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 50.8 years, 51.2% women) at baseline and after 4.8 years. RESULTS: During follow-up, LARS decreased significantly in men (-.90%, P = .033) but not in women (-.23%, P = .60). In stepwise regression analysis, stronger decrease in LARS over time was associated with male sex, a higher age, body mass index (BMI), mean arterial pressure (MAP) and serum insulin at baseline and with a greater increase in BMI and MAP over time (P ≤ .018). Similarly, an increased risk of developing or retaining abnormal LARS was observed in older participants, in subjects with a higher baseline BMI, MAP, heart rate (HR), troponin T and ΔMAP, and in those who used ß-blockers at baseline. Both LAVImax and LAVImin increased significantly over time (P ≤ .0007). This increase was associated with a higher baseline age, pulse pressure and a lower HR at baseline and a greater increase in pulse pressure over time (P ≤ .029). Higher serum insulin and D-dimer were independently associated with a stronger increase in LAVImin (P ≤ .0034). CONCLUSION: Subclinical worsening in LA dysfunction was associated with older age, hypertension, obesity, insulin resistance and troponin T levels. Cardiovascular risk management strategies may delay LA deterioration.

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). We regressed echocardiographic indexes and abnormalities on RH ratios averaged for 30 s time intervals. We derived sex-specific peripheral vasoreactivity profiles from PPG time-series and compared their echocardiographic phenotypes. RESULTS: Higher left ventricular (LV) mass index and lower E/A ratio and e' peak and left atrial reservoir strain were independently related to lower RH ratios. Participants with LV hypertrophy or diastolic dysfunction presented significantly lower RH ratios during the 30 to 240s intervals than normal counterparts. Low RH responders (n = 250) presented higher odds for LV hypertrophy (adjusted OR: 2.60; p = .0040) and LV diastolic dysfunction (adjusted OR: 2.66; p = .0037) than moderate-to-high responders (n = 174). CONCLUSION: The association between subclinical heart maladaptation and decreased microvascular reactivity supports the involvement of endothelial dysfunction in HFpEF pathogenesis. Time-integrated profiling of microvascular vasoreactivity may enable early detection of HFpEF in the community.

Improving cardiovascular risk stratification through multivariate time-series analysis of cardiopulmonary exercise test data.

Nowadays cardiorespiratory fitness (CRF) is assessed using summary indexes of cardiopulmonary exercise tests (CPETs). Yet, raw time-series CPET recordings may hold additional information with clinical relevance. Therefore, we investigated whether analysis of raw CPET data using dynamic time warping combined with k-medoids could identify distinct CRF phenogroups and improve cardiovascular (CV) risk stratification. CPET recordings from 1,399 participants (mean age, 56.4 years; 37.7% women) were separated into 5 groups with distinct patterns. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and a history of CV disease, while cluster 1 represented the most favorable CV profile. Clusters 4 (hazard ratio: 1.30; p = 0.033) and 5 (hazard ratio: 1.36; p = 0.0088) had a significantly higher risk of incident adverse events compared to clusters 1 and 2. The model evaluation in the external validation cohort revealed similar patterns. Therefore, an integrative CRF profiling might facilitate CV risk stratification and management.

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). ASCVD outcome was collected (median follow-up time: 10.2 years). We applied partial least squares (PLS) to identify proteins linked to carotid intima-media thickness (cIMT). Next, we assessed the association between future ASCVD events and protein-based phenogroups derived by unsupervised clustering (Gaussian Mixture modelling) based on proteins selected in PLS. RESULTS: PLS identified 19 proteins as important, which were all associated with cIMT in multivariable-adjusted linear regression. 8 of the 19 proteins were excluded from the clustering analysis because of high collinearity. Based on the 11 remaining proteins, the clustering algorithm subdivided the cohort into two phenogroups. Compared to the first phenogroup (n = 177), participants in the second phenogroup (n = 314) presented: i) a more unfavorable lipid profile with higher total cholesterol and triglycerides and lower HDL cholesterol (p ≤ 0.014); ii) higher cIMT (p = 0.0020); and iii) a significantly higher risk for future ASCVD events (multivariable-adjusted hazard ratio (95 % CI) versus phenogroup 1: 2.05 (1.26-3.52); p = 0.0093). The protein-based phenogrouping supplemented ACC/AHA 10-year ASCVD risk scoring for prediction of a first ASCVD event. CONCLUSIONS: Focused protein-based phenogrouping identified individuals at high risk for future ASCVD and may complement current risk stratification strategies.

