Search

Background While exercise impairments are central to symptoms and diagnosis of heart failure with preserved ejection fraction (HFpEF), prior studies of HFpEF biomarkers have mostly focused on resting phenotypes. We combined precise exercise phenotypes with cardiovascular proteomics to identify protein signatures of HFpEF exercise responses and new potential therapeutic targets. Methods and Results We analyzed 277 proteins (Olink) in 151 individuals (N=103 HFpEF, 48 controls; 62±11 years; 56% women) with cardiopulmonary exercise testing with invasive monitoring. Using ridge regression adjusted for age/sex, we defined proteomic signatures of 5 physiological variables involved in HFpEF: peak oxygen uptake, peak cardiac output, pulmonary capillary wedge pressure/cardiac output slope, peak pulmonary vascular resistance, and peak peripheral O2 extraction. Multiprotein signatures of each of the exercise phenotypes captured a significant proportion of variance in respective exercise phenotypes. Interrogating the importance (ridge coefficient magnitude) of specific proteins in each signature highlighted proteins with putative links to HFpEF pathophysiology (eg, inflammatory, profibrotic proteins), and novel proteins linked to distinct physiologies (eg, proteins involved in multiorgan [kidney, liver, muscle, adipose] health) were implicated in impaired O2 extraction. In a separate sample (N=522, 261 HF events), proteomic signatures of peak oxygen uptake and pulmonary capillary wedge pressure/cardiac output slope were associated with incident HFpEF (odds ratios, 0.67 [95% CI, 0.50–0.90] and 1.43 [95% CI, 1.11–1.85], respectively) with adjustment for clinical factors and B‐type natriuretic peptides. Conclusions The cardiovascular proteome is associated with precision exercise phenotypes in HFpEF, suggesting novel mechanistic targets and potential methods for risk stratification to prevent HFpEF early in its pathogenesis.

Summary: Mitochondrial abnormalities have long been described in the setting of cardiomyopathies and heart failure (HF), yet the mechanisms of mitochondrial dysfunction in cardiac pathophysiology remain poorly understood. Many studies have described HF as an energy-deprived state characterized by a decline in adenosine triphosphate production, largely driven by impaired oxidative phosphorylation. However, impairments in oxidative phosphorylation extend beyond a simple decline in adenosine triphosphate production and, in fact, reflect pervasive metabolic aberrations that cannot be fully appreciated from the isolated, often siloed, interrogation of individual aspects of mitochondrial function. With the application of broader and deeper examinations into mitochondrial and metabolic systems, recent data suggest that HF with preserved ejection fraction is likely metabolically disparate from HF with reduced ejection fraction. In our review, we introduce the concept of the mitochondrial ecosystem, comprising intricate systems of metabolic pathways and dynamic changes in mitochondrial networks and subcellular locations. The mitochondrial ecosystem exists in a delicate balance, and perturbations in one component often have a ripple effect, influencing both upstream and downstream cellular pathways with effects enhanced by mitochondrial genetic variation. Expanding and deepening our vantage of the mitochondrial ecosystem in HF is critical to identifying consistent metabolic perturbations to develop therapeutics aimed at preventing and improving outcomes in HF.

Background The American Heart Association's framework “ideal cardiovascular health” (CVH) focuses on modifiable risk factors to reduce cardiovascular disease (CVD). Metabolomics provides important pathobiological insights into risk factors and CVD development. We hypothesized that metabolomic signatures associate with CVH status, and that metabolites, at least partially, mediate the association of CVH score with atrial fibrillation (AF) and heart failure (HF). Methods and Results We studied 3056 adults in the FHS (Framingham Heart Study) cohort to evaluate CVH score and incident outcomes of AF and HF. Metabolomics data were available in 2059 participants; mediation analysis was performed to evaluate the mediation of metabolites in the association of CVH score and incident AF and HF. In the smaller cohort (mean age, 54 years; 53% women), CVH score was associated with 144 metabolites, with 64 metabolites shared across key cardiometabolic components (body mass index, blood pressure, and fasting blood glucose) of the CVH score. In mediation analyses, 3 metabolites (glycerol, cholesterol ester 16:1, and phosphatidylcholine 32:1) mediated the association of CVH score with incident AF. Seven metabolites (glycerol, isocitrate, asparagine, glutamine, indole‐3‐proprionate, phosphatidylcholine C36:4, and lysophosphatidylcholine 18:2), partly mediated the association between CVH score and incident HF in multivariable‐adjusted models. Conclusions Most metabolites that associated with CVH score were shared the most among 3 cardiometabolic components. Three main pathways: (1) alanine, glutamine, and glutamate metabolism; (2) citric acid cycle metabolism; and (3) glycerolipid metabolism mediated CVH score with HF. Metabolomics provides insights into how ideal CVH status contributes to the development of AF and HF.

