Somers, Virend K.

Background Acute cardiac and vascular events, including myocardial infarction (MI) exhibit a peak onset between 6 AM and 11 AM. Plasminogen activator inhibitor‐1 (PAI‐1) is a main inhibitor of tissue‐plasminogen activator and a key indicator of prothrombotic state. Elevated levels of PAI‐1 have been linked to increased cardiac and vascular risk. Obstructive sleep apnea (OSA) has also been associated with increased risk of MI. Hypoxemia, as well as OSA, increase PAI‐1. We tested the hypothesis that the early morning after awakening is associated with higher PAI‐1 activity in subjects with acute MI who also have OSA. Methods We studied 56 subjects after acute myocardial infarction (mean age 60±2 years; BMI 28±1 kg/m 2 ). All subjects underwent complete overnight sleep polysomnography within 2 weeks after MI to assess for presence and severity of OSA. PAI‐1 activity was measured in all 56 subjects, first before sleep (at 9 PM), and then immediately after awakening in the morning (at 6 AM). A third measurement of PAI‐1 was obtained in all subjects at 11 AM. Results For the group, PAI‐1 activity almost doubled from 12.5±1.6 U/mL at 9 PM to 22.5±2.5 U/mL after waking at 6 AM and decreased to 14.2±2.4 U/mL by 11 AM (P<0.0001). Fifty seven percent of patients were observed to have OSA, with apnea hypopnea index of >10 events/hour. Sleep apnea did not contribute to further elevation of morning PAI‐1 activity level in acute MI patients (9 PM, 6 AM and 11 AM PAI‐1 activity respectively: no OSA 11.6±2.5 U/mL, 24.1±3.8 U/mL and 15.2±3.5 U/mL; OSA 13.3±2.1 U/mL, 21.1±3.3 U/mL and 13.3±3.3 U/mL; P=0.8 for between group comparison). Conclusions Increased PAI‐1 activity in acute MI patients may be an important mechanism underlying the early morning peak in cardiovascular events. The presence of OSA however does not appear to further accentuate the already elevated PAI‐1 activity in acute MI patients. In patients after acute MI, the diurnal variation in PAI‐1, rather than apnea, may drive the impaired fibrinolysis. Support or Funding Information ‐‐R01 grant HL65176‐‐This study was also supported in part by a gift to the Mayo Foundation by the Philips Respironics Foundation. 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 Expression of adiponectin, an anti‐atherogenic and insulin‐sensitizing adipokine, is decreased in obesity. Mechanisms underlying this reduction are not well understood. Increased adiponectin in obesity may protect against development of cardiac and metabolic disorders. Objective To investigate the role of leptin in regulating adiponectin expression in humans. Design 1) determining the relationship between leptin and adiponectin in healthy humans during modest weight gain; 2) in‐vitro validation of the role of leptin in adiponectin expression. Results Modest weight gain of 3.8 ± 1.2 kg over 8 weeks of overfeeding in normal weight healthy subjects (n=33, 22 men, 30±7 years) increased both leptin and adiponectin. Changes in leptin during weight gain correlated with changes in adiponectin (rho=0.35, p=0.045), suggesting that leptin may regulate adiponectin expression. In cultured differentiated human white preadipocytes, leptin increased adiponectin mRNA and protein expression. Conclusions Leptin induces adiponectin expression. In obesity, adipose tissue may be resistant to leptin signaling thus preventing increases in adiponectin. Therefore, impaired leptin signaling may contribute to low adiponectin in obesity. Targeting leptin resistance may be beneficial in preventing obesity‐associated cardiac and metabolic disorders. Support : AHA 11SDG7260046, R21 DK81014

