European Heart Journal

BackgroundInvestigation of sex-specific associations between longitudinal resting heart rate (RHR) and new-onset heart failure (HF) using both change in RHR and RHR trajectories. MethodsParticipants from the Trondelag Health Study attending two or three surveys between 1995-2019 were included. We investigated the association between new-onset HF and RHR, using RHR categories according to the mean baseline RHR standard deviation (12 bpm), continuous RHR in restricted cubic splines (n=47712, mean 12-year follow-up), and latent class trajectory models (n=47162, mean 7-year follow-up). Cox regression was used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (95% CI). ResultsDuring follow-up, 2880 of the 47712 participants developed HF. The HF incidence rate was lower in women than men (4.27 vs. 5.68 per 1000 person-years, ratio (95% CI) 0.67 (0.57-0.77). Baseline RHR was 74 bpm in women and 70 bpm in men, and 74% maintained their RHR ({+/-}12 bpm) from baseline to their second attendance (mean change -2{+/-}12 bpm). Each 10-bpm higher RHR was associated with higher risk of HF for both women and men with HR (95% CI) 1.15 (1.03-1.28) and 1.09 (1.00-1.20), respectively. Participants with a high RHR trajectory had higher risk of HF compared to the low RHR-trajectories with HR (95% CI) 1.43 (1.14-1.79) for women and 1.41 (1.16-1.72) for men. ConclusionAll-cause HF was similarly associated with increased RHR and a high RHR trajectory for women and men. Estimating HF risk using RHR trajectories provided stronger associations between RHR and HF compared to using a single RHR measurement. Lay summaryWe investigated sex-specific associations between longitudinal resting heart rate (RHR) and all-cause new-onset of heart failure for 47 712 participants from the Trondelag Health Study, using RHR categories, continuous RHR in restricted cubic splines, and latent class trajectory models. O_LIEach 10-bpm increase in RHR was associated with a similar higher risk of heart failure for both women and men, with 15% higher risk for women and 9% higher for men C_LIO_LIParticipants with a high RHR trajectory had 41-43% increased risk of heart failure compared to the low RHR-trajectory C_LIO_LILong term RHR measurements can reveal changes in HF risk profiles that might not be detectable with a single RHR measurement and highlight individuals with a high RHR who should be further evaluated C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/25339723v1_figa1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@e276d9org.highwire.dtl.DTLVardef@1e8bbaeorg.highwire.dtl.DTLVardef@79de1dorg.highwire.dtl.DTLVardef@e54643_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Sex-specific associations between longitudinal resting heart rate (RHR) and heart failure (HF) from The Trondelag Health Study, 1995-2019. In fully adjusted analyses, women and men with an increase in RHR or a high RHR trajectory had a similar risk of developing all-cause new-onset HF. C_FIG

AimsElevated body mass index (BMI) is a known risk factor for heart failure (HF), however, the underlying mechanisms are incompletely understood. The aim of this study was to investigate the role of common HF risk factors as potential mediators. Methods and ResultsElectronic health record data from primary care, hospital admissions and death registrations in England were used to perform an observational analysis. Data for 1.5 million individuals aged 18 years or older, with BMI measurements and free from heart failure at baseline, were included between 1998 and 2016. Cox models were used to estimate the association between BMI and HF with and without adjustment for atrial fibrillation (AF), diabetes mellitus (DM), coronary heart disease (CHD), and hypertension (HTN). Univariable and multivariable two-sample Mendelian randomisation was performed to estimate causal effects. Among non-underweight individuals, BMI was positively associated with HF with a 1-SD ([~] 4.8kg/m2) higher BMI associated with a hazard ratio (HR) of 1.31 (95% confidence interval [CI] 1.30, 1.32). Genetically predicted BMI yielded a causal odds ratio (OR) of 1.64 per 4.8 kg/m2 BMI (95% CI 1.58, 1.70) which attenuated by 41% (to OR of 1.38 (95% CI 1.31 - 1.45), when simultaneously accounting for AF, DM, CHD and SBP. ConclusionAbout 40% of the excess risk of HF due to adiposity is driven by SBP, AF, DM and CHD. These findings highlight the importance of the prevention and treatment of excess adiposity and downstream HF risk factors to prevent HF, even in people in whom the above risk factors are well managed. One-sentence summaryThis study of the role of excess adiposity as a risk factor for HF, including an observational analysis of measured BMI 1.5 million individuals and multivariable MR analysis of genetically elevated BMI, provides evidence that adiposity is causally associated with HF, with approximately 40% of the effect being mediated by conventional risk pathways. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/20200360v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@4f7eborg.highwire.dtl.DTLVardef@306863org.highwire.dtl.DTLVardef@15544corg.highwire.dtl.DTLVardef@51675e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Population-level data on preclinical heart failure (HF) remain limited because most epidemiological studies focus on symptomatic HF. We therefore developed an administrative-data algorithm to classify HF stages across the national population and describe temporal trends, stage transitions, and mortality across the HF continuum. Methods Using a claims-based staging framework adapted from the Universal Definition of HF, we classified HF stages from ICD-10 codes, prescription records, and medical procedures. We applied this algorithm to the Czech population, linking the National Registry of Reimbursed Health Services to National mortality records from 2015 to 2024. Results In 2024, 27.8% of the Czech population met criteria for Stage A HF and 8.2% for Stage B. Over 10 years, the prevalence of both preclinical stages increased beyond what could be explained by population aging alone, with age-standardized prevalence rising by 9.5% for Stage A and 19.2% for Stage B. Age-standardized 1-year mortality showed a steep stepwise gradient, from 0.69% in Stage A to 1.69% in Stage B, 3.06% in Stage C, and 7.27% in Stage D. Among 52,172 individuals with incident clinical HF in 2024, more than 95% had previously met administrative criteria for Stage A or Stage B. Conclusion Administrative surveillance of the HF continuum using routinely collected healthcare data provides a scalable administrative framework for population-level monitoring of HF burden. In Czechia, both preclinical and clinical HF burdens increased over time beyond population aging alone, underscoring the need for earlier preventive strategies targeting preclinical disease.

