European Journal of Cardiovascular Medicine

Cardiovascular diseases (CVDs) represent the leading cause of death worldwide, accounting for approximately 31% of all global deaths annually. The persistent burden of CVD highlights a critical challenge for public health systems, particularly in addressing the complex interplay of risk factors that contribute to disease progression. Modifiable risk factors, including hypertension, diabetes, dyslipidemia, smoking, and physical inactivity, are well-recognized contributors to cardiovascular morbidity and mortality. Despite decades of progress in understanding and managing these risk factors, the prevalence of CVD remains alarmingly high, driven by aging populations, lifestyle changes, and urbanization (1, 2).

Classically, CVD prevention strategies have involved addressing each of these risk factors with lifestyle changes (diet, exercise, and smoking cessation) and pharmacological treatment of blood pressure, cholesterol, and glucose. Although these approaches have been somewhat successful, their limitations are reflected in the persistently increasing rates of cardiovascular disease (CVD)-related mortality, especially in low- and middle-income countries (3). Moreover, compliance with preventive measures is suboptimal, and a uniform strategy does not fully accommodate the diverse needs and risk profiles of individual patients (4).

Innovations in Preventing CVD — Advances in Technology and Medical Science Wearable devices, telemedicine platforms, and other digital health tools are changing the way patients and clinicians control cardiovascular risk factors. These tools also allow for early recognition of diseases like atrial fibrillation and hypertension, where early intervention can halt disease progression (5). AI and machine learning algorithms are increasingly used to improve cardiovascular risk stratification by providing more accurate predictions of cardiovascular outcomes from complex datasets than standard models (6).

RNA-based therapies and PCSK9 inhibitors are among multiple pharmacotherapeutic developments that have enhanced the lipid and other cardiovascular risk factor management. Such therapies consist of powerful alternatives for patients with statin intolerance or genetic predispositions driving hyperlipidemia, filling some important undefined space in current preventive approaches (7). Genomic and biomarker advances have led to precision medicine approaches that allow for the tailoring of preventive strategies based on the personal risk profile, increasing the effectiveness and adherence to preventive measures (8).

With community-based interventions and digital health, public health is harnessing these tools to scale heart health initiatives to promote healthy behavior and access to preventive care in marginalized populations. To tackle cardiovascular health outcome disparities, these initiatives combine technology with conventional outreach and develop scalable and sustainable solutions (9).

This review intends to summarize recent evidence on innovative approaches to the prevention of cardiovascular disease, with a specific focus on how new strategies can be employed in conjunction with traditional prevention approaches to maximize effectiveness, reach, and equitable access. This review summarizes the latest advances that will clinically impact the future of cardiovascular health while addressing population health implications.

Literature Search

A systematic search of PubMed, MEDLINE, Scopus, and Web of Science databases was conducted for studies published between January 2000 and October 2023. Keywords included “cardiovascular disease prevention,” “digital health technologies,” “artificial intelligence,” “RNA-based therapies,” “polygenic risk scores,” and “mHealth.” Boolean operators (AND, OR) were used to refine search results, while reference lists of selected articles were manually screened for additional relevant studies.

Inclusion and Exclusion Criteria

Inclusion Criteria:

1. Studies examining novel technologies or pharmacological approaches in CVD prevention.
2. Research evaluating the effectiveness of digital health tools, precision medicine, or public health campaigns.
3. Peer-reviewed randomized controlled trials, cohort studies, systematic reviews, and meta-analyses.

Exclusion Criteria:

1. Articles not directly related to cardiovascular disease prevention.
2. Editorials, opinion pieces, or studies with insufficient outcome data.
3. Non-peer-reviewed publications or conference abstracts.

Data Extraction and Quality Assessment

Data extraction was performed using a standardized template to capture study characteristics, population demographics, intervention details, and outcomes. Study quality was assessed using the Cochrane Risk of Bias Tool for randomized trials and the Newcastle-Ottawa Scale for observational studies. Discrepancies in study selection and quality assessment were resolved through consensus among reviewers.

Table 1: PRISMA Flow Diagram

Digital Health Innovations

Wearable Devices:

Wearable technologies, including smartwatches, fitness trackers, and advanced health monitors, have transformed cardiovascular disease prevention by enabling continuous monitoring of health metrics such as heart rate, physical activity, sleep patterns, and oxygen saturation. These devices empower individuals to proactively manage their health while providing clinicians with real-time data for personalized care. Studies reveal that patients using wearables demonstrate improved adherence to lifestyle modifications and medication regimens, as well as enhanced detection of arrhythmias, hypertension, and early signs of cardiovascular disease. Integration with healthcare systems has shown additional benefits, such as the facilitation of telemedicine consultations and improved follow-up rates.