Background: Decreased testosterone (TTST) and sex hormone binding globin (SHBG) contribute to development of cardiometabolic disorders in obese men. We aimed to determine the effect of weight gain and weight loss on TTST and SHBG in healthy men and to examine their relationship with endothelial function. Method: 25 normal weight healthy men underwent an overfeeding protocol to gain 5% body weight over 8 weeks. Following weight gain, they underwent 8 weeks of weight loss. TTST, SHBG and endothelial function (brachial artery flow‐mediated dilation, FMD) were measured at baseline (T1), after weight gain (T2) and after weight loss (T3). Results: Volunteers gained 4 ± 1.3 kg and subsequently lost 3.2 ± 1.5 kg of body weight. TTST levels did not change (T1: 18.85 ± 6.2 nmol/L, T2: 19.3 ± 6.9 nmol/L, T3: 20.3 ± 7.6 nmol/L). However, SHBG decreased during weight gain (p=0.04) and increased during weight loss (p=0.0005) (T1: 22.8 ± 10.5 nmol/L, T2: 20.8 ± 11.1 nmol/L, T3: 25.58 ± 11.5 nmol/L). Furthermore, SHBG reduction during weight gain tended to correlate with decreases in FMD (p=0.06; R 2 =0.15). During weight loss, increases in SHBG correlate negatively with changes in FMD (p=0.008; R 2 =0.32). Conclusion : In normal weight healthy men, SHBG decreases with modest weight gain and increases with weight loss. TTST does not change. Changes in SHBG partly predict improvements in endothelial function with weight loss. Grant Funding Source : Supported by AHA 11SDG7260046, R21 DK81014, AHA 13POST16420009

Traditionally regarded as a consequence of sleep‐disordered breathing, excessive daytime sleepiness (EDS) is now recognized as an independent risk factor for cardiovascular disease. Evidence also links the shortening of telomeres, responsible for protecting DNA, with increased cardiovascular disease risk. Interestingly, recent data illustrate telomere lengthening in patients with obstructive sleep apnea (OSA); however, whether EDS is associated with telomere length independently of OSA remains unknown. To examine this potential relationship, we studied 169 subjects with polysomnography assessment who answered “yes” or “no” to experiencing EDS; 70 subjects (41%) reported EDS (59M, 44±12yrs, 30.3±5.5kg/m 2 ) whereas 99 did not (59%, 71M, 40±15yrs, 28.6±7.5kg/m 2 ). Telomere length was quantified via qPCR of genomic DNA isolated from peripheral blood samples and did not differ between individuals reporting EDS compared to those who did not (4,856±280 vs. 4,880±340bp, respectively, P =0.31). However, 50% of subjects reporting EDS had moderate‐to‐severe OSA (apnea‐hypopnea index ≥15events/hr) compared to 28% of the non‐EDS group ( P <0.01). To account for the confounding effects of OSA on telomere length, we conducted a series of subgroup analyses. Subjects without OSA who reported EDS (n=35, 27M, 39±11yrs, 27.4±5.0kg/m 2 ) had shorter telomeres (4,805±165 vs. 4,875±231bp, P =0.05) than those not reporting EDS (n=71, 47M, 35±11yrs, 26.8±5.4kg/m 2 ). Interestingly, within the OSA subgroup, there were no differences in telomere length (4,897±235 vs. 4,858±251bp, P =0.45) between those with (n=35, 32M, 48±11yrs, 33.2±4.5kg/m 2 ) and without EDS (n=28, 24M, 53±16yrs, 33.1±9.7kg/m 2 ). These effects do not appear to be influenced by subject demographics as no differences were found between compared groups (EDS‐OSA vs. nEDS‐OSA and OSA+EDS vs. OSA‐EDS). Collectively, our data suggest individuals reporting EDS may be at greater risk of developing cardiovascular disease due to accelerated telomere shortening. It also appears OSA and EDS exert differential effects on telomere length.

