Real-world walking behaviors are associated with early-stage heart failure: a Project Baseline Health Study.

BACKGROUND: Data collected via wearables may complement in-clinic assessments to monitor subclinical heart failure (HF). OBJECTIVES: Evaluate the association of sensor-based digital walking measures with HF stage and characterize their correlation with in-clinic measures of physical performance, cardiac function and participant reported outcomes (PROs) in individuals with early HF. METHODS: The analyzable cohort included participants from the Project Baseline Health Study (PBHS) with HF stage 0, A, or B, or adaptive remodeling phenotype (without risk factors but with mild echocardiographic change, termed RF-/ECHO+) (based on available first-visit in-clinic test and echocardiogram results) and with sufficient sensor data. We computed daily values per participant for 18 digital walking measures, comparing HF subgroups vs stage 0 using multinomial logistic regression and characterizing associations with in-clinic measures and PROs with Spearman's correlation coefficients, adjusting all analyses for confounders. RESULTS: In the analyzable cohort (N=1265; 50.6% of the PBHS cohort), one standard deviation decreases in 17/18 walking measures were associated with greater likelihood for stage-B HF (multivariable-adjusted odds ratios [ORs] vs stage 0 ranging from 1.18-2.10), or A (ORs vs stage 0, 1.07-1.45), and lower likelihood for RF-/ECHO+ (ORs vs stage 0, 0.80-0.93). Peak 30-minute pace demonstrated the strongest associations with stage B (OR vs stage 0=2.10; 95% CI:1.74-2.53) and A (OR vs stage 0=1.43; 95% CI:1.23-1.66). Decreases in 13/18 measures were associated with greater likelihood for stage-B HF vs stage A. Strength of correlation with physical performance tests, echocardiographic cardiac-remodeling and dysfunction indices and PROs was greatest in stage B, then A, and lowest for 0. CONCLUSIONS: Digital measures of walking captured by wearable sensors could complement clinic-based testing to identify and monitor pre-symptomatic HF.

Quantifying assumptions underlying peak oxygen consumption equations across the body mass spectrum.

The goal of this study is to quantify the assumptions associated with the Wasserman-Hansen (WH) and Fitness Registry and the Importance of Exercise: A National Database (FRIEND) predictive peak oxygen consumption (pVO2 ) equations across body mass index (BMI). Assumptions in pVO2 for both equations were first determined using a simulation and then evaluated using exercise data from the Stanford Exercise Testing registry. We calculated percent-predicted VO2 (ppVO2 ) values for both equations and compared them using the Bland-Altman method. Assumptions associated with pVO2 across BMI categories were quantified by comparing the slopes of age-adjusted VO2 ratios (pVO2 /pre-exercise VO2 ) and ppVO2 values for different BMI categories. The simulation revealed lower predicted fitness among adults with obesity using the FRIEND equation compared to the WH equations. In the clinical cohort, we evaluated 2471 patients (56.9% male, 22% with BMI >30 kg/m2 , pVO2 26.8 mlO2 /kg/min). The Bland-Altman plot revealed an average relative difference of -1.7% (95% CI: -2.1 to -1.2%) between WH and FRIEND ppVO2 values with greater differences among those with obesity. Analysis of the VO2 ratio to ppVO2 slopes across the BMI spectrum confirmed the assumption of lower fitness in those with obesity, and this trend was more pronounced using the FRIEND equation. Peak VO2 estimations between the WH and FRIEND equations differed significantly among individuals with obesity. The FRIEND equation resulted in a greater attributable reduction in pVO2 associated with obesity relative to the WH equations. The outlined relationships between BMI and predicted VO2 may better inform the clinical interpretation of ppVO2 values during cardiopulmonary exercise test evaluations.

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.

Improving Reporting of Exercise Capacity Across Age Ranges Using Novel Workload Reference Equations.

