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Plasma proteomic profiles predict future dementia in healthy adults

Nature Aging volume 4, pages 247–260 (2024)Cite this article

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Abstract

The advent of proteomics offers an unprecedented opportunity to predict dementia onset. We examined this in data from 52,645 adults without dementia in the UK Biobank, with 1,417 incident cases and a follow-up time of 14.1 years. Of 1,463 plasma proteins, GFAP, NEFL, GDF15 and LTBP2 consistently associated most with incident all-cause dementia (ACD), Alzheimer’s disease (AD) and vascular dementia (VaD), and ranked high in protein importance ordering. Combining GFAP (or GDF15) with demographics produced desirable predictions for ACD (area under the curve (AUC) = 0.891) and AD (AUC = 0.872) (or VaD (AUC = 0.912)). This was also true when predicting over 10-year ACD, AD and VaD. Individuals with higher GFAP levels were 2.32 times more likely to develop dementia. Notably, GFAP and LTBP2 were highly specific for dementia prediction. GFAP and NEFL began to change at least 10 years before dementia diagnosis. Our findings strongly highlight GFAP as an optimal biomarker for dementia prediction, even more than 10 years before the diagnosis, with implications for screening people at high risk for dementia and for early intervention.

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Fig. 1: Associations of plasma proteins with incident dementia.
Fig. 2: Protein importance ranking and SHAP visualization of modeling on all incident dementia populations.
Fig. 3: Predictive accuracy of plasma proteins, alone or in combination with other variables.
Fig. 4: Predictive performance of baseline protein levels on the risk of clinical progression.
Fig. 5: Temporal trajectories of plasma proteins preceding the dementia diagnosis.

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Data availability

The data used in the present study are available from UK Biobank with restrictions applied. Data were used under license and are thus not publicly available. Access to the UK Biobank data can be requested through a standard protocol (https://www.ukbiobank.ac.uk/register-apply/). Data used in this study are available in the UK Biobank under application number 19542. All data supporting the findings described in this manuscript are available in the article and in the supplementary materials and from the corresponding author upon request. Source data are provided with this paper.

Code availability

All software used in this study is publicly available. The code used in this study can be accessed at https://github.com/jasonHKU0907/DementiaProteomicPrediction.

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Acknowledgements

We thank all the participants and researchers from the UK Biobank. W.C. was funded by National Key Research and Development Program of China (grant no. 2023YFC3605400). J.T.-Y. was funded by grants from the Science and Technology Innovation 2030 Major Projects (grant no. 2022ZD0211600), National Natural Science Foundation of China (grant nos. 82071201, 92249305), Research Start-up Fund of Huashan Hospital (grant no. 2022QD002) and Excellence 2025 Talent Cultivation Program at Fudan University (grant no. 3030277001). J.F.-F. was funded by National Key R&D Program of China (grant nos. 2018YFC1312904, 2019YFA0709502), Shanghai Municipal Science and Technology Major Project (grant no. 2018SHZDZX01) and the 111 Project (no. B18015). J.Y. was funded by Shanghai Pujiang Talent Program (grant no. 23PJD006). The funders had no role in study design, data collection and analysis; decision to publish or preparation of the manuscript. Further, we would like to thank the support from the ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.

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  1. These authors contributed equally: Yu Guo, Jia You, Yi Zhang.

Authors and 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

    Yu Guo, Jia You, Yi Zhang, Wei-Shi Liu, Yu-Yuan Huang, Ya-Ru Zhang, Qiang Dong, Wei Cheng & Jin-Tai Yu

  2. Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China

    Jia You, Wei Zhang, Jian-Feng Feng & Wei Cheng

  3. Key Laboratory of Computational Neuroscience and Brain-inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Jian-Feng Feng & Wei Cheng

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Contributions

J.-T.Y. undertook conceptualization and design of the study, interpretation of the data and revision of the manuscript. Y.G., J.Y. and Y.Z. collected, analyzed and interpreted the data, and drafted and revised the manuscript. J.-F.F., W.C. and J.-T.Y. were responsible for funding, administrative, technical or material support. All authors carried out revision of the manuscript. All authors had full access to all the study data and accepted responsibility for submitting it for publication.

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Correspondence to Jian-Feng Feng, Wei Cheng or Jin-Tai Yu.

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Nature Aging thanks Keenan Walker and Alexa Pichet Binette for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flowchart for participants’ enrollment.

From the UK Biobank cohort, we excluded individuals with dementia at baseline or with self-reported dementia and those who did not undergo plasma proteomic assay. The remaining participants were classified based on their first reported years of ACD or AD or VaD after baseline. Abbreviations: ACD, all-cause dementia; AD, Alzheimer’s disease; VaD, vascular dementia.

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Guo, Y., You, J., Zhang, Y. et al. Plasma proteomic profiles predict future dementia in healthy adults. Nat Aging 4, 247–260 (2024). https://doi.org/10.1038/s43587-023-00565-0

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