Integrative Omics to enhance therapeutics development for healthy aging

摘要/总结 已经确定了一些药物和干预措施，以遏制与个人年龄相关的发病率- 一些化合物已经被确定可以延长小鼠和其他非哺乳动物的寿命。 人类物种然而，很少有，如果有的话，已经明确显示，通过减缓衰老过程， 从而同时延缓多种疾病的发作，并最终延长人类寿命。 因此，目前寻找药物和干预目标，可以导致长寿的发展- 加强“老年保护者”，即，预防多种与年龄有关的疾病， 通过减缓或破坏衰老过程的各个方面来延长寿命，需要改进。只有非常整合 考虑到需要调和围绕着这些问题的复杂性， 与年龄有关的疾病的发病机制与过程有助于同步衰减的多个 定义老化的系统。我们认为，这种融合实际上可以通过以下方式实现：1。追求 多项人类纵向研究，重点是发现与糖尿病相关的代谢物和蛋白质。 生物学上令人信服的定义，在不同组织中缓慢和健康的人类衰老; 2.开发新的交叉， 涉及多种组织的物种寿命研究，以深入了解影响 长寿，其元素可能与人类研究中发现的因素一致， 他们真正与长寿有关，而不仅仅是疾病; 3.将项目1-2产生的数据沿着汇总 利用相关的可用公共领域数据来生成/验证假设，此外还进行GWAS， 确定疾病的保护因素，并使用新的统计和推理方法。我们建议的研究 他们是最早拥护这一概念的人，即不同科学研究的“三角测量”， 发现，即，试图统一的结果，从不同的研究设计的基础上，他们的生物连贯性，是 最佳的方法，以促进识别人类目标的长寿增强老年保护药物， 干预措施。重要的是，尽管我们认为我们提出的每一项研究本身都是 足够强大，以确定潜在的老年保护目标，他们的集体和综合使用与新的 分析方法将具有前所未有的力量，并为抗衰老药物发现提供范式 学术界的研究。例如，我们提出了第一个人类纵向研究， 寻找与表观遗传时钟和其他经验证的衰老指标相关的药物因素 率/健康老龄化在成千上万的人;最大的代谢组学和蛋白质组学跨物种多， 组织研究（N=60种）与寿命相关的因素;最大的人类纵向比较 对血液、肌肉、脂肪和皮肤样本进行的衰老组织研究;以及最大规模的汇总数据研究 从公共领域来源进行关联分析，包括标准GWAS和独特的多基因风险 基于评分的健康基因组GWAS

ABSTRACT/SUMMARY A number of drugs and interventions have been identified that curb morbidities associated with individual age- related diseases, and a few compounds have been identified that increase longevity in mice and other non- human species. However, few, if any, have been definitively shown to work by slowing the aging process and thereby simultaneously delaying the onset of multiple diseases and ultimately extending human longevity. Thus, current searches for drug and intervention targets that can lead to the development of longevity- enhancing `geroprotectors,' i.e., interventions which stave off multiple age-related diseases and increase longevity by slowing or disrupting aspects of the aging process, need to be improved. Only very integrated approaches are likely to lead to successful searches given the need to reconcile complexities surrounding the pathogenesis of age-related diseases with processes contributing to the synchronized decay of multiple systems that defines aging. We believe that such integration can be achieved practically by: 1. pursuing multiple human longitudinal studies focusing on the discovery of metabolites and proteins associated with a biologically-compelling definition of slow and healthy human aging in different tissues; 2. exploiting novel cross- species longevity studies involving multiple tissues to obtain insights into conserved pathways impacting longevity whose elements may be consistent with factors discovered in human studies and hence validate them as truly related to longevity and not just disease; and 3. aggregating data arising from items 1-2 along with relevant available public domain data to generate/validate hypotheses, in addition to pursuing a GWAS to identify protective factors for disease and using novel statistical and inferential methods. Our proposed studies are some of the first to champion the notion that the `triangulation' of disparate scientific studies and discoveries, i.e., the attempt to unify results from different study designs based on their biological coherence, is the optimal way to advance identification of human targets for longevity-enhancing geroprotective drugs and interventions. Importantly, although we believe that each of the individual studies we are proposing is itself powerful enough to identify potential geroprotector targets, their collective and integrated use with novel analytic methods will have unprecedented power and provide a paradigm for anti-aging drug discovery research within the academic community. For example, we are proposing the first human longitudinal study to search for druggable factors associated with the epigenetic clock and other validated measures of the aging rate/healthy aging in thousands of individuals; the largest metabolomics and proteomic cross-species multi- tissue study (N=60 species) of factors associated with lifespan; the largest human longitudinal comparative tissue study of aging exploring blood, muscle, fat and skin samples; and the largest study of aggregated data from public domain sources for association analyses including standard GWAS and a unique polygenic risk score-based healthy genome GWAS.