Plasma PCSK9 correlates with apoB-48-containing triglyceride-rich lipoprotein production in men with insulin resistance
Bibliographic record
Abstract
Intestinal triglyceride (TG)-rich lipoproteins (TRLs) are important in the pathogenesis of atherosclerosis in insulin resistance (IR). We investigated the association of plasma proprotein convertase subtilisin/kexin type 9 (PCSK9) concentrations with apoB-48-containing TRL metabolism in 148 men displaying various degrees of IR by measuring in vivo kinetics of TRL apoB-48 during a constant-fed state after a primed-constant infusion of L-[5,5,5-D3]leucine. Plasma PCSK9 concentrations positively correlated with TRL apoB-48 pool size ( r = 0.31, P = 0.0002) and production rate ( r = 0.24, P = 0.008) but not the fractional catabolic rate ( r = −0.04, P = 0.6). Backward stepwise multiple linear regression analysis identified PCSK9 concentrations as a positive predictor of TRL apoB-48 production rate (standard β = +0.20, P = 0.007) independent of BMI, age, T2D/metformin use, dietary fat intake during the kinetic study, and fasting concentrations of TGs, insulin, glucose, LDL cholesterol, or C-reactive protein. We also assessed intestinal expression of key genes involved in chylomicron processing from duodenal samples of 71 men. Expression of PCSK9 and HMG-CoAR genes was positively associated ( r = 0.43, P = 0.002). These results support PCSK9 association with intestinal secretion and plasma overaccumulation of TRL apoB-48 in men with IR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".