Glutamate Cysteine Ligase Catalytic Subunit Promoter Polymorphisms and Associations with Type 1 Diabetes Age-at-onset and GAD65 Autoantibody Levels
Bibliographic record
Abstract
The purpose of this study was to test the hypothesis that glutamate cysteine ligase catalytic subunit (GCLC) promoter polymorphisms are susceptibility factors for type 1 diabetes (T1D), T1D age-at-onset and T1D autoantibodies. T1D patients and control subjects from the Swedish Childhood Diabetes Registry and the Swedish Diabetes Incidence Study registry were genotyped for two GCLC promoter polymorphisms; the GCLC -129 C to T single nucleotide polymorphism (GCLC -129 SNP) and the GCLC GAG trinucleotide repeat polymorphism (GCLC TNR). Glutamate decarboxylase antibody (GAD65Ab) positive T1D patients with the GCLC -129 SNP C/T genotype have increased GAD65Ab levels (p-value, <0.05) compared to the GCLC -129 SNP C/C genotype. T1D patients with an age-at-onset of 14-35 years who possess the GCLC -129 SNP T/T genotype have a higher GAD65Ab index than T1D patients with the GCLC -129 SNP C/C genotype (p-value <0.05). In addition, T1D patients with an age-at-onset of 14-35 years possess the GCLC TNR 7/8 genotype at a lower frequency than the control subjects (OR, 0.33, 95% CI, 0.13-0.82). The GCLC -129 SNP and GCLC TNR appear to be in linkage disequilibrium (p-value<0.0001). These results suggest that GCLC promoter polymorphisms may influence GAD65Ab levels and may influence the age at which T1D is diagnosed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".