Longitudinal Changes and Predictive Value of the Renal Resistive Index in the General Population.

Aim: Haemodynamic imaging markers like the renal-resistive index (RRI) provide insights into cardiovascular-renal interactions. However, longitudinal epidemiological data on the RRI's natural history, correlates and predictive value are lacking. We investigated factors associated with longitudinal RRI changes (ΔRRI) and its predictive value for cardiovascular events in the community.

PWV to GLS Ratio in the General Population: Reference Values, Correlates, and Predictive Value of a Noninvasive Ventricular-Arterial Index.

Purpose: Noninvasive imaging markers combining ventricular and arterial properties may help predict cardiac disease. We conducted a general population study to determine reference values, clinical correlates, and the predictive value of the ratio of the carotid-femoral pulse wave velocity (cfPWV) to the left ventricular global longitudinal strain (GLS).

Methods: We measured cfPWV by applanation tonometry and 4-chamber GLS by echocardiography in 1026 individuals (mean age 50.

Clinical and biochemical predictors of longitudinal changes in left atrial structure and function: A general population study.

Purpose: There is a need for better understanding the factors that modulate left atrial (LA) dysfunction. Therefore, we determined associations of clinical and biochemical biomarkers with serial changes in echocardiographic indexes of LA function in the general population.

Methods: We measured LA maximal and minimal volume indexes (LAVImax and LAVImin) by echocardiography and LA reservoir strain (LARS) by two-dimensional speckle-tracking in 627 participants (mean age 50.

Feature-based clustering of the left ventricular strain curve for cardiovascular risk stratification in the general population.

Objective: Identifying individuals with subclinical cardiovascular (CV) disease could improve monitoring and risk stratification. While peak left ventricular (LV) systolic strain has emerged as a strong prognostic factor, few studies have analyzed the whole temporal profiles of the deformation curves during the complete cardiac cycle. Therefore, in this longitudinal study, we applied an unsupervised machine learning approach based on time-series-derived features from the LV strain curve to identify distinct strain phenogroups that might be related to the risk of adverse cardiovascular events in the general population.

Serum proteomic profiling of carotid arteriopathy: A population outcome study.

Background And Aims: Circulating proteins reflecting subclinical vascular disease may improve prediction of atherosclerotic cardiovascular disease (ASCVD). We applied feature selection and unsupervised clustering on proteomic data to identify proteins associated with carotid arteriopathy and construct a protein-based classifier for ASCVD event prediction.

Methods: 491 community-dwelling participants (mean age, 58 ± 11 years; 51 % women) underwent carotid ultrasonography and proteomic profiling (CVD II panel, Olink Proteomics).

Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction: A Machine Learning Approach.

Integrative interpretation of cardiopulmonary exercise tests (CPETs) may improve assessment of cardiovascular (CV) risk. Here, we identified patient phenogroups based on CPET summary metrics and evaluated their predictive value for CV events. We included 2280 patients with diverse CV risk who underwent maximal CPET by cycle ergometry.

Cardiorespiratory fitness components in relation to clinical characteristics, disease state and medication intake: A patient registry study.

Background: Interpretation of cardiopulmonary exercise testing (CPET) results requires thorough understanding of test confounders such as anthropometrics, comorbidities and medication. Here, we comprehensively assessed the clinical determinants of cardiorespiratory fitness and its components in a heterogeneous patient sample.

Methods: We retrospectively collected medical and CPET data from 2320 patients (48.

Novel left ventricular mechanical index in pulmonary arterial hypertension.

Ventricular interdependence plays an important role in pulmonary arterial hypertension (PAH). It can decrease left ventricular (LV) longitudinal strain (LVLS) and lead to a leftward displacement ("transverse shortening") of the interventricular septum (sTS). For this study, we hypothesized the ratio of LVLS/sTS would be a sensitive marker of systolic ventricular interactions in PAH.

Unsupervised Time-Series Clustering of Left Atrial Strain for Cardiovascular Risk Assessment.

Background: Early identification of individuals at high risk for developing cardiovascular (CV) events is of paramount importance for efficient risk management. Here, the authors investigated whether using unsupervised machine learning methods on time-series data of left atrial (LA) strain could distinguish clinically meaningful phenogroups associated with the risk for developing adverse events.

Methods: In 929 community-dwelling individuals (mean age, 51.

Improving predictive performance in incident heart failure using machine learning and multi-center data.