BackgroundCardiovascular risk prediction models, such as PCE, QRISK3, and SCORE2 are recommended tools to guide treatment initiation/intensification in primary care. In clinical practice, the absence of one or more required predictors is common, which precludes routine application of such models. MethodsWe developed a set of partial models predicting the 10-year risk of cardiovascular disease (CVD) and major CVD (additionally considering atrial fibrillation, heart failure, and peripheral arterial disease) using combinations of 14 predictors, allowing application in settings were only a subset of variables is available. The set of partial models was evaluated across five studies jointly comprising 105,550 participants. FindingsWe trained 4,096 unique models to predict 10-year major CVD risk, observing near identical performance evaluated against CVD and major CVD. The c-statistic ranged between: quartiles (Q1) 0.71 and Q3: 0.73 across the five studies. This was comparable to the performance of the PCE (Q1: 0.70, Q3: 0.74, 10 predictors) and SCORE2 (Q1: 0.71, Q3: 0.75, 8 predictors). Due to large number of required predictors (22/23 for men/women) the QRISK3 was evaluated in a single cohort: c-statistic 0.72 (95% CI 0.72; 0.73). Model performance remained adequate when focussing on the set of partial models using 2-4 predictors: c-statistic Q1: 0.70 and Q3: 0.71. Partial models demonstrated reasonable calibration across most studies, observing a limited risk underestimation in two cohorts. Partial models excluding blood pressure and lipids demonstrated similar performance to models incorporating these variables. The set of partial models has been made available through a python-based application programming interface. InterpretationWe show that in the presence of partially missing data, clinically relevant predictions of the 10-years risk of major CVD can be obtained by using a subset of features, facilitating improved and more timely treatment decisions. FundingDutch Research Council, British Heart Foundation, UK Research and Innovation. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSBefore submitting our article on May 5, 2025, we searched PubMed articles published from database inception, using the terms "missing data" [tiab] or "incomplete data"[tiab], "cardiovascular disease" [tiab], and "risk score" [tiab] or "prediction"[tiab]. Studies unrelated to cardiovascular disease (CVD) prediction were excluded. None of the identified CVD prediction models allowed for missing input data and instead considered missing data solely at the stage of model derivation. Added value of this studyThe applicability of widely recommended cardiovascular risk prediction models, such as SCORE2 (Europe), PCE (US), and QRISK3 (UK), is constrained by the need to measure all included variables. The absence of even a single variable, such as total cholesterol used in all three aforementioned models - precludes risk prediction. For instance, among individuals aged 40 to 69 years without a history of cardiovascular disease, only 10.8% have a recorded cholesterol measurement at any point in their medical history. To overcome these limitations, this study introduces an approach using 4,096 partial models to predict 10-year risk of (major) cardiovascular disease using combinations of 14 variables, specifically designed to address the challenge of missing data. Performance was assessed across five datasets from the UK and the Netherlands. Models including between 2 - 4 predictors already provided a discriminative ability comparable to guideline-recommended models: PCE (10 predictors), SCORE2 (8 predictors), and QRISK3 (22 predictors for women, 23 for men). Implications of all the available evidenceWe show that even when only a subset of predictor variables is available, our partial models approach can make clinically relevant predictions of the 10-years risk of (major) cardiovascular disease, enabling earlier and more effective treatment decisions. The set of partial models are accessible through a python-based API, allowing for integration in personal or clinical care dashboards.

Background: Left ventricular (LV) remodeling is associated with impaired cardiac function and future cardiovascular disease (CVD). Current remodeling definitions use broad categorizations based on hypertrophy and mean wall thickness. Cardiac magnetic resonance (CMR) imaging provides detailed characterization of regional myocardial wall properties, which may enhance sex-specific cardiovascular disease (CVD) risk stratification. Objectives: We aimed to identify detailed LV remodeling patterns, their associations with CVD risk, and their clinical predictors. Methods: LV wall thickness data were obtained by CMR in three independent population-based cohorts (SHIP-TREND-0, n=931; SHIP-START-2, n=490; KORA-FF4, n=368). Sex-specific remodeling patterns were identified by k-means clustering and associated with established CVD risk scores and incident morbidity and all-cause mortality. Bootstrapped multinomial regression with LASSO regularization was used to select relevant clinical predictors of remodeling patterns. Results: The sample comprised 991 men (mean age 52.9 years, prevalent CVD 7.8%) and 798 women (52.5 years, 2%). Four remodeling clusters were found for men and women, respectively. For a subset of these clusters, significant associations with an increased CVD risk were found, e.g. in women, the high-risk cluster was associated with a 10.6 (95% confidence interval: 8.9, 12.3) percentage point increase in the 10-year Framingham Risk Score. Associations were independent of blood pressure and myocardial mass. Only in women, associations were also independent of average wall thickness and LV concentricity. Variable selection identified distinct clinical predictors of remodeling patterns. Conclusion: Particularly in women, regional LV wall thickness patterns detect unfavorable cardiac remodeling and might improve CVD risk stratification beyond existing strategies. Automated implementation during image acquisition and integration with shape-based models may facilitate clinical application.

