Genetic and Clinical Characteristics of Monogenic Diabetes in Japan: A Nationwide Study by the Japan Diabetes Society
Bibliographic record
Abstract
CONTEXT: Monogenic diabetes is often underdiagnosed because of limited genetic testing opportunities and varying screening criteria. OBJECTIVE: To investigate the genetic and clinical characteristics of monogenic diabetes in Japan and assess the utility of classical screening criteria and the maturity-onset diabetes of the young (MODY) probability calculator. DESIGN AND SETTING: This study included 232 probands with diabetes onset before age 35, body mass index <30 kg/m2, and negative islet autoantibodies, recruited from 2019 to 2024. Targeted sequencing of 11 causal genes was performed, followed by multiplex ligation-dependent probe amplification when indicated. RESULTS: Pathogenic or likely pathogenic (P/LP) variants were identified in 67 (28.9%) probands: 25 in GCK, 22 in HNF1A, 7 in HNF1B, 6 in HNF4A, 4 in ABCC8, and 1 each in NEUROD1, PDX1, and INSR. Of these, 64 (95.5%) carried P/LP variants in actionable genes potentially affecting treatment strategies (GCK, HNF1A, HNF1B, HNF4A, ABCC8). P/LP variant carriers were younger at diagnosis, had lower body mass index, and better metabolic control than noncarriers. However, clinical heterogeneity was substantial. Notably, 35 cases (52.2%) did not meet classical screening criteria of young onset (≤25 years) and a three-generation family history. Although MODY probability scores were higher in probands with P/LP variants (median: 75.5% vs 58.0%; P < .001), early insulin initiation substantially lowered the probability scores, warranting caution. CONCLUSION: We established a nationwide genetic testing platform and identified carriers of actionable variants, offering possibilities for precision medicine. Neither classical criteria nor the MODY probability calculator identified all monogenic cases, highlighting the need for broader genetic testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".