scholarly journals Cardiovascular disease manifestations in Chernobyl liquidators: 25 years later. Clinico-analytical review

2012 ◽  
Vol 11 (3) ◽  
pp. 62-69 ◽  
Author(s):  
I. L. Telkova
Keyword(s):  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Vascular Pathology ◽  
Radiation Doses ◽  
Cardiovascular Monitoring ◽  
Early Diagnostics ◽  
Analytical Review ◽  
Atherosclerosis Development ◽  
Low Radiation Doses

Aim. To analyse the results of clinical cardiovascular disease (CVD) monitoring and the specifics of CVD manifestations in Chernobyl liquidators, who were exposed to “low” radiation doses 25 years ago. Material and methods. In total, 402 Chernobyl liquidators were examined. One hundred eighty five individuals have been followed up for 15 years (1994-2009) and underwent primary and repeat cardiologic examination, such as coronary artery (CA) angiography, endomyocardial biopsy, computed spiral tomography of CA, peripheral artery ultrasound, and the assessment of hormone levels, carbohydrate metabolism, and cardiovascular function. Results. At the first stage of cardiovascular monitoring (1995-1999), the most prevalent forms of CVD among Chernobyl liquidators were arterial hypertension, AH (70,3%) and/or coronary microvascular coronary heart disease, MVCHD (58,9%). Ten-fifteen years later, the prevalence of AH had not changed substantially. The prevalence of atherosclerotic coronary heart disease (ACHD) had increased, partly due to CA atherosclerosis development in patients with earlier diagnosed MVCHD. Severity, manifestations, and outcomes of CVD in Chernobyl liquidators were dependent on a range of factors, such as age, duration and doses of radiation exposure, and subsequent lifestyle and work characteristics. Conclusion. Stabilisation of AH pathomorphosis and prevention of atherosclerotic progression of MVCHD were associated with early diagnostics of vascular pathology and effective CVD prevention.

Mortality and associated risk factors in diabetics

1985 ◽  
Vol 110 (4_Suppl) ◽  
pp. S21-S26 ◽  
Author(s):  
R. J. Jarrett ◽  
M. J. Shipley
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Mortality Risk ◽  
Mortality Rates ◽  
Potential Risk Factors ◽  
Diabetic Population ◽  
Associated Risk Factors ◽  
The Relationship

Summary. In 168 male diabetics aged 40-64 years participating in the Whitehall Study, ten-year age adjusted mortality rates were significantly higher than in non-diabetics for all causes, coronary heart disease, all cardiovascular disease and, in addition, causes other than cardiovascular. Mortality rates were not significantly related to known duration of the diabetes. The predictive effects of several major mortality risk factors were similar in diabetics and non-diabetics. Excess mortality rates in the diabetics could not be attributed to differences in levels of blood pressure or any other of the major risk factors measured. Key words: diabetics; mortality rates; risk factors; coronary heart disease. There are many studies documenting higher mortality rates - particularly from cardiovascular disease -in diabetics compared with age and sex matched diabetics from the same population (see Jarrett et al. (1982) for review). However, there is sparse information relating potential risk factors to subsequent mortality within a diabetic population, information which might help to explain the increased mortality risk and also suggest preventive therapeutic approaches. In the Whitehall Study, a number of established diabetics participated in the screening programme and data on mortality rates up to ten years after screening are available. We present here a comparison of diabetics and non-diabetics in terms of relative mortality rates and the influence of conventional risk factors as well as an analysis of the relationship between duration of diabetes and mortality risk.

Abstract MP013: Strong Inverse Relation between Physical Activity and Incidence of Cardiovascular Disease in African Americans: the Atherosclerosis Risk in Communities Study

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Elizabeth J Bell
Keyword(s):  
Physical Activity ◽  
Heart Failure ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
African Americans ◽  
Risk Reduction ◽  
Atherosclerosis Risk In Communities ◽  
Baseline Visit ◽  
Atherosclerosis Risk

