Dalane W. Kitzman

Heart failure with preserved ejection fraction (HFpEF) is a complex systemic syndrome characterized by severe exercise intolerance. In addition to cardiac dysfunction, abnormalities in peripheral skeletal muscle are key contributors to reduced exercise capacity in HFpEF. Multiple studies show that exercise training and caloric restriction improve exercise capacity and quality of life in HFpEF patients. To map the HFpEF-associated molecular signatures in skeletal muscle and the effect of lifestyle intervention, we generated single-nucleus (sn) multiome (joint snRNA-seq and snATAC-seq) profiles of 140 biopsies. Our study included 95 individuals: 25 healthy controls and 70 HFpEF patients, of whom 45 had paired biopsies collected before and after a supervised diet and exercise intervention. From 132,568 nuclei, we resolved 15 skeletal muscle cell types and identified HFpEF-specific remodeling, including type 1 fiber and pericyte depletion and type 2x fiber enrichment, reflecting the cellular basis of exercise intolerance. Transcriptional changes in HFpEF muscle indicated dysregulation of metabolic pathways, stress-responsive programs, cytoskeletal integrity, and calcium signaling. Transcription factor (TF) motifs enriched in differentially accessible chromatin corresponded to concordantly differentially expressed TF-genes, revealing coordinated regulatory programs underlying these changes. Importantly, HFpEF-related changes in both chromatin accessibility and transcription were partially reversible following diet and exercise. Integrative multiomic velocity analyses revealed increased coordinated repression in both chromatin accessibility and transcription of calcium handling genes in HFpEF. HFpEF-linked chromatin was enriched for HF and cardiometabolic GWAS signals, and cross-modal analyses further nominated candidate causal variants and effector genes linking genetic risk to peripheral muscle dysfunction in HFpEF. Together, these findings highlight the molecular mechanisms underlying skeletal muscle remodeling in HFpEF and identify pathways responsive to lifestyle interventions.

Background: Improving health status is a fundamental goal after myocardial infarction (MI). Whether health status differs between type 1 and type 2 MI is unknown. Methods: In this prospective study, 150 patients hospitalized with type 1 (n=75) and type 2 MI (n=75) were enrolled. Health status was assessed at baseline, 1, and 4 months using the EuroQol 5‐Dimension 5‐Level visual analogue scale, 7‐item Seattle Angina Questionnaire, Patient‐Reported Outcomes Measurement Information System Physical Function, Patient‐Reported Outcomes Measurement Information System Fatigue, FRAIL Scale, and Patient Health Questionnaire‐9. Results: A total of 149 baseline, 92 one‐month, and 92 four‐month assessments were analyzed. Patients with type 2 MI were older, were more frequently women, and had more comorbidities. Baseline health status was similar between type 2 and type 1 MI (EuroQol 5‐Dimension 5‐Level visual analog scale, 66.20±22.17 versus 69.36±20.67; P =0.37). At 1 and 4 months, the EuroQol 5‐Dimension 5‐Level visual analog scale was lower for type 2 MI, and adjusted analyses demonstrated less improvement (difference, − 4.8 points; P =0.04). Patients with type 2 MI had lower physical function and greater frailty throughout follow‐up. Baseline fatigue and depression were similar but modestly worse for type 2 MI at follow‐up. Baseline 7‐item Seattle Angina Questionnaire scores were higher for type 2 MI but were similar between groups at follow‐up. Conclusions: Although baseline overall health status was similar, patients with type 2 MI experienced smaller improvements in health status and reported a greater burden of frailty and impaired physical function over 4 months. These findings highlight the need to address health status domains beyond angina after type 2 MI.

BACKGROUND: Biomarkers in heart failure (HF) provide mechanistic and prognostic insights, but their role in predicting treatment response is less understood. We evaluated whether multiple baseline biomarker profiles from the REHAB-HF trial (Rehabilitation Therapy in Older Acute Heart Failure Patients) could stratify functional improvement following a 12-week physical rehabilitation intervention (RI). METHODS: Participants ≥60 years hospitalized with heart failure were randomized to a 12-week outpatient RI or attention control. Functional outcomes included changes in the short physical performance battery and 6-minute walk distance. Blood collected at baseline and 12 weeks was analyzed for cardiac (cTnI and cTnT [cardiac troponin I and T], NT-proBNP [N-terminal pro-B-type natriuretic peptide]), renal (creatinine), and inflammatory (CRP [C-reactive protein]) biomarkers. Associations between baseline biomarker levels and 12-week functional gains by treatment group were evaluated using adjusted linear regression models and machine learning–based decision trees. RESULTS: Baseline biomarker data were available for 242 of 349 participants (69%). Using linear regression, higher cTnI and T were associated with greater 12-week gains in the short physical performance battery and 6-minute walk distance, respectively, among RI participants versus attention control (interaction P =0.040 and 0.032). In the decision tree, analyses combining all biomarkers, CRP emerged as the primary biomarker for both outcomes. Among participants with CRP ≥9.9 mg/L, RI was associated with a +2.4 point (95% CI, 1.8–3.1) greater increase in the short physical performance battery and a +79 m (95% CI, 50–109) greater increase in 6-minute walk distance compared with attention control. In contrast, for those with CRP <9.9 mg/L, the differential benefit of the RI was limited (+0.8 in short physical performance battery [95% CI, 0.1–1.6]; +30 m in 6-minute walk distance [95% CI, –1.0 to 61]). The biomarker levels (except for creatinine) decreased by 12 weeks posthospitalization, but with no differences based on treatment assignment. CONCLUSIONS: Higher inflammation, measured by CRP, may identify older adults recently hospitalized for heart failure with the greatest functional benefit from a physical RI. Biomarker profiling may predict the benefits of this treatment. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02196038.

