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Brandon Tejada,1,2 Roby Joehanes,1,2 Shih-Jen Hwang,1,2 Tianxiao Huan,1– 3 Chen Yao,1,2 Jennifer E Ho,4 Daniel Levy1,2,5 1Framingham Heart Study, Framingham, MA, USA; 2Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA; 3Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA, USA; 4CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, Unites States; 5Boston University School of Medicine, Boston, MA, USACorrespondence: Daniel Levy, Framingham Heart Study, 73 Mt. Wayte Avenue, Suite 2, Framingham, MA, 01702, USA, Email Levyd@nih.govPurpose: Assessing an individual’s systemic inflammatory state is vital to understand inflammation’s role in cardiometabolic diseases and identify those at the greatest risk of disease. We generated global inflammation scores and investigated their associations with cardiometabolic risk factors and adverse outcomes.Patients and Methods: Aggregate Inflammation Scores (AIS) and Principal Component Analysis (PCA) scores were generated for 7287 Framingham Heart Study participants using up to 26 inflammation-related proteins, with higher scores reflecting a pro-inflammatory milieu. Multivariable regression and proportional hazards analyses were conducted to investigate the associations of inflammation with cardiometabolic risk factors and outcomes. The primary outcomes for cross-sectional analyses included age, cigarette smoking, fasting lipid and glucose levels, blood pressure, body mass index (BMI), and hypertension, diabetes, and obesity. For prospective analyses, new-onset hypertension, diabetes, obesity, cardiovascular disease and all-cause mortality were investigated.Results: Higher inflammation scores were associated with smoking and older age, higher BMI, systolic blood pressure, lipids, and glucose levels, and with greater odds of hypertension and diabetes after adjusting for age, sex, cohort, and BMI (all p < 0.001). Higher baseline scores were associated with greater odds of new-onset hypertension after adjusting for traditional risk factors (OR [95% CI] per one standard deviation [1-SD] increase, AIS: 1.33 [1.21– 1.47], PCA score: 1.26 [1.12– 1.42], p < 0.001). The AIS also was associated with new-onset diabetes (1.32 [1.14– 1.52], p < 0.001). Proportional hazards analyses revealed greater risk of new-onset cardiovascular disease events and all-cause mortality (HR [95% CI] per 1-SD, AIS: 1.25 [1.14– 1.37] and 1.32 [1.23– 1.42], PCA score: 1.22 [1.13– 1.33] and 1.40 [1.31– 1.49], p < 0.001).Conclusion: Global inflammation scores encompassing an array of pro- and anti-inflammatory proteins and pathways may enhance risk assessment for cardiometabolic diseases. The AIS and PCA scores provide further opportunities to investigate the mechanisms of inflammation-related risk of disease.Keywords: inflammation scores, cardiovascular disease, heart failure, pathways

Observational and trial data have revealed significant improvement in cardiogenic shock (CS) mortality due to acute myocardial infarction (AMI) after introducing early coronary revascularization. Less is known about CS mortality due to heart failure (HF), which is increasingly recognized as a distinct entity from AMI-CS.In this nationwide observational study, the CDC WONDER database was used to identify national trends in age-adjusted mortality rates (AAMR) due to CS (HF vs. AMI related) per 100,000 people aged 35-84. AAMR from AMI-CS decreased significantly from 1999 to 2009 (AAPC: -6.9% [95%CI -7.7, -6.1]) then stabilized from 2009 to 2020. By contrast, HF-CS associated AAMR rose steadily from 2009 to 2020 (AAPC: 13.3% [95%CI 11.4,15.2]). The mortality rate was almost twice as high in males compared to females in both AMI-CS and HF-CS throughout the study period. HF-CS mortality in the non-Hispanic Black population is increasing more quickly than that of the non-Hispanic White population (AAMR in 2020: 4.40 vs. 1.97 in 100,000). The AMI-CS mortality rate has been consistently higher in rural than urban areas (30% higher in 1999 and 28% higher in 2020).These trends highlight the fact that HF-CS and AMI-CS represent distinct clinical entities. While mortality associated with AMI-CS has primarily declined over the last two decades, the mortality related to HF-CS has increased significantly, particularly over the last decade, and is increasing rapidly among individuals younger than 65. Accordingly, a dramatic change in the demographics of CS patients in modern intensive care units is expected.

Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR.We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRRAmong 56,793 individuals (mean age 57 years, 51% women), the HRRDeep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study.

The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.