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, 51.6 years; 52.9% women), clinical and echocardiographic data were acquired, including LA strain traces, at baseline, and cardiac events were collected on average 6.3 years later. Two unsupervised learning techniques were used: (1) an ensemble of a deep convolutional neural network autoencoder with k-medoids and (2) a self-organizing map to cluster spatiotemporal patterns within LA strain curves. Clinical characteristics and cardiac outcome were used to evaluate the validity of the k clusters using the original cohort, while an external population cohort (n = 378) was used to validate the trained models. RESULTS: In both approaches, the optimal number of clusters was five. The first three clusters had differences in sex distribution and heart rate but had a similar low CV risk profile. On the other hand, cluster 5 had the worst CV profile and a higher prevalence of left ventricular remodeling and diastolic dysfunction compared with the other clusters. The respective indexes of cluster 4 were between those of clusters 1 to 3 and 5. After adjustment for traditional risk factors, cluster 5 had the highest risk for cardiac events compared with clusters 1, 2, and 3 (hazard ratio, 1.36; 95% CI, 1.09-1.70; P = .0063). Similar LA strain patterns were obtained when the models were applied to the external validation cohort, and clinical characteristics revealed similar CV risk profiles across all clusters. CONCLUSION: Unsupervised machine learning algorithms used in time-series LA strain curves identified clinically meaningful clusters of LA deformation and provide incremental prognostic information over traditional risk factors.

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. Method: We prospectively studied 1,185 community-dwelling individuals (mean age, 53.2 years; 51.3% women), in whom we acquired clinical and echocardiographic data including LV strain traces at baseline and collected adverse events on average 9.1 years later. A Gaussian Mixture Model (GMM) was applied to features derived from LV strain curves, including the slopes during systole, early and late diastole, peak strain, and the duration and height of diastasis. We evaluated the performance of the model using the clinical characteristics of the participants and the incidence of adverse events in the training dataset. To ascertain the validity of the trained model, we used an additional community-based cohort (n = 545) as external validation cohort. Results: The most appropriate number of clusters to separate the LV strain curves was four. In clusters 1 and 2, we observed differences in age and heart rate distributions, but they had similarly low prevalence of CV risk factors. Cluster 4 had the worst combination of CV risk factors, and a higher prevalence of LV hypertrophy and diastolic dysfunction than in other clusters. In cluster 3, the reported values were in between those of strain clusters 2 and 4. Adjusting for traditional covariables, we observed that clusters 3 and 4 had a significantly higher risk for CV (28% and 20%, P ≤ 0.038) and cardiac (57% and 43%, P ≤ 0.024) adverse events. Using SHAP values we observed that the features that incorporate temporal information, such as the slope during systole and early diastole, had a higher impact on the model's decision than peak LV systolic strain. Conclusion: Employing a GMM on features derived from the raw LV strain curves, we extracted clinically significant phenogroups which could provide additive prognostic information over the peak LV strain.

