Genetic screening for individuals at high risk for type 1 diabetes in the general population using HLA Class II alleles as disease markers. A comparison between three European populations with variable rates of disease incidence
Bibliographic record
Abstract
BACKGROUND: To develop screening strategies for identification of individuals at increased genetic risk for type 1 diabetes in three populations with variable disease incidence rates and distinct ethnic origin. METHODS: A stepwise HLA DQB1-DQA1-DRB1-based screening approach was evaluated. Patients with childhood-onset type 1 diabetes were recruited from Finland (n = 1739), Hungary (n = 149), and Greece (n = 119). Consecutive newborns (2568 from Finland and 1047 from Greece) or healthy schoolchildren (n = 177 from Hungary) served as controls. RESULTS: The DQB1*02/0302 genotype conferred the highest disease risk in all populations. The DQB1*02/y (y not equal DQB1*0301,*0302,*0602,*0603, *0604) genotypes were more common and conferred a higher disease risk in the Greek population (OR 4.9) compared to the Finns (OR 1.2). DQB1*0302/x (x not equal DQB1*02, *0301, *0602, *0603, *0604) genotypes were, in contrast, more prevalent among Finnish cases (32.7%) as compared to Hungarians (18.1%) or Greeks (13.5%). The protective DQB1*0602 or *0603 positive genotypes were most common in the Finns, while DQB1*0301 was more common in Hungarians and Greeks. In all groups, DQA1 and DRB1*04 typing considerably increased the sensitivity of the DQB1-based screening. The different high-risk genotype combinations present in about 10% of the background population had a diagnostic sensitivity of 60% in Finland and 80% in Hungary and Greece. CONCLUSIONS: HLA DR-DQ-based screening is a feasible tool for the identification of individuals at increased genetic risk for type 1 diabetes in populations with diverse genetic background. The risk markers should, however, be individually selected for the target population since the screening efficiency of various markers is highly dependent on the ethnic group studied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".