Biological AgeNCT06791486
AI-Driven Prediction of Biological Age With EHR
An observational study of Biological Age, run by The Eye Hospital of Wenzhou Medical University at 4 sites in China.
Trial summary
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). Read more about this study
- Who is it for?
- Ages 0 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Biological Age
- What is being tested?
- AI-assisted predictive model
- How many people
- 1,000,000 participants
- Where
- 4 sitesChina
- Study dates
- 1 Mar 2023to 2 Apr 2025
- 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 0 to 100 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The study population consists of individuals who have received care at participating hospitals or healthcare centers with accessible electronic health records (EHR). Participants will include those with complete EHR data, including medical history, laboratory test results, imaging data, and lifestyle factors such as diet, physical activity, and smoking habits. The cohort will comprise both individuals who are healthy and those with chronic conditions or comorbidities to analyze biological age prediction across different health statuses. The study will be conducted across multiple healthcare facilities to ensure a diverse patient population representing a wide range of age groups, health conditions, and demographics.
Who may be able to join
- Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).
- Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.
- All participants must provide informed consent for the use of their medical data for research purposes.
Who cannot join
- Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.
- atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.
- Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites, 4 recruiting
- Recruiting
Nanfang Hospital
Guangzhou, Guangdong
Zhuomin Li, +86-0577-85397527, chetneyli.1001@gmail.com
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Sian Liu, +86-0577-88002888, liusan@mail3.sysu.edu.cn
- Recruiting
The Eye Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Lan Wang, +86-0577-85397527, wl2832300533@163.com
About this study
Official title: Predicting Biological Age Using Electronic Health Records: An AI-Based Approach
This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.
Read the detailed description
Biological age prediction is crucial for assessing overall health, determining the risk of age-related diseases, and providing personalized healthcare. While chronological age is a key factor, it does not always reflect an individual's true biological aging process. Early identification of accelerated biological aging and associated health risks can significantly impact early interventions and long-term health outcomes. In clinical practice, healthcare providers integrate a wide range of patient data, including medical history, laboratory test results, and clinical observations, to understand an individual's health status and predict potential future risks. As precision medicine becomes more important, the ability to predict biological age and personalize care plans is essential. Recent advancements in artificial intelligence and data analysis techniques have shown promise in enhancing the accuracy of biological age predictions. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, laboratory results, clinical observations, and patient demographics. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized healthcare for patients by predicting biological age, identifying at-risk individuals, and improving health outcomes.
- Conditions
- Biological Age
- Being tested
- AI-assisted predictive model
- Keywords
- Biological Age, electronic health records, AI prediction, aging
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted predictive modelOther
This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes.
| Study group | Role | What they receive |
|---|---|---|
| Biologically Younger Group | Participants whose biological age is predicted to be younger than their chronological age. Receives: Other: AI-assisted predictive model | |
| Biologically Older Group | Participants whose biological age is predicted to be older than their chronological age. Receives: Other: AI-assisted predictive model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Biological Age Prediction Accuracy
The accuracy of the AI model in predicting biological age compared to chronological age. This will be evaluated using the Pearson Correlation Coefficient (PCC) to assess the strength of the correlation between predicted biological age and chronological age. Additionally, R-squared (R²) will be used to evaluate the proportion of variance in biological age explained by the model.
Measured: 1 year
Secondary outcomes (1)
Health Risk Correlation
The correlation between predicted biological age and various health risks, such as the development of chronic diseases (e.g., cardiovascular disease, diabetes), using PCC to evaluate the relationship between biological age predictions and health outcomes.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start1 Mar 2023
- Primary completion2 Apr 2025 (estimated)
- Study completion2 Apr 2025 (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 Biological AgeStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791486
- Sponsor's protocol ID
- Biological Age
- Status last verified
- Apr 2025
- First submitted
- 19 Jan 2025
- Last updated
- 2 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Liu S, Zheng W, Kang J, Xu T, Chen S, Li G, Li J, Wong H, Wang M, Bai X, Hu C, Tang C, Jin S, Zou Z, Chong I, Lu Y, Wong IN, Xu H, Zhang CL, Shi J, Feng E, Gu J, Sun Z, Chen H, Yang L, Zhang Y, Zhu X, Huang H, Xu X, Li X, Zhao Z, Qi H, Lu X, Cheng N, Pan S, Sun N, Yin Y, Williams M, Oermann E, Rasko JEJ, Li J, Wang K, Zhang K, Wu H, Xia Y, Zeng F; International Consortium of Digital Twins in Healthcare and Medicine. Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent. Nat Med. 2026 Sep 4. doi: 10.1038/s41591-026-04694-y. Online ahead of print.