Abstract Introduction Our aim was to investigate if the accuracy of diagnosing mild cognitive impairment (MCI) using the Mini–Mental State Examination (MMSE) and logical memory (LM) test could be enhanced by adding MRI data. Methods Data of individuals with normal cognition and MCI were obtained from the National Alzheimer Coordinating Center database (n = 386). Deep learning models trained on MRI slices were combined to generate a fused MRI model using different voting techniques to predict normal cognition versus MCI. Two multilayer perceptron (MLP) models were developed with MMSE and LM test results. Finally, the fused MRI model and the MLP models were combined using majority voting. Results The fusion model was superior to the individual models alone and achieved an overall accuracy of 90.9%. Discussion This study is a proof of principle that multimodal fusion of models developed using MRI scans, MMSE, and LM test data is feasible and can better predict MCI.

Background Obesity is a precursor to heart failure with preserved ejection fraction. Biomarkers that identify preclinical metabolic heart disease (MHD) in young obese patients would help identify high‐risk individuals for heart failure prevention strategies. We assessed the predictive value of GAL3 (galectin–3), FSTL3 (follistatin‐like 3 peptide), and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) to identify stage B MHD in young obese participants free of clinically evident cardiovascular disease. Methods and Results Asymptomatic obese patients (n=250) and non‐obese controls (n=21) underwent echocardiographic cardiac phenotyping. Obese patients were classified as MHD positive (MHD‐POS; n=94) if they had abnormal diastolic function or left ventricular hypertrophy and had estimated pulmonary artery systolic pressure ≥35 mm Hg. Obese patients without such abnormalities were classified as MHD negative (MHD‐NEG; n=52). Serum biomarkers timed with echocardiography. MHD‐POS and MHD‐NEG individuals were similarly obese, but MHD‐POS patients were older, with more diabetes mellitus and metabolic syndrome. Right ventricular coupling was worse in MHD‐POS patients (P

BackgroundLimited data exist regarding cardiac manifestations of Chagas disease in migrants living in non-endemic regions.MethodsA retrospective cohort analysis of 109 patients with Chagas disease seen at Boston Medical Center (BMC) between January 2016 and January 2023 was performed. Patients were identified by screening and testing migrants from endemic regions at a community health center and BMC. Demographic, laboratory, and cardiac evaluation data were collected.ResultsMean age of the 109 patients was 43 years (range 19-76); 61% were female. 79% (86/109) were diagnosed with Chagas disease via screening and 21% (23/109) were tested given symptoms or electrocardiogram abnormalities. Common symptoms included palpitations (25%, 27/109) and chest pain (17%, 18/109); 52% (57/109) were asymptomatic. Right bundle branch block (19%, 19/102), T-wave changes (18%, 18/102), and left anterior fascicular block (11%, 11/102) were the most common electrocardiogram abnormalities; 51% (52/102) had normal electrocardiograms. Cardiomyopathy stage was ascertained in 94 of 109 patients: 51% (48/94) were indeterminate stage A and 49% (46/94) had cardiac structural disease (stages B1-D). Clinical findings that required clinical intervention or change in management were found in 23% (25/109), and included cardiomyopathy, apical hypokinesis/aneurysm, stroke, atrial or ventricular arrhythmias, and apical thrombus.ConclusionsThese data show high rates of cardiac complications in a cohort of migrants living with Chagas disease in a non-endemic setting. We demonstrate that Chagas disease diagnosis prompts cardiac evaluation which often identifies actionable cardiac disease and provides opportunities for prevention and treatment.