Abstract 13175: Social Jet Lag in Eating Patterns as a Marker of Meal Timing Variability is Associated With Elevated Cardiometabolic Risk in the AHA Go Red for Women Strategically Focused Research Network

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Nour Makarem ◽  
Dorothy D Sears ◽  
Marie-Pierre St-Onge ◽  
Faris M Zuraikat ◽  
Linda Gallo ◽  
...  
Keyword(s):  
Cardiometabolic Risk ◽  
Fasting Glucose ◽  
Circadian Disruption ◽  
Jet Lag ◽  
Eating Patterns ◽  
Cvd Risk ◽  
Timing Variability ◽  
Meal Timing ◽  
The Difference ◽  
Nighttime Eating

Introduction: Social jet lag, typically defined as the difference in sleep timing on work vs. free days, is related to elevated cardiovascular disease (CVD) risk, via circadian disruption. Irregular meal timing may also lead to circadian disruption, but the role of social jet lag in eating patterns in CVD risk has not been examined. Hypothesis: Social jet lag in eating patterns, a measure of meal timing variability, will be associated with elevated cardiometabolic risk in women. Methods: Women from the AHA Go Red for Women SFRN at Columbia (n=116, mean age: 33 y, 45% Hispanic) completed a 1-wk food record using the web-based NIH Automated Self-Administered 24-h Dietary Assessment Tool. Social jet lag in eating patterns was evaluated using the difference between weekday vs. weekend: 1) nightly fasting duration (NFD), 2) time of first meal, and 3) time of last meal. Standard deviation (SD) of % kcal consumed after 5PM and 8PM was used to capture variability in nighttime eating. Cardiovascular health (CVH) was assessed with the AHA Life Simple 7 (LS7) score. Linear regression models adjusted for age, socioeconomic status, and sleep were used to examine associations with CVH, BMI, waist circumference (WC), systolic blood pressure (SBP), diastolic BP (DBP), and fasting glucose. Results: The average time of first meal and NFD on weekdays vs. weekends was 8:54AM vs. 10:11AM and 12.5 h vs. 13.7 h, respectively; average time of last meal was similar (8:27PM). Each 1-h increase in the difference between first meal time on weekdays vs. weekends was associated with higher BMI (β=0.81, p=0.018), WC (β=0.67, p=0.029), DBP (β=1.34, p=0.049), and SBP (β=1.45, p=0.062). Each 1-h increase in the difference between weekday vs. weekend NFD was associated with higher BMI (β=0.55, p=0.045), fasting glucose (β=2.46, p=0.028), and SBP (β=1.17, p=0.058) and lower LS7 score (β=-0.24, p=0.014). Greater variability in nighttime eating (higher %kcal after 5PM and 8PM SD) was associated with higher WC (β=0.014, p=0.020 and β=0.030, p=0.040). Conclusions: Greater meal timing variability was related to poorer CVH and higher BMI, WC, BP, and fasting glucose. Results suggest that stabilizing meal timing patterns should be considered for lowering CVD risk and warrant confirmation in a larger sample.

scholarly journals Variability in Daily Eating Patterns and Eating Jetlag Are Associated With Worsened Cardiometabolic Risk Profiles in the American Heart Association Go Red for Women Strategically Focused Research Network

2021 ◽  
Vol 10 (18) ◽  
Author(s):  
Nour Makarem ◽  
Dorothy D. Sears ◽  
Marie‐Pierre St‐Onge ◽  
Faris M. Zuraikat ◽  
Linda C. Gallo ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Systolic Blood Pressure ◽  
Diastolic Blood Pressure ◽  
Body Mass ◽  
Cardiometabolic Risk ◽  
Circadian Disruption ◽  
Eating Patterns ◽  
Fasting Duration

