Background/Objectives: Epilepsy is a long-term condition that affects the brain and has a big impact on a person’s daily life, especially in areas where people do not have a lot of money or access to good healthcare. This study aimed to evaluate the relationship between medication adherence and QoL and to assess the role of seizure severity in the association between them among patients with epilepsy. Methods: A cross-sectional study of 1100 adult patients with epilepsy was conducted using registry data and structured interviews. The main outcomes that were assessed are quality of life (QoL), medication adherence, and seizure severity. Results: Reduced QoL was observed in 62% of patients. Low medication adherence was significantly associated with reduced QoL (OR = 4.33 [3.24–5.79] unadjusted; 3.90 [3.07–5.80] fully adjusted). Seizure severity was also associated with reduced QoL (OR = 1.62, p = 0.002; OR = 2.05, p < 0.001). Cognitive impairment showed the strongest association with reduced QoL, with ORs of 14.6 for mild and 80.8 for moderate-severe impairment in unadjusted models, remaining significant after adjustment. Medication adherence was significantly associated with seizure severity (OR = 1.18, p = 0.002), and attenuation of its effect after adjustment suggests that these variables are interrelated, although causality cannot be determined in this study. Additional factors associated with reduced QoL included lower education, longer disease duration, polytherapy, structural brain abnormalities, and comorbidities. Conclusions: Reduced QoL in epilepsy is strongly influenced by cognitive impairment and medication nonadherence, with seizure severity potentially contributing to this association, although causality cannot be inferred. These findings support integrated care strategies targeting adherence, cognition, and seizure control to improve patient outcomes.

Background/Objectives: In resource-limited regions, limited access to genetic testing may delay etiological diagnosis in selected patients with epilepsy and complex neurological manifestations, particularly when the phenotype raises suspicion of an underlying genetic etiology. This study aimed to assess the diagnostic value and clinical utility of phenotype-guided WES in selected patients aged ≥17 years with epilepsy and complex neurological manifestations in Southern Kazakhstan and to characterize clinical, therapeutic, and molecular features associated with drug-resistant epilepsy (DRE). Methods: This observational cohort included 78 patients aged ≥17 years with epilepsy and complex neurological manifestations identified through 25 outpatient medical centers. After enrollment, all patients underwent structured HPO-based phenotyping, assessment of antiseizure medication exposure, seizure burden, treatment response, and proband-only WES. Variants were interpreted according to ACMG/AMP guidelines. Exploratory multivariable logistic regression was used to assess factors associated with DRE. Results: P/LP variants were identified in 12/78 patients, corresponding to a diagnostic yield of 15.4%. VUS were detected in 16/78 patients (20.5%). DRE was present in 41 patients (52.6%), all of whom were receiving polytherapy. Carbamazepine was the most frequently used antiseizure medication (52/78, 66.7%), followed by valproic acid (32/78, 41.0%) and levetiracetam (20/78, 25.6%). Patients with DRE had earlier seizure onset, more frequent definite structural MRI abnormalities, and a higher proportion of P/LP variants. In the adjusted model, P/LP variant presence and definite structural MRI abnormality were associated with DRE. Conclusions: Phenotype-guided proband-only WES provided clinically relevant diagnostic information in a real-world epilepsy cohort with complex neurological manifestations in a resource-limited outpatient setting. The observed burden of DRE and polytherapy supports the potential value of integrating therapeutic profiling, neuroimaging, and genomic testing in the etiological evaluation of patients with epilepsy and additional neurological features.

Введение. Труднодиагностируемые неврологические заболевания у детей, включая эпилептические энцефалопатии (ЭЭ), нейродегенеративные заболевания (НДЗ) и комплексные неврологические фенотипы (КНФ), характеризуются высокой генетической гетерогенностью и клиническим полиморфизмом. Глубокое фенотипирование в сочетании с экзомным секвенированием может повысить точность диагностики и выявления молекулярно-генетических причин этих патологий. Целью настоящей работы является глубокое фенотипирование пациентов с ЭЭ, НДЗ, КНФ и выявление молекулярно-генетических причин их путем экзомного секвенирования среди детей Туркестанского региона для совершенствования диагностики. Методы. Проведено многоцентровое кросс-секционное исследование 79 детей с предположительно наследственной патологией, отобранных из 250 пациентов с труднодиагностируемыми неврологическими нарушениями. Диагностика включала клинико-неврологическое обследование, глубокое фенотипирование (точный и всесторонний анализ фенотипических отклонений) с использованием HPO и Phenomizer, экзомное секвенирование для выявления однонуклеотидных вариантов (SNV) и вариаций числа копий (CNV). Произведен частотный анализ - расчет описательных статистик для дискретных данных (средние величины - среднее арифметическое, относительные величины – интенсивный показатель) с использованием программного обеспечения IBM SPSS Statistics 29.0.1.0. Результаты. Экзомное секвенирование выявило генетические вариации у 51,9% пациентов: 87,8% SNV и 12,2% CNV. Патогенные и вероятно патогенные SNV составили 75%. Глубокое фенотипирование позволило классифицировать пациентов на группы: ЭЭ (15,2%), НДЗ (49,4%), КНФ (35,4%) и выявить частые клинические проявления (судорожный синдром – 68,3%, малые аномалии развития – 60,7%, тяжелые двигательные расстройства – 46,2%). Обратное фенотипирование подтвердило клиническую значимость выявленных мутаций. Выводы: Результаты показывают важность глубокого фенотипирования и экзомного секвенирования в диагностике редких неврологических заболеваний. Включение этих методов в клиническую практику повышает точность диагностики и способствует выявлению молекулярных механизмов патологий, особенно в регионах с ограниченным доступом к молекулярно-генетическим исследованиям. Исследование вносит вклад в глобальную геномику, расширяя представления о молекулярных основах редких неврологических заболеваний. Introduction. Hard-to-diagnose neurological disorders in children, including epileptic encephalopathies (EE), neurodegenerative diseases (NDD), and complex neurological phenotypes (CNP), are characterized by high genetic heterogeneity and clinical polymorphism. Deep phenotyping combined with exome sequencing can improve diagnostic accuracy and the identification of the molecular-genetic causes of these pathologies. This study aims to improve diagnostics by conducting deep phenotyping of patients with EE, NDD, and CNP and identifying their molecular-genetic causes through exome sequencing among children in the Turkestan region. Materials and Methods. A multicenter cross-sectional study was conducted on 79 children with suspected hereditary pathology, selected from a cohort of 250 patients with hard-to-diagnose neurological disorders. The diagnostic approach included clinical-neurological examination, deep phenotyping (precise and comprehensive analysis of phenotypic abnormalities) using HPO and Phenomizer, and exome sequencing to identify single nucleotide variants (SNVs) and copy number variations (CNVs). A frequency analysis was performed, calculating descriptive statistics for discrete data (mean values – arithmetic mean, relative values – intensive indicator) using IBM SPSS Statistics 29.0.1.0 software. Results. Exome sequencing revealed genetic variations in 51.9% of patients: 87.8% SNVs and 12.2% CNVs. Pathogenic and likely pathogenic SNVs accounted for 75%. Deep phenotyping enabled the classification of patients into groups: EE (15.2%), NDD (49.4%), and CNP (35.4%) and identified common clinical manifestations...