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生物学年龄在健康老龄化中的研究进展 被引量:1

Advances in the study of biological age in healthy aging
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摘要 人口老龄化增加了人类衰老研究及其临床应用的需求。传统上一般采用时序年龄(chronologicalage,CA)即日历年龄来描述人体的衰老情况,然而个体之间衰老过程和速度是不一致的,存在明显的生物衰老个体差异,因而CA不能真实地反应个体组织结构和生理功能的不同状况,存在不准确、错误判断等缺陷,无法准确地描述人体的衰老程度。近年来提出采用生物学年龄(biologicalage,BA)来更加综合准确地描述人体衰老情况,其通过数学算法结合多种生物标志物构建预测模型来实现量化生物衰老的目的,因其更加精准而得到越来越多的关注与研究。本文将对BA的意义、目前常用计算方法及在健康老龄化中的进展和前景进行综述。 Population aging increases the demand for human aging research and its clinical applications.Traditionally,the chronological age(CA),that is,the age based on the calendar,is used to describe the state of aging.However,the aging process and speed among individuals are not consistent and often show clear individual differences in biological aging.Therefore,CA cannot truly reflect people's conditions of body structure and function,has drawbacks leading to unreliable and wrong assessment,and is unable to accurately describe the human bodys state of aging.In recent years,it has been proposed that the biological age(BA)should be used to more comprehensively and accurately describe the stage of human aging.Combining mathematical algorithms with a variety of biomarkers,predictive models can be constructed to quantify BA.These approaches have been increasingly appreciated for their improved accuracy and received further investigation.This article reviews the value of BA,currently commonly used calculation methods and their progress and prospects in healthy aging.
作者 陈晨 张莹 杨睿悦 周起 栗向辉 杨泽 孙亮 Chen Chen;Zhang Ying;Yang Ruiyue;Zhou Qi;Li Xianghui;Yang Ze;Sun Liang(The Key Laboratory of Geriatrics,Beijing Institute of Geriatrics,Beijing Hospital,National Center of Gerontology,National Health Com mission,Institute of Geriatric Medicine,Chinese Academy of Medical Sciences,Beijing 100730,China;Peking University Fifth School of Clinical Medicine,Beijing 100730,China)
出处 《中华老年医学杂志》 CAS CSCD 北大核心 2021年第6期798-802,共5页 Chinese Journal of Geriatrics
基金 国家自然科学基金(91849132、81571385) 北京医院科技新星项目(BJ-2018-139) 北京医院博士启动基金(BJ-2018-024)。
关键词 生物学年龄 衰老生物标志物 统计模型 Biological age Aging biomarkers Statistical model
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