Heart rate and autonomic biomarkers distinguish convulsive epileptic vs. functional or dissociative seizures.

OBJECTIVE: 20-40% of individuals whose seizures are not controlled by anti-seizure medications exhibit manifestations comparable to epileptic seizures (ES), but there are no EEG correlates. These events are called functional or dissociative seizures (FDS). Due to limited access to EEG-monitoring and inconclusive results, we aimed to develop an alternative diagnostic tool that distinguishes ES vs. FDS. We evaluated the temporal evolution of ECG-based measures of autonomic function (heart rate variability, HRV) to determine whether they distinguish ES vs. FDS. METHODS: The prospective study includes patients admitted to the University of Rochester Epilepsy Monitoring Unit. Participants are 18-65 years old, without therapies or co-morbidities associated with altered autonomics. A habitual ES or FDS is recorded during admission. HRV analysis is performed to evaluate the temporal changes in autonomic function during the peri­ictal period (150-minutes each pre-/post-ictal). We determined if autonomic measures distinguish ES vs. FDS. RESULTS: The study includes 53 ES and 46 FDS. Temporal evolution of HR and autonomics significantly differ surrounding ES vs. FDS. The pre-to-post-ictal change (delta) in HR differs surrounding ES vs. FDS, stratified for convulsive and non-convulsive events. Post-ictal HR, total autonomic (SDNN & Total Power), vagal (RMSSD & HF), and baroreflex (LF) function differ for convulsive ES vs. convulsive FDS. HR distinguishes non-convulsive ES vs. non-convulsive FDS with ROC>0.7, sensitivity>70%, but specificity<50%. HR-delta and post-ictal HR, SDNN, RMSSD, LF, HF, and Total Power each distinguish convulsive ES vs. convulsive FDS (ROC, 0.83-0.98). Models with HR-delta and post-ictal HR provide the highest diagnostic accuracy for convulsive ES vs. convulsive FDS: 92% sensitivity, 94% specificity, ROC 0.99). SIGNIFICANCE: HR and HRV measures accurately distinguish convulsive, but not non-convulsive, events (ES vs. FDS). Results establish the framework for future studies to apply this diagnostic tool to more heterogeneous populations, and on out-of-hospital recordings, particularly for populations without access to epilepsy monitoring units.

GWAS in Mice Maps Susceptibility to HIV-Associated Nephropathy to the Ssbp2 Locus.

BACKGROUND: To gain insight into the pathogenesis of collapsing glomerulopathy, a rare form of FSGS that often arises in the setting of viral infections, we performed a genome-wide association study (GWAS) among inbred mouse strains using a murine model of HIV-1 associated nephropathy (HIVAN). METHODS: We first generated F1 hybrids between HIV-1 transgenic mice on the FVB/NJ background and 20 inbred laboratory strains. Analysis of histology, BUN, and urinary NGAL demonstrated marked phenotypic variation among the transgenic F1 hybrids, providing strong evidence for host genetic factors in the predisposition to nephropathy. A GWAS in 365 transgenic F1 hybrids generated from these 20 inbred strains was performed. RESULTS: We identified a genome-wide significant locus on chromosome 13-C3 and multiple additional suggestive loci. Crossannotation of the Chr. 13 locus, including single-cell transcriptomic analysis of wildtype and HIV-1 transgenic mouse kidneys, nominated Ssbp2 as the most likely candidate gene. Ssbp2 is highly expressed in podocytes, encodes a transcriptional cofactor that interacts with LDB1 and LMX1B, which are both previously implicated in FSGS. Consistent with these data, older Ssbp2 null mice spontaneously develop glomerulosclerosis, tubular casts, interstitial fibrosis, and inflammation, similar to the HIVAN mouse model. CONCLUSIONS: These findings demonstrate the utility of GWAS in mice to uncover host genetic factors for rare kidney traits and suggest Ssbp2 as susceptibility gene for HIVAN, potentially acting via the LDB1-LMX1B transcriptional network.

GeneLiFT: A novel test to facilitate rapid screening of genetic literacy in a diverse population undergoing genetic testing.

With the broader introduction of genomic medicine in research and clinical care, an increasing number of persons are offered genetic testing. Many factors, including genetic literacy, may impact the utilization of genetic results by patients and their families. We developed a rapid, self-administered measure of genetic literacy, called Genetic Literacy Fast Test (GeneLiFT). We next evaluated the association of GeneLiFT scores with the comprehension of limitations of genomic medicine in participants undergoing genetic testing in the NIH-sponsored eMERGE III study at Columbia University Irving Medical Center, New York. All participants underwent genetic screening for variants in 74 actionable genes associated with adult-onset disorders. A diverse cohort of 724 participants completed the survey (60% women, 45% less than 40 years old, and 53% self-reported White non-Hispanic ancestry). The GeneLiFT was validated using known group differences based on education, health literacy, and numeracy, and with questions assessing genetic knowledge. GeneLiFT identified multiple standard genetics terms, that is, jargon, not recognized by more than 50% of participants (including actionability and pathogenicity). Low genetic literacy, identified in 210 participants (29%), was significantly associated with poor understanding of the limitations of genetic testing (p-values < 10-9 ). This association was independent of education, health literacy, and numeracy levels, highlighting the importance of directly measuring genetic literacy. Low genetic literacy was also associated with low satisfaction with the informed consent process. GeneLiFT is a practical tool for rapid assessment of genetic literacy in large studies or clinical care. GeneLiFT will allow future research to efficiently assess the role of genetic literacy on the clinical impact of genetic testing.

Autonomic and Cardiac Repolarization Lability in Long QT Syndrome Patients.

OBJECTIVE: Long QT-Syndrome (LQTS) patients are at risk of arrhythmias and seizures. We investigated whether autonomic and cardiac repolarization measures differed based on LQTS genotypes, and in LQTS patients with vs. without arrhythmias and seizures. METHODS: We used 24-h ECGs from LQTS1 (n = 87), LQTS2 (n = 50), and LQTS genotype negative patients (LQTS(-), n = 16). Patients were stratified by LQTS genotype, and arrhythmias/seizures. Heart rate variability (HRV) and QT variability index (QTVI) measures were compared between groups during specific physiological states (minimum, middle, & maximum sympathovagal balance, LF/HF). Results were further tested using logistic regression for each ECG measure, and all HRV measures in a single multivariate model. RESULTS: Across multiple physiological states, total autonomic (SDNN) and vagal (RMSSD, pNN50) function were lower and repolarization dynamics (QTVI) were elevated in LQTS(+), LQTS1, and LQTS2, compared to LQTS(-). Many measures remained significant in the regression models. Multivariate modeling demonstrated that SDNN, RMSSD, and pNN50 were independent markers of LQTS(+) vs. LQTS(-), and SDNN and pNN50 were markers for LQTS1 vs. LQTS(-). During sympathovagal balance (middle LF/HF), RMSSD and pNN50 distinguished LQTS1 vs. LQTS2. LQTS1 patients with arrhythmias had lower total (SDNN) and vagal (RMSSD and pNN50) autonomic function, and SDNN remained significant in the models. In contrast, ECG measures did not differ in LQTS2 patients with vs. without arrhythmias, and LQTS1 and LQTS2 with vs. without seizures. CONCLUSION: Autonomic (HRV) and cardiac repolarization (QTVI) ECG measures differ based on LQTS genotype and history of arrhythmias in LQTS1. SDNN, RMSSD, and pNN50 were each independent markers for LQTS genotype.