Exercise capacity (EC) is an important predictor of survival in the general population and in subjects with cardiopulmonary disease. Despite its relevance, considering the percent-predicted workload (%pWL) given by current equations may overestimate EC in older adults. Therefore, to improve the reporting of EC in clinical practice, our main objective was to develop workload reference equations (pWL) that better reflect the relation between workload and age. Using the Fitness Registry and the Importance of Exercise National Database (FRIEND), we analyzed a reference group of 6,966 apparently healthy participants and 1,060 participants with heart failure who underwent graded treadmill cardiopulmonary exercise testing. For the first group, the mean age was 44 years (18 to 79); 56.5% of participants were males and 15.4% had obesity. Peak oxygen consumption was 11.6 ± 3.0 METs in males and 8.5 ± 2.4 METs in females. After partition analysis, we first developed sex-specific pWL equations to allow comparisons to a healthy weight reference. For males, pWL (METs) = 14.1-0.9×10-3×age2 and 11.5-0.87×10-3×age2 for females. We used those equations as denominators of %pWL, and based on their distribution, we determined thresholds for EC classification, with average EC defined by the range corresponding to 85% to 115%pWL. Compared with %pWL using current equations, the new equations yielded better-calibrated %pWL across different age ranges. We also derived body mass index-adjusted pWL equations that better assessed EC in subjects with heart failure. In conclusion, the novel pWL equations have the potential to impact the report of EC in practice.

Exercise Systolic Blood Pressure Response During Cycle Ergometry is Associated with Future Hypertension in Normotensive Individuals.

AIMS: We aimed to investigate the association between the exercise systolic blood pressure (SBP) response and future hypertension (HTN) in normotensive individuals referred for cycle ergometry, with special regard to reference exercise SBP values, and exercise capacity. METHODS: In this longitudinal cohort study, data from 14,428 exercise tests were cross-linked with Swedish national registries on diagnoses and medications. We excluded individuals with a baseline diagnosis of cardiovascular disease or HTN. The peak exercise SBP (SBPpeak) was recorded and compared to the upper limit of normal (ULN) derived from SBPpeak reference equations incorporating age, sex, resting SBP and exercise capacity. To evaluate the impact of exercise capacity, three SBP to work rate slopes (SBP/W-slopes), were calculated, relative to either supine or seated SBP at rest or to the first exercise SBP. Adjusted hazard ratios (HRadjusted [95% Confidence interval, CI]) for incident HTN during follow-up, in relation to SBP response metrics, were calculated. RESULTS: We included 3,895 normotensive individuals (49±14 years, 45% females) with maximal cycle ergometer tests. During follow-up (median 7.5 years) 22% developed HTN. Higher SBPpeak and SBPpeak>ULN were associated with incident HTN (HRadjusted 1.19 [1.14-1.23] per 10 mmHg, and 1.95 [1.54-2.47], respectively). All three SBP/W-slopes were positively associated to incident HTN, particularly the SBP/W-slope calculated as supine-to-peak SBP (HRadjusted 1.25 [1.19-1.31] per 1 mmHg/10W). CONCLUSION: Both SBPpeak>ULN based on reference values and high SBP/W-slopes were associated with incident HTN in normotensive individuals and should be considered in the evaluation of the cycle ergometry SBP response.

We examined the systolic blood pressure response during maximal bicycle exercise testing in individuals without hypertension or established cardiovascular disease, and found that: When applying reference values for peak systolic blood pressure during cycling exercise, accounting for age, sex, resting blood pressure and exercise capacity, exceeding the upper limit of normal was associated with twice as high relative risk of future hypertension, compared to having a peak systolic blood pressure within normal limits. A steep increase in exercise blood pressure in relation to the increase in work rate was also associated with future hypertension but did not always coincide with elevated peak systolic blood pressure.

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.

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.

Epicardial fat and Stage B heart failure among overweight/obese and normal weight individuals with diabetes mellitus.