Background Sleep variability and social jetlag are associated with adverse cardiometabolic outcomes via circadian disruption. Variable eating patterns also lead to circadian disruption, but associations with cardiometabolic health are unknown. Methods and Results Women (n=115, mean age: 33±12 years) completed a 1‐week food record using the Automated Self‐Administered 24‐Hour Dietary Assessment Tool at baseline and 1 year. Timing of first and last eating occasions, nightly fasting duration, and %kcal consumed after 5 pm (%kcal 5 pm ) and 8 pm (%kcal 8 pm ) were estimated. Day‐to‐day eating variability was assessed from the SD of these variables. Eating jetlag was defined as weekday‐weekend differences in these metrics. Multivariable‐adjusted linear models examined cross‐sectional and longitudinal associations of day‐to‐day variability and eating jetlag metrics with cardiometabolic risk. Greater jetlag in eating start time, nightly fasting duration, and %kcal 8 pm related to higher body mass index and waist circumference at baseline ( P <0.05). In longitudinal analyses, a 10% increase in %kcal 8 pm SD predicted increased body mass index (β, 0.52; 95% CI, 0.23–0.81) and waist circumference (β, 1.73; 95% CI, 0.58–2.87); greater %kcal 8 pm weekday‐weekend differences predicted higher body mass index (β, 0.25; 95% CI, 0.07–0.43). Every 30‐minute increase in nightly fasting duration SD predicted increased diastolic blood pressure (β, 0.95; 95% CI, 0.40–1.50); an equivalent increase in nightly fasting duration weekday‐weekend differences predicted higher systolic blood pressure (β, 0.58; 95% CI, 0.11–1.05) and diastolic blood pressure (β, 0.45; 95% CI, 0.10–0.80). Per 10% increase in %kcal 5 pm SD, there were 2.98 mm Hg (95% CI, 0.04–5.92) and 2.37mm Hg (95% CI, 0.19–4.55) increases in systolic blood pressure and diastolic blood pressure; greater %kcal 5 pm weekday‐weekend differences predicted increased systolic blood pressure (β, 1.83; 95% CI, 0.30–3.36). For hemoglobin A1c, every 30‐minute increase in eating start and end time SD and 10% increase in %kcal 5 pm SD predicted 0.09% (95% CI, 0.03–0.15), 0.06% (95% CI, 0.001–0.12), and 0.23% (95% CI, 0.07–0.39) increases, respectively. Conclusions Variable eating patterns predicted increased blood pressure and adiposity and worse glycemic control. Findings warrant confirmation in population‐based cohorts and intervention studies.

scholarly journals Physical Activity, Measures of Obesity, and Cardiometabolic Risk: The Multi-Ethnic Study of Atherosclerosis (MESA)

2014 ◽  
Vol 11 (4) ◽  
pp. 831-837 ◽  
Author(s):  
Paul A. McAuley ◽  
Haiying Chen ◽  
Duck-chul Lee ◽  
Enrique Garcia Artero ◽  
David A. Bluemke ◽  
...  
Keyword(s):  
Physical Activity ◽  
Insulin Resistance ◽  
Waist Circumference ◽  
Impaired Fasting Glucose ◽  
Central Obesity ◽  
Cardiometabolic Risk ◽  
Fasting Glucose ◽  
Reference Group ◽  
Model Assessment ◽  
Ethnic Study

Background:The influence of higher physical activity on the relationship between adiposity and cardiometabolic risk is not completely understood.Methods:Between 2000–2002, data were collected on 6795 Multi-Ethnic Study of Atherosclerosis (MESA) participants. Self-reported intentional physical activity in the lowest quartile (0–105 MET-minutes/week) was categorized as inactive and the upper three quartiles (123–37,260 MET-minutes/week) as active. Associations of body mass index (BMI) and waist circumference categories, stratified by physical activity status (inactive or active) with cardiometabolic risk factors (dyslipidemia, hypertension, upper quartile of homeostasis model assessment of insulin resistance [HOMA-IR] for population, and impaired fasting glucose or diabetes) were assessed using logistic regression analysis adjusting for age, gender, race/ethnicity, and current smoking.Results:Among obese participants, those who were physically active had reduced odds of insulin resistance (47% lower; P < .001) and impaired fasting glucose/diabetes (23% lower; P = .04). These associations were weaker for central obesity. However, among participants with a normal waist circumference, those who were inactive were 63% more likely to have insulin resistance (OR [95% CI] 1.63 [1.24–2.15]) compared with the active reference group.Conclusions:Physical activity was inversely related to the cardiometabolic risk associated with obesity and central obesity.

