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Record W4390194129 · doi:10.1002/alz.080302

Effects of Alzheimer’s disease genetic risk on brain morphometric development in three multiple‐ancestry pediatric datasets

2023· article· en· W4390194129 on OpenAlexaff
Jacob W. Vogel, Laura M. Schultz, Bart Larsen, Jakob Seidlitz, Noor Al‐Sharif, Ran Barzilay, Matthew Cieslak, Sydney Covitz, Raquel E. Gur, Ruben C. Gur, Guillaume Huguet, Renaud La Joie, Corey T. McMillan, Nathalie Nilsson, Jean‐Baptiste Poline, Kosha Ruparel, Russell T. Shinohara, Laura E.M. Wisse, Daniel H. Wolf, David A. Wolk, Aaron Alexander‐Bloch, Laura Almasy, Theodore D. Satterthwaite

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University InstituteUniversité de Montréal
Fundersnot available
KeywordsBrain sizePolygenic risk scoreMedicineApolipoprotein ECohortDementiaMultiple comparisons problemDiseasePsychologyOncologyInternal medicineBiologyMagnetic resonance imagingGeneticsSingle-nucleotide polymorphismGenotypeStatistics

Abstract

fetched live from OpenAlex

Abstract Background Few prior studies have investigated whether genetic risk for Alzheimer’s disease (AD) can manifest through altered neurodevelopment of AD‐vulnerable brain regions. This pre‐registered study tests the effects of AD polygenic risk on morphometric neurodevelopment across 3 large datasets totaling 8,364 youths aged 3‐22 (Table 1) Method Polygenic risk score (PRS) was calculated using PRS‐CS software, with summary scores from Kunkle et al. (2021) for African ancestry individuals (AA) and Jansen et al. (2019) for European ancestry individuals (EA). For EA only, PRS were calculated with (PRS‐APOE+) and without (PRS‐APOE‐) the APOE locus included. All individuals had T1‐weighted MRI processed with FreeSurfer that passed quality assessment checks. ComBAT was used to harmonize sites within‐cohort. Primary analyses investigated effects of PRS, age and their interaction on bilateral hippocampal volume and AD MetaROI cortical thickness using generalized additive models. Age interactions were not evaluated in ABCD due to limited age range. Exploratory analysis examined effects of age and PRS on cortical thickness, volume, surface area and subcortical volume across Desikan‐Killiany regions, adjusting for multiple comparisons (FDR). For exploratory analyses only, ABCD was split into training and test sets. All analyses were conducted for EA and AA separately; sex, total intracranial volume, image quality (Euler number) and ten genetic principal components were included as model covariates. Result Table 1 summarizes primary results. Hippocampal volume increased with age in 3/6 datasets, while MetaROI thickness decreased with age in all datasets. Surprisingly, increased PRS was associated with larger hippocampi in three datasets. In one dataset only, greater PRS was associated with increased metaROI thickness (Fig 1). 2/3 AA datasets showed age*PRS interactions on hippocampal volume, but in opposite directions. An age*PRS interaction on metaROI thickness was found in one dataset (Fig. 2A‐C). For exploratory analyses, no regions survived multiple comparisons for any contrast in 5/6 datasets; APOE only PRS was associated with larger supramarginal surface area in one dataset (Fig 2E). Effects were not correlated across studies (Fig 2D) Conclusion This large, pre‐registered study provides little evidence that AD genetic risk manifests during pediatric brain morphometric development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.279
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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