Phenome-wide associations of human aging uncover sex-specific dynamics

IF 17 Q1 CELL BIOLOGY Nature aging Pub Date : 2024-11-05 DOI:10.1038/s43587-024-00734-9
Lee Reicher, Noam Bar, Anastasia Godneva, Yotam Reisner, Liron Zahavi, Nir Shahaf, Raja Dhir, Adina Weinberger, Eran Segal
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Abstract

Aging varies significantly among individuals of the same chronological age, indicating that biological age (BA), estimated from molecular and physiological biomarkers, may better reflect aging. Prior research has often ignored sex-specific differences in aging patterns and mainly focused on aging biomarkers from a single data modality. Here we analyze a deeply phenotyped longitudinal cohort (10K project, Israel) of 10,000 healthy individuals aged 40–70 years that includes clinical, physiological, behavioral, environmental and multiomic parameters. Follow-up visits are scheduled every 2 years for a total of 25 years. We devised machine learning models of chronological age and computed biological aging scores that represented diverse physiological systems, revealing different aging patterns among sexes. Higher BA scores were associated with a higher prevalence of age-related medical conditions, highlighting the clinical relevance of these scores. Our analysis revealed system-specific aging dynamics and the potential of deeply phenotyped cohorts to accelerate improvements in our understanding of chronic diseases. Our findings present a more holistic view of the aging process, and lay the foundation for personalized medical prevention strategies. The authors analyzed data from a deeply phenotyped longitudinal cohort to uncover sex-specific aging patterns. They found that biological age scores, derived from diverse biomarkers, correlate with age-related diseases, providing insights for personalized medical interventions.

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人类衰老的全貌关联揭示了性别特异性动态变化。
同一年龄段的个体之间的衰老差异很大,这表明根据分子和生理生物标志物估算的生物年龄(BA)可以更好地反映衰老。之前的研究往往忽视了衰老模式中的性别差异,并主要关注来自单一数据模式的衰老生物标志物。在这里,我们分析了一个由 10,000 名 40-70 岁健康人组成的深度表型纵向队列(10K 项目,以色列),其中包括临床、生理、行为、环境和多组学参数。每两年进行一次随访,共持续 25 年。我们设计了计时年龄的机器学习模型,并计算了代表不同生理系统的生物衰老分数,揭示了不同性别的衰老模式。生物衰老评分越高,与年龄相关的疾病发病率越高,这突出表明了这些评分的临床意义。我们的分析揭示了特定系统的衰老动态,以及深度表型队列在加速改善我们对慢性疾病的理解方面所具有的潜力。我们的研究结果提出了一个更全面的衰老过程视角,为个性化医疗预防策略奠定了基础。
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