Evidence map›Paper›PMID 35654594›Full record

ArticleNeurology2022

Genetic Variation in Targets of Antidiabetic Drugs and Alzheimer Disease Risk: A Mendelian Randomization Study.

Bowen Tang, Yunzhang Wang, Xia Jiang, Madhav Thambisetty, Luigi Ferrucci, Kristina Johnell, Sara Hägg

Open access · bronzeAbstract read
In one paragraph

Article in Neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
9.3field-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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 4 syntheses or guidelines pooled it, 67 citations in OpenAlex.

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  18. Glucagon-like peptide-1 receptor agonists for major neurocognitive disorders.Journal of neurology, neurosurgery, and psychiatry · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 2 countries.

Bowen TangFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.
Yunzhang WangFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.
Xia JiangFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.ORCID https://orcid.org/0000-0001-5878-8986
Madhav ThambisettyFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.
Luigi FerrucciFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.
Kristina JohnellFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging.
Sara HäggFrom the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (B.T., Y.W., K.J., S.H.); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (X.J.); Brain Aging and Behavior Section, National Institute on Aging (M.T.); and Longitudinal Studies Section (L.F.), National Institute on Aging. sara.hagg@ki.se.ORCID https://orcid.org/0000-0002-2452-1500
Karolinska Institutet · SE

Funding

Understanding mechanisms and seeking novel biomarkers in Alzheimer's diseaseZIAAG000200 · NIA · NATIONAL INSTITUTE ON AGING · PI RESNICK, SUSAN · 2013 to 2024
$12.2M
Repurposing drugs for Alzheimer´s disease using a reverse translational approachRF1AG067996 · NIA · KAROLINSKA INSTITUTE · PI HAGG, SARA · 2021 to 2021
$1.6M
Repurposing drugs for Alzheimer´s disease using a reverse translational approachR01AG067996 · NIA · KAROLINSKA INSTITUTE · PI HAGG, SARA · 2024 to 2025
$973k
New Cures from Old Medicines - Targeting abnormal metabolism in Alzheimer's DiseaseZIAAG000436 · NIA · NATIONAL INSTITUTE ON AGING · PI RESNICK, SUSAN · 2019 to 2024
$950k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NIA NIH HHS R01 AG067996NIA NIH HHS RF1 AG067996
6 · The paper itself

Abstract

BACKGROUND AND

objectivesPrevious studies have highlighted antidiabetic drugs as repurposing candidates for Alzheimer disease (AD), but the disease-modifying effects are still unclear.

methodsA 2-sample mendelian randomization study design was applied to examine the association between genetic variation in the targets of 4 antidiabetic drug classes and AD risk. Genetic summary statistics for blood glucose were analyzed using UK Biobank data of 326,885 participants, whereas summary statistics for AD were retrieved from previous genome-wide association studies comprising 24,087 clinically diagnosed AD cases and 55,058 controls. Positive control analysis on type 2 diabetes mellitus (T2DM), insulin secretion, insulin resistance, and obesity-related traits was conducted to validate the selection of instrumental variables.

resultsIn the positive control analysis, genetic variation in sulfonylurea targets was associated with higher insulin secretion, a lower risk of T2DM, and an increment in body mass index, waist circumference, and hip circumference, consistent with drug mechanistic actions and previous trial evidence. In the primary analysis, genetic variation in sulfonylurea targets was associated with a lower risk of AD (odds ratio [OR] = 0.38 per 1 mmol/L decrement in blood glucose, 95% CI 0.19-0.72, DISCUSSION: Genetic variation in sulfonylurea targets was associated with a lower risk of AD, and future studies are warranted to clarify the underlying mechanistic pathways between sulfonylureas and AD.

Indexed as

Alzheimer DiseaseDiabetes Mellitus, Type 2Blood GlucoseDrug RepositioningGenome-Wide Association StudyGlucagon-Like Peptide 1HumansHypoglycemic AgentsMendelian Randomization AnalysisPolymorphism, Single NucleotideBlood GlucoseGlucagon-Like Peptide 1Hypoglycemic Agents

Identifiers

PMID35654594
PMCPMC9484609
OpenAlexW4281784971

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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.