Integrative Omics to enhance therapeutics development for healthy aging

摘要/摘要 已经确定了一些药物和干预措施来遏制与个人年龄相关的疾病-- 相关疾病，以及一些化合物已被确定可以延长小鼠和其他非 人类物种。然而，几乎没有人，如果有的话，已经被明确地证明通过减缓衰老过程和 从而同时延缓多种疾病的发病，最终延长人类寿命。 因此，目前正在寻找可以导致长寿的药物和干预靶点-- 加强“老年保护者”，即预防多种与年龄有关的疾病和增加 通过延缓或破坏衰老过程的各个方面来延长寿命，需要加以改进。只有非常完整的 考虑到需要协调围绕 与年龄相关的疾病的发病机制与多种疾病的同步衰退有关 定义老化的系统。我们认为，这种一体化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 生物学上令人信服的对不同组织中缓慢而健康的人类衰老的定义；2.开发新的交叉 涉及多个组织的物种寿命研究，以深入了解影响 长寿，其元素可能与人体研究中发现的因素一致，从而验证 它们确实与长寿有关，而不仅仅是与疾病有关；以及3.汇总项目1-2产生的数据 使用相关的可用公共领域数据来生成/验证假设，此外，还需要进行GWA以 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 是最先支持这样一个概念的人，即对不同的科学研究和 发现，即试图统一来自不同研究设计的结果，基于它们的生物学一致性，是 促进延年益寿药物人体靶点识别的最佳方法和 干预措施。重要的是，尽管我们认为我们提出的每一项个别研究都是 强大到足以识别潜在的虫族保护者目标，他们的集体和综合使用与新颖 分析方法将具有前所未有的力量，并为抗衰老药物的发现提供一种范式 学术界内的研究。例如，我们正在提议进行第一项人类纵向研究 寻找与表观遗传时钟和其他有效的衰老指标相关的可用药因素 数以千计的人的衰老速度/健康；最大的代谢组学和蛋白质组学跨物种多 与寿命相关的因素的组织研究(N=60种)；人类最大的纵向比较 探索血液、肌肉、脂肪和皮肤样本的衰老组织研究；以及最大规模的聚合数据研究 来自公共领域来源，用于关联分析，包括标准GWAs和独特的多基因风险 基于分数的健康基因组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.