A study published in Nature Cardiovascular Research presents a new approach for the primary prevention of myocardial infarction. The research team from Uppsala University developed a predictive model based on biomarkers and artificial intelligence that detects asymptomatic individuals but at risk of developing myocardial infarction (AMI) in the next 6 months.
Through a blood test, along with a instrument onlinesimple to use for individual risk assessment, high-risk individuals can be identified who could benefit from correcting lifestyle factors or administering pharmacological treatments.
Such innovations can motivate and actively involve every citizen in managing their own health, contributing significantly to reducing the impact of cardiovascular diseases.
Prediction of AMI is difficult because of the complex interplay of genetic, environmental, and lifestyle factors that contribute to cardiovascular disease. These factors can vary significantly between individuals, making it difficult to identify a universally applicable predictive model. So far, studies have evaluated different risk factors and biomarkers, following long periods of 5-10 years, identifying factors that are stable over time. However, the authors explained that, in the months preceding a myocardial infarction, they occur variable events that can influence risk (e.g. after a cancer diagnosis). In this way, it was observed how the dynamic biological processes in the human body could be evaluated in the period before an AMI, through a simple blood test.
The study included more than 2000 people without documented cardiovascular disease from six European cohorts, of whom 240 had AMI within the first 6 months of the first sampling. The analysis targeted 817 proteins and 1,025 metabolites, along with 16 clinical variables.
So identified 48 proteins and 43 metabolites that, in combination with demographic and physiological markers such as age, sex, and systolic blood pressure, show a significant correlation with impending AMI risk.
Image by kjpargeter on Freepik
Among these biomarkers, peptidul natriuretic de tip B (BNP) stood out for its consistent association with acute cardiac events, highlighting its potential as a clinical predictive tool. It shows increased accuracy to identify people at increased risk of AMI over a six-month horizon. BNP is a hormone produced by the heart, released in response to pressure changes that occur when heart failure develops or worsens. BNP testing is used to diagnose heart conditions, guide treatment, and determine prognosis in patients with heart failure. In the context of a myocardial infarction, the role of BNP and NT-proBNP can indicate the degree of cardiac stress and damage.
By using advanced statistical models and machine learning techniques, the research team was able to analyze a complex array of clinical and biological data. This model not only integrates traditional risk factors, but also incorporates new biomarkers, providing a holistic view of an individual’s risk profile.
Later, it was developed un instrument online to use this data, allowing anyone to estimate their risk of having an AMI in the next six months. This tool could have a significant impact on public health, as it could motivate people to take prescribed preventive drugs or adopt lifestyle changes to lower their risk. By identifying individuals at high risk of myocardial infarction before clinical symptoms appear, prevention strategies can be tailored to individual risk profiles, avoiding adverse events.
The researchers plan to conduct further in-depth studies of these newly identified risk molecules to better understand their functions and treatment potential. The diverse cohort, spanning several European countries, provided a rich data set that improves the applicability of the findings in different populations. This approach sets a precedent for future studies targeting cardiovascular disease as well as other complex conditions.
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2024-02-18 17:53:11
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