Somers, Virend K.

Central sleep apnea (CSA) with nocturnal hypoxia is frequent in heart failure (HF). Hypoxia causes increased circulating erythropoietin (EPO) in healthy normals. EPO promotes increased vasoconstriction and exogenous EPO administration is associated with adverse cardiovascular events. No prior studies have related EPO concentration to apnea or hypoxia due to CSA. EPO is elevated in HF patients with nocturnal hypoxia due to CSA. Ambulatory, non-anemic HF patients (n = 29) with LVEF < 45% and healthy controls (n = 18) underwent polysomnography (PSG). Subjects with obstructive sleep apnea (OSA) were excluded. CSA was defined as apnea-hypopnea index (AHI) ≥ 15. Hypoxia was quantified as the proportion of sleep with arterial oxygen saturation < 90% (T90%). Blood for EPO was drawn post-PSG. Other clinical characteristics were summarized from the medical record. HF subjects and controls were similar age (54 vs 60 y, p = 0.09). CSA was present in 14 HF subjects; 13 were men compared to 8 of 15 without CSA (p = 0.04). HF subjects had 42% higher mean EPO than controls (p < 0.01) despite similar hemoglobin (13.9 vs 14.0 g/dL, p = 0.8). NYHA class III–IV HF subjects had 42% higher mean EPO than class I–II HF subjects (p = 0.05, figure ). EPO concentration was correlated with severity of nocturnal hypoxia by simple linear regression (r = 0.4; p = 0.02). By multivariate analysis, elevated EPO was associated with NYHA class III–IV HF and elevated AHI (p = 0.01 and 0.03, respectively; r = 0.6) after adjusting for age, gender, LVEF, renal function and hemoglobin. Nocturnal hypoxia due to CSA promotes increased endogenous EPO concentration in HF patients.

Central obesity has been associated with adverse events in patients with cardiovascular disease (CVD). We undertook this study to evaluate predictors of overweight awareness, desire to lose weight and weight loss attempts in patients with CVD and central obesity. Association of physician-rendered diagnosis of overweight with weight loss attempts was also evaluated. Data from National Health and Nutrition Examination Survey (NHANES) 1999 –2004 were used. CVD was defined as self-referred history of coronary artery disease or stroke. Central obesity was determined based on a waist circumference > 102 cm in men and > 88 cm in women. Motivational factors considered as determinant for behavioral change that were evaluated included: participants’ awareness of their overweight status; desire to lose weight; any weight loss attempts in last year. We examined demographic, anthropometric and clinical determinants of each stage of change using multivariate models. Of the 1484 participants with CVD that were identified in NHANES, 824 had central obesity (56%). 76% of centrally obese participants were aware of their overweight status and 79% were desirous of losing weight. Despite this awareness and desire, only 41% of centrally obese had attempted weight loss in the last year. Only 61% reported that they had been informed that they were overweight by physicians. On multivariate analysis, after adjusting for sociodemographic factors and body mass index, physician rendered diagnosis of overweight was a significant predictor of weight loss attempts (OR=2.5, 95% CI 1.3– 4.9, p= 0.006). Awareness of one’s overweight status strongly predicted desire to lose weight in multivariate models (OR=107.6, 95 % CI 42.9 –270.0, p<.0001) and desire to lose weight was linked to recent attempts to lose weight (OR 10.4, 95 % CI 2.8– 39.2, p= 0.0005), confirming that the Transtheoretical Model of behavioral change applies to the study population. In a nationally representative sample of participants with CVD and central obesity, physician-rendered diagnosis of overweight emerged as an important predictor of weight loss attempts. Efforts to promote weight loss should focus on interventions based on Transtheoretical Model of behavioral change.

