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Record W3112852434 · doi:10.1002/alz.044985

A blood cholesterol polygenic score in two independent AD cohorts

2020· article· en· W3112852434 on OpenAlexaff
Nathalie Nilsson, Cynthia Picard, Anne Labonté, Judes Poirier

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsAlzheimer Society of CanadaMcGill UniversityDouglas College
Fundersnot available
KeywordsMedicineInternal medicineCohortExplained variationAnalysis of varianceCholesterolCorrelationOncology

Abstract

fetched live from OpenAlex

Abstract Background While blood total cholesterol (TC) levels is a recognized factor contributing to Alzheimer’s disease (Alz. Ass., 2017), its role is debated (Wood et al., 2014). E.g. several studies have shown that midlife hypercholesterolemia is associated with increased risk of developing AD (Reiman et al., 2010; Solomon et al., 2009; Toro et al., 2014), but others have shown no association (Li et al., 2005; Tan et al., 2003). High TC has also been associated with increased amyloid load in the hippocampus (Pappolla et al., 2003) and hypometabolism in brain regions affected by AD (Reiman et al., 2010). Studies with statins have shown similar discrepancies with retrospective studies indicating a protective effect but have had no consistent positive effects in randomized controlled trials (Shobab et al., 2005). Thus, in an attempt to address these discrepancies, we constructed a polygenic score (TC‐pgs) capturing some of the variance in peripheral TC levels and evaluated it for associations with AD risk and biomarkers. Method A weighted TC‐pgs was constructed using summary data from Willer et al., (2013; http://lipidgenetics.org/ ) in two AD cohorts; PREVENT‐AD ( https://preventad.loris.ca/ ) and ROSMAP ( https://www.radc.rush.edu/home.htm ). The TC‐pgs was optimized for correlation with TC levels in the PREVENT‐AD cohort, and then evaluated for correlations with AD risk and CSF biomarkers using both cohorts. Results We found that by stratifying for statin use and sex we more than doubled the amount of variance explained by the score (from ∼7.5% to ∼17.5%) specifically in statin free females. Furthermore, the TC‐pgs improved the prediction of hypercholesterolemia (AUC 0.805 vs 0.646, p = 0.0016) in the same group. The TC‐pgs were evaluated for associations with CSF Aβ42, p‐tau and tau in PREVENT‐AD and for clinically and pathologically defined AD in ROSMAP. We did not find any significant associations, but for a trend towards a positive association between TC‐pgs and CSF p‐tau levels (p = 0.09). Conclusion Our TC‐pgs did improve prediction of hypercholesterolemia but failed to correlate with AD or AD biomarkers. Thus, the findings do not support that an increased cumulative genetic risk of hypercholesterolemia influence the risk of AD.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.317
Teacher spread0.276 · 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
Published2020
Admission routes1
Has abstractyes

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