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Record W2078480719 · doi:10.2217/clp.09.64

Lipid-altering gene variants and cardiovascular risk in the older population

2009· article· en· W2078480719 on OpenAlexaff
Tisha Joy, Robert A. Hegele

Bibliographic record

VenueClinical Lipidology · 2009
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern University
Fundersnot available
KeywordsSingle-nucleotide polymorphismSNPTriglycerideInternal medicineGenome-wide association studyQuartilePopulationEndocrinologyBiologyAlleleMedicineGenotypeGeneticsCholesterolGene

Abstract

fetched live from OpenAlex

Evaluation of: Murray A, Cluett C, Bandinelli S et al.: Common lipid-altering gene variants are associated with therapeutic intervention thresholds of lipid levels in older people. Eur. Heart J. 30, 1711–1719 (2009). Increased plasma LDL-C and triglyceride concentrations and reduced plasma HDL-C concentrations are associated with increased cardiovascular disease risk. Recent genome-wide association studies have identified numerous genetic variants – single nucleotide polymorphisms (SNPs) – influencing plasma LDL‑C, HDL-C and triglyceride concentrations. Murray et al. evaluated approximately 600 older people (≥65 years; 43% males) from the Invecchiare in Chianti, Aging in the Chianti Area (InCHIANTI) study and genotyped 21 lipidassociated SNPs identified from early genome‑wide association studies. While individual SNP effects on plasma lipid concentrations were small, effect sizes were somewhat larger when SNPs were combined into an allele score. The extreme quartiles of these scores were significantly associated with an approximately 4–8% variation of plasma lipids; an approximately threefold‑increased risk of crossing a lipid threshold value for intervention; and an approximately threefold‑increased risk of vascular disease. With some caveats, the results suggest that combining SNP genotypes might have some clinical relevance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.346
Teacher spread0.303 · 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 teacher head, 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
Published2009
Admission routes1
Has abstractyes

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