Traumatic brain injury (TBI) heavily impacts the body: it damages the brain tissue and the peripheral nervous system and shifts homeostasis in many types of tissue. An acute brain injury compromises the “brain–gut-microbiome axis”, a well-balanced network formed by the brain, gastrointestinal tract, and gut microbiome, which has a complex effect: damage to the brain alters the composition of the microbiome; the altered microbiome affects TBI severity, neuroplasticity, and metabolic pathways through various bacterial metabolites. We modeled TBI in rats. Using a bioinformatics approach, we sought to identify correlations between the gut microbiome composition, TBI severity, the rate of neurological function recovery, and blood metabolome. We found that the TBI caused changes in the abundance of 26 bacterial genera. The most dramatic change was observed in the abundance of Agathobacter species. The TBI also altered concentrations of several metabolites, specifically citrulline and tryptophan. We found no significant correlations between TBI severity and the pre-existing gut microbiota composition or blood metabolites. However, we discovered some differences between the two groups of subjects that showed high and low rates of neurological function recovery, respectively. The present study highlights the role of the brain–gut-microbiome axis in TBI.

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.

Geriatric syndromes (GSs) and aging-associated diseases (AADs) are common side effects of aging. They are affecting the lives of millions of older adults and placing immense pressure on healthcare systems and economies worldwide. It is imperative to study the factors causing these conditions and develop a holistic framework for their management. The so-called long-lived individuals—people over the age of 90 who managed to retain much of their health and functionality—could be holding the key to understanding these factors and their health implications. We analyzed the health status and lifestyle of the long-lived individuals and identified risk factors for GSs. Family history greatly contributes to the health and prevention of cognitive decline in older adults. Lifestyle and certain socioeconomic factors such as education, the age of starting to work and retiring, job type and income level, physical activity, and hobby were also associated with certain GSs. Moreover, the levels of total protein, albumin, alpha-1 globulins, high-density lipoprotein, free triiodothyronine, and 25-hydroxyvitamin D were direct indicators of the current health status. The proposed mathematical model allows the prediction of successful aging based on family history, social and economic factors, and life-long physical activity (f1 score = 0.72, AUC = 0.68, precision = 0.83 and recall = 0.64).

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.

Aging is associated with a high prevalence of insomnia, which is linked to somatic and neuropsychiatric diseases, as well as metabolic and immunological dysfunction. This study aims to identify alterations in the transcriptome profiles and functional metabolic pathways in older adults with different types of sleep disorders. This cross-sectional study included 1002 participants (60–90 years) who were screened for sleep disorders using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Two types of sleep disorders were identified in the study cohort, i.e., sleep onset insomnia and sleep maintenance insomnia. Both types of insomnia were further analyzed for associations with clinical characteristics, laboratory testing results, and socioeconomic backgrounds. The transcriptomic profiles of peripheral blood samples were examined in 236 individuals, supplemented with differential gene and dsRNA expression analyses (DESeq2). Both sleep onset insomnia and middle insomnia were associated with depression, chronic pain syndrome, and osteoarthritis, while only middle insomnia was associated with cardiometabolic diseases. No associations were observed between sleep onset insomnia or reduced sleep duration and transcriptomic profiles. In contrast, 244 genes were differentially expressed in patients with middle insomnia, indicating the activation of pathways related to viral infection response and inhibition of protein synthesis. Additionally, differential expression analysis of double-stranded RNA (dsRNA) identified 2139 significant changes. Middle insomnia in older adults is associated with transcriptomic changes indicative of an activated antiviral immune response, likely resulting from changes in dsRNA expression levels. The chronic inflammation arising from these transcriptomic alterations may underlie the observed association between middle insomnia and cardiometabolic conditions.