Objective: Epilepsies are severe chronic neurological diseases that impair several domains in life and are often accompanied by various somatic and psychiatric comorbidities. Associations between epilepsy and its comorbidities remain poorly understood. As epidemiological research mainly relies on cross-sectional designs and descriptive results, homogeneities regarding comorbidities in individuals suffering from epilepsy remain uncovered. Therefore, we aimed to identify clusters of individuals based on selected seizure-related variables and somatic comorbidities, and their respective risk of experiencing affective disorders, using a Latent Class Analysis (LCA). Methods: Latent class analysis, is a model-driven statistical approach, which aims at latent, unobservable clusters on selected disease features. LCA has therefore the potential for uncovering previously unobservable groups or classes with similar comorbidity patterns. It allows for comparisons between those classes regarding risk or promotive factors – such as affective disorders. Our data derives from the Austrian cohort of the European Study on Burden and Care of Epilepsy (ESBACE; http://www.esbace.eu/). In ESBACE, multiple factors were collected to get a detailed picture on prevalence, epilepsy-related variables and comorbidities in a population-based cohort from the region of Salzburg, Austria. We used LCA to identify epilepsy-somatic-comorbidity-clusters and further, compared them to the observed the risk of suffering from affective disorders. Results: The prevalence of epilepsy in the study region was 9.14/1000 inhabitants. LCA unveiled a three-cluster solution, of which one cluster, mainly consisting of individuals with mixed seizure types, higher age, and discrete somatic comorbidities (stroke, cardiovascular – and respiratory/pulmonary diseases) had a higher risk of experiencing affective disorders. Significance: To our knowledge, this is the first large scale study that uses LCA to identify epilepsy-related comorbidity phenotypes, and therefore it might open a new way for epidemiological research.
- Affective disorders
- Latent class analysis