Genetic Landscape of Common Epilepsies: Advancing towards Precision in Treatment.

Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.

Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population.

PURPOSE: This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). METHODS: Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into 'good responder (GR)' and 'poor responder (PR)' based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models. RESULTS: Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework. SIGNIFICANCE: Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier's predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.

Serum copper in Alzheimer's disease and vascular dementia.

Alzheimer's disease is the most common form of dementia in the elderly and it's prevalence is rapidly rising. Oxidative stress plays important role in the pathophysiology of Alzheimer's disease. Metals like copper, iron derived through diet can act as pro-oxidant under oxidative stress. In the present study, serum copper levels were evaluated in 50 patients with Alzheimer's disease, 24 patients with Vascular Dementia and 30 controls. All the groups were also investigated for serum ceruloplsmin levels. The mean copper levels in Alzheimer's disease and Vascular Dementia were significantly raised compared to controls. An attempt has been made to study the relationship of serum copper with ceruloplasmin. Our study found weak correlation between copper and ceruloplasmin levels in Alzheimer's disease and Vascular Dementia.

A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson's disease.

BACKGROUND: PD is a progressive neurodegenerative disorder commonly treated by levodopa. The findings from genetic studies on adverse effects (ADRs) and levodopa efficacy are mostly inconclusive. Here, we aim to identify predictive genetic biomarkers for levodopa response (LR) and determine common molecular link with disease susceptibility. A systematic review for LR was conducted for ADR, and drug efficacy, independently. All included articles were assessed for methodological quality on 14 parameters. GWAS of PD were also reviewed. Protein-protein interaction (PPI) analysis using STRING and functional enrichment using WebGestalt was performed to explore the common link between LR and PD. RESULTS: From 37 candidate studies on levodopa toxicity, 18 genes were found associated, of which, CAn STR 13, 14 (DRD2) was most significantly associated with dyskinesia, followed by rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Similarly, 8 studies on efficacy resulted in 4 genes in which rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were significant. To establish the molecular connection between LR with PD, we identified 35 genes significantly associated with PD. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed. Among the 67 nodes (263 edges) in LR, and 62 nodes (190 edges) in PD pathophysiology, UBC, SNCA, FYN, SRC, CAMK2A, and SLC6A3 were identified as common potential candidates. CONCLUSION: Our study revealed the genetically significant polymorphism concerning the ADRs and levodopa efficacy. The six common genes may be used as predictive markers for therapy optimization and as putative drug target candidates.

Effect of apolipoprotein E (APO E) polymorphism on leptin in Alzheimer's disease.

BACKGROUND: Leptin, a 16 kDa peptide hormone synthesized and secreted specifically from white adipose cells protects neurons against amyloid ß-induced toxicity, by increasing Apolipoprotein E (APO E)-dependent uptake of ß amyloid into the cells, thereby, protect individuals from developing Alzheimer's disease (AD). The APO E ε4 allele is a known genetic risk factor for AD by accelerating onset. It is estimated that the lifetime risk of developing AD increases to 29% for carriers with one ε4 allele and 9% for those with no ε4 allele. OBJECTIVES: To determine the levels of serum leptin, cholesterol, low density lipoprotein (LDL-C), and high density lipoprotein (HDL-C) in the diagnosed cases of AD and the association of them with cognitive decline and Apolipoprotein E (APO E) genotypes in AD. MATERIALS AND METHODS: Serum levels of serum leptin, cholesterol, LDL-C, and HDL-C along with APO E polymorphism were studied in 39 subjects with probable AD and 42 cognitive normal individuals. RESULTS: AD group showed significantly lower levels of leptin (P = 0.00) as compared to control group. However, there was no significant difference in cholesterol, triglycerides, LDL-C, and HDL-C levels in AD and control groups. The frequency of ε4 allele in AD (38.5%) was found to be significantly higher than in control (10.3%). ε3 allele was more frequent than ε4 allele in AD and control group.