623-P: Impact of a Low-Carbohydrate vs. Low-Fat Breakfast on Blood Glucose Control in Type 2 Diabetes
Bibliographic record
Abstract
Postprandial hyperglycemia and glycemic variability are independent risk factors for cardiovascular disease and mortality in people living with type 2 diabetes (T2D). The highest glucose spike often occurs after the first morning meal, highlighting the major influence that breakfast has on overall glycemic control. We examined how advice and guidance to consume a low-carbohydrate (LC) versus standard dietary guidelines low-fat control (CTL) breakfast influenced glycemic control assessed by continuous glucose monitoring (CGM). Participants with T2D (N=121, 53% female, mean age 64 years) completed a remote 3-month parallel-group RCT comparing LC to CTL with 14-day CGM at the start and end of the intervention. Daily mean glucose, maximum glucose, area under the curve (AUC), mean amplitude of glycemic excursions, standard deviation (SD), and time above range (>10mmol/L) were all significantly lower, and time in range (3.9-10mmol/L) significantly higher, in the LC group versus CTL (all P<0.05). Post-breakfast mean and maximum glucose, SD and iAUC were also significantly lower in the LC group (all P<0.001). A low-carbohydrate breakfast appears to be a simple dietary strategy to improve several CGM metrics when compared to a typical dietary guidelines breakfast in persons living with T2D. Disclosure B.Oliveira: None. C.Chang: None. K.Falkenhain: None. K.Oetsch: None. M.E.Francois: None. J.P.Little: None. Funding Egg Nutrition Center; Egg Farmers of Canada
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".