Epigenetics and NCD Prevention in Kazakhstan: Personalized Approaches and Biological Age Prediction

NCT06953180 | Recruiting

• 1 other identifier: BR27304987
• Study Type: observational
• Target: 6,720
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to enhance personalized and preventive care for non-communicable diseases (NCDs) in Kazakhstan by examining epigenetic factors, predicting biological age and reproductive function using machine learning, and developing health improvement recommendations.

Conditions

Cardiovascular Diseases (CVD); Type 2 Diabetes; Chronic Respiratory Diseases; Obesity (Disorder); Chronic Kidney Diseases

Keywords

DNA methylation; precision medicine; preventive medicine; artificial intelligence; biomedical modeling

Outcome Measures

Primary Outcomes (2)

• Accuracy of Machine Learning Model for Predicting Biological Age

  Evaluation of the model's performance (based on telomere length and DNA methylation) using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R².

  Within 10 months from start of data collection

• Accuracy of Reproductive Function Prediction Model

  Development and validation of machine learning model to predict reproductive function using biomarkers. Model performance evaluated via MAE, MSE, and R².

  Within 10 months from start of data collection

Study Arms (1)

The study includes 1 cohort divided into 4 age subgroups.

The study follows a multistage cluster sampling design with age and gender stratification. A total of 1 cohorts have been identified, further divided into 4 age subgroups: 1. 18-29 years 2. 30-44 years 3. 45-59 years 4. 60-69 years Each age group is designed to have an equal distribution of men and women, ensuring gender balance across all subgroups.

Other: Genetic: DNA analysis

Interventions

Genetic: DNA analysis OTHER

Investigation of telomere length (TL) and DNA methylation level analysis

The study includes 1 cohort divided into 4 age subgroups.

Eligibility Criteria

• Age: 18 Years - 69 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population consists of 6,720 adult volunteers aged 18 to 69 years, residing across 17 regions of Kazakhstan. Participants are stratified by age and gender to ensure balanced representation within the following age groups: * 18-29 years * 30-44 years * 45-59 years * 60-69 years Each age group includes an equal proportion of men and women, ensuring gender balance. The selection follows a multistage cluster sampling design, incorporating various socioeconomic backgrounds and health statuses to enhance the generalizability of findings.

You may qualify if:

• Adults aged 18 to 69 years.
• Residents of 17 regions of Kazakhstan.
• Willingness to participate and provide informed consent.

You may not qualify if:

• Age less than 18 years old or over 69 years old.
• Failure to provide informed consent or incomplete participation in data collection procedures.

Sponsors & Collaborators

• Asfendiyarov Kazakh National Medical University: lead

Study Sites (1)

Asfendiyarov Kazakh National Medical University

Almaty, Kazakhstan, 050000, Kazakhstan

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

Venous blood samples and peripheral blood mononuclear cells (PBMCs) will be collected. DNA will be extracted and analyzed for telomere length (via quantitative real-time polymerase chain reaction (qPCR)) and methylation patterns (using methylation-sensitive high-resolution melting (MS-HRM)). Only high-quality DNA (assessed by spectrophotometry and fluorometry) will be retained. Samples will be stored at -80°C.

MeSH Terms

Conditions

Cardiovascular Diseases; Diabetes Mellitus, Type 2; Obesity; Renal Insufficiency, Chronic

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Overweight; Overnutrition; Nutrition Disorders; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes

Central Study Contacts

Ildar Fakhradiyev, Ph.D

CONTACT

+7 (727) 338 7090; fakhradiyev.i@kaznmu.kz

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice-rector

Study Record Dates

• First Submitted: March 31, 2025
• First Posted: May 1, 2025
• Study Start: March 3, 2025
• Primary Completion: January 1, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 11, 2025

Record last verified: 2025-04

Locations

KA (1)