2025 AHA科学声明：心力衰竭基于风险的一级预防

THE PARADIGM OF RISK-BASED PREVENTION: ASCVD AS A MODEL

Contemporary guidelines for primary prevention of AS CVD recommend a risk-based framework to prevent ASCVD across the life course. 20 This framework was orig inally described at the Bethesda Conference in 1996. 36,37

The underlying premise of the conceptual framework of risk-based prevention is that the absolute benefit of any given therapy is directly proportional to the predicted risk of an individual. Therefore, the higher the predicted risk, the greater the benefit or absolute risk reduction when preventive therapies provide similar relative risk reduction across the spectrum of baseline absolute patient risk (as is true for statins and BP lowering). This information can then be used to determine the threshold for net benefit of specific therapies, which integrates the benefit and risk for harm on the basis of data from clinical trials.

In 2013, the ACC/AHA expert working group opera tionalized the concept of net benefit for the first time and recommended that tatin therapy for primary preven tion of ASCVD be considered on the basis of absolute risk as estimated with the pooled cohort equations. 38 In addition, the guidelines emphasized the importance of the clinician–patient risk discussion as a core tenet of personalized, patient-centered care. This approach was extended in the blood cholesterol $(20\,1\,8)^{21}$ and the primary prevention (2019) 20 guidelines with the intro duction of the calculation/personalization/reclassifica tion (CPR) framework (Figure2). This approach relies on sequential testing based on Bayesian principles and clinician judgment, which maximizes efficiency in select ing patients for additional testing and identifying those

Table 2. Crosswalk Between Heart Failure Stages, Cardiovascular-Kidney-Metabolic Stages, Life’s Essential 8, and Prevention Approaches

|Factors| No risk factors|At risk for HF|Pre-HF|HF| |---|---|---|---|---| |Characteristics| Healthy, asymptomatic; optimal lifestyle | Presence of ≥1 established risk factors for HF | Presence of subclinical disease or target organ damage indicating higher risk for HF | Clinical signs or symptoms of HF| | Prevention stages |Primordial|Primary|Early secondary ("primary-and-a-half")| Secondary/tertiary| | Universal definition of HF stages ||||C| | CKM syndrome stages ||1—２|①|A| |LE8CVH| Ideal levels of LE8 (high CVH) | Nonideal levels for 1 or 2 factors (moderate CVH) | Nonideal levels for multiple factors (mod- erateCVH) | Poor levels of several factors (low CVH | | Stage-based clinical approaches for management | Screen for risk factors; use lifestyle interventions to promote and maintain optimal levels | Screen for risk factors; use lifestyle interventions and initiation of drug therapy for risk factor modification in higher-risk situations with net benefit | Targeted diagnosis/detection of subclinical disease; use lifestyle interventions and intensifying preventive efforts targeting known risk factors and slowing progression of subclinical disease | Management of clinical HF; use GDMT (eg, quad pillars for HFrEF), lifestyle interventions, drug and device therapy to optimize symp- toms and prevent secondary events |

CKM denotes cardiovascular-kidney-metabolic; CVH, cardiovascular health; GDMT, guideline-directed medical therapy; HF, heart failure; HFrEF, heart failure with

who need more (or less) intensive preventive therapy. In the ASCVD prevention guidelines, 20,21,39 this has been termed the CPR approach, in which one calculates the 10-year risk of a clinical event, personalizes the risk on the basis of other clinical factors unique to that patient, and reclassifies the risk if clinical or patient uncertainty remains regarding preventive drug therapy. However, until recently, all of the necessary components to extend this risk-based CPR framework to HF prevention were not available.

RISK PREDICTION OF INCIDENT HF: CALCULATION AS THE FIRST STEP

Although quantitative risk assessment and risk-based prevention is well-established for ASCVD, defining riskbased strategies to avert incident HF is more challeng ing, as no primary prevention trials for HF have been conducted with a risk-based approach. In addition, few trials considering HF risk factors have focused on inci dent HF as a primary outcome. Nevertheless, the premise

 Figure 2. Proposed risk-based algorithm applying the C-P-R framework established for atherosclerotic cardiovascular disease prevention and adapted for heart failure prevention. ASCVD indicates atherosclerotic cardiovascular disease; BNP, brain natriuretic peptide; CAC, coronary artery calcium; HF, heart failure; and TTE, transthoracic echocardiography. Adapted from Lloyd-Jones et $a|^{39}$ with permission. Copyright $\copyright$ 2019 American Heart Association.

of absolute risk assessment to guide initiation and in tensity of preventive strategies (eg, intensive BP low ering) can now be extended to HF with the availability of a well-validated multivariable risk tool—PREVENT. 40 The importance of identifying risk for HF has become increasingly relevant given the growing burden of HF morbidity and mortality and was part of the impetus for developing the PREVENT model with HF as an inde pendent end point as well as inclusion of HF in the total CVD end point.

The considerations and decisions that informed PRE VENT model development for risk prediction of HF are summarized in Table3. 29,30,36 A key strength of the model is its development in a large contemporary population of ${\tt>}3.2$ million adults with broad racial and ethnic, geo graphic, and socioeconomic diversity, which supports its generalizability. Race was not included as a predictor, given that it is a social construct, but the PREVENT mod els were tested to ensure accuracy and precision in all racial and ethnic groups. Second, the model expands risk assessment for younger adults, and includes patients from 30 to 79 years of age, with the goal to estimate risk over the short- and long-term time horizon with 10- and 30-year risk estimates. Third, the model includes predictors that are aligned with HF risk and the shifting burden toward CKM syndrome, with the inclusion of tra ditional risk factors (eg, body mass index, BP, estimated glomerular filtration rate) as well as additional kidney and metabolic health markers (eg, urine albumin-to-creatinine ratio, HbA1C) in add-on models. PREVENT also incorporates social determinants of health (SDoH) with a zip code–based measure of deprivation (social deprivation index). A highly accurate and precise risk prediction model, such as PREVENT, is central to the success of a risk-based prevention paradigm for HF.

The PREVENT models did not include HF-specific blood-based biomarkers (eg, BNP) or imaging measures (eg, LV hypertrophy). Whereas these biomarkers are well-established prognostic markers for HF and have a strong magnitude of association with HF, the focus on a population-based initial risk assessment strategy led to their exclusion from the base model with the intent that they be incorporated into a sequential testing strategy, as detailed in the following. This decision was further supported by the importance of prioritizing the imple mentation of risk assessment and prediction in primary care settings, where extended biomarker testing for BNP and echocardiography are not routinely performed in asymptomatic individuals and cost may be a barrier for implementation. 36 Incorporation of BNP, echocardio graphic measures, or other biomarkers into a multivari able equation should be considered in future studies to determine whether this will improve risk discrimination or calibration. However, whether thresholds for BNP should be personalized on the basis of demographic characteristics (eg, age, sex) or clinical comorbidities (eg, obesity) that influence levels remains an unan swered question.

In addition, it remains unclear whether risk models for the prediction of HF should estimate risk for each HF subtype separately (eg, HF with preserved ejec tion fraction [HFpEF], HF with reduced ejection frac tion [HFrEF]). The clinical utility of subtype-specific risk prediction would need to be predicated on evidence that tailored prevention strategies would differ in efficacy for each subtype (eg, type of therapy, intensity of risk factor modification). No data exist that support subtypespecific prevention strategies, but prevention of HFpEF (compared with HFrEF) may be particularly tailored to therapies focused on CKM health.

Table 3. Overview of Key Considerations in Development of the PREVENT Equations for Heart Failure

|Key considerations|PREVENTmodel description| |---|---| |1. Derivation population| Adults age 30 to 79 y free of CVD (ASCVD or HF) with >3 million individuals included in the derivation from community-based research cohorts and electronic health system data sets withbaseline examinations from 1992 through 2022 | |2. Outcome of interest|Heart failure (across the spectrum of ejection fraction with preserved, midrange, or reduced ejection fraction)| |3. Time horizon|10 or 30 y after baseline measurement of risk factors| |4. Stratification|Sex-specific| |5. Predictors| Base: age, smoking status, body mass index, systolic blood pressure, antihypertensive treatment, presence or absence of diabetes, estimated glomerular filtration rate | |5. Predictors|Optional:urine albumin-to-creatinine ratio,hemoglobin A1C, social deprivation index (zip codelevel)| |6. External validation| Validated in >3 million distinct individuals in external datasets from community-based research cohorts and electronic health system datasetsfrom1992through2022 | |7. Model discrimination|C statistics ranged from 0.81 to 0.84| |8. Model calibration|Observed versus predicted slope ranged from 0.81 to 1.00| |9. Clinical implementation|Calculator and code available online through the American Heart Association40|

ASCVD indicates atherosclerotic cardiovascular disease; CVD, cardiovascular disease; HF, heart failure; and PREVENT, Predicting Risk of Cardiovascular Disease

Events.

RISK-ENHANCING FACTORS FOR HF: PERSONALIZATION AS THE SECOND STEP

A growing amount of observational and epidemiologic data support the role of several nontraditional risk factors for HF, in addition to traditional CVD risk factors. These factors may be termed risk-enhancing factors, analogous to the definition proposed in the 2018 AHA/ACC blood cholesterol guidelines, 21 the 2019 ACC/AHA primary prevention guidelines for CVD prevention, 20 and the AHA presidential advisory defining CKM health 30 ( Supplemen tal Table 1 ). The incorporation of risk-enhancing factors is crucial; assessments that only consider traditional HF risk factors will underestimate HF risk in many patients. In a large population-based study, traditional risk factors accounted for ${\approx}50\%$ of the population-attributable frac tion of lifetime risk of HF, highlighting the importance of nontraditional risk factors in the development of HF. 41,42 The evidence base for selected conditions proposed as putative HF risk-enhancing factors is reviewed briefly in this section.

Chronic Inflammatory Diseases

Given that systemic inflammation appears to be a com mon pathobiologic feature predisposing to $\mathsf{H F},^{43}$ the presence of chronic inflammatory conditions should be considered a risk-enhancing factor due to dysfunctional immune regulation, defective endothelial cell function, reduced nitric oxide bioavailability, and triggering of inter stitial fibrosis deposition seen with chronic inflammation, all of which can contribute to increased HF risk. People with human immunodeficiency virus infection have been reported to have an increased risk of HF, with multiple studies linking human immunodeficiency virus infection to LV dysfunction (cross-sectionally) and HF (longitudi nally), which appears to be blunted when optimal viral suppression is achieved. 44–46 Therefore, evidence-based human immunodeficiency virus infection treatment with viral suppression is also crucial for HF prevention, in ad dition to HF-specific preventive therapies. More recently, since the onset of the pandemic, COVID-19 has been associated with higher risk of HF. 47

Exposure to Cardiotoxins

Patients receiving treatment for malignancy can be ex posed to multiple cardiotoxic treatments associated with a higher risk of HF. Anthracyclines, the backbone of adjuvant chemotherapy for many cancer types (eg, breast cancer, lymphoma, sarcoma), are the most wellrecognized cardiotoxic treatment, with increased risk for HF, exposing cancer survivors to increased morbidity and mortality attributable to HF. 48 Cancer therapy–related cardiac dysfunction extends beyond the anthracyclines

to HER-2 therapies, alkylating agents (eg, cyclophos phamide, melphalan), vascular endothelial growth factor inhibitors (eg, tyrosine kinase inhibitors), immune check point inhibitors, and radiotherapy. 49,50 Therefore, HF pre vention should be a focus in patients receiving treatment for malignancy. $\upbeta$ -Blockers or angiotensin-converting enzyme inhibitors (ACEis)/angiotensin receptor blockers (ARBs) are the mainstay for HF prevention and cardiac function preservation. 51 Most recently, randomization to atorvastatin compared with placebo was more effec tive at preventing LV dysfunction in the STOP-CA trial (Statins to Prevent the Cardiotoxicity From Anthracy clines) of 300 patients with lymphoma undergoing treat ment with anthracyclines. 52 In addition, many shared risk factors exist for cancer and HF that support prioritizing preventive measures for shared benefits in cancer and HF outcomes, which has been the focus of 2 previous AHA scientific statements. 53,54 Other pharmacotherapies have also been associated with direct myocardial toxicity, such as immunomodulatory therapies (eg, tumor necro sis factor– α inhibitors), which was the focus of a previ ous AHA scientific statement. 55

Adverse Pregnancy Outcomes

CVD is the leading cause of maternal mortality and mor bidity in the peripartum period. 56–58 Beyond peripartum cardiomyopathy, an emerging body of evidence demon strates that adverse pregnancy outcomes are also as sociated with development of pre-HF and HF later in life. 57,59–63 Adverse pregnancy outcomes and HF share traditional risk factors, 64,65 such as high BP, that may con tribute to shared pathophysiologic mechanisms leading to vascular injury and future HF. 59 The postpartum period is an underused opportunity for improvement in delivery of care to use HF prevention efforts after adverse preg nancy outcomes.

Metabolic Dysfunction–Associated Steatotic Liver Disease

Metabolic dysfunction–associated steatotic liver disease, formerly known as nonalcoholic fatty liver disease, is the most common form of liver disease in the United States, affecting $10\%$ to $30\%$ of the US population. 1 Nonalco holic fatty liver disease itself has been identified as a risk factor for pre-HF 66,67 and ${\mathsf{H F}},^{68-70}$ independent of shared cardiac risk factors that contribute to both development of nonalcoholic fatty liver disease and HF. Liver-related proinflammatory state and higher thrombin generation with nonalcoholic fatty liver disease potentially contribute through their effects on myocardial stiffness and fibro sis promoting HF. 71,72 Screening for overt pre-HF among patients with metabolic dysfunction–associated steatotic liver disease and future studies examining approaches to prevent HF in this risk-enriched group are warranted.