Valvular heart disease is associated with a high global burden of disease. Even mild aortic stenosis confers increased morbidity and mortality, prompting interest in understanding normal variation in valvular function at scale.We developed a deep learning model to study velocity-encoded magnetic resonance imaging in 47,223 UK Biobank participants. We calculated eight traits, including peak velocity, mean gradient, aortic valve area, forward stroke volume, mitral and aortic regurgitant volume, greatest average velocity, and ascending aortic diameter. We then computed sex-stratified reference ranges for these phenotypes in up to 31,909 healthy individuals. In healthy individuals, we found an annual decrement of 0.03cm2in the aortic valve area. Participants with mitral valve prolapse had a 1 standard deviation [SD] higher mitral regurgitant volume (P=9.6 × 10−12), and those with aortic stenosis had a 4.5 SD-higher mean gradient (P=1.5 × 10−431), validating the derived phenotypes’ associations with clinical disease. Greater levels of ApoB, triglycerides, and Lp(a) assayed nearly 10 years prior to imaging were associated with higher gradients across the aortic valve. Metabolomic profiles revealed that increased glycoprotein acetyls were also associated with an increased aortic valve mean gradient (0.92 SD, P=2.1 x 10−22). Finally, velocity-derived phenotypes were risk markers for aortic and mitral valve surgery even at thresholds below what is considered relevant disease currently.Using machine learning to quantify the rich phenotypic data of the UK Biobank, we report the largest assessment of valvular function and cardiovascular disease in the general population.

BACKGROUND: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. OBJECTIVE: To evaluate if artificial intelligence–enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. METHODS: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression (“LVH-Net”). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I (“LVH-Net Lead I”) or lead II (“LVH-Net Lead II”) from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. RESULTS: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93–0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90–0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. CONCLUSION: An artificial intelligence–enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

This study aimed to evaluate hemodynamic correlates of inducible blood pressure (BP) pulsatility with exercise in heart failure with preserved ejection fraction (HFpEF), to identify relationships to outcomes, and to compare this with heart failure with reduced ejection fraction (HFrEF).In HFpEF, determinants and consequences of exercise BP pulsatility are not well understood.We measured exercise BP in 146 patients with HFpEF who underwent invasive cardiopulmonary exercise testing. Pulsatile BP was evaluated as proportionate pulse pressure (PrPP), the ratio of pulse pressure to systolic pressure. We measured pulmonary arterial catheter pressures, Fick cardiac output, respiratory gas exchange, and arterial stiffness. We correlated BP changes to central hemodynamics and cardiovascular outcome (nonelective cardiovascular hospitalization) and compared findings with 57 patients with HFrEF from the same referral population.In HFpEF, only age (standardized beta = 0.593; P 0.001), exercise stroke volume (standardized beta = 0.349; P 0.001), and baseline arterial stiffness (standardized beta = 0.182; P = 0.02) were significant predictors of peak exercise PrPP in multivariable analysis (R = 0.661). In HFpEF, lower PrPP was associated with lower risk of cardiovascular events, despite adjustment for confounders (HR:0.53 for PrPP below median; 95% CI: 0.28-0.98; P = 0.043). In HFrEF, lower exercise PrPP was not associated with arterial stiffness but was associated with lower peak exercise stroke volume (P = 0.013) and higher risk of adverse cardiovascular outcomes (P = 0.004).In HFpEF, greater inducible BP pulsatility measured using exercise PrPP reflects greater arterial stiffness and higher risk of adverse cardiovascular outcomes, in contrast to HFrEF where inducible exercise BP pulsatility relates to stroke volume reserve and favorable outcome.

There is growing recognition that reproductive factors are associated with increased risk of future cardiovascular disease. Infertility has been less well studied, although emerging data support its association with increased risk of cardiovascular disease. Whether infertility is associated with future risk of heart failure (HF) is not known.This study sought to examine the development of HF and HF subtypes in women with and without history of infertility.We followed postmenopausal women from the Women's Health Initiative prospectively for the development of HF. Infertility was self-reported at study baseline. Multivariable cause-specific Cox models were used to evaluate the association of infertility with incident overall HF and HF subtypes (heart failure with preserved ejection fraction [HFpEF]: left ventricular ejection fraction of ≥50% vs heart failure with reduced ejection fraction [HFrEF]: left ventricular ejection fraction of 50%]).Among 38,528 postmenopausal women (mean age: 63 ± 7 years), 5,399 (14%) participants reported a history of infertility. Over a median follow-up of 15 years, 2,373 developed incident HF, including 807 with HFrEF and 1,133 with HFpEF. Infertility was independently associated with future risk of overall HF (HR: 1.16; 95% CI: 1.04-1.30; P = 0.006). Notably, when examining HF subtypes, infertility was associated with future risk of HFpEF (HR: 1.27; 95% CI: 1.09-1.48; P = 0.002) but not HFrEF (HR: 0.97; 95% CI: 0.80-1.18).Infertility was significantly associated with incident HF. This was driven by increased risk of HFpEF, but not HFrEF, and appeared independent of traditional cardiovascular risk factors and other infertility-related conditions. Future research should investigate mechanisms that underlie the link between infertility and HFpEF.

