Digital Biomarkers Enable Automated, Longitudinal Monitoring in a Mouse Model of Aging.

To understand the growing needs of an aging human population, there is demand for scalable and reproducible approaches to study animal models of aging and to test novel therapeutic interventions. We investigated the sensitivity and utility of a continuous monitoring platform and its digital biomarkers (motion, breathing rate, and wheel running) to evaluate behavioral and physiological differences between "young" (12 weeks) and "old" (23 months) male C57BL/6J mice with or without running wheels in the home cage. Compared to young mice, old mice showed marked reductions in motion and breathing rate, as well as altered circadian rhythms. Mice without running wheels possessed lower breathing rates compared to their counterparts with running wheels. Digital biomarkers showed age-dependent changes in response to routine procedures (cage changes and blood sampling) and alterations in subjects that unexpectedly reached endpoint. Continuous collection of digital biomarkers in the home cage can enhance current approaches by providing unbiased longitudinal monitoring for large-scale aging studies.

Automated and Continuous Monitoring of Animal Welfare through Digital Alerting.

A primary goal in preclinical animal research is respectful and responsible care aimed toward minimizing stress and discomfort while enhancing collection of accurate and reproducible scientific data. Researchers use hands-on clinical observations and measurements as part of routine husbandry procedures or study protocols to monitor animal welfare. Although frequent assessments ensure the timely identification of animals with declining health, increased handling can result in additional stress on the animal and increased study variability. We investigated whether automated alerting regarding changes in behavior and physiology can complement existing welfare assessments to improve the identification of animals in pain or distress. Using historical data collected from a diverse range of therapeutic models, we developed algorithms that detect changes in motion and breathing rate frequently associated with sick animals but rare in healthy controls. To avoid introducing selec- tion bias, we evaluated the performance of these algorithms by using retrospective analysis of all studies occurring over a 31-d period in our vivarium. Analyses revealed that the majority of the automated alerts occurred prior to or simultaneously with technicians' observations of declining health in animals. Additional analyses performed across the entire duration of 2 studies (animal models of rapid aging and lung metastasis) demonstrated the sensitivity, accuracy, and utility of automated alerting for detecting unhealthy subjects and those eligible for humane endpoints. The percentage of alerts per total subject days ranged between 0% and 24%, depending on the animal model. Automated alerting effectively complements standard clinical observations to enhance animal welfare and promote responsible scientific advancement.