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NCT Number: NCT06953180

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

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.

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Key information

Conditions

Cardiovascular Diseases (CVD) Body Weight Cardiovascular Diseases Chronic Disease Chronic Kidney Diseases Chronic Respiratory Diseases Diabetes Mellitus Diabetes Mellitus, Type 2 Disease Attributes Endocrine System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Glucose Metabolism Disorders Kidney Diseases Male Urogenital Diseases Metabolic Diseases Nutrition Disorders Nutritional and Metabolic Diseases Obesity Obesity (Disorder) Overnutrition Overweight Pathologic Processes Pathological Conditions, Signs and Symptoms Renal Insufficiency Renal Insufficiency, Chronic Signs and Symptoms Type 2 Diabetes Urogenital Diseases Urologic Diseases

Age range

18 year–69 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Asfendiyarov Kazakh National Medical University

Almaty, Kazakhstan, 050000

Location status: Recruiting

Location contact

Ildar Fakhradiyev, PhD

CONTACT

[email protected]

+7 (727) 338 7090

Ildar Fakhradiyev, PhD

PRINCIPAL_INVESTIGATOR

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Genetic: DNA analysis

Other

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

Primary outcomes

  1. Accuracy of Machine Learning Model for Predicting Biological Age

    Time frame: Within 10 months from start of data collection

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

  2. Accuracy of Reproductive Function Prediction Model

    Time frame: Within 10 months from start of data collection

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

Study contacts

Contact information is provided by the study sponsor or research team.

Ildar Fakhradiyev, Ph.D

CONTACT

[email protected]

+7 (727) 338 7090

Sponsors and collaborators

Lead sponsor

Asfendiyarov Kazakh National Medical University

Other

Registry information

Official study title

Epigenetics and Prevention of Non-communicable Diseases in Kazakhstan: a Personalized Approach and Biological Age Prediction

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
May 1, 2025
Registry last updated
Jul 11, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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