Circadian Rhythms of Risk Factors and Management in Atherosclerotic and Hypertensive Vascular Disease: Modern Chronobiological Perspectives of an Ancient Disease

Atherosclerosis, a chronic inflammatory disease of the arteries that appears to have been as prevalent in ancient as in modern civilizations, is predisposing to life-threatening and life-ending cardiac and vascular complications, such as myocardial and cerebral infarctions. The pathogenesis of atherosclerosis involves intima plaque buildup caused by vascular endothelial dysfunction, cholesterol deposition, smooth muscle proliferation, inflammatory cell infiltration and connective tissue accumulation. Hypertension is an independent and controllable risk factor for atherosclerotic cardiovascular disease (CVD). Conversely, atherosclerosis hardens the arterial wall and raises arterial blood pressure. Many CVD patients experience both atherosclerosis and hypertension and are prescribed medications to concurrently mitigate the two disease conditions. A substantial number of publications document that many pathophysiological changes caused by atherosclerosis and hypertension occur in a manner dependent upon circadian clocks or clock gene products. This article reviews progress in the research of circadian regulation of vascular cell function, inflammation, hemostasis and atherothrombosis. In particular, it delineates the relationship of circadian organization with signal transduction and activation of the renin-angiotensin-aldosterone system as well as disturbance of the sleep/wake circadian rhythm, as exemplified by shift work, metabolic syndromes and obstructive sleep apnea (OSA), as promoters and mechanisms of atherogenesis and risk for non-fatal and fatal CVD outcomes. This article additionally updates advances in the clinical management of key biological processes of atherosclerosis to optimally achieve suppression of atherogenesis through chronotherapeutic control of atherogenic/hypertensive pathological sequelae

A Study of the Relationship Between Uric Acid and Substantia Nigra Brain Connectivity in Patients With REM Sleep Behavior Disorder and Parkinson’s Disease

Low levels of the natural antioxidant uric acid (UA) and the presence of REM sleep behavior disorder (RBD) are both associated with an increased likelihood of developing Parkinson’s disease (PD). RBD and PD are also accompanied by basal ganglia dysfunction including decreased nigrostriatal and nigrocortical resting state functional connectivity. Despite these independent findings, the relationship between UA and substantia nigra (SN) functional connectivity remains unknown. In the present study, voxelwise analysis of covariance was used in a cross-sectional design to explore the relationship between UA and whole-brain SN functional connectivity using the eyes-open resting state fMRI method in controls without RBD, patients with idiopathic RBD, and PD patients with and without RBD. The results showed that controls exhibited a positive relationship between UA and SN functional connectivity with left lingual gyrus. The positive relationship was reduced in patients with RBD and PD with RBD, and the relationship was found to be negative in PD patients. These results are the first to show differential relationships between UA and SN functional connectivity among controls, prodromal, and diagnosed PD patients in a ventral occipital region previously documented to be metabolically and structurally altered in RBD and PD. More investigation, including replication in longitudinal designs with larger samples, is needed to understand the pathophysiological significance of these changes

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs