Genetic basis for idiosyncratic reactions to antiepileptic drugs.

PURPOSE OF REVIEW: In recent years, there has been an explosion of genetic research in epilepsy, including a search for genetic markers of adverse reactions to antiepileptic drugs. This article will focus on recent findings concerning genetic factors affecting susceptibility to idiosyncratic reactions to antiepileptic drugs. RECENT FINDINGS: Recent studies have investigated the role of genetic factors in the development of antiepileptic drug-induced cutaneous reactions, carbamazepine and valproate-induced liver toxicity, vigabatrin-induced visual field defects, and antiepileptic drug-induced teratogenicity. The greatest progress has been an improved definition of the role of human leukocyte antigen-related genes as predictors of the risk of serious antiepileptic drug-induced cutaneous reactions. This has led to the recommendation that patients of Asian ancestry be tested for the HLA-B*1502 allele, in order to identify those at high risk of developing Stevens-Johnson syndrome and toxic epidermal necrolysis after administration of carbamazepine and, possibly, phenytoin and other antiepileptic drugs. SUMMARY: Future research will probably lead to discovery of additional genetic predictors of susceptibility to adverse reactions to antiepileptic drugs. Identification of genetic markers should, in turn, allow unravelling of the molecular mechanisms underlying these reactions. Ultimately, these advances should lead not only to improved personalization of therapy but also to development of safer drugs

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings