Analysing Genetic Information Can Help Prevent Complications after Myocardial Infarction
Researchers at HSE University have developed a machine learning (ML) model capable of predicting the risk of complications—major adverse cardiac events—in patients following a myocardial infarction. For the first time, the model incorporates genetic data, enabling a more accurate assessment of the risk of long-term complications. The study has been published in Frontiers in Medicine.
Coronary artery disease (CAD), or ischaemic heart disease (IHD), is a condition characterised by insufficient blood and oxygen supply to the heart from narrowing or blockage of the coronary arteries. It is typically caused by plaques composed of fats and cholesterol that build up on the walls of blood vessels. Coronary heart disease may present as angina (chest pain), myocardial infarction (heart attack), or other problems.
According to WHO, ischaemic heart disease is the world’s biggest killer, responsible for 13% of the total deaths. Therefore, it is crucial to prescribe appropriate treatment to minimise the risks of complications and recurrences. Researchers at HSE University developed a model capable of predicting the probability of major adverse cardiac events following a myocardial infarction.
The scientists analysed data from patients admitted with myocardial infarction to the Surgut District Centre for Diagnostics and Cardiovascular Surgery between 2015 and 2024. Upon admission to the emergency department, medical researchers (cardiologists) explained the main points of the study protocol to the patients and obtained their informed consent to participate. The cardiologists then assessed the condition of the coronary arteries supplying the heart and based on their evaluation, either balloon angioplasty with stenting or coronary artery bypass grafting were performed. All patients received guideline-based therapy, including RAAS-blockers, beta-blockers, statins, and dual antiplatelet therapy. The information was documented in the patients' hospital medical records. For each patient, standard clinical measurements were taken, including blood pressure, body mass index, and cholesterol and glucose levels.
During the laboratory stage, DNA was isolated from the leukocyte rings in the collected blood samples and then frozen at −80°C for future genetic testing. The genotypes were determined based on a specific genetic variation (polymorphism) in the VEGFR-2 gene. The genetic marker VEGFR-2 is a component of the body's signalling system that regulates the growth of new blood vessels. There are three variations of the genotype—C/C, C/T, and T/T—differing in the variation of the DNA nucleotides cytosine (C) or thymine (T) in this region of the gene. Although the marker has been known for a long time, this was the first study to examine its impact on the prognosis of complications following myocardial infarction.
The authors evaluated the impact of 39 factors on the prognosis of risks such as cardiac death, recurrent acute coronary syndrome, stroke, and the need for repeat revascularisation, a procedure that helps restore blood flow in the arteries. To select the best model, the researchers trained and tested several machine learning algorithms: Gradient Boosting (CatBoost and LightGBM), Random Forest, Logistic Regression, and an AutoML approach.
The CatBoost model, a gradient boosting algorithm optimised for working with categorical data rather than numeric values, demonstrated the best performance. It makes predictions by sequentially building and training 'weak' decision trees, where each new tree corrects the errors of the previous ones. When building trees, the algorithm splits the data into two parts: the model is trained on one portion, while errors are calculated on the other. This reduces the risk of overfitting, where the model simply memorises the correct answers, and helps it identify general patterns for making predictions in new, unseen cases.
The influence of features on the model's accuracy was evaluated using the method of sequential feature addition, which assesses their contribution at each stage. The researchers selected the 9 most significant features: gender, body mass index, Charlson comorbidity index (which accounts for the presence of serious concomitant diseases), condition of the lateral wall of the left ventricle, degree of damage to the left coronary artery trunk, number of affected arteries, variant of the VEGFR-2 gene, choice between percutaneous coronary intervention or coronary artery bypass grafting, and statin dosage.
The results showed that the dose of statins, medications used to lower cholesterol levels in the blood, is the most important factor influencing the risk of complications. High doses of statins reduce this risk, particularly in patients with an unfavourable genotype. The VEGFR-2 polymorphism, specifically the presence of the T allele, was ranked fourth in terms of importance.
'Previously, genetic factors were not included in ML models, primarily because sequencing or even genotyping of individual nucleotides is not routinely performed in hospitals. In addition to standard measurements, we had access to data on polymorphism in the VEGFR-2 gene. This allowed us to compare this indicator with others and determine that the risk allele of the VEGFR-2 variant is one of the five most important factors for predicting long-term outcomes in patients with myocardial infarction,' explains Maria Poptsova, Head of the HSE International Laboratory of Bioinformatics and co-author of the paper.
The researchers emphasise that analysing genetic data contributes to creating more accurate and personalised models for predicting the risk of major adverse cardiovascular events in patients following a myocardial infarction.
'Cardiovascular diseases require resources for diagnosis, treatment, rehabilitation, and prevention, and therefore place a significant burden on the healthcare system. The introduction of such models into clinical practice could reduce mortality and the frequency of recurrent infarctions, optimise treatment, and ease the burden on healthcare professionals,' according to Alexander Kirdeev, Research Assistant at the International Laboratory of Bioinformatics and lead author of the paper.
The study was carried out in the framework of HSE University's 'Mirror Laboratories' project.
See also:
HSE University Scholars Uncover E-Learning Preferences of Top Students
HSE University experts have analysed students’ digital footprints and shown for the first time that final grades depend on one’s personal approach to an online course. Balanced students have proven to be more successful than those who follow a more traditional and practical approach. The findings from this study will help create a more adaptive and personalised educational system. This research has been published in the journal The Internet and Higher Education.
HSE Scientists Develop Method to Stabilise Iodine in Solar Cells
Scientists at HSE MIEM, in collaboration with colleagues from China, have developed a method to improve the durability of perovskite solar cells by addressing iodine loss from the material. The researchers introduced quaternary ammonium molecules into the perovskite structure; these molecules form strong electrostatic pairs with iodine ions, effectively anchoring them within the crystal lattice. As a result, the solar cells retain more than 92% of their power after a thousand hours of operation at 85°C. The study has been published in Advanced Energy Materials.
HSE Researchers Create Genome-Wide Map of Quadruplexes
An international team, including researchers from HSE University, has created the first comprehensive map of quadruplexes—unstable DNA structures involved in gene regulation. For the first time, scientists have shown that these structures function in pairs: one is located in a DNA region that initiates gene transcription, while the other lies in a nearby region that enhances this process. In healthy tissues, quadruplexes regulate tissue-specific genes, whereas in cancerous tissues they influence genes responsible for cell growth and division. These findings may contribute to the development of new anticancer drugs that target quadruplexes. The study has been published in Nucleic Acids Research.
Mathematician from HSE University–Nizhny Novgorod Solves Equation Considered Unsolvable in Quadratures Since 19th Century
Mathematician Ivan Remizov from HSE University–Nizhny Novgorod and the Institute for Information Transmission Problems of the Russian Academy of Sciences has made a conceptual breakthrough in the theory of differential equations. He has derived a universal formula for solving problems that had been considered unsolvable in quadratures for more than 190 years. This result fundamentally reshapes one of the oldest areas of mathematics and has potential to have important implications for fundamental physics and economics. The paper has been published in Vladikavkaz Mathematical Journal.
Scientists Reveal How Language Supports Complex Cognitive Processing in the Brain
Valeria Vinogradova, a researcher at HSE University, together with British colleagues, studied how language proficiency affects cognitive processing in deaf adults. The study showed that higher language proficiency—regardless of whether the language is signed or spoken—is associated with higher activity and stronger functional connectivity within the brain network responsible for cognitive task performance. The findings have been published in Cerebral Cortex.
HSE AI Research Centre Simplifies Particle Physics Experiments
Scientists at the HSE AI Research Centre have developed a novel approach to determining robustness in deep learning models. Their method works eight times faster than an exhaustive model search and significantly reduces the need for manual verification. It can be applied to particle physics problems using neural networks of various architectures. The study has been published in IEEE Access.
Scientists Show That Peer Influence Can Be as Effective as Expert Advice
Eating habits can be shaped not only by the authority of medical experts but also through ordinary conversations among friends. Researchers at HSE University have shown that advice from peers to reduce sugar consumption is just as effective as advice from experts. The study's findings have been published in Frontiers in Nutrition.
HSE University Develops Tool for Assessing Text Complexity in Low-Resource Languages
Researchers at the HSE Centre for Language and Brain have developed a tool for assessing text complexity in low-resource languages. The first version supports several of Russia’s minority languages, including Adyghe, Bashkir, Buryat, Tatar, Ossetian, and Udmurt. This is the first tool of its kind designed specifically for these languages, taking into account their unique morphological and lexical features.
HSE Scientists Uncover How Authoritativeness Shapes Trust
Researchers at the HSE Institute for Cognitive Neuroscience have studied how the brain responds to audio deepfakes—realistic fake speech recordings created using AI. The study shows that people tend to trust the current opinion of an authoritative speaker even when new statements contradict the speaker’s previous position. This effect also occurs when the statement conflicts with the listener’s internal attitudes. The research has been published in the journal NeuroImage.
Language Mapping in the Operating Room: HSE Neurolinguists Assist Surgeons in Complex Brain Surgery
Researchers from the HSE Center for Language and Brain took part in brain surgery on a patient who had been seriously wounded in the SMO. A shell fragment approximately five centimetres long entered through the eye socket, penetrated the cranial cavity, and became lodged in the brain, piercing the temporal lobe responsible for language. Surgeons at the Burdenko Main Military Clinical Hospital removed the foreign object while the patient remained conscious. During the operation, neurolinguists conducted language tests to ensure that language function was preserved.