Publications by authors named "B Essen"
Aims: Regular heavy alcohol consumption may lead to the development of alcohol-related cardiomyopathy and symptomatic heart failure (HF) later in life. However, the dose-response relationship between alcohol consumption and risk for incident HF, and whether these associations vary by sex and type of alcoholic beverage remains unclear.
Methods And Results: A total of 407 014 participants (52% women, age 56 years) from the UK Biobank who completed alcohol-related questionnaires and without a history of HF at baseline were included in the study.
Background And Aims: Current estimates for the lifetime risk to develop heart failure with either a reduced (HFrEF) or preserved ejection fraction (HFpEF) and their associated risk factors are derived from two studies from the USA. The sex-specific lifetime risk and population attributable fraction of potentially modifiable risk factors for incident HFpEF and HFrEF are described in a large European community-based cohort with 25 years of follow-up.
Methods: A total of 8558 participants from the PREVEND cohort were studied at baseline from 1997 onwards and followed until 2022 for cases of new-onset HFrEF (ejection fraction < 50%) and HFpEF (ejection fraction ≥ 50%) by assessment of hospital records.
Background: Previous studies have examined clinical predictors of incident heart failure (HF) in men and women. However, potential mechanisms through which these clinical predictors relate to the onset of HF remain to be established.
Objectives: The authors studied the association between clinical and proteomic risk profiles of new-onset HF in men and women.
Background: We evaluated the potential of circulating bone morphogenetic protein 10 (BMP10) as a biomarker for atrial stress and remodelling in patients with heart failure (HF), in comparison to N-terminal pro-B-type natriuretic peptide (NT-proBNP). We also assessed the predictive value of BMP10 for adverse clinical outcomes.
Methods: BMP10 levels were quantified in 2085 chronic HF patients from the European BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) cohort and in 1487 patients from the Scottish validation cohort.
Background: Clustering algorithms can identify distinct heart failure (HF) subgroups. The choice of algorithms, modelling process, and input variables can impact clustering outcomes. Therefore, we reviewed analytical methods and variables used in studies that performed clustering in patients with HF.
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