Abstract 521: Impact of Insulin-resistance, Body Mass Index and Dietary Fat Intakes on Apolipoprotein B-48 Kinetic
Bibliographic record
Abstract
Substantial evidence exists indicating that elevated plasma levels of apolipoprotein (apo) B-48-containing lipoproteins are associated with increased cardiovascular disease risk. Overaccumulation of apoB-48-containing lipoproteins of intestinal origin observed in patients with insulin-resistance (IR) is thought to be attributable to both elevated intestinal production and reduced clearance of these lipoproteins. The objective of this study was to assess the relative contribution of IR, anthropometry, and dietary fat content on triglyceride (TG)-rich lipoprotein (TRL) apoB-48 kinetic. The in vivo kinetic of TRL apoB-48 was measured in 131 men with primed-constant infusion of L-[5,5,5-D3]leucine. TRL apoB-48 production rate (PR) and fractional catabolism rate (FCR) were determined by multi-compartmental modeling, while TRL apoB-48 pool size (PS) was measured by ELISA. IR was defined by TG levels > 1.5 mmol/L and Homeostasis Model Assessment of IR (HOMA-IR) > 2.5. Age was inversely associated with TRL apoB-48 FCR (ρ=-0.21; P=0.02), while BMI was positively correlated with TRL apoB-48 PR (ρ=0.33; P=0.0001) and PS (ρ=0.44; P<0.0001). Fat intake was associated with TRL apoB-48 PR (ρ=0.34; P<0.0001) and PS (ρ=0.34; P<0.0001). HOMA-IR was positively associated with TRL apoB-48 PR (ρ=0.26; P=0.005) and PS (ρ=0.35; P=0.0001). Furthermore, when adjusted for fat intake, TRL apoB-48 PR was significantly higher in IR subjects (n=73) than in insulin-sensitive subjects (Δ=+64%; P<0.0001), and TRL apoB-48 FCR was lower (Δ=-13%; P=0.005). Multiple linear regression analysis showed that IR was the most significant constitutive predictor of TRL apo B-48 PR variance (6.6%; P=0.01), while age and BMI were not significantly associated with PR. In conclusion, these results suggest that IR is a major factor contributing to TRL apoB-48 overaccumulation, possibly through increased production and decreased clearance, independent of age, BMI and fat intake.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".