Introduction : Obesity is a common risk factor in cardiac rehabilitation (CR), yet many CR patients do not realize significant weight loss, despite engaging in regular exercise training while in CR. Hypothesis : We hypothesized that standard CR would demonstrate favorable body composition (BC) changes even in the absence of weight loss and sought to measure the performance characteristics of weight loss to predict BC changes. Methods : We included consecutive patients enrolled in early outpatient CR who underwent air displacement plethysmography with measurements of height, weight, and waist circumference at initiation and completion of CR. We calculated the accuracy of weight loss to identify significant improvements in BC. Results : Data from 112 patients were available for analysis; mean age 60±12 yr., 76% male, mean body mass index (BMI) 30.2±5.3 kg/m 2 . The indication for CR was myocardial infarction (n=33), percutaneous coronary intervention (n=29), coronary artery bypass or valve surgery (n=45), and heart transplant (n=5). Repeat testing occurred after a median of 81 days and 21 phase II CR sessions. There was no change in BMI (-0.53±1.3 kg/m 2 , p=0.43) and weight (-1.6±4.0 kg, p=0.51). Fat mass showed a borderline significant decrease of 2.9±3.9 kg (p=0.08). However, BC improved significantly as measured by body fat % (-2.5±3.2%, p=0.03) and waist circumference (-4.6±5.2 cm; p=0.01). There was a correlation between weight loss and fat mass loss (r=0.85, p=0.0001, positive predictive value=0.94), and % fat change (r=0.67, p=0.0001, positive predictive value=0.91). See table 1. However, the negative predictive value to detect no fat change for patients who did not lose weight was low (0.49, when assessed by % body fat loss, and 0.57, when assessed by fat mass loss). Among patients who did not lose weight, 29 (51%) had a greater than 1% body fat loss. Conclusions : The diagnostic performance of weight change to predict favorable BC changes is poor and highlights the potential utility of BC measurements in the CR setting. Table. Diagnostic Characteristics of Weight Loss as a Predictor of Improvements in Body Composition Weight Loss (kg) Body Fat % Loss ≤ 1% Fat Mass Loss (kg) > 1kg > 1% ≤ 1kg > 1kg 49 5 51 3 ≤ 1kg or gain 29 28 25 33 Sensitivity 0.63 0.67 Specificity 0.85 0.92 PPV * 0.91 0.94 NPV ** 0.49 0.57 * PPV = Positive Predictive Valve, ** NPV = Negative Predictive Value

Background : The significant bidirectional changes in cardiovascular(CV) risk factor prevalence make the net trend in incident CV risk unknown. We assessed the trends in the net 10-year risk for incident CV disease using the Framingham risk model and data from the National Health and Nutrition Examination Surveys(NHANES). Methods : We used data from NHANES II (1976–1980), NHANES III (1988–1994), and NHANES 1999–2004, including major CV risk factors, all collected in a standardized manner. People with a history of CV disease, pregnant women, people with missing data and those outside the Framingham age range were excluded. The Framingham risk function estimated the 10-year risk for CV disease. The slope of change or rate of change per year between NHANES II/III, and between NHANES III/1999–2004 were determined. The slope difference was calculated and compared to zero. Results : The mean age-adjusted 10-year CV risk decreased from 10 to 7.9% between NHANES II and III, with a significant slope (p<0.001). However, the mean age-adjusted risk decreased to a lesser extent between NHANES III and NHANES 1999–2004 from 7.9 to 7.4% (p<0.001). The slopes were different (p<0.001). In women, CV risk did not change between NHANES III and 1999–2004, 7.9 to 7.5%, (p=0.40). Predicted 10-year risk by age group (Figure ) shows that the decrease in CV risk was lower in the second period compared to the first one in all age groups, except in the elderly. Conclusion : The estimated net risk for CV disease in the US population decreased from 1976–1980 to 1988–1994 but minimally changed from 1988–1994 to 1999–2004, particularly in women. The increased prevalence of obesity may have offset the gain in CV risk reduction. Predicted 10-year Framingham Risk by Age Group

Introduction: Idiopathic Dilated (DCM) and hypertrophic cardiomyopathies (HCM) are leading causes of sudden cardiac death. The incidence and prevalence of both DCM and HCM were originally estimated in Olmsted County, MN over 1975-1984. Incidence was 6.0 and prevalence was 36.5 per 100,000 person-years, for DCM; with incidence and prevalence rates of 2.5 and 19.7 per 100,000 person-years for HCM. This study aims to determine the incidence and mortality rates of individuals diagnosed with HCM and DCM in Olmsted County, MN from 1984 to 2016. Methods: The Rochester Epidemiology Project is a validated long term medical record linkage system that collects and links medical information for the approximately 145000 residents of Olmsted County, Minnesota. Using this database and the Electronic Health Record systems at Mayo Clinic and Olmsted Medical Center in Rochester, MN, we manually reviewed and validated the medical records of 4440 patients. This cohort includes all patients with any mention of a cardiomyopathy based on ICD and HICDA codes during the period 1984 to 2016. Data were summarized with mean and standard deviation, frequency and percentage and compared between groups using two sample t-test for continuous data and chi-square for proportions. Survival was computed using Kaplan-Meier methods and compared to expected survival based on age and gender. Incidence was computed using Olmsted County population numbers during the time period and adjusted to the US 1980 white population to allow for comparison with the previous study. Results: The calculated crude incidence rate for DCM and HCM are 10.1 and 7.8 per 100,000 respectively. Incidence rates adjusted for comparison to US 1980 white population (age and sex adjusted) are 10.4 (95% CI: 9.3-11.4) and 8.0 (95% CI: 7.1-8.8) per 100,000 person-years, respectively. The observed number of deaths in this cohort (HCM and DCM) was 420 vs 194 expected. Conclusions: Calculated incidence, both crude and adjusted rates are higher than previously estimated rates. The crude incidence rate for HCM is almost 4-fold higher than that for DCM is 2.4 times higher than the original estimate by Codd et al. Individuals with cardiomyopathies were found to have a higher overall mortality rate than the general population with an observed to expected hazard ratio of 1.73 (95% CI 1.49-2.01, p<0.001) for DCM and 1.44 (95% CI 1.21-1.71, p<0.001) for HCM. Our findings suggest that previous studies may have underestimated the true burden of HCM and DCM in the population which has important implications for healthcare provision and cost.

Background Despite strenuous public health efforts, obesity rates continue to rise globally, and contribute importantly to increased cardiometabolic morbidity and mortality. Dietary habits play a key role in the development and progression of obesity, and are the target of choice in preventative and therapeutic programs. Although preclinical studies have shown that caffeine may exert anti‐adipogenic effects, in humans the implications of coffee consumption on risks of excess adiposity are unclear. Objective The objective of this study was to investigate the association between habitual coffee consumption and indices of total and regional body fat in healthy adults. Methods Seventy‐eight subjects (46 males, mean age 27.3±6.1 years) who took part in research studies conducted at the Mayo Clinic Cardiovascular/Sleep Laboratory and underwent body composition assessment were selected. Absence of cardiovascular or metabolic diseases was ascertained by physical exam and review of medical charts. Height, weight, waist and hip circumference measures were collected for computation of body mass index (BMI, kg/m 2 ) and waist‐to‐hip ratio (WHR). Measurements of total body fat percentage, upper body fat (kg), and lower body fat (kg) were obtained from dual‐energy X‐ray absorptiometry (DXA). Abdominal computed tomography (CT) was used to quantify subcutaneous adipose tissue (SAT, cm 2 ) and visceral adipose tissue (VAT, cm 2 ). Habitual coffee consumption (cups/day; range 0–3) was self‐reported and coded as a binary variable (yes/no). Results Sixty percent of the subjects (n=47) reported habitual coffee consumption. Age and gender distribution were comparable between groups (P=0.52 and P=0.73, respectively). There was no significant difference between those who reported consuming coffee and those who did not in terms of BMI (23.6±2.7 kg/m 2 vs 24±2.8 kg/m 2 , P=0.56) and WHR (0.85±0.08 vs 0.85±0.07, P=0.7). Likewise, body composition and fat distribution indices were similar between groups: total body fat percentage (coffee drinkers vs non‐coffee drinkers: 30.5±7.2% vs 28.6±7.8%, P=0.27), upper body fat mass (6.6±2.4 kg vs 6.6±3.1 kg, P=0.99), lower body fat mass (9.4±5.2 kg vs 8.4±4.1 kg, P=0.39), SAT (143.4±61.3 cm 2 vs 135.9±63.7 