Louis, Erik K. St.

AbstractRoom tilt illusion is a rare phenomenon in which a person transiently perceives the surrounding environment as tilted to one side. Epilepsy is one of the presumed causes of room tilt illusion, but this has never been proven. We present a case of room tilt illusion that is epileptic in nature, documented by video‐ EEG monitoring. Our Patient is a 30‐year‐old woman with 11 years history of bi‐monthly spells of sudden tilt of the whole environment counterclockwise for a duration of one to two seconds. These spells were initially diagnosed as psychogenic. She was then admitted to our video‐EEG monitoring unit. She had three of her typical spells during monitoring, with corresponding changes of brief generalized spike‐wave burst on EEG that coincided temporarily with her symptoms. The video otherwise did not reveal any sway or tilt of the patient herself. She was started on topiramate with resolution of her symptoms. She remained spell‐free when seen during follow‐up, six months later. This case illustrates a first example of room tilt illusion that is documented to be epileptic in nature. This case adds to the varied nature of how epilepsy can manifest in patients, which may support improved diagnosis and treatment of epileptic patients.

Background Sudden cardiac arrest is the leading mode of death in the United States. Epilepsy affects 1% of Americans; yet epidemiological data show a prevalence of 4% in cases of sudden cardiac arrest. Sudden unexpected death in epilepsy (SUDEP) may share features with sudden cardiac arrest. The objective of this study was to report autopsy and genomic findings in a large cohort of SUDEP cases. Methods and Results Mayo Clinic Sudden Death Registry containing cases (ages 0–90 years) of sudden unexpected and unexplained deaths 1960 to present was queried. Exome sequencing performed on decedent cases. From 13 687 cases of sudden death, 656 (4.8%) had a history of seizures, including 368 confirmed by electroencephalography, 96 classified as SUDEP, 58 as non‐SUDEP, and 214 as unknown (insufficient records). Mean age of death in SUDEP was 37 (±19.7) years; 56 (58.3%) were male; 65% of deaths occurred at night; 54% were found in bed; and 80.6% were prone. Autopsies were obtained in 83 cases; bystander coronary artery disease was frequently reported as cause of death; nonspecific fibrosis was seen in 32.6% of cases, in structurally normal hearts. There were 4 cases of Dravet syndrome with pathogenic variants in SCN1A gene. Using whole exome sequencing in 11 cases, 18 ultrarare nonsynonymous variants were identified in 6 cases including CACNB2, RYR2, CLNB, CACNA1H, and CLCN2 . Conclusions This study examined one of the largest single‐center US series of SUDEP cases. Several cases were reclassified as SUDEP, 15% had an ECG when alive, and 11 (11.4%) had blood for whole exome sequencing analysis. The most frequent antemortem genetic finding was pathogenic variants in SCN1A ; postmortem whole exome sequencing identified 18 ultrarare variants.

Abstract Video-polysomnography (v-PSG) is essential for diagnosing rapid eye movement (REM) sleep behavior disorder (RBD). Although there are current American Academy of Sleep Medicine standards to diagnose RBD, several aspects need to be addressed to achieve harmonization across sleep centers. Prodromal RBD is a stage in which symptoms and signs of evolving RBD are present, but do not yet meet established diagnostic criteria for RBD. However, the boundary between prodromal and definite RBD is still unclear. As a common effort of the Neurophysiology Working Group of the International RBD Study Group, this manuscript addresses the need for comprehensive and unambiguous v-PSG recommendations to diagnose RBD and identify prodromal RBD. These include: (1) standardized v-PSG technical settings; (2) specific considerations for REM sleep scoring; (3) harmonized methods for scoring REM sleep without atonia; (4) consistent methods to analyze video and audio recorded during v-PSGs and to classify movements and vocalizations; (5) clear v-PSG guidelines to diagnose RBD and identify prodromal RBD. Each section follows a common template: The current recommendations and methods are presented, their limitations are outlined, and new recommendations are described. Finally, future directions are presented. These v-PSG recommendations are intended for both practicing clinicians and researchers. Classification and quantification of motor events, RBD episodes, and vocalizations are however intended for research purposes only. These v-PSG guidelines will allow collection of homogeneous data, providing objective v-PSG measures and making future harmonized multicentric studies and clinical trials possible.

