BackgroundLong-living adults often maintain cognitive function despite neuropathological changes, which is often attributed to cognitive resilience (CR)—a combined effect of cognitive and cerebral reserves. CR is influenced by genetic, clinical, sociodemographic, and environmental factors.Materials and methodsWe investigated genetic, clinical, and environmental predictors of CR in 198 dementia-free long-living adults via two neuropsychological examinations over a 2-year period, a geriatric assessment, and a genome-wide association study (GWAS).ResultsLimited mobility, reduced walking, hearing impairment, depression, anemia, lower quality of life, and decreased BMI were key accelerators of CI. Depression, hypercholesterolemia, and lack of hobbies increased the risk of mild cognitive impairment (MCI)-to-dementia progression. GWAS identified CR-associated genetic variants, including a missense mutation in SYNGAP1 (Ile1115Thr) not previously linked to cognitive disorders.ConclusionOur findings corroborated established risk factors for cardiovascular diseases and identified population-specific patterns, with APOE ε4 showing no significant association. Both protein-coding regions and non-coding elements were implicated in CI, suggesting that it is underlain by complex regulatory mechanisms.

This publication addresses one of the most critical challenges in modern medicine: human aging and longevity. The authors have assembled the most current scientific knowledge on how the human body ages and how aging processes may be modulated. Volume Two explores demographic aspects, theoretical mechanisms of aging, and methods for assessing the rate of aging. Phenomena of lifespan extension and geroprotective technologies are examined. The book presents the patterns of organ and systems aging at the cellular, tissue, and organ levels. This unique volume is an indispensable resource for a broad range of physicians and medical researchers, particularly experts in the biology of aging, gerontology, and geriatrics.

Most genomic studies compare the genomes of long-living adults to those of the general population to identify potential genetic markers of longevity. We propose a refined approach: focusing on the genetic makeup of healthy, long-living adults to detect mechanisms promoting both longer lifespan and improved quality of life. To this end, we analyzed medical and genomic data from 3,703 long-living adults aged ≥90 years and 22,354 individuals aged 18-75 years (total N = 26,057). Using whole-genome sequencing (WGS) and a genome-wide association study (GWAS), we found that variants with significant and negative associations with longevity in the GWAS were located in genes such as APOE, APOC1, and CFAP46, which are implicated in an increased risk of age-related diseases. However, the presence or absence of these variants should not be considered a definitive determinant of longevity or sustained health after the age of 90. We found that healthy longevity was positively associated with variants within the MYO18B, TBC1D28, and LOC105376454 genes. To demonstrate the multifactorial nature of the examined phenotypes, we constructed polygenic score models that accounted for nonlinear interactions among the predictors. Trial registration: Clinical Trials NCT06268132 (for long-living adults). Registered 22 February 2024 (retrospectively registered).

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.

Damaging mutations of the Angiotensin I-converting enzyme (ACE) that result in low ACE levels may increase the risk of developing late-onset Alzheimer’s disease (AD). We quantified blood ACE levels in EDTA-plasma from 147 subjects with 23 different heterozygous ACE mutations (and 70 controls) and estimated the effect of these mutations on ACE phenotype, using a set of monoclonal antibodies (mAbs) to ACE and two ACE substrates. We identified several mutations in both ACE domains (including the most frequent ACE mutation, Y215C), which led to decreased ACE levels in the blood, and thus could be considered as putative risk factors for late-onset AD. The precipitation of several ACE mutants (Q259R, A725P, C734Y) by specific mAbs changed significantly, and therefore, these mAbs could be markers of these mutations. Analysis of 50 of the most frequent ACE mutations demonstrates that more than 1.5% of the adult population may have mutations which lead to decreased ACE levels, and thus, the role of low ACE levels in the development of AD may be underappreciated. Intriguingly, statistical and cluster analyses of longevity patients revealed trends towards higher frequency of cognitive impairment among affected individuals with damaging ACE mutations. Systematic analysis of blood ACE levels in patients with various ACE mutations identifies individuals with low blood ACE levels who may be at increased risk for late-onset AD. Patients with transport-deficient ACE mutations theoretically could benefit from therapeutic treatment with a combination of chemical and pharmacological chaperones and proteasome inhibitors, as was demonstrated previously on a cell model of the transport-deficient ACE mutation Q1069R. Moreover, clinical association analysis suggests a trend linking damaging ACE mutations with increased risk of cognitive impairment.

