PhenoAge模型的开发与验证的研究进展

摘要/Abstract

摘要：

衰老是多系统功能退化的复杂过程，随着全球人口老龄化，研究衰老机制及延长健康寿命成为医学与公共健康的重点。表型年龄（phenotypic age，PhenoAge）作为创新的生物年龄评估工具，基于9项常规血液指标，涵盖代谢、炎症和器官功能，相较DNA甲基化时钟更具临床实用性和成本效益。本文回顾PhenoAge的开发与验证历程，强调大数据和机器学习在指标选择和避免过拟合中的作用。PhenoAge在多队列中验证了其预测全因死亡率、心血管疾病及功能性衰退的能力。然而，数据异质性、可重复性及与GrimAge等模型的竞争仍是挑战。未来，通过标准化数据流程和跨学科合作，PhenoAge的动态监测能力有望推动精准衰老研究，尤其在职业健康检测和公共卫生政策中的临床转化方面。本文为PhenoAge的优化与应用提供理论支持和方向指引。

关键词: 表型年龄, 生物年龄, 衰老生物标志物, 职业健康

Abstract:

Aging is a complex biological process involving multisystem functional decline. With global population aging，studying aging mechanisms and extending healthy lifespan have become key priorities in medicine and public health. Phenotypic age（PhenoAge），an innovative biological age assessment tool，is based on nine routine blood biomarkers covering metabolism，inflammation，and organ function，offering greater clinical utility and cost-effectiveness compared to DNA methylation clocks. This paper reviews the development and validation process of PhenoAge，highlighting the critical role of big data and machine learning in biomarker selection and avoiding overfitting. PhenoAge has been validated across multiple cohorts for predicting all-cause mortality，cardiovascular disease，and functional decline. However，challenges such as data heterogeneity，reproducibility，and competition with models like GrimAge persist. Looking forward，standardized data workflows and interdisciplinary collaboration can enhance PhenoAge's dynamic monitoring capabilities，advancing precision aging research，particularly in occupational health and public health policy. This paper provides theoretical support and guidance for optimizing and applying PhenoAge globally.

Key words: PhenoAge, Biological age, Aging biomarkers, Occupational health

中图分类号:

R181.2

黄磊, 单宝荣, 王大宇. PhenoAge模型的开发与验证的研究进展. [J]职业与健康, 2026, 42(15): 2148-2153.

HUANG Lei, SHAN Baorong, WANG Dayu. Research progress on the development and validation of the PhenoAge model. [J]OCCUPATION AND HEALTH, 2026, 42(15): 2148-2153.

