Diabetes Mellitus Type 2 (T2DM)NCT06957093
Therapeutic Efficacy and Safety Evaluation of AI in the Management of Diabetes: A RCT Trial
A study testing artificial intelligence and Routine diagnosis and treatment group for diabetes in people with Diabetes Mellitus Type 2 (T2DM) and Artificial Intelligence (AI), run by The First Hospital of Jilin University at 1 site in China.
Trial summary
Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Diabetes Mellitus Type 2 (T2DM), Artificial Intelligence (AI)
- What is being tested?
- artificial intelligence and Routine diagnosis and treatment group for diabetes
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 15 Jun 2025to Dec 2026
- Phase
- Interventional
- 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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age: ≥18 years，≤75 years;
- Diagnosed with type 2 diabetes for ≥1 year;
- 7.0% ≤HbA1c ≤11%;
- Body mass index ≥18.5 kg/m2;
- Proficient ability to use smart phones;
- Agreed to utilize a digital integrated healthcare platform for diabetes care and research;
- Informed consents are obtained from the participants.
Who cannot join
- Presence of other types of diabetes, such as type 1 diabetes and gestational diabetes;
- Severe diabetic complications;
- Medical history of chronic liver diseases, including hemochromatosis, hepatocellular carcinoma, autoimmune liver disease, cirrhosis, viral hepatitis (including hepatitis A, B, and C), or hepatolenticular degeneration;
- Kidney injury (serum creatinine ≥1.5 times the upper limit of the reference) ; Serum ALT and AST levels elevated >2-fold;
- Medical history of mental disorders, such asschizophrenia, depression, or bipolar affective disorder;
- Excessive alcohol intake or drug abuse in the past 3 months;
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- Use of medications affecting glucose metabolism, such as corticosteroids or ·consumption of immunosuppressive and anti-obesity medications in the past 3 months;
- Pregnancy, planning for pregnancy, or lactation; or any other conditions unsuitable for trial participation;
- Participatingor plan to participate in other clinical trials; and other cases that are inappropriate to participate.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The First Hospital of Jilin University
Changchun, Jilin, 130000
Sun, +8688782075, chenglins@jlu.edu.cn
About this study
Official title: Evaluation of the Therapeutic Efficacy and Safety of Artificial Intelligence-based Decision-making Technology in the Integrated Management of Diabetes Mellitus: a Longitudinal, Open-labeled, Randomized Controlled Trial
Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group.
Methods: For the AI Intervention group, the patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks, offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. The platform is a digitally integrated healthcare platform that patients can use independently without the need for monitoring and assistance by healthcare professionals. The glucometer and pedometer bracelet will automatically connect to the platform through Bluetooth. The patient lab sheet identification and structured conversion system, AI for food picture identification and calorie calculation systems, and the AI decision-making system are on the cloud server. Patients upload image information, such as lab sheets and meal pictures, through the patient's diabetes mobile health system, and the cloud platform intelligently analyzes the patient's disease, medication, and daily life status to develop personalized solutions according to individual control goals. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. For the conventional treatment group, patients will receive a free blood glucometer and will have regular outpatient appointments. There is no limit to the number of outpatient visits; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team objectively conduct their diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level.
Expected results: A significant difference in HbA1c change from baseline to 48 weeks and improved FPG and 2-hour postprandial blood glucose levels in the AI intervention group were observed.
Read the detailed description
Follow-up Plan::
Visit 1(-4W~-1W): Obtain the written informed consents of the patients, conduct the demographic survey, medical record survey, drug history investigation, subject compliance investigation, vital signs checkup, laboratory tests, imaging, and other instrument examinations, as well as evaluate the comorbidities of diabetes.
Visit 2 (D0): Educate the intervention group operating the platform system, evaluating diabetic hypoglycemia events, enhancing patients' self-management abilities, and knowledge mastery. Lab tests will be conducted at 12-week intervals, including Visit 3 (12W), Visit 4 (24W) or Visit 5 (36W), and Visit 6 (48W).
- Being tested
- artificial intelligence, Routine diagnosis and treatment group for diabetes
- Keywords
- Diabetes mellitus type 2 (T2DM), Artificial Intelligence (AI)
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligenceOther
The platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform.
