Authors :
Presenting Author: Temitayo Oyegbile-Chidi, MD, PhD – University of California Davis
Danielle Harvey, PhD – University of California Davis; David Dunn, MD – Indiana University; Jana Jones, PhD – University of Wisconsin School of Medicine and Public Health; Anna Byars, PhD – Cincinnati Children’s Hospital at the University of Cincinnati; Philip Fastenau, PhD – University Hospitals Cleveland Medical Center and Case Western Reserve University School of Medicine; Joan Austin, PhD – Indiana University; Bruce Hermann, PhD – University of Wisconsin School of Medicine and Public Health
Rationale:
Accumulating evidence indicates that children with new-onset epilepsies may exhibit co-morbidities including cognitive dysfunction, which adversely affects academic performance. Application of unsupervised machine learning techniques has demonstrated the presence of discrete cognitive phenotypes at or near the time of diagnosis, but there is limited knowledge of the subsequent course or trajectories of these cognitive phenotypes. Here we investigate the presence and progression of cognitive and academic status in youth with new-onset epilepsy and their siblings over a three year period.
Methods:
A total of 252 subjects (aged 6-16 years) were recruited within six weeks of their first recognized seizure along with 223 of their unaffected siblings. Each child underwent a comprehensive neuropsychological assessment evaluating intelligence, language, immediate & delayed verbal and visual memory, executive function, and speeded fine motor dexterity as well as targeted measures of academic achievement at baseline, 18 months later and 36 months from baseline. Factor analysis of the neuropsychological tests revealed four underlying factor domains – language, processing speed, executive function, and verbal memory. Using these factor scores, latent trajectory analysis identified categories within prototypical cognitive trajectories over 36 months. To determine clinical predictors, data on clinical epilepsy characteristics (age of onset, seizure syndrome, seizure frequency and anti-seizure medication), presence/absence of magnetic resonance imaging (MRI) and electroencephalography (EEG) abnormalities were collected in the children with epilepsy. In addition, sociodemographic data at baseline (child’s self-identified race, mother’s highest education level, household income, and parental marital status) were collected and collated into a socio-disadvantage index.
Results:
Three phenotypic groups with different cognitive trajectories over the 36-month period were identified: high average, low average, and borderline phenotypes. The high average phenotype exhibited the highest baseline Full-Scale IQ (mean IQ=112.4), neuropsychological factor scores and academic performance; while the borderline phenotype showed the polar opposite with the worst performances across all testing metrics (mean IQ = 81.52). Findings remained significant and stable over 36 months. Linear regression indicated that abnormal EEG and neurological examination were constant and stable predictors of these cognitive phenotype classification. In addition, the socio-disadvantage index also consistently predicted cognitive phenotype classification over the three year period.
Conclusions:
This study demonstrates the presence of diverse latent cognitive trajectory phenotypes over 36 months in youth with new-onset epilepsy that are associated with a stable neuropsychological and academic performance longitudinally. Both disease related and social factors are linked to these phenotypic groups. Future studies would be beneficial to determine if these cognitive phenotypic categories are modifiable with early intervention.
Funding:
NINDS (NS22416, J. Austin, P.I.)
NCATS (UL1 TR001860 and linked award KL2 TR001859, T. Oyegbile-Chidi)