Orthostatic hypotension is a sharp decrease in blood pressure when an individual transitions from a supine to an upright position. OH affects at least 30% of older adults. It is attributed to the dysfunction of the autonomic innervation and decreased vascular bed capacity. Genomic (n = 2526), methylomic (n = 910), and transcriptomic (n = 391) data from centenarians aged 90 years and older were used to examine molecular and genetic factors for OH. No statistically significant genetic predictors of OH were identified. However, the study revealed numerous epigenetic markers of OH indicative of general aging, such as DNA hypomethylation. The predictive DNA methylation-based model for orthostatic hypotension demonstrated an average accuracy of 79%. The transcriptome analyses highlighted associations between OH and inflammation pathways, as well as other age-related biological processes. Integrated omics and clinical data have identified six key mechanisms associated with orthostatic hypotension: metabolic dysregulation, impaired muscle tone, altered cell proliferation, inflammation, humoral regulation, and neural regulation.

Background. Cognitive impairment (CI) is one of the most common geriatric syndromes, which has a significant negative impact on the autonomy and quality of life of older patients. It seems promising to study the association of the neuropsychological status of individuals over 90 years old with the parameters of their clinical and geriatric status in order to identify potential modifiable risk factors for the occurrence and progression of CI in this age group. Aim. To assess the relationship between hormonal and metabolic parameters and the neuropsychological status of long-livers. Materials and methods. The study included 3494 people aged 90 years and older. During a medical team visit, the study participants underwent a comprehensive geriatric assessment, as well as blood sampling to determine a number of metabolic and hormonal parameters. Cognitive functions were assessed using the Mini Mental State Examination, as well as the Frontal Assessment Battery. Depression symptoms were assessed using the Geriatric Depression Scale. Statistical analysis of the data was performed using the R programming languages version 4.2.2. and Python version 3.9.12. Results. Despite the correlation between the test results on the scales described above, different types of dementia have their own hormonal and metabolic markers. Thus, it is neurodegenerative conditions diagnosed with Mini Mental State Examination, that are associated with HDL-cholesterol and apolipoprotein A1. Whereas frontal dysfunction diagnosed with Frontal Assessment Battery is more associated with adiponectin. For depression, no unique markers not associated with the progression of CI were found. Conclusion. Different hormonal and metabolic parameters make a significant contribution to determining the severity of cognitive impairment in long-livers, depending on the specific type of neurological disorders. The obtained data require confirmation in prospective studies and may further be promising for optimizing guidelines for diagnosis, treatment and prevention of CI in long-livers.

Biological age is a personalized measure of the health status of an organism, organ, or system, as opposed to simply accounting for chronological age. To date, there have been known attempts to create estimators of biological age based on various biomedical data. In this work, we focused on developing an approach for assessing heart biological age using echocardiographic data. The current study included echocardiographic data from more than 5,000 different cases. As a result, indicators such as EA (E/A ratio of maximum flow rates in the first and second phases), IVS (thickness of the interventricular septum), LVPW (thickness of the posterior left ventricular wall), LVCO (cardiac output), and RWT (relative wall thickness) showed the greatest predictive potential. Based on these parameters, we created and trained neural network models to determine heart biological age, with a Mean Absolute Error (MAE) of approximately 3.5 years, an R-squared (R2) value of around 0.87, and a Spearman's rank correlation coefficient (rho) greater than 0.9 in men. In women, the MAE was approximately 3.4 years, with an R2 value of around 0.89 and a rho greater than 0.9. In addition, we have applied an AI explanation algorithm to improve understanding of how the model performs an assessment. Furthermore, the EchoAGE model was tested on echo cardiographic data from patients with age-related diseases, patients with multimorbidity, children with progeria syndrome, and diachronic data series.

