Somers, Virend K.

Background Sarcoidosis is an inflammatory, noncaseating, granulomatous disorder of unknown cause that can affect any body system and is associated with cardiovascular disease including sudden cardiac death (SCD). Cardiac involvement in sarcoidosis is associated with higher risk of SCD, but the SCD risk in the general sarcoidosis population is unknown. We aimed to determine the risk of SCD in people with sarcoidosis versus the matched general population. Methods and Results A population‐based cohort of sarcoidosis and age‐ and sex‐matched comparators from January 1, 1976 to December 31, 2013 was used; presence of other comorbidities in the comparator group was not an exclusion criterion. Mortality, including time, place, and cause of death were measured and manually adjudicated for SCD events. Incidence rates are reported per 100 000 person‐years, and Cox models were used for group comparisons. Of the 345 incident cases of sarcoidosis (171 men; 50%) there were 58 reported deaths; 10 were definite/probable SCD versus 57 all‐cause and 9 SCDs in comparators. Median follow‐up was 12.9 years (interquartile range, 6.0–23.4 years) . Incidence rate of SCD in sarcoidosis was 192 (95% CI, 92–352) versus 155 (95% CI, 71–294) in comparators (hazard ratio [HR], 1.28 (95% CI, 0.52–3.17). Nocturnal deaths were more frequent in sarcoidosis 57 (95% CI, 12–168) versus 17 (95% CI, 0.4–95) (HR, 3.76 [95% CI, 0.39–36.47]). No significant differences were detected between the groups by sex, age, calendar year of diagnosis, or disease duration. Conclusions In a population‐based cohort of patients with sarcoidosis, the risk for SCD compared with matched comparators was not increased. There were more nocturnal deaths among patients with sarcoidosis, yet this was statistically insignificant.

SummaryThis SERVE‐HF (Treatment of Predominant Central Sleep Apnea by Adaptive Servo Ventilation in Patients With Heart Failure) sub study analysis evaluated polysomnography (PSG) data in patients with heart failure with reduced ejection fraction (HFrEF) and predominant central sleep apnea (CSA) randomised to guideline‐based medical therapy, with or without adaptive servo ventilation (ASV). Patients underwent full overnight PSG at baseline and at 12 months. All PSG recordings were analysed by a core laboratory. Only data for patients with baseline and 3‐ or 12‐month values were included. The sub study included 312 patients; the number with available PSG data differed for each variable (94–103 in the control group, 77–99 in the ASV group). After 12 months, baseline‐adjusted respiratory measures were significantly better in the ASV group versus control. Although some between‐group differences in sleep measures were seen at 12 months (e.g., better sleep efficiency in the ASV group), these were unlikely to be clinically significant. The number of periodic leg movements during sleep (PLMS) increased in the ASV group (p = 0.039). At 12 months, the respiratory arousal index was significantly lower in the ASV versus control group (p < 0.001), whilst the PLMS‐related arousal index was significantly higher in the ASV group (p = 0.04 versus control). ASV attenuated the respiratory variables characterising sleep apnea in patients with HFrEF and predominant CSA in SERVE‐HF. Sleep quality improvements during ASV therapy were small and unlikely to be clinically significant. The increase in PLMS and PLMS‐related arousals during ASV warrants further investigation, particularly relating to their potential association with increased cardiovascular risk.

Background: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. Methods: UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. Results: Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. Conclusions: In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.

Introduction: Growing evidence suggests that aberrant sleep architecture is linked to heightened risk of cardiovascular disease independently of sleep apnea. Rapid Eye Movement (REM) sleep is characterized by increased sympathetic activity and blood pressure (BP) compared to non-REM sleep stages. However, whether REM sleep is associated with BP across the 24 hours has not been investigated. Hypothesis: We hypothesized that increased REM sleep would be associated with higher ambulatory BP. Methods: We recruited 137 individuals (58% men, aged 39±14 years, office systolic/diastolic BP [SBP/DBP] 125±14/77±11 mmHg) who completed overnight polysomnography followed by 24-h ambulatory BP monitoring. Sleep was scored according to standard criteria. 24-h, daytime, and nighttime averages were calculated for SBP, DBP and heart rate. REM sleep duration was computed as percentage of total sleep time and used to group participants into tertiles as follows: T1 (REM% ≤18.8), T2 (REM% 18.9-24), and T3 (REM% ≥24.1). Results: Sex ratio, body mass index, and apnea-hypopnea index were similar across REM tertiles, but age increased with increasing tertiles (p=0.029). 24-h SBP (T1 vs T2 vs T3: 121±12 vs 117±7 vs 124±12 mmHg, p=0.015) and DBP (72±7 vs 70±6 vs 76±8 mmHg, p<0.001) differed across REM sleep tertiles, with significantly higher values in those in the highest tertile. Results were consistent when considering daytime SBP (T1 vs T2 vs T3: 126±12 vs 122±8 vs 132±14 mmHg, p<0.001) and DBP (77±7 vs 76±6 vs 83±9 mmHg, p<0.001), and nighttime DBP (65±9 vs 61±7 vs 65±8 mmHg, p=0.047). No differences were noted on nighttime SBP or any heart rate measures. A significant interaction was found between hypertension status and REM sleep in multiple regression analysis, with the observed associations being significant only among hypertensives (n=69). In those with hypertension, REM sleep duration was associated with 24-h mean BP independently of age, sex, body mass index, apnea-hypopnea index and antihypertensive medications (standardized beta 0.22, p=0.041). Conclusions: Longer REM sleep duration is independently associated with elevated ambulatory BP in individuals with hypertension. REM sleep may be a novel cardiovascular risk indicator in this population.