Background and Aims: Epicardial adipose tissue (EAT) has emerged as an important cardiovascular biomarker that reflects both inflammatory and cardiometabolic risk. EAT volume and density vary significantly across populations, yet there is a lack of multicenter studies investigating the predictive value of population-specific EAT percentiles. Methods: In this multicenter study, we retrospectively analyzed low-dose computed tomography correction scans from 42,842 patients undergoing myocardial perfusion imaging. A derivation cohort of 15,082 patients was used to establish sex- and age-specific nomograms for EAT density and EAT volume indexed to body surface area. Percentile-based thresholds were tested for outcome prediction in a validation cohort of 27,760 patients. For clinical implementation, we developed an online EAT percentile calculator. Results: Percentile curves demonstrated increased BSA-indexed EAT volume and decreasing EAT density with age. Over a median follow-up of 3.6 years (IQR: 1.83 - 5.14), 4,956 patients experienced a nonfatal myocardial infarction or death. In multivariable Cox models, patients above the 95th sex- and age-specific percentile had significantly worse outcomes for BSA- indexed EAT volume [adjusted hazard ratio 1.30, 95% CI: 1.14 - 1.49, p < 0.001] and EAT density [adjusted hazard ratio 1.7, 95% CI: 1.51 - 1.92, p<0.001] when compared to patients below the 50th percentile (p<0.001). Conclusion: Age- and sex-specific EAT percentiles provide a clinically interpretable framework for contextualizing automated EAT measurements and identifying patients at increased cardiovascular risk. EAT density was a stronger prognostic marker and identified elevated risk even among patients with normal BMI, supporting its potential to provide information beyond conventional anthropometric assessment.

IntroductionAcute myocardial infarction (AMI) remains a leading cause of mortality, with the coexistence of other conditions (i.e., multimorbidity) complicating management and outcomes. Currently, healthcare providers see major challenges in consideration of the patient with a multimorbid profile, especially as this is a progressive issue where the temporal evolution of diseases is complex in nature, with a profound impact on clinical outcomes. MethodsData on 12,701 AMI patients from the UK Biobank were selected for analysis from the cohort of 502,000 volunteers and then grouped into pre- (up to 1 year prior) and early (within 5 years) post-AMI periods. Using Dynamic Time Warping (DTW) clustering, sequences of ICD-10 diagnoses accumulated over time in the post-AMI period were used to cluster participants. Topic modelling of cluster-specific diagnoses informed thematic labels for these profiles (clusters) of AMI patients. Using data from pre-AMI, along with socio-demographic variables (age, IMD score, BMI, and sex), four predictive supervised models, namely, Logistic Regression, Random Forest, XGBoost, and CatBoost, were developed, with CatBoost achieving the highest accuracy for profile membership prediction. Model interpretability via SHapley Additive exPlanations (SHAP) identified key diagnostic categories that were driving profile assignments. Then, survival analyses compared SMART (Second Manifestations of Arterial Disease) risk scores across the profiles, adjusting for clinical covariates to evaluate adverse cardiovascular outcomes - death. Finally, Phenome-Wide Association Studies (PheWAS) were employed to link profile-specific diagnostic themes to underlying genetic mechanisms. ResultsUsing the above approaches, three multimorbidity profiles were identified in the post-AMI period: Acute cardio-renal-respiratory instability with chronic metabolic disease (ACUTE-CARD), Cardiometabolic disease with mixed arrhythmic-ischemic burden (CARDIOMIX), and Smoking-related cardiovascular disease with multimorbidity (SMO-CARD). CatBoost predicted profile membership with AUROC 0.77. Participants in the SMO-CARD cluster showed the highest rates of mortality, while ACUTE-CARD had the most favourable outcomes (SMART risk score = 11.2, and 6.8% CVD deaths). SMO-CARD displayed a broad range of cardiopulmonary and systemic associations. PheWAS revealed profile-specific genetic associations and pathway enrichments were consistent with clinical features; for example, cardiometabolic genes were associated with the CARDIOMIX cluster, and immune-related pathways were associated with SMO-CARD, supporting the biological plausibility of these profiles. ConclusionIntegrating temporal clustering with explainable machine learning reveals distinct multimorbidity patterns in AMI patients. This framework supports personalised risk stratification and outcome prediction in clinical care.

Background: Obesity is a major modifiable risk factor for heart failure (HF), but body mass index (BMI) does not distinguish biologically distinct fat depots. Whether imaging-derived adipose tissue depots capture specific cardiometabolic pathways underlying HF risk beyond conventional anthropometric measures remains uncertain. Methods: We performed two-sample Mendelian randomization (MR) and multivariable MR mediation analyses using published genome-wide association study summary statistics. General adiposity traits included BMI, waist-to-hip ratio (WHR), and WHR adjusted for BMI from GIANT and UK Biobank meta-analyses of up to 694,649 individuals. MRI-derived visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT) were derived from 38,965 UK Biobank participants. HF outcome data were obtained from the HERMES consortium, including 1,946,349 individuals and 153,174 HF cases. Cardiometabolic mediators included type 2 diabetes (T2D), systolic blood pressure (SBP), LDL cholesterol, HDL cholesterol, and triglycerides. Primary analyses used inverse-variance weighted MR, with sensitivity analyses and directionality testing. Mediation was estimated using joint multivariable MR conditioning on significant cardiometabolic mediators. Results: Among MRI-derived adipose depots, ASAT was the only trait significantly associated with HF risk (odds ratio [OR] 1.64 per 1-SD increase; 95% CI 1.40-1.93; FDR q<0.001). VAT showed a positive but imprecise association (OR 1.38; 95% CI 0.87-2.20), and GFAT was not associated with HF. Among general adiposity measures, BMI (OR 1.65; 95% CI 1.58-1.71) and WHR (OR 1.30; 95% CI 1.23-1.38) were robustly associated with HF, whereas WHR adjusted for BMI was not. ASAT was significantly associated with T2D, SBP, HDL cholesterol, and triglycerides, but not LDL cholesterol. In joint multivariable MR, 67.9% of ASAT's HF effect was mediated through T2D, SBP, HDL cholesterol, and triglycerides (95% CI 49.2-86.8%). In contrast, BMI demonstrated only 8.5% mediation (95% CI -7.4 to 24.4%), and WHR showed non-significant mediation of 36.7% (95% CI -8.3 to 81.7%). Conclusions: MRI-derived abdominal subcutaneous adipose tissue captures a biologically coherent cardiometabolic signal underlying HF risk that is diluted by conventional anthropometric measures. ASAT may represent an imaging biomarker of metabolic syndrome-mediated HF risk and could support more precise risk stratification and mechanistically targeted prevention in HF.

IntroductionAlthough APOE {varepsilon}4 allele carriage confers a risk of coronary disease, its persistence in human populations might be explained by certain survival advantages (antagonistic pleiotropy). HypothesisCombining data from three British cohorts-1946 National Survey of Health and Development (NSHD), Southall and Brent Revised (SABRE) and UK Biobank-we explored whether APOE {varepsilon}4 carriage associates with beneficial or unfavorable left ventricular (LV) structural and functional parameters by echocardiography and cardiovascular magnetic resonance (CMR) in older age. MethodsBased on the presence of APOE {varepsilon}4, genotypes were divided into: APOE {varepsilon}4 ({varepsilon}2{varepsilon}4, {varepsilon}3{varepsilon}4,{varepsilon} 4{varepsilon}4) and non-APOE {varepsilon}4 carriers. Echocardiographic data included: LV ejection fraction, E/e, systolic and diastolic posterior wall and interventricular septal thickness (LVPWTs/d, IVSs/d), LV mass and the ratio of the LV stroke volume to the LV myocardial volume called myocardial contraction fraction (MCF). CMR data additionally included longitudinal and radial peak diastolic strain rates (PDSR). Generalized linear models explored associations between APOE {varepsilon}4 genotypes as exposures and echocardiographic/CMR biomarkers as outcomes. As APOE genotype is a genetic instrumental variable (unconfounded), Model 1 was unadjusted; Model 2 was adjusted for factors associated with the outcome (age, sex, and socio-economic position) to yield more precise estimates; and subsequent models were individually adjusted for mediators (body mass index, cardiovascular disease [CVD], high cholesterol and hypertension) to explore mechanistic pathways. Results35,568 participants were included. Compared to the non-APOE {varepsilon}4 group, APOE {varepsilon}4 carriers had similar cardiac echocardiographic phenotypes in terms of LV EF, E/e, LVPWTs/d, IVSs/d and LV mass but had a 4% higher MCF (95% confidence interval [CI]: 1-7%, p=0.016) which persisted in Model 2 (95% CI 1-7%, p=0.008) but was attenuated to 3% after adjustment for CVD, diabetes and hypertension (all 95% CI 0-6%; all p<0.070). This was replicated in UK Biobank using CMR data, where APOE {varepsilon}4 carriers had a 1% higher MCF (95% CI 0-1%, p=0.020) which was attenuated only after adjusting for BMI or diabetes. ConclusionsAPOE {varepsilon}4 carriage associates with improved myocardial performance in older age resulting in greater LV stroke volume generation per 1 mL of myocardium and better longitudinal strain rates compared to non APOE {varepsilon}4 carriers. This potentially favorable cardiac phenotype adds to the growing number of reported survival advantages attributed to APOE {varepsilon}4 carriage that might collectively explain its persistence in humans.

