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

RUSS-AGE, CREATING OF A BIOLOGICAL AGE CALCULATOR AND STUDY OF AGING PHENOTYPES IN THE RUSSIAN POPULATION

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.

Study Overview

• Status: Recruiting
• Conditions: Biomarkers; Healthy Aging

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 3500

Contacts and Locations

• Study Contact: Name: Liubov Machekhina, MD, PhD; Phone Number: +79037488543; Email: machehina_lv@rgnkc.ru

Study Locations

• Russia
  • Moscow, Russia, 129226
    • Recruiting
    • Pirogov Russian National Research Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

The study cohort comprises a nationally representative sample of the general Russian adult population. We plan to enroll at least 3,500 volunteers (targeting a 1:1 male-to-female ratio) aged 18 years and older. Participants will be systematically recruited across multiple clinical centers in Russia to ensure geographic and social diversity. A key feature of the design is stratified enrollment by 5-year age groups (from 18 to 90+ years), with a target of 200-300 participants per stratum.

Description

Inclusion Criteria: Signing the informed consent form to participate in the study.

Participant's age was 18 years or older at the time of inclusion in the study.

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Exclusion Criteria:

1. Refusal to participate in the study or to provide informed consent.
2. History or medical records indicating the presence of infectious diseases (Hepatitis C, Hepatitis B, including HBsAg carrier status, HIV infection).
3. Presence of an acute illness/condition, exacerbation of a chronic disease, or surgical intervention within the last month prior to study inclusion.
4. Lack of remission from an oncological disease or ongoing anti-tumor therapy initiated less than three years prior to study inclusion.
5. Severe cognitive or sensory impairments and mental disorders that, in the investigator's opinion, preclude adequate communication with the subject.
6. Severe forms of chronic non-communicable diseases: life-threatening cardiac arrhythmias, chronic heart failure NYHA Class III-IV, left ventricular ejection fraction <40%, ischemic heart disease CCS Class III-IV, chronic kidney disease Stages 4-5, type 1 diabetes mellitus, type 2 diabetes mellitus with terminal stages of complications, systemic connective tissue diseases, chronic obstructive pulmonary disease with respiratory failure of Grade 1 or higher, bronchial asthma requiring glucocorticosteroid therapy, osteoarthritis Kellgren-Lawrence Grade IV, body mass index (BMI) ≥40 kg/m², as well as documented history of myocardial infarction (MI) or acute cerebrovascular accident (stroke).
7. Pregnancy or lactation (breastfeeding).

8. Any other factors that, in the investigator's opinion, may preclude the participant's inclusion in the study.

   Additional exclusion criteria for participants undergoing stool sample collection:

9. Use of systemic antibiotics for 3 or more days within the 3 months prior to the study start.
10. Any invasive procedures on the large intestine within the last 3 weeks prior to the study start.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
A representative Russian adult cohort; A representative Russian adult cohort aged 18+ both men and women

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Aging clock	A composite, unitless numerical score derived from a mathematical model (e.g., via machine learning) that integrates multidimensional data (socioeconomic, clinical, functional, cognitive, and laboratory biomarkers). This score is the output of the developed calculator and represents the estimated biological age of an individual. The score is calculated based on data collected during the single study visit. Its validation as a predictor will be analyzed cross-sectionally against chronological age and health status.	Primary Outcome - through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Characterization of Aging Phenotypes	Descriptive profiles (phenotypes) of aging across 5-year age strata (from 18 to 90+ years), based on the distribution and correlation of collected variables. This includes patterns in physical function, cognitive scores, biomarker levels, and comorbidity burden.	Secondary Outcome - through study completion, an average of 1 year
Establishment of a Biobank Repository	The number and type of biospecimens (whole blood, serum, plasma, stool, urine) successfully collected, processed, and stored for long-term preservation, enabling future research on aging mechanisms.	Through study completion, an average of 1 year

Collaborators and Investigators

• Sponsor: Pirogov Russian National Research Medical University

Publications and helpful links

General Publications

• Melnitskaia A.A., Matchekhina L.V., Tkacheva O.N., Ilyushchenko A.K., Tyazhelnikov A.A., Polunin V.S., Yumukyan A.V., Strazhesko I.D. RUSS-AGE: developed research protocol for the creation of Russian biological age calculators. Russian Journal of Geriatric Medicine. 2023;16(4):239-247. doi:10.37586/2686-8636-4-2023-239-247 (In Russ.)

Study record dates

Study Major Dates

• Study Start (Actual): January 12, 2023
• Primary Completion (Estimated): September 1, 2029
• Study Completion (Estimated): September 1, 2030

Study Registration Dates

• First Submitted: December 3, 2025
• First Submitted That Met QC Criteria: May 4, 2026
• First Posted (Actual): May 7, 2026

Study Record Updates

• Last Update Posted (Actual): May 7, 2026
• Last Update Submitted That Met QC Criteria: May 4, 2026
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: healthy aging; biological age; aging biomarkers; aging clock; biological age calculator; phenotypic age; aging phenotype; machine learning in aging
• Other Study ID Numbers: BA06/2022.1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No