Department of Psychiatry, Yale University School of Medicine, 300 George St, Suite 901, New Haven, CT 06511, USA.
Program in Computational Biology and Bioinformatics, Yale University, 300 George St, Suite 501, New Haven, CT 06511, USA.
Semin Cell Dev Biol. 2021 Aug;116:180-193. doi: 10.1016/j.semcdb.2021.01.003. Epub 2021 Jan 25.
Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.
量化生物衰老对于理解为什么衰老是发病率和死亡率的主要驱动因素以及评估新型治疗方法以对抗病理性衰老至关重要。在过去的十年中,已经使用各种数据类型和建模技术开发了许多与大脑衰老相关的生物标志物。衰老涉及许多相互关联的过程,因此需要许多互补的生物标志物,每个标志物都捕捉到衰老生物学的不同方面。在这里,我们提出了一个层次框架,突出了这些生物标志物之间以及潜在生物学过程的关系。我们回顾了在大脑衰老背景下研究最多的那些指标:表观遗传钟、蛋白质组钟和神经影像学年龄预测器。许多研究已经将这些生物标志物与认知、心理健康、大脑结构和衰老期间的病理学联系起来。我们还深入探讨了解释这些生物标志物所面临的挑战和复杂性,并提出了进一步创新的领域。最终,需要对这些生物标志物有一个强大的机制理解,以便有效地干预衰老过程,预防和治疗与年龄相关的疾病。