Routine diagnosis and treatment group for diabetesOther
There is no limit to the number of outpatient visits for the control group; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team (endocrinologist and nutritionist) objectively conducttheir diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence Intervention Group | Experimental | The patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. Receives: Other: artificial intelligence |
| Conventional Treatment Group | Active comparator | Patients in the control group will receive a free blood glucometer and will have regular outpatient appointments every 12 weeks.. Receives: Other: Routine diagnosis and treatment group for diabetes |
What is being measured
The primary outcome is the main question the study is designed to answer.
HbA1c
Change From Baseline in HbA1c levels at 24 and 48 Weeks
Measured: 48weeks
Fasting Blood Glucose (FBG)
Change from baseline in mean fasting blood glucose at 48 weeks
Measured: 48 weeks
2-hour Postprandial Blood Glucose (2hPPG)
Change from baseline in mean 2-hour postprandial blood glucose at 48 weeks
Measured: 48 weeks
Hypoglycemic events
Number of hypoglycemic events from baseline to 48 weeks
Measured: 48 weeks
Secondary outcomes (7)
Healthcare expenses
Assess the monthly treatment costs from baseline to the 48 weeks
Measured: 48weeks
Insulin and oral hypoglycemic agent dosing
Changes in oral medication and insulin dosage from baseline to week 48
Measured: 48 weeks
Serum lipids
Change From Baseline in serum lipids levels at 48 Weeks
Measured: 48 weeks
blood pressure
Change From Baseline in blood pressure at 48 Weeks
Measured: 48 weeks
BMI
Weight in kilograms, height in meters, weight and height will be combined to report BMI in kg/m^²
Measured: 48 weeks
body weight
weight in kilograms
Measured: 48weeks
Fasting C-peptide level
Change From Baseline in fasting C-peptide level at 6 Weeks
Measured: 48weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 May 2025
- Study start15 Jun 2025
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus Type 2 (T2DM)Studies of the same condition, open and under way.
- More trials run by The First Hospital of Jilin UniversityThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Hospital of Jilin University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Chenglin Sun, Doctor, Principal investigator, The First Hospital of Jilin University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06957093
- Sponsor's protocol ID
- K2024283
- Status last verified
- May 2025
- First submitted
- 17 Apr 2025
- Last updated
- 9 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lee YB, Kim G, Jun JE, Park H, Lee WJ, Hwang YC, Kim JH. An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care. 2023 May 1;46(5):959-966. doi: 10.2337/dc22-1929.
PubMed 36821833Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S, Jang HC, Park KS, Cho YM. The Effect of a Smartphone-Based, Patient-Centered Diabetes Care System in Patients With Type 2 Diabetes: A Randomized, Controlled Trial for 24 Weeks. Diabetes Care. 2019 Jan;42(1):3-9. doi: 10.2337/dc17-2197. Epub 2018 Oct 30.
PubMed 30377185Dobson R, Whittaker R, Jiang Y, Maddison R, Shepherd M, McNamara C, Cutfield R, Khanolkar M, Murphy R. Effectiveness of text message based, diabetes self management support programme (SMS4BG): two arm, parallel randomised controlled trial. BMJ. 2018 May 17;361:k1959. doi: 10.1136/bmj.k1959.
PubMed 29773539Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, Shaw J, Bouck Z, Jamieson T, Onabajo N, Cooper M, Marani H, Jeffs L, Bhatia RS. Mobile App for Improved Self-Management of Type 2 Diabetes: Multicenter Pragmatic Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 10;7(1):e10321. doi: 10.2196/10321.
PubMed 30632972Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-Based Technology in Diabetes Management. Diabetes Ther. 2020 Mar;11(3):607-619. doi: 10.1007/s13300-020-00768-3. Epub 2020 Jan 25.
PubMed 31983028Sun C, Sun L, Xi S, Zhang H, Wang H, Feng Y, Deng Y, Wang H, Xiao X, Wang G, Gao Y, Wang G. Mobile Phone-Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 4;7(1):e10664. doi: 10.2196/10664.
PubMed 30609983Wang H, Yuan X, Wang J, Sun C, Wang G. Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Prim Health Care Res Dev. 2021 Sep 29;22:e48. doi: 10.1017/S1463423621000517.
PubMed 34583801Bulut C, Kato Y. Epidemiology of COVID-19. Turk J Med Sci. 2020 Apr 21;50(SI-1):563-570. doi: 10.3906/sag-2004-172.
PubMed 32299206Mahajan V, Singh T, Azad C. Using Telemedicine During the COVID-19 Pandemic. Indian Pediatr. 2020 Jul 15;57(7):652-657. Epub 2020 May 14.