Sex-based differences in cardiovascular disease presentation, diagnosis, and response to therapies are well established, but mechanistic understanding and translation to clinical applications are limited. Blood-based biomarkers have become an important tool for interrogating biologic pathways. Understanding sexual dimorphism in the relationship between biomarkers and cardiovascular disease will enhance our insights into cardiovascular disease pathogenesis in women, with potential to translate to improved individualized care for men and women with or at risk for cardiovascular disease. In this review, we examine how biologic sex associates with differential levels of blood-based biomarkers and influences the effect of biomarkers on disease outcomes. We further summarize key differences in blood-based cardiovascular biomarkers along central biologic pathways, including myocardial stretch/injury, inflammation, adipose tissue metabolism, and fibrosis pathways in men versus women. Finally, we present recommendations for leveraging our current knowledge of sex differences in blood-based biomarkers for future research and clinical innovation.

Mechanisms underlying sex differences in heart failure with preserved ejection fraction (HFpEF) are poorly understood. We sought to examine sex differences in measures of arterial stiffness and the association of arterial stiffness measures with left ventricular hemodynamic responses to exercise in men and women.We studied 83 men (mean age 62 years) and 107 women (mean age 59 years) with HFpEF who underwent cardiopulmonary exercise testing with invasive hemodynamic monitoring and arterial stiffness measurement (augmentation pressure [AP], augmentation index [AIx], and aortic pulse pressure [AoPP]). Sex differences were compared using multivariable linear regression. We examined the association of arterial stiffness with abnormal left ventricular diastolic response to exercise, defined as a rise in pulmonary capillary wedge pressure relative to cardiac output (∆PCWP/∆CO) ≥ 2 mmHg/L/min by using logistic regression models.Women with HFpEF had increased arterial stiffness compared with men. AP was nearly 10 mmHg higher, and AIx was more than 10% higher in women compared with men (P0.0001 for both). Arterial stiffness measures were associated with a greater pulmonary capillary wedge pressure response to exercise, particularly among women. A 1-standard deviation higher AP was associated with3-fold increased odds of abnormal diastolic exercise response (AP: OR 3.16, 95% CI 1.34-7.42; P = 0.008 [women] vs OR 2.07, 95% CI 0.95-5.49; P = 0.15 [men]) with similar findings for AIx and AoPP.Arterial stiffness measures are significantly higher in women with HFpEF than in men and are associated with abnormally steep increases in pulmonary capillary wedge pressure with exercise, particularly in women. Arterial stiffness may preferentially contribute to abnormal diastolic function during exercise in women with HFpEF compared with men.

IntroductionRight ventricular (RV) dysfunction is associated with increased mortality across a spectrum of cardiovascular diseases. The role of obesity in RV dysfunction and adverse outcomes is unclear.MethodsWe examined patients undergoing right heart catheterization between 2005-2016 in a hospital-based cohort. Linear regression was used to examine the association of obesity with hemodynamic indices of RV dysfunction [pulmonary artery pulsatility index (PAPi), right atrial pressure: pulmonary capillary wedge pressure ratio (RAP:PCWP), RV stroke work index (RVSWI)]. Cox models were used to examine the association of RV function measures with clinical outcomes.ResultsAmong 8285 patients (mean age 63 years, 40% women), higher BMI was associated with worse indices of RV dysfunction, including lower PAPi (β -0.26, SE 0.01, p ConclusionsPatients with obesity had worse hemodynamic measured indices of RV function across a broad hospital-based sample. While RV dysfunction was associated with worse clinical outcomes including mortality and HF hospitalization, this association was especially pronounced among individuals with higher BMI.

Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology–Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women’s Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. Results: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767–0.836]; BWH, 0.752 [95% CI, 0.741–0.763]; UK Biobank, 0.732 [95% CI, 0.704–0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790–0.856]; BWH, 0.747 [95% CI, 0.736–0.759]; UK Biobank, 0.705 [95% CI, 0.673–0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). Conclusions: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Objective: Greater parity has been associated with cardiovascular disease risk. We sought to find whether the effects on cardiac remodeling and heart failure risk are clear.Methods: We examined the association of number of live births with echocardiographic measures of cardiac structure and function in participants of the Framingham Heart Study (FHS) using multivariable linear regression. We next examined the association of parity with incident heart failure with preserved (HFpEF) or reduced (HFrEF) ejection fraction using a Fine-Gray subdistribution hazards model in a pooled analysis of n = 12,635 participants in the FHS, the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular Endstage Disease. Secondary analyses included major cardiovascular disease, myocardia infarction and stroke.Results: Among n = 3931 FHS participants (mean age 48 ± 13 years), higher numbers of live births were associated with worse left ventricular fractional shortening (multivariable β -1.11 (0.31); P = 0.0005 in ≥ 5 live births vs nulliparous women) and worse cardiac mechanics, including global circumferential strain and longitudinal and radial dyssynchrony (P < 0.01 for all comparing ≥ 5 live births vs nulliparity). When examining HF subtypes, women with ≥ 5 live births were at higher risk of developing future HFrEF compared with nulliparous women (HR 1.93, 95% CI 1.19–3.12; P = 0.008); by contrast, a lower risk of HFpEF was observed (HR 0.58, 95% CI 0.37–0.91; P = 0.02).Conclusions: Greater numbers of live births are associated with worse cardiac structure and function. There was no association with overall HF, but a higher number of live births was associated with greater risk for incident HFrEF.

Background: Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease. Methods: In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait. Results: Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 ( P =1.2×10 −26 ), IGFBP3 ( P =4.8×10 −18 ), and PHACTR1 ( P =1.4×10 −13 ), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data. Conclusions: We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.

The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance of the ECG, though the extent to which diseases across the human phenotypic landscape can be detected on the ECG remains unclear. We developed a deep learning variational autoencoder model that encodes and reconstructs ECG waveform data within a multidimensional latent space. We then systematically evaluated whether associations between ECG encodings and a broad range of disease phenotypes could be detected using the latent space model by deriving disease vectors and projecting individual ECG encodings onto the vectors. We developed models for both 12- and single-lead ECGs, akin to those used in wearable ECG technology. We leveraged phecodes to generate disease labels using International Classification of Disease (ICD) codes for about 1,600 phenotypes in three different datasets linked to electronic health record data. We tested associations between ECG encodings and disease phenotypes using a phenome-wide association study approach in each dataset, and meta-analyzed the results. We observed that the latent space ECG model identified associations for 645 (40%) diseases tested in the 12-lead model. Associations were enriched for diseases of the circulatory (n=140, 82% of category-specific diseases), respiratory (n=53, 62%), and endocrine/metabolic (n=73, 45%) systems, with additional associations evident across the human phenome; results were similar for the single-lead models. The top ECG latent space association was with hypertension in the 12-lead ECG model, and cardiomyopathy in the single-lead ECG model (p-308for each). The ECG latent space model demonstrated a greater number of associations than ECG models using standard ECG intervals alone, and generally resulted in improvements in discrimination of diseases compared to models comprising only age, sex, and race. We further demonstrate how a latent space model can be used to generate disease-specific ECG waveforms and facilitate disease profiling for individual patients.

Background: Heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) are distinct clinical entities, yet there is scant evidence for associations of proteomic signatures with future development of HFpEF versus HFrEF. Methods: We evaluated the association of 71 protein biomarkers with incident HFpEF versus HFrEF (left ventricular ejection fraction ≥ versus Results: Among 7038 participants (mean age 49 years; 54% women), 5 biomarkers were associated with increased risk of incident HFpEF (false discovery rate qP P P P P =0.001). Fourteen biomarkers were associated with incident HFrEF (multivariable P Conclusions: We identified 5 protein biomarkers of new-onset HFpEF representing pathways of inflammation, cardiac stress, and vascular stiffness, which partly overlapped with HFrEF. We found 14 biomarkers associated with new-onset HFrEF, with some distinct associations including myeloperoxidase, resistin, and paraoxanase-1. Taken together, these findings provide insights into similarities and differences in the development of HF subtypes. Registration: URL: https://clinicaltrials.gov/ct2/show/NCT00005121 ; Unique identifier: NCT0005121.

We present the case of a woman who developed presumed spontaneous coronary artery dissection of a septal branch. She later developed high-grade atrioventricular block that led to a diagnosis of cardiac sarcoidosis involving the interventricular septum. This case illustrates a rare and challenging presentation of cardiac sarcoidosis. (Level of Difficulty: Beginner.), Central Illustration

AIMS Recent studies suggest an association between cardiovascular disease (CVD) and cancer incidence/mortality, but the pathophysiological mechanisms underlying these associations are unclear. We aimed to examine biomarkers previously associated with CVD and study their association with incident cancer and cancer-related death in a prospective cohort study. METHODS AND RESULTS We used a proteomic platform to measure 71 cardiovascular biomarkers among 5,032 participants in the Framingham Heart Study who were free of cancer at baseline. We used multivariable-adjusted Cox models to examine the association of circulating protein biomarkers with risk of cancer incidence and mortality. To account for multiple testing, we set a 2-sided false discovery rate (FDR Q-value)