Citations

Background There is a scarcity of validated rapid dietary screening tools for patient use in the clinical setting to improve health and reduce cardiovascular risk. The Healthy Eating Index (HEI) 2015 measures compliance with the 2015 to 2020 Dietary Guidelines for Americans but requires completion of an extensive diet assessment to compute, which is time consuming and impractical. The authors hypothesize that a 19‐item dietary survey assessing consumption of common food groups known to affect health will be correlated with the HEI‐2015 assessed by a validated food frequency questionnaire and can be further reduced without affecting validity. Methods and Results A 19‐item Eating Assessment Tool (EAT) of common food groups was created through literature review and expert consensus. A cross‐sectional survey was then conducted in adult participants from a preventive cardiology clinic or cardiac rehabilitation and in healthy volunteers (n=661, mean age, 36 years; 76% women). Participants completed an online 156‐item food frequency questionnaire, which was used to calculate the HEI score using standard methods. The association between each EAT question and HEI group was analyzed by Kruskal‐Wallis test. Linear regression models were subsequently used to identify univariable and multivariable predictors for HEI score for further reduction in the number of items. The final 9‐item model of Mini‐EAT was validated by 5‐fold cross validation. The 19‐item EAT had a strong correlation with the HEI score ( r =0.73) and was subsequently reduced to the 9 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, fish or seafood, legumes/nuts/seeds, low‐fat dairy, high‐fat dairy, and sweets consumption, without affecting the predictive ability of the tool ( r =0.71). Conclusions Mini‐EAT is a 9‐item validated brief dietary screener that correlates well with a comprehensive food frequency questionnaire. Future studies to test the Mini‐EAT's validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

Introduction: Limited validated rapid dietary screening tools are available for patient use for actionable goal setting to improve health and reduce cardiovascular risk. The Healthy Index (HEI) Score measures compliance with the 2015-2020 Dietary Guidelines for Americans but requires completion of an extensive food frequency questionnaire (FFQ) to compute, which is time consuming and impractical. Hypothesis: A 19-item dietary survey that assesses consumption of common food groups known to affect health is correlated to the gold standard FFQ and HEI score and can be further reduced without affecting validity. Methods: An evidence-based 19-item Eating Assessment Tool (EAT™) of common food groups was created through literature review and expert consensus. We then conducted a cross-sectional survey of adult patients from a preventive cardiology clinic or cardiac rehabilitation and of healthy volunteers (n=628, mean age= 37 y, 76% women). Participants completed an online 156-item FFQ which was used to calculate the HEI score using standard methods. The association between each EAT™ question and HEI score category was analyzed by Kruskal-Walis test. Linear regression models were used to identify univariable and multivariable predictors for HEI score. The final model of Mini-EAT™ was validated by 5-fold cross validation. Results: The 19-item EAT™ had good correlation with the HEI (r=0.77) and was subsequently reduced to the 8 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, legumes/nuts/seeds, low-fat dairy, high-fat dairy, and sweets consumption, without affecting the predictive ability of the tool (r=0.76). Conclusions: Mini-EAT™ is an 8-item validated brief dietary screener that correlates well with a comprehensive FFQ. Future studies to test the Mini-EAT™’s validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

In contrast to the decreasing burden related to cardiovascular disease (CVD), the burden related to dysglycemia and adiposity complications is increasing in Czechia, and local drivers must be identified. A comprehensive literature review was performed to evaluate biological, behavioral, and environmental drivers of dysglycemia and abnormal adiposity in Czechia. Additionally, the structure of the Czech healthcare system was described. The prevalence of obesity in men and diabetes in both sexes has been increasing over the past 30 years. Possible reasons include the Eastern European eating pattern, high prevalence of physical inactivity and health illiteracy, education, and income-related health inequalities. Despite the advanced healthcare system based on the compulsory insurance model with free-for-service healthcare and a wide range of health-promoting initiatives, more effective strategies to tackle the adiposity/dysglycemia are needed. In conclusion, the disease burden related to dysglycemia and adiposity in Czechia remains high but is not translated into greater CVD. This discordant relationship likely depends more on other factors, such as improvements in dyslipidemia and hypertension control. A reconceptualization of abnormal adiposity and dysglycemia into a more actionable cardiometabolic-based chronic disease model is needed to improve the approach to these conditions. This review can serve as a platform to investigate causal mechanisms and secure effective management of cardiometabolic-based chronic disease.

