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Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

A growing appreciation of the pathophysiological interrelatedness of metabolic risk factors such as obesity and diabetes, chronic kidney disease, and cardiovascular disease has led to the conceptualization of cardiovascular-kidney-metabolic syndrome. The confluence of metabolic risk factors and chronic kidney disease within cardiovascular-kidney-metabolic syndrome is strongly linked to risk for adverse cardiovascular and kidney outcomes. In addition, there are unique management considerations for individuals with established cardiovascular disease and coexisting metabolic risk factors, chronic kidney disease, or both. An extensive body of literature supports our scientific understanding of, and approach to, prevention and management for individuals with cardiovascular-kidney-metabolic syndrome. However, there are critical gaps in knowledge related to cardiovascular-kidney-metabolic syndrome in terms of mechanisms of disease development, heterogeneity within clinical phenotypes, interplay between social determinants of health and biological risk factors, and accurate assessments of disease incidence in the context of competing risks. There are also key limitations in the data supporting the clinical care for cardiovascular-kidney-metabolic syndrome, particularly in terms of early-life prevention, screening for risk factors, interdisciplinary care models, optimal strategies for supporting lifestyle modification and weight loss, targeting of emerging cardioprotective and kidney-protective therapies, management of patients with both cardiovascular disease and chronic kidney disease, and the impact of systematically assessing and addressing social determinants of health. This scientific statement uses a crosswalk of major guidelines, in addition to a review of the scientific literature, to summarize the evidence and fundamental gaps related to the science, screening, prevention, and management of cardiovascular-kidney-metabolic syndrome.

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs)., Methods: The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains., Results: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics., Conclusions: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs)., Methods: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy., Results: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics., Conclusions: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

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.

Background: Whereas regular exercise is associated with lower risk of cardiovascular disease and mortality, mechanisms of exercise-mediated health benefits remain less clear. We used metabolite profiling before and after acute exercise to delineate the metabolic architecture of exercise response patterns in humans., Methods: Cardiopulmonary exercise testing and metabolite profiling was performed on Framingham Heart Study participants (age 53±8 years, 63% women) with blood drawn at rest (n=471) and at peak exercise (n=411)., Results: We observed changes in circulating levels for 502 of 588 measured metabolites from rest to peak exercise (exercise duration 11.9±2.1 minutes) at a 5% false discovery rate. Changes included reductions in metabolites implicated in insulin resistance (glutamate, -29%; P =1.5×10 -55 ; dimethylguanidino valeric acid [DMGV], -18%; P =5.8×10 -18 ) and increases in metabolites associated with lipolysis (1-methylnicotinamide, +33%; P =6.1×10 -67 ), nitric oxide bioavailability (arginine/ornithine + citrulline, +29%; P =2.8×10 -169 ), and adipose browning (12,13-dihydroxy-9Z-octadecenoic acid +26%; P =7.4×10 -38 ), among other pathways relevant to cardiometabolic risk. We assayed 177 metabolites in a separate Framingham Heart Study replication sample (n=783, age 54±8 years, 51% women) and observed concordant changes in 164 metabolites (92.6%) at 5% false discovery rate. Exercise-induced metabolite changes were variably related to the amount of exercise performed (peak workload), sex, and body mass index. There was attenuation of favorable excursions in some metabolites in individuals with higher body mass index and greater excursions in select cardioprotective metabolites in women despite less exercise performed. Distinct preexercise metabolite levels were associated with different physiologic dimensions of fitness (eg, ventilatory efficiency, exercise blood pressure, peak Vo 2 ). We identified 4 metabolite signatures of exercise response patterns that were then analyzed in a separate cohort (Framingham Offspring Study; n=2045, age 55±10 years, 51% women), 2 of which were associated with overall mortality over median follow-up of 23.1 years ( P ≤0.003 for both)., Conclusions: In a large sample of community-dwelling individuals, acute exercise elicits widespread changes in the circulating metabolome. Metabolic changes identify pathways central to cardiometabolic health, cardiovascular disease, and long-term outcome. These findings provide a detailed map of the metabolic response to acute exercise in humans and identify potential mechanisms responsible for the beneficial cardiometabolic effects of exercise for future study.

There is a lack of consensus on how we define heart failure with preserved ejection fraction (HFpEF), with wide variation in diagnostic criteria across society guidelines. This lack of uniformity in disease definition stems in part from an incomplete understanding of disease pathobiology, phenotypic heterogeneity, and natural history. We review current knowledge gaps and existing diagnostic tools and algorithms. We present a simple approach to implement these tools within the constraints of the current knowledge base, addressing separately (1) hospitalized individuals with rest congestion, where diagnosis is more straightforward; and (2) individuals with exercise intolerance, where diagnosis is more complex. Here, a potential role for advanced or provocative testing, including evaluation of hemodynamic responses to exercise is considered. More importantly, we propose focus areas for future studies to develop accurate and feasible diagnostic tools for HFpEF, including animal models that recapitulate human HFpEF, and human studies that both address a fundamental understanding of HFpEF pathobiology, and new diagnostic approaches and tools, as well. In sum, there is an urgent need to more accurately define the syndrome of HFpEF to inform diagnosis, patient selection for clinical trials, and, ultimately, future therapeutic approaches.

The interrelationships between atrial fibrillation (AF) and heart failure (HF) are complex and poorly understood, yet the number of patients with AF and HF continues to increase worldwide. Thus, there is a need for initiatives that prioritize research on the intersection between AF and HF. This article summarizes the proceedings of a virtual workshop convened by the US National Heart, Lung, and Blood Institute to identify important research opportunities in AF and HF. Key knowledge gaps were reviewed and research priorities were proposed for characterizing the pathophysiological overlap and deleterious interactions between AF and HF; preventing HF in people with AF; preventing AF in individuals with HF; and addressing symptom burden and health status outcomes in AF and HF. These research priorities will hopefully help inform, encourage, and stimulate innovative, cost-efficient, and transformative studies to enhance the outcomes of patients with AF and HF.

