Insulin autoimmune syndrome induced by methimazole in a patient with Graves' disease

Pitfalls in the diagnosis of insulin autoimmune syndrome (Hirata's disease) in a hypoglycemic child: a case report and review of the literature

Background Insulin autoimmune syndrome (IAS) is a rare cause of hyperinsulinemic hypoglycemia (HH) not addressed as a potential differential diagnosis in current pediatric guidelines. We present a case of IAS in a child with no previous history of autoimmune disease, no previous intake of triggering medications and absence of genetic predisposition. Case presentation A 6-year-old boy presented with recurrent HH (blood glucose of 26 mg/dL [1.4 mmol/L] and insulin of 686 μU/mL). Abdominal imaging was normal. After multiple therapeutic failures, we hypothesized misuse of exogenous insulin and factitious hypoglycemia. Council of Guardianship had the child separated from his mother, but insulin levels remained high. A chromatography test was then performed which showed high titers of endogenous insulin autoantibody (IAA) with early dissociation from the insulin molecule. The human leukocyte antigen (HLA) test showed a DRB1 *13:01/*08:02 genotype. The patient was advised to control food intake and physical activity routines. During a 5-year follow-up, hypoglycemic episodes were sparse, despite high insulin levels. Conclusions Misdiagnosis of IAS with factitious hypoglycemia may happen if IAS is not considered as a differential diagnosis, leading to potential traumatic consequences. Further efforts should be made to increase awareness of IAS as a differential diagnosis of hypoglycemia and to include it in pediatric guidelines.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools

Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9-90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0-82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.