Background: Cardiovascular diseases remain a leading cause of death worldwide, yet the prevalence of pathogenic and likely pathogenic genetic variants associated with them is still underassessed in some populations. This study aimed to assess the frequency and geographic distribution of such variants within a representative sample of the Russian population. Additionally, it explored potential links between genotype and phenotype in a cohort of long-lived adults. Methods: We analyzed whole-genome sequencing data from 75,144 adults and 2,872 individuals aged 90 and older. Variants within 37 ACMG v3.1 genes were examined using InterVar, focusing on nonsynonymous variants and indels across exons and splicing sites. Variants were grouped based on ClinVar (as of 24 April 2023) annotations, with most subjected to manual review to confirm their significance. Results: Among the adult participants, 3,817 (5.1%) carried at least one of the variants under consideration. Of these, 141 (0.19%) carried pathogenic, 580 (0.77%) likely pathogenic, and 3,127 (4.16%) variants of uncertain significance. Variants not registered in ClinVar were found in 1,782 individuals (2.37%). Notably, one participant with cardiomyopathy carried a heterozygous TTN variant. In the long-lived cohort, 15 variants were classified as pathogenic or likely pathogenic, alongside 72 uncertain variants; overall, 19 individuals (0.66%) carried pathogenic or likely pathogenic variants. No significant difference was observed in variant frequency between the adult and long-lived groups. Conclusions: This study provided essential insights into the prevalence and geographic distribution of cardiovascular disease-related variants in Russia, laying the foundation for targeted genetic screening disease prevention strategies within this population.

BACKGROUND. A trend toward demographic aging of the population is currently observed in most countries worldwide. More and more people are achieving longevity, both due to environmental factors and lifestyle, as well as to molecular genetic processes. Few large-scale studies have examined all factors contributing to longevity both in Russia and globally. Fewer of them have used a multi-omics approach, which could provide a better insight into the phenomenon of longevity by integrating genomic, epigenomic, transcriptomic and metagenomic data. AIM. To study the longevity phenomenon in the Russian Federation using a comprehensive analysis of lifestyle, environmental, socio-economic, clinical and laboratory factors, as well as molecular genetic factors.MATERIALS AND METHODS. The five-year joint longevity study conducted by the Center for Strategic Planning and the Russian Clinical Research Center for Gerontology has successfully collected one of the world’s largest repositories, containing a unique database of over 20,000 biological samples from 5,000 long-living individuals from 10 regions of the Russian Federation. Each participant undergoes a detailed medical history analysis, a comprehensive geriatric assessment, a wide range of laboratory tests, and molecular genetic testing.RESULTS. An integrated approach to the study of longevity enabled the determination of main aging phenotypes, as well as the environmental and lifestyle factors associated therewith. The longitudinal design of the study also allowed for the identification of factors related to all-cause mortality, including mortality from COVID-19. State-of-the-art sequencing provided an opportunity to examine the molecular genetic factors underlying both longevity (86 genetic variants passed the genome-wide significance threshold for longevity, 2 of which were located in the coding region in the APOE gene) and socially significant geriatric syndromes, such as cognitive impairment (19 genetic variants passed the genome-wide significance threshold, several of which were also located in the APOE gene). The results of the genome-wide association studies have been used in the development of a polygenic score model that enables to assess an early risk for cognitive impairment with 69% accuracy and the longevity probability with 78% accuracy. The results were validated on independent cohorts, as well as in vitro and in vivo models. In addition to the genetic data analysis, the epigenetics of longevity has also been studied. The epigenetic data obtained during the study have enabled the development of a biological age assessment tool with an accuracy of 95%, outperforming other comparable models.CONCLUSIONS. This comprehensive approach, considering both multi-omics and clinical data, could facilitate the development of highly accurate predictive models for aging-associated diseases and biological age assessment models. It provides a better insight into the fundamental mechanisms of longevity and promotes personalized geriatric medicine.