Intestinal lipoprotein overproduction in insulin-resistant states
Bibliographic record
Abstract
PURPOSE OF REVIEW: Excessive postprandial lipemia is highly prevalent in obese and insulin-resistant/type 2 diabetic individuals and substantially increases the risk of atherosclerosis and cardiovascular disease. This article will review our current understanding of the link between insulin resistance and intestinal lipoprotein overproduction and highlight some of the key recent findings in the field. RECENT FINDINGS: Emerging evidence from several animal models of insulin resistance as well as insulin-resistant humans clearly supports the link between insulin resistance and aberrant intestinal lipoprotein metabolism. In insulin-resistant states, elevated free fatty acid flux into the intestine, downregulation of intestinal insulin signaling and upregulation of microsomal triglyceride transfer protein all appear to stimulate intestinal lipoprotein production. Gut peptides, GLP-1 and GLP-2, may be important regulators of intestinal lipid absorption and lipoprotein production. SUMMARY: Available evidence in humans and animal models strongly favors the concept that the small intestine is not merely an absorptive organ but rather plays an active role in regulating the rate of production of triglyceride-rich lipoproteins. Metabolic signals in insulin resistance and type 2 diabetes and in some cases an aberrant intestinal response to these factors all contribute to the enhanced formation and secretion of triglyceride-rich lipoproteins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".