Artificial Intelligence (AI):

A growing number of tools powered by AI are being used to enhance cardiovascular risk measurement and prevention. It employs advanced machine learning algorithms that assess large datasets, such as instantaneous computerized medical records, genetic data, and wearied gadget outputs, to anticipate cardiovascular risk with unmatched precision. AI has outperformed traditional models (e.g., Framingham risk score) in predictive power, especially when dealing with complex cases and multiple comorbidities (10, 11). AI applications enable such optimization of pharmacological therapies and tailoring of lifestyle interventions according to patient profiles, allowing for more effective prevention strategies (12).

Table 2: Key Digital Health Innovations in CVD Prevention

Pharmacological Advances

RNA-Based Therapies:

Another clinical strategy to target dyslipidemia is the use of (small interfering RNA) siRNAs and antisense oligonucleotides (AOs), which are RNA-based therapies. Earlier this year, the SIINCI study demonstrated that inclisiran, a small interfering RNA (siRNA) product directed against proprotein convertase subtilisin/kexin type 9 (PCSK9), is effective in renal population, capable of lowering LDL-C for up to 18 months after two annual injections in a very safe way according to the results of the ORION-11 and ORION-4 phase 2 and 3 studies. It has been shown in clinical trials to reduce major adverse cardiovascular events by lowering LDL cholesterol by up to 50% (13, 14) Repatha injection (evolocumab) is a newer class of therapies that provides a novel therapeutic alternative for patients with a reported intolerance to statin therapy or are unable to achieve optimal goal outcomes on currently available therapies.

Table 3: Pharmacological Innovations in CVD Prevention

PCSK9 Inhibitors:

PCSK9 inhibitors, such as evolocumab and alirocumab, have revolutionized lipid management in high-risk populations. These monoclonal antibodies work by enhancing LDL receptor recycling, leading to substantial reductions in circulating LDL cholesterol levels. Studies report a 15–20% reduction in major cardiovascular events among patients using these agents, highlighting their transformative impact on cardiovascular prevention (15, 16). Their use in conjunction with statins or as standalone therapies expands options for personalized prevention.

Public Health Strategies

Mobile Health (mHealth):

mHealth interventions, including smartphone applications and text messaging programs, leverage technology to promote heart-healthy behaviors and facilitate remote monitoring. These tools have shown success in improving medication adherence, increasing physical activity, and promoting dietary changes among diverse populations (17, 18). mHealth programs are particularly impactful in resource-limited settings, where they address disparities in access to preventive care and reduce cardiovascular risk at the population level.

Community-Based Programs:

Community-driven initiatives remain a cornerstone of public health strategies in cardiovascular prevention. Programs emphasizing smoking cessation, physical activity, and dietary improvements have achieved significant reductions in cardiovascular risk factors. Digital campaigns and group-based interventions extend the reach of these efforts, promoting awareness and sustained behavioral changes in large populations (19, 20).

Table 4: Public Health Innovations in CVD Prevention

The integration of modern innovations into cardiovascular disease (CVD) prevention has brought significant advancements that are transforming the field. These innovations address longstanding challenges in traditional prevention strategies, such as suboptimal adherence, limited access to care, and the inability to personalize interventions for diverse populations. The expanded toolbox of digital health technologies, pharmacological breakthroughs, and precision medicine approaches has not only enhanced the efficacy of prevention but also created opportunities for more equitable healthcare delivery (21, 22).

Impact of Digital Health Technologies

Digital health technologies, including wearable devices and mobile health (mHealth) platforms, have revolutionized the way cardiovascular health is monitored and managed. Wearables provide real-time data on vital signs, physical activity, and sleep patterns, enabling earlier detection of high-risk conditions such as hypertension, arrhythmias, and diabetes. For example, studies have shown that smartwatches with electrocardiogram (ECG) capabilities can identify atrial fibrillation with high accuracy, allowing timely intervention and reducing the risk of stroke and other complications. Furthermore, wearable devices foster greater patient engagement by empowering individuals to take an active role in their health. This increased engagement often translates into better adherence to lifestyle changes and medication regimens.

mHealth tools, such as smartphone applications and SMS-based interventions, have similarly demonstrated success in promoting heart-healthy behaviors, improving medication adherence, and providing remote monitoring. These platforms are particularly valuable in underserved populations, where traditional healthcare infrastructure may be inadequate. For example, community-driven mHealth programs have been shown to reduce cardiovascular risk factors such as smoking and physical inactivity, even in resource-limited settings. However, barriers such as digital literacy and access to technology must be addressed to fully realize the potential of these innovations.

