Systemic disease manifestations associated with epilepsy in tuberous sclerosis complex

Epilepsy is one of the most disabling symptoms of tuberous sclerosis complex (TSC) and is a leading cause of morbidity and mortality in affected individuals. The relationship between systemic disease manifestations and the presence of epilepsy has not been thoroughly investigated. This study utilizes a multicenter TSC Natural History Database including 1,816 individuals to test the hypothesis that systemic disease manifestations of TSC are associated with epilepsy.

Univariate analysis was used to identify patient characteristics (e.g., age, gender, race, and TSC mutation status) associated with the presence of epilepsy. Individual logistic regression models were built to examine the association between epilepsy and each candidate systemic or neurologic disease variable, controlling for the patient characteristics found to be significant on univariate analysis. Finally, a multivariable logistic regression model was constructed, using the variables found to be significant on the individual analyses as well as the patient characteristics that were significant on univariate analysis.

Nearly 88% of our cohort had a history of epilepsy. After adjusting for age, gender, and TSC mutation status, multiple systemic disease manifestations including cardiac rhabdomyomas (odds ratio [OR] 2.3, 95% confidence interval [CI] 1.3-3.9, p = 0.002), retinal hamartomas (OR 2.1, CI 1.0-4.3, p = 0.04), renal cysts (OR 2.1, CI 1.3-3.4, p = 0.002), renal angiomyolipomas (OR 3.0, CI 1.8-5.1, p < 0.001), shagreen patches (OR 1.7, CI 1.0-2.7, p = 0.04), and facial angiofibromas (OR 1.7, CI 1.1-2.9, p = 0.03) were associated with a higher likelihood of epilepsy. In the multivariable logistic regression model, cardiac rhabdomyomas (OR 1.9, CI 1.0-3.5, p = 0.04) remained significantly associated with the presence of epilepsy.

The identification of systemic disease manifestations such as cardiac rhabdomyomas that confer a higher risk of epilepsy development in TSC could contribute to disease prognostication and assist in the identification of individuals who may receive maximal benefit from potentially novel, targeted, preventative therapies.