Abstract P139: Association of Energy Flux with Change in Cardiovascular Risk Factors in Weight Stable Individuals

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Baqiyyah Conway ◽  
Peter Giacobbi ◽  
Clemens Drenowatz ◽  
Stephen Blair ◽  
Gregory Hand
Keyword(s):  
Risk Factors ◽  
Energy Flux ◽  
Impaired Fasting Glucose ◽  
Fasting Glucose ◽  
Exercise Group ◽  
C Reactive Protein ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Reactive Protein ◽  
Group Assignment

Background: It is generally accepted that body weight is maintained when there is energy balance between intake and expenditure. Energy balance can be achieved at different rates of expenditure through exercise and caloric intake which has been referred to as energy flux: high flux reflects high expenditure and high intake while low flux describes low energy expenditure and intake. Overweight, obesity, and diabetes are major risk factors for cardiovascular disease and CVD risk factors tend to increase with hyperglycemia and BMI. Exercise is a viable way to achieve weight maintenance, however, there is limited data about the role of energy flux on CVD risk factors when individuals maintain their body weight. We investigated the effect of energy flux and change in energy flux on CVD risk factors in when body weight is maintained. Methods: One hundred and thirteen overweight or obese class I adults ages 21 to 45 were randomized to a control group, moderate exercise (17.5 kcal/kg/week) or high exercise group (35 kcal/kg/week). The exercise groups performed supervised exercise at and intensity of 70-75% of their heart rate maximum. Impaired fasting glucose was defined as a fasting glucose of 100-125 mg/dL. General linear models were used to test the relationship of exercise intensity and impaired fasting glucose on change in energy flux from baseline to six months, as well as the relationship of 6-month change in energy flux with change in CVD risk factors, namely, HDLc, LDLc, vLDLc, total cholesterol, triglycerides, Apolipoprotein B (ApoB), and C-reactive protein. Results: Seventy-two percent of the population was overweight and 22% were obese. Mean change in energy flux from baseline to month six was 128.8 kcal/day. In multivariable analyses including age, sex, BMI, impaired fasting glucose, and energy expenditure group assignment, neither exercise group assignment nor baseline obesity status had any effect on change in energy flux, lipids, or inflammatory markers. Impaired fasting glucose was associated with a significantly greater increase in energy flux from baseline to six months (p=0.03). There was a stepwise change in C-reactive protein from baseline to six months, with a decrease (-2.46 mg/dL) in controls, a moderate increase (+0.32 mg/dL) in the moderate intensity exercise group and a larger increase (+0.82 mg/dL) in the very intensive exercise group, p= 0.03 for moderate intensity and p=0.02 for very intensive exercise groups compared to controls. Finally, increases in energy flux from baseline to six months were associated with increased ApoB (p=0.04), though there were no significant changes in energy flux by group assignment. Conclusion: Intensification of exercise and increases in energy flux while maintaining stable weight is associated with increases in certain cardiovascular risk factors, namely C-reactive protein and ApoB.

scholarly journals Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242365
Author(s):  
Tsung-Ying Tsai ◽  
Pai-Feng Hsu ◽  
Chung-Chi Lin ◽  
Yuan-Jen Wang ◽  
Yaw-Zon Ding ◽  
...  
Keyword(s):  
Risk Factors ◽  
Factor Analysis ◽  
General Population ◽  
Sedentary Behavior ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Validation Group ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Clustering Pattern