参考文献 38

[1]	United Nations, Department of Economic and Social Affairs,Population Division. World population prospects 2022:Summary of results[EB/OL].(2022-07-11)[2025-04-06]. https://www.un.org/development/desa/pd/content/World-Population-Prospects-2022.
[2]	KENNEDY B K, BERGER S L, BRUNET A, et al. Geroscience:Linking aging to chronic disease[J]. Cell, 2014, 159(4):709-713.
[3]	CHEN S H, EPEL E S, MELLON S H, et al. Adverse childhood experiences and leukocyte telomere maintenance in depressed and healthy adults[J]. J Affect Disord, 2014, 169:86-90.
[4]	HO S Y, DUCHENE S. Molecular-clock methods for estimating evolutionary rates and timescales[J]. Mol Ecol, 2014, 23(24):5947-5965.
[5]	BLACKBURN E H, EPEL E S, LIN J. Human telomere biology:A contributory and interactive factor in aging,disease risks,and protection[J]. Science, 2015, 350(6265):1193-1198.
[6]	CAWTHON R M, SMITH K R, O'BRIEN E, et al. Association between telomere length in blood and mortality in people aged 60 years or older[J]. Lancet, 2003, 361(9355):393-395.
[7]	FINKEL T, HOLBROOK N J. Oxidants,oxidative stress and the biology of ageing[J]. Nature, 2000, 408(6809):239-247.
[8]	FRANCESCHI C, BONAFÈ M, VALENSIN S, et al. Inflamm-aging:An evolutionary perspective on immunosenescence[J]. Ann N Y Acad Sci, 2000, 908(1):244-254.
[9]	VIDYA M K, KUMAR V G, SEJIAN V, et al. Toll-like receptors:Significance,ligands,signaling pathways,and functions in mammals[J]. Int Rev Immunol, 2018, 37(1):20-36.
[10]	RIDKER P M, CUSHMAN M, STAMPFER M J, et al. Plasma concentration of C-reactive protein and risk of developing peripheral vascular disease[J]. Circulation, 1998, 97(5):425-428.
[11]	LIUZZO G, BIASUCCI L M, GALLIMORE J R, et al. The prognostic value of C-reactive protein and serum amyloid a protein in severe unstable angina[J]. N Engl J Med, 1994, 331(7):417-424.
[12]	HORVATH S. DNA methylation age of human tissues and cell types[J]. Genome Biol, 2013, 14(10):R115.
[13]	MARIONI R E, SHAH S, MCRAE A F, et al. DNA methylation age of blood predicts all-cause mortality in later life[J]. Genome Biol, 2015, 16(1):25.
[14]	WANG T, TSUI B, KREISBERG J F, et al. Epigenetic aging signatures in mice livers are slowed by dwarfism,calorie restriction and rapamycin treatment[J]. Genome Biol, 2017, 18(1):57.
[15]	HANNUM G, GUINNEY J, ZHAO L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates[J]. Mol Cell, 2013, 49(2):359-367.
[16]	LEVINE M E, LU A T, QUACH A, et al. An epigenetic biomarker of aging for lifespan and healthspan[J]. Aging, 2018, 10(4)573-591.
[17]	PUTIN E, MAMOSHINA P, ALIPER A, et al. Deep biomarkers of human aging:Application of deep neural networks to biomarker development[J]. Aging, 2016, 8(5):1021-1033.
[18]	CRONJÉ H T, NIENABER-ROUSSEAU C, MIN J L, et al. Comparison of DNA methylation clocks in Black South African men[J]. Epigenomics, 2021, 13(6):437-449.
[19]	BORTZ J, GUARIGLIA A, KLARIC L, et al. Biological age estimation using circulating blood biomarkers[J]. Commun Biol, 2023, 6(1):1089.
[20]	BELSKY D W, CASPI A, ARSENEAULT L, et al. Quantification of the pace of biological aging in humans through a blood test,the DunedinPoAm DNA methylation algorithm[J]. Elife, 2020, 9:e54870.
[21]	BELSKY D W, MOFFITT T E, COHEN A A, et al. Eleven telomere, epigenetic clock,and biomarker-composite quantifications of biological aging:Do they measure the same thing?[J]. Am J Epidemiol, 2018, 187(6):1220-1230.
[22]	张业先. 基于多组学整合分析的癌症生物标志物识别算法研究[D]. 长春: 吉林大学, 2020.
[23]	NUSINOVICI S, RIM T H, LI H, et al. Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs:A cohort development and validation study[J]. Lancet Healthy Longev, 2024, 5(10):100593.
[24]	GIALLUISI A, TIROZZI A, COSTANZO S, et al. Blood-based biological ageing and red cell distribution width are associated with prevalent Parkinson's disease:Findings from a large Italian population cohort[J]. Front Endocrinol, 2024, 15:1376545.
[25]	FITZGERALD K N, HODGES R, HANES D, et al. Potential reversal of epigenetic age using a diet and lifestyle intervention:A pilot randomized clinical trial[J]. Aging, 2021, 13(7):9419-9432.
[26]	LI D L, HODGE A M, CRIBB L, et al. Body size, diet quality,and epigenetic aging:cross-sectional and longitudinal analyses[J]. J Gerontol A Biol Sci Med Sci, 2024, 79(4):glae026.
[27]	FUJISHIRO K, DIEZ-ROUX A V, LANDSBERGIS P A, et al. Current employment status,occupational category,occupational hazard exposure and job stress in relation to telomere length:The Multiethnic Study of Atherosclerosis(MESA)[J]. Occup Environ Med, 2013, 70(8):552-560.
[28]	黄思芳. 职业锰暴露工人血细胞重金属浓度与相对端粒长度的关联研究[D]. 南宁: 广西医科大学, 2023.
[29]	范亚慧. 氧化应激和端粒在焦炉逸散物致线粒体损伤中的中介效应[D]. 郑州: 郑州大学, 2024.
[30]	吴维超, 郭燕, 赵祥凯, 等. 职业酸雾暴露与作业工人生物衰老加速的关联性分析[J]. 吉林大学学报(医学版), 2024, 50(6):1741-1750.
[31]	GRAF G H, CROWE C L, KOTHARI M, et al. Sex differences in epigenetic age acceleration and its association with health outcomes[J]. Aging, 2021, 13(6):8882-8895.
[32]	YAP L E, HUNT J E, TURNER R S. Aging as a target for the prevention and treatment of Alzheimer's disease[J]. Front Neurol, 2024, 15:1376104.
[33]	XIE K, FUCHS H, SCIFO E, et al. Deep phenotyping and lifetime trajectories reveal limited effects of longevity regulators on the aging process in C57BL/6J mice[J]. Nat Commun, 2022, 13(1):6830.
[34]	刘斯洋, 林星辰, 程丝, 等. 多组学大数据与医学发展[J]. 科技导报, 2024, 42(12):51-74.
[35]	黄子铭. 基于AI的多组学数据分析方法在药物设计中的应用[D]. 北京: 中国科学院大学(中国科学院上海药物研究所), 2022.
[36]	LU A T, QUACH A, WILSON J G, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan[J]. Aging, 2019, 11(2): 303-327.
[37]	MCCRORY C, FIORITO G, HERNANDEZ B, et al. GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality[J]. J Gerontol A Biol Sci Med Sci, 2021, 76(5):741-749.
[38]	MAJUMDER S, MONDAL T, DEEN M J. Wearable sensors for remote health monitoring[J]. Sensors(Basel), 2017, 17(1):130.