Inherited Risk of HF

The growing availability of genetic testing along with substantial decline in costs of testing has led to an explosion of genetic information, including clinically in dicated testing in probands with an inherited cardiomy opathy and first-degree relatives (which was the focus of another scientific statement 73 ), direct-to-consumer genetic testing, and research-based testing with return of results (eg, All of Us). As a result, many asymptom atic individuals may now know they have a pathogenic or likely pathogenic variant in a cardiomyopathy or arrhyth mia gene without any evidence of structural or symp tomatic HF. This at-risk group may benefit from more intensive screening and risk factor modification and are identified in the HF guidelines in stage A. With the advent of targeted molecular therapies for inherited car diomyopathies, a precision medicine approach to treat ment is now also possible, specifically among those with TTR variants with amyloid cardiomyopathy (eg, tafami dis) 74 or sarcomere variants with hypertrophic cardiomy opathy (eg, mavacamten). 75 Therefore, genotype-first or genomic-enhanced screening on a population scale may be considered in the future to enable prevention, earlier detection, and treatment. However, additional research is needed to understand penetrance, expressivity, and efficacious and cost-effective strategies for preven tion for genotype+/phenotype− individuals. Moreover, whereas the contribution of polygenic risk is high for HF, limited research has been conducted in developing and evaluating the incremental utility of polygenic risk scores for HF.

patients with type 2 diabetes enrolled in CANVAS (Cana gliflozin Cardiovascular Assessment Study), a post hoc analysis demonstrated that half of participants without a history of HF had a median NT-proBNP (N-terminal pro– B-type natriuretic peptide) level ≥ 81 pg/mL. 80 This trial included individuals with diabetes either with prevalent ASCVD or at least 2 risk factors for CVD, suggesting that biomarker testing may have the highest yield and utility in those with prevalent ASCVD or at increased risk of HF. The American Diabetes Association recommends that all patients with diabetes have annual BNP screening. 81 A comorbidity-based approach such as this may represent an adjunctive strategy that could be combined with quan titative risk assessment to identify who may benefit from cardiac biomarker testing.

However, further data are needed to understand the pretest probability for biomarker testing on the basis of different comorbidities, number of comorbidities, pre dicted risk with PREVENT, or a combination of these approaches. Future research should also consider yield and cost-effectiveness of various screening strategies, including who should be tested and with what fre quency biomarker testing should be repeated. This will allow us to define the most efficient and cost-effective strategy to identify individuals at risk, quantify that risk, and then apply optimal thresholds for treatment. This could then be extended to examine how echocardiogra phy may best be considered. For example, echocardiog raphy testing may be reserved for those with elevated biomarkers or at high predicted risk for earlier detection of pre-HF.

RISK REFINEMENT: RECLASSIFICATION AND PRE-HF DETECTION AS THE THIRD STEP

For HF, the third step of the CPR framework developed for ASCVD prevention needs to be adapted to recognize the dual purposes of additional diagnostic testing with cardiac biomarkers and imaging of (1) risk prediction and (2) earlier detection of pre-HF. One of the fundamental unanswered questions for HF prevention remains how to identify those who would be most likely to benefit from additional testing with cardiac biomarkers and imaging.

Whereas universal testing of cardiac biomarkers may be considered, this approach could lead to sub stantial overtesting of individuals and excessive medical expenses for those at very low pretest probability of sub clinical disease. 76–78 For example, the yield of natriuretic peptide levels or echocardiography for stage B findings among younger individuals and middle-aged individu als without adverse traditional risk factors would be low (akin to the low yield of coronary artery calcium measure ment in younger individuals), unless the group of patients can be enriched to improve yield. 78,79 In contrast, among

CLINICAL IMPLEMENTATION OF RISKBASED PREVENTION OF HF: ADAPTATION OF THE CPR FRAMEWORK

Clinicians wishing to implement risk-based HF preven tion in practice could adapt the CPR paradigm as out lined in Figure2. 39 The first step would be to perform quantitative risk assessment with PREVENT. This ap proach is broadly applicable to the general clinic popula tion seen in primary care, family practice, obstetrics and gynecology, and preventive cardiology settings, and is able to be implemented easily given the use of routine clinical measures. The PREVENT equations are avail able as an online tool or could be programmed into an electronic health record for automatic calculation. 40 Of note, quantitative risk estimation with PREVENT is not indicated for individuals who do not have HF but repre sent a subset of individuals at particularly increased risk, specifically those with asymptomatic LV systolic dysfunc tion (which has also been termed “primary and a half pre vention”), a genetic disorder associated with HF or LV dysfunction, or ASCVD. These individuals should receive evidence-based guideline-directed medical therapy and

diagnostic testing with cardiac biomarkers and imaging as indicated.

Once 10-year (and perhaps 30-year) risk for HF has been calculated, it is useful to personalize the risk for the individual patient. Risk-enhancing factors, such as those detailed previously, can be used by the clini cian to revise the risk estimate. Thus, in a patient–clini cian discussion, the quantitative 10-year risk estimate can be presented, and framed as an underestimate for that patient if risk-enhancing factors are present. For example, the clinician might indicate to a patient that their 10-year risk estimate for HF on the basis of their risk factors is $10\%$ (or, “10 out of 100 patients like you would develop HF in the next 10 years”), but the presence of a chronic inflammatory condition (such as human immunodeficiency virus infection), meta bolic dysfunction–associated steatotic liver disease, or a pregnancy complicated by preeclampsia means that that patient’s risk is likely even higher. In that set ting, it would be appropriate to use the information to recommend more intensive lifestyle adherence, upti tration of drug therapies to ensure more intensive con trol of risk factors, or consideration of additional drug therapy when indicated (such as a sodium-glucose cotransporter-2 inhibitor [SGLT2i] in those with diabe tes or CKD). Shared decision-making with consideration of patient-specific factors and appropriate framing of potential risks and benefits will lead to greater patient satisfaction and adherence ( Supplemental Figure 1 ).

There remains uncertainty regarding specific thresh olds or cut points for HF risk that would result in therapeu tic action, which should be a focus of future guidelines. Defining net clinical benefit of different therapies that prevent HF would be a fruitful area of research in HF prevention on the basis of available clinical trial data in risk-enriched populations (eg, estimating distribution of predicted risk with PREVENT in clinical trial populations when HF prevention was successfully achieved, such as in EMPA-REG OUTCOME [BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients] for empagliflozin). Design and conduct of randomized trials with prevention of HF as the primary end point should be considered.

For ASCVD, additional testing to reclassify the risk and refine the patient’s potential for benefit is recom mended when there is clinical uncertainty or patient reluctance regarding the potential benefit of intensifying preventive strategies after the patient–clinician discus sion. For HF, the guidelines suggest additional benefit of systematically identifying those with pre-HF or stage B and may warrant a combined strategy to consider addi tional biomarker and imaging testing for risk refinement with clinical uncertainty and for the earlier detection of pre-HF. Clinician judgment could then be used to revise recommendations for more (or less) intensive preventive strategies with greater confidence.