Background: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequently overlooked or attributed to other conditions. This study proposes a novel, externally validated artificial intelligence (AI) tool using ECG data (ECG‐AI) for the simultaneous detection of subtypes of LV dysfunction and HFpEF. Methods: Two ECG‐AI models, using 12‐lead or single‐lead ECG, were developed using data from Atrium Health Wake Forest Baptist and University of Tennessee Health Science Center (UTHSC) to classify ECGs into 4 categories: reduced LV ejection fraction (rEF; EF<40), midrange EF (mEF; 40≤mEF<50), HFpEF, and controls (no LV dysfunction or HFpEF). Next, a boosting algorithm was used to incorporate clinical risk factors. Finally, the models were independently validated on pediatric data from UTHSC. Results: The Atrium Health Wake Forest Baptist cohort included 1 078 198 digital ECGs from 165 243 patients (5% rEF, 8% mEF, 2% HFpEF), and the UTHSC external validation cohort comprised 72 832 ECGs from 42 880 patients (1% rEF, 1% mEF, 1% HFpEF). For the 12‐lead (Lead‐I) ECG‐AI model, areas under the curve for rEF/mEF/HFpEF were 0.90/0.81/0.80 (0.89/0.78/0.75) in the Atrium Health Wake Forest Baptist holdout data and 0.92/0.76/0.73 (0.90/0.75/0.74) in the UTHSC data. Clinical data‐only machine‐learning models lacked generalizability; clinical + ECG‐AI models did not show any significant improvement compared with ECG‐alone models. With 8418 ECGs (142 cases, 8276 controls) from UTHSC pediatric data, the 12‐lead model achieved areas under the curve of 0.97/0.71/0.64 for rEF/mEF/HFpEF, and the Lead I model had areas under the curve of 0.94/0.77/0.67. Conclusions: ECG‐AI can accurately detect LV dysfunction and HFpEF even from single‐lead ECG, enabling potential low‐cost screening.

BACKGROUND: Although chronic obstructive pulmonary disease (COPD) and heart failure with preserved ejection fraction often coexist with overlapping clinical features, they are usually studied separately. The SPIROMICS HF (Subpopulations and Intermediate Outcome Measures in COPD and Heart Failure Study) is testing hypotheses that new computed tomography emphysema subtypes are associated with specific cardiovascular phenotypes (eg, cor pulmonale , cor pulmonale parvus ), common airway branch variants are associated with right heart dysfunction, and symptomatic tobacco-exposed persons with preserved spirometry have signs of increased left ventricular afterload. METHODS: SPIROMICS is a multicenter observational study of COPD with extensive pulmonary phenotyping of participants with ≥20 pack-years smoking and nonsmoking controls. COPD and COPD severity were defined by standard spirometric criteria and symptomatic tobacco-exposed persons with preserved spirometry by ≥20 pack-years, normal spirometry, and COPD Assessment Test score >10. SPIROMICS HF selected all participants in SPIROMICS visit 5 at major sites. Its comprehensive speckle-tracking echocardiography, which included physiological perturbations of leg raise and low-intensity exercise, was harmonized prospectively with the Multi-Ethnic Study of Atherosclerosis Early Heart Failure and HeartSHARE (Combining Omics, Deep Phenotyping, and Electronic Health Records for Heart Failure Subtypes and Treatment Targets) studies. The cardiopulmonary magnetic resonance imaging protocol with gadolinium administration included myocardial fibrosis sequences, pulmonary angiography, time-resolved 3-dimensional cine magnetic resonance imaging (4-dimensional flow) of venous return, and metronome-paced tachypnea to induce dynamic hyperinflation. Coronary artery calcium was assessed on computed tomography scans. The Kansas City Cardiomyopathy Questionnaire was administered. RESULTS: Of the final sample of 753 participants, 57% had COPD (15% mild, 27% moderate, and 15% severe), 18% had symptomatic tobacco-exposed persons with preserved spirometry, 16% were smoking controls, and 8% were nonsmoking controls. Reproducibility of the main measures from speckle-tracking echocardiography (intraclass correlation coefficient, 0.83–0.99), exercise echocardiography (intraclass correlation coefficient, 0.71–0.99) and magnetic resonance imaging (intraclass correlation coefficient, 0.57–0.99) were good-to-excellent, including in severe COPD. CONCLUSIONS: SPIROMICS HF aims to characterize and understand cardiopulmonary interactions in COPD and COPD-related phenotypes to inform targeted treatments for combined cardiopulmonary failure.