cm 2 , P=0.6), and VAT (57.7±35.5 cm 2 vs 70.3±44 cm 2 , P=0.22). Analysis stratified by gender did not reveal any differential effect of regular coffee consumption on body fat (all Ps>0.26). Conclusion In healthy, young individuals moderate habitual coffee consumption is not associated with measures of total or regional adiposity. Whether the anti‐obesity effects of caffeine emerge only when it is consumed in larger quantities remains to be determined. Support or Funding Information Research support: 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 Abdominal (ABD) and lower‐body (LB) fat depots exert opposing effects on cardiometabolic risk factors. However, molecular mechanisms underlying these differences are not completely understood. Studies suggest an inflammatory role of CD95 in obesity‐associated adipose tissue inflammation and dysfunction. Also, leptin deficiency is associated with increased CD95 expression. Objective To examine the expression of CD95 in the ABD and LB fat depots and to investigate leptin‐dependent CD95 regulation. Methods Transcription of CD95 and other adipokines was determined in paired subcutaneous abdominal (ABD) and lower‐body (femoral, LB) fat samples obtained from 29 healthy individuals (10 females, age: 29.4 ± 7.6 years, BMI: 24 ± 4.1 kg/m 2 ). The underlying mechanisms and down‐stream effects of altered CD95 expression were evaluated using differentiated human white preadipocytes (dHWP). Results Reduced mRNA expression of CD95 was apparent in adipose tissue obtained from LB fat depot [ABD: 4.30 (13.91 – 0.06), LB: 2.49 (10.88 – 0.04), p = 0.01]. mRNA expression of MCP1 [ABD: 0.51 (1.35 – 0.34), LB: 0.51 (1.21 – 0.28), p = 0.51], CCL5 [ABD: 6.60 (43.45 – 1.40), LB: 4.44 (11.28 – 1.27), p = 0.20], TNFα [ABD: 1.10 (6.14 – 0.03), LB: 0.07 (5.84 – 0.03), p = 0.30], and leptin [ABD: 21.86 (31.33 – 14.6), LB: 27.23 (41.69 – 20.35), p = 0.08] was not different between the two fat depots. A positive relationship between CD95 mRNA and other inflammatory cytokines was evident in both ABD (MCP1: ρ = 0.79, p < 0.001; CCL5: ρ = 0.83, p < 0.001; TNFα: ρ = 0.87, p < 0.001) and LB fat depot (MCP1: ρ = 0.87, p < 0.001; CCL5: ρ = 0.78, p < 0.001; TNFα: ρ = 0.83, p < 0.001). Furthermore, leptin‐dependent decreases in CD95 mRNA (p = 0.04) and protein expression (p = 0.03) was seen in dHWP. Also, 24 hour pre‐treatment of dHWP with leptin protected cells from FasL (p = 0.03) induced apoptosis. Conclusions Compared to ABD adipose tissue, LB fat has reduced transcription of CD95. Additionally, expression of CD95 mRNA was associated with increased expression of inflammatory cytokines in both ABD and LB subcutaneous fat. Moreover, we show that leptin may mediate decreases in CD95 expression thereby protecting adipose tissue from FasL mediated apoptosis. Our studies suggest that the decreased CD95 mRNA expression in LB fat depot may be related to higher localized leptin expression and possibly contribute to depot specific alterations in adipose tissue function and consequent cardiometabolic risk. Support or Funding Information Prachi Singh is supported by AHA grant‐in‐Aid 17GRNT33660138. 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 Osteoporosis, a condition associated with decreased bone density, contributes significantly to morbidity and mortality. Importantly, the incidence and economic burden of osteoporosis is rising partly due to increases in the aging population. Moreover, while the role of obesity on bone health is controversial, studies suggest that obesity alters bone density. Effects of obstructive sleep apnea (OSA), which is more prevalent in ageing and obesity, on any relationship between obesity and BMD are not well known. Objective To examine the effects of BMI and OSA on total and regional bone density. Methods A database of all research participants with nocturnal polysomnography and whole body dual energy X‐Ray absorptiometry scan (DEXA) was created. The study population included 72 subjects without OSA (AHI<5, Age: 29.1 ± 7.3 years, BMI: 24.3 ± 3.6 Kg/m 2 ) and 22 subjects with OSA (apnea hypopnea index, AHI>5; 4 females, Age: 43.3 ± 11.2 years, BMI: 30.8 ± 4.1 Kg/m 2 ). We examined the relationship between BMI and BMD in all our subjects. Linear regression models adjusted for age and BMI were used to assess changes in bone density in the presence of OSA. Additional analyses examining the effects of OSA in overweight and obese individuals (BMI>25 kg/m 2 ) and relationship between BMI and BMD in OSA subjects