The relation of low glycaemic index fruit consumption to glycaemic control and risk factors for coronary heart disease in type 2 diabetes
Bibliographic record
Abstract
AIMS/HYPOTHESIS: Sugar has been suggested to promote obesity, diabetes and coronary heart disease (CHD), yet fruit, despite containing sugars, may also have a low glycaemic index (GI) and all fruits are generally recommended for good health. We therefore assessed the effect of fruit with special emphasis on low GI fruit intake in type 2 diabetes. METHODS: This secondary analysis involved 152 type 2 diabetic participants treated with glucose-lowering agents who completed either 6 months of high fibre or low GI dietary advice, including fruit advice, in a parallel design. RESULTS: Change in low GI fruit intake ranged from -3.1 to 2.7 servings/day. The increase in low GI fruit intake significantly predicted reductions in HbA(1c) (r = -0.206, p =0.011), systolic blood pressure (r = -0.183, p = 0.024) and CHD risk (r = -0.213, p = 0.008). Change in total fruit intake ranged from -3.7 to 3.2 servings/day and was not related to study outcomes. In a regression analysis including the eight major carbohydrate foods or classes of foods emphasised in the low GI diet, only low GI fruit and bread contributed independently and significantly to predicting change in HbA(1c). Furthermore, comparing the highest with the lowest quartile of low GI fruit intake, the percentage change in HbA(1c) was reduced by -0.5% HbA(1c) units (95% CI 0.2-0.8 HbA(1c) units, p < 0.001). CONCLUSIONS/INTERPRETATION: Low GI fruit consumption as part of a low GI diet was associated with lower HbA(1c), blood pressure and CHD risk and supports a role for low GI fruit consumption in the management of type 2 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT00438698.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".