Evidence map›Paper›PMID 38820112›Full record

ArticleBrain : a journal of neurology2024

Towards cascading genetic risk in Alzheimer's disease.

Andre Altmann, Leon M Aksman, Neil P Oxtoby, Alexandra L Young, ADNI, Daniel C Alexander, Frederik Barkhof, Maryam Shoai, John Hardy, Jonathan M Schott

Abstract read
In one paragraph

Article in Brain : a journal of neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. TMEM106B is a selective modulator of TDP-43 pathology in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Genetic drivers of progression in Alzheimer's disease are distinct from disease risk.medRxiv : the preprint server for health sciences · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Andre AltmannUCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.ORCID 0000-0002-9265-2393
Leon M AksmanStevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.ORCID 0000-0003-2342-0780
Neil P OxtobyUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK.ORCID 0000-0003-0203-3909
Alexandra L YoungUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK.
ADNI
Daniel C AlexanderUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK.
Frederik BarkhofUCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
Maryam ShoaiUCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK.
John HardyUCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK.
Jonathan M SchottUK Dementia Research Institute, University College London, London, WC1E 6BT, UK.ORCID 0000-0003-2059-024X

Funding

Research Education ComponentP30AG066518 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI Lisa C Silbert · 2020 to 2026
$28.6M
Agence Nationale de la Recherche ANR-19-JPW2-000Alzheimer's AssociationAlzheimer's Research UKBrain Research UKBritish Heart FoundationEarly Detection of Alzheimer's Disease Subtypes (E-DADS) projectEU Joint Programme-Neurodegenerative Disease Research (JPND) projectItalian Ministry of HealthMedical Research Council MR/T046422/1National Health & Medical Research Council 1191535National Institute for Health Research University College London Hospitals Biomedical Research CentreNational Research, Development and Innovation Office 2019-2.1.7-ERA-NET-2020-00008NIA NIH HHS P30 AG066518Wellcome TrustWellcome Trust 227341/Z/23/ZWeston Brain InstituteWolfson FoundationZonMw 733051106
6 · The paper itself

Abstract

Alzheimer's disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimer's disease. Here, we investigate whether genetic risk for Alzheimer's disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimer's disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimer's disease, in addition to opening therapeutic windows for targeted interventions.

Indexed as

Alzheimer DiseaseGenetic Predisposition to Diseasetau ProteinsAgedAged, 80 and overAmyloid beta-PeptidesApolipoproteins EBiomarkersCohort StudiesDisease ProgressionFemaleGenome-Wide Association StudyHumansMaleMiddle AgedMultifactorial InheritanceAmyloid beta-PeptidesApolipoproteins EBiomarkerstau ProteinsAlzheimer’s diseaseAPOEbiomarkerlongitudinal progressionpolygenic risk

Identifiers

PMID38820112
PMCPMC11292901

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.