IPM Take
Cardiology is starting to look like oncology did two decades ago: genomics, biomarkers, risk stratification, AI and targeted therapies are moving from research into practice. But the same warning applies. If reimbursement, testing pathways and data infrastructure lag behind, precision cardiology will not democratise prevention. It will create another two-tier system: one for patients whose risk is found early, and one for everyone else who waits until the event happens.
Executive Summary
Cardiovascular prevention is entering a precision medicine era. Elevated lipoprotein(a), or Lp(a), is a largely inherited cardiovascular risk factor that affects around 1.4 billion people worldwide, yet it remains under-tested despite European guidance recommending that Lp(a) should be measured at least once in adults. At the same time, polygenic risk scores are increasingly available and may help identify inherited risk beyond family history, although European guidance does not yet recommend routine clinical use.
The precision shift goes beyond Lp(a). Genetic testing is increasingly embedded in cardiomyopathy and inherited arrhythmia care, where it can influence diagnosis, risk stratification, family screening and management. AI is also moving into cardiovascular risk prediction, imaging, ECG interpretation and personalised care pathways. New Lp(a)-lowering therapies, including pelacarsen, olpasiran and lepodisiran, are in late-stage or outcomes-focused development, raising the possibility that identifying inherited risk could soon become more clinically actionable.
The policy problem is clear: science can now reveal cardiovascular risk years before disease becomes visible, but most health systems still lack reimbursement pathways, HTA frameworks, workforce capacity and implementation guidance for broad precision cardiovascular screening. Cardiology may be entering its oncology moment. The question is whether health systems will repeat oncology’s access mistakes.
Why it matters
- Policymakers: Precision cardiovascular prevention requires funding for testing, data infrastructure, genetic counselling and referral pathways, not only reimbursement for medicines after disease develops.
- Public authorities: Population-level prevention strategies may need to include inherited and biomarker-based risk, especially for Lp(a), familial cardiomyopathies and high-risk families.
- HTA bodies: Traditional assessment models often separate drugs, diagnostics and care pathways. Precision cardiology will require evaluation of the full intervention chain: test, risk classification, counselling, treatment decision, follow-up and outcome.
- Payers: If new Lp(a)-lowering therapies prove outcome benefit, payers will face a basic question: who pays for the test that identifies eligible patients before the drug is prescribed?
- Clinicians: Cardiologists will need to interpret Lp(a), polygenic risk scores, genetic cardiomyopathy results and AI-generated risk outputs in ways that are clinically useful and understandable to patients.
- Data / AI leaders: AI-enabled cardiovascular prediction will only be credible if models are externally validated, bias-tested, clinically integrated and connected to real interventions rather than producing risk scores with no pathway.
- Industry / innovation partners: The commercial opportunity is not only the next cardiovascular drug. It is the ecosystem around early risk detection, diagnostics, decision support, data platforms and targeted prevention.
- Patients / advocates: People with inherited cardiovascular risk may look healthy until they do not. Fair access to testing could mean the difference between prevention and a first catastrophic event.
Cardiology is approaching its oncology moment.
For years, the contrast was easy. Oncology became the flagship of precision medicine: molecular testing, biomarkers, targeted therapies, companion diagnostics and personalised treatment pathways. Cardiology, by comparison, remained the territory of blood pressure, LDL cholesterol, smoking, diabetes, obesity and lifestyle advice.
That distinction is collapsing.
The future of cardiovascular medicine is no longer only about treating heart attacks, strokes, heart failure and arrhythmias after they happen. It is increasingly about identifying who is silently carrying risk years before the first clinical event.
The tools are already here.
Lp(a) testing can reveal one of the most common inherited cardiovascular risk factors. Polygenic risk scores can estimate inherited risk across many common genetic variants. Genetic testing can identify cardiomyopathies and inherited arrhythmia syndromes before relatives develop symptoms. AI can detect patterns in ECGs, imaging and health records that clinicians may not see. Advanced biomarkers and imaging can expose disease biology earlier than symptoms ever could.
This is precision cardiology.
But the system around it is still built for yesterday.
The clearest example is Lp(a). Elevated Lp(a) is a causal cardiovascular risk factor and a risk factor for aortic valve stenosis. It is mostly genetically determined, largely unaffected by lifestyle, and often invisible in routine cholesterol testing. A person can have normal LDL cholesterol, a healthy lifestyle, no symptoms and still carry high inherited cardiovascular risk.
The European Atherosclerosis Society recommends that Lp(a) should be measured at least once in adults. The same consensus statement estimates that elevated Lp(a) affects around 1.4 billion people worldwide. That is not a niche population. That is a population-level screening problem hiding inside a specialist lipid marker.
And yet many people are never tested.
For decades, part of the argument against widespread Lp(a) testing was brutally practical: why test for something if there is no specific approved treatment? That argument is becoming weaker. Several Lp(a)-lowering therapies are now in advanced clinical development. Pelacarsen, olpasiran and lepodisiran are all designed to address elevated Lp(a), with outcomes trials intended to determine whether lowering Lp(a) reduces cardiovascular events.
If those trials succeed, cardiovascular prevention could change quickly. But success would expose the next bottleneck: health systems cannot prescribe targeted prevention to people they have never identified.
This is where cardiology starts to mirror oncology.
Oncology learned that a targeted therapy is only as useful as the biomarker system that finds the patient. No test, no match. No reimbursement, no access. No pathology capacity, no implementation. No data system, no monitoring. Precision medicine is never just the molecule. It is the infrastructure around the molecule.
Cardiology is now walking into the same trap.
Lp(a) is only one part of the story. Polygenic risk scores are also moving closer to clinical debate. A 2025 European Heart Journal clinical consensus statement notes that polygenic risk scores may capture inherited cardiovascular risk in a single metric and may help refine prevention beyond family history. But it also warns that enthusiasm has outpaced rigorous evaluation of clinical utility, and that ESC guidelines do not currently recommend routine use.
That is not a rejection of polygenic risk. It is a warning about premature implementation without evidence, standards and equity safeguards.
The equity issue matters because genetic risk tools have historically performed better in populations that were overrepresented in genomic datasets. If precision cardiology is built mainly on European-ancestry data, it risks reproducing the same bias that has already weakened trust in other areas of precision medicine. A risk tool that works best for the populations already best served by healthcare is not precision medicine. It is precision inequality.
Inherited cardiomyopathies and arrhythmias show a more mature side of the field. ESC guidance states that genetic testing should be performed in patients with cardiomyopathy and may influence risk stratification and management. It also emphasises genetic counselling and psychological support for patients and relatives. In these conditions, genetic testing can change family screening, surveillance, device decisions and reproductive counselling.
But even here, access is not automatic.
Testing requires specialist knowledge, variant interpretation, counselling, follow-up and family cascade pathways. Many systems still struggle to fund genetic counsellors, organise family outreach or ensure that a positive test leads to practical care rather than anxiety and paperwork. Genetic information without an implementation pathway can become another burden placed on patients.
AI adds another layer.
Recent cardiovascular AI reviews and policy reports describe applications across prevention, early risk prediction, imaging, ECG interpretation, diagnosis, personalised treatment and health-system optimisation. AI could make cardiovascular risk detection cheaper, faster and more scalable. An AI-enabled ECG may one day help identify heart failure, atrial fibrillation or cardiomyopathy risk before standard clinical pathways would detect it.
But AI is not magic. It needs validation, calibration, workflow integration, clinician trust, data quality and reimbursement. A model that predicts risk but does not trigger an evidence-based intervention is not prevention. It is surveillance without care.
This is the real policy question.
Will governments and payers fund risk identification before disease occurs, or will they continue to pay mainly for hospital admissions, procedures and late-stage treatment?
At present, reimbursement systems are far better at paying for events than preventing them. A heart attack generates codes, procedures, hospital stays, devices and drugs. A genetic risk test, Lp(a) test, AI risk model or family cascade programme is harder to fit into traditional payment logic, especially when the benefit may appear years later and across multiple budget holders.
That is why precision cardiology’s biggest challenge may not be science.
It may be reimbursement.
The European Health Data Space could help by enabling more structured use and reuse of health data for care, research, innovation and policy. Cardiovascular AI initiatives could help standardise risk prediction and support earlier intervention. National genomic medicine programmes could make inherited cardiovascular testing more systematic. But none of this will matter if the last mile is missing: payment, workforce, access and clinical accountability.
For IPM, the political lesson is sharp.
Cardiology should not repeat oncology’s mistakes. Precision oncology delivered real scientific progress, but access to biomarker testing and targeted treatments remains uneven across countries, regions and hospitals. Patients in major academic centres often benefit first. Rural, lower-income and under-resourced systems wait. Reimbursement delays create invisible rationing. Diagnostics become the weak link.
Precision cardiology could go the same way.
The wealthy and well-informed may get Lp(a) testing, genomic risk interpretation and AI-supported prevention. Everyone else may continue with standard risk calculators until disease declares itself through a stroke, myocardial infarction, sudden death or heart failure admission.
That would be a policy failure.
The promise of precision cardiology is not simply to make cardiovascular care more sophisticated. It is to find risk earlier, act sooner and prevent catastrophic events before they happen. But prevention only becomes equitable when systems pay for the infrastructure that makes it possible.
The next cardiovascular blockbuster may not be a drug.
It may be the decision to finally reimburse finding risk before the body sends the bill.
