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

Asfendiyarov Kazakh National Medical University6,720 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

6,720

Conditions

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

Interventions

Eligibility

Sex: ALLMin age: 18 YearsMax age: 69 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * Adults aged 18 to 69 years. * Residents of 17 regions of Kazakhstan. * Willingness to participate and provide informed consent. Exclusion Criteria: * Age less than 18 years old or over 69 years old. * Failure to provide informed consent or incomplete participation in data collection procedures.

Locations (1)

Asfendiyarov Kazakh National Medical University

Almaty, Kazakhstan, Kazakhstan

RECRUITING

Outcomes

Primary Outcomes

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.

Time frame: 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.

Time frame: Within 10 months from start of data collection

Central Contacts

Ildar Fakhradiyev, Ph.D

CONTACT

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

Data from ClinicalTrials.gov

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