The Low-Density Lipoprotein Receptor Genotype Is a Significant Determinant of the Rebound in Low-Density Lipoprotein Cholesterol Concentration After Lipoprotein Apheresis Among Patients With Homozygous Familial Hypercholesterolemia
Bibliographic record
Abstract
I n homozygous familial hypercholesterolemia (HoFH) caused by mutations in the low-density lipoprotein (LDL) receptor (LDLR) gene, patients with 2 receptornegative mutations have higher cholesterol concentrations and coronary heart disease risk than patients with double receptor-defective mutations.1 Pharmacological treatment is insufficient to achieve an efficient reduction in LDL-cholesterol (C) or lipoprotein (a) (Lp(a)) concentrations in patients with HoFH, and repetitive long-term lipoprotein apheresis (LA) remains the gold-standard therapy.LA induces an acute decrease in LDL-C and Lp(a) concentrations, which is then followed by a rebound in the following days.The rebound after LA constitutes a major determinant of LA efficacy because it directly affects the average concentrations between treatments, considered the best estimate of the physiological effects of long-term LA. 2 However, our understanding of the determinants of rebound after treatment in LDL-C and Lp(a) is limited. 2 This study aimed to determine the extent to which the LDLR genotype modulates the rebound after LA in LDL-C and Lp(a) concentrations among patients with HoFH.We hypothesized that the rebound in LDL-C and Lp(a) concentrations is greater among patients with receptor-negative HoFH than among patients with receptor-defective HoFH.Data on all consecutive LA treatments performed between August 2008 and February 2016 among patients with HoFH with genetically defined defective/defective LDLR mutations (n=3), negative/negative LDLR mutations (n=8), and defective/negative LDLR mutations (n=4) treated at the CHU de Québec-Université Laval were collected.For each patient, the compiled data included: (1) date of LA, (2) cumulative number of LA treatments received, (3) interval between LA treatments, (4) LA system used, (5) volume of filtered plasma per treatment, (6) duration of treatments, (7) lipoprotein concentrations before and after LA, and (8) cumulative interval since the first compiled LA treatment.Data on LDL-C and Lp(a) rebound covered 1999 and 1567 treatments, respectively.The rebound was calculated as the percentage difference between concentrations after LA and before the subsequent LA treatment.Mixed models for repeated measures with patients as a random effect were used for statistics.The study was approved by the Laval University Medical Center ethical review committee, and informed consent was obtained from each patient.At baseline, patients (34.2±14.3 years of age; women, n=8/15; coronary heart disease history, n=8/15) were treated with a maximally tolerated dose of statin (atorvastatin: 80 mg, n=7; 40 mg, n=1; rosuvastatin: 40 mg, n=6; 5 mg, n=1) and ezetimibe and had cutaneous and tendinous xanthomas.Patients were French-Canadian (n=13), Lebanese (n=1), and Hondurian (n=1).The LDLR genotype was significantly associated with LDL-C rebound (P=0.003).Negative/negative patients had a greater mean rebound in LDL-C concentrations compared with defective/defective patients and defective/negative patients,
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".