Objective: To mitigate the burden associated with heart failure (HF), primary prevention is of the utmost importance. To improve early risk stratification, advanced computational methods such as machine learning (ML) capturing complex individual patterns in large data might be necessary. Therefore, we compared the predictive performance of incident HF risk models in terms of (a) flexible ML models and linear models and (b) models trained on a single cohort (single-center) and on multiple heterogeneous cohorts (multi-center).

Temporal shift and predictive performance of machine learning for heart transplant outcomes.

Background: Outcome prediction following heart transplant is critical to explaining risks and benefits to patients and decision-making when considering potential organ offers. Given the large number of potential variables to be considered, this task may be most efficiently performed using machine learning (ML). We trained and tested ML and statistical algorithms to predict outcomes following cardiac transplant using the United Network of Organ Sharing (UNOS) database.

Evaluation of diastole by echocardiography for detecting early cardiac dysfunction: an outcome study.

Aims: Timely detection of subclinical left ventricular diastolic dysfunction (LVDDF) is of importance for precise risk stratification of asymptomatic subjects. Here, we evaluated the prevalence of LVDDF and its prognostic significance in the general population using two grading approaches: the 2016 ASE/EACVI recommendations and population-derived, age-specific criteria.

Methods And Results: We randomly recruited 1407 community-dwelling participants (mean age, 51.

Subclinical heart remodeling and dysfunction in relation to peripheral endothelial dysfunction: A general population study.

Aims: Epidemiological studies should substantiate the paradigm that endothelial dysfunction contributes to the development of heart failure with preserved ejection fraction (HFpEF). We investigated the association of cardiac remodeling and dysfunction with peripheral vasoreactivity in the general population.

Methods: In 424 individuals, we echocardiographically assessed cardiac structure and function and determined digital vasomotor function by photoplethysmography (PPG) during reactive hyperemia (RH).

Temporal changes in soluble angiotensin-converting enzyme 2 associated with metabolic health, body composition, and proteome dynamics during a weight loss diet intervention: a randomized trial with implications for the COVID-19 pandemic.

Background: Angiotensin-converting enzyme 2 (ACE2) serves protective functions in metabolic, cardiovascular, renal, and pulmonary diseases and is linked to COVID-19 pathology. The correlates of temporal changes in soluble ACE2 (sACE2) remain understudied.

Objectives: We explored the associations of sACE2 with metabolic health and proteome dynamics during a weight loss diet intervention.

Proteomic profiling for detection of early-stage heart failure in the community.

Aims: Biomarkers may provide insights into molecular mechanisms underlying heart remodelling and dysfunction. Using a targeted proteomic approach, we aimed to identify circulating biomarkers associated with early stages of heart failure.

Methods And Results: A total of 575 community-based participants (mean age, 57 years; 51.

Echocardiographic phenogrouping by machine learning for risk stratification in the general population.

Aims: There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left heart structure and function could identify phenotypically distinct groups of asymptomatic individuals in the general population.

Methods And Results: We prospectively studied 1407 community-dwelling individuals (mean age, 51.

Subclinical Heart Dysfunction in Relation to Metabolic and Inflammatory Markers: A Community-Based Study.

Background: Population studies investigating the contribution of immunometabolic disturbances to heart dysfunction remain scarce. We combined high-throughput biomarker profiling, multidimensional network analyses, and regression statistics to identify immunometabolic markers associated with subclinical heart dysfunction in the community.

Methods: In 1,236 individuals (mean age, 51.

Subclinical left atrial dysfunction profiles for prediction of cardiac outcome in the general population.

Objective: Echocardiographic definitions of subclinical left atrial dysfunction based on epidemiological data remain scarce. In this population study, we derived outcome-driven thresholds for echocardiographic left atrial function parameters discriminating between normal and abnormal values.

Methods: In 1306 individuals (mean age, 50.

Applying machine learning to detect early stages of cardiac remodelling and dysfunction.

Aims: Both left ventricular (LV) diastolic dysfunction (LVDD) and hypertrophy (LVH) as assessed by echocardiography are independent prognostic markers of future cardiovascular events in the community. However, selective screening strategies to identify individuals at risk who would benefit most from cardiac phenotyping are lacking. We, therefore, assessed the utility of several machine learning (ML) classifiers built on routinely measured clinical, biochemical, and electrocardiographic features for detecting subclinical LV abnormalities.