Artificial intelligence (AI) and machine learning (ML) are further augmenting the capabilities of digital health technologies. By analyzing complex datasets from electronic health records, genetic profiles, and wearable devices, AI can identify patterns and predict cardiovascular risk with remarkable precision. AI-powered risk models outperform traditional scoring systems, such as the Framingham Risk Score, particularly in populations with multiple comorbidities or atypical presentations. Moreover, AI facilitates personalized care by tailoring interventions to individual risk profiles and optimizing treatment strategies, ultimately improving outcomes.

Advances in Pharmacological Therapies

With improvements in pharmacological strategy, the armamentarium for cardiac risk reduction has expanded extensively. Therapies that target RNA, including small-interfering RNA (siRNA) and antisense oligonucleotides, are a new class of long-acting lipid-lowering agents. Inclisiran, a PCSK9 target siRNA-based drug, proved to be highly effective in lowering LDL-C with a biannual dosing schedule overcoming potential issues with adherence (23). These therapies provide a potential alternative for patients with statin intolerance or inadequate statin response.

PCSK9 inhibitors are a monoclonal antibody class that has revolutionized lipid management by offering very effective LDL-lowering properties. These agents can be particularly advantageous among high-risk populations, such as patients with familial hypercholesterolemia or known cardiovascular disease. PCSK9 inhibitors reduce major cardiovascular events by 15–20%, as demonstrated in large clinical trials, and have become fundamental to modern prevention strategies. Unfortunately, the prohibitive pricing for these therapies is a major hurdle for their widespread implementation, and progress must be made toward optimizing cost-effectiveness, affordability, and accessibility (24, 25).

Personalized Prevention and Precision Medicine

Precision medicine for CVD prevention is a paradigm shift toward personalized risk-based interventions. Polygenic risk scores (PRS) combine genetic factors and identify individuals with increased susceptibility to cardiovascular disease. When PRS is integrated with established clinical risk factors, it improves risk stratification which will facilitate early and targeted prevention strategies. For example, individuals with elevated aPRS may benefit from intensified lipid-lowering therapies or closer monitoring, whereas individuals at the lower end of the genetic spectrum can avoid unnecessary therapy.

Biomarker-guided prevention can further personalize cardiovascular care. The use of high-sensitivity troponins, natriuretic peptides, and other biomarkers has allowed for earlier detection of subclinical disease, facilitating the timely management before progression to overt cardiovascular events. These biomarkers also allow risk stratification in patients with established disease, informing treatment choices and improving outcomes.

Strategies in Public Health and Operational Capacities

At the population level, cardiovascular risk prevention still relies heavily on public health initiatives. Digital health campaigns that utilize social media and online platforms have been shown to be effective in increasing awareness and fostering heart-healthy behaviors. Notably, these campaigns are most effective in targeting younger populations and modifying disease risk factors (i.e., smoking, physical inactivity, and unhealthy diets). Third, community-based programs that couple digital tools with traditional outreach expand the scope of prevention, especially in underserved communities.

This public health strategy has the benefit of scalable implementation in low-resource environments potentially enabling widespread application. Yet, many challenges such as digital literacy, language barriers, and cultural differences need to be addressed if they are to provide equitable access and effectiveness. In addition, public health programs should promote sustainability by being implemented under the umbrella of broader health systems and ensuring a long-term funding strategy.

Challenges and Future Directions

While the innovations discussed offer immense potential, their integration into routine clinical practice and public health frameworks is not without challenges. Cost remains a significant barrier, particularly for advanced pharmacological therapies such as PCSK9 inhibitors and RNA-based drugs. Addressing this challenge will require efforts to improve affordability through policy changes, subsidies, and generic alternatives.

Accessibility is another critical issue, particularly in low- and middle-income countries where healthcare infrastructure may be inadequate. Digital health technologies, while promising, are often inaccessible to populations lacking reliable internet connectivity or digital literacy. Bridging these gaps will require targeted interventions, including education programs, infrastructure development, and collaborations with technology providers.

