Journal of Neuroendocrinology · 2024 · 10 citations · 15 references
MedicineEndocrinologySurgeryClinical EpidemiologyEpidemiologic MethodsEpidemiologyCancer RegistriesMorbidity AnalysisObesity EpidemiologyObesity ManagementWeight ManagementAnnual IncidenceClaims Database AnalysisHypothalamic DisordersObesity As A Selection CriterionObesity IncidenceSellar Tumors
Hypothalamic obesity (HO) is defined as abnormal weight gain resulting in severe persistent obesity due to physical, tumor- and/or treatment-related damage to the hypothalamus. HO epidemiology is poorly understood. We developed a database algorithm supporting the standardized identification of tumor/treatment-related HO (TTR-HO) patients. The algorithm is used to estimate incidence rates of TTR-HO patients in the German healthcare context from a representative claims database (n = 5.42 million) covering 2010-2020. Patients were identified based on surgery/radiotherapy procedures and HO-associated tumor diagnoses (n = 3976). HO was defined by incident obesity and validated based on incident diabetes insipidus diagnoses and desmopressin prescription within a 12-month period after surgery/radiotherapy. Uncertainty due to algorithm definitions is explored in sensitivity analyses. Estimated annual incidence of TTR-HO in Germany is between 0.7 and 1.7 cases per 1,000,000 persons (2019 prevalence: n = 1262 patients). With observed cases in all age groups, two HO-incidence peaks are identified: children/young adults aged 10-24 years and adults aged 40-44 years. Most frequent HO-validated tumor diagnoses are benign sellar/suprasellar tumors (6.1/1,000,000 persons over 9 years), including tumors of the craniopharyngeal duct (1.3/1,000,000), neoplasms of the pituitary gland (4.1/1,000,000), and nonspecific brain tumors of endocrine glands (2.4/1,000,000). This is the first real-world database analysis of TTR-HO epidemiology, refining current estimates of HO epidemiology and early patient identification. A more comprehensive characterization of patients with HO as well as a better understanding of clinical implications will be crucial in developing optimal treatment strategies to improve patient outcomes.
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