Continuous spectrum of glucose dysmetabolism due to the <scp><i>KCNJ11</i></scp> gene mutation—Case reports and review of the literature
Bibliographic record
Abstract
) channel, which plays a key role in insulin secretion. Monogenic diseases caused by KCNJ11 gene mutation are rare and easily misdiagnosed. It has been shown that mutations in the KCNJ11 gene are associated with neonatal diabetes mellitus (NDM), maturity-onset diabetes of the young 13 (MODY13), type 2 diabetes mellitus (T2DM), and hyperinsulinemic hypoglycemia. We report four patients with KCNJ11 gene mutations and provide a systematic review of the literature. A boy with diabetes onset at the age of 1 month was misdiagnosed as type 1 diabetes mellitus (T1DM) for 12 years and received insulin therapy continuously, resulting in poor glycemic control. He was diagnosed as NDM with KCNJ11 E322K gene mutation, and glibenclamide was given to replace exogenous insulin. The successful transfer time was 4 months, much longer than the previous unsuccessful standard of 4 weeks. The other three patients were two sisters and their mother; the younger sister was misdiagnosed with T1DM at 13 years old, while the elder sister was diagnosed with diabetes (type undefined) at 16 years old. They were treated with insulin for 3 years, with poor glycemic control. Their mother was diagnosed with T2DM and achieved good glycemia control with glimepiride. They were diagnosed as MODY13 because of the autosomal dominant inheritance of two generations, early onset of diabetes before 25 years of age in the two sisters, and the presence of the KCNJ11 N48D gene mutation. All patients successfully transferred to sulfonylureas with excellent glycemic control. Therefore, the wide spectrum of clinical phenotypes of glucose dysmetabolism caused by KCNJ11 should be recognized to reduce misdiagnosis and implement appropriate treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".