Combined Analysis of Three Biomarkers May Help Identify High Heart Disease Risk Earlier

Adults with elevated levels of three biomarkers for heart disease—lipoprotein a (Lp(a)), remnant cholesterol and high-sensitivity C-reactive protein (hsCRP)—had nearly triple the risk of heart attack compared to those without elevated levels, according to preliminary research to be presented at the American Heart Association's Scientific Sessions 2025. The study found that people with elevated levels of only one of the three biomarkers had a 45% increased risk of heart attack, while those with elevated levels of two biomarkers had double the risk.

The three biomarkers measure different pathways to cardiovascular disease—genetics, cholesterol metabolism and inflammation. Lipoprotein(a) is a type of cholesterol that is largely inherited and can cause plaque buildup in arteries. Remnant cholesterol refers to harmful fat particles in the blood that standard cholesterol tests can miss but can also clog arteries. High-sensitivity C-reactive protein (hsCRP) measures inflammation in the body, with elevated levels potentially signaling risk of damage to the arteries.

"Each of the blood tests on its own indicate only a modest increase in heart attack risk, however, when we found elevated levels for all three, the risk of heart attack was nearly three times higher," said Richard Kazibwe, M.D., M.S., lead researcher and an assistant professor of internal medicine at Wake Forest University School of Medicine. "These biomarkers work together like pieces of a puzzle. One piece cannot show the full picture, yet when combined, we can see a much clearer and more complete depiction of heart attack risks."

Researchers reviewed health data from the UK Biobank, one of the world's largest health databases, for more than 300,000 participants who were free of heart disease when first enrolled. Scientists tracked heart attack rates for these participants over a median follow-up of 15 years and calculated how many participants had each of the three blood test results in the highest 20% of values. The analysis revealed a clear stepwise pattern in heart attack risk based on the number of elevated biomarkers.

Although these blood tests are not yet part of routine screening guidelines, Kazibwe notes this combination approach may be more accessible than it initially appears. Lp(a) and hsCRP tests are available at most labs upon request, and health care professionals can calculate remnant cholesterol from standard cholesterol panels already performed during many routine checkups. Remnant cholesterol is calculated as total cholesterol minus LDL cholesterol and HDL cholesterol.

The findings support recommendations that consideration of risk enhancers including Lp(a), hsCRP and remnant cholesterol can play an important role in personalizing risk estimates. The 2025 AHA/ACC High Blood Pressure Guideline recommends that health care professionals use the Predicting Risk of cardiovascular disease EVENTs (PREVENT™) equation to help assess cardiovascular disease risk and optimize primary prevention of cardiovascular disease.

"Advances in the accuracy and precision of risk for atherosclerotic cardiovascular disease mean that clinicians can now more precisely identify individuals who are likely to benefit from preventive therapies," said Pamela Morris, M.D., FAHA, an American Heart Association volunteer expert and professor of cardiology at the Medical University of South Carolina. "The findings indicate that these biomarkers can aid in clinical decision making, particularly for those patients in whom treatment decisions are uncertain."

The research has important limitations as an observational study that cannot prove elevated levels of these biomarkers directly caused the heart attacks. Additional research is needed to determine if using these tests to guide treatment decisions leads to improved patient outcomes or saves lives. The findings also come from the UK Biobank, with approximately 95% of participants identifying as white, requiring additional research to confirm whether the results apply broadly to diverse populations.