Endocrine Abstracts

JOINT1920

Introduction: Obesity results from the interaction of genetic factors with numerous environmental factors. The comprehension of the molecular mechanisms of obesity progressed enormously in the last years thanks to the development of faster and more precise genetic screening tools applied in cohort studies or in examinations with focus on subjects and their families. In particular, whole-exome sequencing showed its power to identify new syndromes associated with obesity or new forms of obesity due to a single naturally occurring dysfunctional gene (i.e. monogenic obesity). In particular, obesity is a global medical and social issue, leading to complications such as cardiovascular diseases, type 2 diabetes mellitus, and oncological pathologies. One of the main challenges is the accurate diagnosis of obesity type, as different forms (endocrine, hypothalamic, alimentary, genetic, etc.) require specific treatment approaches. The implementation of innovative technologies, such as chatbots, can significantly improve the quality of diagnosis and treatment.

The aim of the study: To improve diagnosis of obesity types using a TypeObese Telegram bot to increase patient-centered treatment and improve the efficiency of medical care.

Materials and Methods: Based on literature analysis, international guidelines, and clinical observations, an algorithm for the differential diagnosis of obesity types was developed. This algorithm was implemented in the form of the TypeObese Telegram bot, which applies an individualized approach to each patient. The bot collects clinical data, analyzes examination results, and provides recommendations for additional diagnostics.

Results: The TypeObese Telegram bot supports physicians of various specialties working with obese patients. Its functionality includes іystematic collection of information on body mass, lifestyle, medical history, and comorbidities, differentiation of endocrine, hypothalamic, and other types of obesity, recommendations for further examinations, such as hormone, leptin, and insulin level analyses. Testing of TypeObese demonstrated its usability in both primary and secondary healthcare settings, high accuracy in identifying obesity types, reduction of cognitive load on physicians, and increased awareness of rare obesity forms, such as hypothalamic or genetic obesity.

Conclusions: TypeObese is an effective tool for optimizing the diagnosis of obesity types. Its implementation enhances treatment individualization, improves healthcare efficiency, and helps prevent complications. The integration of similar solutions into clinical practice is recommended to improve obesity diagnosis and management.