衰老异质性的研究进展
收稿日期: 2024-11-12
网络出版日期: 2026-04-08
基金资助
上海交通大学医学院大学生创新训练计划(1723Y091)
版权
Research progress on heterogeneity of aging
Received date: 2024-11-12
Online published: 2026-04-08
Copyright
王廷旭 , 王旭 , 周廉尧 , 尹硕 , 代敬宇 , 叶静 . 衰老异质性的研究进展[J]. 内科理论与实践, 2026 , 21(01) : 74 -79 . DOI: 10.16138/j.1673-6087.2026.01.11
With the development of Chinese society, the proportion of the population aged 65 and above is increasing, and China is gradually entering an aging society. How to achieve healthy aging and how to delay aging have become important social issues. Due to the heterogeneity of aging, different individuals have different rates of aging. Aging is jointly regulated by internal and external factors. The internal factors include the twelve classic characteristics of aging, and there are multiple aging "clocks" that can indicate the level of cellular senescence. Therefore, the internal factors are clearly studied at present. The external factors include contributing factors and harmful factors. At present, the research on external factors is less and more concentrated in specific risk areas, and the research on the real situation is relatively limited. External factors interact with internal factors to influence aging. The use of multivariate cross analysis calculation to establish a comprehensive model of the interaction between internal and external factors of aging in a universal environment can provide theoretical support and technical means for the prevention and early warning of aging and related diseases.
Key words: aging; molecular marker; environment
| [1] | 世界卫生组织. 老龄化与健康[EB/OL]. (2025)[2025-10-15]. https://www.who.int/zh/news-room/fact-sheets/detail/ageing-and-health. |
| World Health Organization. Ageing and health [EB/OL]. (2025)[2025-10-15]. https://www.who.int/zh/news-room/fact-sheets/detail/ageing-and-health. | |
| [2] | Nie C, Li Y, Li R, et al. Distinct biological ages of organs and systems identified from a multi-omics study[J]. Cell Rep, 2022, 38(10):110459. |
| [3] | Niccoli T, Partridge L. Ageing as a risk factor for disease[J]. Curr Biol, 2012, 22(17):R741-752. |
| [4] | López-Otín C, Blasco MA, Partridge L, et al. Hallmarks of aging: an expanding universe[J]. Cell, 2023, 186(2):243-278. |
| [5] | Hayflick L. The limited in vitro lifetime of human diploid cell strains[J]. Exp Cell Res, 1965, 37:614-636. |
| [6] | Yang JH, Hayano M, Griffin PT, et al. Loss of epigenetic information as a cause of mammalian aging [J]. Cell, 2023, 186(2):305-326. e27. |
| [7] | Ruby JG, Wright KM, Rand KA, et al. Estimates of the heritability of human longevity are substantially inflated due to assortative mating[J]. Genetics, 2018, 210(3):1109-1124. |
| [8] | Al-Jumayli M, Brown SL, Chetty IJ, et al. The biological process of aging and the impact of ionizing radiation[J]. Semin Radiat Oncol, 2022, 32(2):172-178. |
| [9] | Sadhu S, Decker C, Sansbury BE, et al. Radiation-induced macrophage senescence impairs resolution programs and drives cardiovascular inflammation[J]. J Immunol, 2021, 207(7):1812-1823. |
| [10] | Shi W, Gao X, Cao Y, et al. Personal airborne chemical exposure and epigenetic ageing biomarkers in healthy Chinese elderly individuals: evidence from mixture approaches[J]. Environ Int, 2022, 170:107614. |
| [11] | Gao X, Huang J, Cardenas A, et al. Short-term exposure of PM2.5 and epigenetic aging:a quasi-experimental study[J]. Environ Sci Technol, 2022, 56(20):14690-14700. |
| [12] | Hahad O, Frenis K, Kuntic M, et al. Accelerated aging and age-related diseases (CVD and neurological) due to air pollution and traffic noise exposure[J]. Int J Mol Sci, 2021, 22(5):2419. |
| [13] | Liu H, Luo H, Yang T, et al. Association of leukocyte telomere length and the risk of age-related hearing impairment in Chinese Hans[J]. Sci Rep, 2017, 7(1):10106. |
| [14] | Cai J, Chen S, Yu G, et al. Comparations of major and trace elements in soil, water and residents' hair between longevity and non-longevity areas in Bama, China[J]. Int J Environ Health Res, 2021, 31(5):581-594. |
| [15] | Azqueta A, Slyskova J, Langie SA, et al. Comet assay to measure DNA repair: approach and applications[J]. Front Genet, 2014, 5:288 |
| [16] | Zhang C, Song X, Cui W, et al. Antioxidant and anti-ageing effects of enzymatic polysaccharide from Pleurotus eryngii residue[J]. Int J Biol Macromol, 2021, 173:341-350. |
| [17] | Gao Y, Zhang W, Zeng LQ, et al. Exercise and dietary intervention ameliorate high-fat diet-induced NAFLD and liver aging by inducing lipophagy[J]. Redox Biol, 2020, 36:101635. |