PURPOSE: Although up to 20% of people with type 2 diabetes (DM) have normal BMI (< 25 kg/m2), it remains unclear whether there is a difference in the development of cardiac dysfunction between those with normal and higher BMI. Furthermore, little is known about the relationship of visceral fat with BMI or fitness in asymptomatic patients with DM. METHODS: We prospectively enrolled asymptomatic patients with DM and divided into two groups: BMI ≥ 25kg/m2 (overweight/obese group) versus < 25kg/m2(normal-weight group). Resting echocardiogram followed by exercise stress echocardiogram and exercise gas exchange analysis (in a subgroup) was performed. Cardiac function was evaluated using left ventricular longitudinal strain (LVLS), E/e', and relative wall thickness (RWT). In addition, epicardial fat thickness (EFT) was measured to estimate visceral fat. RESULTS: Normal-weight patients with DM had more EFT compared with overweight/obese patients (0.66 ± 0.17 cm vs. 0.59 ± 0.22 cm, p < 0.05), despite the overlap between the groups. There was no significant difference in the prevalence of LV remodeling (p = 0.49), impaired LVLS (p = 0.22), or increased E/e' (p = 0.26), and these were consistently observed when matched for race. The majority of patients (63%) achieved ≥ 85% of percent peak-predicted VO2. At peak, there was no significant difference in peak VO2 normalized by eLBM (36.4 ± 7.7 vs. 37.8 ± 7.1 ml/kg eLBM/min, p = 0.43) while VO2 normalized by weight (23.6 ± 6.5 vs. 29.6 ± 6.7 ml/kg/min, p < 0.001) and VO2 ratio (5.7 ± 1.7 vs. 7.3 ± 2.4 METs, p = 0.001) were significantly lower in patients with obese/overweight group. There was no significant difference between patients with higher and lower EFT. CONCLUSIONS: Patients with DM and normal BMI have excess epicardial fat compared to those with overweight/obese. Epicardial fat was not directly linked to prevalence of subclinical dysfunction.

Deep neural network-based clustering of deformation curves reveals novel disease features in PLN pathogenic variant carriers.

Echocardiographic deformation curves provide detailed information on myocardial function. Deep neural networks (DNNs) may enable automated detection of disease features in deformation curves, and improve the clinical assessment of these curves. We aimed to investigate whether an explainable DNN-based pipeline can be used to detect and visualize disease features in echocardiographic deformation curves of phospholamban (PLN) p.Arg14del variant carriers. A DNN was trained to discriminate PLN variant carriers (n = 278) from control subjects (n = 621) using raw deformation curves obtained by 2D-speckle tracking in the longitudinal axis. A visualization technique was used to identify the parts of these curves that were used by the DNN for classification. The PLN variant carriers were clustered according to the output of the visualization technique. The DNN showed excellent discriminatory performance (C-statistic 0.93 [95% CI 0.87-0.97]). We identified four clusters with PLN-associated disease features in the deformation curves. Two clusters showed previously described features: apical post-systolic shortening and reduced systolic strain. The two other clusters revealed novel features, both reflecting delayed relaxation. Additionally, a fifth cluster was identified containing variant carriers without disease features in the deformation curves, who were classified as controls by the DNN. This latter cluster had a very benign disease course regarding development of ventricular arrhythmias. Applying an explainable DNN-based pipeline to myocardial deformation curves enables automated detection and visualization of disease features. In PLN variant carriers, we discovered novel disease features which may improve individual risk stratification. Applying this approach to other diseases will further expand our knowledge on disease-specific deformation patterns. Overview of the deep neural network-based pipeline for feature detection in myocardial deformation curves. Firstly, phospholamban (PLN) p.Arg14del variant carriers and controls were selected and a deep neural network (DNN) was trained to detect the PLN variant carriers. Subsequently, a clustering-based approach was performed on the attention maps of the DNN, which revealed 4 distinct phenotypes of PLN variant carriers with different prognoses. Moreover, a cluster without features and a benign prognosis was detected.

