The Effect of PCSK9 Loss-of-Function Variants on the Postprandial Lipid and ApoB-Lipoprotein Response
Bibliographic record
Abstract
Context: Proprotein convertase subtilisin kexin 9 (PCSK9) mediates degradation of the low-density lipoprotein receptor (LDLR), thereby increasing plasma low-density lipoprotein cholesterol (LDL-C). Variations in the PCSK9 gene associated with loss of function (LOF) of PCSK9 result in greater expression of hepatic LDLR, lower concentrations of LDL-C, and protection from cardiovascular disease (CVD). Apolipoprotein-B (apoB) remnants also contribute to CVD risk and are similarly cleared by the LDLR. We hypothesized that PCSK9-LOF carriers would have lower fasting and postprandial remnant lipoproteins on top of lower LDL-C. Objective: To compare fasting and postprandial concentrations of triglycerides (TGs), total apoB, and apoB48 as indicators of remnant lipoprotein metabolism in PCSK9-LOF carriers with those with no PCSK9 variants. Design: Case-control, metabolic study. Setting: Clinical Research Center of The Ottawa Hospital. Participants: Persons with one or more copies of the L10ins/A53V and/or I474V and/or R46L PCSK9 variant and persons with no PCSK9 variants. Intervention: Oral fat tolerance test. Main Outcomes Measures: Fasting and postprandial plasma TG, apoB48, total apoB, total cholesterol, and PCSK9 were measured at 0, 2, 4, and 6 hours after an oral fat load. Results: Participants with PCSK9-LOF variants (n = 22) had reduced fasting LDL-C (-14%) as well as lower fasting TG (-21%) compared with noncarrier controls (n = 23). LOF variants also had reduced postprandial total apoB (-17%), apoB48 (-23%), and TG (-18%). Postprandial PCSK9 declined in both groups (-24% vs -16%, respectively). Conclusions: Participants carrying PCSK9-LOF variants had attenuated levels of fasting and postprandial TG, apoB48, and total apoB. This may confer protection from CVD and further validate the use of PCSK9 inhibitors to lower CVD risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".