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Record W2982089964 · doi:10.1093/eurheartj/ehz746.0014

4944The contribution of familial hypercholesterolemia (FH) to premature coronary artery disease decreased by 2-fold between 1998 and 2018 in a founder population with high prevalence of FH

2019· article· en· W2982089964 on OpenAlexaffabout
A Lauziere, D. Brisson, Sylvain Bédard, Étienne Khoury, G Tremblay, Melinda Barabas, Daniel Gaudet

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineFamilial hypercholesterolemiaCoronary artery diseaseEzetimibePopulationInternal medicinePCSK9DiseaseLDL receptorStatinCholesterolLipoproteinEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Familial hypercholesterolemia (FH) is an autosomal dominant trait associated with high risk of premature coronary artery disease (CAD). The worldwide prevalence of FH is estimated at 1:250 to 1:500. In certain populations, including French Canadians (FC), the prevalence is significantly higher however. From 1995 to 1998, FH contributed to 9.6% of angiographically proven CAD in a FC founder population, the burden being the highest in men aged <50 years (20.6%). In the past 2 decades, powerful statins, ezetimibe and other low-density lipoprotein-Cholesterol (LDL-C) modulators, such as PCSK9 inhibitors, have been progressively introduced and several FH diagnosis scoring systems or guidelines have been developed and disseminated in order to facilitate FH recognition and management. The impact of these measures on the FH burden is however not documented. Purpose To compare the burden of FH twenty years apart in FC patients hospitalized for CAD. Methods Lipid profiles, cardiovascular risk factors and FH status of 1,132 FC patients who were hospitalized for a CAD event and who consecutively attended the cardiovascular disease clinic in 2017 and 2018 were compared to those of 2,506 who consecutively presented angiographically proven CAD two decades ago. FH status was based on Simon Broome and FH Canada definitions. In 1998, all consenting CAD patients were also molecularly screened for the most prevalent FH causing mutations in FC. Comparisons between groups were performed using Chi-square and independent samples Student's t-test. Results Most patients in both cohorts were males (74.5% vs 73.9% in 1998 vs. 2018, respectively). At admission, mean LDL-C (± SD) was 3.99±1.67 in 1998 vs. 2.22±1.06 in 2018 (p<0.001). The proportion of patients who were treated with a statin or another lipid lowering agent was 32.9% in 1998 compared to 67.6% in 2018 (p<0.001) and the drug regimen was also significantly different. In 1998, 24.6% of patients had LDL-C >5.0 mmol/L at admission compared to 4.2% in 2018. Definite FH was diagnosed in 9.6% of patients in the 1998 cohort compared to 4.7% in the 2018 cohort (p<0.001). FH patients hospitalized for CAD were significantly older in 2018 than in 1998 (56.3±11.3 vs. 49.2±10.9 in men, p=0.001; 61.1±11.8 vs. 53.2±12.3 in women, p=0.02). In the same period, the relative burden of diabetes and other lipid disorders, including high-density lipoprotein dysmetabolism significantly increased (p<0.001). Conclusions Over a period of 20 years, in a founder population with a high prevalence of FH, the contribution of FH to hospitalizations for CAD decreased by 2-fold and affected patients now tend to be hospitalized at an older age than 2 decades ago. This suggests that early diagnosis and more effective management of FH in the last 2 decades have contributed to significantly decrease its burden. Acknowledgement/Funding ECOGENE-21, Amgen, Sanofi

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 score0.133
Threshold uncertainty score0.265

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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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Citations0
Published2019
Admission routes2
Has abstractyes

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