Vytautas Zabiela

Background and Objectives: This study aimed to determine the diagnostic yield of familial hypercholesterolemia (FH) genotyping and to describe the clinical phenotype and pharmacogenomic (PGx) implementation readiness of adults referred for clinically suspected FH at a Lithuanian tertiary cardiology center. The study was designed as a descriptive retrospective analysis of existing clinical and genetic test results generated using heterogeneous historical sequencing platforms. Materials and Methods: This single-center descriptive cohort included 48 adults with clinically suspected FH evaluated between February 2022 and June 2023. Patients were classified according to Dutch Lipid Clinic Network criteria before genetic testing. Historical targeted next-generation sequencing (NGS) and whole-exome sequencing (WES) results were reviewed. Genetic findings were interpreted using a three-tier reporting framework: diagnostic FH-associated findings, pharmacogenomic findings relevant to future implementation, and VUS, common polymorphisms, or other non-diagnostic findings. Because harmonized PGx star-allele/diplotype calling was not available across historical platforms, PGx findings were not analysed as a mutually exclusive patient-level category. Results: Pathogenic FH-associated variants were identified in 13 of 48 patients (27.1%), including eight LDLR and five APOB variant carriers. In the remaining 35 patients (72.9%), no pathogenic or likely pathogenic FH-associated variant was identified within the analytical scope of the historical test used. This non-diagnostic category could include VUS, common polymorphisms, exploratory findings, or no reportable variant. Patients carrying pathogenic LDLR variants had higher total cholesterol and LDL-C concentrations than patients without detected pathogenic variants. PGx-related findings were considered observations relevant to reporting and future implementation; they were not evaluated as predictors of statin response, statin intolerance, or LDL-C reduction. Conclusions: In this Lithuanian real-world cohort, pathogenic LDLR or APOB variants were identified in 27.1% of patients referred for suspected FH. Retrospective analyses of heterogeneous historical genetic results can inform diagnostic reporting and implementation planning but cannot establish PGx associations with statin efficacy or intolerance without harmonized diplotype calling and prospectively collected treatment outcomes.

Background and Objectives: Cardiovascular disorders contribute substantially to medical ineligibility for compulsory military service in Lithuania. This study aimed to describe cardiovascular disease patterns and assess their association with military service eligibility among conscription-age individuals treated at a tertiary care center, considering gender, place of residence, age, and the most common cardiovascular causes of medical ineligibility. Materials and Methods: This retrospective hospital-based study included men and women aged 18–26 years with cardiovascular disease diagnoses defined according to ICD-10 codes specified by the Order of the Minister of National Defense of the Republic of Lithuania. Anonymized medical records from the Hospital of the Lithuanian University of Health Sciences Kaunas Clinics were reviewed. Participants were categorized into four military service eligibility classes based on medical history data and were stratified by gender, age, and place of residence. Results: The study included 521 participants (56.6% male, 43.4% female). Gender and residence showed no significant impact on military service eligibility. Younger individuals, particularly those aged 18–19, were more often deemed eligible, while eligibility declined with age. Males more commonly had essential hypertension and hypertensive heart disease, whereas females more frequently presented with paroxysmal tachycardia and other arrhythmias. Hypertension and other severe cardiovascular conditions most strongly reduced eligibility for compulsory military service, whereas rhythm disorders were more often compatible with service. Conclusions: In this hospital-based cohort of conscription-age individuals with cardiovascular disease, gender and place of residence did not significantly influence eligibility for military service. Eligibility declined with increasing age, and hypertension-related cardiovascular disorders were the leading cause of ineligibility among conscripts.

Hypercholesterolemia, characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), along with inflammation, is a well-known risk factor for developing atherosclerosis and coronary artery disease (CAD). Many patients with hypercholesterolemia may carry inherited genetic variants that are not part of the commonly recognized mutations in the LDLR, APOB, LDLRAP1, and PCSK9 genes. These genetic variants may have cumulative effects that contribute to increased LDL-C levels and CAD development. The polygenic risk score (PRS) may provide an essential tool for evaluating an individual’s genetic predisposition to these conditions. This pilot study aimed to investigate the impact of the PRS calculated from specific single nucleotide polymorphisms (SNPs) associated with LDL cholesterol (LDL-C)—namely, CELSR2 rs629301, APOB rs1367117, ABCG8 rs6544713, LDLR rs6511720, APOE rs429358, and rs7412—on LDL-C levels in both healthy individuals with elevated LDL-C levels (>2.6 mmol/L) and those diagnosed with ST-segment elevation myocardial infarction (STEMI). A total of 61 healthy individuals with high LDL-C levels (>2.6 mmol/L) and 93 STEMI patients were selected for the study. The High-Resolution Melting Polymerase Chain Reaction (HRM PCR) method was adopted and sequencing techniques were employed to identify the specific single nucleotide polymorphisms (SNPs) of interest. The patient group exhibited a PRS of 0.824 (with a range of −0.62 to 1.174) compared to 0.674 (range: −0.176 to 0.974) in healthy individuals, indicating a higher genetic predisposition to elevated LDL-C levels (p = 0.001) in patients. Interestingly, patients had lower LDL-C concentrations than healthy individuals. Additionally, a more significant number of patients were past smokers and statin users. The PRS calculations revealed that patients with a higher PRS had increased odds of experiencing an MI, with an odds ratio of 12.044 (95% confidence interval: 1.551–93.517, p = 0.017). Similarly, smokers showed even higher odds, with an odds ratio of 24.962 (95% CI: 7.171–86.890, p < 0.001). Among healthy individuals, those with a higher PRS had increased odds of having an LDL-C concentration greater than 4.9 mmol/L (odds ratio: 20.391, 95% CI: 1.116–358.486, p = 0.039). However, no significant association was found between the PRS and LDL-C levels in the patient group during hospitalization (p = 0.782). This pilot study shows that PRS can be employed to evaluate the risk of MI and to estimate concentrations greater than 4.9 mmol/L LDL-C in healthy individuals.