Future research should focus on evaluating the long-term impact of these innovations on cardiovascular outcomes, particularly in diverse populations. Studies assessing the cost-effectiveness of emerging therapies and technologies will be crucial for informing policy decisions and optimizing resource allocation. Additionally, efforts to integrate digital health tools with traditional care models will enhance the scalability and sustainability of these interventions.

However, recent innovations in cardiovascular disease prevention, including digital health technologies, pharmacological advances, precision medicine, and public health strategies, have markedly improved our capacity to detect, manage, and attenuate cardiovascular risk. Wearable devices, AI-driven tools, and new treatment modalities, such as RNA therapies and PCSK9 inhibitors, bridge the gap in real-time insights and personalized consideration of risk allocation. First, precision medicine strategies like polygenic risk scores, biomarker-driven interventions, etc., provide customized preventive pathways with more effective outcomes. Public health programs utilizing mobile health technologies and community-based approaches help to widen the net of prevention to vulnerable communities. These innovations are other paradigm shifts in the field of cardiovascular prevention, despite issues such as cost and accessibility, and digital literacy. In conclusion, by combining these innovative interfaces with standards of care and addressing the current barriers to uptake, the field can make substantial strides to reduce the burden of cardiovascular disease worldwide and improve human health.

1. World Health Organization. Cardiovascular diseases (CVDs). 2021.
2. Yusuf S, et al. Modifiable risk factors for cardiovascular disease. Lancet. 2004;364:937–952.
3. Fuster V, et al. The future of prevention of cardiovascular diseases. J Am Coll Cardiol. 2011;58(12):1217–1227.
4. Benjamin EJ, et al. Heart disease and stroke statistics—2019 update. Circulation. 2019;139(10):e56–e528.
5. Topol EJ. Digital medicine: empowering both patients and clinicians. Lancet. 2016;388(10055):740–741.
6. Steinhubl SR, et al. The emerging role of mobile health in cardiovascular care. JAMA Cardiol. 2015;1(7):753–759.
7. Piwek L, et al. The rise of consumer health wearables: promises and barriers. PLoS Med. 2016;13(2):e1001953.
8. Turakhia MP, et al. Wearable devices for detecting atrial fibrillation: the FITBIT Heart Study. JAMA Cardiol. 2021;6(5):563–569.
9. Tison GH, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol. 2018;3(5):409–416.
10. Krittanawong C, et al. Artificial intelligence in cardiovascular medicine. J Am Coll Cardiol. 2017;69(21):2657–2664.
11. Gulati G, et al. Machine learning in cardiovascular medicine. Eur Heart J. 2019;40(34):2791–2800.
12. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
13. Ray KK, et al. Inclisiran in patients at high cardiovascular risk with elevated LDL cholesterol. N Engl J Med. 2020;382(16):1520–1530.
14. Fitzgerald K, et al. A highly durable RNAi therapeutic inhibitor of PCSK9. Nature. 2017;546(7657):574–578.
15. Sabatine MS, et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. N Engl J Med. 2017;376(18):1713–1722.
16. Robinson JG, et al. Efficacy and safety of alirocumab in reducing lipids and cardiovascular events. J Am Coll Cardiol. 2015;65(5):511–526.
17. McCarthy CP, et al. Biomarkers in cardiovascular disease: beyond troponin. Nat Rev Cardiol. 2019;16(9):595–609.
18. Daniels LB, Maisel AS. Natriuretic peptides. J Am Coll Cardiol. 2007;50(25):2357–2368.
19. Khera AV, et al. Polygenic risk scores for prediction of coronary artery disease. Circ Genom Precis Med. 2018;11(6):e002165.
20. Mega JL, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385(9984):2264–2271.
21. Angraal S, et al. mHealth interventions in cardiovascular disease prevention: closing gaps in equity and access. J Am Heart Assoc. 2017;6(12):e006744.
22. Chow CK, et al. Effectiveness of a mobile phone text messaging intervention on cardiovascular risk factors. Circulation. 2015;132(6):589–596.
23. Mahmood S, et al. Cardiovascular risk factors: impact of lifestyle intervention on improving outcomes. Am J Prev Med. 2014;47(5):704–711.
24. D’Agostino RB, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753.

Wilson PWF, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–1847.