Evidence map›Paper›PMID 42469913›Full record

ArticleGenome medicine2026

Multi-threshold polygenic risk improves hippocampal-based cognitive decline prediction.

Mohammed Janahi, Luigi Lorenzini, Neil P Oxtoby, Frederik Barkhof, Younes Mokrab, Jonathan M Schott, Andre Altmann, For The Alzheimer's Disease Neuroimaging Initiative

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In one paragraph

Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Mohammed JanahiThe UCL Hawkes Institute and Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK. rmapmja@ucl.ac.uk.ORCID https://orcid.org/0000-0001-7442-2298
Luigi LorenziniDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, 1081 BT, Amsterdam, The Netherlands.
Neil P OxtobyThe UCL Hawkes Institute and Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK.
Frederik BarkhofThe UCL Hawkes Institute and Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK.
Younes MokrabMedical and Population Genomics Lab, Research Branch, Sidra Medicine, Doha, Qatar. y.mokrab@sidra.org.
Jonathan M SchottDementia Research Centre (DRC), Queen Square Institute of Neurology, University College London, Gower Street, London, WC1E 6BT, UK. j.schott@ucl.ac.uk.
Andre AltmannThe UCL Hawkes Institute and Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK. a.altmann@ucl.ac.uk.
For The Alzheimer's Disease Neuroimaging Initiative

Funding

Agence Nationale de la Recherche ANR-19-JPW2-000Medical Research Council MR/T046422/1National Health & Medical Research Council, Australia 1191535National Research, Development and Innovation Office, Hungary 2019-2.1.7-ERA-NET-2020-00008ZonMw 733051106
6 · The paper itself

Abstract

backgroundPredicting cognitive decline from brain MRI is a central question in neuroscience. Hippocampal volume (HV) is a key cognitive biomarker, and normative models can be augmented with multimodal information. Here we augment normative models with genetic information and show improvements in cognitive decline prediction across multiple experimental setups.

methodsWe improve normative models for HV by integrating multi-threshold polygenic scores (PGS) with demographic and imaging data using Gaussian Process Regression (GPR). Models were trained on 23,997 participants from UK Biobank (UKBB) and validated on 3,000 out-of-sample participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimer's Disease (EPAD) cohorts.

resultsOur genetically-informed models significantly strengthened associations across six experimental designs and 13 key neurocognitive measures, including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Alzheimer's Disease Assessment Scale (ADAS), while enhancing prediction of future cognitive decline.

conclusionsTogether, these findings underscore the promise of integrating multi-threshold PGS with neuroimaging-based predictive models to improve prognostication and early intervention strategies for neurodegenerative diseases.

Indexed as

Cognitive DysfunctionHippocampusMultifactorial InheritanceAgedAlzheimer DiseaseFemaleGenetic Risk ScoreHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingBrain ModellingDementiaNeurodegenerative DisordersNormative ModellingPolygenic Scores

Identifiers

PMID42469913
PMCPMC13662996

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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.