Assessment of Associations Between Serum Lipoprotein (a) Levels and Atherosclerotic Vascular Diseases in Hungarian Patients With Familial Hypercholesterolemia Using Data Mining and Machine Learning

Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment

Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.

Applications of machine learning in familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

Assessment of Hypertensive Patients' Complex Metabolic Status Using Data Mining Methods

Cardiovascular diseases are among the leading causes of mortality worldwide. Hypertension is a preventable risk factor leading to major cardiovascular events. We have not found a comprehensive study investigating Central and Eastern European hypertensive patients' complex metabolic status. Therefore, our goal was to calculate the prevalence of hypertension and associated metabolic abnormalities using data-mining methods in our region. We assessed the data of adults who visited the University of Debrecen Clinical Center's hospital (n = 937,249). The study encompassed data from a period of 20 years (2001-2021). We detected 292,561 hypertensive patients. The calculated prevalence of hypertension was altogether 32.2%. Markedly higher body mass index values were found in hypertensive patients as compared to non-hypertensives. Significantly higher triglyceride and lower HDL-C levels were found in adults from 18 to 80 years old. Furthermore, significantly higher serum glucose and uric acid levels were measured in hypertensive subjects. Our study confirms that the calculated prevalence of hypertension is akin to international findings and highlights the extensive association of metabolic alterations. These findings emphasize the role of early recognition and immediate treatment of cardiometabolic abnormalities to improve the quality of life and life expectancy of hypertensive patients.

Determining the prevalence of childhood hypertension and its concomitant metabolic abnormalities using data mining methods in the Northeastern region of Hungary

Objective

Identifying hypertension in children and providing treatment for it have a marked impact on the patients' long-term cardiovascular outcomes. The global prevalence of childhood hypertension is increasing, yet its investigation has been rather sporadic in Eastern Europe. Therefore, our goal was to determine the prevalence of childhood hypertension and its concomitant metabolic abnormalities using data mining methods.

Methods

We evaluated data from 3 to 18-year-old children who visited the University of Debrecen Clinical Center's hospital throughout a 15-year study period (n = 92,198; boys/girls: 48/52%).

Results

We identified a total of 3,687 children with hypertension (2,107 boys and 1,580 girls), with a 4% calculated prevalence of hypertension in the whole study population and a higher prevalence in boys (4.7%) as compared to girls (3.2%). Among boys we found an increasing prevalence in consecutive age groups in the study population, but among girls the highest prevalences are identified in the 12-15-year age group. Markedly higher BMI values were found in hypertensive children as compared to non-hypertensives in all age groups. Moreover, significantly higher total cholesterol (4.27 ± 0.95 vs. 4.17 ± 0.88 mmol/L), LDL-C (2.62 ± 0.79 vs. 2.44 ± 0.74 mmol/L) and triglyceride (1.2 (0.85-1.69) vs. 0.94 (0.7-1.33) mmol/L), and lower HDL-C (1.2 ± 0.3 vs. 1.42 ± 0.39 mmol/L) levels were found in hypertensive children. Furthermore, significantly higher serum uric acid levels were found in children with hypertension (299.2 ± 86.1 vs. 259.9 ± 73.3 μmol/L), while glucose levels did not differ significantly.

Conclusion

Our data suggest that the calculated prevalence of childhood hypertension in our region is comparable to data from other European countries and is associated with early metabolic disturbances. Data mining is an effective method for identifying childhood hypertension and its metabolic consequences.

Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods

Background: There are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of the FCS score. Methods: Analyzing medical records of 1,342,124 patients, the FCS score of each patient was calculated. Based on the data of previously diagnosed FCS patients, we trained machine learning models to identify other features that may improve FCS score calculation. Results: We identified 26 patients with an FCS score of ≥10. From the trained models, boosting tree models and support vector machines performed the best for patient recognition with overall AUC above 0.95, while artificial neural networks accomplished above 0.8, indicating less efficacy. We identified laboratory features that can be considered as additions to the FCS score calculation. Conclusions: The estimated prevalence of FCS was 19.4 per million in our region, which exceeds the prevalence data of other European countries. Analysis of larger regional and country-wide data might increase the number of FCS cases. Although FCS score is an excellent tool in identifying potential FCS patients, consideration of some other features may improve its accuracy.

Clinical Aspects of Genetic and Non-Genetic Cardiovascular Risk Factors in Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is the most common monogenic metabolic disorder characterized by considerably elevated low-density lipoprotein cholesterol (LDL-C) levels leading to enhanced atherogenesis, early cardiovascular disease (CVD), and premature death. However, the wide phenotypic heterogeneity in FH makes the cardiovascular risk prediction challenging in clinical practice to determine optimal therapeutic strategy. Beyond the lifetime LDL-C vascular accumulation, other genetic and non-genetic risk factors might exacerbate CVD development. Besides the most frequent variants of three genes (LDL-R, APOB, and PCSK9) in some proband variants of other genes implicated in lipid metabolism and atherogenesis are responsible for FH phenotype. Furthermore, non-genetic factors, including traditional cardiovascular risk factors, metabolic and endocrine disorders might also worsen risk profile. Although some were extensively studied previously, others, such as common endocrine disorders including thyroid disorders or polycystic ovary syndrome are not widely evaluated in FH. In this review, we summarize the most important genetic and non-genetic factors that might affect the risk prediction and therapeutic strategy in FH through the eyes of clinicians focusing on disorders that might not be in the center of FH research. The review highlights the complexity of FH care and the need of an interdisciplinary attitude to find the best therapeutic approach in FH patients.

The Importance of Arterial Stiffness Assessment in Patients with Familial Hypercholesterolemia

Cardiovascular diseases are still the leading cause of mortality due to increased atherosclerosis worldwide. In the background of accelerated atherosclerosis, the most important risk factors include hypertension, age, male gender, hereditary predisposition, diabetes, obesity, smoking and lipid metabolism disorder. Arterial stiffness is a firmly established, independent predictor of cardiovascular risk. Patients with familial hypercholesterolemia are at very high cardiovascular risk. Non-invasive measurement of arterial stiffness is suitable for screening vascular dysfunction at subclinical stage in this severe inherited disorder. Some former studies found stiffer arteries in patients with familial hypercholesterolemia compared to healthy controls, while statin treatment has a beneficial effect on it. If conventional drug therapy fails in patients with severe familial hypercholesterolemia, PCSK9 inhibitor therapy should be administered; if these agents are not available, performing selective LDL apheresis could be considered. The impact of recent therapeutic approaches on vascular stiffness is not widely studied yet, even though the degree of accelerated athero and arteriosclerosis correlates with cardiovascular risk. The authors provide an overview of the diagnosis of familial hypercholesterolemia and the findings of studies on arterial dysfunction in patients with familial hypercholesterolemia, in addition to presenting the latest therapeutic options and their effects on arterial elasticity parameters.