Abstract Aims/Hypothesis The need for understanding obesity as a chronic disease, its stigmatization, and the lack of actionability related to it demands a new approach. The adiposity‐based chronic disease (ABCD) model is based on adiposity amount, distribution, and function, with a three stage complication‐centric rather than a body mass index (BMI)‐centric approach. The prevalence rates and associated risk factors are presented. Methods In total, 2159 participants were randomly selected from Czechia. ABCD was established as BMI ≥ 25 kg/m 2 or high body fat percent, or abdominal obesity and then categorized by their adiposity‐based complications: Stage 0: none; Stage 1: mild/moderate; Stage 2: severe. Results ABCD prevalence was 62.8%. Stage 0 was 2.3%; Stage 1 was 31.4%; Stage 2 was 29.1%. Comparing with other classifiers, participants in Stage 2 were more likely to have diabetes, hypertension, and metabolic syndrome than those with overweight, obesity, abdominal obesity, and increased fat mass. ABCD showed the highest sensitivity and specificity to detect participants with peripheral artery disease, increased intima media, and vascular disease. Conclusion/Interpretation The ABCD model provides a more sensitive approach that facilitates the early detection and stratification of participants at risk compared to traditional classifiers.

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.

Abstract Background : The term arterial stiffness (ArSt) describes structural changes in arterial wall related to the loss of elasticity and is known as an independent predictor of cardiovascular diseases (CVD). The evidence relating to ArSt and triglycerides (TG) shows contradictory results. This paper means to survey the association between high TG and ArSt, utilizing the cardio-ankle vascular index (CAVI). Methods : Subjects aged between 25 and 64 years from a random population-based sample were evaluated between 2013-2016. Data from questionnaires, blood pressure, anthropometric measures, and blood samples were collected and analyzed. CAVI was measured using VaSera VS-1500N devise. Subjects with a history of CVD or chronic renal disease were excluded. Results : 1934 participants, 44.7 % of males, were included. The median age was 48 (Interquartile Range [IQR] 19) years, TG levels were 1.05 (0.793) mmol/L, and CAVI 7.24 (1.43) points. Prevalence of high CAVI was 10.0 % (14.5 % in males and 6.4 % in females; P < 0.001) and prevalence of hypertriglyceridemia was 20.2 % (29.2 % in males and 13 % in females; P <0.001). Correlation between TG and CAVI was 0.136 ( P <0.001). High CAVI values were more prevalent among participants with metabolic syndrome (MetS), high blood pressure, dysglycemia, abdominal obesity, high LDL-cholesterol, and high total cholesterol. Using binary regression analysis, high TG were associated with high CAVI, even after adjustment for other MetS components, age, gender, smoking status, LDL-c, and statin treatment (β=0.474, OR= 1.607, 95 % CI = 1.063-2.429, P =0.024). Conclusion : TG levels were correlated with ArSt, measured as CAVI. High TG was associated with high CAVI independent of multiple cardiometabolic risk factors. Awareness of the risks and targeted treatment of hypertriglyceridemia could further benefit in reducing the prevalence of CVD and events.

