Danyluk, Alexandria

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.

Background Compelling evidence links obstructive sleep apnea (OSA), a prevalent sleep disorder, to heightened morbidity and mortality. Individuals with family history (FH) of OSA exhibit increased risk of developing this sleep disorder, suggesting that genetics, in addition to demographic and lifestyle factors, are likely implicated in the pathogenesis of OSA. However, it is unclear whether a positive FH of OSA predisposes to other unfavorable health outcomes. In this regard, data on its impact on vulnerability to obesity, both a precursor and a consequence of OSA, are lacking. Objective We sought to assess the association between FH of OSA and risk of obesity. Methods The study population consisted of 612 individuals (66% male, age 48.1±17.3 years) who took part in research studies performed in the Cardiovascular/Sleep Laboratory at Mayo Clinic and had complete family history data. FH of OSA and obesity were determined from questionnaires. Demographics and medical comorbidities including OSA diagnosis were obtained from self‐reports. Body mass index (BMI, kg/m 2 ) was calculated from height and weight measures and used to determine obesity status as follows: normal weight (BMI <25 kg/m 2 ), overweight (BMI 25–29.9 kg/m 2 ), and obese (BMI ≥30 kg/m 2 ). Measurements of waist circumference were obtained to determine central obesity (defined as waist circumference ≥88 cm in women and ≥102 cm in men). Results FH of OSA was reported by twelve percent of the sample (n=72). Individuals with FH of OSA were younger and more likely to be female than those without (age: 40.8±12.6 years vs 49.1±17.6 years, P<0.0001; sex: 48.6% vs 32.2%, P=0.008). Those with positive FH had higher BMI (31.5±6.8 kg/m 2 vs 29.2±7 kg/m 2 , P=0.009), were more frequently obese (59.7% vs 39.8%, P=0.007), and had higher prevalence of central obesity (67.7% vs 53.8%, P=0.036). Multivariable regression models showed the FH of OSA was associated with 3.2‐times greater risk of obesity (95% CI 1.7–5.9, P=0.0002) and with 2.3‐times greater risk of central obesity (95% CI 1.2–4.5, P=0.011) after correcting for age, sex, OSA diagnosis, and FH of obesity. Conclusion Family history of OSA is accompanied by an increased likelihood of obesity, especially central obesity, even if subjects with FH of OSA are much younger. This relationship is independent of conventional risk factors, including OSA diagnosis, suggesting that comorbid OSA in the offspring is unlikely to be implicated. Research addressing the potential pathophysiological mechanisms underlying our findings is warranted. Support or Funding Information Research support: VKS and PS are supported by NIH HL65176. NC is supported by American Heart Association grant 16SDG27250156, and Mayo Clinic CCaTS 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 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 Dietary habits are critical determinants of health and especially of cardiometabolic risk status. Previous studies have examined the relationship between breakfast consumption and indices of obesity, with conflicting findings. Such discrepancy may be owed to the use of surrogate measures of adiposity such as body mass index. Furthermore, it is unknown whether frequency of breakfast consumption is associated with different patterns of regional body fat distribution. Objective In this study, we aimed at investigating the association between frequency of breakfast consumption and body fat distribution. Methods We compiled data from 184 individuals (64% males, mean age 35.8±12.6 years, ranging from 18 to 63 years old) who participated in research studies performed in the Mayo Clinic Cardiovascular/Sleep Laboratory from 2005 to 2019 and underwent whole body dual‐energy X‐ray absorptiometry (DXA) for body composition. Breakfast eating habits were assessed by questionnaire and frequency of consumption was determined as number of times per week (range 0 to 7). Responses were recoded as low frequency (0 to 2 times/week), middle frequency (3 to 4 times/week), and high frequency (5 to 7 times/week). Measures of height and weight were collected to compute body mass index (BMI, kg/m 2 ). Total body fat percentage, fat mass, and lean mass were obtained from DXA. Regions of interest were manually adjusted with the DXA software to quantify abdominal fat and lower body fat. Abdominal fat/total body fat and lower body fat/total body fat ratios were calculated. Results Low frequency of breakfast consumption was reported by 14% of the subjects (n=26), while 18% (n=34) reported middle‐frequency of consumption. The proportion of men/women (p=0.1) and BMI values (p=0.78) were similar across breakfast eating categories. Compared to those who had breakfast with high‐frequency, those who ate it less frequently were younger (p=0.038). In age, sex, and BMI‐adjusted analysis, low‐frequency of breakfast consumption was significantly associated with greater abdominal fat mass (standardized beta=0.11, 95% CI 0.09‐ 1.29, p=0.024) and higher abdominal fat/total body fat ratio (standardized beta=0.16, 95% CI 0.003–0.024, p=0.013). Total body fat percentage (p=0.26), fat mass (p=0.26), and lean mass (p=0.22) were not associated with frequency of breakfast consumption. Similarly, no relations were seen with lower body fat (p=0.85) or lower body fat/total body fat ratio (p=0.51). Conclusion Our study showed that infrequent breakfast consumption is selectively associated with greater abdominal fat mass. As accumulation of fat in the abdominal depot is a more robust predictor of adverse outcomes than total adiposity, these findings suggest that abdominal fat may mediate the increased cardiometabolic risk previously observed in conjunction with infrequent breakfast consumption.