Nutrient intakes and dysglycaemia in populations of West African origin
Bibliographic record
Abstract
Examining the relationship between glucose intolerance and dietary intake in genetically similar populations with different dietary patterns and rates of type 2 diabetes may provide important insights into the role of diet in the pathogenesis of this disease. The objective of the present study was to assess the relationship between dietary variables and dysglycaemia/type 2 diabetes among three populations of African origin. The study design consists of a cross-sectional study of men and women of African descent aged 24-74 years from Cameroon (n 1790), Jamaica (n 857) and Manchester, UK (n 258) who were not known to have diabetes. Each participant had anthropometric measurements and underwent a 2 h 75 g oral glucose tolerance test. Habitual dietary intake was estimated with quantitative FFQ, developed specifically for each country. The age-adjusted prevalence of undiagnosed type 2 diabetes in Cameroon was low (1·1 %), but it was higher in Jamaica (11·6 %) and the UK (12·6 %). Adjusted generalised linear and latent mixed models used to obtain OR indicated that each 1·0 % increment in energy from protein, total fat and saturated fats significantly increased the odds of type 2 diabetes by 9 (95 % CI 1·02, 1·16) %, 5 (95 % CI, 1·01, 1·08) % and 16 (95 % CI 1·08, 1·25) %, respectively. A 1 % increase in energy from carbohydrates and a 0·1 unit increment in the PUFA:SFA ratio were associated with significantly reduced odds of type 2 diabetes. The results show independent effects of dietary factors on hyperglycaemia in African origin populations. Whether modifying intake of specific macronutrients helps diabetes prevention needs testing in randomised trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".