Statistical Models and Mechanisms Linking Biomarkers of Aging to Cognitive-Physical Decline and Dementia

痴呆症影响着全世界4400万成年人，阿尔茨海默病(AD)和相关的痴呆症 老年人(ADRD)占所有病例的60%-80%。身体残疾往往是最终的结果 死前痴呆症的后果。三分之一的痴呆症病例可能归因于可改变的因素， 由于AD治疗的益处尚不清楚，因此有必要确定预防痴呆症的干预目标 和身体残疾。因为这两种情况之前可能会出现认知和身体表现不佳的情况 十年来，共同的认知-身体双重衰退的生物决定因素影响神经和 肌肉骨骼系统可以为预防痴呆症和身体残疾的治疗目标提供信息。这个 老年学假说认为，以衰老生物学为目标可能更好地影响人类健康，包括 预防痴呆症和身体残疾，而不是针对特定的疾病。事实上，单独的几行 对认知和身体终点的研究表明，反映衰老潜在生物学的生物标志物 都与认知和身体衰退有关。这项工作包括炎症的生物标志物和 衰老的特征，如细胞衰老，细胞通讯改变，表观遗传学变化，端粒磨损， 营养信号和蛋白平衡的丧失。然而，流行病学研究尚未进行严格的调查。 衰老的生物机制是否影响认知和身体衰退之间的关系和动态 或痴呆症和肢体残疾的发作。因此，识别生物衰老机制的早期生物标志物 在最初健康的老年人中，与认知-身体双重衰退和关节痴呆-残疾发病有关 成人是迈向老年科学指导的预防试验的关键一步。然而，对纵向认知和认知的研究 物理终端很容易受到生存偏差和无法测量的混淆的影响。现有统计学的局限性 方法是准确识别共享生物机制的生物标志物的关键障碍，这些生物机制可能影响 或预测认知和身体终点。因此，需要新的计算模型来克服这些问题。 障碍。这项建议的具体目的是：1)检验衰老生物标志物与纵向双重的关系 认知-体力衰退；2)衰老生物标志物与发病时间的关系检验 3)开发/验证衰老风险评分的生物标记物，以预测关节痴呆-残疾。为此， 我们提出了一个生物老化指数和新的多变量纵向时间和时间的计算模型。 事件结果，并将其应用于来自11,000个社区住宅的8个队列研究的协调数据 年龄至少65岁的成年人有测量的生物标志物。我们假设衰老的生物标记物可以预测和 部分解释认知和身体终点之间的关系，而不是已知的风险因素。新的计算- 作为联合研究认知和身体终点的基本工具而开发的模型将与 科学界。这个项目对公众健康的最终影响是新的模型和新的生物标记物 为老年科学指导的预防ADRD和老年人身体残疾的新战略提供信息。

Dementia affects over 44 million adults worldwide, and Alzheimer’s disease (AD) and related dementias (ADRD) account for 60%-80% of all cases among older adults. Physical disability is often the final consequence of dementia before death. One-third of dementia cases may be attributable to modifiable factors, and due to unclear benefit of AD treatments, there is a need to identify intervention targets to prevent dementia and physical disability. Since both conditions may be preceded by poor cognitive and physical performance by over a decade, shared biological determinants of dual cognitive-physical decline that impact neurological and musculoskeletal systems may inform therapeutic targets to prevent dementia and physical disability. The geroscience hypothesis posits that targeting the biology of aging may better impact human health, including prevention of dementia and physical disability, than targeting specific diseases. Indeed, separate lines of research on cognitive and physical endpoints indicate that biomarkers reflecting the underlying biology of aging are related to both cognitive and physical decline. This work includes biomarkers of inflammation and hallmarks of aging such as cell senescence, altered cell communication, epigenetic changes, telomere attrition, nutrient signaling, and loss of proteostasis. However, epidemiologic studies have not rigorously investigated whether biological mechanisms of aging affect relations and dynamics between cognitive and physical decline or dementia and physical disability onset. Thus, identifying early biomarkers of biological aging mechanisms that are related to dual cognitive-physical decline and joint dementia-disability onset in initially health older adults is a key step toward geroscience-guided prevention trials. However, studies of longitudinal cognitive and physical endpoints are vulnerable to survival bias and unmeasured confounding. Limitations of extant statistical methods are a key barrier to accurately identifying biomarkers of shared biological mechanisms that may affect or predict cognitive and physical endpoints. Thus, new computational models are needed to overcome these barriers. Specific aims of this proposal are to: 1) test relations of biomarkers of aging with longitudinal dual cognitive-physical decline; 2) test relations of biomarkers of aging with time to incident joint dementia-disability onset; and 3) develop/validate a biomarker of aging risk score to predict joint dementia-disability. To this end, we propose a biological aging index and novel computational models for multivariate longitudinal and time-to- event outcomes and to apply them to harmonized data from 8 cohort studies of >11,000 community-dwelling adults aged at least 65 years with measured biomarkers. We hypothesize that biomarkers of aging predict and explain, in part, relations between cognitive and physical endpoints beyond known risk factors. New computa- tional models developed as essential tools to jointly study cognitive and physical endpoints will be shared with the scientific community. The ultimate public health impact of this project is new models and novel biomarkers of aging to inform new geroscience-guided strategies to prevent ADRD and physical disability in older adults.