郑州大学学报（医学版）2025，Vol.60Issue(4)：445-451,7.DOI:10.13705/j.issn.1671-6825.2025.04.213
生物学年龄研究进展
Progress in biological age research
摘要
Abstract
In the context of aging population,accurate measurement of individual aging is the key to achieve"healthy aging".By integrating multidimensional biomarker data,biological age has constructed an aging assessment system that goes beyond the traditional chronological age.In this paper,we systematically review the research progress of biological age as-sessment system,and analyze the current major algorithms for assessing biological age,including the Klemera-Doubal meth-od,phenotypic age,metabolic age score,deep neural network,convolutional neural network,etc.The Klemera-Doubal method is widely applicable and has better mortality prediction efficacy than traditional methods,but is dependent on the distribution of biomarkers in the reference population and has weak cross-group generalization;phenotypic age is clinically accessible and has cross-subgroup robustness(e.g.,disease-free population),but lacks longitudinal data validation;the metabolic age score reflects dynamic changes in metabolic pathways but has limited applicability across races.It is suggested that integra-ting multidimensional biomarker data through different methods can more accurately quantify the aging process and predict disease risk.Accurate assessment of biological age is expected to promote personalized health interventions,disease risk stratification and social policy optimization,and help realize"healthy aging".关键词
衰老/生物学年龄/健康老龄化/算法/生物标志物Key words
aging/biological age/healthy aging/algorithm/biomarker分类
医药卫生引用本文复制引用
高晨阳,李梦涵,谷志广,赵祥凯,王彭彭,刘足云,王威..生物学年龄研究进展[J].郑州大学学报（医学版）,2025,60(4):445-451,7.基金项目
国家自然科学基金项目(82171584 （）
81872597) （）
河南省中原英才计划基础研究领军人才项目(33220026) （33220026）
浙江大学医学院老化病防治研究中心项目(2022010002) （2022010002）
