Clinical and laboratory profile of children with suspected monogenic diabetes

Abstract

Introduction: Diabetes mellitus (DM) is a common clinical condition with multiple presentations and the differential diagnosis between type 1, type 2 and Maturity Onset Diabetes of the Young (MODY) is a frequent challenge. MODY is a rare type of monogenic diabetes, characterized by a distinct hereditary pattern and specific diagnostic criteria. Objective: This study aims to describe the clinical and laboratory profile of patients with suspected MODY assisted at the Childhood Diabetes Outpatient Clinic (AENDI- Ambulatório de Diabetes Infantil) of the Clinical Hospital of the Faculty of Medicine of Ribeirão Preto - USP (HC-FMRP-USP- Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto – USP). Methods: This is a cross-sectional retrospective study carried out between 2012 and 2022, comprising the analysis of 12 medical records of patients with suspected MODY assisted at AENDI. The data collected included were as follows: demographic and anthropometric data, symptoms, treatment, laboratory exams, and family background, alongside the use of the MODY prediction calculator. The analyses were conducted using descriptive statistics. Results: Out of all the patients assisted at AENDI, only 1.25% were classified with suspected MODY. No patient in the sample was diagnosed with obesity. Polydipsia, polyphagia, polyuria and weight loss were observed in 22.22%, 25%, 44.44% and 28.57% cases, respectively. Drug therapy was not needed in 66.67% patients; however, when indicated, 50% required oral antidiabetic drugs. Fasting glycemia and glycated hemoglobin mean were 128 mg/dL and 6.02%, respectively. Family history of diabetes in first-and-second degree relatives was positive in 91.66% cases. The molecular analysis of genes associated with MODY was carried out, but the results are not available yet. Conclusion: The findings suggest that the MODY prediction calculator is a useful tool to screen patients with high MODY probability. The creation of specific treatments to track and monitor such patients is essential to secure accurate diagnosis and proper clinical management.