Clinical, Metabolic, and Immunoinflammatory Characteristics of Adult-Onset Diabetes at Diagnosis: A Cross-Sectional Study in the Kazakh Population
Clinical, Metabolic, and Immunoinflammatory Characteristics of Adult-Onset Diabetes at Diagnosis: A Cross-Sectional Study in the Kazakh Population
The goal of this observational study is to better understand how diabetes develops in adults at the time of diagnosis. The study focuses on adults with newly diagnosed diabetes in the Kazakh population.
The main questions it aims to answer are:
Participants undergo clinical and laboratory assessments at diagnosis. These include:
Adult-onset diabetes is increasingly recognized as a heterogeneous condition with diverse underlying pathophysiological mechanisms. At the time of diagnosis, individuals may present with varying degrees of insulin resistance, impaired beta-cell function, and immune activation. However, traditional classifications based mainly on glycemic parameters do not fully capture this complexity.
This study aims to provide an integrated assessment of metabolic and immunoinflammatory features in adults with newly diagnosed diabetes. Particular attention is given to the interplay between insulin resistance, beta-cell function, and immune-mediated processes, including islet autoimmunity and inflammatory signaling pathways.
The study focuses on key metabolic indicators such as body mass index, glycemic control, C-peptide levels, and indices derived from the HOMA2 model, as well as immunological markers including islet autoantibodies and cytokines involved in the interleukin-1 pathway. The balance between pro-inflammatory and anti-inflammatory activity is assessed through the IL-1β/IL-1Ra ratio.
By integrating metabolic and immunological data, this study seeks to better characterize early-stage heterogeneity of adult-onset diabetes and to contribute to improved phenotypic classification. A more comprehensive understanding of these mechanisms may support the development of more personalized approaches to diagnosis and disease management in diverse populations.
Inclusion Criteria:
Exclusion Criteria: