.png)
Explore the future of preventive cardiology. Discover emerging technologies, innovations, and approaches revolutionizing heart disease prevention and care.
Cardiology has traditionally focused on treating heart disease after it appears, using bypass surgery, stents, or medications. Despite these interventions, heart disease remains the leading cause of death worldwide.
According to the World Health Organization, 80% of heart attacks and strokes are preventable. As a result, there’s a growing push for earlier detection and prevention, but this idea isn’t new.
The Bogalusa Heart Study found that narrowing and hardening of arteries begin in childhood. In autopsies of people aged 2 to 39, all had fatty streaks in the aorta. Half of the children aged 2 to 15 already had them in their coronary arteries. This shows prevention must begin early in life.
This article explores the future of preventive cardiology, covering new diagnostic tools, AI-driven risk prediction, biomarker therapies, and other innovations that are changing how we prevent heart disease.
New technologies are changing how we detect, track, and prevent heart disease, often before any symptoms show up. Here are the key advances:
Wearable devices come in different forms, including smartwatches, fitness bands, rings, patches, earbuds, and even smart clothing. These gadgets use special sensors to track things your body does, like:
Even more sophisticated is the integration of wearables with digital health platforms that flag abnormalities and notify care teams, reducing hospitalizations and emergency events.
Here’s how these devices provide benefits in the following areas:
Global data shows that one in four people does not get enough physical activity, raising the risk of developing cardiovascular diseases (CVDs). Devices like Fitbit and Apple iWatch track movement and step counts, often encouraging healthier habits through real-time feedback.
In a study involving older women, those who walked up to 7,500 steps per day experienced lower mortality rates. Another analysis showed that walking at least 8,200 steps daily reduced the risk of several conditions, including:
All of which are linked to CVDs.
These wearables are often paired with tools like gamification. Features like points, challenges, and leaderboards make movement more engaging.
One study examined how Fitbit leaderboards affected daily activity. On average, users walked 370 more steps per day. The biggest gains were seen in previously inactive users, who added 1,300 steps daily, reflecting a 15% improvement.
Atrial fibrillation (AF or Afib) is an irregular heartbeat that often occurs without symptoms. In some cases, episodes are intermittent, known as paroxysmal AF. Detecting how often AF happens is important for treatment decisions.
Standard tools like Holter monitors help, but have several drawbacks:
Devices like the Zio® Patch, a disposable, waterproof, stick-on patch worn on the chest for up to 14 days, can continuously record heart rhythms and offer a more practical option.
In a pilot study, the Zio® Patch matched Holter monitors in detecting AF during the first 24 hours. After that, it identified additional issues, such as dangerous pauses, that led to pacemaker placement or medication changes. About 28% of patients had their treatment adjusted based on Zio® Patch data.
Another study tested an Apple Watch app for detecting AF. Among 419,000 users, 0.52% received a notification about an irregular pulse. These alerts matched ECG patch results 84% of the time. More importantly, 57% of those who received a notification went on to seek medical evaluation outside the study.
In a separate trial, researchers tested a smart T-shirt equipped with textile-based electrodes that can record a 12-lead ECG, which is a detailed view of heart activity. Across various resting positions (lying down, sitting, or standing), the smart T-shirt produced ECG readings on par with Holter monitors. It successfully captured critical features of the heartbeat in more than 97% of the recordings.
An experience shared on a popular online forum, Reddit, described how their Apple Watch may have helped prevent a stroke. The poster shared that on a Monday morning, they felt unusual quick pulses but ignored them. That night, feeling uneasy, they decided to sleep with their Apple Watch on. By Tuesday morning, the watch had flagged three AFib alerts and high heart rates up to 170 bpm during sleep.
After seeing a doctor, a clinical ECG confirmed the AFib, and they were admitted to the hospital that same day. By Wednesday, they were discharged with medication to help prevent a stroke.
Machine learning models now incorporate:
These tools can detect subtle patterns that humans may overlook. For example, some AI systems can now estimate a person's "vascular age."
Researchers trained an AI model to estimate aortic stiffness, an indicator of how "aged" your arteries are and a known risk factor for heart disease. The gold standard method for this is called carotid-femoral pulse wave velocity (cfPWV), but it’s difficult to perform in routine settings.
Tested on over 7,000 people from the Framingham Heart Study, the AI’s predictions closely matched traditional methods. Those with higher AI-VA had a 50-79% greater risk of heart problems, even after accounting for other risk factors.
Notably, the AI worked across different recording sites, meaning even simple devices on your arm or wrist could provide enough information.
Other models can estimate a person’s “heart age” based on their electrocardiogram (ECG). This could help doctors spot high-risk patients early, even from a single ECG test.
Your genetic makeup is one of the earliest personal factors that can influence your risk of developing heart disease.
For instance, data from the Bogalusa Heart Study tracked people for up to 42 years, beginning when they were around 10 years old. The study found that even before blood pressure or weight becomes abnormal, a person’s genetic profile can already indicate a higher risk.
Another study, the Young Finns Study, tracked individuals from age 3 into their 40s. It found that combining genetic data with basic health information improved the ability to forecast adult conditions such as high blood pressure, type 2 diabetes, and abnormal lipid levels.
Genetic screening makes it possible to begin monitoring and make changes earlier in life. It also helps physicians decide how a person might respond to medications like statins, beta-blockers, or blood thinners. This can reduce the chance of side effects and support better treatment decisions from the beginning.
In one study, researchers created a genetic testing panel calleArtificial intelligence (AI) and machine learning (ML) technologies are revolutionizing how cardiologists assess risk. Traditional risk calculators like the Framingham score use a handful of metrics, such as cholesterol levels, blood pressure, and age, but modern algorithms can analyze thousands of variables at once.and tested it in 709 adult patients. The findings shoInstead of directly measuring the cfPWV, the AI used blood pressure wave shapes to predict vascular age (AI-VA).
This type of testing allows clinicians to address risk earlier, guide medication choices more effectively, and start preventive steps before major health issues arise.
Cardiac imaging helps doctors examine the heart’s structure and how well it functions. It can reveal problems like weak contractions, valve damage, poor blood flow, or plaque buildup in the arteries. Common imaging methods include:
With recent technological advances, even more tools are available to give a clearer picture of the heart’s condition and performance. These include:
4D flow MRI is a special type of heart and blood vessel scan that tracks how blood moves through the body in three dimensions and over time. Aside from basic flow measures like direction, speed, and whether blood is leaking backward (regurgitation), 4D flow MRI can also measure advanced flow characteristics, such as:
4D flow MRI gives a full picture of how blood flows through the heart and vessels, allowing doctors to spot abnormalities that may signal heart problems.
Strain imaging measures how the heart muscle moves and stretches. While ultrasound usually does this, a newer MRI method, called fast strain-encoded imaging (fast-SENC), captures the heart’s movement in just one heartbeat.
A 2021 study found that fast-SENC was more effective than traditional MRI measures like ejection fraction at detecting early, hidden heart damage and more accurately classifying stages of heart failure.
Using fast-SENC, researchers reclassified 37% of patients initially labeled as Stage A (those with risk factors but no symptoms or structural changes) to Stage B, based on findings that less than 80% of their heart muscle was functioning normally. These subtle abnormalities were not detected by conventional measures such as ejection fraction.
Follow-up results confirmed the clinical importance of this reclassification. Patients identified by fast-SENC as having Stage B heart damage experienced significantly higher rates of all-cause mortality and more hospitalizations for heart failure than those with normal heart muscle function.
This newer CT type uses detectors that count individual photons. This offers ultra-high resolution images that help doctors see the tiny details inside the heart’s arteries more clearly. It can also target specific substances (like calcium or iodine) and provide more data from one scan instead of needing several scans at different settings.
Early lab tests (in models and animals) showed that PCD-CT could:
In its first human study, PCCT produced significantly clearer images compared to regular CT, especially in challenging areas. It was found to be:
These improvements are essential for accurately diagnosing coronary artery disease. Importantly, PCCT delivered superior image quality while using a lower radiation dose.
In a separate case study, a 68-year-old man came to the hospital with chest pain. He was considered low-risk for coronary artery disease. His troponin levels (a heart damage marker) were normal, and his echocardiogram showed the heart’s pumping function was normal.
Doctors then performed a PCD-CT scan using the NAEOTOM Alpha® system. The results revealed:
Doctors then performed an invasive, high-precision imaging method, which validated the PCD-CT findings. The patient had stents placed successfully in the problematic areas and restored blood flow.
FFRCT is a non-invasive imaging tool used to assess how well blood is flowing through the heart’s arteries. It gives functional information about whether a blockage is actually reducing blood flow enough to cause problems, not just whether the artery looks narrowed.
This makes it a non-invasive alternative to the traditional invasive FFR test, which requires inserting a wire into the coronary arteries during a heart catheterization procedure. It’s especially helpful in the following cases:
Alongside these advancements, the use of AI, machine learning, and deep learning is steadily expanding in heart imaging. This implies a shift toward more automated, accurate, and efficient diagnostic processes, allowing earlier detection and less need for invasive procedures.
Routine cholesterol and glucose tests have long been used to gauge cardiovascular risk, but a new generation of biomarkers promises more accurate, dynamic insights.
Emerging markers include:
This blood test detects subtle increases in a protein called C-reactive protein (CRP), which your liver produces. Normally, CRP levels stay low, but they rise when there’s inflammation in the body. Higher levels may signal underlying inflammation or a more serious health issue.
Here's how the results are typically interpreted:
Lp(a) is considered one of the strongest genetic risk factors for heart disease. What makes Lp(a) concerning is that it can raise your cardiovascular risk even if your LDL ("bad") cholesterol is at a healthy level and you follow all the lifestyle recommendations.
Research continues to confirm that high Lp(a) is directly involved in causing atherosclerosis and calcification of the heart valves. However, the exact biological mechanisms behind these effects are still not completely understood.
Despite this, expert guidelines now recommend that everyone have their Lp(a) checked at least once in their life. However, many doctors and clinics haven’t yet put this into routine practice, partly because there’s no standard treatment plan if a patient’s Lp(a) is high.
miRNAs are small RNA molecules that help regulate how genes work. Abnormal miRNA levels are linked to problems like:
Because miRNAs remain stable in the bloodstream and reflect internal changes in the heart, researchers are actively studying them as potential tools for diagnosing heart disease.
In some cases, heart-specific miRNAs increase within hours after a heart attack, though they haven’t proven more accurate than troponin, the current standard. But, certain miRNAs may help distinguish between different types of heart attacks.
Some miRNAs are associated with heart failure and may even help differentiate between types, such as heart failure with preserved ejection fraction (HFpEF) versus reduced ejection fraction (HFrEF), sometimes with greater precision than traditional tests.
For now, there’s no standardized miRNA test in clinical use, and larger, well-designed studies are still needed.
Other than blood-based biomarkers, another promising method is the use of molecular tests to detect early signs of disease and help predict how patients will do over time.
One such approach is called liquid biopsy. It’s a non-invasive method that uses blood, saliva, or urine samples to look for signs of disease. In CVDs, it could be useful for:
Liquid biopsy has already been used successfully in cancer. Though not yet widely adopted in the context of CVD, it is especially appealing.
An exciting new frontier involves the creation of The high-sensitivity CRP (hs-CRP) test is a more refined version of the standard CRP test. While the regular test detects CRP in a broader range (8 to 1,000 mg/L), the hs-CRP test is designed to pick up much smaller amounts, from 0.3 to 10 mg/L, making it useful for detecting low-grade inflammation.
A digital twin is a virtual copy of your body or a specific system, like your heart. This digital version is constantly updated with real-time data using medical records, imaging scans (like MRIs), and wearable devices. The goal is to mirror what's happening in your body as accurately as possible, both now and in the future.
A cardiovascular digital twin, in particular, would allow the simulation of:
Although digital twins have been in use for some time in sectors like government and defense, their adoption in healthcare is still emerging.
Companies such as Siemens Healthineers are leading the way, having developed advanced heart models that support surgical planning, diagnostics, and simulations of cardiac function.
Beyond the individual level, healthcare systems are adopting predictive models to manage heart disease across populations. These tools help identify which groups are most at risk and target preventive resources accordingly.
In one study, researchers used statistical and spatial analysis methods to analyze heart disease risk across Ethiopia. They found that some areas had significantly higher 10-year heart disease risk than others. These "hot spots" were found in several regions, including the capital, Addis Ababa. They also discovered that areas with higher humidity tended to have higher heart disease risk.
Combined with community outreach, these analytics help public health officials better target prevention efforts.
With tools like genetic testing, wearables, advanced imaging, and AI, we can now detect risk with greater accuracy and intervene much earlier in the disease process. But it’s not just about having the technology. It also depends on supporting long-term healthy habits.
As technology advances, the future of heart care will be smarter, more connected, and more proactive than ever.
