No abstract

Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological phenomena can provide new insight into the causes of multiday cycles. The brain-heart link is highly relevant for epilepsy, with implications for seizure prediction, therapy (i.e. vagal nerve stimulation) and mortality (i.e. sudden unexpected death in epilepsy).We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study quantified multiday cycles of heart rate and seizures in human participants (N=26) using wearable smartwatches and mobile seizure diaries over at least two months (M=6.4, SD=4.0). Participants had an epilepsy diagnosis and uncontrolled or partially controlled seizures. Significant cycles in heart rate were detected using a continuous wavelet transform. Significant co-modulation between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase (Omnibus test). Weekly trends were also investigated from a retrospective cohort of continuous video-EEG-ECG records of people (N=29) undergoing diagnostic epilepsy monitoring for at least eight days (M=9.7, SD=1.3).Heart rate cycles were found in all participants, with circadian (100%), about-weekly (88%) and about-monthly (92%) rhythms being the most prevalent. Seizures were significantly locked onto at least one heart rate cycle in 76% of eligible participants. Seizures occurred preferentially during the rising phase of the heart rate cycles. Significant weekly trends were also verified in 52% of the video-EEG-ECG cohort.The existence and prevalence of multiday heart rate cycles may have clinical implications for cardiology. Heart rate cycles showed striking similarities to multiday epileptic rhythms and were co-modulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

BackgroundWe aimed to assess the differences in the gut microbiome among participants with different uric acid levels (hyperuricemia [HUA] patients, low serum uric acid [LSU] patients, and controls with normal levels) and to develop a model to predict HUA based on microbial biomarkers.MethodsWe sequenced the V3-V4 variable region of the 16S rDNA gene in 168 fecal samples from HUA patients (n=50), LSU patients (n=61), and controls (n=57). We then analyzed the differences in the gut microbiome between these groups. To identify gut microbial biomarkers, the 107 HUA patients and controls were randomly divided (2:1) into development and validation groups and 10-fold cross-validation of a random forest model was performed. We then established three diagnostic models: a clinical model, microbial biomarker model, and combined model.ResultsThe gut microbial α diversity, in terms of the Shannon and Simpson indices, was decreased in LSU and HUA patients compared to controls, but only the decreases in the HUA group were significant (P=0.0029 and P=0.013, respectively). The phylum Proteobacteria (P<0.001) and genus Bacteroides (P=0.02) were significantly increased in HUA patients compared to controls, while the genus Ruminococcaceae_Ruminococcus was decreased (P=0.02). Twelve microbial biomarkers were identified. The area under the curve (AUC) for these biomarkers in the development group was 84.9% (P<0.001). Notably, an AUC of 89.1% (P<0.001) was achieved by combining the microbial biomarkers and clinical factors.ConclusionsThe combined model is a reliable tool for predicting HUA and could be used to assist in the clinical evaluation of patients and prevention of HUA.

The impact of dietary inflammatory potential on serum cytokine concentrations in second and third trimesters of Chinese pregnant women is not clear. A total of 175 pregnant women from the Tianjin Maternal and Child Health Education and Service Cohort (TMCHESC) were included. The dietary inflammatory index (DII) was calculated based on 24-h food records. Serum tumor necrosis factor-α (TNF-α), interleukin 1β (IL-1β), IL-6, IL-8, IL-10, C-reactive protein (CRP), and monocyte chemoattractant protein-1 (MCP-1) levels in the second and third trimesters were measured. The mean DII scores (mean ± SD) were −0.07 ± 1.65 and 0.06 ± 1.65 in the second and third trimesters, respectively. In the third trimester, IL-1β (p = 0.039) and MCP-1 (p = 0.035) levels decreased and then increased with increasing DII scores. IL-10 concentrations decreased in pregnant women whose DII scores increased between the second and third trimesters (p = 0.011). Thiamin and vitamin C were negatively correlated with MCP-1 (β = −0.879, and β = −0.003) and IL-6 (β = −0.602, and β = −0.002) levels in the third trimester. In conclusion, the DII score had a U-shaped association with cytokine levels during the third trimester. Changes in DII scores between the second and third trimesters of pregnancy were correlated with cytokine levels during the third trimester.

Electroencephalography (EEG) has been used to forecast seizures with varying success. There is an increasing interest to use electrocardiogram (ECG) to help with seizure forecasting. The neural and cardiovascular systems may exhibit critical slowing, which is measured by an increase in variance and autocorrelation of the system, when change from a normal state to an ictal state. To forecast seizures, the variance and autocorrelation of long-term continuous EEG and ECG data from 16 patients were used for analysis. The average period of recordings was 161.9 h, with an average of 9 electrographic seizures in an individual patient. The relationship between seizure onset times and phases of variance and autocorrelation in EEG and ECG data was investigated. The results of forecasting models using critical slowing features, seizure circadian features, and combined critical slowing and circadian features were compared using the receiver-operating characteristic curve. The results demonstrated that the best forecaster was patient-specific and the average area under the curve (AUC) of the best forecaster across patients was 0.68. In 50% of patients, circadian forecasters had the best performance. Critical slowing forecaster performed best in 19% of patients. Combined forecaster achieved the best performance in 31% of patients. The results of this study may help to advance the field of seizure forecasting and lead to the improved quality of life of people who suffer from epilepsy.