Oksana Kopylova

Aim. To evaluate the effectiveness of lipid-lowering therapy (LLT) in patients with genetically confirmed familial hypercholesterolemia (FH). Material and methods. The study included 140 patients with genetically confirmed FH, followed at the Lipid Center of the National Medical Research Center for Therapy and Preventive Medicine. Participants ranged in age from 19 to 80 years. Lipid profile parameters, clinical and genetic data were analyzed. Results. In 89% of the patients studied, a variant in the LDLR gene was identified as the cause of FH, while in 11%, a variant in the APOB gene was identified. No variants in the PCSK9 gene were detected. High penetrance of the identified FH variants was demonstrated. In patients with a LDLR gene, low-density lipoprotein cholesterol (LDL-C) levels without LLT (median [interquartile range] 8,49 [7,43; 9,31] mmol/L) were higher than in patients with a variant in the APOB gene (7,55 [6,22; 8,33] mmol/L) (pConclusion. The study results demonstrate that patient genetic characteristics influence baseline LDL-C levels but do not affect the efficacy of LLT. To achieve the target LDL-C level of 36,4%, patients in the very high CVD group should add bempedoic acid or lipid apheresis to the triple therapy (statin/ezetimibe/PCSK9 inhibitors).

Aim. To study the adequacy and effectiveness of lipid-lowering therapy (LLT) prescribed to patients with heterozygous familial hypercholesterolemia (HeFH), including fixed-dose combinations (FDC) of rosuvastatin with ezetimibe, as well as the frequency of achieving target low-density lipoprotein cholesterol (LDL-C) levels.Material and methods. The clinical and demographic data of 3151 patients with HeFH at high or very high cardiovascular risk from the RENAISSANCE registry were retrospectively analysed. Among the 1612 individuals receiving LLT, 477 patients were treated with a combination of rosuvastatin at various doses and ezetimibe 10 mg daily, either as a free combination or as a FDC.Results. The median age of the patients was 46.0 [32.0; 57.0] years. The median LDL-C level was 6.4 [5.3; 7.8] mmol/L. Although LLT was recommended to all participants, approximately half (49%, n=1539) were not receiving treatment. Among the 1612 patients on LLT, 44% (n=703) received statin monotherapy, and 43% (n=697) were treated with a combination of statins and ezetimibe. Of the 477 patients taking rosuvastatin with ezetimibe, the majority were active adults of working age (35-64 years): 335 (80%) vs 86 (20%), p Conclusion. Among patients with НeFH, the use of a FDC of rosuvastatin and ezetimibe is associated with higher treatment adherence and a greater likelihood of achieving target LDL-С levels.

The accumulation of biospecimens associated with large-scale clinical data (data from electronic medical records, epidemiological and other large-scale studies) has made it possible to conduct studies aimed at studying associations between genetic variants and phenotypes, in particular, phenome-wide association studies (PheWAS). It was originally designed to test one or more genetic variants associated with a disease or clinical symptom for associations with other phenotypes. PheWAS can identify novel genetic and phenotypic associations, differentiate true pleiotropy and clinical comorbidity, identify new disease subtypes, and identify new drug targets. Future efforts to integrate broad and robust phenotypic data collection and improve PheWAS tools will provide a valuable resource for more efficient genome-phenome analysis, leading to new discoveries in personalized medicine.

Aim. To present an approach to collection of biosamples of patients with rare and scientifically interesting clinical situations for solving the problems of personalized medicine, as well as to analyze related 8-year experience.Material and methods. The approach and a collection of biosamples of blood and its derivatives is developed at the National Medical Research Center for Therapy and Preventive Medicine within the project "Interesting Cases at the National Medical Research Center for Therapy and Preventive Medicine". The collection of biomaterial from project patients was carried out on a planned basis with the inpatient department, as well as upon referral from outpatient doctors. All included patients signed informed consent. Each biosample is accompanied by an extensive annotation, including socio-demographic, clinical, genetic and other types of data. The article presents the project results as of August 12, 2024.Results. An expert group developed 15 disease groups and related inclusion criteria. At the time of analysis, 4525 inpatients and outpatients were included in the project. Positive changes in the number of people included annually is noted. Genetic testing was performed on >2500 patients. The proposed approach allows solving a wide range of clinical and research problems in personalized medicine as follows: timely diagnosis or clarification; formation of patient cohorts to study the genetic aspects of diseases; identification of new genetic variants of hereditary diseases; development of genetic diagnostic panels; study of rare diseases; reduction of sample creation time in case of novel scientific ideas.Conclusion. The proposed approach to the collection and preservation of biosamples and related clinical, socio-demographic, genetic and other types of data in patients with rare clinical cases of scientific interest is important and effective for solving practical and research problems of personalized medicine. The algorithm is well developed, standardized and easily implemented within the clinics, regardless of their size. Preanalytical phase standardization creates the prerequisites for multicenter national and international cooperation.