EVIDENCE-BASED INTERVENTIONS FOR THE PRIMARY PREVENTION OF HF

HF is eminently preventable and the evidence base for this comes from various randomized controlled trials (RCTs) that enrolled individuals with specific comorbidi ties. The armamentarium of therapeutic options that may be considered for the primary prevention of HF can best be summarized by the comorbidity or clustering of co morbidities that are present (eg, diabetes with or without CKD). However, few RCTs investigated incident HF as a sole primary end point, resulting in challenges defin ing the optimal evidence-based approach to selection and sequence of therapies for the prevention of inci dent HF. Furthermore, the syndrome of HF encapsulates substantial pathophysiologic heterogeneity (eg, risk fac tors, symptoms, clinical markers). Thus, even when HF is included as an end point, most RCTs relegate it to a component of a composite primary end point (eg, ma jor adverse cardiovascular events [MACEs]), as an in dividual secondary end point, or only examine it in post hoc analyses. RCTs evaluating the benefit on prevention of HF also typically contain eligibility criteria enriching for individuals at greater HF risk on the basis of clinical factors or biomarkers (eg, acute coronary syndrome or stable CAD, uncontrolled BP, diabetes with vascular dis ease, elevated natriuretic peptide levels, LV hypertrophy, asymptomatic LV dysfunction). 18,82 This highlights the importance of first ensuring implementation of existing disease-specific guidelines (eg, chronic CAD) and pre vention guidelines (eg, hypertension, cholesterol, primary prevention) in HF prevention (Figure1). This section briefly summarizes key comorbidities and the evidence base for their management relevant to primary preven tion of HF and adapts the concept of therapeutic pil lars inspired by stage C HF guideline-directed medical therapy to prevention domains (eg, metabolic health, BP control, kidney protection, ASCVD treatment) relevant for the primary prevention of HF (Figure3). 83

Lifestyle Interventions for Prevention of HF

The overarching concept of CVH can be widely applied to populations to prioritize lifestyle and behavior change for health promotion and prevention of HF. 31 Since the formal definition of CVH in 2010 (Life’s Simple 7) 32 and its revision in 2023 (Life’s Essential 8), 31 numerous stud ies have demonstrated a strong inverse relationship be tween total CVH score and incident HF. 84 In a study of 11092 participants pooled from 2 diverse cohort stud ies, high or more favorable CVH scores were associated with a significantly lower lifetime risk of HF compared with lower or worse CVH scores. 85 Furthermore, a sub group of participants in the ARIC study (Atherosclerosis Risk in Communities) free of CVD for whom echocardio graphic data were available upon follow-up had a $63\%$

 Figure 3. Pillars of primary prevention for a risk-based paradigm to prevent heart failure.

\*Pillars or key areas for evidence-based preventive therapies to target prevention of heart failure (HF) and include those in whom quantitative risk-based prevention with PREVENT (Predicting Risk of Cardiovascular Disease Events) can be applied: (1) metabolic health (ie, weight management with lifestyle or pharmacotherapy or both, cardioprotective antihyperglycemic agents); (2) blood pressure (BP) control (ie, use of lifestyle or antihypertensive therapies or both to achieve goal of <130/80 mm Hg in those at increased risk); (3) kidney protection (ie, preventing kidney function decline with sodiumglucose cotransporter 2 inhibitors, glucagon-like-peptide 1 receptor agonists, or nonsteroidal mineralocorticoid receptor antagonists or a combination of therapies in eligible patients); as well as (4) secondary prevention of atherosclerotic cardiovascular disease (ASCVD; ie, aspirin, statin, and initiation of guideline-directed medical therapy if indicated for post–myocardial infarction asymptomatic left ventricular dysfunction). LE8 indicates Life’s Essential 8.

and $57\%$ lower risk of LV hypertrophy and diastolic dys function, respectively, with an optimal CVH score versus an inadequate score. 86

Physical inactivity and poor cardiorespiratory fitness are key risk factors for the incidence of HF. A strong dose–response inverse association exists between level of physical activity as well as cardiorespiratory fitness and risk of developing HF, with a greater magnitude of association for HFpEF compared with HFrEF. 87 Guide lines recommend at least 150 min/wk of moderate intensity physical activity, or 75 min/week of vigorous intensity physical activity, or a combination of moderate and vigorous intensity physical activity for optimal CVH. 88

Available data support adherence to a healthy lifestyle to reduce the risk of HF in patients at risk. For example, a plant-based diet with whole grains and adherence to the DASH (Dietary Approaches to Stop Hypertension) diet is recommended for lowering BP and may have pro tective benefits for HF prevention independent of BPlowering benefits. 22,89 According to a systematic review of 14 studies that evaluated dietary patterns and the primary prevention of HF, adhering to Mediterranean

and DASH diets may protect against the incidence of HF. 91 Higher sodium intake and lower potassium intake have both been associated with higher BP in observa tional studies, with evidence from a large randomized trial of sodium substitution demonstrating reduced rates of stroke and major cardiovascular events but no significant difference in HF. 92,93 Therefore, reducing dietary sodium intake is likely to have benefit in HF prevention. However, individual diet studies have not demonstrated conclusive results for HF prevention, with the majority of studies of nutritional epidemiology limited by residual confounding.

Hypertension

Several RCTs demonstrated reduction in incident HF with BP-lowering therapies, particularly thiazide diuret ics. 94–97 Among individuals with hypertension and LV hy pertrophy, ARB use was superior to $\upbeta$ -blockade for BP lowering and HF risk reduction. 82 Many hypertension RCTs also demonstrated that on-treatment reduction in LV mass or hypertrophy was associated with lower in cident HF risk independent of BP lowering, suggesting LV hypertrophy itself as a modifiable target for HF pre vention. 97 More intensive BP lowering than that achieved in the aforementioned RCTs demonstrated further HF risk reduction. 98,99 This benefit has been demonstrated across multiple clinical and demographic subgroups, in cluding among older adults, with $28\%$ of participants en rolled in SPRINT (Systolic Blood Pressure Intervention Trial) ${\geq}75$ years of age at baseline. In addition to SPRINT, several other RCTs have demonstrated that intensive BP lowering reduces MACEs, which is largely driven by the prevention of HF. 98,100,101 The absolute benefit for BP lowering is directly proportional to the baseline predicted risk and magnitude of BP lowering. Therefore, those with a higher BP at baseline and a higher predicted risk are likely to have the greatest absolute risk reduction. In a post hoc analysis of SPRINT, individuals in the highest predicted HF risk category at baseline had a significantly greater relative risk reduction with intensive BP control compared with those with the lowest predicted HF risk at baseline, suggesting that those who are at higher base line risk of HF (predicted 10-year HF risk $\geq\!9\%$ ) may derive the greatest benefit from intensive BP-lowering therapy. 102 Therefore, the HF, CVD primary prevention, and hypertension guidelines all consistently recommend initiation and treatment goal of $\mathsf{B P{<}130/{<}80\ m m\ H g}$ in those at increased risk of CVD (Table1).

