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

Shi-Dong Chen; Wei Zhang; Yu-Zhu Li; Liu Yang; Yu-Yuan Huang; Yue-Ting Deng; Bang-Sheng Wu; John Suckling; Edmund T Rolls; Jian-Feng Feng; Wei Cheng; Qiang Dong; Jin-Tai Yu

doi:10.1016/j.biopsych.2022.08.002

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

Biol Psychiatry. 2023 May 1;93(9):790-801. doi: 10.1016/j.biopsych.2022.08.002. Epub 2022 Aug 17.

Authors

Shi-Dong Chen¹, Wei Zhang², Yu-Zhu Li², Liu Yang¹, Yu-Yuan Huang¹, Yue-Ting Deng¹, Bang-Sheng Wu¹, John Suckling³, Edmund T Rolls⁴, Jian-Feng Feng⁵, Wei Cheng⁶, Qiang Dong⁷, Jin-Tai Yu⁸

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.
² 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.
³ Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
⁴ 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.
⁵ 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.
⁶ 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.
⁷ 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. Electronic address: dong_qiang@fudan.edu.cn.
⁸ 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. Electronic address: jintai_yu@fudan.edu.cn.

PMID: 36788058
DOI: 10.1016/j.biopsych.2022.08.002

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: Alzheimer’s disease; Mendelian randomization analysis; Phenome-wide association study; Time-to-event analysis; UK Biobank.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / genetics
Biological Specimen Banks
Genome-Wide Association Study
Humans
Mendelian Randomization Analysis*
Polymorphism, Single Nucleotide
Prospective Studies
United Kingdom / epidemiology