NAT2 polymorphism associated with plasma glucose concentration in Canadian Oji-Cree
Bibliographic record
Abstract
The prevalence of type 2 diabetes in the Oji-Cree of Northern Ontario is among the highest of any population in the world. We previously demonstrated that markers D8S264 and D22S683 were both linked and associated with type 2 diabetes in the Oji-Cree. Among the possible candidate genes for type 2 diabetes and related traits on chromosomes 8p and 22q were NAT2 and CYP2D6, respectively. We thus explored the possible association of NAT2 and CYP2D6 alleles and diabetes-related traits in a sample of 112 Oji-Cree subjects with type 2 diabetes and 481 Oji-Cree subjects without type 2 diabetes. We found no difference in the allele and genotype frequencies of the NAT2 G191A, C282T, C481T, G590A, A803G and G857A, and the CYP2D6 G1934A polymorphisms between Oji-Cree subjects with and without type 2 diabetes. However, we found a significant association between the NAT2 C282T polymorphism and plasma fasting glucose concentration. Specifically, NAT2 282T/T homozygotes had significantly higher plasma fasting glucose than 282C/C homozygotes, and heterozygotes had intermediate levels of this trait. Thus, variation in NAT2 or CYP2D6 was not associated with the presence of type 2 diabetes, and would not be causative for this phenotype in Oji-Cree. However, NAT2 might be a 'modifier gene' affecting the level of glycaemia in non-diabetic subjects.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".