Letter to the Editor in response to the article “Prediction of future insulin deficiency in glutamic acid decarboxylase autoantibody enzyme‐linked immunosorbent assay‐positive patients with slowly progressive type 1 diabetes”

Dear Editor, To the best of our knowledge, the first study on “Prediction of future insulin deficiency in glutamic acid decarboxylase autoantibody enzyme-linked immunosorbent assay-positive patients with slowly progressive type 1 diabetes”, penned by Mr. Kawasaki et al.,1 was published in your honored journal. Their work provides valuable insights; however, methodological refinements could further strengthen the predictive model and clinical applicability. We highlight four key considerations: First, the optimal cut-off values for age, BMI, and fasting C-peptide were derived from ROC analysis in a cohort of 60 patients (including 30 progressors). The modest sample size may affect the stability of these cut-offs. Reporting bootstrapped confidence intervals, as recommended in diagnostic study guidelines2, could help assess their generalizability. Second, defining insulin deficiency by a single F-CPR value (<0.6 ng/mL) may not fully capture progressive β-cell decline. Longitudinal measurements could reduce misclassification3. Third, predictive values (positive predictive value [PPV] and negative predictive value [NPV]) are prevalence-dependent. The specialized Japanese type 1 diabetes database study (TIDE-J) registry may not reflect general slowly progressive insulin-dependent diabetes mellitus (SPIDDM) prevalence. Reporting sensitivity, specificity, and likelihood ratios would improve interpretability across settings. Finally, the combined predictive ability of the identified factors (age, BMI, F-CPR, insulinoma-associated antigen-2 autoantibodies [IA-2A]) is described only by an increase in positive predictive value. A formal multivariable model (such as logistic regression or a nomogram) would more rigorously quantify each variable's independent contribution and align with contemporary prognostic-model standards4. We believe that addressing these points will strengthen the conclusions of the study and provide a more reliable tool for prognostic assessment in SPIDDM. The author declares no conflict of interest. Approval of the research protocol: None. Informed consent: None. Registry and the registration no. of the study/trial: None. Animal studies: None. The data that support the findings of this study are available in PubMed at https://pubmed.ncbi.nlm.nih.gov/. These data were derived from the following resources available in the public domain: J Diabetes Investig 2024; 15: 835–842. https://doi.org/10.1111/jdi.14178.