PubMed 42742183
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Biological Age Using Electronic Health Records: An AI-Based Approach
- ClinicalTrials.gov ID
- NCT06791486
- Acronym / sponsor ID
- Biological Age
- Conditions
- Biological Age
- Keywords
- Biological Age, electronic health records, AI prediction, aging
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 2 Apr 2025 (estimated)
- Study completion
- 2 Apr 2025 (estimated)
- First posted
- 24 Jan 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 is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.
Detailed description
Biological age prediction is crucial for assessing overall health, determining the risk of age-related diseases, and providing personalized healthcare. While chronological age is a key factor, it does not always reflect an individual's true biological aging process. Early identification of accelerated biological aging and associated health risks can significantly impact early interventions and long-term health outcomes. In clinical practice, healthcare providers integrate a wide range of patient data, including medical history, laboratory test results, and clinical observations, to understand an individual's health status and predict potential future risks. As precision medicine becomes more important, the ability to predict biological age and personalize care plans is essential. Recent advancements in artificial intelligence and data analysis techniques have shown promise in enhancing the accuracy of biological age predictions. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, laboratory results, clinical observations, and patient demographics. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized healthcare for patients by predicting biological age, identifying at-risk individuals, and improving health outcomes.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Biologically Younger Group | – | Other: AI-assisted predictive modelParticipants whose biological age is predicted to be younger than their chronological age. |
| Biologically Older Group | – | Other: AI-assisted predictive modelParticipants whose biological age is predicted to be older than their chronological age. |
Who may qualify
- Ages
- 0 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of individuals who have received care at participating hospitals or healthcare centers with accessible electronic health records (EHR). Participants will include those with complete EHR data, including medical history, laboratory test results, imaging data, and lifestyle factors such as diet, physical activity, and smoking habits. The cohort will comprise both individuals who are healthy and those with chronic conditions or comorbidities to analyze biological age prediction across different health statuses. The study will be conducted across multiple healthcare facilities to ensure a diverse patient population representing a wide range of age groups, health conditions, and demographics.
Key inclusion criteria (3 listed)
- Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).
- Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.
- All participants must provide informed consent for the use of their medical data for research purposes.
Key exclusion criteria (3 listed)
- Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.
- atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.
- Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.
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 |
|---|---|---|
| AI-assisted predictive modelThis study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes. | Other | Biologically Older Group, Biologically Younger Group |
Primary outcome
Biological Age Prediction Accuracy
Time frame: 1 year
The accuracy of the AI model in predicting biological age compared to chronological age. This will be evaluated using the Pearson Correlation Coefficient (PCC) to assess the strength of the correlation between predicted biological age and chronological age. Additionally, R-squared (R²) will be used to evaluate the proportion of variance in biological age explained by the model.
1 secondary outcome
Health Risk Correlation
Time frame: 1 year
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 2 Apr 2025.
- Derived from this study: PubMed 42742183Liu S, Zheng W, Kang J, Xu T, Chen S, Li G, Li J, Wong H, Wang M, Bai X, Hu C, Tang C, Jin S, Zou Z, Chong I, Lu Y, Wong IN, Xu H, Zhang CL, Shi J, Feng E, Gu J, Sun Z, Chen H, Yang L, Zhang Y, Zhu X, Huang H, Xu X, Li X, Zhao Z, Qi H, Lu X, Cheng N, Pan S, Sun N, Yin Y, Williams M, Oermann E, Rasko JEJ, Li J, Wang K, Zhang K, Wu H, Xia Y, Zeng F; International Consortium of Digital Twins in Healthcare and Medicine. Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent. Nat Med. 2026 Sep 4. doi: 10.1038/s41591-026-04694-y. Online ahead of print.
- Individual participant dataSharing planned: No
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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.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Biological Age, run by The Eye Hospital of Wenzhou Medical University at 4 sites in China.
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 3 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 2 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. 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 Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Biological Age Prediction Accuracy (time frame: 1 year). 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 NCT06791486, 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 2 Apr 2025.