小鼠的病理时钟(PathoClock)和生理时钟(PhysioClock)再现了人类的多病和异质性衰老。

Shabnam Salimi, Christina Pettan-Brewer, Warren Ladiges
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摘要

背景:多病是一个公共卫生问题,也是衰老和健康寿命的一个重要组成部分,但由于缺乏可转化的调查工具来捕捉不同物种衰老过程的相似性和差异性以及个体和单个器官之间的变异性,因此研究不足:为了帮助满足这一需求,我们利用贝叶斯推断法,将从人类研究中借鉴的身体器官疾病数(BODN)应用于8、16、24和32月龄的C57BL/6(B6)和CB6F1小鼠品系,作为基于病理学病变的系统发病率衡量标准,从而开发出一种与临床上基于人类身体时钟的小鼠病理时钟(PathoClock)。小鼠生理时钟(PhysioClock)也是根据相同的两个小鼠品系的心血管、神经肌肉和认知功能等生理领域的测量结果开发的,以便与 BODN 保持一致:PathoClock和PhysioClock的年龄间和年龄内变异以及品系间变异。与 C57BL/6 相比,CB6F1 的 PathoClock 和 PhysioClock 与年龄的相关性更强。然后,利用病理学和生理学测量指标对计时年龄的回归模型,建立了预测模型,命名为病理年龄(PathoAge)和生理年龄(PhysioAge)。PathoAge比PhysioAge更能预测实际年龄,因为PhysioAge预测的实际年龄和观察到的实际年龄是复杂的,而不是线性的:结论:PathoClock 和 PhathoAge 可用于捕捉生物变化,预测 BODN(一种在人类中开发的指标),并比较不同物种的多病症。这些小鼠时钟是潜在的转化工具,可用于老龄化干预研究。
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PathoClock and PhysioClock in mice recapitulate human multimorbidity and heterogeneous aging.

Background: Multimorbidity is a public health concern and an essential component of aging and healthspan but understudied because investigative tools are lacking that can be translatable to capture similarities and differences of the aging process across species and variability between individuals and individual organs.

Methods: To help address this need, body organ disease number (BODN) borrowed from human studies was applied to C57BL/6 (B6) and CB6F1 mouse strains at 8, 16, 24, and 32 months of age, as a measure of systems morbidity based on pathology lesions to develop a mouse PathoClock resembling clinically-based Body Clock in humans, using Bayesian inference. A mouse PhysioClock was also developed based on measures of physiological domains including cardiovascular, neuromuscular, and cognitive function in the same two mouse strains so that alignment with BODN was predictable.

Results: Between- and within-age variabilities in PathoClock and PhysioClock, as well as between-strain variabilities. Both PathoClock and PhysioClock correlated with chronological age more strongly in CB6F1 than C57BL/6. Prediction models were then developed, designated as PathoAge and PhysioAge, using regression models of pathology and physiology measures on chronological age. PathoAge better predicted chronological age than PhysioAge as the predicted chronological and observed chronological age for PhysioAge were complex rather than linear.

Conclusion: PathoClock and PhathoAge can be used to capture biological changes that predict BODN, a metric developed in humans, and compare multimorbidity across species. These mouse clocks are potential translational tools that could be used in aging intervention studies.

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