Evidence map›Paper›PMID 38995775›Full record

ArticleJournal of Alzheimer's disease : JAD2024

Exploring the Genetic Heterogeneity of Alzheimer's Disease: Evidence for Genetic Subtypes.

Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
  2. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Jeremy A ElmanDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Nicholas J SchorkDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Aaditya V RanganDepartment of Psychiatry, University of California San Diego, La Jolla, CA, USA.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Systems BiologyU19AG023122 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI FRED H GAGE · 2004 to 2026
$102.6M
Regulatory and Human Study Operations (RHSO) Core CU19AG065169 · NIA · UNIVERSITY OF ARIZONA · PI SCHORK, NICHOLAS JOSEPH · 2021 to 2025
$59.8M
The VETSA Longitudinal Twin Study of Cognition and Aging (VETSA 4)R01AG050595 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A, FRANZ, CAROL ELAINE · 2015 to 2024
$28.0M
Exceptional Longevity Data Management and Coordinating CenterU24AG078753 · NIA · SAGE BIONETWORKS · PI Christine M. Fabre Suver, Solveig Sieberts · 2022 to 2026
$10.3M
The VETSA Longitudinal MRI Twin Study of Aging (VETSA MRI 4)R01AG076838 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANDERS M DALE, Jeremy A Elman · 2022 to 2026
$8.7M
Integrative Omics to enhance therapeutics development for healthy agingUH3AG064706 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI MILLER, RICHARD A, SCHORK, NICHOLAS JOSEPH · 2021 to 2024
$3.3M
Linking genetic subtypes of Alzheimer's disease to biological and cognitive heterogeneityK01AG063805 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ELMAN, JEREMY A · 2021 to 2024
$516k
NIA NIH HHS K01 AG063805NIA NIH HHS R01 AG050595NIA NIH HHS R01 AG076838NIA NIH HHS U01 AG024904NIA NIH HHS U19 AG023122NIA NIH HHS U19 AG065169NIA NIH HHS U24 AG078753NIA NIH HHS UH3 AG064706
6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective: We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods: We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases = 2,739, controls = 5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases = 500, controls = 470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n = 399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results: PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased cerebrospinal fluid p-tau and cognitive decline over time. Conclusions: This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.

Indexed as

Alzheimer DiseaseCognitive DysfunctionGenetic HeterogeneityAgedAged, 80 and overFemaleGenetic Predisposition to DiseaseHumansMaleMiddle AgedPrincipal Component Analysistau Proteinstau ProteinsAlzheimer’s diseasebiclusteringgenetic riskgenetic subtypesgenotyping

Identifiers

PMID38995775
PMCPMC11636402

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.