Abstract Purpose Compare agreements between polysomnography-based (PSG) diagnosis of isolated REM-sleep-behavior-disorder (iRBD) and Non-REM-Hypertonia (NRH), a novel biomarker independently associated with synucleinopathy-related neurodegenerative diseases. Methods Sixteen patients with histories of dream-enactment-behavior (DEB)(women=38%; age:64.6±13.0) underwent PSG with simultaneously-recorded Sleep Profiler (SP). Two boarded sleep neurologists independently characterized iRBD. Physician1 combined abnormal qualitative REM-sleep-without-atonia (RSWA) by submental electromyography, with video-confirmation of probably DEB. Physician2 relied solely on qualitative RSWA. SP was auto-staged, technically reviewed, and reprocessed for automated abnormal NRH detection. Kappa scores measured physician and NRH agreements. Results In the 14 records with REM sleep, iRBD was characterized in: Physician1=64%, Physician2=79%, NRH=71% of the records. Across the three methods, unanimous iRBD agreement occurred in 57% of the records (positive=7, negative=1). The between-physician agreement in iRBD classifications was fair (kappa=0.32). The agreement between NRH and Physician1 was moderate (kappa=0.52) versus slight with Physician2 (kappa=0.05). NRH comparisons to consensus physician agreement yielded one false-positive and one false-negative iRBD finding. Physician2 classified: a) iRBD in two cases that were negative by Physician1 and NRH, and b) one negative case that Physician1 and NRH characterized as iRBD. Physician1 identified one negative case that was classified iRBD by Physician2 and NRH. Additionally, NRH was abnormal in one of the two records with no REM sleep. Discussion NRH may assist in iRBD risk assessment, given it agreed with at least one physician in 86% of the cases and the between-physician iRBD agreement was only fair. NRH also characterized iRBD-risk in patients with insufficient REM sleep for RSWA assessment.

Abstract Purpose Evaluate the sleep staging agreement between polysomnography (PSG) and Sleep Profiler (SP) in patients with suspected isolated REM-sleep-behavior-disorder. Methods Twenty-six patients with reported dream-enactment-behavior (Site1=16, Site2=10; 27% women; age 64±13 years) underwent a diagnostic PSG with simultaneously recorded SP. A registered sleep-technologist at each site performed PSG-staging, while SP was auto-staged and technically reviewed/edited. Across technicians, the initial staging was blinded. Site1 then performed unblinded restaging of PSG=N3(N2) vs. SP=N2(N3) epochs, while Site2 conducted a blinded, carefully-targeted restaging of N3. Statistics included Cohen’s kappa and Chi-square analyses. Results Agreement between SP and Site1 vs. Site2 were significantly different for Wake (kappa:Site1=0.816;Site2=0.650;combined=0.736), stage N1 (kappa:Site1=0.149;Site2=0.228;combined=0.188), stage N2 (kappa:Site1=0.632;Site2=0.718;combined=0.659), stage N3 (kappa:Site1=0.715;Site2=0.368;combined=0.525) and REM (kappa:Site1=0.827;Site2=0.719;combined=0.766)(all P<0.001). After restaging of N3, the kappa values improved at Site1 (unblinded:N2=0.659/N3=0.883) and Site2 (blinded:N2=0.775/N3=0.736)(combined:N2=0.735/N3=0.851). The proportion of PSG-epochs restaged from N3 to N2 was 17% at Sites1 and 38% at Site2 (P<0.001), while Site1 had fewer remaining PSG=N3 vs. SP=N2 conflicts (5.6% vs. 20.8%, P<0.001). Compared to Site2, Site1 had a superior: REM kappa due to fewer SP=N2 disagreements (8.5% vs. 16.8%, P<0.001), and Wake kappa resulting from fewer SP=N1 (6.6 vs. 15.6%, P<0.001) and SP=N2 conflicts (5.9 vs. 12.0%, P<0.001). Conversely, the Site1 N1 kappa was inferior due to greater SP=wake disagreement (41.6% vs. 19.8%, P<0.001). Discussion N3 was excessively stage by both PSG technicians before restaging. At Site1, Wake, N3, and REM had almost-perfect-agreement with SP, while N2 had substantial-agreement. At Site2 and across-site, substantial-agreement was observed for Wake, N2, N3, and REM.

