Cardiovascular Diseases (CVD), Chronic Kidney Diseases, Chronic Respiratory Diseases, Obesity (Disorder), Type 2 Diabetes
Conditions
Keywords
DNA methylation, precision medicine, preventive medicine, artificial intelligence, biomedical modeling
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.
Interventions
OTHERGenetic: DNA analysis
Investigation of telomere length (TL) and DNA methylation level analysis
Sponsors
Asfendiyarov Kazakh National Medical University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 69 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of Machine Learning Model for Predicting Biological Age | 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. |
| Accuracy of Reproductive Function Prediction Model | 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. |
Countries
Kazakhstan
Contacts
Primary ContactIldar Fakhradiyev, Ph.D
Outcome results
None listed