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Record W2534792282 · doi:10.1016/j.jalz.2016.06.1747

P3‐088: Genetic Influence of Plasma Homocysteine Level on Alzheimer's Disease

2016· article· en· W2534792282 on OpenAlexaff
Tina Roostaei, Daniel Felsky, Arash Nazeri, Aristotle N. Voineskos

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMendelian randomizationHomocysteineConfoundingSingle-nucleotide polymorphismGenome-wide association studyMedicineAlzheimer's Disease Neuroimaging InitiativeGenetic associationSNPInternal medicineOncologyDiseaseBioinformaticsDementiaGeneticsGenotypeBiologyGenetic variantsGene

Abstract

fetched live from OpenAlex

Observational studies have proposed elevated blood homocysteine level as a risk factor for late-onset Alzheimer’s disease (AD). Homocysteine level is easily modifiable using available treatments. However, clinical trials have reported inconsistent results for the effect of altering homocysteine on AD-related cognitive and clinical outcomes. To determine whether blood homocysteine is merely a byproduct of or is a causal contributing factor to AD pathophysiology, we used a mendelian randomization approach to test for association between genetic polymorphisms influencing plasma homocysteine level and AD, while avoiding reverse causation and confounding. Top SNPs from the 13 loci influencing plasma homocysteine level (van Meurs et al., 2013; meta-analysis of n=44,147, European descent) were used in this study. Mendelian randomization estimates of association between plasma homocysteine level and AD risk were calculated using an inverse-variance weighted approach from IGAP SNP-AD associations’ summary statistics (meta-analysis of AD GWASs, AD=17,008, nondemented-controls=37,154). Additional mega-analyses were performed using linear mixed models and plasma homocysteine polygenic scores derived from imputed genomic data from ADNI, ADGC, GenADA, and ROS/MAP participants (age>=65, European descent), while accounting for fixed effects of age, sex, and number of APOE-ε4 alleles, as well as random effects of study groups/genotyping platforms. Mendelian randomization analysis using IGAP data did not support causal association between genetically-predicted plasma homocysteine level and risk for AD (O.R. for a one-SD predicted increase in plasma homocysteine[95%CI]=1.01[0.89-1.15]). We also found no evidence for a significant association between plasma homocysteine polygenic score and risk for AD in a mega-analysis of ADNI, ADGC, GenADA, and ROS/MAP data (AD=3,866, nondemented-controls=2,691)(O.R.[95%CI]=1.02[0.96-1.08]). Moreover, additional analyses among AD patients did not provide significant evidence for association between plasma homocysteine polygenic score and baseline MMSE score (n=1,179 GenADA, ROS/MAP, and ADNI participants, t=0.24, P=0.81) or longitudinal change in MMSE score (i.e. interaction between polygenic score and follow-up month while assuming random intercepts and slopes for each individual; 775 observations on 239 ADNI participants, mean follow-up=17months, t=-1.35, P=0.18). Our analyses demonstrate that genetically-determined plasma homocysteine levels do not influence risk for, and severity and progression of AD. This suggests that plasma homocysteine is a biomarker of AD rather than a causal factor.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.001

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 head0.049
GPT teacher head0.304
Teacher spread0.255 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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