Diabetes

Among patients with diabetes, neither intensive gly cemic control nor insulin, sulfonylureas, or metformin reduced the risk of HF in RCTs. In contrast, there is concern that dipeptidyl peptidase-4 inhibitors and some thiazolidinediones may exacerbate risk of HF. 103–108 The

emergence of SGLT2is offers a potential promising strategy for HF prevention, and RCTs have consistently demonstrated lowering of the risk of incident HF across a range of populations (eg, diabetes with or without CKD, diabetes with or without ASCVD, CKD without diabetes) with multiple agents within the SGLT2i class (eg, empagliflozin, dapagliflozin, canagliflozin). These mechanisms are not well understood, but the benefits of SGLT2i are unlikely to be explained by glucose low ering alone. 108–110 Emerging data for higher-potency glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide) and GLP-1/GIP (glucose-dependent in sulinotropic polypeptide) RAs (tirzepatide) demonstrate impressive benefits among patients with diabetes on overall MACEs. However, in SUSTAIN-6 (Trial to Evalu ate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes), which randomized 3297 patients with type 2 diabe tes to semaglutide versus placebo, there was no dif ference in hospitalization for HF, although few events occurred (59 versus 54) over a median follow-up of 2.1 years. 111 Among patients with diabetes, particularly those with hypertension or nephropathy, use of ACEis or ARBs, mineralocorticoid receptor antagonists, and SGLT2i reduced HF risk. 112–119 Determining the choice among these therapies, in what order, and how to man age polypharmacy requires real-world empiric data, but modeling exercises can suggest the potential benefit that may be realized with combination therapy. A re cent analysis leveraged data from 12 RCTs of SGLT2i, GLP-1 RAs, and finerenone, and estimated that among people with diabetes and urine albumin-to-creatinine ratio ≥ 30 mg/g, combination therapy with SGLT2is, GLP-1RAs, and nonsteroidal mineralocorticoid recep tor antagonists compared with ACEis or ARBs would be associated with greater survival free of hospital ized HF by 3.2 years $\textcircled{95\%}$ CI, 2.4–4.0) using actuarial methods. 120 Several observational studies demonstrate lower HF incidence with better global risk factor control among patients with diabetes. 121

Obesity

Given that obesity is a powerful contributor to risk of HF, 122,123 weight loss and maintenance of a healthy body mass index is an important strategy for HF prevention. Excess weight confers HF risk both directly through adverse effects on the myocardium as well as indirectly through the development of intermediate HF risk fac tors. 122,124 Although a moderate weight loss of $5\%$ body weight can improve HF risk factor levels such as BP, a greater weight loss of $\ge10\%$ body weight in the Look AHEAD trial (Action for Health in Diabetes) was associ ated with better cardiovascular outcomes in a post hoc analysis. 6,7 However, among the subset without prevalent HF, intensive lifestyle intervention in Look AHEAD did

not result in lower risk of HF (hazard ratio, 0.96 [95% CI, 0.75–1.23] over a median follow-up of 12.4 years). 125 Data on bariatric surgery for effective weight loss are as sociated with a significant reduction in risk of HF and favorable reverse remodeling in pre-HF. 126 There is grow ing enthusiasm of the potential for GLP-1 RAs for CVD risk reduction, but their role for HF prevention remains unclear. Semaglutide has been studied in 1914 people with prevalent HFpEF in a pooled analysis of SELECT (Semaglutide Effects on Cardiovascular Outcomes in People With Overweight or Obesity), FLOW (A Research Study to See How Semaglutide Works Compared to Pla cebo in People With Type 2 Diabetes and Chronic Kidney Disease), STEP-HFpEF (Research Study to Investigate How Well Semaglutide Works in People Living With Heart Failure and Obesity), and STEP-HFpEF DM (Re search Study to Look at How Well Semaglutide Works in People Living With Heart Failure, Obesity and Type 2 Diabetes), and reduced the risk of worsening HF. 127 SE LECT also demonstrated benefit in terms of MACEs and HF in patients with obesity, ASCVD, and any HF in a prespecified analysis of SELECT. 128,129 Whether similar benefits for GLP-1 RAs extend to individuals to prevent HF in those without HF at baseline remains to be seen.

Chronic Kidney Disease

CKD, which is most commonly a consequence of dia betes and hypertension, is a major underaddressed risk factor for HF. Lower estimated glomerular filtration rate, greater albuminuria, and particularly the combination of the two confer substantial risk for HF. However, CKD was not included in the HF guidelines as one of the stage A risk factors, and was excluded from biomarker screening thresholds to define stage B. Therefore, CKD has been systematically underemphasized despite its outsized in fluence on HF risk with regard to HF prevention to date. In contrast, the CKM framework centrally incorporates kidney health. In addition, the PREVENT risk equations include measures of kidney function to enable the ac curate prediction of HF risk among patients with CKD.

There has been growth in the availability of therapeutic options to preserve kidney health and reduce HF risk on top of established ACEis/ARBs (with proteinuria), includ ing SGLT2i, and, among those with CKD due to diabetes, GLP-1 RAs and nonsteroidal mineralocorticoid receptor agonists (finerenone). 130–132 In a prespecified analysis from the FLOW trial of patients with CKD due to diabe tes, semaglutide demonstrated $30\%$ reduction in HF risk among patients without HF at baseline (hazard ratio, 0.68 $[95\%$ CI, 0.50–0.91]; P =0.0113). 133 Increased use of therapies that are kidney protective and decrease the risk of worsening kidney function should be considered a component of multifactorial risk factor control for reducing HF risk, particularly given the common clustering of CKD with other risk factors, as evidenced in the CKM staging

construct where presence of high-risk CKD (G4 or G5 per Kidney Disease:Improving Global Outcomes) is con sidered a risk equivalent of subclinical CVD (stage 3). 29,30

Coronary Artery Disease

RCTs in patients with acute coronary syndromes exam ining the outcome of HF were largely conducted in the 1980s and 1990s, when fibrinolysis was the predomi nant acute myocardial infarction reperfusion method, and included patients with substantial LV systolic dysfunction (LVEF below $35\%–40\%$ or stage B). Several RCTs pro vide evidence for renin-angiotensin-aldosterone system inhibitors (ACEi or ARB therapy) for HF risk reduction in patients after myocardial infarction, with subsequent exten sion of indications to patients with chronic CAD regardless of LVEF. 113,134–141 The 2023 release of the chronic CAD guideline 23 highlighted prevention of HF with the use of renin-angiotensin-aldosterone system inhibitors (ACEi or ARB among those with CAD with comorbid hypertension, diabetes, CKD, and LVEF ${\leq}40\%$ ) and the prioritization of SGLT2i among those with CAD and comorbid diabetes for those at risk of HF (Table1). Since the publication of the 2023 guideline, SELECT was published, which ran domized 17604 patients with overweight or obesity and ASCVD. SELECT demonstrated a significant reduction in MACEs (hazard ratio, 0.80 [0.72–0.90]) and the HF composite end point (0.82 [0.71–0.96]) for semaglutide compared with placebo. 142 Among patients with diabetes and ASCVD, a CVD outcome trial of tirzepatide is being conducted (SURPASS-CVOT [A Study of Tirzepatide (LY3298176) Compared With Dulaglutide on Major Car diovascular Events in Participants With Type 2 Diabetes]), with anticipated completion in June 2025.