Abstract Background The term arterial stiffness (ArSt) describes the reduced capability of an artery to expand and contract in response to pressure changes and is recognized as an independent predictor of cardiovascular diseases (CVD). The evidence relating ArSt and triglycerides (TG) shows contradictory results. The aim of this paper is to assess the association between high TG and ArSt, using the cardio-ankle vascular index (CAVI).Methods Subjects aged between 25 and 64 years from a random population-based sample were evaluated between 2013–2016. Data from questionnaires, blood pressure, anthropometric measures and blood samples were collected to identify the metabolic syndrome (MetS). CAVI was measured using VaSera VS-1500N devise (Fukuda Denshi Co., Ltd., Japan). Subjects with history of CVD or chronic renal disease were excluded.Results 1934 participants, 44.7% males, were included. Median age was 48 (Interquartile Range [IQR] 19) years, TG levels were 1.05 (0.793) mmol/L, and CAVI 7.24 (1.43) points. Prevalence of high CAVI was 10.0% (14.5% in males and 6.4% in females; p < 0.001). Correlation between TG and CAVI was 0.136 (p < 0.001). High CAVI values were more prevalent among participants with MetS, high blood pressure, dysglycemia, abdominal obesity, and high total cholesterol. Using binary regression analysis, high TG were associated with high CAVI, even after adjustment for other MetS components, age, gender, smoking status and cholesterol (OR = 1.630, 95% CI = 1.061–2.505, p = 0.026).Conclusion TG levels were correlated with ArSt, measured as CAVI. High TG was associated with high CAVI independent of multiple cardiometabolic risk factors.

Obesity and hypertension independently promote pathological left ventricular remodelling (LVR) and left ventricular hypertrophy (LVH), but to what extent they do so when they do not coexist is unclear. We used data from the Cardiovision Brno 2030 study to assess—for the first time in a region where no investigations have been previously carried out—the independent association of obesity and hypertension with LV geometry, and to evaluate the effects of hypertension in normal weight patients and the effects of obesity in normotensive patients. Overall, 433 individuals, aged 25–65 years, with no history of cardiovascular disease and/or antihypertensive treatment, were stratified into four groups according to BMI and hypertension: normal weight non-hypertensive (NWNH), normal weight hypertensive (NWH), overweight/obese non-hypertensive (ONH) and overweight/obese hypertensive (OH). LVR was classified as normal, concentric LVR (cLVR), concentric LVH (cLVH) or eccentric LVH (eLVH). Linear regression analysis demonstrated that body mass index (BMI) and systolic blood pressure (SBP) are the main predictors of LV mass and that they interact: SBP had a stronger effect in overweight/obese (β = 0.195; p = 0.033) compared to normal weight patients (β = 0.134; p = 0.048). Hypertension increased the odds of cLVR (OR = 1.78; 95%CI = 1.04–3.06; p = 0.037) and cLVH (OR = 8.20; 95% CI = 2.35–28.66; p = 0.001), independent of age, sex and BMI. Stratified analyses showed that NWH had a greater odd of cLVH (OR = 7.96; 95%CI = 1.70–37.08; p = 0.008) and cLVR (OR = 1.62; 95%CI = 1.02–3.34; p = 0.047) than NWNH. In the absence of hypertension, obesity was not associated with LVM and abnormal LV geometry, suggesting that it is not per se a determinant of LVR. Thus, antihypertensive therapy still remains the first-line approach against LVH in hypertensive patients, though weight loss interventions might be helpful in those who are obese.

In the European Union, Czech Republic ranks 3rd and 6th for the incidence of obesity and cardiovascular diseases, respectively. Worldwide, short sleep duration and excessive daytime sleepiness (EDS) characterize obese subjects, which in turn exhibit scarce physical activity and unhealthy diet. We aimed to understand the relationship between irregular sleep patterns, obesity and lifestyle factors, such as diet and physical activity, in a vulnerable Czech population. 1482 members of the Kardiovize cohort, a random sample of the Czech urban population, were included in a cross-sectional study. Exposure variables included self-reported sleep duration and EDS, assessed by the Epworth Sleepiness Scale. Primary outcomes were BMI and waist-to-hip ratio or prevalence of obesity and central obesity. Covariates included physical activity and diet. Associations and interactions between variables were evaluated using logistic regression analyses. After adjustment for covariates, short sleep duration (<7 h) was associated with greater odds of overweight (BMI > 25; OR = 1.42; 95%CI = 1.06–1.90; p = 0.020) and obesity (BMI > 30; OR = 1.40; 95%CI = 1.02–1.94; p = 0.047), while EDS was associated with greater odds of central obesity (OR = 1.72; 95%CI = 1.06–2.79; p = 0.030), independent of diet and physical activity. However, due to the cross-sectional nature of our study, further prospective, large-scale studies are needed to evaluate the etiological link and causality between sleep disturbances and obesity.