Background. Biological age is a better predictor of morbidity and mortality associated with chronic age-related diseases than chronological age. The estimated difference between biological and chronological age can reveal an individual’s rate of aging.Aim. The aim of this study was to assess the association of cardiovascular risk factors with the rate of aging in people without cardiovascular diseases. Materials and methods. We calculated biological artery age and found associations of “old” arteries and rate of aging with risk factors of cardiovascular diseases in 143 adults without cardiovascular diseases. The data were analyzed by their categorization into 3 tertiles using regression methods.Results. “Old” arteries were associated with chronological age (p < 0,001; ОR = 0,55; 95% CI: 0,43 — 0,71) and hypertension (p = 0,002; ОR = 6,04; 95% CI: 1,98 — 18,42) in general group, age (p < 0,001; ОR = 0,45; 95% CI: 0,30 — 0,68), hypertension (p = 0,004; ОR = 12,79; 95% CI: 2,25 — 72,55) and family history of oncology (p = 0,036; ОR = 0,14; 95% CI: 0,02 — 0,88) in women subgroup and age (p = 0,001; ОR = 0,45; 95% CI: 0,28 — 0,76) and 3rd tertile of glycated hemoglobin (p = 0,041; ОR = 65,05; 95% CI: 1,19 — 3548,29) in men subgroup. Difference between biological and chronological age in a group of “old” arteries was associated with chronological age (p = 0,001; β = -1,24; 95% CI: -1,95 — -0,53) and with chronological age (p < 0,001; β = 1,71; 95% CI: 1,06 — 2,36) and 3rd tertile of glycated hemoglobin (p = 0,009; β = -4,78; 95% CI: -8,32 — -1,24) in group of “young” arteries.Conclusion. This study demonstrates that accelerated arterial aging is associated with hypertension and high levels of glycated haemoglobin.

Background . Life expectancy is increasing around the globe. However, chronological age is not the best indicator of health. For a more accurate assessment of body condition throughout life, in general, and aging, in particular, and identify potential points of geroprotective intervention, a specialized tool is needed. A tool that could prove beneficial is a biological age calculator, utilizing a range of biomarkers to analyze the degree of functional preservation of the body. Many existing biological age calculators are limited by a small number of parameters to analyze and sensitivity to use in a specific population. Aim . Large-scale studies to create a mathematical model for calculating biological age based on the Russian population have not previously been carried out. In 2022, the RUSS-AGE study was launched to create biochemical, cognitive and microbiotic calculators of biological age and determine possible points of geroprotective interventions. Materials and methods . The study intends to enroll at least 3,500 participants and analyze more than a hundred biomarkers using laboratory tests, questionnaires, neurocognitive and functional testing, and collection of anthropometric and physical indicators. Results. Currently, the recruitment of participants is supported by a government grant under the Priority 2030 program. By November 2023, 510 participants had been enrolled in the study. Conclusion. Further statistical processing of the information received and the development of prototypes of biological age calculators are planned.

The expression of the gene of pattern recognition receptor TLR2, proinflammatory cytokine IL-1β, and anti-inflammatory cytokine IL-10 was analyzed in the peripheral blood of nonagenarians (n=219; mean age 92.1 years, 77 men and 142 women) in comparison with healthy young donors (n=24; mean age 22.5 years, 16 women and 8 men). Nonagenarians were interviewed, medical records were analyzed, and a comprehensive geriatric assessment was performed according to the Clinical Guidelines on Frailty. The level of gene expression was determined by real-time PCR. The participation of inflammatory mechanisms in the immunosenescence was revealed. It was shown that increased expression of IL1B and TLR2 genes is associated with the development of frailty in nonagenarians and can be a factor of pathological aging. Increased expression of IL10 gene can be considered as a factor of successful aging in nonagenarians.

