RUSS-AGE: Creating of a Biological Age Calculator and Study of Aging Phenotypes in the Russian Population

Biomarkers, Healthy Aging

biological age, aging biomarkers, aging clock, biological age calculator, phenotypic age, aging phenotype, healthy aging, machine learning in aging

This is a multi-center, cross-sectional, observational study aimed at developing biological age calculators specifically for the Russian population investigating various aging phenotypes. Aging is a complex process that varies greatly between individuals, meaning that chronological age does not always reflect one's biological health status. The primary goal of this study is to identify and analyze a comprehensive set of markers (including socioeconomic factors, lifestyle, physical parameters, cognitive function, and laboratory biomarkers) that best reflect the aging process. Using this data, researchers will create a mathematical model to estimate a person's "biological age." The study plans to enroll at least 3,500 male and female volunteers aged 18 years and older from across Russia. Participants will be divided into 5-year age groups (e.g., 18-24, 25-29, up to 90+ years) to ensure broad representation. Participation involves a single visit to a clinical center. During this visit, participants will undergo: Interview and questionnaires (assessing health history, lifestyle, socioeconomic status, diet, sleep, and quality of life). Physical examination and anthropometric measurements (height, weight, blood pressure, grip strength). Functional and cognitive tests (e.g., walking speed, balance tests, memory and attention tasks tailored to age). Collection of biomaterials: blood (50 ml), urine, and stool samples for extensive laboratory analysis, including routine tests and specialized aging biomarkers. Part of the biomaterials will be biobanked for future scientific research. Instrumental examinations for a subset of participants: Depending on the center's capabilities and the study protocol, some participants may also undergo additional assessments such as densitometry (bone density scan), bioimpedance analysis (body composition), and brain MRI. The results are expected to lead to the creation of a validated biological age calculator for the Russian population. This tool could help identify targets for interventions to promote healthy aging and, in the future, potentially predict the risk of developing age-related chronic diseases.

Background and Rationale Chronological age is an imprecise measure of an individual's functional health and aging trajectory. Biological age, an integrative measure of systemic physiological state, is a more accurate predictor of age-related morbidity and mortality. While multiple biomarkers and models for estimating biological age exist, they are primarily derived from non-Russian populations and may not account for region-specific genetic, lifestyle, and environmental factors. The RUSS AGE study aims to address this gap by establishing a comprehensive, population-specific biological age calculator for the Russian population. Primary Objectives To develop and validate a mathematical model (calculator) for estimating biological age in the Russian adult population based on a multidimensional panel of biomarkers, clinical parameters, and functional assessments. Secondary Objectives * To characterize aging phenotypes (profiles) across different adult age groups (from 18 to 90+ years) in Russia. * To analyze the associations between socio-economic status, lifestyle factors, health history, and the rate of biological aging. * To establish a biobank of blood, serum, plasma, and urine samples for future research on aging mechanisms. * To identify potential targets for geroprotective interventions and lay the groundwork for future prospective studies on predicting age-associated disease risk. Study Design This is a multi-center, cross-sectional, observational study. The study involves several visits per participant, with no interventional procedures. Data and biospecimen are collected to create a reference database and develop the predictive model. Some participants will be re-enrolled in the study with repeated data collection Methodology Overview Data collection is structured into comprehensive domains to capture the multifactorial nature of aging: * Sociodemographic & Clinical History: Collected via detailed questionnaires covering socio-economic status, lifetime risk factors, medical history (chronic and past diseases), and medication use. * Functional & Physical Assessment: Physical Exam & Anthropometry: Height, weight, BMI, waist/hip circumference, blood pressure, resting heart rate. * Physical Function: Handgrip strength (dynamometry), the 5-times sit-to-stand test and balance tests. For those 65+, the Short Physical Performance Battery (SPPB) is used. * Functional Independence: For participants aged 65+, the Barthel Index of basic activities of daily living is assessed. \*Cognitive & Psychological Assessment: * Cognitive Status: Age-tailored tests including the Stroop test, Digit Symbol Substitution Test (DSST) and Trail Making Test; the Montreal Cognitive Assessment (MoCA) for those 40+; and the Mini-Mental State Examination (MMSE) for those 90+. * Emotional State: Assessed using the Hospital Anxiety and Depression Scale (HADS). Sleep quality is evaluated with the Insomnia Severity Index (ISI). * Patient-Reported Outcomes: Quality of life is measured using the SF-12 questionnaire. Nutritional status is evaluated via a study-specific dietary questionnaire. * Laboratory & Biomarker Analysis: Fasting blood, stool and urine samples are collected. Core analyses include complete blood count with differential, clinical biochemistry (lipid profile, liver and kidney function, glucose, etc.), urine tests and 16S rRNA sequencing of stool samples Data Analysis & Modeling: Statistical analysis will involve descriptive statistics and inter-group comparisons across age strata. The core analytical task will employ advanced machine learning and statistical modeling techniques (e.g., deep learning approaches, regression models) to integrate the multidimensional data (clinical, functional, cognitive, laboratory) and derive a composite index of biological age. The model's development and validation will adhere to standard practices for predictive analytics. Governance: The study is coordinated by the Russian Gerontological Research and Clinical Center (RGNKC), a division of the Pirogov Russian National Research Medical University. All procedures will be conducted in accordance with the Declaration of Helsinki, ICH Good Clinical Practice guidelines, and relevant Russian regulatory requirements, following approval by the appropriate Independent Ethics Committee.

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