Background and aims: Familial hypercholesterolemia (FH) is one of the most frequent diseases with monogenic inheritance. Previous data indicated that the heterozygous form occurred in 1:250 people. Based on these reports, around 36 000-40 000 people are estimated to have FH in Hungary, however, there are no exact data about the frequency of the disease in our country. Therefore, we initiated a cooperation with a clinical site partner company that provides modern data mining methods on the basis of medical and statistical records and we applied them on two major hospitals in the Northern Great Plain region of Hungary to find patients with a possible diagnosis of FH. Methods: Medical records of 1 342 124 patients were included our study. From the mined data, we calculated Dutch Lipid Clinic Network (DLCN) scores for each patient and grouped them according to the criteria to assess the likelihood of the diagnosis of FH. We also calculated the mean lipid levels that were taken before the diagnosis and treatment. Results: We identified 225 patients with a DLCN score of 6-8 (mean total cholesterol: 9.38±3.0 mmol/L, mean LDL-C: 7.61±2.4 mmol/L), and 11 706 patents with a DLCN score of 3-5 (mean total cholesterol: 7.34±1.2 mmol/L, mean LDL-C: 5.26±0.8 mmol/L). Conclusions: Analyzing more regional and country-wide data and more frequent measurements of total cholesterol and LDL-C levels would increase the number of the discovered FH cases. Data mining seems to be ideal for filtering and screening for FH in Hungary. Highlights  There are not exact data about the frequency of familial hypercholesterolemia (FH) in Hungary.  We aimed to identify patients with FH using data mining methods.  Medical records of 1,342,124 patients were included.  We calculated Dutch Lipid Clinic Network (DLCN) scores and lipid levels.  We identified 11,937 patients with a DLCN score of 3-8.

Background and aims: Premature mortality due to atherosclerotic vascular disease is very high in Hungary in comparison with international prevalence rates, though the estimated prevalence of familial hypercholesterolemia (FH) is in line with the data of other European countries. Previous studies have shown that high lipoprotein(a)- Lp(a) levels are associated with an increased risk of atherosclerotic vascular diseases in patients with FH. We aimed to assess the associations of serum Lp(a) levels and such vascular diseases in FH using data mining methods and machine learning techniques in the Northern Great Plain region of Hungary.Methods: Medical records of 590,500 patients were included in our study. Based on the data from previously diagnosed FH patients using the Dutch Lipid Clinic Network scores (≥7 was evaluated as probable or definite FH), we trained machine learning models to identify FH patients.Results: We identified 459 patients with FH and 221 of them had data available on Lp(a). Patients with FH had significantly higher Lp(a) levels compared to non-FH subjects [236 (92.5; 698.5) vs. 167 (80.2; 431.5) mg/L, p < .01]. Also 35.3% of FH patients had Lp(a) levels >500 mg/L. Atherosclerotic complications were significantly more frequent in FH patients compared to patients without FH (46.6 vs. 13.9%). However, contrary to several other previous studies, we could not find significant associations between serum Lp(a) levels and atherosclerotic vascular diseases in the studied Hungarian FH patient group.Conclusion: The extremely high burden of vascular disease is mainly explained by the unhealthy lifestyle of our patients (i.e., high prevalence of smoking, unhealthy diet and physical inactivity resulting in obesity and hypertension). The lack of associations between serum Lp(a) levels and atherosclerotic vascular diseases in Hungarian FH patients may be due to the high prevalence of these risk factors, that mask the deleterious effect of Lp(a).

The prevalence of hypertriglyceridemia has been increasing worldwide. Attention is drawn to the fact that the frequency of a special hypertriglyceridemia entity, named chylomicronemia syndrome, is variable among its different forms. The monogenic form, termed familial chylomicronemia syndrome, is rare, occuring in 1 in every 1 million persons. On the other hand, the prevalence of the polygenic form of chylomicronemia syndrome is around 1:600. On the basis of the genetical alterations, other factors, such as obesity, alcohol consumption, uncontrolled diabetes mellitus and certain drugs may significantly contribute to the development of the multifactorial form. In this review, we aimed to highlight the recent findings about the clinical and laboratory features, differential diagnosis, as well as the epidemiology of the monogenic and polygenic forms of chylomicronemias. Regarding the therapy, differentiation between the two types of the chylomicronemia syndrome is essential, as well. Thus, proper treatment options of chylomicronemia and hypertriglyceridemia will be also summarized, emphasizing the newest therapeutic approaches, as novel agents may offer solution for the effective treatment of these conditions.

ObjectiveIdentifying 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.MethodsWe 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%).ResultsWe 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.ConclusionOur 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.

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.