PubMed 32412914
Study at a glance
From the registry record, last updated by the sponsor on 9 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Therapeutic Efficacy and Safety of Artificial Intelligence-based Decision-making Technology in the Integrated Management of Diabetes Mellitus: a Longitudinal, Open-labeled, Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT06957093
- Acronym / sponsor ID
- K2024283
- Keywords
- Diabetes mellitus type 2 (T2DM), Artificial Intelligence (AI)
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- The First Hospital of Jilin UniversityUniversity, hospital or other
- Study start
- 15 Jun 2025 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 4 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.
Purpose: To evaluate the efficacy of artificial intelligence (AI)-based decision-making technology in managing glycated hemoglobin (HbA1c) and blood glucose levels compared to the control group.
Methods: For the AI Intervention group, the patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks, offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. The platform is a digitally integrated healthcare platform that patients can use independently without the need for monitoring and assistance by healthcare professionals. The glucometer and pedometer bracelet will automatically connect to the platform through Bluetooth. The patient lab sheet identification and structured conversion system, AI for food picture identification and calorie calculation systems, and the AI decision-making system are on the cloud server. Patients upload image information, such as lab sheets and meal pictures, through the patient's diabetes mobile health system, and the cloud platform intelligently analyzes the patient's disease, medication, and daily life status to develop personalized solutions according to individual control goals. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. For the conventional treatment group, patients will receive a free blood glucometer and will have regular outpatient appointments. There is no limit to the number of outpatient visits; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team objectively conduct their diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level.
Expected results: A significant difference in HbA1c change from baseline to 48 weeks and improved FPG and 2-hour postprandial blood glucose levels in the AI intervention group were observed.
Detailed description
Follow-up Plan::
Visit 1(-4W~-1W): Obtain the written informed consents of the patients, conduct the demographic survey, medical record survey, drug history investigation, subject compliance investigation, vital signs checkup, laboratory tests, imaging, and other instrument examinations, as well as evaluate the comorbidities of diabetes.
Visit 2 (D0): Educate the intervention group operating the platform system, evaluating diabetic hypoglycemia events, enhancing patients' self-management abilities, and knowledge mastery. Lab tests will be conducted at 12-week intervals, including Visit 3 (12W), Visit 4 (24W) or Visit 5 (36W), and Visit 6 (48W).
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence Intervention Group | Experimental | Other: artificial intelligenceThe patients will be trained to independently use the diabetes telemedicine platform application. Each patient will be equipped with a glucometer and exercise bracelet, and the data will be automatically transmitted to the medical server via Bluetooth. The healthcare platform will analyze the uploaded data and provide feedback suggestions on medication, diet, and exercise automatically. The platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. Free outpatient visits will be provided to both the intervention and control groups every twelve weeks. |
| Conventional Treatment Group | Active comparator | Other: Routine diagnosis and treatment group for diabetesPatients in the control group will receive a free blood glucometer and will have regular outpatient appointments every 12 weeks.. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- Age: ≥18 years，≤75 years;
- Diagnosed with type 2 diabetes for ≥1 year;
- 7.0% ≤HbA1c ≤11%;
- Body mass index ≥18.5 kg/m2;
- Proficient ability to use smart phones;
Key exclusion criteria (9 listed)
- Presence of other types of diabetes, such as type 1 diabetes and gestational diabetes;
- Severe diabetic complications;
- Medical history of chronic liver diseases, including hemochromatosis, hepatocellular carcinoma, autoimmune liver disease, cirrhosis, viral hepatitis (including hepatitis A, B, and C), or hepatolenticular degeneration;
- Kidney injury (serum creatinine ≥1.5 times the upper limit of the reference) ; Serum ALT and AST levels elevated >2-fold;
- Medical history of mental disorders, such asschizophrenia, depression, or bipolar affective disorder;
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 |
|---|---|---|
| artificial intelligenceThe platform will also monitor the medical and lifestyle data of the patients every two weeks,offer feedback based on the analyses, and remind the patient to adhere to the self-management protocol based on the platform. | Other | Artificial Intelligence Intervention Group |
| Routine diagnosis and treatment group for diabetesThere is no limit to the number of outpatient visits for the control group; however, they are required to regularly monitor and record their blood glucose, diet, and exercise data to ensure that the medical team (endocrinologist and nutritionist) objectively conducttheir diagnosis and treatment activities. The medical team will provide free outpatient visits every 12 weeks, along with advice on medication, diet, and exercise based on the individual's blood glucose level. | Other | Conventional Treatment Group |
Primary outcome
HbA1c
Time frame: 48weeks
Change From Baseline in HbA1c levels at 24 and 48 Weeks
Fasting Blood Glucose (FBG)
Time frame: 48 weeks
Change from baseline in mean fasting blood glucose at 48 weeks
2-hour Postprandial Blood Glucose (2hPPG)
Time frame: 48 weeks
Change from baseline in mean 2-hour postprandial blood glucose at 48 weeks
Hypoglycemic events
Time frame: 48 weeks
Number of hypoglycemic events from baseline to 48 weeks
7 secondary outcomes
Healthcare expenses
Time frame: 48weeks
Insulin and oral hypoglycemic agent dosing
Time frame: 48 weeks
Serum lipids
Time frame: 48 weeks
blood pressure
Time frame: 48 weeks
BMI
Time frame: 48 weeks
body weight
Time frame: 48weeks
Fasting C-peptide level
Time frame: 48weeks
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 9 Dec 2025.
