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Record W4366609292 · doi:10.1212/wnl.0000000000207305

Genetic Moderation of the Association of β-Amyloid With Cognition and MRI Brain Structure in Alzheimer Disease

2023· article· en· W4366609292 on OpenAlexfundno aff
Philip S. Insel, Atul Kumar, Oskar Hansson, Niklas Mattsson

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchParkinsonfondenNational Institutes of HealthIXICOGenentechMarcus och Amalia Wallenbergs minnesfondSkånes universitetssjukhusSveriges LäkarförbundVetenskapsrådetEisaiLunds UniversitetGHR FoundationNorthern California Institute for Research and EducationServierKnut och Alice Wallenbergs StiftelseKonung Gustaf V:s och Drottning Victorias FrimurarestiftelseFoundation for Neurologic DiseasesPfizerBiogenBioClinicaAustralian GovernmentF. Hoffmann-La RocheSynarcUniversity of Southern CaliforniaBrigham and Women's HospitalMedpaceBristol-Myers SquibbEli Lilly and CompanyNovartis Pharmaceuticals CorporationAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsParkinson Research FoundationAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsCognitionNeuroimagingAlzheimer's diseaseMinor allele frequencyCognitive declineAlleleAlzheimer's Disease Neuroimaging InitiativeDiseasePsychologyImaging geneticsGenetic associationMedicineOncologyInternal medicineNeuroscienceDementiaAllele frequencyGeneticsBiologyGeneGenotypeSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Objectives: There is considerable heterogeneity in the association between increasing amyloid β (Aβ) pathology and early cognitive dysfunction in preclinical Alzheimer’s disease (AD). At this stage, some individuals show no signs of cognitive dysfunction, while others show clear signs of decline. The factors explaining this heterogeneity are particularly important for understanding progression in AD but remain largely unknown. In this study, we examine an array of genetic variants that may influence the relationships among Aβ, brain structure, and cognitive performance in two large cohorts. Methods: In 2953 cognitively-unimpaired participants from the A4 study, interactions between genetic variants and 18F-Florbetapir PET standardized uptake value ratio (SUVR) to predict the Preclinical Alzheimer’s Cognitive Composite (PACC) were assessed. Genetic variants identified in the A4 study were evaluated in the Alzheimer’s Disease Neuroimaging Initiative (ADNI, N=527) for their association with longitudinal cognition and brain atrophy in both cognitively unimpaired participants and those with mild cognitive impairment. Results: In A4, four genetic variants significantly moderated the association between Aβ load and cognition. Minor alleles of three variants were associated with additional decreases in PACC scores with increasing Aβ SUVR (rs78021285, β =-2.29, SE=0.40, pFDR=0.02, nearest gene ARPP21; rs71567499, β =-2.16, SE=0.38, pFDR=0.02, nearest gene PPARD; rs10974405, β =-1.68, SE=0.29, pFDR=0.02, nearest gene GLIS3). The minor allele of rs7825645 was associated with less decrease in PACC scores with increasing Aβ SUVR (β =0.71, SE=0.13, pFDR=0.04, nearest gene FGF20). The genetic variant rs76366637, in linkage disequilibrium with rs78021285, was available in both A4 and ADNI. In A4, rs76366637 was strongly associated with reduced PACC scores with increasing Aβ SUVR (β =-1.01, SE=0.21, t=-4.90, p<0.001). In ADNI, rs76366637 was associated with accelerated cognitive decline (χ2=15.3, p=0.004) and atrophy over time (χ2=26.8, p<0.001), with increasing Aβ SUVR. Conclusion: Patterns of increased cognitive dysfunction and accelerated atrophy due to specific genetic variation may explain some of the heterogeneity in cognition in preclinical and prodromal AD. The genetic variant near ARPP21 associated with lower cognitive scores in A4 and accelerated cognitive decline as well as brain atrophy in ADNI may help to identify those at the highest risk of accelerated progression of Alzheimer’s disease.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.271
Teacher spread0.261 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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