Bibliographic record
Abstract
Background: Familial Hypercholesterolemia (FH) is associated with premature atherosclerotic cardiovascular disease caused by excessive accumulation of LDL-C in circulation. Early treatment can normalize life expectancy. There are many barriers to care in FH that may lead to low diagnosis rates and unfavourable patient outcomes. For example, despite recommendations of genetic screening for diagnosis of FH by several national organizations, it is not routinely available as part of clinical care in Canada. Additionally, sex has been identified as a potential barrier to optimal care in cardiovascular diseases, which needs to be further explored in FH specifically.Methods/Results: First, the impact of unbiased genetic testing on re-classification of patients with a clinical diagnosis of FH in a single centre cohort in Québec was determined. Next-generation sequencing of the LDLR, APOB and PCSK9 genes and multiplex ligation-dependent probe amplification of the LDLR gene to detect genetic variants, including copy number variants was performed. All mutations were reviewed by a geneticist and cross-referenced in ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/). Among 335 FH cases seen at the lipid clinic of the McGill University Health Centre (55% men, 45% women), baseline LDL-C was 7.0 ± 1.8 mmol/L. Women were diagnosed 6 years later than men and presented with higher LDL-C and apoB levels. In 229 patients who underwent genetic testing, a pathogenic FH-causing variant was identified in 169 (74%) individuals. A majority had variants in the LDLR (86%) or ABOP (14%) genes. Interestingly, the genetic panels currently available in Québec, which includes 11 common variants in French Canadians, only accounted for 49% of identified mutations. Importantly, 67% of patients initially defined as “probable FH” were re-classified as “definite FH” following genetic screening. Next, we investigated how sex can act as a barrier to care in FH, potentially leading to less-than-optimal patient outcomes. A preliminary retrospective registry analysis of 292 patients with FH at from the lipid clinic at the McGill University Health Centre was performed. In this cohort, less women were on high-intensity statins compared to men (35% vs. 74%, P=0.002) and less women reached an LDL-C target of ≤ 2.5 mmol/L compared to men (32% vs. 55%, P=0.02). To further investigate sex differences in treatment of FH, a global scale systematic review was performed. Publicly available databases were searched for peer-reviewed, English publications. Publications went through two rounds of screening in duplicate and were kept if the population was labelled as FH and data demonstrating a sex comparison in treatment was available. A thorough data extraction was performed. After duplicates were excluded, the search identified 3,979 records. After screening all items for inclusion criteria, 50 records remained. Conclusion: Genetic testing in patients suspected of having FH provided diagnostic certainty and permitted re-classification of many individuals with a probable diagnosis of FH. The limited genetic panel offered by Québec, focusing only on common French Canadian variants provided incomplete data in half of the cases. Our data supports unbiased genetic testing for a diagnosis of FH. The preliminary results of our single centre registry analysis revealed important sex differences in treatment and lipid level achievement in FH. The final selection of records of our systematic review will allow us to compare and contrast existing data on sex differences in treatment of FH. This review has the potential to reveal sex as a barrier to optimal treatment in FH. Identifying these imbalances will allow us to reduce barriers in care through educational initiatives, adequate training, and public advocacy to improve the quality of life and life expectancy of all individuals with FH
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".