BackgroundCardiovascular disease (CVD) is the leading cause of premature death worldwide. Effective and individualized treatment requires exact knowledge about both risk factors and risk estimation. Most evidence for risk prediction currently comes from population-based studies on first incident cardiovascular events. In contrast, little is known about the relevance of risk factors for the outcome of patients with established CVD or those who are at high risk of CVD, including patients with type 2 diabetes. In addition, most studies focus on individual diseases, whereas less is known about disease overarching risk factors and cross-over risk. AimThe aim of BeLOVE is to improve short- and long-term prediction and mechanistic understanding of cardiovascular disease progression and outcomes in very high-risk patients, both in the acute as well as in the chronic phase, in order to provide the basis for improved, individualized management. Study designBeLOVE is an observational prospective cohort study of patients of both sexes aged >18 in selected Berlin hospitals, who have a high risk of future cardiovascular events, including patients with a history of acute coronary syndrome (ACS), acute stroke (AS), acute heart failure (AHF), acute kidney injury (AKI) or type 2 diabetes with manifest target-organ damage. BeLOVE includes 2 subcohorts: The acute subcohort includes 6500 patients with ACS, AS, AHF, or AKI within 2-8 days after their qualifying event, who undergo a structured interview about medical history as well as blood sample collection. The chronic subcohort includes 6000 patients with ACS, AS, AHF, or AKI 90 days after event, and patients with type 2 diabetes (T2DM) and target-organ damage. These patients undergo a 6-8 hour deep phenotyping program, including detailed clinical phenotyping from a cardiological, neurological and metabolic perspective, questionnaires including patient-reported outcome measures (PROMs)as well as magnetic resonance imaging. Several biological samples are collected (i.e. blood, urine, saliva, stool) with blood samples collected in a fasting state, as well as after a metabolic challenge (either nutritional or cardiopulmonary exercise stress test). Ascertainment of major adverse cardiovascular events (MACE) will be performed in all patients using a combination of active and passive follow-up procedures, such as on-site visits (if applicable), telephone interviews, review of medical charts, and links to local health authorities. Additional phenotyping visits are planned at 2, 5 and 10 years after inclusion into the chronic subcohort. Future perspectiveBeLOVE provides a unique opportunity to study both the short- and long-term disease course of patients at high cardiovascular risk through innovative and extensive deep phenotyping. Moreover, the unique study design provides opportunities for acute and post-acute inclusion and allows us to derive two non-nested yet overlapping sub-cohorts, tailored for upcoming research questions. Thereby, we aim to study disease-overarching research questions, to understand crossover risk, and to find similarities and differences between clinical phenotypes of patients at high cardiovascular risk.

Backgrounddrug development and disease prevention of heart failure (HF) and atrial fibrillation (AF) are impeded by a lack of robust early-stage surrogates. We determined to what extent cardiac magnetic resonance (CMR) measurements act as surrogates for the development of HF or AF in healthy individuals. MethodsGenetic data was sourced on the association with 22 atrial and ventricular CMR measurements. Mendelian randomization was used to determine CMR associations with atrial fibrillation (AF), heart failure (HF), non-ischemic cardiomyopathy (CMP), and dilated cardiomyopathy (DCM). Additionally, for the CMR surrogates of AF and HF, we explored their association with non-cardiac traits. ResultsIn total we found that 10 CMR measures were associated with the development of HF, 8 with development of non-ischemic CMP, 5 with DCM, and 11 with AF. Left-ventricular (LV) ejection fraction (EF), and LV end diastolic volume (EDV) were associated with all 4 cardiac outcomes. Increased LV-MVR (mass to volume ratio) affected HF (odds ratio (OR) 0.83, 95%CI 0.79; 0.88), DCM (OR 0.26, 95%CI 0.20; 0.34), non-ischemic CMP (OR 0.44 95%CI, 0.35; 0.57). We were able to identify 9 CMR surrogates for HF and AF (including LV-MVR, biventricular EDV, right-ventricular EF, and left-atrial maximum volume) which associated with non-cardiac traits such as blood pressure, cardioembolic stroke, diabetes, and late-onset Alzheimers disease. ConclusionCMR measurements may act as surrogate endpoints for the development of HF (including non-ischemic CMP and DCM) or AF. Additionally, we show that changes in cardiac function and structure measured through CMR, may affect diseases of other organs leading to diabetes and late-onset Alzheimers disease.