Granzymes, IL-16, and poly(ADP-ribose) polymerase 1 increase during wildfire smoke exposure.

Background: Given the increasing prevalence of wildfires worldwide, understanding the effects of wildfire air pollutants on human health-particularly in specific immunologic pathways-is crucial. Exposure to air pollutants is associated with cardiorespiratory disease; however, immune and epithelial barrier alterations require further investigation. Objective: We sought to determine the impact of wildfire smoke exposure on the immune system and epithelial barriers by using proteomics and immune cell phenotyping. Methods: A San Francisco Bay area cohort (n = 15; age 30 ± 10 years) provided blood samples before (October 2019 to March 2020; air quality index = 37) and during (August 2020; air quality index = 80) a major wildfire. Exposure samples were collected 11 days (range, 10-12 days) after continuous exposure to wildfire smoke. We determined alterations in 506 proteins, including zonulin family peptide (ZFP); immune cell phenotypes by cytometry by time of flight (CyTOF); and their interrelationship using a correlation matrix. Results: Targeted proteomic analyses (n = 15) revealed a decrease of spondin-2 and an increase of granzymes A, B, and H, killer cell immunoglobulin-like receptor 3DL1, IL-16, nibrin, poly(ADP-ribose) polymerase 1, C1q TNF-related protein, fibroblast growth factor 19, and von Willebrand factor after 11 days' average continuous exposure to smoke from a large wildfire (P < .05). We also observed a large correlation cluster between immune regulation pathways (IL-16, granzymes A, B, and H, and killer cell immunoglobulin-like receptor 3DL1), DNA repair [poly(ADP-ribose) 1, nibrin], and natural killer cells. We did not observe changes in ZFP levels suggesting a change in epithelial barriers. However, ZFP was associated with immune cell phenotypes (naive CD4+, TH2 cells). Conclusion: We observed functional changes in critical immune cells and their proteins during wildfire smoke exposure. Future studies in larger cohorts or in firefighters exposed to wildfire smoke should further assess immune changes and intervention targets.

Associations of long-term mortality with serum uric acid at admission in acute decompensated heart failure with different phenotypes.

BACKGROUND AND AIMS: It remains unclear whether the long-term prognostic value of serum uric acid (SUA) at admission differs in acute decompensated heart failure (HF) patients across the spectrum of left ventricular ejection fraction (EF). METHODS AND RESULTS: In 2375 patients (38.9% women; mean age, 68.8 years), we assessed the risk of long-term (>1 year) all-cause mortality associated with per 1-SD increase in SUA at admission, using multivariable Cox regression in HF with preserved (HFpEF), mildly reduced (HFmrEF) and reduced (HFrEF) EF. During a median follow-up of 4.1 years, the long-term mortality rate was 39.9%. In all patients, the multivariable-adjusted hazard ratio (HR) expressing the risk of long-term mortality associated with SUA was 1.18 (95% CI, 1.11-1.26; P < 0.001). Compared with the low tertile of the SUA distribution, the sex- and age-adjusted cumulative incidence of long-term mortality was higher in the top tertile. In patients with HFpEF and HFrEF, SUA predicted the risk of long-term mortality with HRs amounting to 1.12 (95% CI, 1.02-1.21; P = 0.012) and 1.28 (95% CI, 1.12-1.47; P < 0.001), respectively. However, there were no associations between the risk of mortality and SUA in HFmrEF. Furthermore, age, sex, NYHA class, and the prevalence of coronary heart disease interacted significantly with SUA for predicting long-term mortality. CONCLUSION: Higher levels of SUA at admission were associated with higher risk of long-term mortality in patients with different HF subtypes. The risk conferred by SUA was age and sex dependent. Our observations highlight that measuring SUA at admission may help to improve risk stratification.

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.

Clinical and Echocardiographic Diversity Associated With Physical Fitness in the Project Baseline Health Study: Implications for Heart Failure Staging.

BACKGROUND: Clinical and echocardiographic features may carry diverse information about the development of heart failure (HF). Therefore, we determined heterogeneity in clinical and echocardiographic phenotypes and its association with exercise capacity. METHODS: In 2036 community-dwelling individuals, we defined echocardiographic profiles of left and right heart remodeling and dysfunction. We subdivided the cohort based on presence (+) or absence (-) of HF risk factors (RFs) and echocardiographic abnormalities (RF-/Echo-, RF-/Echo+, RF+/Echo-, RF+/Echo+). Multivariable-adjusted associations between subgroups and physical performance metrics from 6-minute walk and treadmill exercise testing were assessed. RESULTS: The prevalence was 35.3% for RF-/Echo-, 4.7% for RF-/Echo+, 39.3% for RF+/Echo-, and 20.6% for RF+/Echo+. We observed large diversity in echocardiographic profiles in the Echo+ group. Participants with RF-/Echo+ (18.6% of Echo+) had predominantly echocardiographic abnormalities other than left ventricular (LV) diastolic dysfunction, hypertrophy and reduced ejection fraction, whereas their physical performance was similar to RF-/Echo-. In contrast, participants with RF+/Echo+ presented primarily with LV hypertrophy or dysfunction, features that related to lower 6-minute walking distance and lower exercise capacity. CONCLUSIONS: Subclinical echocardiographic abnormalities suggest HF pathogenesis, but the presence of HF risk factors and type of echo abnormality should be considered so as to distinguish adverse from benign adaptation and to stratify HF risk.

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.

Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial.

BACKGROUND & AIMS: Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention. DESIGN: Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention. RESULTS: A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM0.66 × FM0.066] was body size and sex independent (p = 0.91 and p = 0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r = -0.47 for female and r = -0.44 for male, both p < 0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p < 0.001) and between individuals who are overweight and obese (p < 0.001) while no significant differences were observed when indexed to REE/[LBM0.66 × FM0.066], p > 0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p = 0.614). CONCLUSION: Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

Defining left ventricular remodeling using lean body mass allometry: a UK Biobank study.

PURPOSE: The geometric patterns of ventricular remodeling are determined using indexed left ventricular mass (LVM), end-diastolic volume (LVEDV) and concentricity, most often measured using the mass-to-volume ratio (MVR). The aims of this study were to validate lean body mass (LBM)-based allometric coefficients for scaling and to determine an index of concentricity that is independent of both volume and LBM. METHODS: Participants from the UK Biobank who underwent both CMR and dual-energy X-ray absorptiometry (DXA) during 2014-2015 were considered (n = 5064). We excluded participants aged ≥ 70 years or those with cardiometabolic risk factors. We determined allometric coefficients for scaling using linear regression of the logarithmically transformed ventricular remodeling parameters. We further defined a multiplicative allometric relationship for LV concentricity (LVC) adjusting for both LVEDV and LBM. RESULTS: A total of 1638 individuals (1057 female) were included. In subjects with lower body fat percentage (< 25% in males, < 35% in females, n = 644), the LBM allometric coefficients for scaling LVM and LVEDV were 0.85 ± 0.06 and 0.85 ± 0.03 respectively (R2 = 0.61 and 0.57, P < 0.001), with no evidence of sex-allometry interaction. While the MVR was independent of LBM, it demonstrated a negative association with LVEDV in (females: r = - 0.44, P < 0.001; males: - 0.38, P < 0.001). In contrast, LVC was independent of both LVEDV and LBM [LVC = LVM/(LVEDV0.40 × LBM0.50)] leading to increased overlap between LV hypertrophy and higher concentricity. CONCLUSIONS: We validated allometric coefficients for LBM-based scaling for CMR indexed parameters relevant for classifying geometric patterns of ventricular remodeling.