Genetic overdiagnosis of long QT syndrome (LQTS) becomes a critical concern due to the high clinical significance of DNA diagnosis. Current guidelines for LQTS genetic testing recommend a limited scope and strict referral based on the Schwartz score. Nevertheless, LQTS may be underdiagnosed in patients with borderline phenotypes. We aimed to evaluate the total yield of rare variants in cardiac genes in LQTS patients. The cohort of 82 patients with LQTS referral diagnosis underwent phenotyping, Schwartz score counting, and exome sequencing. We assessed known LQTS genes for diagnostics, as per guidelines, and a broader set of genes for research. Diagnostic testing yield reached 75% in index patients; all causal variants were found in KCNQ1, KCNH2, and SCN5A genes. Research testing of 248 heart-related genes achieved a 50% yield of molecular diagnosis in patients with a low Schwartz score (p value = 0.04), reflecting the more severe disease in such patients than in those with causal variants but without additional findings. We conclude that the current LQTS genetic diagnosis framework is highly specific but may lack sensitivity for patients with a Schwartz score <3.5. Improving referral criteria for these patients could enhance DNA diagnosis. Also, our results suggest that additional variants in cardiac genes may affect the severity of the disease in the carriers of LQTS-causing variants, which may aid in identifying new modifier genes.

Aim. To validate and evaluate the accuracy of 14 genetic risk scores (GRSs) for type 2 diabetes (T2D), created earlier in other countries, using a Russian population sample from the biobank of the National Medical Research Center for Therapy and Preventive Medicine. Material and methods. For genetic analysis, next generation sequencing data was used on a sample from the Russian population (n=1165) based on the biobank collection. The study included 14 GRSs associated with T2D. Results. The study demonstrated that the predictive power of 12 out of 14 GRSs for T2D was replicated in the Russian population. As quality metrics, we used the area under the ROC curve, which for models including only GRS varied from 54,49 to 59,46%, and for models including GRS, sex and age — from 77,56 to 78,75%. Conclusion. For the first time in Russia, a study of 14 T2D GRSs developed on other populations was conducted. Twelve GRSs have been validated and can be used in the future to improve risk prediction and prevention of T2D in Russia.

Aim. To validate and evaluate the accuracy of 14 genetic risk scores (GRSs) for type 2 diabetes (T2D), created earlier in other countries, using a Russian population sample from the biobank of the National Medical Research Center for Therapy and Preventive Medicine.Material and methods. For genetic analysis, next generation sequencing data was used on a sample from the Russian population (n=1165) based on the biobank collection. The study included 14 GRSs associated with T2D.Results. The study demonstrated that the predictive power of 12 out of 14 GRSs for T2D was replicated in the Russian population. As quality metrics, we used the area under the ROC curve, which for models including only GRS varied from 54,49 to 59,46%, and for models including GRS, sex and age — from 77,56 to 78,75%.Conclusion. For the first time in Russia, a study of 14 T2D GRSs developed on other populations was conducted. Twelve GRSs have been validated and can be used in the future to improve risk prediction and prevention of T2D in Russia.