Background and AimsThe H2FPEF score is a widely used prediction tool used during the diagnostic work-up of heart failure with preserved ejection fraction (HFpEF). However, having angina symptoms is not included in the score, despite being common in patients with HFpEF. We hypothesize that incorporating angina in the H2FPEF score may improve its performance. Given the known sex differences in HFpEF, sex-specific analyses are warranted. MethodsWe included 1,266 individuals suspected HFpEF, with 515 from the UHFO-DM cohort and 751 from a combination cohort of STRETCH, TREE and UHFO-COPD. Participants underwent standardized symptom collection, including angina, using WHO questionnaires and expert-panel adjudication of HFpEF. Following evaluation of H2FPEF, we assessed the association of angina with HFpEF independent of H2FPEF using logistic regression. By adding angina to H2FPEF, we developed a modified algorithm and evaluated it by AUC, calibration, reclassification, and decision curve analysis. All analyses were stratified by sex. ResultsIn the UHFO-DM cohort, HFpEF prevalence was 24%. Overall H2FPEF discrimination (AUC) was 0.72, with 0.69 in women and 0.74 in men. Angina was independently associated with HFpEF in women (OR 3.96, 95% CI 1.72-9.11, P=0.001) but not in men (1.90, 0.88-4.10, 0.102). This was also found in the combination cohort (women: 2.13, 1.14-3.97, 0.018; men: 0.85, 0.44-1.66, 0.638). In the UHFO-DM cohort, adding one point for angina in a modified H2FPEF score in women improved AUC from 0.69 to 0.71 (DeLong P=0.030), increased sensitivity (0.53 to 0.60) and negative predictive value (0.80 to 0.82), and yielded a continuous net reclassification improvement of 0.449, with preserved calibration and higher net clinical benefit on decision curves. No performance gain was observed with the same modification in men. ConclusionsIn women with suspected HFpEF, presence of angina provides diagnostic information independent of H2FPEF to uncover HFpEF. A simple sex-specific modification of H2FPEF, adding one point for angina in women, may slightly improve discrimination and rule-out performance in women. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/25339191v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@b9eee9org.highwire.dtl.DTLVardef@f41af6org.highwire.dtl.DTLVardef@162595org.highwire.dtl.DTLVardef@1fd8f9c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Case studies conducted after recovery from acute infection with SARS-CoV-2 have frequently identified abnormalities on CMR imaging, suggesting the possibility that SARS-CoV-2 infection commonly leads to cardiac pathology. However, these observations have not been able to distinguish between associations that reflect pre-existing cardiac abnormalities (that might confer a greater likelihood of more severe infection) from those that arise as consequences of infection. To address this question, UK Biobank volunteers (n=1285; 54.5% women; mean age at baseline, 59.8 years old; 96.3% white) who attended an imaging assessment including cardiac magnetic resonance (CMR) before the start of the COVID-19 pandemic were invited to attend a second imaging assessment in 2021. Cases with evidence of previous SARS-CoV-2 infection were identified through linkage to PCR-testing or other medical records, or a positive antibody lateral flow test; n=640 in data available on 22 Sep 2021) and were matched to controls with no evidence of previous infection (n=645). The majority of these infections were milder and did not involve hospitalisation. Measures of cardiac and aortic structure and function were derived from the CMR images obtained on the cases before and after SARS-CoV-2 infection from images for the controls obtained over the same time interval using a previously validated, automated algorithm. Cases and controls had similar cardiac and aortic imaging phenotypes at their first imaging assessment. Changes between CMR imaging measures in cases before and after infection were not significantly different from those in the matched control group. Additional adjustment for comorbidities made no material difference to the results. While these results are preliminary and limited to imaging metrics derived from automated analyses, they do not suggest clinically significant persistent cardiac pathology in the UK Biobank population after generally milder (non-hospitalised) SARS-CoV-2 infection.

Background: Comprehensive assessment of hypertension-mediated organ damage (HMOD) across multiple organ systems remains limited in sub-Saharan Africa. We aimed to determine the prevalence and predictors of multidomain HMOD in a geographically diverse Ghanaian adult population. Methods: This secondary analysis of the Ghana Heart Study included 1,106 adults from four regions. Multidomain HMOD was defined as a pre-specified 9-domain TOD composite score ?2, based on the ESH/ESC 2018 guidelines framework. Logistic regression and ROC analysis were used to identify predictors and compare discriminative performance. Results: Mean age was 46.9 (17.2) years and 58% were female. Multidomain HMOD prevalence was 21.2% (235/1,106) and increased steeply with age: 8.6% (<45 years), 20.6% (45?59 years), and 44.4% (?60 years). Hypertension prevalence was 73% in the HMOD group versus 28% in those without HMOD (p < 0.001). The strongest independent associations were peripheral artery disease (OR 41.2), valvular burden (OR 14.4), and ECG-LVH (OR 9.0). baPWV showed superior discriminative performance (AUC 0.827, 95% CI 0.794?0.860) compared with the ASCVD Pooled Cohort Equations (AUC 0.466; ?AUC +0.351, DeLong test p < 0.001). Conclusions: One in five Ghanaian adults has hypertension-mediated organ damage in ?2 organ systems. baPWV is the strongest predictor and substantially improves risk stratification beyond conventional scores. These findings support the use of baPWV to guide hypertension management and HMOD assessment in West Africa.

Identifying individuals at high risk of heart failure during precursor stages could allow for earlier initiation of treatments to modify disease progression. We performed a GWAS meta- analysis to generate a heart failure (HF) polygenic risk score (PRS) then tested the association with phenotypic subtypes (reduced ejection fraction [HFrEF] and preserved ejection fraction [HFpEF]) to evaluate the value of polygenic risk prediction. Results from the European-ancestry analysis showed that an ancestry-matched PRS, calculated from GBMI meta-analysis outperformed the previous HF GWAS (HERMES), yielding an adjusted odds ratio (aOR) of 2.27 (95% CI: 2.05-2.51; p: 1.76x10-56) from GBMI compared to 1.30 (95% CI: 1.18-1.44; p: 1.42x10- 7) from HERMES, and 1.49 (95% CI: 1.33-1.66; p: 8.38x10-13) compared to 1.17 (95% CI: 1.05- 1.31; p: 0.004) for HFrEF and HFpEF, respectively. Next, we evaluated the performance differences between ancestry-matched and multi-ancestry PRS in the African American cohort. The GBMI multi-ancestry GWAS-based PRS had a significant aOR of 1.49 (p: 0.006). Findings suggest that a PRS for heart failure derived from the GBMI multi-ancestry study is useful in predicting HFrEF, but less powerful in predicting HFpEF in an independent cohort. The difficulty in predicting HFpEF could result from the GBMI HF phenotype, preferencing HFrEF over HFpEF, and/or greater genetic heterogeneity in the HFpEF phenotype.

