Starch Source Influences Dietary Glucose Generation at the Mucosal α-Glucosidase Level
Bibliographic record
Abstract
The quality of starch digestion, related to the rate and extent of release of dietary glucose, is associated with glycemia-related problems such as diabetes and other metabolic syndrome conditions. Here, we found that the rate of glucose generation from starch is unexpectedly associated with mucosal α-glucosidases and not just α-amylase. This understanding could lead to a new approach to regulate the glycemic response and glucose-related physiologic responses in the human body. There are six digestive enzymes for starch: salivary and pancreatic α-amylases and four mucosal α-glucosidases, including N- and C-terminal subunits of both maltase-glucoamylase and sucrase-isomaltase. Only the mucosal α-glucosidases provide the final hydrolytic activities to produce substantial free glucose. We report here the unique and shared roles of the individual α-glucosidases for α-glucans persisting after starch is extensively hydrolyzed by α-amylase (to produce α-limit dextrins (α-LDx)). All four α-glucosidases share digestion of linear regions of α-LDx, and three can hydrolyze branched fractions. The α-LDx, which were derived from different maize cultivars, were not all equally digested, revealing that the starch source influences glucose generation at the mucosal α-glucosidase level. We further discovered a fraction of α-LDx that was resistant to the extensive digestion by the mucosal α-glucosidases. Our study further challenges the conventional view that α-amylase is the only rate-determining enzyme involved in starch digestion and better defines the roles of individual and collective mucosal α-glucosidases. Strategies to control the rate of glucogenesis at the mucosal level could lead to regulation of the glycemic response and improved glucose management in the human body.
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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 | 0.000 |
| 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".