The discovery and validation of
biomarkers in neurological and
neurodegenerative diseases is an important challenge for early diagnosis of disease and for the development of
therapeutics.
Epilepsy is often a consequence of brain insults such as
traumatic brain injury or
stroke, but as yet no
biomarker exists to predict the development of
epilepsy in patients at risk. Given the complexity of
epilepsy, it is unlikely that a single
biomarker is sufficient for this purpose, but a combinatorial approach may be needed to overcome the challenge of individual variability and disease heterogeneity. The goal of the present prospective study in the
lithium-
pilocarpine model of
epilepsy in rats was to determine the discriminative utility of combinations of phenotypic
biomarkers by examining their ability to predict
epilepsy. For this purpose, we used a recent model refinement that allows comparing rats that will or will not develop spontaneous recurrent
seizures (SRS) after
pilocarpine-induced
status epilepticus (SE). Potential
biomarkers included in our study were seizure threshold and seizure severity in response to timed i.v. infusion of
pentylenetetrazole (PTZ) and behavioral alterations determined by a battery of tests during the three weeks following SE. Three months after SE, video/EEG monitoring was used to determine which rats had developed SRS. To determine whether a
biomarker or combination of
biomarkers performed better than chance at predicting
epilepsy after SE, derived data underwent receiver operating characteristic (ROC) curve analyses. When comparing rats with and without SRS and
sham controls, the best intergroup discrimination was obtained by combining all measurements, resulting in a ROC area under curve (AUC) of 0.9592 (P<0.01), indicating an almost perfect discrimination or accuracy to predict development of SRS. These data indicate that a combinatorial
biomarker approach may overcome the challenge of individual variability in the prediction of
epilepsy.