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 (48.2% females) referred for cycle ergometry at the University Hospital Leuven, Belgium. We assessed clinical determinants of peak CPET indexes of cardiorespiratory fitness (CRF) and its hemodynamic and ventilatory components using stepwise regression and quantified multivariable-adjusted differences in indexes between cases and references. RESULTS: Lower peak load and peak O2 uptake were related to: higher age, female sex, lower body height and weight, and higher heart rate; to the intake of beta blockers, analgesics, thyroid hormone replacement and benzodiazepines; and to diabetes mellitus, chronic kidney disease, non-ST elevation myocardial infarction and atrial fibrillation (p < 0.05 for all). Lower peak load also correlated with obstructive pulmonary diseases. Stepwise regression revealed associations of hemodynamic and ventilatory indexes (including heart rate, O2 pulse, systolic blood pressure and ventilation at peak exercise and ventilatory efficiency) with age, sex, body composition and aforementioned diseases and medications. Multivariable-adjusted differences in CPET metrics between cases and controls confirmed the associations observed. CONCLUSION: We described known and novel associations of CRF components with demographics, anthropometrics, cardiometabolic and pulmonary diseases and medication intake in a large patient sample. The clinical implications of long-term noncardiovascular drug intake for CPET results require further investigation.

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. Key CPET indices and information on incident CV events (median follow-up time: 5.3 years) were derived. Next, we applied unsupervised clustering by Gaussian Mixture modeling to subdivide the cohort into four male and four female phenogroups solely based on differences in CPET metrics. Ten of 18 CPET metrics were used for clustering as eight were removed due to high collinearity. In males and females, the phenogroups differed significantly in age, BMI, blood pressure, disease prevalence, medication intake and spirometry. In males, phenogroups 3 and 4 presented a significantly higher risk for incident CV events than phenogroup 1 (multivariable-adjusted hazard ratio: 1.51 and 2.19; p ≤ 0.048). In females, differences in the risk for future CV events between the phenogroups were not significant after adjustment for clinical covariables. Integrative CPET-based phenogrouping, thus, adequately stratified male patients according to CV risk. CPET phenomapping may facilitate comprehensive evaluation of CPET results and steer CV risk stratification and management.

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. In a cross-sectional cohort of patients with PAH (n = 57) and matched controls (n = 57), we quantified LVLS and septal TS in the amplitude and time domain. We then characterized LV phenotypes using upset plots, ventricular interactions using network analysis, and longitudinal analysis in a representative cohort of 45 patients. We also measured LV metrics in mice subjected to pulmonary arterial banding (PAB) using a 7 T magnetic resonance imaging at baseline, Week 1, and Week 7 post-PAB (N = 9). Patients with PAH had significantly reduced absolute LVLS (15.4 ± 3.4 vs. 20.1 ± 2.3%, p < 0.0001), higher sTS (53.0 ± 12.2 vs. 28.0 ± 6.2%, p < 0.0001) and lower LVLS/sTS (0.30 ± 0.09 vs. 0.75 ± 0.16, p < 0.0001) compared to controls. Reduced LVLS/sTS was observed in 89.5% of patients, while diastolic dysfunction, impaired LVLS (<16%), and LV atrophy were observed in 73.7%, 52.6%, and 15.8%, respectively. In the longitudinal cohort, changes in LVLS/sTS were closely associated with changes in N-terminal pro B-type natriuretic peptide (r = 0.73, p < 0.0001) as well as survival. Mice subjected to PAB showed significant RV systolic dysfunction and decreased LVLS/sTS compared to sham animals. We conclude that in PAH, LVLV/sTS is a simple ratio that can reflect ventricular systolic interactions.

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). Design and methods: In our analysis, we used the meta-data consisting of 30,354 individuals from 6 cohorts. During a median follow-up of 5.40 years, 1,068 individuals experienced a non-fatal HF event. We evaluated the predictive performance of survival gradient boosting (SGB), CoxNet, the PCP-HF risk score, and a stacking method. Predictions were obtained iteratively, in each iteration one cohort serving as an external test set and either one or all remaining cohorts as a training set (single- or multi-center, respectively). Results: Overall, multi-center models systematically outperformed single-center models. Further, c-index in the pooled population was higher in SGB (0.735) than in CoxNet (0.694). In the precision-recall (PR) analysis for predicting 10-year HF risk, the stacking method, combining the SGB, CoxNet, Gaussian mixture and PCP-HF models, outperformed other models with PR/AUC 0.804, while PCP-HF achieved only 0.551. Conclusion: With a greater number and variety of training cohorts, the model learns a wider range of specific individual health characteristics. Flexible ML algorithms can be used to capture these diverse distributions and produce more precise prediction models.

