Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging

Ling-Zhi Ma¹, Wei-Shi Liu², Yu He², Yi Zhang², Jia You³, Jian-Feng Feng⁴, Lan Tan⁵, Wei Cheng⁶, Jin-Tai Yu⁷

Affiliations

¹ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
² Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
⁴ Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
⁵ Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China. Electronic address: dr.tanlan@163.com.
⁶ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China. Electronic address: wcheng@fudan.edu.cn.
⁷ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China. Electronic address: jintai_yu@fudan.edu.cn.

PMID: 40328427
DOI: 10.1016/j.jare.2025.05.004

Free article

Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging

Ling-Zhi Ma et al. J Adv Res. 2025.

Free article

. 2025 May 4:S2090-1232(25)00297-8.

doi: 10.1016/j.jare.2025.05.004. Online ahead of print.

Authors

Ling-Zhi Ma¹, Wei-Shi Liu², Yu He², Yi Zhang², Jia You³, Jian-Feng Feng⁴, Lan Tan⁵, Wei Cheng⁶, Jin-Tai Yu⁷

Affiliations

¹ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
² Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
³ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
⁴ Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
⁵ Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China. Electronic address: dr.tanlan@163.com.
⁶ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China. Electronic address: wcheng@fudan.edu.cn.
⁷ Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China. Electronic address: jintai_yu@fudan.edu.cn.

PMID: 40328427
DOI: 10.1016/j.jare.2025.05.004

Abstract

Introduction: Plasma proteomics examines levels of thousands of proteins and has the potential to identify clinical biomarkers for healthy aging.

Objectives: This large proteomics study aims to identify clinical biomarkers for healthy aging and further explore potential mechanisms involved in aging.

Methods: This study analyzed data from 51,904 UK Biobank participants to explore the association between 2,923 plasma proteins and nine aging-related phenotypes, including PhenoAge, KDM-Biological Age, healthspan, parental lifespan, frailty, and longevity. Protein levels were measured using proteomics, and associations were assessed with a significance threshold of P < 1.90E-06. We utilized the DE-SWAN method to detect and measure the nonlinear alterations in plasma proteome during the process of biological aging. Mendelian randomization was applied to assess causal relationships, and a PheWAS explored the broader health impacts of these proteins.

Results: We identified 227 proteins significantly associated with aging (P < 1.90E-06), with the pathway of inflammation and regeneration being notably implicated. Our findings revealed fluctuating patterns in the plasma proteome during biological aging in middle-aged adults, pinpointing specific peaks of biological age-related changes at 41, 60, and 67 years, alongside distinct age-related protein change patterns across various organs. Furthermore, mendelian randomization further supported the causal association between plasma levels of CXCL13, DPY30, FURIN, IGFBP4, SHISA5, and aging, underscoring the significance of these drug targets. These five proteins have broad-ranging effects. The PheWAS analysis of proteins associated with aging highlighted their crucial roles in vital biological processes, particularly in overall mortality, health maintenance, and cardiovascular health. Moreover, proteins can serve as mediators in healthy lifestyle and aging processes.

Conclusion: These significant discoveries underscore the importance of monitoring and intervening in the aging process at critical periods, alongside identifying potential biomarkers and therapeutic targets for age-related disorders within the plasma proteomic landscape, thus offering valuable insights into healthy aging.

Keywords: Biological age; Biomarkers; Health aging; Nonlinear change; Proteomics.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging

Affiliations

Plasma proteomics identify novel biomarkers and dynamic patterns of biological aging

Authors

Affiliations

Abstract

Conflict of interest statement

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Full Text Sources

Miscellaneous