One technology that helps overcome the problem of low research reproducibility is biobanking, which involves maintaining strict quality standards at all stages. In addition to data on the biosample (detailed documentation on sampling, transportation, preparation and subsequent storage), one of the key points is the availability of information about the donor (patient). The aim of this article was to analyze creating clinical abstract of biospecimens, combining data from various biobanks and assessing the possibilities of electronic medical records and other modern technologies for this. The search for publications was carried out in the PUBMED, eLIBRARY.RU, RSCI databases. One approach to creating a clinical description is the targeted collection of information by a specially trained employee. Primary information is most often taken from the individual records of the study participant, which are developed and approved when planning work. An alternative method is the use of electronic medical records and other documents that collect information during the assessment and treatment of patients. There are also mixed types of clinical data collection, a prime example of which is the UK Biobank. Completeness, structure, and standardization are essential characteristics of clinical description associated with biospecimens. Various standards are currently being developed to unify clinical description, making biobanks and collections more available to external researchers and organizations, which is necessary for collaboration and more efficient use of stored biospecimens. Harmonization of clinical description methodology between different biobanks open up broad boundaries for large- scale research within personalized and translational medicine.

In connection with the development of genetic and other types of research in translational medicine, collections containing a significant number of biospecimens and large data sets are required. Since in-depth examination of patients is most often carried out during hospitalization, the creation of collections based on electronic medical records (EMR) data is promising. In this regard, the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" was initiated, aimed at total biobanking of blood and related products from patients in inpatient departments and the creation of a unified system that combines information on biosamples, clinical and other types of data. Aim. To describe the collection of biospecimens collected within the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" for August 2023, and to analyze the process of biospecimen description. Material and methods . Recruitment of patients is carried out on the basis of inpatient departments of the National Medical Research Center for Therapy and Preventive Medicine, only if the patient signs informed consent. After blood is taken, the biomaterial enters the biobank, where sample preparation and storage is carried out, as well as data about each biosample is entered into a special FreezerPRO system. At the same time, clinical data about the patient is collected using EMR, which are combined in the Medialog medical information system. Results. A special software for data exchange between the medical information system and the biobank has been developed, which combines information about patients entering the Medialog medical information system and the FreezerPro biospecimen management system, which contains detailed information about biospecimens. As of August 25, 2023, biomaterial from 3996 patients was collected, which was accompanied by a detailed clinical summary. In particular, data is available on diagnoses (for 100% of patients), laboratory test data (100%), paraclinical data, in particular, electrocardiography (100%), echocardiography (76,8%), 24-hour Holter monitoring (59,8%), esophagogastroduodenoscopy (27,3%), etc. The most common diseases were hypertension (76,0%), atherosclerosis (69,9%), coronary artery disease (52,9%), arrhythmias (50,6%), diabetes (19,2%). In addition, 25,0% of patients had prior stenting. Conclusion. Total biobanking within the hospital of a multidisciplinary research medical center allows for the rapid and high-quality collection of biomaterial and related data. A collection of biospecimens with comprehensive and detailed clinical annotations has been collected. A primary analysis of the collection was carried, which showed that it can be used for a wide range of studies.

Aim. To develop an algorithm and manage total biobanking of samples of whole blood, serum and plasma of patients in the hospital of the National Medical Research Center for Therapy and Preventive Medicine in order to create a detailed biosample collection through the integration of electronic medical records and a biobank database. Material and methods. The study includes all patients admitted to the hospital of the National Medical Research Center for Therapy and Preventive Medicine in various departments who signed informed consent for biobanking. Clinical information is collected as part of patient examination and stored in electronic medical records in the Medialog medical information system. Biobanking of blood and related products is carried out in accordance with standard operating procedures. From each patient, 12 aliquots of serum and blood plasma are stored, as well as 1 tube of whole blood with ethylenediaminetetraacetic acid potassium salt. Information about biospecimens, patient identification numbers and storage location coordinates are contained in the biobank database. Results. An algorithm for total biobanking of biomaterial and data from inpatients has been developed and put into practice. This collection is associated with large biomedical data stored in a medical information system, laboratory information system and biobank. The combined databases make it possible to search for samples in the collection according to specified criteria. Conclusion. The collection developed can be used for a wide range of studies by forming patient samples according to the necessary criteria. The developed algorithm for total biobanking in a hospital can be used in various medical centers equipped with biobanks.