Background: Heart failure with preserved ejection fraction (HFpEF) is common, yet there is currently no consensus on how to define HFpEF according to various society and clinical trial criteria. How clinical and hemodynamic profiles of patients vary across definitions is unclear. We sought to determine clinical characteristics, as well as physiologic and prognostic implications of applying various criteria to define HFpEF., Methods: We examined consecutive patients with chronic exertional dyspnea (New York Heart Association class II to IV) and ejection fraction ≥50% referred for comprehensive cardiopulmonary exercise testing with invasive hemodynamic monitoring. We applied societal and clinical trial HFpEF definitions and compared clinical profiles, exercise responses, and cardiovascular outcomes., Results: Of 461 patients (age 58±15 years, 62% women), 416 met American College of Cardiology/American Heart Association (ACC/AHA), 205 met European Society of Cardiology (ESC), and 55 met Heart Failure Society of America (HFSA) criteria for HFpEF. Clinical profiles and exercise capacity varied across definitions, with peak oxygen uptake of 16.2±5.2 (ACC/AHA), 14.1±4.2 (ESC), and 12.7±3.1 mL·kg -1 ·min -1 (HFSA). A total of 243 patients had hemodynamic evidence of HFpEF (abnormal rest or exercise filling pressures), of whom 222 met ACC/AHA, 161 met ESC, and 41 met HFSA criteria. Over a mean follow-up of 3.8 years, the incidence of cardiovascular outcomes ranged from 75 (ACC/AHA) to 298 events per 1000 person-years (HFSA). Application of clinical trial definitions of HFpEF similarly resulted in distinct patient classification and prognostication., Conclusions: Use of different HFpEF classifications variably enriches for future cardiovascular events, but at the expense of not including up to 85% of individuals with physiologic evidence of HFpEF. Comprehensive phenotyping of patients with suspected heart failure highlights the limitations and heterogeneity of current HFpEF definitions and may help to refine HFpEF subgrouping to test therapeutic interventions.

Background: Atrial fibrillation (AF) and heart failure (HF) frequently coexist and together confer an adverse prognosis. The association of AF with HF subtypes has not been well described. We sought to examine differences in the temporal association of AF and HF with preserved versus reduced ejection fraction., Methods and Results: We studied Framingham Heart Study participants with new-onset AF or HF between 1980 and 2012. Among 1737 individuals with new AF (mean age, 75±12 years; 48% women), more than one third (37%) had HF. Conversely, among 1166 individuals with new HF (mean age, 79±11 years; 53% women), more than half (57%) had AF. Prevalent AF was more strongly associated with incident HF with preserved ejection fraction (multivariable-adjusted hazard ratio [HR], 2.34; 95% confidence interval [CI], 1.48-3.70; no AF as referent) versus HF with reduced ejection fraction (HR, 1.32; 95% CI, 0.83-2.10), with a trend toward difference between HF subtypes (P for difference=0.06). Prevalent HF was associated with incident AF (HR, 2.18; 95% CI, 1.26-3.76; no HF as referent). The presence of both AF and HF portended greater mortality risk compared with neither condition, particularly among individuals with new HF with reduced ejection fraction and prevalent AF (HR, 2.72; 95% CI, 2.12-3.48) compared with new HF with preserved ejection fraction and prevalent AF (HR, 1.83; 95% CI, 1.41-2.37; P for difference=0.02)., Conclusions: AF occurs in more than half of individuals with HF, and HF occurs in more than one third of individuals with AF. AF precedes and follows HF with both preserved and reduced ejection fraction, with some differences in temporal association and prognosis. Future studies focused on underlying mechanisms of these dual conditions are warranted., (© 2016 American Heart Association, Inc.)

Background: Heart failure (HF) is a clinical syndrome characterized by signs and symptoms involving multiple organ systems. Longitudinal data demonstrating that asymptomatic cardiac dysfunction precedes overt HF are scarce, and the contribution of noncardiac dysfunction to HF progression is unclear. We hypothesized that subclinical cardiac and noncardiac organ dysfunction would accelerate the manifestation of HF., Methods and Results: We studied 1038 participants of the Framingham Heart Study original cohort (mean age, 76±5 years; 39% men) with routine assessment of left ventricular systolic and diastolic function. Major noncardiac organ systems were assessed with the use of serum creatinine (renal), serum albumin (hepatic), ratio of forced expiratory volume in 1 second to forced vital capacity (FEV(1):FVC ratio; pulmonary), hemoglobin concentration (hematologic/oxygen-carrying capacity), and white blood cell count (systemic inflammation). On follow-up (mean, 11 years), there were 248 incident HF events (146 in women). After adjustment for established HF risk factors, antecedent left ventricular systolic dysfunction (hazard ratio, 2.33; 95% confidence interval, 1.43 to 3.78) and diastolic dysfunction (hazard ratio, 1.32; 95% confidence interval, 1.01 to 1.71) were associated with increased HF risk. After adjustment for cardiac dysfunction, higher serum creatinine, lower FEV1:FVC ratios, and lower hemoglobin concentrations were associated with increased HF risk (all P<0.05); serum albumin and white blood cell count were not. Subclinical dysfunction in each noncardiac organ system was associated with a 30% increased risk of HF (P=0.013)., Conclusions: Antecedent cardiac dysfunction and noncardiac organ dysfunction are associated with increased incidence of HF, supporting the notion that HF is a progressive syndrome and underscoring the importance of noncardiac factors in its occurrence.