A common polymorphism near the interleukin-6 gene modifies the association between dietary fat intake and insulin sensitivity
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggests a role for inflammation in the development of type 2 diabetes. Elevated levels of inflammatory cytokines, including interleukin-6, have been associated with insulin resistance, and dietary lipids can increase cytokine production. The objective of this study was to determine whether a single nucleotide polymorphism near the IL6 gene (rs7801406) modifies the relationship between dietary fat and markers of insulin sensitivity. METHODS: Subjects were healthy men and women aged 20-29 years from the Toronto Nutrigenomics and Health Study. Dietary intake was estimated using a one-month semiquantitative food frequency questionnaire. Fasting blood samples were taken for genotyping and biomarker measurement. RESULTS: The single nucleotide polymorphism was not associated with any of the measures of insulin sensitivity. However, it modified the relationship between total dietary fat and the homeostasis model assessment of insulin resistance (P = 0.053 for interaction). Total fat intake was positively related to HOMA-IR in individuals homozygous for the G allele (β = 0.005 ± 0.002, P = 0.03), but not among heterozygotes. There was an inverse relationship between total fat intake and HOMA-IR in individuals who were homozygous for the A allele (β = -0.012 ± 0.006, P = 0.047). CONCLUSION: These findings suggest that dietary fat influences insulin sensitivity differently depending on genotype.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".