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

总结 健康和老龄化的社会梯度在人类中已经建立;社会联系越大， 社会经济地位越高，过多的疾病负担和死亡率就越低。一致，缺乏 社会支持和低社会经济地位是健康的主要负面决定因素，增加了患病率 和/或预测疾病的发作。不幸的是，疾病往往只在老年时表现出来， 治疗选择和生物学灵活性有限。此外，社会背景对老龄化的因果作用是 很难确定，需要一个实验设计，其中社会因素可以随机推断 因果关系，这是不道德的，往往是不可行的人类。社会的进化保守作用 健康和老龄化的决定因素（SDoHA）和在社会中进行随机实验设计的能力 哺乳动物，提供了将人类的观察结果反向翻译到其他动物的机会。特别是， 使用实验室小鼠研究社会因素对衰老的影响有几个优点，包括： 与其他哺乳动物相比，寿命相对较短， 合理的时间框架;能够进行社会行为变量的意向治疗随机化设计; 它们可以接受复杂的基因操作。然而，SDoHA的作用往往被忽视， 使用小鼠进行生物医学衰老研究，从而错过了人类衰老的关键组成部分。这一目标 （i）发展严谨的社会行为模式，以供研究雄性和雌性老鼠的老化情况; (ii)开发创新的评估工具和“综合老龄化指数”， 在行为、身体功能和生理学方面，以及可以预测功能障碍和寿命，（iii） 确定影响衰老过程中个体变异性的社会因素，最后（iv）确定社会行为 采取干预战略，提高复原力。R61开发、概念验证阶段有两个目标。目的 1将通过使用操纵社会因素的随机设计来确定影响衰老速度的社会因素。 连通性、社会稳定性和社会压力。我们还将开发相关的定量评估工具， 老化研究目标2将开发一种“综合老化指数”，一种基于可量化的 在生命过程中的行为、身体和生理变化。R33实施阶段有两个目标。 目标3将确定社会等级、社会不稳定和/或社会剥夺是否影响男性和女性的寿命。 雌性小鼠，并将实施该算法，以基于在生命过程中收集的数据来预测寿命。 目标4将实施旨在提高弹性的行为策略，包括社会等级逆转，社会 整合和认知刺激/环境丰富。在其完成后，该项目将开发新的 实验范例和评估工具，对实地产生深远影响。它还将产生一个 关于社会因素如何影响健康轨迹和老龄化以及老龄化 过程是服从干预的，而不是那些不服从干预的。

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.