ClinicalTrials.gg
RecruitingNCT06953180Updated Jul 11, 2025

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

An observational study in Cardiovascular Diseases (CVD), Type 2 Diabetes and Chronic Respiratory Diseases, sponsored by Asfendiyarov Kazakh National Medical University. Recruiting at 1 site in Kazakhstan. Open to participants aged 18 Years to 69 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-07-11.

Sponsored by Asfendiyarov Kazakh National Medical University · Observational

From the registry’s dates

  • Primary completion was expected by Jan 2026, 10 months ago, but the record still lists the study as recruiting.
  • Started Mar 2025; still recruiting 1 year 8 months later.
Study type
Observational
Model
Other
Time perspective
Prospective
Enrollment
6,720
Ages
18 Years to 69 Years
Sex
All
01

Study 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.

02

Conditions studied

  • 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
03

In context

Cardiovascular Diseases

5,368 studies on the registry are indexed under Cardiovascular Diseases; 939 are open to participants now.

This study's planned enrollment of 6,720 is above the median of 506 across 1,572 observational studies indexed under Cardiovascular Diseases.

Browse Cardiovascular Diseases studies →

Lead sponsor

Asfendiyarov Kazakh National Medical University is the lead sponsor of 19 studies on the registry; 2 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 69 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

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

Exclusion criteria

Exclusion Criteria:

  • Age less than 18 years old or over 69 years old.
  • Failure to provide or incomplete participation in data collection procedures.
Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

05

Study design

Observational model
Other
Time perspective
Prospective
Enrollment
6,720 participants (estimated)
Patient registry
No
Biospecimen retention
Samples with dna

Groups and cohorts

  • 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

  • OtherGenetic: DNA analysis

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

06

What researchers measure

Primary outcomes

  1. 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

  2. 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

07

Study locations

1 of 1 sites recruiting
08

References and documents

Individual participant data

Plan to share: Undecided

No publications or documents are linked to this record.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started Mar 2025 (actual). Primary completion is estimated for Jan 2026, 9 months after the start.

Of 580 non-industry observational studies under Cardiovascular Diseases, half reached primary completion within 70 months of starting (the median), counting those still running; a quarter within 28 and three quarters within 196. This study's planned 9 months is below that.

Counting every sponsor, half of 664 studies reached it within 61 months; a quarter within 24 and three quarters within 192.

Cardiovascular Diseases covers many different conditions, not one, so the studies compared here vary widely. Read these figures as a rough guide.

How these figures are worked out, and which studies are left out of them.

Registered
Mar 31, 2025
First posted
May 1, 2025
StartActual
Mar 3, 2025
Primary completionEst.
Jan 1, 2026
CompletionEst.
Dec 31, 2026
Last update
Jul 11, 2025
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Jul 11, 2025, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT06953180
Lead sponsor
Asfendiyarov Kazakh National Medical University
Responsible party
Ildar Fakhradiyev (Vice-rector, Asfendiyarov Kazakh National Medical University) — Principal investigator
First posted
May 1, 2025
Start date
Mar 3, 2025
Primary completion
Jan 1, 2026 (estimated)
Completion
Dec 31, 2026 (estimated)
Last update
Jul 11, 2025

Study contacts

Ildar Fakhradiyev, Ph.D
Contact
fakhradiyev.i@kaznmu.kz
+7 (727) 338 7090

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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