Abstract Study Objectives Objective excessive daytime sleepiness (EDS) is associated with systemic inflammation and a higher risk of cardiometabolic morbidity in obstructive sleep apnea (OSA). We hypothesized that OSA with objective EDS is associated with higher levels of sympathetic nerve activity (SNA) when compared with self-reported EDS. We, therefore, examined the associations between objective and self-reported EDS with SNA in patients with OSA. Methods We studied 147 consecutive male patients with OSA from the institutional sleep clinic. Objective EDS and self-reported EDS were defined based on Multiple Sleep Latency Test (MSLT) latency ≤ 8 minutes and Epworth Sleepiness Scale (ESS) &gt; 10, respectively. Twenty-four-hour urinary norepinephrine was used for assessing SNA. Blood pressure (BP) was measured both in the evening and in the morning. Results Twenty-four-hour urinary norepinephrine was significantly higher in patients with OSA with objective EDS compared with those without objective EDS (p = 0.034), whereas it was lower in patients with OSA with self-reported EDS compared with those without self-reported EDS (p = 0.038) after adjusting for confounders. Differences in the sympathetic drive were most striking in those with an objective but not self-reported EDS versus those with self-reported but not objective EDS (p = 0.002). Moreover, shorter MSLT latency was significantly associated with higher diastolic BP (β = −0.156, p = 0.049) but not systolic BP. No significant association between ESS scores and BP was observed. Conclusions Objective, but not self-reported EDS, is associated with increased SNA and diastolic BP among males with OSA, suggesting that objective EDS is a more severe phenotype of OSA that is accompanied by higher sympathetic drive, higher BP, and possibly greater cardiovascular morbidity and mortality.

Objective We studied the effect of a lifestyle therapy program using the resources already available in a cardiac rehabilitation setting for patients at risk for metabolic syndrome (MetS). Methods We created a six-week cardiometabolic program (CMP) using facilities and resources of a cardiac rehabilitation setting. The CMP included a minimum of 6 once-a-week 60 min sessions consisting of exercise training and nutrition instruction, in addition to 6 once-a-week 60 min group behavioral modification classes following the LEARN program. We also assessed the outcomes in 239 patients (pts) who had at either obesity or hyperglycemia: 58 pts had enrolled and completed the CMP, 58 pts had signed up for the CMP but either never attended or dropped out early (Control 1) and had follow-up data, and 123 pts had not signed up for the CMP but had been seen in the outpatient preventive cardiology practice at the Mayo Clinic (Control 2). We compared changes in components of the MetS from baseline to six weeks (for the CMP group) and at 6 months for all pts. Results Of 187 people who signed up for the CMP, only 62 completed the program and from those, 58 gave authorization to use their clinical data in research. Of the 125 that never attended or dropped out early, 58 had follow-up data. Clinical characteristics of the CMP, Control 1, and Control 2 groups and improvement in each of the outcome measures are shown in the Table. In the CMP group, there was a significant improvement at 6 weeks in waist circumference, weight, diastolic blood pressure and total cholesterol. At 6 months the fasting glucose had also significantly improved. In contrast, improvements in Control 1 and Control 2 were modest at best. See Table. When comparing the 6 month changes in the CMP and Control 1, those in the CMP had more pronounced weight loss (-4.5±5 kg vs -0.14±6 kg, p<0.001); decrease in systolic blood pressure (-1.1±17 mmHg vs +9.6±20 mmHg, p=0.004); and decrease in diastolic blood pressure (-4.6±11 mmHg vs +3.4±15 mmHg, p=0.0017). Similarly, comparing CMP vs Control 2, a significantly greater drop in body weight (-4.5±5 kg vs -0.9±3 kg, p<0.001) and in diastolic blood pressure (-4.6±11 mmHg vs -0.7±9 mmHg, p=0.0202) was noted in the CMP. The change in the number of MetS components per patient dropped significantly, favoring the CMP group compared with the control groups. Conclusion A lifestyle therapy program for patients at risk for MetS can be implemented using existing resources of a cardiac rehabilitation program, although enrollment and completion rates are low. Such a program, however, is effective at improving cardiometabolic outcomes in individuals at risk for MetS.

