Nobiletin Prevents High-Fat Diet-Induced Dysregulation of Intestinal Lipid Metabolism and Attenuates Postprandial Lipemia
Bibliographic record
Abstract
Objective: Nobiletin is a dietary flavonoid that improves insulin resistance and atherosclerosis in mice with metabolic dysfunction. Dysregulation of intestinal lipoprotein metabolism contributes to atherogenesis. The objective of the study was to determine if nobiletin targets the intestine to improve metabolic dysregulation in both male and female mice. Approach and Results: Triglyceride-rich lipoprotein (TRL) secretion, intracellular triglyceride kinetics, and intestinal morphology were determined in male and female LDL (low-density lipoprotein) receptor knockout ( Ldlr –/– ), and male wild-type mice fed a standard laboratory diet or high-fat, high-cholesterol (HFHC) diet ± nobiletin using an olive oil gavage, radiotracers, and electron microscopy. Nobiletin attenuated postprandial TRL levels in plasma and enhanced TRL clearance. Nobiletin reduced fasting jejunal triglyceride accumulation through accelerated TRL secretion and lower jejunal fatty acid synthesis with no impact on fatty acid oxidation. Fasting-refeeding experiments revealed that nobiletin led to higher levels of phosphorylated AKT (protein kinase B) and FoxO1 (forkhead box O1) and normal Srebf1c expression indicating increased insulin sensitivity. Intestinal length and weight were diminished by HFHC feeding and restored by nobiletin. Both fasting and postprandial plasma GLP-1 (glucagon-like peptide-1; and likely GLP-2) were elevated in response to nobiletin. Treatment with a GLP-2 receptor antagonist, GLP-2(3-33), reduced villus length in HFHC-fed mice but did not impact TRL secretion in any diet group. In contrast to males, nobiletin did not improve postprandial lipid parameters in female mice. Conclusions: Nobiletin opposed the effects of the HFHC diet by normalizing intestinal de novo lipogenesis through improved insulin sensitivity. Nobiletin prevents postprandial lipemia because the enhanced TRL clearance more than compensates for increased TRL secretion.
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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) | 0.000 | 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 | 0.001 |
| 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".