Background: Transthyretin cardiomyopathy (ATTR-CM) is a progressive, potentially fatal disease requiring accurate risk stratification. Echocardiography is the first-line imaging modality, with AI-based tools increasingly applied for automated analysis, yet their prognostic value remains unknown. Objectives: To examine the prognostic value of AI-derived echocardiographic measurements and their incremental value beyond biomarker staging in ATTR-CM. Methods: This retrospective study included patients from two ATTR-CM registries. Baseline echocardiograms were analyzed using the fully automated AI-based software Us2.ai. Prognostic performance was assessed by Kaplan-Meier analysis, Cox regression, and ROC curves. A two-parameter echocardiographic staging system combining left ventricular (LV) global longitudinal strain (GLS) and right ventricular (RV) fractional area change (FAC) stratified patients into low (both normal), intermediate (one abnormal), and high risk (both abnormal). Results: Among 347 patients (91% male, median age 78 years), 141 experienced all-cause death or heart failure hospitalization over a median follow-up of 2.4 years. In multivariable analysis, AI-derived LV-GLS (HR 1.13 [1.03-1.25], p=0.011) and RV FAC (HR 0.96 [0.93-0.99], p=0.014) were independent outcome predictors. Echo staging stratified risk into groups with 3-fold (95% CI 1.70-5.91) and 6-fold (95% CI 3.22-10.30) increased hazard compared to low risk (p<0.001), with incremental prognostic value beyond National Amyloidosis Centre (NAC) staging and age (chi-square from 53 to 80; p<0.001). AI and human measurements showed comparable 1-year predictive performance (all p>0.05). Conclusion: AI-derived echocardiographic measurements demonstrate independent and incremental prognostic value beyond biomarker-based NAC staging in ATTR-CM, comparable to human measurements, supporting their integration into clinical risk stratification.

Background and aimsHeart Failure with Preserved Ejection Fraction (HFpEF) accounts for approximately half of all heart failure cases, with high levels of morbidity and mortality. However, most cases of HFpEF are undiagnosed as conventional risk scores underestimate risk in non-White populations. Our aim was to develop and validate a diagnostic prediction model to detect undiagnosed HFpEF, AIM-HFpEF. MethodsWe applied natural language processing (NLP) and machine learning methods to routinely collected electronic health record (EHR) data from a tertiary centre hospital trust in London, UK, to derive the AIM-HFpEF model. We then externally validated the model and performed benchmarking against existing HFpEF prediction models (H2FPEF and HFpEF-ABA) for diagnostic power in patients of non-white ethnicity and patients from areas of increased socioeconomic deprivation. ResultsAn XGBoost model combining demographic, clinical and echocardiogram data showed strong diagnostic performance in the derivation dataset (n=3170, AUC=0.88, [95% CI, 0.86-0.91]) and validation cohort (n=5383, AUC: 0.88 [95% CI, 0.87-0.89]). Diagnostic performance was maintained in patients of non-White ethnicity (AUC=0.88 [95% CI, 0.84-0.93]) and patients from areas of high socioeconomic deprivation (AUC=0.89 [95% CI, 0.84-0.94]). and AIM-HFpEF performed favourably in comparison to H2FPEF and HFpEF-ABA models. AIM-HFpEF model probabilities were associated with an increased risk of death, hospitalisation and stroke in the external validation cohort (P<0.001, P=0.01, P<0.001 respectively for highest versus middle tertile). ConclusionAIM-HFpEF represents a validated equitable diagnostic model for HFpEF, which can be embedded within an EHR to allow for fully automated HFpEF detection.

BackgroundRecent studies have shown BMI variability is risk factor for various adverse cardiovascular outcomes. However, the specific associations between BMI variability and the risk of developing HFpEF versus HFrEF, particularly across multiple weight change trends, remain unexplored. Methods and ResultsWe identified a cohort of 52,286 eligible patients with overweight or obesity grouped into three categories based on their BMI change patterns over five years: weight loss, stable weight, and weight gain. BMI variability was assessed in the same 5-year period using average successive variability (ASV). These patients were subsequently followed to monitor the incidence of HFpEF and HFrEF. Cox regression models were applied to examine the differential association between BMI variability and HFpEF and HFrEF risk. Over a median follow-up of 4.81 years, 2,295 patients developed HFpEF, and 1,189 developed HFrEF. After adjusting for relevant confounders, elevated BMI variability was associated with an increased risk of HFpEF. The hazard ratios (HRs) of HFpEF for each 1-SD increment in ASV of BMI were 1.27 (95% CI, 1.10-1.47) in the weight loss group and 1.22 (95% CI, 1.09-1.37) in the stable weight group. Additionally, when analyzed as a binary variable divided by the median, BMI variability above the median was associated with higher risks of HFpEF compared to those below the median, with the corresponding HRs being 1.46 (95% CI, 1.20-1.77) for the weight loss group and 1.17 (95% CI, 1.04-1.31) for the stable weight group. ConclusionsIn this large cohort of patients living with overweight or obesity, greater BMI variability was significantly associated with a higher risk of developing HFpEF compared to patients with reduced and stable weight over time. Clinical Perspective Whats new?1. In patients with weight loss and stable weight, those with higher BMI variability have an increased risk of developing incident HFrEF compared to those experiencing lower BMI variability, after adjusting all potential confounding variables. 2. In patients with weight gain, BMI variability was not significantly linked to the risk of developing HFpEF or HFrEF. However, a larger increase in delta BMI was significantly associated with a higher risk of incident HFpEF and HFrEF in this group. What are the clinical implications?1. Promoting the importance of stable and consistent weight management strategies to reduce heart failure risk, particularly by minimizing BMI variability in patients undergoing weight loss or maintaining stable weight.

