Cardiovascular Diseases (CVD)NCT06953180
Epigenetics and NCD Prevention in Kazakhstan: Personalized Approaches and Biological Age Prediction
An observational study of Cardiovascular Diseases (CVD) and Type 2 Diabetes, run by Asfendiyarov Kazakh National Medical University at 1 site in Kazakhstan.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 69 yearsHealthy volunteers can take part
- Conditions the study is for
- Cardiovascular Diseases (CVD), Type 2 Diabetes, Chronic Respiratory Diseases
- What is being tested?
- Genetic: DNA analysis
- How many people
- 6,720 participants
- Where
- 1 siteKazakhstan
- Study dates
- 3 Mar 2025to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 69 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Adults aged 18 to 69 years.
- Residents of 17 regions of Kazakhstan.
- Willingness to participate and provide informed consent.
Who cannot join
- Age less than 18 years old or over 69 years old.
- Failure to provide informed consent or incomplete participation in data collection procedures.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Kazakhstan1 site, 1 recruiting
- Recruiting
Asfendiyarov Kazakh National Medical University
Almaty, Kazakhstan, 050000
Ildar Fakhradiyev, PhD, +7 (727) 338 7090, fakhradiyev.i@kaznmu.kz
Ildar Fakhradiyev, PhD
About this study
Official title: Epigenetics and Prevention of Non-communicable Diseases in Kazakhstan: a Personalized Approach 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.
- Conditions
- Cardiovascular Diseases (CVD), Type 2 Diabetes, Chronic Respiratory Diseases, Obesity (Disorder), Chronic Kidney Diseases
- Being tested
- Genetic: DNA analysis
- Keywords
- DNA methylation, precision medicine, preventive medicine, artificial intelligence, biomedical modeling
- Medical subject headings
- Cardiovascular Diseases, Diabetes Mellitus, Type 2, Obesity, Renal Insufficiency, Chronic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Genetic: DNA analysisOther
Investigation of telomere length (TL) and DNA methylation level analysis
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Genetic: DNA analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: Within 10 months from start of data collection
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2025
- Study start3 Mar 2025
- Primary completion1 Jan 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Cardiovascular Diseases (CVD)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 6,720 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Asfendiyarov Kazakh National Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ildar Fakhradiyev, Asfendiyarov Kazakh National Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06953180
- Sponsor's protocol ID
- BR27304987
- Status last verified
- Apr 2025
- First submitted
- 31 Mar 2025
- Last updated
- 11 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Sadykov B, Ibrayeva A, Ismoldayev Y, Shoranov M, Saliev T, Sadykova A, Tanabayeva S, Fakhradiyev I. Age-Metabolic Profile of Lower Serum Estradiol Among Women in Kazakhstan: A Large National Survey-Based Analytical Study. Int J Environ Res Public Health. 2026 Jul 21;23(7):934. doi: 10.3390/ijerph23070934.
PubMed 42512238
Study at a glance
From the registry record, last updated by the sponsor on 11 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Epigenetics and Prevention of Non-communicable Diseases in Kazakhstan: a Personalized Approach and Biological Age Prediction
- ClinicalTrials.gov ID
- NCT06953180
- Acronym / sponsor ID
- BR27304987
- 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
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 6,720 participants (estimated)
- Lead sponsor
- Asfendiyarov Kazakh National Medical UniversityUniversity, hospital or other
- Study start
- 3 Mar 2025 (actual)
- Primary completion
- 1 Jan 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 1 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| The study includes 1 cohort divided into 4 age subgroups. | – | Other: Genetic: DNA analysisThe 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. |
Who may qualify
- Ages
- 18 to 69 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Adults aged 18 to 69 years.
- Residents of 17 regions of Kazakhstan.
- Willingness to participate and provide informed consent.
Key exclusion criteria (2 listed)
- Age less than 18 years old or over 69 years old.
- Failure to provide informed consent or incomplete participation in data collection procedures.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Genetic: DNA analysisInvestigation of telomere length (TL) and DNA methylation level analysis | Other | The study includes 1 cohort divided into 4 age subgroups. |
Primary outcome
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jul 2025.
- Derived from this study: PubMed 42512238Sadykov B, Ibrayeva A, Ismoldayev Y, Shoranov M, Saliev T, Sadykova A, Tanabayeva S, Fakhradiyev I. Age-Metabolic Profile of Lower Serum Estradiol Among Women in Kazakhstan: A Large National Survey-Based Analytical Study. Int J Environ Res Public Health. 2026 Jul 21;23(7):934. doi: 10.3390/ijerph23070934.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases (CVD) and Type 2 Diabetes, run by Asfendiyarov Kazakh National Medical University at 1 site in Kazakhstan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 11 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Kazakhstan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ildar Fakhradiyev, Ph.D (+7 (727) 338 7090, fakhradiyev.i@kaznmu.kz), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Machine Learning Model for Predicting Biological Age (time frame: Within 10 months from start of data collection). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06953180, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Jul 2025.