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic challenge. Non-invasive, scalable screening tools may enable earlier recognition, timely intervention, and improved care. To evaluate the performance, reproducibility, and early detection capability of an electrocardiogram-based artificial intelligence (ECG-AI) model designed to identify HFpEF using HeartShare data and real-world ECGs from Wake Forest Baptist Health (WFBH). Methods: The original ECG-AI model was developed and validated using >1 million ECGs. In this study, we examined the external validity and reproducibility over time of this ECG-AI measure in 432 participants from an NIH-funded study of clinically validated HFpEF or controls (HeartShare). Specifically, we assessed model accuracy (AUC, sensitivity, specificity, predictive values) and reproducibility across three serial ECGs. We also analyzed the potential for early (preclinical) detection of HFpEF in 59,705 real-world ECGs from 12,338 patients a large integrated healthcare system (Wake Forest Baptist Health (WFBH)). Results: In HeartShare, ECG-AI achieved an AUC of 0.760 (95% CI: 0.729-0.816), with 65% sensitivity and 75% specificity for detection of HFpEF. We obtained no significantly different AUC when using only lead I ECG as an input, AUC of 0.773 (0.729-0.816). Within-patient reproducibility across three consecutive ECGs showed strong correlations (Pearson r = 0.87-0.89) and strong agreement (Cohen’s κ = 0.68-0.74). Misclassified cases showed fewer risk factors and more normal-like ECG features. In real-world WFBH data, ECG-AI detected HFpEF up to 4 years before clinical diagnosis with AUCs from 0.77 to 0.80. Conclusions: 12 lead ECG-AI model demonstrates strong generalizability, reproducibility and early detection capabilities for HFpEF, supporting its potential as a scalable screening and risk stratification tool. Almost identical single lead AUC demands future investigation for remote monitoring. What is new?: This study is the first to demonstrate that an ECG-AI model for HFpEF maintains strong temporal reproducibility across serial ECGs, supporting its stability as a robust non-invasive tool. We validate the model in both a rigorously phenotyped cohort and a large real-world health system and show that single-lead ECG input achieves accuracy comparable to the full 12-lead model. In addition, we show that the model can identify HFpEF years before clinical diagnosis, extending prior work by establishing ECG-AI as a reproducible, generalizable, and potentially preclinical detection tool. What are the clinical implications?: The strong temporal reproducibility of the ECG-AI measure indicates that it can provide reliable longitudinal tracking of HFpEF risk, making it suitable for both clinical monitoring and remote assessment. Early detection capabilities - up to four years before diagnosis - create opportunities for proactive evaluation and earlier intervention. The comparable performance of single-lead ECGs also opens the door for scalable deployment through wearable or home-based devices, broadening access to HFpEF screening and enabling continuous risk surveillance outside traditional clinical environments.

A demographic shift toward an aging population is occurring worldwide. Commensurate with this trend, age-related diseases are also rising. Although aging is an immutable part of life, biological aging is a highly heterogeneous process influenced by health-related behaviors, genetics, and the environment. The biological effects of aging are evident across all organ systems. Some individuals demonstrate an accelerated aging phenotype, which is associated with substantial morbidity and mortality. Moreover, common cardiovascular conditions, such as heart failure with preserved ejection fraction, can be conceptualized as a consequence of accelerated aging. Physical activity and exercise training modify the biological effects of accelerated aging considerably. They are associated with various salutary benefits and have emerged as potent therapeutics in disease states characterized by the accelerated aging phenotype. In this state-of-the-art review, we discuss the age-related changes observed in cardiovascular and peripheral tissues, the implications of age acceleration, and the effectiveness and mechanisms of physical activity and exercise training to curb the deleterious effects of aging and improve outcomes.

BACKGROUND: The longitudinal impact of blood pressure (BP) control on the components of arterial stiffness has not been studied. METHODS: The SPRINT (Systolic BP Intervention Trial) compared an intensive systolic BP goal (P =0.042). Differences in total stiffness between treatment groups over 3 years of follow-up were driven by intensive BP control reducing load-dependent PWV (−0.71 [−0.58 to −0.85] m/s, P P =0.40). Load-dependent PWV was lower in the intensive treatment group at 1 year and remained lower throughout the follow-up. In contrast, structural PWV was similar between the 2 groups and increased throughout the follow-up period. CONCLUSIONS: Intensive BP control slowed the progression of total arterial stiffness by decreasing load-dependent stiffness, but not through reduced structural stiffness. Future investigations are needed to determine if load-dependent PWV may have potential utility as a biomarker to monitor the efficacy of treatment and guide BP management strategies. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01206062.