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, 51.2 years; 51.1% women; 53.5% with cardiovascular risk factors). We measured left heart dimensions, strain, tricuspid regurgitation, transmitral blood flow, and mitral annular tissue velocities using conventional echocardiography and Doppler imaging. We utilized these measurements to grade of LVDDF according to the 2016 recommendations and population-derived, age-specific approach. According to the 2016 recommendations, 26 subjects (1.85%) were classified as having the advanced stage (Grade 2), whereas in 109 participants (7.75%) diastolic function was indeterminate. When applying the population-derived criteria, the prevalence of advanced LVDDF was 17.9% (n = 252). During the follow-up period (8.4 years), 100 participants experienced adverse cardiac events. After full adjustment, we did not observe any significant differences in the risk of events between subjects with indeterminate or any grade of LVDDF and subjects with normal diastolic function when classified according to the 2016 recommendation (P ≥ 0.25). In contrast, the adjusted risks of adverse cardiac events (HR = 1.28; P = 0.0045) were significantly elevated in participants with LVDDF when classified according to the population-derived criteria. CONCLUSIONS: Our study underscored the importance of considering age- and population-derived thresholds in LVDDF grading in subjects at high cardiovascular risk which led to a better risk stratification and outcome prediction.

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. METHODS: We included 59,590 adult and 8,349 pediatric patients enrolled in the UNOS database between January 1994 and December 2016 who underwent cardiac transplantation. We evaluated 3 classification and 3 survival methods. Algorithms were evaluated using shuffled 10-fold cross-validation (CV) and rolling CV. Predictive performance for 1 year and 90 days all-cause mortality was characterized using the area under the receiver-operating characteristic curve (AUC) with 95% confidence interval. RESULTS: In total, 8,394 (12.4%) patients died within 1 year of transplant. For predicting 1-year survival, using the shuffled 10-fold CV, Random Forest achieved the highest AUC (0.893; 0.889-0.897) followed by XGBoost and logistic regression. In the rolling CV, prediction performance was more modest and comparable among the models with XGBoost and Logistic regression achieving the highest AUC 0.657 (0.647-0.667) and 0.641(0.631-0.651), respectively. There was a trend toward higher prediction performance in pediatric patients. CONCLUSIONS: Our study suggests that ML and statistical models can be used to predict mortality post-transplant, but based on the results from rolling CV, the overall prediction performance will be limited by temporal shifts inpatient and donor selection.

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, 51.2 years; 51.1% women), in whom we performed clinical and echocardiographic examination at baseline and collected cardiac events on average 8.8 years later. Cardiac phenotypes that were correlated at r > 0.8 were filtered, leaving 21 echocardiographic features, and systolic blood pressure for phenogrouping. We employed hierarchical and Gaussian mixture model-based clustering. Cox regression was used to demonstrate the clinical validity of constructed phenogroups. Unsupervised clustering analyses classified study participants into three distinct phenogroups that differed markedly in echocardiographic indexes. Indeed, cluster 3 had the worst left ventricular (LV) diastolic function (i.e. lowest e' velocity and left atrial (LA) reservoir strain, highest E/e', and LA volume index) and LV remodelling. The phenogroups were also different in cardiovascular risk factor profiles. We observed increase in the risk for incidence of adverse events across phenogroups. In the third phenogroup, the multivariable adjusted risk was significantly higher than the average population risk for major cardiovascular events (51%, P = 0.0028). Conclusion: Unsupervised learning algorithms integrating routinely measured cardiac imaging and haemodynamic data can provide a clinically meaningful classification of cardiac health in asymptomatic individuals. This approach might facilitate early detection of cardiac maladaptation and improve risk stratification.

