African Americans May Have to Consume More Than 12 Grams a Day of Resistant Starch to Lower Their Risk for Type 2 Diabetes
Bibliographic record
Abstract
African Americans have a high prevalence rate of type 2 diabetes mellitus (DM). High-maize 260 (National Starch and Chemical Co., Bridgewater, NJ, USA) resistant starch (RS) is a promising food ingredient to reduce risk factors for type 2 DM. A 14-week, double-blind, crossover design study was conducted with African American male (n = 8) and female (n = 7) subjects at risk for type 2 DM. All subjects consumed bread containing 12 g of added RS or control bread (no added RS) for 6 weeks, separated by a 2-week washout period. There were no significant differences in the subjects' fasting plasma glucose levels due to the consumption of the RS bread versus the control bread. Fructosamine levels were significantly lower after consumption of both RS and control bread than at baseline. However, we found no significant difference in fructosamine levels due to treatment effects, i.e., RS bread intake versus the control bread. There were no significant differences in insulin or C-reactive protein levels due to treatment, gender, or sequence effects. Mean homeostasis model assessment of insulin resistance decreased to normal values (>2.5) at the end of the 14-week study, although there were no significant treatment effects. The results of this study suggest that African Americans may need to consume more than 12 g/day of RS to lower their risk for type 2 DM.
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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) | 0.005 | 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".