MULTILEVEL BARRIERS TO OPTIMAL PREVENTION OF HF

To be successful, formulation of preventive strategies should also consider important contextual factors, such as SDoH. SDoH are responsible for up to $50\%$ of health outcomes 143 and often require approaches beyond ad vising patients to change their lifestyle and take medi cations as prescribed to treat cardiometabolic diseases. AHA/ACC/Heart Failure Society of America guidelines for the management of HF have class I recommenda tions to perform HF risk assessments in patients at risk for health disparities and use multidisciplinary strategies to address SDoH at clinic practice and health system lev els. 16 Whereas best practices for addressing SDoH are less clear, because this research focus has been sys tematically opposed and underfunded, 144 multiple stud ies have examined strategies to address social drivers at individual, health system, and policy levels. 145,146

Opportunities to address SDoH are boundless. SDoH include access to quality care, access to quality education,

economic stability, neighborhood and built environment, and social and community contexts, which also encom pass bias and structural racism as overlapping or separate major contributors to CVD. 147–149 At the individual level, lack of personal transportation has been identified by patients as a barrier to receiving follow-up care for HF. 150 Offer ing nonemergent medical transportation for patients at risk of missing appointments related to transportation has been estimated to be cost-saving at a health care system level for patients with HF, as well as strategies for virtual health care delivery that mitigate barriers related to dis tance, particularly for people residing in rural areas, such as telehealth. 143,151 Bias has been associated with many health care decisions and may contribute to inferior care before development of HF. 152 Educational interventions focused on addressing bias have demonstrated feasibility in affecting culture change that may contribute to equity in offering advanced HF therapies. 153 Neighborhood depri vation is known to be associated with higher risk of HF, HF readmission, and mortality, and suboptimal housing is associated with cardiovascular outcomes. 154–156

The need for health systems to assess and address SDoH is clear. In a systematic review of studies addressing SDoH, health system investment in housing interventions were particularly efficacious at improving a wide range of health outcomes. 157 Successful housing interventions ranged from provision of housing vouchers to help with costs of housing, to renovation of unhealthy housing environments, to health care management provision at the housing complex level (eg, medication management, education). A simulation study of costs to provide food assistance, housing programs, nonemergent medical transportation, and community-based care coordination was estimated to cost only $\S60$ per member per month, of which approximately half would be federally funded. 158 Whereas many of these studies did not specifically study patients at risk for HF, these strategies helped improve management of health factors that may increase risk of HF and can be considered at the individual clinic level or systematically at the hospital level. Whereas many health systems may have ad hoc programs to assess SDoH and provide support for identified social needs, there remains a lack of standardization nationally, with considerable heterogeneity across systems. 159 Additional advocacy for policy changes may help improve the financial feasibility of implementing these strategies. Further investigation is needed to identify best strategies to address SDoH in patients with HF and at risk for HF. This was a central focus of the CKM framework detailed in the recent AHA presidential advisory. 30

Coverage and affordability of optimal monitoring and treatment remains a gap. For example, self-measured BP monitors are a valuable tool to improve BP control, but coverage of validated BP monitors continues to be a barrier. In addition, cost of prescribed medications can be a crucial barrier to optimal prevention related to health

insurance coverage, out-of-pocket charges even with insurance, and high-cost therapies that prevent HF. This may substantially interfere with adherence and persis tence of guideline-directed medical therapy for prevention of HF. A review of current list prices for a 90-day supply of selected preventive therapies is outlined in Supplemen tal Table 2 . Many fixed-dose combination antihypertensive therapies are now available at ${\tt<}\Phi{\tt2}0$ for a 3-month supply.

FUTURE DIRECTIONS AND UNANSWERED QUESTIONS

In aggregate, the available evidence supports the fact that HF is eminently preventable in clinical practice to day. But moving from this theoretical concept to routine clinical practice and implementing effective risk-based prevention of HF will require additional work and a mul tidisciplinary care model. The potential design of a mul tidisciplinary care team was detailed in the recent AHA presidential advisory on CKM, which could include phy sicians, pharmacists, advanced practice clinicians, social workers, care navigators, and community health workers

to reduce fragmentation of care and optimize preventive efforts. The first pilot trial of a pharmacist-led interven tion among individuals at high predicted risk of HF is underway (FIT-HF [Feasibility of the Implementation of Tools for Heart Failure Risk Prediction]) 160 and lays the foundation for future evaluations of multidisciplinary care interventions for HF prevention.

Several foundational areas require future research. Ten of the most pressing unanswered questions include the following:

1. What is the predicted HF risk threshold or thresh olds for therapeutic decisions (ie, at what predicted risk threshold does each preventive therapy have net benefit)? 2. Are therapies that prevent HF cost-effective among those with HF risk factors, such as diabe tes, CKD, and hypertension? 3. Is there a role for preventive therapies on the basis of predicted risk alone (ie, beyond the currently defined indications, such as diabetes or CKD) if predicted risk for HF is high enough even in the absence of these comorbidities?

Table 4.

Key Evidence Gaps and Unanswered Questions for the Primary Prevention of Heart Failure

