Smith, Kevin L.

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 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 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 Numerous studies have identified an association between frequency of breakfast consumption and body weight in children and adolescents, indicating that those who skip this meal are more likely to gain weight and develop obesity in adulthood. However, few studies have examined the implications of infrequent breakfast consumption for obesity risk in the adult population, and available data are mixed. The purpose of this study was to investigate the role of breakfast consumption on obesity indices and weight gain in a sample of healthy adults. Methods The study sample was drawn from a cohort of consecutive subjects who were recruited for research studies at the Cardiovascular/Sleep Laboratory at Mayo Clinic from 2005 to 2017 and consisted of 347 healthy adults (215 males, mean age 35.5±15.4 years, ranging from 18 to 87 years old). Physical examination and review of medical records were conducted to ensure absence of overt disease. Measures of height, weight, waist and hip circumference were obtained. Body mass index (BMI, kg/m 2 ) and waist‐to‐hip ratio (WHR) were computed. Obesity was defined as BMI ≥30 kg/m 2 . Weight gain over the past year was quantified from self‐report. Frequency of breakfast consumption (times/week, range 0 to 7) was collected from questionnaire and coded as “never” (0 times/week), “infrequent” (1 to 4 times/week), and “frequent” (5 to 7 times/week). Only those who reported the same breakfast routine for ≥2 years were included in the analysis. Results Four percent of the sample (n=15) reported never consuming breakfast and 24% (n=85) reported eating it infrequently. Compared to the individuals who consumed it frequently, those who never or infrequently had breakfast were predominately men (P=0.049) and older (P=0.001). Waist circumference was larger in those who skipped breakfast in comparison to those who consumed it 1–4 times/week or 5–7 times/week (97.5±17.9 cm vs. 85.6±11 cm vs. 87.7±15 cm, respectively; P=0.017). There were no significant differences in hip circumference, WHR, or BMI across breakfast eating patterns. Prevalence of obesity tended to be higher among those who skipped breakfast than in those who ate it frequently (26.7% vs. 10.9%, P=0.074). Individuals who did not consume breakfast reported the greatest weight gain, followed by those who consumed it 1–4 times/week, while the lowest weight gain was reported by those who had breakfast 5–7 times/week (3.6±6.9 kg vs. 2±2.3 kg vs. 1.3±2.2 kg; P=0.002). In a multivariable model, skipping breakfast predicted weight gain independently of age, gender, and BMI (P=0.004). Conclusions Infrequent breakfast consumption is associated with indices of central obesity and weight gain, with these associations being more evident in individuals who never eat breakfast. Our findings on healthy adults are consistent with prior observations in the young, corroborating the concept that regular consumption of this meal is an important and independent contributor of healthy weight at all ages. Support or Funding Information American Heart Association grant 16SDG27250156; National Institutes of Health grants RO1 HL 114676 and HL 114024; CCaTS Early Stage Investigator Award. 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 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.

Abstract Study Objectives Values for normative REM sleep without atonia (RSWA) remain unclear. Older age and male sex are associated with greater RSWA, and isolated elevated RSWA has been reported. We aimed to describe normative RSWA and characterize isolated RSWA frequency in adults without REM sleep behavior disorder (RBD). Methods We visually quantified phasic, “any,” and tonic RSWA in the submentalis (SM) and anterior tibialis (AT) muscles, and the automated Ferri REM Atonia Index during polysomnography in adults without RBD aged 21–88. We calculated RSWA percentiles across age and sex deciles and compared RSWA in older (≥ 65) versus younger (<65) men and women. Isolated RSWA (exceeding diagnostic RBD cutoffs, or >95th percentile) frequency was also determined. Results Overall, 95th percentile RSWA percentages were SM phasic, any, tonic = 8.6%, 9.1%, 0.99%; AT phasic and “any” = 17.0%; combined SM/AT phasic, “any” = 22.3%, 25.5%; and RAI = 0.85. Most phasic RSWA burst durations were ≤1.0 s (85th percentiles: SM = 1.07, AT = 0.86 seconds). Older men had significantly higher AT RSWA than older women and younger patients (all p < 0.04). Twenty-nine (25%, 18 men) had RSWA exceeding the cohort 95th percentile, while 17 (14%, 12 men) fulfilled diagnostic cutoffs for phasic or automated RBD RSWA thresholds. Conclusions RSWA levels are highest in older men, mirroring the demographic characteristics of RBD, suggesting that older men frequently have altered REM sleep atonia control. These data establish normative adult RSWA values and thresholds for determination of isolated RSWA elevation, potentially aiding RBD diagnosis and discussions concerning incidental RSWA in clinical sleep medicine practice.