ArticleeLife2022
Nomograms of human hippocampal volume shifted by polygenic scores.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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Who cites it
5 citing papers in PubMed.
- Cumulative exposure to cannabis and hippocampus MRI in middle age: results from the coronary artery risk development in young adults (CARDIA) study.Translational psychiatry · 2026Article
- Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.Alzheimer's research & therapy · 2025Article
- Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Normative models for neuroimaging markers: Impact of model selection, sample size and evaluation criteria.NeuroImage · 2023Article
- A Perspective: Challenges in Dementia Research.Medicina (Kaunas, Lithuania) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm
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Registered trials
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