Prevalence and phenotypic features of diabetes due to recessive, non-syndromic WFS1 mutations
Bibliographic record
Abstract
OBJECTIVE: Recessive WFS1 mutations are known to cause Wolfram syndrome, a very rare systemic disorder. However, they were also found in non-syndromic diabetes in Han Chinese misdiagnosed with type 1 diabetes (T1D), a molecular cause that appears to be considerably more common than the fully expressed syndrome. We aimed to better define the incidence and clinical features of non-syndromic diabetes due to recessive WFS1 mutation. DESIGN: We analyzed the genotype and phenotype of 320 consecutive incident Chinese pediatric diabetic patients diagnosed from 2016 to 2019 to search for non-syndromic diabetic cases due to recessive WFS1 mutation. METHODS: A cohort of 105 pancreatic autoantibody-negative patients were recruited for exome sequencing. All patients tested positive for pathogenic diallelic WFS1 mutations were examined for phenotypic features (fundoscopy, audiogram, and urine density). RESULTS: We found three cases of non-syndromic diabetes due to recessive WFS1 mutations (incidence = 0.94% (95% CI: 0.25-2.7%)). All three cases only had mild diabetes when diagnosed. All patients had well-conserved fasting C-peptide when diagnosed but one of them progressed to T1D-like insulin deficiency. In addition, we found a fourth case with previously undetected features of Wolfram syndrome. CONCLUSIONS: Non-syndromic diabetes due to WFS1 mutation may be common among Chinese pediatric patients with diabetes. It is important to differentiate it from other maturity-onset diabetes in the young subtypes with similar phenotype by molecular diagnosis because of different prognosis and, potentially, therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".