Mouse models for the influence of the social environment on health and aging

Summary A social gradient in health and aging is well established in humans; the greater the social connectedness and socioeconomic status (SES), the lower the burden of a plethora of diseases and mortality rate. Consistently, lack of social support and low SES are among the major negative determinants of health, increasing the prevalence

and/or anticipating the onset of diseases. Unfortunately, diseases often only manifest at old age when therapeutic options and biological flexibility are limited. Additionally, the causal role of social context on aging is difficult to ascertain, requiring an experimental design in which social factors can be randomized to infer

causation, which is unethical and often not feasible in humans. The evolutionary conserved role of social determinants of health and aging (SDoHA) and the ability to conduct randomized experimental designs in social mammals, offer the opportunity to reverse-translate observations made in humans to other animals. In particular,

the use of laboratory mice has several advantages to study the effect of social factors on aging, including: their comparatively short lifespan when compared to other mammals enabling the completion of longevity studies in a reasonable timeframe; the ability to conduct intent-to-treat randomization designs of socio-behavioral variables;

they are amenable to sophisticated genetic manipulations. However, the role of SDoHA is often neglected in biomedical aging research using mice, thus missing critical components of human aging. The objectives of this project are to: (i) develop rigorous socio-behavioral models suitable for aging studies in male and female mice;

(ii) develop innovative assessment tools and a “comprehensive aging index” summarizing the global impairment in behavior, physical functions and physiology, and a that can predict functional impairment and longevity, (iii) identify social factors affecting individual variability in aging processes, and finally (iv) identify socio-behavioral

intervention strategies to increase resilience. The R61 – development, proof-of-concept phase has 2 Aims. Aim 1 will identify social factors affecting the pace of aging by using a randomized design that manipulates social connectedness, social stability and social stress. We will also develop quantitative assessment tools relevant for

aging research. Aim 2 will develop a “comprehensive aging index”, an algorithm which is based on quantifiable behavioral, physical and physiological changes over the lifecourse. The R33 – implementation phase has 2 Aims. Aim 3 will determine whether social rank, social instability and/or social deprivation affect lifespan in male and

female mice and will implement the algorithm to predict longevity based on data collected during the lifecourse. Aim 4 will implement behavioral strategies designed to increase resilience, including social rank reversal, social integration and cognitive stimulation/environmental enrichment. At its completion, this project will develop novel

experimental paradigms and assessment tools with far reaching impact to the field. It will also generate an unprecedented new knowledge on how social factors affect health trajectories and aging, and which aging process is amenable to intervention versus those that are not amenable to intervention.