|Tools|Unanswered questions| |---|---| |Risk assessment tools|What are the most accurate and precise strategies to predict an individual's absolute risk of developing HF?| |Risk assessment tools| How do different risk assessment tools compare in terms of sensitivity, specificity, positive predictive value, and negative predictive value, and what is the incremental utlity for reclassification of novel biomarkers and imaging characteristics? | |Risk assessment tools| What decision thresholds should be considered to designate low, intermediate, or high risk of HF, and how should these thresholds vary for the specific preventive intervention (eg, intensive blood pressure lowering or SGLT2i)? | |Risk assessment tools| Is there a risk threshold that may be considered a “risk equivalent”" or be included in the definition of pre-HF or stage B HF that may prompt cardiac biomarker testing (eg, BNP) or echocardiography, or both? | | Biomarkers for reclassification of risk and earlier detection of pre-HF | Which biomarkers demonstrate substantial association and predictive utility for reclassifying individuals' risk for developing HF? | | Biomarkers for reclassification of risk and earlier detection of pre-HF | Can polygenic risk scores or genotype-first approaches for high-effect variants (eg, TTR or sarcomere genes) identify asymptomatic individuals at inherited risk for cardiomyopathy and HF? | | Biomarkers for reclassification of risk and earlier detection of pre-HF | How early can these biomarkers detect pre-HF, what is their predictive accuracy, and are there biomarker combinations that enhance predictive power for early detection? | | Cost value assessment of preventive therapies | What is the cost-effectiveness of screening and detection strategies to identify individuals at increased risk of HF or earlier detection of pre-HF? | | Cost value assessment of preventive therapies |What is the cost-effectiveness of novel high-cost preventive therapies for HF (eg, GLP-1 RAs, SGLT2i)?| | Cost value assessment of preventive therapies | What degree of efficacy and pricing structure would be needed to consider targeted molecular therapies for the prevention of HF among genotype+/phenotype- patients, such as with pathogenic TTR variants or sarcomere variants? | | Al approaches for risk prediction and earlier detection of pre-HF | How can machine learning be applied to extract relevant data from electronic medical records to improve prediction of riskfor HF? | | Al approaches for risk prediction and earlier detection of pre-HF | Can Al be used on high-dimensional image or video data of ECGs and imaging (eg, echocardiograms, magnetic resonance imaging) to extract predictive features that improve risk prediction of HF? | | Al approaches for risk prediction and earlier detection of pre-HF |What are the ethical and privacy considerations when using Al to train models for the prediction of HF?| | Assessing and addressing SDOH for prevention of HF |What are the best approaches to identify adverse social factors among patients at risk for HF?| | Assessing and addressing SDOH for prevention of HF | What interventions that target or address adverse social factors (eg, food insecurity lack of health care access or insurance) are effective at preventing HF? | | CKM stage regression for the prevention of HF |Can earlier intervention that leads to regression from CKM stages 3 to 2 or 2 to 1 prevent HF?| | CKM stage regression for the prevention of HF | What are the most effective strategies for regression from later CKM stages to earlier stages can be implemented in clinical practice to prevent HF? |

AI indicates artificial intelligence; BNP, brain natriuretic peptide; CKM, cardiovascular-kidney-metabolic; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HF, heart failure; SDOH, social determinants of health; and SGLT2i, sodium-glucose cotransporter-2 inhibitor.

4. What advances in artificial intelligence and machine learning can we use to refine risk predic tion for better initial risk assessment or sequential risk reclassification? 5. What are the mechanisms and pathways that underlie progression from those at risk to pre-HF to symptomatic HF that may inform future targets for novel therapies? 6. Can more comprehensive assessment and integra tion of SDoH in risk prediction improve accuracy and precision and confer greater clinical utility? 7. How can barriers to optimal HF prevention be addressed that target adverse SDoH in order to ensure antiracist delivery of care and equitably reduce the burden of HF? 8. What health system interventions, communityengaged solutions, and policies can be enacted to ensure optimal implementation of prevention strat egies, which is a clinical and public health priority? 9. Can clinician-directed nudges improve calculation of risk and implementation of evidence-based pre ventive therapies, and what strategies are needed to address unintended consequences and nudge fatigue? 161 10. How can digital technology and artificial intelli gence be leveraged to optimize prevention of HF given the explosion of smartphone applications (mHealth), wearables, and Bluetooth-enabled remote monitoring devices? Based on the current evidence, a proposed risk-based framework should also consider the sequence and inten sification of therapies that integrates predicted risk and comorbidities to prioritize those who may have greatest benefit from certain therapies. The prioritization of this risk-based approach requires careful consideration for equity in initiation and access, particularly for higher-cost therapies, such as SGLT2i, for which large disparities in prescribing already exist and are likely to grow. 162,163 Whereas SGLT2is have been heralded as the “statins” of HF prevention, 164 at current pricing, cost-effectiveness analyses of SGLT2i declare them to be of “moderate economic value” even in prevalent HFrEF (a cost–value statement was included in the 2022 Heart Failure Guide lines). 16 However, generic options for SGLT2i will soon be available, which will improve the cost–value of this therapy. 165 More broadly, definition of performance mea sures for evidence-based prevention of HF are needed to ensure rapid implementation. Additional unanswered questions that are important to advancing the risk-based prevention of HF are summarized in Table4.

CONCLUSIONS

The publication of 2 key documents—the 2022 AHA/ ACC/Heart Failure Society of America guideline for the

Table 5. Top 10 Takeaways for Risk-Based Primary

Prevention of Heart Failure: A Scientific Statement From the

American Heart Association

1. HF is one of the most common causes of morbidity and mortality, with a growing burden among younger adults, people from underrepresented racial or ethnic groups, and people who experience adverse social determinants of health, with CKM syndrome as the predominant phenotype. 2. Once symptomatic HF is present in an individual, structural changes are often irreversible, such that treatment of prevalent disease is often about symptom management and slowing progression of disease, emphasizing the importance of prevention, earlier detection of subclinical HF, and targeted comorbidity-specific therapeutic interventions among those at high predicted risk of HF (eg, with or without diabetes, CKD, hypertension, or obesity). 3. HF is often the result of known antecedent pathophysiology (eg, stage A: diabetes, CKD, hypertension, or obesity), such that primary prevention through lifestyle modification and risk factor treatment has a substantial role in its prevention (pillars for HF prevention), for which available therapies have a growing body of evidence for HF prevention (eg, RAASi, SGLT2i, GLP-1RA). 4. Once pre-HF is present (evidenced by structural changes to the heart or elevated cardiac biomarker levels) or among individuals at high predicted risk (PREVENT $`\!\geq\!20\%$ ), the downstream development of symptomatic HF can be prevented or delayed by initiation and intensification of preventive therapies. 5. Assessment of absolute risk for development of incident HF is endorsed by the 2022 AHA/ACC/HFSA guideline for the management of heart failure to match the intensity of preventive efforts with the absolute risk of the individual. 16 6. The AHA recently developed PREVENT equations to estimate risk, which integrate readily available clinical variables routinely measured in the primary care setting, estimate risk of HF accurately and precisely, and are available online. 40 7. Several risk-enhancing factors or nontraditional risk factors or markers for HF have been proposed that may facilitate personalization of risk estimation, including chronic inflammatory disease, metabolic dysfunction–associated steatotic liver disease, cardiotoxic chemotherapy exposure, and adverse pregnancy outcomes. 8. More efficient and cost-effective testing for earlier detection of subclinical (stage B) HF, including natriuretic peptide levels and echocardiography, can be prioritized with a sequential stepwise approach among those at increased risk (eg, high predicted risk, highrisk conditions such as prevalent CHD, AF, or diabetes). 9. Equitable implementation of risk-based screening, sequential testing, and therapeutic interventions for prevention of incident HF represents a first step to mitigate persistent and pervasive health disparities in HF, alongside other efforts to address social drivers of health and other structural barriers. 10. Additional research is needed to translate this conceptual framework of risk-based HF prevention into clinical care, including risk-based decision thresholds for preventive therapies, cost-effectiveness data on value of preventive therapies, equitable strategies to assess and address adverse social factors, and evolution of artificial intelligence tools to analyze high-dimensional image and video data from cardiovascular imaging to refine risk prediction of HF and earlier detection of pre-HF.