Background Few studies have reported on the clustering pattern of CVD risk factors, including sedentary behavior, systemic inflammation, and cadiometabolic components in the general population. Objective We aimed to explore the clustering pattern of CVD risk factors using exploratory factor analysis to investigate the underlying relationships between various CVD risk factors. Methods A total of 5606 subjects (3157 male, 51.5±11.7 y/o) were enrolled, and 14 cardiovascular risk factors were analyzed in an exploratory group (n = 3926) and a validation group (n = 1676), including sedentary behaviors. Results Five factor clusters were identified to explain 69.4% of the total variance, including adiposity (BMI, TG, HDL, UA, and HsCRP; 21.3%), lipids (total cholesterol and LDL-cholesterol; 14.0%), blood pressure (SBP and DBP; 13.3%), glucose (HbA1C, fasting glucose; 12.9%), and sedentary behavior (MET and sitting time; 8.0%). The inflammation biomarker HsCRP was clustered with only adiposity factors and not with other cardiometabolic risk factors, and the clustering pattern was verified in the validation group. Conclusion This study confirmed the clustering structure of cardiometabolic risk factors in the general population, including sedentary behavior. HsCRP was clustered with adiposity factors, while physical inactivity and sedentary behavior were clustered with each other.

Light intensity alters the effects of light-induced circadian disruption on glucose and lipid metabolism in mice

2021 ◽  
Author(s):  
Xiaojing Fan ◽  
Defu Chen ◽  
Ying Wang ◽  
Yizhou Tan ◽  
Hongyou Zhao ◽  
...  
Keyword(s):  
Body Weight ◽  
Light Intensity ◽  
High Fat Diet ◽  
Body Weight Gain ◽  
Circadian Disruption ◽  
Light Cycle ◽  
Jet Lag ◽  
Glucose And Lipid Metabolism ◽  
Insulin Tolerance ◽  
Potential Strategy

Circadian disruption induced by rotating light cycles has been linked to metabolic disorders. However, how the interaction of light intensity and light cycle affects metabolism under different diets remains to be explored. Eighty mice were first randomly stratified into the low- (LFD, n = 40) or high-fat diet (HFD, n = 40) groups. Each group was further randomly subdivided into four groups (n = 8-12 per group) in terms of different light intensities (lower [LI, 78 lx] or higher intensity [HI, 169 lx]) and light cycles (12 h light:12 h dark cycle or circadian-disrupting [CD] light cycle consisting of repeated 6-h light phase advancement). Body weight was measured weekly. At the end of the 16-week experiment, mice were sacrificed for serum and pathological analysis. Glucose and insulin tolerance tests were performed during the last 2 weeks. The CD cycle increased body weight gain, adipocyte area, glucose intolerance, and insulin resistance of LFD as well as HFD mice under HI but not LI condition. Moreover, the serum and hepatic triglyceride levels increased with LFD-HI treatment, regardless of light cycle. In addition, the CD cycle improved lipid and glucose metabolism under HFD-LI condition. In summary, the detrimental effects of the CD cycle on metabolism were alleviated under LI condition, especially in HFD mice. These results indicate that modulating light intensity is a potential strategy to prevent the negative metabolic consequences associated with jet lag or shift work.

scholarly journals Is Drinking Alcohol Really Linked to Cardiovascular Health? Evidence from the Kardiovize 2030 Project

Nutrients ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2848
Author(s):  
Andrea Maugeri ◽  
Ota Hlinomaz ◽  
Antonella Agodi ◽  
Martina Barchitta ◽  
Sarka Kunzova ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Alcohol Consumption ◽  
Cardiovascular Health ◽  
Fasting Glucose ◽  
Smoking Status ◽  
Activity Level ◽  
Drinking Patterns ◽  
Drinking Alcohol ◽  
Cvd Risk ◽  
The Relationship

