Dietary Resistant Starch Supplementation Increases High-Density Lipoprotein Particle Number in Pigs Fed a Western Diet
Bibliographic record
Abstract
Resistant starch (RS) has been well characterized for its glycemic control properties; however, there is little consensus regarding the influence of RS on blood lipid concentrations and lipoprotein distribution and size. Therefore, this study aimed to characterize the effect of daily RS supplementation in a controlled capsule delivery on biomarkers of cardiovascular (blood lipids, lipoproteins) and diabetes (glucose, insulin) risk in a pig model. Twelve 8-week-old male Yorkshire pigs were placed on a synthetic Western diet and randomly divided into two groups (n = 6/group) for 30 days: (1) a placebo group supplemented with capsules containing unmodified pre-gelatinized potato starch (0 g/RS/day); and (2) an RS group supplemented with capsules containing resistant potato starch (10 g/RS/day). Serum lipids including total-cholesterol (C), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides did not differ (p > 0.05) between the RS and placebo groups. Although the total numbers of very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) particles were similar (p > 0.05) between the two groups, total high-density lipoprotein (HDL) particles were higher (+28%, p < 0.05) in the RS group compared with placebo, resulting from an increase (p < 0.05) in the small HDL subclass particles (+32%). Compared with the placebo group, RS supplementation lowered (p < 0.05) fasting serum glucose (-20%) and improved (p < 0.05) insulin resistance as estimated by Homeostatic Model Assessment-Insulin Resistance (HOMA-IR) without a change in insulin. Additionally, total serum glucagon-like-peptide 1 (GLP-1) was higher (+141%, p < 0.05) following RS supplementation compared with placebo. This data suggests that in addition to the more well-characterized effect of RS intake in lowering blood glucose and improving insulin sensitivity, the consumption of RS may be beneficial in lipid management strategies by enhancing total HDL particle number.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".