, causation is unproven, and the health risks of raw or imbalanced diets remain significant.

Pollution and chemical exposure emerge as particularly compelling contributors. Air pollutants, tobacco smoke residues, household cleaners, and topical products can damage epithelial barriers, promote dysbiosis, and increase allergen penetration, initiating self-perpetuating cycles of inflammation. Pollutants may also induce epigenetic changes that favor proinflammatory responses. Stress, which is closely linked to dogs and their owners, may further lower the threshold for disease expression.

Overall, canine atopic dermatitis reflects complex interactions between genetics and a modern environment. While not all factors are controllable, modifiable elements—dietary choices, exercise, stress reduction, careful use of antibiotics and topical products, and minimizing pollutant exposure—offer practical opportunities for prevention and mitigation.

Marsella, R. (2026). Environmental factors are responsible for the rise of atopic dermatitis in dogs: veterinarians should focus on modifiable influences. Journal of the American Veterinary Medical Association, 264(1), 11-19. https://doi.org/10.2460/javma.25.06.0391 

Bottom line — There are some practical things we can do.

Deep Learning Gait Analysis In Dogs

Objective gait analysis is increasingly used in veterinary medicine to diagnose orthopedic and neurologic disease, evaluate treatment response, and monitor rehabilitation, but conventional approaches such as force plates, IMUs, and marker-based motion capture are costly, labor-intensive, and impractical for routine clinical use. This study evaluated a deep learning–based, markerless gait analysis system designed specifically for dogs, aiming to provide an accurate, accessible estimation of anatomical landmarks without physical markers. Trained on a large, veterinarian-annotated dataset representing more than 30 breeds and validated against 2-D marker-based ground-truth data, the model demonstrated high accuracy and reliability across multiple performance metrics, including mean average precision, normalized keypoint error, and percentage of correct keypoints. Overall performance was strong, with particularly high accuracy for distal limb landmarks, likely reflecting greater visual prominence and reduced soft tissue interference, although landmark-specific visibility rather than simple anatomical location appeared to be the dominant determinant of accuracy. Compared with previously reported multi-species pose-estimation datasets, the canine-specific dataset and controlled gait environment likely contributed to improved model convergence and performance, while data augmentation helped mitigate overfitting. 

Despite promising results, several limitations were identified. Morphological diversity among dog breeds, coat length, and body condition influenced both markerless detection and the accuracy of marker-based ground truth, which itself was subject to skin motion, fur interference, and imprecision in palpating bony landmarks. Use of a 2-D marker-based reference limited validation of true kinematic and spatiotemporal parameters and required straight-line gait trials to minimize perspective distortion. Inter- and intrarater reliability of training annotations was not formally assessed, although veterinary oversight and standardized labeling likely reduced variability. Practical factors such as lighting, occlusion, motion blur, and single-camera constraints also affected data capture, reflecting real-world tradeoffs between experimental control and clinical feasibility. Overall, the study demonstrates that markerless, deep learning–based gait analysis can accurately localize canine anatomical landmarks across diverse breeds without physical markers, representing an important step toward practical clinical application. Future work should focus on 3-D validation, broader breed and coat inclusion, less controlled environments, and comparison of clinically meaningful gait metrics to establish diagnostic validity and support multicenter adoption in veterinary practice. 

Pahk, J., Park, S., Seo, J., Kim, H., Son, M., Jin, Y., Kim, H., & Kang, B. (2026). A deep learning–based markerless gait analysis model for dogs shows promising accuracy when validated with 2-dimensional marker–based data. American Journal of Veterinary Research https://doi.org/10.2460/ajvr.25.09.0337

Bottom line — Early results show exciting potential

Arthritis Pathways In Older Horses

Osteoarthritis in horses is a common, debilitating condition characterized by progressive cartilage degeneration, synovial inflammation, and subchondral bone remodeling, for which current treatments are largely short-lived and symptom-modifying rather than disease-modifying. This study positions cellular senescence as a potentially important and previously underrecognized contributor to equine OA pathogenesis. Senescent cells, which accumulate with aging and tissue stress, remain metabolically active and secrete proinflammatory and matrix-degrading factors collectively termed the senescence-associated secretory phenotype (SASP), thereby perpetuating chronic inflammation, impaired tissue repair, and joint degeneration. Using transcriptomic analyses of synovial fluid immune cells and peripheral blood mononuclear cells, the study demonstrated a pronounced upregulation of senescence-associated gene signatures locally within OA-affected joints, particularly in synovial fluid immune populations, while systemic signals in circulating cells were comparatively muted. Immune cell subsets such as macrophages, dendritic cells, gamma delta T cells, CD8 T cells, and cycling cell populations showed enrichment of senescence pathways related to SASP, metabolic, inflammatory, stress-induced, and replicative senescence. Macrophages exhibited strong metabolic senescence signatures consistent with proinflammatory activation, while dendritic cells and T-cell subsets showed enhanced inflammatory and NF-κB–associated senescence patterns that may amplify local immune-mediated cartilage damage. These findings parallel observations in human and laboratory animal OA, where senescence drives self-perpetuating cycles of inflammation, matrix breakdown, and tissue dysfunction through paracrine signaling. Evidence from other equine tissues and age-related conditions further supports the biological plausibility that senescence contributes broadly to impaired regeneration, altered immune function, and musculoskeletal disease in horses. Collectively, the results provide the first comprehensive evidence linking cellular senescence signatures to equine OA and suggest that senotherapeutic strategies, including senolytic or senomorphic approaches, may represent promising adjunctive avenues for future disease-modifying interventions. However, the study is descriptive and limited by a small sample size, age imbalance between groups, and lack of causal inference, underscoring the need for larger, longitudinal studies to disentangle the effects of aging versus OA, validate senescence markers as clinical biomarkers, and guide the safe, targeted development of senescence-modulating therapies in horses. 

Singer, J., Chow, L., Ammons, D., Sabino, I., Impastato, R., Dow, S., & Pezzanite, L. M. (2025). Senescence-associated gene pathways are differentially expressed in equine aging-related osteoarthritis. American Journal of Veterinary Research https://doi.org/10.2460/ajvr.25.09.0343

Bottom line — Trying to find different pathways to help with arthritis treatment

Just putting things in perspective …