Although insufficient sleep is associated with increased cardiovascular risk, evidence of a causal relationship is lacking. We investigated the effects of prolonged sleep restriction on 24-hour ambulatory blood pressure (BP) and other cardiovascular measures in 20 healthy young participants (aged 23.4±4.8 years, 9 females), who underwent a randomized, controlled, crossover, 16-day inpatient study consisting of 4 days of acclimation, 9 days of sleep restriction (4 hours of sleep/night) or control sleep (9 hours), and 3 days of recovery. Subjects consumed a weight maintenance diet with controlled nutrient composition throughout. A 24-hour BP (primary outcome) and cardiovascular biomarkers were measured repeatedly. Polysomnographic monitoring was continuous. Comparing sleep restriction versus control sleep, 24-hour mean BP was higher (adjusted mean difference, day 12: 2.1 mm Hg [95% CI, 0.6–3.6], corrected P =0.016), endothelial function was attenuated ( P <0.001), and plasma norepinephrine increased ( P =0.011). Despite increased deep sleep, BP was elevated while asleep during sleep restriction and recovery. Post hoc analysis revealed that 24-hour BP, wakefulness, and sleep BP increased during experimental and recovery phases of sleep restriction only in women, in whom 24-hour and sleep systolic BP increased by 8.0 (5.1–10.8) and 11.3 (5.9–16.7) mm Hg, respectively (both P <0.001). Shortened sleep causes persistent elevation in 24-hour and sleep-time BP. Pressor effects are evident despite closely controlled food intake and weight, suggesting that they are primarily driven by the shortened sleep duration. BP increases are especially striking and sustained in women, possibly suggesting lack of adaptation to sleep loss and thus greater vulnerability to its adverse cardiovascular effects.

Background The prevalence of excessive daytime sleepiness (EDS) in the general population is reported to be 9 to 28 percent. Various social and biological factors have been reported to contribute to the development of EDS, such as cardiovascular disease, metabolic disorders as well as socioeconomic status. Medically, EDS is associated with poorer prognosis in patients with sleep disorders. However, whether sleepiness is an independent negative prognostic indicator in patients without sleep apnea is poorly understood. Objective To evaluate the relationship between self‐reported excessive daytime sleepiness (assessed by Epworth Sleepiness Scale) and the risk of all‐cause mortality in patients without obstructive sleep apnea (OSA). Methods We performed a retrospective cohort study in a large registry of patients seen at the Mayo Clinic Center for Sleep Medicine between November 17 th , 2009 and April 15 th , 2017. All patients received a diagnostic polysomnography and had completed Epworth Sleepiness Scale (ESS). The primary outcome of all‐cause mortality risk was computed using the Kaplan‐Meier analysis and Cox‐proportional hazard model with age, sex, body mass index (BMI), baseline comorbidities and sleep measurements as confounders. Results A total of 2746 non‐OSA patients were identified. The overall median age (IQR) was computed as 53 (40, 63) with 45% of patients identifying as males. The median follow‐up time was approximately 2.3 years. In a univariate analysis, daytime sleepiness was associated with the female sex, younger age and higher BMI. No significant differences were observed in vascular related diseases between sleepy and non‐sleepy patients. EDS was not significantly associated with all‐cause mortality with an adjusted hazard ratio of 1.23 (95% CI 0.86, 1.76; p =0.257). In addition, there was no significant association of EDS and the risk of all‐cause mortality even after stratifying by sex, age and BMI in non‐OSA patients. Conclusion EDS is not associated with all‐cause mortality in non‐OSA patients. Further prospective studies are needed to confirm this finding. Support or Funding Information This research was supported by NIH HL65176 and NIH HL114024. Kaplan–Meier survival curve of 8 years all‐cause mortality of non‐OSA patients with and without excessive daytime sleepiness (EDS) at baseline. image Kaplan–Meier survival curve of 8 years all‐cause mortality of non‐OSA patients with and without excessive daytime sleepiness (EDS) at baseline. