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Record W2594218367 · doi:10.1161/atvb.33.suppl_1.a336

Abstract 336: Overrepresentation of Familial Hypercholesterolemia-causing Mutations in Patients with Familial Combined Hyperlipidemia

2013· article· en· W2594218367 on OpenAlexaff
Mary A. Bamimore, Henian Cao, Jian Wang, Adam D. McIntyre, Christopher Johansen, Robert A. Hegele

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2013
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsPCSK9Missense mutationFamilial hypercholesterolemiaGeneticsApolipoprotein BCandidate geneLDL receptorBiologySanger sequencingPopulationMutationGeneLipoproteinEndocrinologyMedicineCholesterol

Abstract

fetched live from OpenAlex

Background_ Elevated plasma low-density lipoprotein (LDL) cholesterol is a feature of both familial hypercholesterolemia (FH) and familial combined hyperlipidemia (FCH). FH is predominantly a monogenic _autosomal dominant_ condition that results from mutations in the genes coding for low density lipoprotein receptor (LDLR), apolipoprotein B-100 (APOB) and proprotein convertase subtilisin/kexin type 9 (PCSK9). In contrast, the genetic basis of FCH, a very common dyslipidemia in the human population, is less well understood, but is considered to be polygenic. Investigations in other polygenic lipid disorders indicate that genetic susceptibility is comprised of both common polymorphisms and rare heterozygous mutations. Re-sequencing technologies have identified greater accumulation of missense rare variants in candidate genes of diseased cohorts relative to healthy controls, thus indicating a potential role of candidate genes in disease etiology. Objective_ Our aim was to understand the genetic etiology of FCH. We hypothesized missense rare variants in the 3 known autosomal dominant FH-causing genes and the newly characterized inducible degrader of the low density lipoprotein receptor (IDOL), would accumulate in FCH patients (n=138) (cases) relative to controls (n=94). We tested this hypothesis using Sanger sequencing of coding sequences, intron-exon boundaries and some regulatory regions of the 4 candidate genes. Results & Discussion_ For cases vs controls, the total missense rare variant carrier counts were 7/138 vs 4/94 in LDLR and 14/138 vs 9/94 in APOB. Of all these missense rare variants found in our entire cohort, we found functionally verified FH-causing variants only in our cases and not in our controls (P =0.09), namely LDLR G314S, D333V, V806I and APOB R3500W variants. These variants likely explain hypercholesterolemia in those particular FCH patients. Conclusions_ This preliminary analysis of FCH candidate gene sequences supports the idea that rare FH-causing mutations are over-represented in FCH. The findings also support future expanded case-control and family studies to identify possible monogenic causes of FCH using various next-generation sequencing technologies.

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)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.025
GPT teacher head0.273
Teacher spread0.248 · 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
Published2013
Admission routes1
Has abstractyes

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