Associations Between Soluble LDLR and Lipoproteins in a White Cohort and the Effect of PCSK9 Loss-of-Function
Bibliographic record
Abstract
Context: Elevated circulating cholesterol-rich low-density lipoprotein (LDL) particles increase coronary artery disease risk. Cell-surface hepatic LDL receptors (LDLRs) clear 70% of these particles from circulation. The ectodomain of LDLR is shed into circulation, preventing it from removing LDL particles. The role that LDLR ectodomain shedding plays as a regulatory mechanism is unknown. Objective: We describe LDLR shedding via the relationships between circulating soluble LDLRs (sLDLRs) and serum lipoproteins, serum proprotein convertase subtilin/kexin type 9 (PCSK9; a negative regulator of LDLR), and clinical parameters in a white Canadian population. Design: Population-based, cross-sectional study. Settings: Clinical Research Center, The Ottawa Hospital, and Faculty of Medicine, University of Ottawa. Participants: Two hundred seventy-three white Canadians. Intervention: None. Main Outcome Measures: sLDLR measured by ELISA; serum lipids and PCSK9, PCSK9 genotypes, and clinical parameters from previous analyses. Results: sLDLRs correlated strongly with triglycerides (TG; r = 0.624, P < 0.0001) and moderately with LDL cholesterol (r = 0.384, P < 0.0001), and high-density lipoprotein cholesterol (r = -0.307, P = 0.0003). Only TG correlations were unaffected by PCSK9 variations. sLDLR levels were significantly elevated in those with TG >50th or LDL cholesterol >75th percentiles. Conclusions: Serum sLDLR levels correlate with several lipoprotein parameters, especially TG, and the presence of PCSK9 loss-of-function variants alters sLDLR levels and correlations, except for TG. Ectodomain LDLR shedding has a role in LDL metabolism, distinct from PCSK9, with interplay between these two pathways that regulate cell-surface LDLRs. Findings suggest alteration of LDLR shedding could emerge as a target to treat dyslipidemia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".