TY - JOUR
T1 - Causal Associations Between Modifiable Risk Factors and the Alzheimer's Phenome
AU - collaborators of the Alzheimer's Disease Genetics Consortium
AU - Andrews, Shea J.
AU - Fulton-Howard, Brian
AU - O'Reilly, Paul
AU - Marcora, Edoardo
AU - Goate, Alison M.
AU - Farrer, Lindsay A.
AU - Haines, Jonathan L.
AU - Mayeux, Richard
AU - Naj, Adam C.
AU - Pericak-Vance, Margaret A.
AU - Schellenberg, Gerard D.
AU - Wang, Li San
N1 - Funding Information:
S.J.A., B.F.H., E.M., and A.M.G. were supported by the JPB Foundation ( http://www.jpbfoundation.org ) and by the National Institutes of Health (U01AG052411 and U01AG058635; principal investigator Alison Goate). P.F.O. was supported by funding from the UK Medical Research Council (MR/N015746/1) and the National Institute of Health (R01MH122866). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This analysis was possible due to the generous sharing of genomewide association summary statistics. We would like to thank the research participants and employees of 23andMe for making this work possible. ADGC: The Alzheimer's Disease Genetics Consortium supported collection and genotyping of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. NCRAD: Samples from the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. NIAGADS: Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24 AG041689). The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA‐funded ADCs (Supplementary Table S7 ).
Funding Information:
S.J.A., B.F.H., E.M., and A.M.G. were supported by the JPB Foundation (http://www.jpbfoundation.org) and by the National Institutes of Health (U01AG052411 and U01AG058635; principal investigator Alison Goate). P.F.O. was supported by funding from the UK Medical Research Council (MR/N015746/1) and the National Institute of Health (R01MH122866). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This analysis was possible due to the generous sharing of genomewide association summary statistics. We would like to thank the research participants and employees of 23andMe for making this work possible. ADGC: The Alzheimer's Disease Genetics Consortium supported collection and genotyping of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. NCRAD: Samples from the National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. NIAGADS: Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24 AG041689). The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs (Supplementary Table S7).
Funding Information:
The members of the Alzheimer's Disease Genetics Consortium and their institutional affiliations are: , PhD (Departments of Neurology, Biostatistics, Epidemiology, Medicine (Biomedical Genetics), Neurology, and Ophthalmology, Boston, MA, USA); , PhD (Department of Population and Quantitative Health Sciences and Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA); , MSc, MD (Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Department of Neurology, and Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA); , PhD (Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; supported by funding from the National Institute of Aging; R01 AG054060 and RF1 AG061351); , PhD (The John P. Hussman Institute for Human Genomics and Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA); , PhD (Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA); and , PhD (Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA) contributed to the acquisition of data for the Alzheimer's Disease Genetics Consortium. Lindsay A. Farrer Jonathan L. Haines Richard Mayeux Adam C. Naj Margaret A. Pericak‐Vance Gerard D. Schellenberg Li‐San Wang
Publisher Copyright:
© 2020 American Neurological Association
PY - 2021/1
Y1 - 2021/1
N2 - Objective: The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the “AD phenome”: AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42), tau, and ptau181, and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). Methods: Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. Results: PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. Interpretation: Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54–65.
AB - Objective: The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the “AD phenome”: AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid-β (Aβ42), tau, and ptau181, and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). Methods: Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two-sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. Results: PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total- and LDL-cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. Interpretation: Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54–65.
UR - http://www.scopus.com/inward/record.url?scp=85095691159&partnerID=8YFLogxK
U2 - 10.1002/ana.25918
DO - 10.1002/ana.25918
M3 - Article
C2 - 32996171
AN - SCOPUS:85095691159
SN - 0364-5134
VL - 89
SP - 54
EP - 65
JO - Annals of Neurology
JF - Annals of Neurology
IS - 1
ER -