Plasma proteomic signature of frailty in 50,506 adults

Xueqing Jia; Weijing Gao; Hampus Hagelin; Yanjie Zhao; Jingyun Zhang; Xingqi Cao; Liming Zhang; Youheng Wu; Lina Ma; Liangkai Chen; Liang Sun; Huan Guo; Cuntai Zhang; Juulia Jylhävä; Zixin Hu; Emiel O Hoogendijk; Sara Hägg; Zuyun Liu

doi:10.1016/j.cmet.2026.02.013

Plasma proteomic signature of frailty in 50,506 adults

Cell Metab. 2026 May 5;38(5):1012-1028.e6. doi: 10.1016/j.cmet.2026.02.013. Epub 2026 Mar 16.

Authors

Xueqing Jia¹, Weijing Gao¹, Hampus Hagelin², Yanjie Zhao¹, Jingyun Zhang³, Xingqi Cao⁴, Liming Zhang¹, Youheng Wu¹, Lina Ma⁵, Liangkai Chen⁶, Liang Sun⁷, Huan Guo⁸, Cuntai Zhang⁹, Juulia Jylhävä¹⁰, Zixin Hu¹¹, Emiel O Hoogendijk¹², Sara Hägg¹³, Zuyun Liu¹⁴

Affiliations

¹ Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
² Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
³ Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China.
⁴ Department of General Practice, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
⁵ Department of Geriatrics, National Research Center for Geriatric Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
⁶ School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁷ The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, China.
⁸ Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁹ Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
¹⁰ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Faculty of Medicine and Health Technology and Gerontology Research Center (GEREC), Tampere University, Tampere, Finland; Tampere Institute for Advanced Study, Tampere, Finland.
¹¹ Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China; Shanghai Academy of Artificial Intelligence for Science, Shanghai, China. Electronic address: huzixin@fudan.edu.cn.
¹² Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-location VU University Medical Center, Amsterdam, the Netherlands.
¹³ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Electronic address: sara.hagg@ki.se.
¹⁴ Second Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. Electronic address: zuyunliu@zju.edu.cn.

PMID: 41844148
DOI: 10.1016/j.cmet.2026.02.013

Abstract

Proteomics enables the systematic elucidation of biological mechanisms underlying frailty. Through a large proteome-wide association study of 2,911 plasma proteins from 50,506 UK Biobank participants, we identified 1,339 proteins significantly associated with frailty, highlighting collagen-containing extracellular matrix and vesicle lumen pathways. Replication in the TwinGene study confirmed partial but consistent associations. Mendelian randomization analyses identified five potentially causal proteins for frailty. Moreover, we developed a proteomic frailty score (PFS) that showed strong predictive performance for 199 incident diseases across 13 categories and broad responsiveness to 84 modifiable risk factors. Longitudinal analyses revealed accelerated PFS progression with advancing age and increasing baseline frailty severity. An online tool (https://zipoa.shinyapps.io/frailty/) was created for public PFS calculation. Finally, we observed a biphasic pattern of frailty-associated proteomic dysregulation across the lifespan, with peaks at ages ∼50 and ∼63. Together, we establish PFS as a biomarker of biological aging while identifying critical windows and molecular targets for frailty interventions.

Keywords: Mendelian randomization; aging; disease prediction; frailty; modifiable factor; proteomics.

MeSH terms

Adult
Aged
Aged, 80 and over
Aging
Biomarkers / blood
Blood Proteins* / metabolism
Female
Frailty* / blood
Frailty* / genetics
Frailty* / metabolism
Humans
Male
Middle Aged
Proteome* / metabolism
Proteomics* / methods

Substances

Proteome
Biomarkers
Blood Proteins