ACC indicates American College of Cardiology; AF, atrial fibrillation; AHA, American Heart Association; CHD, coronary heart disease; CKD, chronic kid ney disease; CKM, cardiovascular-kidney-metabolic; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HF, heart failure; HFSA, Heart Failure Society of America; PREVENT, Predicting Risk of Cardiovascular Disease Events; RAASi, renin-angiotensin-aldosterone system inhibitor; and SGLT2i, sodium-glucose cotransporter-2 inhibitor.

management of heart failure, 16 which endorsed the use of multivariable risk assessment tools and natriureticbased biomarker screening in those at risk, and the

AHA PREVENT equations, 40 which estimate absolute 10-year and 30-year risk of incident HF—have resulted in a timely opportunity to fundamentally advance the paradigm of risk-based prevention for HF. In concert, the emergence of novel therapies with a growing evi dence base for their unique effects on the prevention of HF underscore the need for a conceptual framework that considers which therapies to prioritize, in which se quence, and for which patients. This scientific statement leverages the established paradigm for risk-based pre vention that has been successful for ASCVD prevention and efficiently adapts this to the rapidly growing area of need in HF prevention, with the top 10 takeaways for this scientific statement summarized in Table5. As we advance the risk-based framework proposed here for the primary prevention of HF, the path forward re quires that we prioritize systems-based approaches, team-based care, and assessing and addressing SDoH to equitably reduce the burden of HF.

ARTICLE INFORMATION

The American Heart Association makes every effort to avoid any actual or poten tial conflicts of interest that may arise as a result of an outside relationship or a

personal, professional, or business interest of a member of the writing panel. Spe cifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.

This statement was approved by the American Heart Association Sci ence Advisory and Coordinating Committee on January 6, 2025, and the American Heart Association Executive Committee on January 16, 2025. A copy of the document is available at https://professional.heart.org/state ments by using either “Search for Guidelines & Statements” or the “Browse by Topic” area. To purchase additional reprints, call 215-356-2721 or email Meredith.Edelman@wolterskluwer.com

The American Heart Association requests that this document be cited as follows: Khan SS, Breathett K, Braun LT, Chow SL, Gupta DK, Lekavich C, LloydJones DM, Ndumele CE, Rodriguez CJ, Allen LA; on behalf of the American Heart Association Prevention Science Committee of the Council on Epidemiology and Prevention; Council on Cardiovascular and Stroke Nursing; Council on Basic Car diovascular Sciences; Council on Clinical Cardiology; Council on Hypertension; and Council on Quality of Care and Outcomes Research. Risk-based primary prevention of heart failure: a scientific statement from the American Heart Asso ciation. Circulation. 2025;152:e•••–e•••. doi: 10.1161/CIR.0000000000001307

The expert peer review of AHA-commissioned documents (eg, scientific statements, clinical practice guidelines, systematic reviews) is conducted by the AHA Office of Science Operations. For more on AHA statements and guidelines development, visit https://professional.heart.org/statements. Select the “Guide lines & Statements” drop-down menu, then click “Publication Development.”

Permissions: Multiple copies, modification, alteration, enhancement, and dis tribution of this document are not permitted without the express permission of the American Heart Association. Instructions for obtaining permission are located at https://www.heart.org/permissions. A link to the “Copyright Permissions Request Form” appears in the second paragraph (https://www.heart.org/en/about-us/ statements-and-policies/copyright-request-form).

Disclosures

Writing Group Disclosures

| Writing group member |Employment|Research grant| Other research support | Speakers' bureau/ honoraria | Expert witness | Ownership interest | Consultant/advisory board |Other| |---|---|---|---|---|---|---|---|---| | Sadiya S. khan | Northwestern University Feinberg School of Medicine |None|None|None|None|None|None|None| | Larry A. Allen | University of Colorado School of Medicine, Aurora |NIH (co-I)+; PCORI (PI)+|None|None|None|None| WCG ACI Clinicalt; Novartis*; UpToDate*; Quidelt; American Heart Association* |None| | Lynne T. Braun | Rush University Medical Center, Rush College of Nursing |None|None|None|None|None|None|None| | Khadijah Breathett | Indiana University| NHLBI (R01HL159216 grant on disparities in heart transplant)t; Indi- ana University (CTSI funded grant)+ |None|None|None|None|None|None| | Sheryl L. Chow | Western University of Health Sciences |None|None|None|None|None|None|None| | Deepak K. Gupta | Vanderbilt University Medical Center |NIH/NHLBI (R01)*|None|None|None|None|None|None| | Carolyn Lekavich | Duke Clinical Research Institute and Duke University School of Medicine |None|None|None|None|None|None|None| | Donald M. Lloyd- Jones | Northwestern University Feinberg School of Medicine |None|None|None|None|None|None|None|

Writing Group Disclosures Continued

| Writing group member |Employment|Research grant| Other research support | Speakers' bureau/ honoraria | Expert witness | Ownership interest | Consultant/advisory board |Other| |---|---|---|---|---|---|---|---|---| | Chiadi E. Ndumele | Johns Hopkins Uni- versity School of Medicine |None|None|None|None|None|None|None| | Carlos J. Rodriguez | Albert Einstein Col- lege of Medicine and Montefiore Medical Center | Amgen, Inc. (lipoprotein [a] research)*; NIH/NHLBI (heart failure research)t; AHA (tobacco and CVD research)* |None|None|None|None|Merck, Inc*|None|

This table represents the relationships of writing group members that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all members of the writing group are required to complete and submit. A relationship is considered to be “significant” if (a) the person receives $\tt\ge\!\Phi5000$ during any 12-month period, or ${\geq}5\%$ of the person’s gross income; or (b) the person owns ${\geq}5\%$ of the voting stock or share of the entity, or owns $\tt\ge5000$ of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.

\*Modest.

†Significant.

Reviewer Disclosures

|Reviewer|Employment| Research grant| Other research support | Speakers' bureau/ honoraria | Expert witness | Ownership in- terest | Consultant/ advisory board |Other| |---|---|---|---|---|---|---|---|---| |Nisha Bansal|University of Washington|None|None|None|None|None|None|None| |Jennifer E. Ho| Beth Israel Deaconess Medical Center | NIH (R01 grant focused on obesity-related cardio- vascular disease)* |None|None|None| Pfizer, Inc (immediate family members)* |None|None| | Roy O. Mathew | Loma Linda VA Health Care System |None|None|None|None|None|None|None| | Tiffany M. Powell-wiley | National Heart, Lung, and Blood Institute, National Institutes of Health |None|None|None|None|None|None|None| | Laurence S. Sperling | Emory University School of Medicine |None|None|None|None|None|None|None|

This table represents the relationships of reviewers that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Ques tionnaire, which all reviewers are required to complete and submit. A relationship is considered to be “significant” if (a) the person receives $\tt\ge5000$ during any 12-month period, or ${\geq}5\%$ of the person’s gross income; or (b) the person owns ${\geq}5\%$ of the voting stock or share of the entity, or owns $\tt\ge\!\Phi5000$ of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.

\*Significant.

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