Tomofumi Takayoshi

Aims/Introduction: Maturity‐onset diabetes of the young (MODY) accounts for at least 1%–5% of diabetes cases and is usually caused by single gene variants. Accurate diagnosis of MODY is important for effective management, especially in young individuals who are lean and lack islet autoantibodies. Only a few MODY cases associated with multiple gene variants have been reported. We here describe a rare case of MODY with a pathogenic HNF1A variant and a coexisting NEUROD1 variant. Materials and Methods: We conducted an extensive clinical analysis and explored the complex genetic architecture of the case in order to provide insight into the etiology of MODY. The candidate variants were identified by comprehensive whole‐exome and Sanger sequencing, and their functional impact was assessed by analysis of their effects on the surface charge or structural stability of HNF1A and NEUROD1. Results: The proband, a 21‐year‐old woman with early‐onset diabetes, was found to harbor a pathogenic HNF1A p.Arg159Trp variant and a coexisting NEUROD1 p.Arg98Ser variant of uncertain significance. The NEUROD1 variant was also present in her mother and sister, both of whom manifested impaired glucose tolerance, but was absent in her father and brother. Structural analyses suggested that the HNF1A variant alters the surface charge of the protein, whereas the NEUROD1 variant may affect protein structural stability. Conclusion: We have identified an unusual case of MODY with a pathogenic HNF1A variant and a coexisting NEUROD1 variant of uncertain significance, and structural analyses suggested that the NEUROD1 variant may affect protein structure.

Background: Although several studies have evaluated the impact of prolonged infusion set use in insulin pump users on glycemic management with the use of continuous glucose monitoring (CGM), real-world assessments without intervention have been unavailable. Methods: This retrospective observational study recruited individuals with type 1 diabetes who received insulin pump therapy with real-time CGM. Insulin pump and CGM logs were extracted from the Medtronic CareLink system, and a dataset was constructed programmatically, counting tracking days from infusion set replacement every 24 hours up to day 4. The primary outcome was mean sensor glucose (SG) level, and the impact of infusion set usage duration on glycemic management was assessed. Results: The study enrolled 45 individuals with a median age of 40 (interquartile range = 32-51) years and median body mass index of 22.5 (21.2-23.8) kg/m². Mean SG was significantly higher on day 4 (median of 151.9 [136.5-173.7] mg/dL) than on day 2 (144.4 [124.0-162.9] mg/dL, P = .024). Similarly, time above range (TAR), time in range (TIR), and time in tight range (TITR) had worsened on day 4 compared with day 2. The TAR increased from a median of 22.0% (7.3%-35.8%) on day 2 to 27.6% (17.7%-44.9%) on day 4, whereas TIR decreased from 74.2% (59.9%-87.1%) to 66.5% (52.0%-79.8%) and TITR decreased from 47.6% (37.1%-67.5%) to 42.2% (34.6%-54.1%). Conclusions: Our evaluation of the real-world impact of prolonged infusion set use revealed an association between longer use and worsening of glycemic management.

Objective: Type A insulin resistance syndrome (IRS), characterized by impaired insulin receptor function due to variants of the insulin receptor gene, manifests as severe insulin-resistant diabetes. Differentiation of type A IRS from type 2 diabetes on the basis of hyperinsulinemia can be challenging. Given the association between insulin receptor dysfunction and reduced insulin clearance, we evaluated the potential of the circulating C-peptide reactivity (CPR)/immunoreactive insulin (IRI) molar ratio, a marker of insulin clearance, for distinguishing type A IRS from type 2 diabetes. Methods: We retrospectively analyzed CPR and IRI levels measured during a 75-g oral glucose tolerance test (OGTT) in 18 individuals with type A IRS and 126 with type 2 diabetes. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic performance of the CPR/IRI molar ratio and IRI levels. Results: IRI levels were significantly higher and the CPR/IRI molar ratio significantly lower in individuals with type A IRS compared with those with type 2 diabetes. The area under the ROC curve for the CPR/IRI molar ratio at baseline, 1 hour, and 2 hours after OGTT initiation was 0.997 (sensitivity 100%, specificity 99.2%), 0.999 (sensitivity 100%, specificity 97.6%), and 0.997 (sensitivity 100%, specificity 95.1%), respectively. The CPR/IRI molar ratio demonstrated robust diagnostic performance regardless of body mass index or hyperinsulinemia severity. Conclusion: The CPR/IRI molar ratio, both at baseline and during OGTT, exhibited higher sensitivity and specificity than IRI levels alone for distinguishing type A IRS from type 2 diabetes. This ratio may serve as a reliable clinical marker for early and accurate diagnosis of type A IRS.

Context: Adverse events related to insulin therapy remain common in individuals with type 1 diabetes (T1D). Optimization of insulin dose can reduce the frequency of these events and help to prevent macrovascular and microvascular complications. Objective: The aim of the present study was to develop machine-learning models to predict the total daily dose (TDD) of insulin on the basis of data available in routine clinical practice, to evaluate the performance of the models, and to interpret the relation between its predictions and features. Methods: This retrospective observational study conducted at a single center recruited individuals diagnosed with T1D who visited Kobe University Hospital and used continuous glucose monitoring in combination with continuous subcutaneous insulin infusion between April 1, 2010 and February 29, 2024. We developed TDD prediction models based on machine learning and evaluated its performance on the basis of the mean absolute percentage error (MAPE). Explainable artificial intelligence frameworks were applied to the machine-learning model to facilitate interpretability. Results: A total of 110 individuals with T1D were included in the study. The best-performing model, the random forest, achieved a MAPE of 19.8%. The most important feature of the model for prediction of the TDD of insulin was body weight, followed by waist circumference and carbohydrate intake. Conclusion: We here developed machine-learning models that predict the TDD of insulin from clinical information. Such models could contribute to the treatment of many individuals undergoing insulin therapy, with further developments being warranted.