Genetics of C-Peptide and Age at Diagnosis in Type 1 Diabetes
Bibliographic record
Abstract
Identified genetic loci for C-peptide and age at diagnosis (AAD) in individuals with type 1 diabetes (T1D) explain only a small proportion of their variation. Here, we aimed to perform large meta–genome-wide association studies (GWAS) of C-peptide and AAD in T1D and to identify the HLA allele/haplotypes associated with C-peptide and AAD. A total of 7,252 and 7,923 European individuals with T1D were included in C-peptide and AAD GWAS, respectively. HLA-DQB1*06:02, which is strongly protective against T1D, was associated with higher C-peptide. HLA-DQB1*03:02, HLA-DRB1*03:01, and HLA-A*24:02, which increase T1D risk, were independently associated with younger AAD. HLA-DR3-DR4 haplotype combination, the strongest T1D susceptibility factor, was associated with younger AAD. Outside the HLA region, rs115673528 on chromosome 5 (Chr5) (GABRG2) was associated with C-peptide, and an indel, rs111970692, on Chr15 within CTSH, a known T1D locus, was associated with AAD. Genetically predicted CTSH expression, methylation, and protein levels were associated with AAD. Mendelian randomization analysis suggested that higher levels of pro-cathepsin H reduced AAD. In conclusion, some HLA allele/haplotypes associated with T1D also contribute to variability of C-peptide and AAD. Outside HLA, T1D loci were generally not associated with C-peptide or AAD. CTSH could be a potential therapeutic target to delay development/progression of T1D. Article Highlights Identified genetic loci for C-peptide and type 1 diabetes (T1D) age at diagnosis (AAD) explain only a small proportion of their variation. We aimed to identify additional genetic loci associated with C-peptide and AAD. Some HLA allele/haplotypes associated with T1D also contributed to variability of C-peptide and AAD, whereas outside the HLA region, T1D loci were mostly not associated with C-peptide or AAD. Genetic variation within CTSH can affect AAD. There is still residual heritability of C-peptide and AAD outside of HLA that could benefit from larger meta–genome-wide association studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".