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 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) | 0.000 | 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".