Starch chemistry affects the kinetics of glucose and SCFA absorption, and insulin and incretin release in pigs
Bibliographic record
Abstract
The objective was to understand the role of starch chemistry on kinetics of glucose and SCFA absorption, and on insulin and incretin release. Four purified starches (crystallinity, 24 to 40%; granule size, 12 to 229 sq. μm; amylose, 0 to 80%; resistant starch, 1.9 to 70.2%; reflected by max. rate of in vitro digestion: from slow, 0.22; 0.38; 1.02; to rapid digestible, 1.92%/min) were included at 70% in diets fed to 4 porto‐arterial catheterized pigs (38.7 ± 1.7 kg BW). Slow vs. rapid digestible starch lowered ( P <0.05) postprandial (PP) net portal appearance (NPA) of glucose 53%, insulin and C‐peptide (peak and 0.5 to 1 h) 55%, NPA glucose to insulin ratio (2 to 10 h) 71%, portal GIP (2 to 2.5 h) 81%, and GLP‐1 (1.5 to 2 h) 36%, and it increased ( P <0.01) NPA of SCFA 196%. Incremental area under the curve (iAUC) of insulin NPA was linearly positively related ( P <0.01) with iAUC of glucose (R 2 =0.49) and incretins NPA (R 2 =0.27) and iAUC of incretin NPA was similarly related with iAUC of glucose (R 2 =0.78) and SCFA NPA (R 2 =0.35) in a regression model. Addition of predicted gastric emptying in the model improved the relation of iAUC of insulin NPA with iAUC of incretins and glucose NPA (R 2 =0.86). Conclusion: slow digestible starch reduces glucose and insulin responses and increases SCFA absorption. Glucose absorption, incretin release, and gastric emptying appear important factors for insulin release. Funding Source: ALMA, Provimi, Alberta Pulse Growers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".