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Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank

Nature Human Behaviour volume 7, pages 1185–1195 (2023)Cite this article

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Abstract

Previous hypothesis-driven research has identified many risk factors linked to dementia. However, the multiplicity and co-occurrence of risk factors have been underestimated. Here we analysed data of 344,324 participants from the UK Biobank with 15 yr of follow-up data for 210 modifiable risk factors. We first conducted an exposure-wide association study and then combined factors associated with dementia to generate composite scores for different domains. We then evaluated their joint associations with dementia in a multivariate Cox model. We estimated the potential impact of eliminating the unfavourable profiles of risk domains on dementia using population attributable fraction. The associations varied by domain, with lifestyle (16.6%), medical history (14.0%) and socioeconomic status (13.5%) contributing to the majority of dementia cases. Overall, we estimated that up to 47.0%–72.6% of dementia cases could be prevented.

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Fig. 1: Overview of analytic design.
Fig. 2: Associations between modifiable risk factors and incident dementia.
Fig. 3: Summary heat map for significant factors in EWAS analysis across the full sample and subgroups.
Fig. 4: Associations between six domains and dementia.

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Data availability

The data used in the present study are available from UKB with restrictions applied. Data were used under license and are thus not publicly available. Access to the UKB data can be requested through a standard protocol (https://www.ukbiobank.ac.uk/register-apply/). Publicly available UKB-based summary statistics for the GWAS of risk factors can be obtained from the MRC IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). The summary statistics of AD GWAS can be accessed from https://gwas.mrcieu.ac.uk/datasets/ieu-b-2/. The summary statistics of all-cause dementia GWAS can be accessed from https://r8.finngen.fi/pheno/F5_DEMENTIA.

Code availability

Scripts used to perform the analyses are available at https://github.com/atticatto/UKB_AD_EWAS.git.

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Acknowledgements

J.-T.Y. was supported by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600); the National Natural Science Foundation of China (82071201, 81971032, 92249305); Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01); Research Start-up Fund of Huashan Hospital (2022QD002); Excellence 2025 Talent Cultivation Program at Fudan University (3030277001); Shanghai Talent Development Funding for The Project (2019074); ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute; and the State Key Laboratory of Neurobiology and Frontiers Centre for Brain Science of the Ministry of Education, Fudan University. W.C. was supported by grants from the National Natural Sciences Foundation of China (no. 82071997) and the Shanghai Rising-Star Program (no. 21QA1408700). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all the participants and professionals contributing to the UK Biobank; the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for these analyses; and the participants and investigators of the FinnGen study. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control participants, the patients and their families. i–Select chips was funded by the French National Foundation on Alzheimer’s disease and related disorders. EADI was supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD/PERADES was supported by the Medical Research Council (Grant no. 503480), Alzheimer’s Research UK (Grant no. 503176), the Wellcome Trust (Grant no. 082604/2/07/Z) and the German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant no. 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by NIH/NIA grant R01 AG033193 and NIA AG081220, and AGES contract N01–AG–12100, NHLBI grant R01 HL105756, the Icelandic Heart Association, the Erasmus Medical Center and Erasmus University. ADGC was supported by NIH/NIA grants U01 AG032984, U24 AG021886 and U01 AG016976, and Alzheimer’s Association grant ADGC–10–196728.

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  1. These authors contributed equally: Yi Zhang, Shi-Dong Chen.

Authors and Affiliations

  1. Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China

    Yi Zhang, Shi-Dong Chen, Yue-Ting Deng, Xiao-Yu He, Xin-Rui Wu, Bang-Sheng Wu, Liu Yang, Ya-Ru Zhang, Kevin Kuo, Wei Cheng & Jin-Tai Yu

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

    Jia You, Jian-Feng Feng & Wei Cheng

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

    Jia You, Jian-Feng Feng & Wei Cheng

  4. Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Jia You, Jian-Feng Feng & Wei Cheng

  5. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Jian-Feng Feng

  6. Department of Psychiatry, University of Cambridge, Cambridge, UK

    John Suckling

  7. Department of Pharmacology, University of Oxford, Oxford, UK

    A. David Smith

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Contributions

J.-T.Y. and W.C. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. J.-T.Y. conceived and designed the project. All authors acquired, analysed or interpreted data. Y.Z., S.-D.C., Y.-T.D., A.D.S., J.S. and J.-T.Y. wrote the initial draft of the manuscript. Y.Z., S.-D.C., Y.-T.D., J.Y., X.-Y.H., X.-R.W., B.-S.W., L.Y., Y.-R.Z., K.K., A.D.S., J.S., W.C. and J.-T.Y. critically revised the manuscript for important intellectual content. Y.Z., S.-D.C., Y.-T.D. and J.Y. conducted statistical analysis. J.-F.F., W.C. and J.-T.Y. acquired funding. J.-F.F., W.C. and J.-T.Y. provided administrative, technical or material support. All authors read and approved the final manuscript.

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Correspondence to Jin-Tai Yu.

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Ethical approval

UK Biobank has received ethical approval from the North West Multi-centre Research Ethics Committee (MREC, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics), and informed consent through electronic signature was obtained from study participants. This study utilized the UK Biobank Resource under application number 19542.

Extended data

Extended Data Fig. 1 Associations between modifiable risk factors and incident dementia when excluding participants who developed dementia within the first 5 years or 10 years.

Models were adjusted for baseline age, sex, APOE ε4 status, and assessment center. The color of cells indicates the effect sizes (HR) between each risk factor and incident dementia (N = 344,324). Asterisks in cells represent significant associations after correction for multiple testing (Bonferroni-corrected, P < 1.87×10-4).

Extended Data Fig. 2 Mendelian randomization estimates of factors in relation to dementia risk.

Estimates were generated using inverse-variance weighted method after removing outliers. Results generated using other methods are available in Supplementary Table 11. Dots represent odds ratios and lines represent 95% CIs.

Extended Data Fig. 3 Associations between six domains and dementia based on factors selected by machine learning.

The favourable profile was set as reference in each domain. The associations were estimated applying Cox model including all six domains mutually adjusted and with adjustment of age, sex, APOE ε4 status, and assessment center. Dots represent hazard ratios; horizontal lines indicate corresponding 95% CIs. Z-tests were used to assess statistical significance and derive Z statistics and corresponding two-sided P values. HR, hazard ratio; CI, confidence interval. SES, socioeconomic status.

Supplementary information

Supplementary Information

Supplementary Note, Discussion, Tables 1–19 and Figs. 1–10.

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Zhang, Y., Chen, SD., Deng, YT. et al. Identifying modifiable factors and their joint effect on dementia risk in the UK Biobank. Nat Hum Behav 7, 1185–1195 (2023). https://doi.org/10.1038/s41562-023-01585-x

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