Understanding How Dogs Age: Longitudinal Analysis of Markers of Inflammation, Immune Function, and Oxidative Stress
Bibliographic record
Abstract
As in human populations, advances in nutrition and veterinary care have led to an increase in the lifespan of companion animals. Detrimental physiological changes occurring later in life must be understood before interventions can be made to slow or reduce them. One important aspect of human aging is upregulation of the inflammatory response and increase in oxidative damage resulting in pathologies linked to chronic inflammation. To determine whether similar processes occur in the aging dog, changes in markers of inflammation and oxidative stress were investigated in 80 Labrador retrievers from adulthood to the end of life. Serum levels of immunoglobulin M (p < .001) and 8-hydroxy-2-deoxyguanosine (p < .001) increased with age, whereas no effect of age was detected for immunoglobulin G or C-reactive protein unless the last year of life was included in the analysis (p = .002). Baseline levels of heat shock protein 70 decreased with age (p < .001) while those after exposure to heat stress were maintained (p = .018). However, when excluding final year of life data, a decline in the heat shock protein 70 response after heat stress was observed (p = .004). These findings indicate that aging dogs undergo changes similar to human inflammaging and offer the possibility of nutritional or pharmacological intervention to delay or reduce these effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".