Existing data have described benefits and drawbacks of alcohol consumption on cardiovascular diseases (CVD), but no research has evaluated its association with the cardiovascular health (CVH) score proposed by the American Heart Association. Here, we conducted a cross-sectional analysis on the Kardiovize cohort (Brno, Czech Republic), to investigate the relationship between alcohol consumption and CVH. We included 1773 subjects (aged 25–64 years; 44.2% men) with no history of CVD. We compared CVD risk factors, CVH metrics (i.e., BMI, healthy diet, physical activity level, smoking status, blood pressure, fasting glucose, and total cholesterol) and CVH score between and within several drinking categories. We found that the relationship between drinking habits and CVH was related to the amount of alcohol consumed, drinking patterns, and beverage choices. Heavy drinkers were more likely to smoke tobacco, and to report diastolic blood pressure, fasting glucose, triglycerides, and low-density lipoprotein (LDL)-cholesterol at higher level than non-drinkers. Among drinkers, however, people who exclusively drank wine exhibited better CVH than those who exclusively drank beer. Although our findings supported the hypothesis that drinking alcohol was related to the CVH in general, further prospective research is needed to understand whether the assessment of CVH should incorporate information on alcohol consumption.

Non-invasive assessment of endothelial activity in patients with coronary heart disease and cardiovascular risk factors

VASA ◽  
2008 ◽  
Vol 37 (2) ◽  
pp. 137-142 ◽  
Author(s):  
Fronek ◽  
Allison
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Cvd Risk ◽  
Hypertension Status ◽  
The Difference ◽  
Highly Correlated ◽  
And Gender

Background: The aim of this study was first to compare the widely used flow mediated dilation ( FMD ) method with the iontophoretically induced acetylcholine vasodilation (IAV ) procedure. The ultimate goal was to examine the endothelial activity ( EA ) in patients with various cardiovascular risk factors compared with control subjects. Patients and methods: In the upper extremities of 27 subjects, comparisons of EA by FMD and IAV measured with laser Doppler flux method (LDF) were conducted. IAV-EA was then measured using LDF in an additional 93 subjects with various cardiovascular ( CVD ) risk factors and/or a diagnosis of coronary heart disease (CHD). Results: The mean age of the subjects was 56.2 years and 54% were male. There was a robust and significant correlation between FMD vs IAV endothelial activity (r = 0.87, p = 0.025). After adjustment for age, there were significant differences in LDF-measured, acetylcholine-induced EA by diagnosis of CHD (p = 0.02), hyperlipidemia (p = 0.03) and diabetes (p < 0.01), as well as by sex (p < 0.01). The difference by hypertension status was of borderline significance (p = 0.07). LDF EA was higher in non-smokers compared to smokers but this difference was not statistically significant (p = 0.3). After adjustment for age and gender, a 10-unit increase in LDF-measured EA was associated with a 12% lower odds for a diagnosis of CHD (p = 0.07). Conclusions: Measurement of IAV-EA by LDF is a simple, noninvasive methodology which is highly correlated with post-occlusive FMD EA and is also significantly associated with a diagnosis of CHD.

scholarly journals High TSH Level within Normal Range Is Associated with Obesity, Dyslipidemia, Hypertension, Inflammation, Hypercoagulability, and the Metabolic Syndrome: A Novel Cardiometabolic Marker

2019 ◽  
Vol 8 (6) ◽  
pp. 817 ◽  
Author(s):  
Yi-Cheng Chang ◽  
Shih-Che Hua ◽  
Chia-Hsuin Chang ◽  
Wei-Yi Kao ◽  
Hsiao-Lin Lee ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Blood Pressure ◽  
Metabolic Syndrome ◽  
Central Obesity ◽  
Cardiometabolic Risk ◽  
Fasting Glucose ◽  
Reference Group ◽  
Elevated Blood ◽  
Normal Range ◽  
The Metabolic Syndrome

