Dietary patterns in Greenland and their relationship with type 2 diabetes mellitus and glucose intolerance
Bibliographic record
Abstract
OBJECTIVE: Traditional Inuit dietary patterns have been found to be beneficial for CVD but have not been investigated in relation to glucose intolerance. We examined the association between dietary patterns and type 2 diabetes mellitus (T2DM), impaired glucose tolerance (IGT) and impaired fasting glucose (IFG). DESIGN: Cross-sectional design with a priori derived dietary patterns from an FFQ resulted in five patterns: imported meat (n 196), traditional food (n 601), balanced diet (n 126), unhealthy diet (n 652) and standard diet (n 799). SETTING: Associations between dietary patterns and glucose-related outcomes were tested by linear and logistic regression analyses. Data included: dietary intake by FFQ, waist circumference, ethnicity, frequency of alcohol intake and smoking, physical activity, and oral glucose tolerance test results. Fasting participants and those without diagnosed T2DM were classified into normal glucose tolerance, IGT, IFG or T2DM. HOMA-IR (homeostatic model assessment-insulin resistance index) and HOMA-β (homeostatic model assessment of β-cell function) were calculated. SUBJECTS: Data included 2374 Inuit, aged 18+ years. RESULTS: Participants with a traditional dietary pattern had higher fasting plasma glucose (mean 5·73 (95% CI 5·68, 5·78) mmol/l, P < 0·0001) and lowest HOMA-β (48·66 (95% CI 46·86, 50·40), P < 0·0001). The traditional diet gave significantly higher odds for IFG and T2DM than the balanced diet, imported meat diet, standard diet and unhealthy diet. CONCLUSIONS: Traditional food was positively associated with T2DM, IFG and fasting plasma glucose, and negatively associated with β-cell function, compared with a standard diet. The imported meat diet seemed the best in relation to glucose intolerance, with lowest fasting plasma glucose and lowest odds for IFG and T2DM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".