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Record W2399280193 · doi:10.18632/oncotarget.9563

MS4A6A genotypes are associated with the atrophy rates of Alzheimer's disease related brain structures

2016· article· en· W2399280193 on OpenAlexfundno aff
Jing Ma, Wei Zhang, Lin Tan, Hui-Fu Wang, Yu Wan, Fu-Rong Sun, Chen‐Chen Tan, Jin‐Tai Yu, Lan Tan

Bibliographic record

VenueOncotarget · 2016
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOServierEisaiNorthern California Institute for Research and EducationUniversity of California, San DiegoPfizerBiogenBioClinicaF. Hoffmann-La RocheSynarcUniversity of Southern CaliforniaMedpaceNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsPrecuneusNeuroimagingGenome-wide association studyAtrophyEntorhinal cortexBrain Structure and FunctionBrain sizeAlzheimer's Disease Neuroimaging InitiativeGenotypeDiseaseMedicineNeuroscienceSingle-nucleotide polymorphismAlzheimer's diseaseBiologyPathologyGeneticsMagnetic resonance imagingHippocampusGeneFunctional magnetic resonance imaging

Abstract

fetched live from OpenAlex

// Jing Ma 1,* , Wei Zhang 1,* , Lin Tan 2 , Hui-Fu Wang 1 , Yu Wan 1 , Fu-Rong Sun 1 , Chen-Chen Tan 1 , Jin-Tai Yu 1 , Lan Tan 1,2 and Alzheimer’s Disease Neuroimaging Initiative ** 1 Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, Qingdao, China 2 College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, China * These authors have contributed equally to this work ** Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Correspondence to: Lan Tan, email: // Keywords : Alzheimer’s disease; MS4A6A; polymorphisms; phenotypes; brain structure; Gerotarget Received : February 13, 2016 Accepted : April 26, 2016 Published : May 23, 2016 Abstract Membrane-spanning 4-domains, subfamily A, member 6A ( MS4A6A ) has been identified as susceptibility loci of Alzheimer’s disease (AD) by several recent genome-wide association studies (GWAS), whereas little is known about the potential roles of these variants in the brain structure and function of AD. In this study, we included a total of 812 individuals from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database. Using multiple linear regression models, we found MS4A6A genotypes were strongly related to atrophy rate of left middle temporal (rs610932: Pc = 0.017, rs7232: Pc = 0.022), precuneus (rs610932: Pc = 0.015) and entorhinal (rs610932, Pc = 0.022) on MRI in the entire group. In the subgroup analysis, MS4A6A SNPs were significantly accelerated the percentage of volume loss of middle temporal, precuneus and entorhinal, especially in the MCI subgroup. These findings reveal that MS4A6A genotypes affect AD specific brain structures which supported the possible role of MS4A6A polymorphisms in influencing AD-related neuroimaging phenotypes.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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".

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Citations22
Published2016
Admission routes1
Has abstractyes

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