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; 51.7% women) underwent echocardiography and proteomic profiling (CVD II panel, Olink Proteomics). We applied partial least squares-discriminant analysis (PLS-DA) and a machine learning algorithm [eXtreme Gradient Boosting (XGBoost)] to identify key proteins associated with echocardiographic abnormalities. We used Gaussian mixture modelling for unbiased clustering to construct phenogroups based on influential proteins in PLS-DA and XGBoost. Of 87 proteins, 13 were important in PLS-DA and XGBoost modelling for detection of left ventricular remodelling, left ventricular diastolic dysfunction, and/or left atrial reservoir dysfunction: placental growth factor, kidney injury molecule-1, prostasin, angiotensin-converting enzyme-2, galectin-9, cathepsin L1, matrix metalloproteinase-7, tumour necrosis factor receptor superfamily members 10A, 10B, and 11A, interleukins 6 and 16, and α1-microglobulin/bikunin precursor. Based on these proteins, the clustering algorithm divided the cohort into two distinct phenogroups, with each cluster grouping individuals with a similar protein profile. Participants belonging to the second cluster (n = 118) were characterized by an unfavourable cardiovascular risk profile and adverse cardiac structure and function. The adjusted risk of presenting echocardiographic abnormalities was higher in this phenogroup than in the other (P < 0.0001). CONCLUSIONS: We identified proteins related to renal function, extracellular matrix remodelling, angiogenesis, and inflammation to be associated with echocardiographic signs of early-stage heart failure. Proteomic phenomapping discriminated individuals at high risk for cardiac remodelling and dysfunction.

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. METHODS AND RESULTS: We included 1407 participants (mean age, 51 years, 51% women) randomly recruited from the general population. We used echocardiographic parameters reflecting LV diastolic function and structure to define LV abnormalities (LVDD, n = 252; LVH, n = 272). Next, four supervised ML algorithms (XGBoost, AdaBoost, Random Forest (RF), Support Vector Machines, and Logistic regression) were used to build classifiers based on clinical data (67 features) to categorize LVDD and LVH. We applied a nested 10-fold cross-validation set-up. XGBoost and RF classifiers exhibited a high area under the receiver operating characteristic curve with values between 86.2% and 88.1% for predicting LVDD and between 77.7% and 78.5% for predicting LVH. Age, body mass index, different components of blood pressure, history of hypertension, antihypertensive treatment, and various electrocardiographic variables were the top selected features for predicting LVDD and LVH. CONCLUSION: XGBoost and RF classifiers combining routinely measured clinical, laboratory, and electrocardiographic data predicted LVDD and LVH with high accuracy. These ML classifiers might be useful to pre-select individuals in whom further echocardiographic examination, monitoring, and preventive measures are warranted.

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, 51.0 years; 51.5% women), we measured 39 immunometabolic markers and assessed echocardiographic indexes of left ventricular diastolic dysfunction (LVDD) and left atrial (LA) reservoir dysfunction. We used partial least squares (PLS) to filter the most relevant biomarkers related to the echocardiographic characteristics. Subsequently, we assessed the associations between the echocardiographic features and biomarkers selected in PLS while accounting for clinical confounders. RESULTS: Influential biomarkers in PLS of echocardiographic characteristics included blood sugar, γ-glutamyl transferase, d-dimer, ferritin, hemoglobin, interleukin (IL)-4, IL-6, and serum insulin and uric acid. In stepwise regression incorporating clinical confounders, higher d-dimer was independently associated with higher E/e' ratio and LA volume index (P ≤ 0.05 for all). In multivariable-adjusted analyses, the risk for LVDD increased with higher blood sugar and d-dimer (P ≤ 0.048). After full adjustment, higher serum insulin and serum uric acid were independently related to worse LA reservoir strain and higher risk for LA reservoir dysfunction (P ≤ 0.039 for all). The biomarker panels detected LVDD and LA reservoir dysfunction with 87% and 79% accuracy, respectively (P < 0.0001). CONCLUSIONS: Biomarkers of insulin resistance, hyperuricemia, and chronic low-grade inflammation were associated with cardiac dysfunction. These biomarkers might help to unravel cardiac pathology and improve the detection and management of cardiac dysfunction in clinical practice.