Background: In recent years, an increase in the number of long-living adults has been a dominant demographic trend in Russia. This population group is highly susceptible to cognitive dysfunctions. Cognitive impairment disrupts the lives of those affected and puts an immense burden on their caregivers. Currently, there are no clinically applicable therapies or prevention strategies for cognitive impairment. Therefore, it is critically important to determine which lifestyle factors contribute to cognitive decline and to develop well-informed preventive strategies. The aim of the study: The study sought to identify the association between cognitive status and lifestyle. Materials and methods: The participants (n=2762) were recruited from 2019 to 2021 from the central regions of Russia. Detailed medical/case histories were obtained, including marital status, education, and social and economic background. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. The Mann-Whitney U test and Chi-squared test were used to test the associations between the sex and the factors under study. Logistic regression was used to assess the associations between the factors and cognitive impairment. Results: Age, sex, lower levels of education, and lower income were risk factors of cognitive impairment. Engaging in physical activity, hobbies, and having a pet were protective against cognitive impairment. In women, cognitive dysfunctions were correlated with the duration of menopause. The predictive model for cognitive dysfunctions based on sex, lifestyle and socioeconomic factors generated (ROC AUC=0.687). Conclusion: The findings confirmed that cognitive dysfunctions in long-living adults were associated with the socioeconomic factors, marital status, and lifestyle. The proposed model makes it possible to assess in advance the risk of developing cognitive impairments in old age and take measures to correct lifestyle to preserve brain functions.

Background. One of the important tasks of modern science is to search for key biomarkers of aging of various body systems. Parameters of carbohydrate metabolism play an essential role in maintaining vital activity. The prevalence of carbohydrate metabolism disorders increases with age, but the time course of changes in individual markers remains poorly understood. Therefore, it is important to investigate the patterns of changes in carbohydrate metabolism markers in different age groups among healthy participants, which is the objective of the RUSS-AGE study. Aim. To evaluate changes in carbohydrate status markers (adiponectin, leptin, glucose, glycated hemoglobin, insulin, and carboxymethyllysine – CML) in different age groups of a healthy Russian population. Materials and methods. The study was conducted at the Pirogov Russian National Research Medical University in collaboration with the Moscow City Outpatient Clinic No. 220. The study group included subjects 18 years of age and older who signed an informed consent form; the exclusion criteria were current acute disease, exacerbation of a chronic disease, surgical intervention within the last month, and moderate to severe chronic age-associated diseases. Blood samples were taken to measure aging markers: glucose (enzymatic ultraviolet method), insulin (chemiluminescent enzyme immunoassay), glycated hemoglobin (calorimetric method), CML, adiponectin, and leptin (enzyme immunoassay). The study was approved by the local ethics committee (Minutes No. 59 dated 13.09.2022). Statistical analysis was carried out using the R programming language version 4.4.0. The significance threshold for the p-value values given in the article is 0.05. Results. The study included 711 participants, which were divided into eight age groups. According to the intergroup comparison, a statistically significant direct relationship of age with adiponectin (p0.001), glucose (p0.001), and glycated hemoglobin (p0.001) was found. No significant correlation with age was found for leptin (p=0.116), insulin (p=0.078), and CML (p=0.506). After conducting a statistical analysis using linear regression to assess the dependence of variables on age, it was found that only adiponectin, glucose, and glycated hemoglobin significantly increase with age (p0.001). Conclusion. The study showed a significant increase in adiponectin, glucose, and glycated hemoglobin, while leptin, insulin and CML had no significant correlation with age.

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.

Biological age is a personalized measure of the health status of an organism, organ, or system, as opposed to simply accounting for chronological age. To date, there have been known attempts to create estimators of biological age based on various biomedical data. In this work, we focused on developing an approach for assessing heart biological age using echocardiographic data. The current study included echocardiographic data from more than 5,000 different cases. As a result, we created EchoAGE - neural network model to determine heart biological age, that was tested on echocardiographic data from patients with age-related diseases, patients with multimorbidity, children with progeria syndrome, and diachronic data series. The model estimates biological age with a Mean Absolute Error of approximately 3.5 years, an R-squared value of around 0.88, and a Spearman's rank correlation coefficient greater than 0.9 in men and women. EchoAGE uses indicators such as E/A ratio of maximum flow rates in the first and second phases, thicknesses of the interventricular septum and the posterior left ventricular wall, cardiac output, and relative wall thickness. In addition, we have applied an AI explanation algorithm to improve understanding of how the model performs an assessment.