| [18] | García-Calzón S, Zalba G, Ruiz-Canela M, et al. Dietary inflammatory index and telomere length in subjects with a high cardiovascular disease risk from the PREDIMED-NAVARRA study: cross-sectional and longitudinal analyses over 5 y[J]. Am J Clin Nutr, 2015, 102(4):897-904. |
| [19] | Flanagan EW, Most J, Mey JT, et al. Calorie Restriction and Aging in Humans[J]. Annu Rev Nutr, 2020, 40:105-133. |
| [20] | Campisi J, Kapahi P, Lithgow GJ, et al. From discoveries in ageing research to therapeutics for healthy ageing[J]. Nature, 2019, 571(7764):183-192. |
| [21] | Bianchi A, Marchetti L, Hall Z, et al. Moderate exercise inhibits age-related inflammation, liver steatosis, senescence, and tumorigenesis[J]. J Immunol, 2021, 206(4):904-916. |
| [22] | 韩璐璐. 健康人生物学年龄积分及生物学衰老结构方程模型的统计建模研究 [D].沈阳: 中国医科大学, 2010. |
| Han LL. Applying statistical technique to develop the biological aging score and aging structural equation modeling in health population[D].Shenyang: China Medical University, 2010. | |
| [23] | Jylh?v? J, Pedersen NL, H?gg S. Biological age predictors[J]. EBioMedicine, 2017, 21:29-36. |
| [24] | Jia L, Zhang W, Chen X. Common methods of biological age estimation[J]. Clin Interv Aging, 2017, 12:759-772. |
| [25] | Klemera P, Doubal S. A new approach to the concept and computation of biological age[J]. Mech Ageing Dev, 2006, 127(3):240-248. |
| [26] | Jee H, Park J. Selection of an optimal set of biomarkers and comparative analyses of biological age estimation models in Korean females[J]. Arch Gerontol Geriatr, 2017, 70:84-91. |
| [27] | Zhang W, Jia L, Cai G, et al. Model construction for biological age based on a cross-sectional study of a healthy Chinese Han population[J]. J Nutr Health Aging, 2017, 21(10):1233-1239. |
| [28] | 张俭, 张婕, 申勐韬, 等. 基于机器学习的宁夏地区老年人生物学年龄研究[J]. 现代预防医学, 2023, 50(1):6-9,32. |
| Zhang J, Zhang J, Shen MT, et al. Machine learning-based research on the biological age of elderly people, Ningxia[J]. Modern Preventive Medicine, 2023, 50(1):6-9,32. | |
| [29] | Bernard D, Doumard E, Ader I, et al. Explainable machine learning framework to predict personalized physiological aging[J]. Aging Cell, 2023, 22(8):e13872. |
| [30] | Lassen JK, Wang T, Nielsen KL, et al. Large-scale metabolomics: predicting biological age using 10, 133 routine untargeted LC-MS measurements[J]. Aging Cell, 2023, 22(5):e13813. |
| [31] | Rutledge J, Oh H, Wyss-Coray T. Measuring biological age using omics data[J]. Nat Rev Genet, 2022, 23(12):715-727. |
| [32] | 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. |
| [33] | Holzscheck N, Falckenhayn C, S?hle J, et al. Modeling transcriptomic age using knowledge-primed artificial neural networks[J]. NPJ Aging Mech Dis, 2021, 7(1):15. |
| [34] | Zhong X, Lu Y, Gao Q, et al. Estimating biological age in the Singapore longitudinal aging study[J]. J Gerontol A Biol Sci Med Sci, 2020, 75(10):1913-1920. |
| [35] | Behrad F, Abadeh MS. An overview of deep learning methods for multimodal medical data mining[J]. Expert Syst Appl, 2022, 200:117006. |
| [36] | Xu Y. Deep Learning in Multimodal Medical Image Analysis[M].Cham: Springer, 2019: 193-200. |
| [37] | Antonelli L, Guarracino MR, Maddalena L, et al. Integrating imaging and omics data: a review[J]. Biomed Signal Process Control, 2019, 52:264-280. |
| [38] | Graves A. Generating sequences with recurrent neural networks [EB/OL]. arXiv, (2014)[2024-10-15]. https://arxiv.org/pdf/1308.0850. |
| [39] | Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [EB/OL]. arXiv, (2017)[2024-10-15]. https://arxiv.org/pdf/1706.03762v5. |
| [40] | Mirza M, Osindero S. Conditional generative adversarial nets [EB/OL]. arXiv, (2014)[2024-10-15]. https://arxiv.org/pdf/1411.1784. |
| [41] | Rahman SA, Adjeroh DA. Deep learning using convolutional LSTM estimates biological age from physical activity[J]. Sci Rep, 2019, 9(1):11425. |
| [42] | 王佳荣. 基于三维卷积神经网络的脑龄预测及脑疾病分类研究[D].2023. 兰州: 兰州理工大学. |
| Wang JR. Brain age prediction and brain disease classification based on 3D convolutional neural network[D].2023. Lanzhou: Lanzhou University of Technology. | |
| [43] | Wang J, Gao Y, Wang F, et al. Accurate estimation of biological age and its application in disease prediction using a multimodal image transformer system[J]. Proc Natl Acad Sci U S A, 2024, 121(3):e2308812120. |
| [44] | Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks:visualising image classification models and saliency maps[EB/OL]. arXiv, (2013)[2024-10-15]. https://arxiv.org/pdf/1312.6034v1. |
/
| 〈 |
|
〉 |