Minimum effective sodium valproate dose in genetic generalized epilepsies.

INTRODUCTION: Sodium valproate (VPA) is the most effective antiseizure medication (ASM) in genetic generalized epilepsies (GGEs). However, the frequent adverse effects and the high risk inflicted on the exposed offspring make it imperative to search for the lowest daily VPA dose able to control seizures for most patients. In the current published series, the VPA value of <1000 mg was the most adopted. OBJECTIVE: This study aims to provide a cutoff VPA value below which a given daily dose can be considered a low dose in patients with GGEs. METHODS: This retrospective, observational cohort study included patients with clinical and electroencephalographic diagnoses of GGEs based on the ILAE criteria. Patients were followed up for at least two years using VPA in mono- or polytherapy. Clinical data, VPA dose, and associated ASMs were analyzed. Adverse effects were also evaluated. We related seizure control to VPA doses through uni- and multivariate statistical analyses. RESULTS: From 225 patients, 169 (75%) had good seizure control, with most (60%) receiving monotherapy. The cutoff daily VPA dose capable of distinguishing these patients from those without seizure control was up to 1000 mg (p = 0.006) in univariate analyses and up to 700 mg in multivariate analyses. For patients in polytherapy, the cutoff was up to 1750 mg and 1800 mg in uni- and multivariate analyses, respectively. CONCLUSIONS: The lowest daily VPA dose in monotherapy able to control seizures for most GGE patients was up to 700 mg, a value that can be used as a low dose criterion in studies assessing the therapeutic VPA ranges. Patients using higher VPA doses or in polytherapy present a lower probability of seizure control.

Association Analysis of Candidate Variants in Admixed Brazilian Patients With Genetic Generalized Epilepsies.

Genetic generalized epilepsies (GGEs) include well-established epilepsy syndromes with generalized onset seizures: childhood absence epilepsy, juvenile myoclonic epilepsy (JME), juvenile absence epilepsy (JAE), myoclonic absence epilepsy, epilepsy with eyelid myoclonia (Jeavons syndrome), generalized tonic-clonic seizures, and generalized tonic-clonic seizures alone. Genome-wide association studies (GWASs) and exome sequencing have identified 48 single-nucleotide polymorphisms (SNPs) associated with GGE. However, these studies were mainly based on non-admixed, European, and Asian populations. Thus, it remains unclear whether these results apply to patients of other origins. This study aims to evaluate whether these previous results could be replicated in a cohort of admixed Brazilian patients with GGE. We obtained SNP-array data from 87 patients with GGE, compared with 340 controls from the BIPMed public dataset. We could directly access genotypes of 17 candidate SNPs, available in the SNP array, and the remaining 31 SNPs were imputed using the BEAGLE v5.1 software. We performed an association test by logistic regression analysis, including the first five principal components as covariates. Furthermore, to expand the analysis of the candidate regions, we also interrogated 14,047 SNPs that flank the candidate SNPs (1 Mb). The statistical power was evaluated in terms of odds ratio and minor allele frequency (MAF) by the genpwr package. Differences in SNP frequencies between Brazilian and Europeans, sub-Saharan African, and Native Americans were evaluated by a two-proportion Z-test. We identified nine flanking SNPs, located on eight candidate regions, which presented association signals that passed the Bonferroni correction (rs12726617; rs9428842; rs1915992; rs1464634; rs6459526; rs2510087; rs9551042; rs9888879; and rs8133217; p-values <3.55e-06). In addition, the two-proportion Z-test indicates that the lack of association of the remaining candidate SNPs could be due to different genomic backgrounds observed in admixed Brazilians. This is the first time that candidate SNPs for GGE are analyzed in an admixed Brazilian population, and we could successfully replicate the association signals in eight candidate regions. In addition, our results provide new insights on how we can account for population structure to improve risk stratification estimation in admixed individuals.