bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.08.20.671317

Dogs and humans share biomarkers of mortality

Abstract

There is growing interest in the use of molecular features as predictors of age, age-related disease risk and age-related mortality. A major shortcoming of this field, however, is the lack of suitable translational research models to identify and understand the underlying mechanisms of these predictive biomarkers in human populations. In particular, we lack a system which, like humans, is genetically variable, lives in diverse environments, and experiences aging-related chronic conditions treated in the context of a sophisticated health care system. Here, we present results from our analysis of data from the Dog Aging Project, a long-term longitudinal study of aging in more than 50,000 companion dogs. In particular, using longitudinal survival models, we present the striking finding of a strong, highly significant positive correlation between the effect of individual metabolites on all-cause mortality in humans, and the association of those same metabolites on all-cause mortality in dogs. We also find that across these independent human studies, the biomarkers identified are also highly correlated, strongly suggesting a general signature of mortality within the plasma metabolome across humans, and now in dogs as well. Given the many similarities between dogs and humans with respect to genetics, environment, disease, and disease treatment, and the fact that dogs are so much shorter lived than humans, we argue that dogs represent an extremely valuable translational model in our ongoing effort to understand the underlying molecular causes and consequences of age-related morbidity and mortality in humans.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Harrison, B. R., Akey, J. M., Snyder-Mackler, N., Raftery, D., DAP Consortium,, Creevy, K. E., Promislow, D.. 2025-08-25. Dogs and humans share biomarkers of mortality. https://doi.org/10.1101/2025.08.20.671317

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The impacts of exogenous noise on stochastic disease dynamics

Much of the literature and intuition associated with mathematical epidemiology is driven by deterministic models, which are a reasonable assumption when the population size is large. Stochastic models, especially individual based models, are however considered vital when dealing with small population sizes, especially at times of invasion or extinction. The overwhelming majority of these models (both deterministic and stochastic) assume that the underlying parameters are fixed (or follow a regular seasonal pattern). Here, we consider an analytic framework for dealing with randomly varying parameters through the use of stochastic differential equations - thereby capturing the action of external noisy processes such as weather. In particular, we focus on when the transmission rate, {beta}, varies as the solution to a Cox-Ingersoll-Ross Model, such that {beta} is gamma distributed with autocorrelation. We consider the impact of this parameter variation on a stochastic version of the Susceptible-Infected-Recovered model, and for this 'double-stochastic' model show through simulation and analytical results that exogenous noise increases the impact of stochasticity, potentially leading to more early extinctions, wider variations in the number of cases at equilibrium, but that early growth rate can be faster or slower depending on the precise parameters.

systems biology↗

Site-resolved spatial and structural interactome of a human cell

The spatial and structural arrangement of proteins determine virtually every process in human cells. We combined gentle subcellular fractionation by differential ultracentrifugation with cross-linking mass spectrometry to systematically map this cellular proteome architecture with residue-level evidence, identifying 164,146 residue-to-residue links in HEK293 cells. These links capture spatial protein arrangement at a resolution sufficient to pinpoint protein localizations at sub-organelle level, determine protein orientations within cellular membranes, and identify inter-organelle contact sites. The residue-level information provides evidence for 18,074 direct protein-protein interactions (PPIs), which we integrate into AlphaFold-based pipelines to nominate PPI-mediating short linear motifs and generate assembly models of large protein complexes. Guided by these spatial and structural readouts, we discover new PPIs within the endomembrane system that regulate the compartmental localization of trafficking machinery. Leveraging a network topology-driven strategy, we augment our HEK293 dataset with PPI data from different cell lines and methods, expanding the spatial and structural interactome of human cells.

systems biology↗

Sensory variation and behavioural degeneracy: a framework for interpreting heterogeneity in the gut-brain axis

Gut microbiome differences are frequently interpreted as reflecting underlying biological differences between individuals. When outcomes are mediated by behaviour, however, this mapping may be fundamentally non-unique. This limits causal inference in gut-brain research, where microbiome differences in autism and depression are routinely attributed to intrinsic neurobiology despite highly variable, overlapping findings. I built a minimal agent-based model grounded in the known sensory variation across the autism spectrum. Dietary behaviour emerges from latent sensory traits, including sensory drive, predictability preference, and context sensitivity, through reinforcement learning and environmental interaction. This behaviour shapes gut microbiome composition. Behavioural variation organizes endogenously into a continuum of specialist, opportunist, and explorer strategies that maps onto the autism sensory spectrum. The system is fundamentally degenerate. Similar microbiome states arise from distinct behavioural pathways. Similar dietary patterns emerge from divergent latent traits. This many-to-one mapping reflects the structural interaction of behaviour, learning, and environmental variability, not stochasticity alone. Microbiome similarity therefore does not uniquely identify underlying cause. As such, the model provides a theoretical framework for interpreting heterogeneity in gut microbiome research, particularly in autism, and generalizes to any condition where behaviour mediates between neural processes and ecological outcomes.

systems biology↗