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A Phenome-wide Association and Mendelian Randomization Study for Alzheimer’s Disease: A Prospective Cohort Study of 502,493 Participants From the UK Biobank

  • Author Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Shi-Dong Chen
    Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Affiliations
    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
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  • Author Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Wei Zhang
    Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Affiliations
    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
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  • Author Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Yu-Zhu Li
    Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.
    Affiliations
    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
    Search for articles by this author
  • Liu Yang
    Affiliations
    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
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  • Yu-Yuan Huang
    Affiliations
    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
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  • Yue-Ting Deng
    Affiliations
    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
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  • Bang-Sheng Wu
    Affiliations
    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
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  • John Suckling
    Affiliations
    Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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  • Edmund T. Rolls
    Affiliations
    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Oxford Centre for Computational Neuroscience, Oxford, United Kingdom

    Department of Computer Science, University of Warwick, Coventry, United Kingdom
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  • Jian-Feng Feng
    Affiliations
    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

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

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

    Zhangjiang Fudan International Innovation Center, Shanghai, China
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  • Wei Cheng
    Affiliations
    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

    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

    Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
    Search for articles by this author
  • Qiang Dong
    Correspondence
    Qiang Dong, M.D., Ph.D.
    Affiliations
    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
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  • Jin-Tai Yu
    Correspondence
    Address correspondence to Jin-Tai Yu, M.D., Ph.D.
    Affiliations
    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

    Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    1 S-DC, WZ, and Y-ZL contributed equally to this work.

      Abstract

      Background

      Considerable uncertainty remains regarding associations of multiple risk factors with Alzheimer’s disease (AD). We aimed to systematically screen and validate a wide range of potential risk factors for AD.

      Methods

      Among 502,493 participants from the UK Biobank, baseline data were extracted for 4171 factors spanning 10 different categories. Phenome-wide association analyses and time-to-event analyses were conducted to identify factors associated with both polygenic risk scores for AD and AD diagnosis at follow-up. We performed two-sample Mendelian randomization analysis to further assess their potential causal relationships with AD and imaging association analysis to discover underlying mechanisms.

      Results

      We identified 39 factors significantly associated with both AD polygenic risk scores and risk of incident AD, where higher levels of education, body size, basal metabolic rate, fat-free mass, computer use, and cognitive functions were associated with a decreased risk of developing AD, and selective food intake and more outdoor exposures were associated with an increased risk of developing AD. The identified factors were also associated with AD-related brain structures, including the hippocampus, entorhinal cortex, and inferior/middle temporal cortex, and 21 of these factors were further supported by Mendelian randomization evidence.

      Conclusions

      To our knowledge, this is the first study to comprehensively and rigorously assess the effects of wide-ranging risk factors on AD. Strong evidence was found for fat-free body mass, basal metabolic rate, computer use, selective food intake, and outdoor exposures as new risk factors for AD. Integration of genetic, clinical, and neuroimaging information may help prioritize risk factors and prevention targets for AD.

      Keywords

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