were also undertaken. Results Total body bone density in all our study subjects was positively correlated with BMI (Std Beta = 0.42, 95% CI 0.004 – 0.015, p < 0.001) but not age (Std Beta = 0.04, 95% CI −0.002 −0.003, p = 0.71) or presence of OSA (Std Beta = 0.042, −0.029 − 0.040, p = 0.75). Similarly, bone density in spine was increased with increases in BMI (Std Beta = 0.45, 95% CI 0.006 − 0.019, p = 0.0003) and not affected by age (Std Beta = 0.14, 95% CI −0.001 – 0.005, p = 0.23) or presence of OSA (Std Beta = 0.18, 95% CI −0.011 – 0.065, p = 0.16). Also, increases in BMD in pelvic region were only associated with BMI (Std Beta = 0.30, 0.001 – 0.019, p = 0.024). Age (Std Beta = −0.09, −0.005 – 0.002, p = 0.45) or presence of OSA (Std Beta = 0.19, −0.016 – 0.086, p = 0.17) did not alter BMD in pelvic area. Interestingly, overweight and obese subjects with OSA had a lower pelvic BMD compared to overweight and obese subjects without OSA (1.10 ± 0.12 g/cm 3 Vs. 1.19 ± 0.16 g/cm 3 , p = 0.043), even when OSA subjects had a higher BMI (31.4± 3.7 kg/m 2 Vs. 27.8± 3.2 Kg/m 2 , p = 0.001) and were older (43.4 ± 11.3 years Vs 30.6 ± 8.8 years, p<0.001). Also, the positive relationship between BMI and BMD was not present in OSA subjects in our study population (Total BMD: p = 0.73; Spine BMD: p = 0.94; Pelvic BMD: p=0.77). Conclusion A positive association between bone density and BMI was observed in our study population. However, this relationship was not evident in OSA subjects. Further, our study suggests that OSA may be accompanied by decreased bone density in the pelvic region. Cross‐sectional and longitudinal studies examining the effects of OSA on regional bone density, fractures, and frailty in BMI, age and gender matched populations are warranted. Support or Funding Information VKS and PS are supported by NIH grant HL65176 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 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 Increases in circulating free‐fatty acids (FFAs) contribute to obesity‐associated cardiometabolic pathology and may be considered a surrogate marker for adipose tissue dysfunction. However, alterations in FFA metabolism resulting from modest weight gain have not been previously evaluated. Objective Examine the changes in FFAs during modest weight gain in healthy individuals and to investigate the relationship between regional fat depots. Study Design We evaluated the effects of overfeeding in 8 healthy, non‐obese participants (4 men; age 29 ± 5.7 years; BMI: 25.1 ± 2.1 kg/m2). FFA concentrations were measured using liquid chromatography/mass spectrometry under fasting conditions and during oral‐glucose tolerance test (OGTT) at baseline and after 8 weeks of weight gain. Prior to the baseline and weight gain visits, subjects consumed isocaloric macro‐nutrient stable diet for three days (50% carbohydrate, 35% fat and 15% protein). Body composition and regional fat depots were assessed using dual‐energy X‐ray absorptiometry and abdominal computed tomography scans. Results Overfeeding was accompanied by 3.9 ± 1.6 kg weight gain (p=0.01) resulting from 2.9 ± 0.9 kg (p=0.01) increase in fat mass and 1.0 ± 0.9 kg (p=0.04) increase in fat‐free mass. Compared to baseline, fasting FFA (439 ± 162 μM vs. 368 ± 123 μM, p=0.55) and area under the curve (AUC) for FFA during OGTT (23319 ± 11686 μM. min−1 vs. 20495 ± 4847 μM.min−1, p=0.55) did not change with weight gain. However, at 120 min. of the OGTT FFA were significantly greater after weight gain (60 ± 18 μM vs. 77 ± 34 μM, p=0.04). We found a negative relationship between relative changes in visceral fat area and FFA at 120 min of glucose challenge (rho=−0.76, p=0.03). Furthermore, relative changes in fasting FFA and AUC for FFA during OGTT with weight gain were associated with baseline total fat mass (fasting FFA: rho=0.76, p=0.03; AUC FFA: rho=0.81; p=0.02) and baseline abdominal fat area (fasting FFA: rho=0.76, p=0.03; AUC FFA: rho=0.79; p=0.02). Conclusions Modest weight gain does not alter fasting FFA or overall FFA response to oral glucose challenge. However, our data suggest development of early stages of adipose tissue insulin resistance which is apparent via lesser suppression of FFA at 120 min. of OGTT. Furthermore, the relationships between fasting FFA, AUC‐FFA, and baseline fat mass suggest that individuals with high baseline adiposity may be more susceptible to develop adipose tissue insulin resistance with weight gain. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .