Role of the Gut in Diabetic Dyslipidemia
Bibliographic record
Abstract
Type 2 diabetes (T2D) is associated with increased risk of cardiovascular disease (CVD). In insulin resistant states such as the metabolic syndrome, overproduction and impaired clearance of liver-derived very-low-density lipoproteins and gut-derived chylomicrons (CMs) contribute to hypertriglyceridemia and elevated atherogenic remnant lipoproteins. Although ingested fat is the major stimulus of CM secretion, intestinal lipid handling and ultimately CM secretory rate is determined by numerous additional regulatory inputs including nutrients, hormones and neural signals that fine tune CM secretion during fasted and fed states. Insulin resistance and T2D represent perturbed metabolic states in which intestinal sensitivity to key regulatory hormones such as insulin, leptin and glucagon-like peptide-1 (GLP-1) may be altered, contributing to increased CM secretion. In this review, we describe the evidence from human and animal models demonstrating increased CM secretion in insulin resistance and T2D and discuss the molecular mechanisms underlying these effects. Several novel compounds are in various stages of preclinical and clinical investigation to modulate intestinal CM synthesis and secretion. Their efficacy, safety and therapeutic utility are discussed. Similarly, the effects of currently approved lipid modulating therapies such as statins, ezetimibe, fibrates, and PCSK9 inhibitors on intestinal CM production are discussed. The intricacies of intestinal CM production are an active area of research that may yield novel therapies to prevent atherosclerotic CVD in insulin resistance and T2D.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".