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. METHODS: We analyzed 457 healthy individuals (mean ± SD age: 39.8 ± 6.6 y) with BMI 28-40 kg/m2 in the DIETFITS (Diet Intervention Examining the Factors Interacting with Treatment Success) study. Biochemical markers of metabolic health and 236 proteins were measured by Olink CVDII, CVDIII, and Inflammation I arrays at baseline and at 6 mo during the dietary intervention. We determined clinical and routine biochemical correlates of the diet-induced change in sACE2 (ΔsACE2) using stepwise linear regression. We combined feature selection models and multivariable-adjusted linear regression to identify protein dynamics associated with ΔsACE2. RESULTS: sACE2 decreased on average at 6 mo during the diet intervention. Stronger decline in sACE2 during the diet intervention was independently associated with female sex, lower HOMA-IR and LDL cholesterol at baseline, and a stronger decline in HOMA-IR, triglycerides, HDL cholesterol, and fat mass. Participants with decreasing HOMA-IR (OR: 1.97; 95% CI: 1.28, 3.03) and triglycerides (OR: 2.71; 95% CI: 1.72, 4.26) had significantly higher odds for a decrease in sACE2 during the diet intervention than those without (P ≤ 0.0073). Feature selection models linked ΔsACE2 to changes in α-1-microglobulin/bikunin precursor, E-selectin, hydroxyacid oxidase 1, kidney injury molecule 1, tyrosine-protein kinase Mer, placental growth factor, thrombomodulin, and TNF receptor superfamily member 10B. ΔsACE2 remained associated with these protein changes in multivariable-adjusted linear regression. CONCLUSIONS: Decrease in sACE2 during a weight loss diet intervention was associated with improvements in metabolic health, fat mass, and markers of angiotensin peptide metabolism, hepatic and vascular injury, renal function, chronic inflammation, and oxidative stress. Our findings may improve the risk stratification, prevention, and management of cardiometabolic complications.This trial was registered at clinicaltrials.gov as NCT01826591.

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, 50.7 years; 51.6% women), we echocardiographically assessed left atrial function and LV global longitudinal strain. We derived cut-off values for left atrial emptying fraction (LAEF), left atrial function index (LAFI) and left atrial reservoir strain (LARS) to define left atrial dysfunction using receiver-operating curve threshold analysis. Main outcome was the incidence of cardiac events and atrial fibrillation (AFib) on average 8.5 years later. RESULTS: For prediction of new-onset AFib, left atrial cut-offs yielding the best balance between sensitivity and specificity (highest Youden index) were: LAEF less than 55%, LAFI less than 40.5 and LARS less than 23%. Applying these cut-offs, abnormal LAEF, LAFI and LARS were, respectively, present in 27, 37.1 and 18.1% of the cohort. Abnormal LARS (<23%) was independently associated with higher risk for cardiac events and new-onset AFib (P ≤ 0.012). Participants with both abnormal LAEF and LARS presented a significantly higher risk to develop cardiac events (hazard ratio: 2.10; P = 0.014) and AFib (hazard ratio: 6.45; P = 0.0036) than normal counterparts. The concomitant presence of an impaired LARS and LV global longitudinal strain improved prognostic accuracy beyond a clinical risk model for cardiac events and the CHARGE-AF Risk Score for AFib. CONCLUSION: Left atrial dysfunction based on outcome-driven thresholds predicted cardiac events and AFib independent of conventional risk factors. Screening for subclinical left atrial and LV systolic dysfunction may enhance cardiac disease prediction in the community.