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background Sleep disturbances are increasingly common and associated with adverse health outcomes. Inadequate sleep may also produce daytime symptoms, such as excessive sleepiness. Growing research identifies daytime sleepiness as a risk factor for morbidity and mortality. However, it is unclear whether these associations are independent of the presence of sleep disordered breathing, a primary cause of hypersomnolence. Objective In this study, we sought to assess the relation between daytime sleepiness and established cardiometabolic risk factors, independently of obstructive sleep apnea. Methods We compiled data from 456 individuals (69% male, age 51.3±15.7 years) who participated in research studies performed in the Cardiovascular/Sleep Laboratory at Mayo Clinic and had complete sleepiness data. Height and weight were measured to calculate body mass index (BMI, kg/m 2 ). Obesity was defined as BMI ≥30 kg/m 2 . Hypertension was determined as self‐reported diagnosis or taking antihypertensive medications. Diabetes mellitus was defined as patient‐reported diabetes or usage of insulin or antidiabetic drugs. Obstructive sleep apnea (OSA) was self‐reported or determined from polysomnography. Subjective daytime sleepiness was defined as an affirmative answer to the question “Are you usually sleepy during the daytime?”. Results Daytime sleepiness was reported by 51% of the subjects (n=231). Those with daytime sleepiness were more likely to be male (74.9% vs 62.3%, P=0.005), but the average age was similar between groups (51.6±17.3 years vs 51±14.1 years, P=0.67). BMI was greater among the subjects with perceived daytime sleepiness than in those without (32.2±7 kg/m 2 vs 28.9±6.8 kg/m 2 , P<0.0001), and obesity was more frequent in the former group (62.9% vs 35.4%, P<0.0001). Prevalence of hypertension (37% vs 27.5%, P=0.03), diabetes (14% vs 6.7%, P=0.01), and OSA (18.2% vs 10.4%, P=0.035) was also higher in individuals reporting sleepiness during the day. In multivariable analysis adjusted for age, sex, and OSA, daytime sleepiness was associated with increased risk of obesity (OR=2.88, 95% CI 1.84–4.49, P<0.0001), hypertension (OR=1.73, 95% CI 1.04–2.87, P=0.034), and diabetes (OR=2.58, 95% CI 1.18–5.61, P=0.017). Conclusion Daytime sleepiness is associated with greater risk of obesity, hypertension and diabetes independently of comorbid OSA. Daytime sleepiness may be a novel target for preventative and therapeutic strategies aimed at improving cardiometabolic health. Support or Funding Information NC is supported by American Heart Association grant 16SDG27250156, and Mayo Clinic Center for Clinical and Translational Science grant Marie Ingalls Cardiovascular Research Career Development Fund. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background The prevalence of inadequate sleep in the general population is rising. While the detrimental health consequences of sleep disorders such as sleep‐disordered breathing are well established, little is known on the contributory role of perceived sleep quality on cardiometabolic risk profile. Importantly, poor sleep quality may be reported even in absence of objective findings of suboptimal sleep. We hypothesized that subjective poor sleep quality would be associated with markers of cardiometabolic risk in individuals without sleep disorders. Methods We compiled data from 667 research subjects (446 males, age 36.4±13.8 years) studied in the Cardiovascular/Sleep Laboratory at Mayo Clinic from 2000 to 2017. Subjects with history of sleep disorders and/or overt cardiovascular diseases, as determined from medical record or self‐report, were excluded from this study. Health status was further confirmed by physical exam. Supine systolic and diastolic blood pressure (SBP/DBP), height, weight, and waist and hip circumferences were collected. Body mass index (BMI, kg/m 2 ) was derived from height and weight. Presence of hypertension (SBP and/or DBP ≥140/80 mmHg) and obesity (BMI ≥30 kg/m 2 ) was recorded. Subjective sleep quality was self‐reported as either good or poor. Analysis of variance and Chi‐square tests were performed to examine differences in outcome measures according to sleep quality. Logistic regression models were run to examine the predictive value of poor sleep quality for hypertension