Abstract Introduction Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model. Methods An IRB approved survey was presented to opting-in users from August to November 2020. COVID-19 test results were reported by 2003/6878 respondents (116 positive; 1887 negative). From the positive group, data from 82 responders (44.7±11.3 yrs.) who reported the date of symptom onset were used. From the negative group, data from 1519 responders (48.4±12.9 yrs.) who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud. Data from January to October 2020 were considered. The predictive model consists of two levels: 1) the daily probability of staying healthy calculated by logistic regression and 2) a continuous density Hidden Markov Model to refine the daily prediction considering the past decision history. Results With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Evaluation of the predictive model resulted in cross-validated area under the receiving-operator curve (AUC) estimate of 0.84±0.09 which is similar to values reported for wearable-sensors. Considering additional days to confirm prediction improved the AUC estimate to 0.93±0.05. Conclusion The results obtained on the smart bed user population suggest that unobtrusive sleep metrics may offer rich information to predict and track the development of symptoms in individuals infected with COVID-19. Support (if any):

Abstract Introduction Narcolepsy is a rare, lifelong disorder that requires long-term treatment and is associated with multiple comorbidities, including cardiovascular conditions. Many available treatments have cardiovascular-related warnings and precautions in their labels. The objective of this study was to estimate the incidence of cardiovascular comorbidities in adult patients with a narcolepsy diagnosis in the US. Methods Claims from IBM® MarketScan®, an administrative claims database, between January 2014 and June 2019 were analyzed. Eligible patients were ≥18 years and had continuous medical and prescription coverage (gaps &lt;30 days allowed). The narcolepsy cohort was defined by ≥2 outpatient claims containing a diagnosis of narcolepsy type 1 or type 2 on separate days and no more than 6 months apart, with ≥1 non-diagnostic office-visit. Non-narcolepsy patients were matched 3:1 to narcolepsy patients by calendar date of cohort entry, age, gender, US geographic region, and insurance type. Each incidence calculation required a 6 month wash-out period prior to cohort entry. Differences between cohorts were evaluated using a Cox proportional hazard model adjusted for age, gender, region, insurance type, and relevant morbidities/comorbidities and medications in the baseline period. Results Of 54,239,110 adults in the database, 12,816 and 38,441 were included in the narcolepsy and matched non-narcolepsy cohort. Approximately 67% were female patients and mean age was approximately 38 years in both cohorts. Incidence rates (per 1,000 person-years) for newly recorded cardiovascular comorbidities or events in narcolepsy/non-narcolepsy were: CVD without hypertension (13.29/7.99), MACE+ (11.75/6.86), heart failure (5.72/3.41), stroke (4.28/2.17), ischemic stroke (3.69/1.91), edema (9.84/4.22), and a composite of stroke, atrial fibrillation, and edema (17.73/8.88). Conclusion Physicians should consider the increased cardiovascular risk when considering risk modification strategies and treatment options for narcolepsy patients. Further research is needed to understand treatment-specific risks. Support (if any) Jazz Pharmaceuticals

The carotid body (CB) is the main peripheral chemoreceptor for arterial respiratory gases O 2 and CO 2 and pH, eliciting reflex ventilatory, cardiovascular, and humoral responses to maintain homeostasis. This review examines the fundamental biology underlying CB chemoreceptor function, its contribution to integrated physiological responses, and its role in maintaining health and potentiating disease. Emphasis is placed on 1) transduction mechanisms in chemoreceptor (type I) cells, highlighting the role played by the hypoxic inhibition of O 2 -dependent K + channels and mitochondrial oxidative metabolism, and their modification by intracellular molecules and other ion channels; 2) synaptic mechanisms linking type I cells and petrosal nerve terminals, focusing on the role played by the main proposed transmitters and modulatory gases, and the participation of glial cells in regulation of the chemosensory process; 3) integrated reflex responses to CB activation, emphasizing that the responses differ dramatically depending on the nature of the physiological, pathological, or environmental challenges, and the interactions of the chemoreceptor reflex with other reflexes in optimizing oxygen delivery to the tissues; and 4) the contribution of enhanced CB chemosensory discharge to autonomic and cardiorespiratory pathophysiology in obstructive sleep apnea, congestive heart failure, resistant hypertension, and metabolic diseases and how modulation of enhanced CB reactivity in disease conditions may attenuate pathophysiology.