RELEVANCE: Sarcopenia is one of the leading geriatric syndromes that increases the risk of disability, falls and injuries. This syndrome is of particular importance for centenarians aged 90 years and older, for whom a detailed analysis of the course of sarcopenia has not previously been carried out and the hormonal and metabolic characteristics of this condition have not been described.AIM OF THE STUDY: To analyze the features of hormonal and metabolic status in nonagenarians with sarcopenia and to identify factors that increase the risk of developing this conditionMATERIALS AND METHODS: The study included 2221 people over the age of 90 years. Study participants underwent a comprehensive geriatric assessment during a visit with a doctor and nurse, as well as blood tests to measure a number of metabolic and hormonal parameters. Statistical data analysis was carried out using the R programming language version 4.2.2.RESULTS: Apolipoprotein A1, free triiodothyronine, vitamin D, albumin, C-reactive protein, hemoglobin, red blood cells and hematocrit were significantly associated with the presence of sarcopenia. In a survival analysis of people with sarcopenia, the strongest protective factors for participants with sarcopenia were any physical activity and increasing free T3. Malnutrition is the leading destructive factor.CONCLUSION: Hormonal metabolic status, in particular low concentrations of vitamin D, triiodothyronine, albumin, and apolipoprotein A1, largely determines the presence of sarcopenia in centenarians, but when assessing risks, it is necessary to take into account a number of other important parameters, such as physical activity and nutritional status.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

Rationale. The impact of vitamin D3 deficiency on the risk and prognosis of numerous chronic diseases has been actively studied for years. Recent research has demonstrated that vitamin D is not merely involved in the control of calcium-phosphorus metabolism, but can also enhance insulin sensitivity, decrease the incidence of type 2 diabetes mellitus (T2DM), obesity and autoimmune destruction of pancreatic β-cells. The influence of vitamin D3 on some cardiometabolic risk factors and cardiovascular disease (CVD) was described. Thus studying the role of vitamin D3 in the development of arteries wall changes in T2DM and IR, and their relationship with biology telomere seems to be quite relevant. Aim. To study the relationship between vitamin D3 deficiency and vascular wall condition, telomere biology in patients with varying insulin sensitivity. Materials and methods. The cross-sectional study involved 305 patients (106 men and 199 women) aged 51.5 ± 13.3 y.o. All patients underwent laboratory and instrumental research methods, study of morphofunctional state vascular wall. Telomere length and telomerase activity were determined using polymerase chain reaction. Results. Totally, 18 patients out of 248 (7.2%) were found to have normal vitamin D3 level (more than 30 ng/ml). In 92.8% of those studied Vitamin D3 insufficiency or deficiency was determined. As increase in vitamin D3 deficiency, an increase in fasting glucose was noted, HbA1c and its elevated concentration, HOMA index, glucose disorders up to T2DM, higher vascular stiffness. Telomerase activity in group with vitamin D3 deficiency was significantly lower than in groups with vitamin D3 insufficiency and normal content. Multiple linear regression analysis revealed that they are independently associated with vitamin D3 in T2DM (B=1.43; st. OR. 0.106; p=0.0001), vascular stiffness (B=0.075; st. OR. 2.11; p=0.017), fasting glucose (B=0.169; st. OR 1.62; p=0.004), HbA1c level (B=0.062; st. OR. 7.4; p=0.001) and the presence of “short” telomeres (B=0.09; st. OR. 1.154; p=0.001). ROC analysis revealed relationships between BMI (0.634, p=0.001), duration of T2DM (0.651, p=0.022), high intima media thickness (0.614, p=0.004), vascular stiffness (0.605, p<0.001), HbA1c (0.588, p=0.022) and presence of vitamin D3 deficiency. Conclusion. In persons with varying insulin sensitivity — from insulin resistance up T2DM is advisable assess vitamin D3 levels for effective prevention of arterial wall changes in addition to traditional CVD risk factors. Availability Vitamin D3 deficiency requires active prevention metabolic disorders and vascular changes.