(1) Background: Overt and subclinical hypothyroidism has been associated with increased cardiometabolic risks. Here we further explore whether thyroid function within normal range is associated with cardiometabolic risk factors in a large population-based study. (2) Methods: We screened 24,765 adults participating in health examinations in Taiwan. Participants were grouped according to high-sensitive thyroid-stimulating hormone (hsTSH) level as: <50th percentile (0.47–1.48 mIU/L, the reference group), 50–60th percentile (1.49–1.68 mIU/L), 60–70th percentile (1.69–1.94 mIU/L), 70–80th percentile (1.95–2.3 mIU/L), 80–90th percentile (2.31–2.93 mIU/L), and >90th percentile (>2.93 mIU/L). Cardiometabolic traits of each percentile were compared with the reference group. (3) Results: Elevated hsTSH levels within normal range were dose-dependently associated with increased body mass index, body fat percentage, waist circumferences, blood pressure, hemoglobin A1c (HbA1c), fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), high homeostasis model of assessment of beta-cell (HOMA-β), triglycerides, total cholesterols, fibrinogen, and uric acids (p-for-trend <0.001), but not with fasting glucose levels. The association remained significant after adjustment of age, sex, and lifestyle. As compared to the reference group, subjects with the highest hsTSH percentile had significantly increased risk of being overweight (adjusted odds ratio (adjOR): 1.35), increased body fat (adjOR: 1.29), central obesity (adjOR: 1.36), elevated blood pressure (adjOR: 1.26), high HbA1c (adjOR: 1.20), hyperinsulinemia (adjOR: 1.75), increased HOMA-IR (adjOR: 1.45), increased HOMA-β (adjOR: 1.40), hypertriglyceridemia (adjOR: 1.60), hypercholesterolemia (adjOR: 1.25), elevated hsCRP (adjOR: 1.34), increased fibrinogen (adjOR: 1.45), hyperuricemia (adjOR: 1.47), and metabolic syndrome (adjOR: 1.42), but significant risk of low fasting glucose (adjOR: 0.89). Mediation analysis indicates that insulin resistance mediates the majority of the association between thyroid hormone status and the metabolic syndrome. (4) Conclusion: Elevated hsTSH within the normal range is a cardiometabolic risk marker associated with central obesity, insulin resistance, elevated blood pressure, dyslipidemia, hyperuricemia, inflammation, and hypercoagulability.

scholarly journals Effects of Meal Timing on Postprandial Glucose Metabolism and Blood Metabolites in Healthy Adults

Nutrients ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1763 ◽  
Author(s):  
Masaki Takahashi ◽  
Mamiho Ozaki ◽  
Moon-Il Kang ◽  
Hiroyuki Sasaki ◽  
Mayuko Fukazawa ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Tricarboxylic Acid Cycle ◽  
Area Under The Curve ◽  
Random Order ◽  
Postprandial Glucose ◽  
Healthy Adults ◽  
Blood Metabolites ◽  
Blood Collection ◽  
Meal Timing ◽  
The Difference

We examined the effects of meal timing on postprandial glucose metabolism, including the incretin response and metabolites in healthy adults. Nineteen healthy young men completed two trials involving blood collection in a fasting state and at 30, 60 and 120 min after meal provision in a random order: (1) morning (~0900 h) and (2) evening (~1700 h). The blood metabolome of eight participants was analyzed using capillary electrophoresis-mass spectrometry. Postprandial glucose concentrations at 120 min (p = 0.030) and glucose-dependent insulinotropic polypeptide concentrations (p = 0.005) at 60 min in the evening trials were higher than those in the morning trials. The incremental area under the curve values of five glycolysis, tricarboxylic acid cycle and nucleotide-related metabolites and 18 amino acid-related metabolites were higher in the morning trials than those in the evening trials (p < 0.05). Partial least-squares analysis revealed that the total metabolic change was higher in the morning. Our study demonstrates that a meal in the evening exacerbates the state of postprandial hyperglycemia in healthy adults. In addition, this study provides insight into the difference of incretion and blood metabolites between breakfast and dinner, indicating that the total metabolic responses tends to be higher in the morning.

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