Evidence map›Paper›PMID 35090530›Full record

ArticleAlzheimer's research & therapy2022

Mendelian randomization highlights significant difference and genetic heterogeneity in clinically diagnosed Alzheimer's disease GWAS and self-report proxy phenotype GWAX.

Haijie Liu, Yang Hu, Yan Zhang, Haihua Zhang, Shan Gao, Longcai Wang, Tao Wang, Zhifa Han, Bao-Liang Sun, Guiyou Liu

Open access · goldAbstract read
In one paragraph

Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
10.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Haijie LiuDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Yang HuSchool of Life Science and Technology, Harbin Institute of Technology, Harbin, 150080, China.
Yan ZhangDepartment of Pathology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China.
Haihua ZhangBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China.
Shan GaoBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China.
Longcai WangDepartment of Anesthesiology, The Affiliated Hospital of Weifang Medical University, Weifang, 261053, China.
Tao WangChinese Institute for Brain Research, Beijing, China.
Zhifa HanState Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.
Bao-Liang SunKey Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
Guiyou LiuBeijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, 100069, China. liuguiyou1981@163.com.ORCID 0000-0002-1126-2888
Capital Medical University · CNWeifang Medical University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNChinese Institute for Brain Research · CNHarbin Institute of Technology · CNShandong First Medical University · CN

Funding

National Centralized Repository for Alzheimer's Disease and Related Dementias (NCRAD)U24AG021886 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI TATIANA M. FOROUD · 2002 to 2026
$119.8M
NATIONAL ALZHEIMERS COORDINATING CENTER (NACC)U01AG016976 · NIA · UNIVERSITY OF WASHINGTON · PI KUKULL, WALTER ANTHONY · 1999 to 2020
$72.8M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
CHARGE Consortium: Omics Discovery for CVD and Aging PhenotypesR01HL105756 · NHLBI · UNIVERSITY OF WASHINGTON · PI Bruce M Psaty, NICHOLAS L SMITH · 2011 to 2026
$9.5M
Collaborative GWAS of Dementia, AD and related MRI and Cognitive EndophenotypesR01AG033193 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI SESHADRI, SUDHA · 2009 to 2016
$4.4M
AGES STUDY-THE REYKJAVIK STUDY OF HEALTHY AGING FOR THE NEW MILLENNIUM-26012100N01AG012100 · NIA · ICELANDIC HEART ASSOCIATION · 2002 to 2004
–
Medical Research Council 503480NHLBI NIH HHS R01 HL105756NIA NIH HHS N01 AG012100NIA NIH HHS R01 AG033193NIA NIH HHS U01 AG016976NIA NIH HHS U01 AG032984NIA NIH HHS U24 AG021886Wellcome TrustWellcome Trust 082604/2/07/Z
6 · The paper itself

Abstract

backgroundUntil now, Mendelian randomization (MR) studies have investigated the causal association of risk factors with Alzheimer's disease (AD) using large-scale AD genome-wide association studies (GWAS), GWAS by proxy (GWAX), and meta-analyses of GWAS and GWAX (GWAS+GWAX) datasets. However, it currently remains unclear about the consistency of MR estimates across these GWAS, GWAX, and GWAS+GWAX datasets.

methodsHere, we first selected 162 independent educational attainment genetic variants as the potential instrumental variables (N = 405,072). We then selected one AD GWAS dataset (N = 63,926), two AD GWAX datasets (N = 314,278 and 408,942), and three GWAS+GWAX datasets (N = 388,324, 455,258, and 472,868). Finally, we conducted a MR analysis to evaluate the impact of educational attainment on AD risk across these datasets. Meanwhile, we tested the genetic heterogeneity of educational attainment genetic variants across these datasets.

resultsIn AD GWAS dataset, MR analysis showed that each SD increase in years of schooling (about 3.6 years) was significantly associated with 29% reduced AD risk (OR=0.71, 95% CI: 0.60-0.84, and P=1.02E-04). In AD GWAX dataset, MR analysis highlighted that each SD increase in years of schooling significantly increased 84% AD risk (OR=1.84, 95% CI: 1.59-2.13, and P=4.66E-16). Meanwhile, MR analysis suggested the ambiguous findings in AD GWAS+GWAX datasets. Heterogeneity test indicated evidence of genetic heterogeneity in AD GWAS and GWAX datasets.

conclusionsWe highlighted significant difference and genetic heterogeneity in clinically diagnosed AD GWAS and self-report proxy phenotype GWAX. Our MR findings are consistent with recent findings in AD genetic variants. Hence, the GWAX and GWAS+GWAX findings and MR findings from GWAX and GWAS+GWAX should be carefully interpreted and warrant further investigation using the AD GWAS dataset.

Indexed as

Alzheimer DiseaseGenome-Wide Association StudyGenetic HeterogeneityHumansMendelian Randomization AnalysisPhenotypePolymorphism, Single NucleotideSelf ReportAlzheimer’s diseaseGenetic heterogeneityGWASGWAXMendelian randomization

Identifiers

PMID35090530
PMCPMC8800228
OpenAlexW4210363323

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.