and obesity risk. Results Poor subjective sleep quality was reported by 231 individuals out of 667 (34.6%). Subjects with perceived poor sleep were older (poor sleep vs good sleep: 42.3±12.9 years vs 33.2±13.3 years, p<0.0001), with greater BMI (31.4±7 kg/m 2 vs 26.5±6.3 kg/m 2 , p<0.0001), waist (104.6±17.8 kg/m 2 vs 89.7±14.8 kg/m 2 , p<0.0001) and hip (111.8±14.8 kg/m 2 vs 102.3±11.5 kg/m 2 , p<0.0001) circumference than good sleepers. Compared to the latter group, SBP and DBP values were also higher in self‐evaluated poor sleepers (poor sleep vs good sleep, SBP: 128.8±15.5 mmHg vs 122.9±14.4 mmHg, p<0.0001; DBP: 74.5±10.6 mmHg vs 71.8±11.4 mmHg, p=0.012). Obesity was more prevalent in those with subjective poor sleep quality than in good sleepers (53.3% vs 19.7%, p<0.0001). Similarly, 72.5% of patients with hypertension reported poor sleep quality while only 27.5% of them were good sleepers (p<0.0001). In comparison to good sleep quality, perceived poor sleep was associated with 3.6 times greater risk of obesity (95% CI 2.5, 5.2, p<0.0001) and 3.5 times greater risk of hypertension (95% CI 1.7, 6.9, p=0.0004) after correcting for conventional covariates. Significance was retained after further adjustment for reported snoring and witnessed apneas. Conclusions Poor perceived sleep quality is independently associated with elevated risk of high blood pressure and excess body weight in individuals without sleep disorders. These findings favor the concept that promoting adequate sleep quality, even subjectively, may benefit cardiometabolic health. Support or Funding Information American Heart Association grant 16SDG27250156; National Institutes of Health grants RO1 HL 065176 and HL 134808. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background Increasing evidence suggests that insufficient sleep is associated with increased morbidity and mortality. Studies linking short sleep with metabolic derangements such as obesity, insulin resistance and dyslipidemia, are especially persuasive. Nevertheless, the vast majority of these data have been obtained from observational studies. We sought to evaluate the effects of 9 days of partial sleep deprivation compared to normal sleep on lipid profile in healthy subjects undergoing an in‐laboratory, experimental protocol. Methods Eight healthy, nonobese, normolipidemic subjects (4 males, age 25.4±6.8 years, body mass index 25.2±3.9 kg/m 2 ) completed a randomized, controlled, crossover, 16‐day in‐patient study. Each sequence consisted of a 4‐day acclimation period, followed by 9 days of experimental partial sleep deprivation (4 hours of sleep opportunity; 00:30 AM – 4:30 AM); or normal sleep (9 hours of sleep opportunity; 10:00 PM – 7:00 AM), and a subsequent 3‐day recovery segment. Subjects consumed a controlled, weight maintenance diet (15% protein, 55% carbohydrates, and 30% fat) during the study. Fasting blood samples for measures of total cholesterol, high‐density lipoprotein (HDL) cholesterol, low‐density lipoprotein (LDL) cholesterol, and triglycerides were obtained in both study sequences on Days 4, 7, 10, 13, and 16. Results Compared to normal sleep, experimental partial sleep deprivation did not cause significant changes in total cholesterol (partial sleep deprivation vs normal sleep: 160.1±21.8 mg/dL vs 164.9±23.7 mg/dL, P=0.724), HDL cholesterol (54.1±14.7 mg/dL vs 52±12.8 mg/dL, P=0.75), LDL cholesterol (86.5±18.9 mg/dL vs 89.5±19.7 mg/dL, P=0.329), or triglycerides (97.7±26.6 mg/dL vs 96.6±38.3 mg/dL, P=0.633). Analysis of time course of lipid values across study days did not reveal appreciable patterns (all P>0.295). Likewise, body weight was similar during both study sequences (partial sleep deprivation vs normal sleep: 69.6±14.2 kg vs 69.2±13.5 kg, P=0.459). Conclusions Experimental sleep restriction does not alter circulating lipids or weight in healthy subjects. Aberrant metabolic effects of insufficient sleep may be mediated by dietary intake and occur when access to food is unrestricted, or may emerge only after longer exposure to sleep curtailment. Support or Funding Information American Heart Association grant 16SDG27250156; National Institutes of Health R01 grants HL114676 and HL114024; European Union project FNUSA‐ICRC no. CZ.1.05/1.1.00/02.0123. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .