Prevalence and risk factors of heart failure in the USA: NHANES 2013 – 2014 epidemiological follow-up study

Komanduri, Saketram; Jadhao, Yogesh; Guduru, Sai; Cheriyath, Pramil; Wert, Yijin

doi:10.1080/20009666.2016.1264696

Cited by 61 publications

(34 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last decades several heart failure (HF) risk scores have been published, mostly from large cohort studies. Although differences between these models exist, most risk models have reported a quite consistent list of risk factors including age, sex, coronary artery disease (CAD), myocardial infarction (MI), hypertension, diabetes mellitus, and obesity as the most important risk factors, 7–12 In turn, some of these endo-phenotypes, such as CAD and MI, are explained by several established cardiovascular (CV) risk factors, such as diet, sedentary lifestyle, and smoking. Furthermore, genetic risk scores are emerging as a tool to assess the risk of developing HF 13 …”

Section: Risk Factors For Heart Failurementioning

confidence: 99%

Common risk factors for heart failure and cancer

Meijers

Boer

2019

Cardiovascular Research

227

142

View full text Add to dashboard Cite

Cardiovascular (CV) disease and cancer are the leading causes of death. 1 , 2 Over the last decades, it has been appreciated that both CV disease and cancer are more common in individuals in whom risk factors for disease development accumulate, and preventative measures have been extremely important in driving down the incidence of disease. 3–6 In general, the field of epidemiology, risk reduction, and preventative trials is divided into health care professionals who have an interest in either CV disease or cancer. As a result, the medical literature and medical practice has largely focused on the one disease, or the other. However, human individuals do not behave according to this dogma. Emerging data clearly suggest that identical risk factors may lead to CV disease in the one individual, but may cause cancer in another, or even both diseases in the same individual. This overlap exists between risk factors that are historically classified as ‘CV risk factors’ as these factors do equally strong predict cancer development. Therefore, we propose that a holistic approach might better estimate actual risks for CV disease and cancer. In this review, we summarize current insights in common behavioural risk factors for heart failure, being the most progressed and lethal form of CV disease, and cancer.

show abstract

Section: Risk Factors For Heart Failurementioning

confidence: 99%

Common risk factors for heart failure and cancer

Meijers

Boer

2019

Cardiovascular Research

227

142

View full text Add to dashboard Cite

show abstract

“…Congestive heart failure is observed in 1% to 2% of the population [20,21]. The prevalence of CHF depends on the age of the population.…”

Section: Discussionmentioning

confidence: 99%

“…Our studies had limitations and therefore it was not possible to assess the most important risk factors of heart failure, such as the presence of coronary heart disease, arterial hypertension, obesity, lipid abnormalities, duration of diabetes, and compensation of diabetes metabolism, and such factors are emphasised by such authors as Komanduri et al [21] or Rydén et al [26,27].…”

Section: Discussionmentioning

confidence: 99%

Diabetes mellitus and congestive heart failure: the prevalence of congestive heart failure in patients with and without diabetes in Poland

et al. 2021

View full text Add to dashboard Cite

Introduction: Diabetes mellitus is a systemic disease and has a negative effect on the cardiovascular system. This paper aimed to present a retrospective analysis of morbidity associated with heart failure in subgroups of patients with and without diabetes in Poland in 2012. Material and methods: Data from the National Health Fund were used for the study. In general, 656,937 patients with heart failure, including 281,538 males and 375,354 females, were studied. In this population, additionally, 201,043 patients with heart failure (main diagnosis) and diabetes were studied, including 82,117 males and 118,926 females. Results: The mean index of morbidity associated with heart failure in the whole subpopulation of diabetes patients was 9.03%; 8.42% for males and 9.50% for females. Morbidity associated with heart failure in the population of patients diagnosed with diabetes in Poland in 2012 was seven times higher compared to morbidity associated with heart failure in non-diabetes patients. Morbidity associated with heart failure in females was significantly higher compared to morbidity in males in the whole population, in both the subpopulations of patients with and without diabetes. Conclusions: Diabetes mellitus significantly increases risk of heart failure in both women and men. The risk is significantly high after the age of 60 years and higher in females.

show abstract

“…Heart failure (HF) affects nearly 6 million adults (Benjamin et al, 2019;Mozaffarian et al, 2016). Within the U.S. population, there are differences in timing and progression of HF between individuals of different racial/ethnic groups and between males and females, and as the population ages the incidence of HF increases (Komanduri et al, 2017). The aging heart is characterized as having increased stiffness and changes in myocardial performance and size (Chiao and Rabinovitch, 2015;Oxenham and Sharpe, 2003).…”

Section: Introductionmentioning

confidence: 99%

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

Pottinger¹,

Pesce²,

Gacita³

et al. 2020

Preprint

View full text Add to dashboard Cite

Approximately 6 million adults in the US have heart failure (HF). HF progression is variable due in part to differences in sex, age, and genetic ancestry. Previous population-based genetic studies have largely focused on cross-sectional data related to HF, a disease known to change over time. Utilizing longitudinal data trajectory probabilities as a continuous trait may increase the likelihood of finding significant, biologically relevant associations in a genome-wide association (GWA) analysis. We analyzed data from the electronic health record in a medical biobank from a single, metropolitan US center to gather clinically pertinent data for analyses. We evaluated whole genome sequencing of 896 unrelated biobank participants, including 494 with at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (~3 observations per person). A censored normal distribution multivariable mixture model was used to cluster phenotype measures for genome-wide analyses. GWA analysis on the trajectory probability of the corrected QT measurement (QTc) taken from electrocardiograms identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes the Wnt ligand secretion mediator, Wntless. WLS was previously associated with QT length using of approximately 16,000 participants supporting the utility of this method to uncover significant genetic associations in small datasets. GWA analysis on the trajectory probability of left ventricular diameter as taken from echocardiograms identified novel significant associations with variants in regulatory regions near MYO10, which encodes the unconventional Myosin-10. We found that trajectory probabilities improved the ability to discover significant and relevant genetic associations. This novel approach increased yield from smaller, well-phenotyped cohorts with longitudinal data from a medical biobank. AUTHOR SUMMARYApproximately 6 million adults in the US have heart failure, a disease known to change over time. In a hospital based electronic health record, electrocardiograms and echocardiograms, used to evaluate heart failure, can be tracked over time. We utilized these data to create a novel trait that can be applied to genetic analyses. We analyzed genome sequence of 896 biobank participants from diverse racial/ethnic backgrounds. Genome-wide association (GWA) analyses were performed on a subset of these individuals for heart failure outcomes. A statistical model that incorporates cardiac data that are tracked over time was used to cluster these data using a probabilistic approach. These probabilities were used for a GWA analysis for corrected QT measurement (QTc) and left ventricular diameter (LVID). The QTc interval analysis identified significant correlations with variants in regulatory regions near the WLS gene which encodes the Wnt ligand secretion mediator, Wntless. Analysis of LVID identified significant associations with variants in regulatory regions near the MYO10 gene which encodes the unconventional Myosin-10. Through t...

show abstract

Prevalence and risk factors of heart failure in the USA: NHANES 2013 – 2014 epidemiological follow-up study

Cited by 61 publications

References 26 publications

Common risk factors for heart failure and cancer

Common risk factors for heart failure and cancer

Diabetes mellitus and congestive heart failure: the prevalence of congestive heart failure in patients with and without diabetes in Poland

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

Contact Info

Product

Resources

About