Background: Physical activity (PA) prevents cardiovascular (CV) disease and maintains health. It is unclear which factors predict the adoption or maintenance of PA at a worksite wellness center. Methods: To assess the predictors of increase in PA we conducted a retrospective study on 7237 adults attending a worksite wellness center between September 2008 and October 2011. Upon joining the wellness center, participants completed an electronic or paper survey that assessed for the presence of CV risk factors, height, weight, self-perceived health behaviors and psychosocial factors using eleven validated 0-10 point scale. We included 1927 participants who completed the standard survey at the time of enrollment and at 12 months (+/- 90 days). For logistic regression analysis PA increase at 12 months was defined as an increase of ≥2 points of self-reported PA, on a 0 to 10 point scale, or maintenance of a high level of PA at follow up (FU). Results: Mean (SD) age was 38 (11) yr., BMI was 26.93 (6.06) kg/m 2 and 32% were men. From the study sample, 717 (37%) participants increased their PA level or maintained a high level of PA at FU. When comparing people who increased or maintained a high level of PA with the group without improvement or a decline in PA, there were differences in age and BMI: age [39 (11) vs. 38 (11) yr.; p=0.04] and BMI [27.37 (5.92) vs. 26.68 (5.92) kg/m 2 ; p=0.024]. The Table reports the results of the multivariate logistic regression model. While CV risk factors (tobacco use, hyperglycemia/diabetes mellitus, and high blood cholesterol), perceived stress level and self-perception of being overweight were not significant predictors of increase PA, low muscular strength and flexibility and interactions with close friends were significantly associated with PA. Conclusion: A significant proportion of people, over one-third, enrolled in a wellness center increase their PA level or maintain high levels of PA at 12 months after enrollment. Increasing age and BMI, and a measure of social support; interaction with close friends, and perceived muscular strength and flexibility, predict increase in PA. Presence of CV risk factors do not predict increase in PA. Tailoring programs to address these domains may help younger, less fit, and those with less social support improve their PA level.

Introduction: Estimating body fat content has shown to better predict adiposity-related cardiovascular risk than the commonly used body mass index (BMI). The white-light 3D Body Volume Index Scanner (BVI) is a non-invasive device normally used in the clothing industry to asses body shapes and sizes. We assessed the hypothesis that volume obtained by BVI is comparable to the volume obtained by air displacement plethysmography (BodPod) (COSMED Concord, CA, USA) and thus capable of assessing body fat mass using the bicompartmental principles of body composition. Methods: We compared BVI to BodPod, a validated bicompartmental method to assess body fat percent that uses pressure/volume relationships in isothermal conditions to estimate body volume. Volume is then used to calculate body density (BD) applying the formula density= Body mass/Volume. Body fat mass percentage is then calculated using the Siri formula (4.95/BD – 4.50) х 100. Subjects were members of the Mayo Clinic Dan Abraham Healthy Living Center undergoing a wellness evaluation. Measurements from both devices were obtained the same day. A prediction model for total BodPod volume was developed using linear regression based on 80% of the observations (N=971), as follows: Predicted BodPod Volume (L) = 9.498 + 0.805*(BVI volume, L) - 0.0411*(Age, years) - 3.295*(Male=0, Female=1) + 0.0554*(BVI volume, L)*(Male=0, Female=1) + 0.0282*(Age, years)*(Male=0, Female=1). Predictions for BodPod volume based on the estimated model were then calculated for the remaining 20% (N=243) and compared to the volume measured by the BodPod. Results: Mean (SEM) age was 41.5(0.41) years, 39.4% were men, weight 81.6(0.67) kg, BMI was 27.8 (0.20) kg/m2. Average difference between volume measured by BodPod- predicted volume by BVI was 0.0 L, median: -0.4 L, IQR: -1.8 L to 1.5 L. Average difference between Body fat % measured - predicted was -1%, median: -2.7%, IQR: -13.2 to 9.9, R2 = 0.9845 (Figure 1-A, B). Conclusion: Body fat mass can be estimated using volume measurements obtained by a white- light 3D body scanner (Figure 1-A).