The effectiveness of lipid-lowering therapies may be insufficient in high-risk cardiovascular patients and depends on the genetic variability of drug-metabolizing enzymes. Customizing statin therapy, including treatment with atorvastatin, may improve clinical outcomes. Currently, there is a lack of guidelines allowing the prediction of the therapeutic efficacy of lipid-lowering statin therapy. This study aimed to determine the effects of clinically significant gene variants of CYP2C19 on atorvastatin therapy in patients with acute coronary syndromes. In total, 92 patients with a confirmed diagnosis of ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI) were sequenced for target regions within the CYP2C19 gene on the Illumina Miniseq system. The CYP2C19 poor metabolizer phenotype (carriers of CYP2C19*2, CYP2C19*4, and CYP2C19*8 alleles) was detected in 29% of patients. These patients had significantly lower responses to treatment with atorvastatin than patients with the normal metabolizer phenotype. CYP2C19-metabolizing phenotype, patient age, and smoking increased the odds of undertreatment in patients (∆LDL-C (mmol/L) < 1). These results revealed that the CYP2C19 phenotype may significantly impact atorvastatin therapy personalization in patients requiring LDL lipid-lowering therapy.

Background. Cardiac side effects associated with anthracycline-based treatment may seriously compromise the prognosis of patients with breast cancer (BC). Evidence shows that genes that operate in drug metabolism can influence the risk of anthracycline-induced cardiotoxicity (AIC). ATP-binding cassette (ABC) transporters could serve as one of the potential biomarkers for AIC risk stratification. We aimed to determine the link between single-nucleotide polymorphisms (SNPs) in several ABC genes (ABCB1 rs1045642, ABCC1 rs4148350, ABCC1 rs3743527) and cardiotoxicity. Methods. The study included 71 patients with BC, who were treated with doxorubicin-based chemotherapy. Two-dimensional echocardiography and speckle-tracking echocardiography were performed. AIC was defined as a new decrease of 10 percentage points in the left ventricular ejection fraction (LVEF). SNPs in ABCB1 and ABCC1 genes were evaluated using real-time PCR. Results. After a cumulative dose of 236.70 mg/m² of doxorubicin, 28.2% patients met the criteria of AIC. Patients who developed AIC had a larger impairment in left ventricular systolic function compared to those who did not develop AIC (LVEF: 50.20 ± 2.38% vs. 55.41 ± 1.13%, p < 0.001; global longitudinal strain: −17.03 ± 0.52% vs. −18.40 ± 0.88%, p < 0.001). The ABCC1 rs4148350 TG genotype was associated with higher rates of cardiotoxicity (TG vs. GG OR = 8.000, 95% CI = 1.405–45.547, p = 0.019). Conclusions. The study showed that ABCC1 rs4148350 is associated with AIC and could be a potential biomarker to assess the risk of treatment side effects in patients with BC.

Background and Objectives: Heart failure (HF) is a threatening health condition that is associated with an increasing prevalence and high expenses because of frequent patient hospitalizations. The purpose of this study was to evaluate the factors that influence the length of in-hospital stay in HF patients. Materials and Methods: A total of 220 patients (43.2% men), admitted to the Department of Cardiology, Kaunas Hospital of Lithuanian University of Health Sciences from the 1st of January 2021 to the 31st of May 2021, were included in this study. According to the length of in-hospital stay, patients were stratified into two groups: the first group’s length of stay (LOS) was from 1 to 8 days, and the second group’s LOS was 9 days or more. Results: The median LOS was 8 (6–10) days. Multivariate logistic regression analysis revealed five predictors as independent factors associated with prolonged hospitalization. These predictors included treatment interruption (OR 3.694; 95% CI 1.080–12.630, p = 0.037), higher value of NT-proBNP (OR 3.352; 95% CI 1.468–7.659, p = 0.004), estimated glomerular filtration rate (eGFR) ≤ 50 mL/min/1.73 m² (OR 2.423; 95% CI 1.090–5.383, p = 0.030), systolic blood pressure (BP) ≤ 135 mmHg (OR 3.100; 95% CI 1.421–6.761, p = 0.004) and severe tricuspid valve regurgitation (OR 2.473; 95% CI 1.086–5.632, p = 0.031). Conclusions: Several variables were identified as significant clinical predictors for prolonged length of in-hospital stay in HF patients where treatment interruption, higher NT-proBNP value and lower systolic BP at admission were the most important.

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study’s data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.