Supplementary Material for: Evidence of Interaction between Type 2 Diabetes Susceptibility Genes and Dietary Fat Intake for Adiposity and Glucose Homeostasis-Related Phenotypes
Bibliographic record
Abstract
Background/Aims: Genome-wide association studies have led to the identification of several susceptibility genes for type 2 diabetes mellitus (T2DM). The objective of this study was to test the hypothesis that the associations between single nucleotide polymorphisms (SNPs) in these genes and adiposity and glucose homeostasis-related phenotypes are influenced by dietary fat intake. Methods: Thirty-three SNPs in 9 T2DM genes (CDKAL1, CDKN2A/B, HHEX, HNF1B, IGF2BP2, KCNJ11, SLC30A8, TCF7L2 and WFS1) were tested in a maximum of 669 subjects from the Quebec Family Study. Subjects were measured for several adiposity indices and underwent a 75-gram oral glucose tolerance test. Total fat intake was estimated from a 3-day dietary record. Results: We observed 13 significant (p ≤ 0.01) SNP-dietary fat interactions. Among them, IGF2BP2 rs4402960, alone or in interaction with dietary fat intake, influenced abdominal total fat (ATF: SNP effect, p = 0.006, interaction effect, p = 0.009) and abdominal visceral fat (AVF: SNP effect, p = 0.007, interaction effect, p = 0.01). Similarly, TCF7L2 rs12573128 alone or in interaction with dietary fat intake, influenced insulin sensitivity (SNP effect and interaction effect, p ≤ 0.008) and glucose tolerance (SNP effect p ≤ 0.009 and interaction effect, p ≤ 0.01). Conclusion: These results suggest that gene-dietary fat interactions may influence glucose homeostasis-related phenotypes and play an important role in determining the increased risk of diabetes associated with the T2DM susceptibility genes.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.852 | 0.312 |
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".