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

摘要 健康和老龄化方面的社会梯度在人类中得到了很好的确立；社会联系越紧密， 社会经济地位(SES)越低，疾病负担越多，死亡率越低。始终如一地缺乏 社会支持和社会经济地位低是健康的主要负面决定因素，增加了患病率 和/或预测疾病的发生。不幸的是，疾病通常只有在老年时才会出现 治疗选择和生物灵活性是有限的。此外，社会环境对老龄化的因果作用是 难以确定，需要一种实验设计，其中社会因素可以随机进行推断 因果关系，这是不道德的，在人类身上往往是不可行的。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试验设计的能力 哺乳动物提供了将在人类身上观察到的结果反向翻译给其他动物的机会。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，从而能够在 合理的时间框架；对社会行为变量进行意向治疗随机设计的能力； 它们易于接受复杂的基因操作。然而，SDoHA的作用在 利用小鼠进行生物医学衰老研究，从而遗漏了人类衰老的关键组成部分。这样做的目的是 该项目的目的是：(I)开发适用于雄性和雌性小鼠衰老研究的严格的社会行为模型； (2)开发创新的评估工具和总结全球减损情况的“综合老化指数” 在行为、身体功能和生理方面，以及可以预测功能障碍和寿命的指标，(Iii) 确定影响老龄化过程中个人变异性的社会因素，最后(4)确定社会行为 提高复原力的干预策略。R61-开发、概念验证阶段有两个目标。目标 1将通过使用操纵社会的随机设计来确定影响老龄化速度的社会因素 连通性、社会稳定和社会压力。我们还将开发与以下方面相关的量化评估工具 老龄化研究。目标2将开发一个“综合老化指数”，这是一种基于可量化的算法 在生命过程中的行为、生理和生理变化。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 并将根据在生命过程中收集的数据来实施预测寿命的算法。 目标4将实施旨在提高韧性的行为策略，包括社会等级颠倒、社会 整合和认知刺激/环境丰富。该项目完成后，将开发出新颖的 对实地产生深远影响的实验范例和评估工具。它还将生成一个 关于社会因素如何影响健康轨迹和老龄化，以及哪些老龄化 过程服从于干预，而不是那些不服从干预。

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.