Total<i>n</i>-3 fatty acid and SFA intakes in relation to insulin resistance in a Canadian First Nation at risk for the development of type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: The present study sought to investigate the associations of total n-3 fatty acid and SFA intakes with insulin resistance in a Canadian First Nation sample at risk for type 2 diabetes. DESIGN: Fasting values for glucose and insulin were used to estimate insulin resistance by homeostasis model assessment (HOMA-IR). Intakes of n-3 fatty acids and SFA were computed from dietary food and drink data obtained using 3 d food records. Associations between HOMA-IR and dietary n-3 and SFA consumption were tested using linear regression models accounting for age, sex, community, education, physical activity, waist circumference, fibre, protein and carbohydrate intakes, and HDL-cholesterol and TAG concentrations. SETTING: Rural Okanagan region of British Columbia, Canada. SUBJECTS: On-reserve First Nation individuals (Interior Salishan) aged 18 years and over, recruited for community-based diabetes screening and determined to be normoglycaemic (n 126). RESULTS: HOMA-IR was negatively associated with dietary n-3 fatty acid intake (β = -0·22; 95 % CI -0·39, -0·04; P = 0·016) and positively associated with dietary SFA intake (β = 0·34; 95 % CI 0·15, 0·53; P = 0·0 0 1). CONCLUSIONS: Intake of dietary n-3 fatty acids may be protective against whereas SFA intake may promote insulin resistance in this high-risk Canadian First Nation sample. Reduced dietary SFA intake and greater n-3 fatty acid intake may assist the prevention of glycaemic disease among First Nations peoples. More rigorous, controlled trials are required to test whether dietary supplementation with n-3 fatty acids in natural or supplement-based form might reduce diabetes risk in high-risk aboriginal groups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".