鉴定(生物学)
健康衰老
透视图(图形)
生物年龄
过程(计算)
转化研究
心理学
老年学
计算机科学
数据科学
生物
医学
生态学
人工智能
生物技术
操作系统
作者
Alessandro Bartolomucci,Alice E. Kane,Lauren Gaydosh,Maria Razzoli,Brianah M. McCoy,Dan Ehninger,Brian H. Chen,Susan E. Howlett,Noah Snyder‐Mackler
出处
期刊:The Journals of Gerontology
[Oxford University Press]
日期:2024-08-10
卷期号:79 (9)
被引量:2
标识
DOI:10.1093/gerona/glae135
摘要
For centuries, aging was considered inevitable and immutable. Geroscience provides the conceptual framework to shift this focus toward a new view that regards aging as an active biological process, and the biological age of an individual as a modifiable entity. Significant steps forward have been made toward the identification of biomarkers for and measures of biological age, yet knowledge gaps in geroscience are still numerous. Animal models of aging are the focus of this perspective, which discusses how experimental design can be optimized to inform and refine the development of translationally relevant measures and biomarkers of biological age. We provide recommendations to the field, including: the design of longitudinal studies in which subjects are deeply phenotyped via repeated multilevel behavioral/social/molecular assays; the need to consider sociobehavioral variables relevant for the species studied; and finally, the importance of assessing age of onset, severity of pathologies, and age-at-death. We highlight approaches to integrate biomarkers and measures of functional impairment using machine learning approaches designed to estimate biological age as well as to predict future health declines and mortality. We expect that advances in animal models of aging will be crucial for the future of translational geroscience but also for the next chapter of medicine.
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