- Background: PubMed 36821833Lee YB, Kim G, Jun JE, Park H, Lee WJ, Hwang YC, Kim JH. An Integrated Digital Health Care Platform for Diabetes Management With AI-Based Dietary Management: 48-Week Results From a Randomized Controlled Trial. Diabetes Care. 2023 May 1;46(5):959-966. doi: 10.2337/dc22-1929.
- Background: PubMed 30377185Kim EK, Kwak SH, Jung HS, Koo BK, Moon MK, Lim S, Jang HC, Park KS, Cho YM. The Effect of a Smartphone-Based, Patient-Centered Diabetes Care System in Patients With Type 2 Diabetes: A Randomized, Controlled Trial for 24 Weeks. Diabetes Care. 2019 Jan;42(1):3-9. doi: 10.2337/dc17-2197. Epub 2018 Oct 30.
- Background: PubMed 29773539Dobson R, Whittaker R, Jiang Y, Maddison R, Shepherd M, McNamara C, Cutfield R, Khanolkar M, Murphy R. Effectiveness of text message based, diabetes self management support programme (SMS4BG): two arm, parallel randomised controlled trial. BMJ. 2018 May 17;361:k1959. doi: 10.1136/bmj.k1959.
- Background: PubMed 30632972Agarwal P, Mukerji G, Desveaux L, Ivers NM, Bhattacharyya O, Hensel JM, Shaw J, Bouck Z, Jamieson T, Onabajo N, Cooper M, Marani H, Jeffs L, Bhatia RS. Mobile App for Improved Self-Management of Type 2 Diabetes: Multicenter Pragmatic Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 10;7(1):e10321. doi: 10.2196/10321.
- Background: PubMed 31983028Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-Based Technology in Diabetes Management. Diabetes Ther. 2020 Mar;11(3):607-619. doi: 10.1007/s13300-020-00768-3. Epub 2020 Jan 25.
- Background: PubMed 30609983Sun C, Sun L, Xi S, Zhang H, Wang H, Feng Y, Deng Y, Wang H, Xiao X, Wang G, Gao Y, Wang G. Mobile Phone-Based Telemedicine Practice in Older Chinese Patients with Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2019 Jan 4;7(1):e10664. doi: 10.2196/10664.
- Background: PubMed 34583801Wang H, Yuan X, Wang J, Sun C, Wang G. Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic. Prim Health Care Res Dev. 2021 Sep 29;22:e48. doi: 10.1017/S1463423621000517.
- Background: PubMed 32299206Bulut C, Kato Y. Epidemiology of COVID-19. Turk J Med Sci. 2020 Apr 21;50(SI-1):563-570. doi: 10.3906/sag-2004-172.
- Background: PubMed 32412914Mahajan V, Singh T, Azad C. Using Telemedicine During the COVID-19 Pandemic. Indian Pediatr. 2020 Jul 15;57(7):652-657. Epub 2020 May 14.
- 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.
Chenglin Sun, Doctor principal investigator
The First Hospital of Jilin University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing artificial intelligence and Routine diagnosis and treatment group for diabetes in people with Diabetes Mellitus Type 2 (T2DM) and Artificial Intelligence (AI), run by The First Hospital of Jilin University at 1 site 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 7 inclusion and 9 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 9 Dec 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 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 Chenglin Sun, Doctor (+86 13944855718, clsun213@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
HbA1c (time frame: 48weeks). The study lists 4 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 NCT06957093, which also lists 9 linked publications.
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 9 Dec 2025.