BackgroundDespite established relationship between atherosclerosis and coronary artery disease (CAD), evidence on subclinical atherosclerosis and its differential associations with premature coronary artery disease (PCAD) versus late-onset coronary artery disease (LCAD) remains limited. AimsThis study aims to delve deeper into the associations between subclinical atherosclerosis and the incidence risk of both PCAD and LCAD. MethodsUsing UK Biobank data, we identified PCAD (male <55/female <65 years; n=7,398) and LCAD (male [&ge;]55/female [&ge;]65 years; n=39,085) cohorts. Conditional inference tree classification optimized carotid intima-media thickness (cIMT) stratification in both cohorts. ResultsConditional inference tree categorized the PCAD cohort into two subgroups: cIMT [&le;]700m and cIMT >700m, with the latter demonstrating a HR of 2.079 for cardiovascular risk. In the LCAD cohort, four cIMT strata were identified: [&le;] 620m, 620-763m (HR=1.401), 763-1054m (HR=1.810), and >1054m (HR=2.850). Multivariable-adjusted Cox models demonstrated significant associations between subclinical atherosclerosis and PCAD (HR=2.079, 95%CI:1.477-2.925) and LCAD (HR=1.776, 95%CI:1.455-2.169), highlighting the prognostic value of cIMT stratification in coronary artery disease risk assessment. ConclusionsA UK Biobank prospective cohort study revealed subclinical atherosclerosis significantly associated with PCAD and LCAD risks. Even within conventional cIMT "safe thresholds", incremental increases predicted elevated risks, underscoring limitations of current thresholds. Future research need to develop multimodal frameworks integrating dynamic cIMT trajectories to refine risk stratification and early interventions.

Background and aimsThe utility of cardiovascular and inflammatory biomarkers to detect coronary obstruction and predict survival benefit of revascularisation in chronic coronary syndrome (CCS) remains unclear. MethodsPatients undergoing coronary angiography for suspected CCS were studied. High-sensitivity cardiac troponin T (hsTnT), N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and copeptin were measured. Diagnostic performance to detect anatomical coronary stenosis (i.e. [&ge;]50%) was assessed using Receiver Operating Characteristic (ROC) analysis with Net Reclassification Improvement (NRI). Survival analysis using multivariate Cox regression assessed treatment-stratified biomarker performance and biomarker x treatment interaction. ResultsAmong 2,251 patients, 888 (39.4%) had coronary obstruction. Only hsTnT provided meaningful diagnostic capacity (Area Under the Curve; AUC 0.669), comparable with risk factor-weighted clinical likelihood (RF-CL) estimate recommended by the current guidelines (AUC 0.663). Reclassification value demonstrated an inverse relationship to RF-CL: very low (NRI=38.4%), low (NRI=19.3%), and intermediate/high likelihood (NRI=12.4%). NT-proBNP was the strongest universal mortality predictor across all treatment categories: Optimal Medical Therapy (OMT; HR 1.488, 95%CI:1.288-1.720, p<0.001), Percutaneous Coronary Intervention (PCI; HR 1.220, 95%CI:1.020-1.458, p=0.029), Coronary Artery Bypass Grafting (CABG; HR 1.220, 95%CI:1.049-1.420, p=0.010). Interaction analysis (p=0.024) demonstrated that in patients with NT-proBNP <150 pg/mL the revascularisation group has an improved survival (HR 0.64, 95%CI:0.47-0.87, p=0.005), whilst this was not observed for the patients with NT-proBNP [&ge;]150 pg/mL (HR 0.96, 95%CI:0.71-1.30, p=0.782). ConclusionsHsTnT provides meaningful diagnostic value with RF-CL category-specific incremental benefit following an inverse gradient pattern. The NT-proBNP 150 pg/mL threshold identifies patients with improved survival after revascularisation. Clinical Trial RegistrationClinicalTrials.gov NCT00497887 KEY LEARNING POINTSO_ST_ABSWhat is already knownC_ST_ABS- Cardiac biomarkers predict outcomes in chronic coronary syndrome - ESC 2024 guidelines introduced risk-factor weighted clinical likelihood assessment - The incremental value of biomarkers to detect coronary stenosis and guide risk stratification after revascularization remains uncertain What this study adds- High-sensitivity troponin T diagnostic utility follows an inverse gradient pattern, with greatest incremental benefit in very low baseline risk patients - NT-proBNP <150 pg/mL identifies a patient subgroup with significant survival improvement after revascularisation - Cardiac biomarker patterns provide biological validation of ESC 2024 guideline evolution