Introduction: Although there is substantial evidence that physical activity reduces risk of cardiovascular disease (CVD), the few studies that included African Americans offer inconclusive evidence and did not study stroke and heart failure separately. Objective: We examined, in African Americans and Caucasians in the Atherosclerosis Risk in Communities study (ARIC), the association of physical activity with CVD incidence (n=1,039) and its major components - stroke (n=350), heart failure (n=633), and coronary heart disease (n=442) - over a follow-up period of 21 years. Methods: ARIC is a population-based biracial cohort study of 45– to 64-yr-old adults at the baseline visit in 1987–89. Physical activity was assessed using the modified Baecke physical activity questionnaire and categorized by the American Heart Association’s ideal CVD health guidelines: poor, intermediate, and ideal physical activity. An incident CVD event was defined as the first occurrence of 1) heart failure, 2) definite or probable stroke, or 3) coronary heart disease, defined as a definite or probable myocardial infarction or definite fatal coronary heart disease. Results: We included 3,707 African Americans and 10,018 Caucasians free of CVD at the baseline visit. After adjustment for age, sex, cigarette smoking, alcohol intake, hormone therapy use, education, and ‘Western’ and ‘Prudent’ dietary pattern scores, higher physical activity was inversely related to CVD, heart failure, and coronary heart disease incidence in African Americans and Caucasians (p-values for trend tests <.0001), and with stroke in African Americans. Hazard ratios (95% confidence intervals) for CVD for intermediate and ideal physical activity, respectively, compared to poor, were similar by race: 0.65 (0.56, 0.75) and 0.59 (0.49, 0.71) for African Americans, and 0.74 (0.66, 0.83) and 0.67 (0.59, 0.75) for Caucasians (p-value for interaction = 0.38). Physical activity was also associated similarly in African Americans and Caucasians for each of the individual CVD outcomes (coronary heart disease, heart failure, and stroke), with an approximate one-third reduction in risk for intermediate and ideal physical activity versus poor physical activity- this reduction was statistically significant. Conclusions: In conclusion, our findings reinforce public health recommendations that regular physical activity is important for CVD risk reduction, including reductions in stroke and heart failure. They provide strong new evidence that this risk reduction applies to African Americans as well as Caucasians and support the idea that some physical activity is better than none.

Abstract P253: Proton Pump Inhibitor Use is Positively Associated with Incidence of Cardiovascular Disease

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Elizabeth J Bell ◽  
Jennifer L St. Sauver ◽  
Veronique L Roger ◽  
Nicholas B Larson ◽  
Hongfang Liu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Language Processing ◽  
Proton Pump ◽  
Hazard Ratio ◽  
Time Varying ◽  
Increased Risk ◽  
The Impact

Introduction: Proton pump inhibitors (PPIs) are used by an estimated 29 million Americans. PPIs increase the levels of asymmetrical dimethylarginine, a known risk factor for cardiovascular disease (CVD). Data from a select population of patients with CVD suggest that PPI use is associated with an increased risk of stroke, heart failure, and coronary heart disease. The impact of PPI use on incident CVD is largely unknown in the general population. Hypothesis: We hypothesized that PPI users have a higher risk of incident total CVD, coronary heart disease, stroke, and heart failure compared to nonusers. To demonstrate specificity of association, we additionally hypothesized that there is not an association between use of H 2 -blockers - another commonly used class of medications with similar indications as PPIs - and CVD. Methods: We used the Rochester Epidemiology Project’s medical records-linkage system to identify all residents of Olmsted County, MN on our baseline date of January 1, 2004 (N=140217). We excluded persons who did not grant permission for their records to be used for research, were <18 years old, had a history of CVD, had missing data for any variable included in our model, or had evidence of PPI use within the previous year.We followed our final cohort (N=58175) for up to 12 years. The administrative censoring date for CVD was 1/20/2014, for coronary heart disease was 8/3/2016, for stroke was 9/9/2016, and for heart failure was 1/20/2014. Time-varying PPI ever-use was ascertained using 1) natural language processing to capture unstructured text from the electronic health record, and 2) outpatient prescriptions. An incident CVD event was defined as the first occurrence of 1) validated heart failure, 2) validated coronary heart disease, or 3) stroke, defined using diagnostic codes only. As a secondary analysis, we calculated the association between time-varying H 2 -blocker ever-use and CVD among persons not using H 2 -blockers at baseline. Results: After adjustment for age, sex, race, education, hypertension, hyperlipidemia, diabetes, and body-mass-index, PPI use was associated with an approximately 50% higher risk of CVD (hazard ratio [95% CI]: 1.51 [1.37-1.67]; 2187 CVD events), stroke (hazard ratio [95% CI]: 1.49 [1.35-1.65]; 1928 stroke events), and heart failure (hazard ratio [95% CI]: 1.56 [1.23-1.97]; 353 heart failure events) compared to nonusers. Users of PPIs had a 35% greater risk of coronary heart disease than nonusers (95% CI: 1.13-1.61; 626 coronary heart disease events). Use of H 2 -blockers was also associated with a higher risk of CVD (adjusted hazard ratio [95% CI]: 1.23 [1.08-1.41]; 2331 CVD events). Conclusions: PPI use is associated with a higher risk of CVD, coronary heart disease, stroke and heart failure. Use of a drug with no known cardiac toxicity - H 2 -blockers - was also associated with a greater risk of CVD, warranting further study.

scholarly journals Association of lung function with coronary heart disease and cardiovascular disease outcomes in elderly: The Rancho Bernardo study

2014 ◽  
Vol 108 (12) ◽  
pp. 1779-1785 ◽  
Author(s):  
Hwa Mu Lee ◽  
Michael A. Liu ◽  
Elizabeth Barrett-Connor ◽  
Nathan D. Wong
Keyword(s):  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Lung Function ◽  
Disease Outcomes

scholarly journals Relationship Between a Plant‐Based Dietary Portfolio and Risk of Cardiovascular Disease: Findings From the Women's Health Initiative Prospective Cohort Study

2021 ◽  
Author(s):  
Andrea J. Glenn ◽  
Kenneth Lo ◽  
David J. A. Jenkins ◽  
Beatrice A. Boucher ◽  
Anthony J. Hanley ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Postmenopausal Women ◽  
Prospective Cohort ◽  
Health Initiative ◽  
Dietary Portfolio ◽  
Diet Score

Background The plant‐based Dietary Portfolio combines established cholesterol‐lowering foods (plant protein, nuts, viscous fiber, and phytosterols), plus monounsaturated fat, and has been shown to improve low‐density lipoprotein cholesterol and other cardiovascular disease (CVD) risk factors. No studies have evaluated the relation of the Dietary Portfolio with incident CVD events. Methods and Results We followed 123 330 postmenopausal women initially free of CVD in the Women's Health Initiative from 1993 through 2017. We used Cox proportional‐hazard models to estimate adjusted hazard ratios (HRs) and 95% CI of the association of adherence to a Portfolio Diet score with CVD outcomes. Primary outcomes were total CVD, coronary heart disease, and stroke. Secondary outcomes were heart failure and atrial fibrillation. Over a mean follow‐up of 15.3 years, 13 365 total CVD, 5640 coronary heart disease, 4440 strokes, 1907 heart failure, and 929 atrial fibrillation events occurred. After multiple adjustments, adherence to the Portfolio Diet score was associated with lower risk of total CVD (HR, 0.89; 95% CI, 0.83–0.94), coronary heart disease (HR, 0.86; 95% CI, 0.78–0.95), and heart failure (HR, 0.83; 95% CI, 0.71–0.99), comparing the highest to lowest quartile of adherence. There was no association with stroke (HR, 0.97; 95% CI, 0.87–1.08) or atrial fibrillation (HR, 1.10; 95% CI, 0.87–1.38). These results remained statistically significant after several sensitivity analyses. Conclusions In this prospective cohort of postmenopausal women in the United States, higher adherence to the Portfolio Diet was associated with a reduction in incident cardiovascular and coronary events, as well as heart failure. These findings warrant further investigation in other populations.

scholarly journals Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation (Preprint)

2019 ◽  
Author(s):  
Zhenzhen Du ◽  
Yujie Yang ◽  
Jing Zheng ◽  
Qi Li ◽  
Denan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Big Data ◽  
Heart Disease ◽  
Large Population ◽  
High Accuracy ◽  
Learning Methods ◽  
Health Records ◽  
Machine Learning Methods

BACKGROUND Predictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated. OBJECTIVE Based on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learning METHODS Data from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance. RESULTS An ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores. CONCLUSIONS We demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

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