There is no single universal biomarker yet to estimate overall health status and longevity prospects. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Clinicians routinely use several indicators that could be biomarkers of aging. Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Biological aging is a complex characteristic of chronological age (usually), health-to-age concordance, and medically estimated life expectancy. This study is a review of the most promising techniques that could soon be used in routine clinical practice. Two main selection criteria were applied: a sufficient sample size and reliability based on validation. The selected biological age calculators were grouped according to the type of biomarker used: (1) standard clinical and laboratory markers; (2) molecular markers; and (3) epigenetic markers. The most accurate were the calculators, which factored in a variety of biomarkers. Despite their demonstrated effectiveness, most of them require further improvement and cannot yet be considered for use in standard clinical practice. To illustrate their clinical application, we reviewed their use during the COVID-19 pandemic.

Previous studies examining the molecular and genetic basis of cognitive impairment, particularly in cohorts of long-living adults, have mainly focused on associations at the genome or transcriptome level. Dozens of significant dementia-associated genes have been identified, including APOE, APOC1, and TOMM40. However, most of these studies did not consider the intergenic interactions and functional gene modules involved in cognitive function, nor did they assess the metabolic changes in individual brain regions. By combining functional analysis with a transcriptome-wide association study, we aimed to address this gap and examine metabolic pathways in different areas of the brain of older adults. The findings from our previous genome-wide association study in 1155 older adults, 179 of whom had cognitive impairment, were used as input for the PrediXcan gene prediction algorithm. Based on the predicted changes in gene expression levels, we conducted a transcriptome-wide association study and functional analysis using the KEGG and HALLMARK databases. For a subsample of long-living adults, we used logistic regression to examine the associations between blood biochemical markers and cognitive impairment. The functional analysis revealed a significant association between cognitive impairment and the expression of NADH oxidoreductase in the cerebral cortex. Significant associations were also detected between cognitive impairment and signaling pathways involved in peroxisome function, apoptosis, and the degradation of lysine and glycan in other brain regions. Our approach combined the strengths of a transcriptome-wide association study with the advantages of functional analysis. It demonstrated that apoptosis and oxidative stress play important roles in cognitive impairment.

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.

COVID-19 is caused by an airborne virus, SARS-CoV-2. The upper respiratory tract (URT) is, therefore, the first system to endure the attack. Inhabited by an assemblage of microbial communities, a healthy URT wards off the invasion. However, once invaded, it becomes destabilised, which could be crucial to the establishment and progression of the infection. We examined 696 URT samples collected from 285 COVID-19 patients at three time-points throughout their hospital stay and 100 URT samples from 100 healthy controls. We used 16S ribosomal RNA sequencing to evaluate the abundance of various bacterial taxa, α-diversity, and β-diversity of the URT microbiome. Ordinary least squares regression was used to establish associations between the variables, with age, sex, and antibiotics as covariates. The URT microbiome in the COVID-19 patients was distinctively different from that of healthy controls. In COVID-19 patients, the abundance of 16 genera was significantly reduced. A total of 47 genera were specific to patients, whereas only 2 were unique to controls. The URT samples collected at admission differed more from the control than from the samples collected at later stages of treatment. The following four genera originally depleted in the patients grew significantly by the end of treatment: Fusobacterium, Haemophilus, Neisseria, and Stenotrophomonas. Our findings strongly suggest that SARS-CoV-2 caused significant changes in the URT microbiome, including the emergence of numerous atypical taxa. These findings may indicate increased instability of the URT microbiome in COVID-19 patients. In the course of the treatment, the microbial composition of the URT of COVID-19 patients tended toward that of controls. These microbial changes may be interpreted as markers of recovery.

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.

BACKGROUND. More than one hundred candidate genes have been associated with longevity, including APOE, FOXO1A, GF1, IGF1, INCR, TP53, etc. Despite extensive research, the precise molecular and genetic underpinnings remain elusive due to the low reproducibility of the findings. Moreover, most studies have focused on exomes and have neglected intergenic regulatory regions of DNA.AIM. To examine the genetic and molecular basis of longevity and create a prognostic model that assesses the chance of reaching the age of 90.MATERIALS AND METHODS. To study the genetic predictors of longevity, 10,157 people were recruited, including 3,066 long-living individuals (90+ years). The recruitment of long-living individuals was carried out jointly with the Russian Clinical Research Center for Gerontology; other participants have been included from the database of the Center for Strategic Planning and Management of Biomedical Health Risks” of the Federal Medical-Biological Agency. A comprehensive medical history was collected for all participants (including data on chronic conditions), and wholegenome sequencing of blood cell DNA was performed. When determining the healthy long-living individuals, we have utilized the machine learningbased calculator of aging phenotype previously developed by us [1].RESULTS. A genome-wide association study (GWAS) involving 3,066 longliving individuals (90+ years) and 7,091 people aged 18 to 75 years confirmed the known association of APOE gene variants with longevity. Additionally, we discovered new associations in the TMEM59, LRRC7, FHIT, CPLX1, DDX31, GRIN1, DHX32, CACNA1C, FBXO21 and UBBP4 genes. The obtained results helped construct a polygenic score, which assesses an individual chance of becoming a long-living individual (ROC AUC = 0.78). However, long-living individuals are a phenotypically heterogeneous group, so determining only the chance of living to 90 years may not be enough. The functional status at this age should also be addressed. The genomes of healthy long-living individuals were compared with the genomes of ailing long-living individuals and those of a healthy population sample. Healthy longevity is mediated by completely different molecular mechanisms than the broader phenotype, and is associated with the genes NAV1, SDK1, ARHGAP39 and ITGAE. Regulatory RNAs, particularly the antisense RNA to the mRNA of the IPO9 gene product, were shown to significantly affect healthy longevity.CONCLUSIONS. This study of the genetic basis of longevity has shown that there is no single mechanism that would be a single and key predictor for life expectancy. Longevity has been and remains a multifactorial phenomenon. However, this work confirmed the association of variants in the APOE gene with life expectancy in the Russian population, as well as detected many new polymorphisms associated with longevity. A polygenic scale was proposed that predicts the chance of reaching the age of longevity with an accuracy of over 78%. The important role of regulatory regions of the genome in the phenotype formation was specifically highlighted. Moreover, the achievement of healthy longevity turned out to be mediated by completely different molecular mechanisms than the broader phenotype, and is associated with the genes NAV1, SDK1, ARHGAP39, and ITGAE.