Monogenic DiabetesNCT07564518
Application of FreeStyle Libre 2 for Evaluating Glycemic Variability Characteristics in Patients With Extreme Glucose Metabolism Phenotypes
An observational study of Monogenic Diabetes and Hyperinsulinemia, run by Peking University People's Hospital.
Trial summary
This cross-sectional study aims to further subdivide diabetes mellitus into more homogeneous subgroups by focusing on extreme glucose metabolism phenotypes, including monogenic diabetes with β cell dysfunction, hyperinsulinemia caused by excessive β cell secretion, and postprandial hypoglycemia phenotypes. Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Monogenic Diabetes, Hyperinsulinemia
- How many people
- 120 participants
- Where
- No sites listed yet
- Study dates
- 25 May 2026to 9 Jan 2027
- 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 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: this study will use CGM to objectively and quantitatively compare glycemic variability parameters and patterns among four groups: patients with β cell dysfunction monogenic diabetes versus patients with type 2 diabetes mellitus, and patients with hyperinsulinemia versus healthy controls. This approach will reveal the effects of extreme β cell function on diurnal glycemic variability patterns and characterize distinctive dynamic glycemic profiles. 60 patients with β cell dysfunction monogenic diabetes and 60 patients with hyperinsulinemia and normal glucose tolerance who meet the inclusion criteria outlined will be recruited from the endocrinology outpatient clinic of our hospital. Groups A1 and A2, as well as Groups B1 and B2, will be matched at a 1:1 ratio by age, sex, and BMI. In addition, Groups A1 and A2 will be matched at a 1:1 ratio by HbA1c.
- Inclusion Criteria:
- Group A1:
- Age ≥ 18 years;
- Patients with β cell dysfunction monogenic diabetes confirmed by DNA sequencing or other diagnostic testing.
- Group A2:
- Age ≥ 18 years;
- Patients with confirmed type 2 diabetes mellitus;
- Derived from this center's existing continuous glucose monitoring (CGM) database.
- Group B1:
- Age ≥ 18 years;
- Normal fasting plasma glucose (≥ 3.6 and < 6.1 mmol/L) and normal 2-hour plasma glucose during OGTT (≥ 3 and < 7.8 mmol/L);
- Fasting insulin ≥ 25 µU/mL and/or 2-hour insulin during OGTT greater than 10 times the fasting insulin level.
- Group B2:
- Age ≥ 18 years;
- Normal glucose tolerance meeting the 2024 ADA criteria: fasting plasma glucose < 5.6 mmol/L, 2-hour plasma glucose during OGTT < 7.8 mmol/L;
- According to laboratory reference standards, fasting insulin ≥ 2.6 and < 25 µU/mL, and 2-hour insulin during OGTT 5-10 times the fasting insulin level.
- Derived from this center's existing continuous glucose monitoring (CGM) database.
- Exclusion Criteria:
- Neonates younger than 4 months of age (congenital diabetes);
- Pregnancy;
- Patients with positive pancreatic autoantibody test results;
- Patients with severe cardiovascular or cerebrovascular diseases, hepatic disease, or renal disease;
- Patients who have participated in other clinical trials.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
This cross-sectional study aims to further subdivide diabetes mellitus into more homogeneous subgroups by focusing on extreme glucose metabolism phenotypes, including monogenic diabetes with β cell dysfunction, hyperinsulinemia caused by excessive β cell secretion, and postprandial hypoglycemia phenotypes. By utilizing continuous glucose monitoring (CGM) technology and the FreeStyle Libre 2 glucose monitoring device, this study will evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes and perform comparative analyses using existing CGM data from healthy populations and patients with type 2 diabetes in our center's database. The study aims to address current gaps in understanding glycemic variability characteristics under extreme β cell functional states, provide novel dynamic monitoring evidence to support early identification, precise classification, and personalized management of these special metabolic states, and simultaneously screen for biomarkers to enable more accurate disease identification, thereby offering potential avenues for improving personalized treatment of diabetes mellitus.
Read the detailed description
Diabetes mellitus is one of the leading causes of death and disability worldwide, affecting individuals regardless of race, sex, or age. Over the past decade, the prevalence of diabetes mellitus in China has increased markedly. Statistics indicate that there were 140.9 million adults with diabetes mellitus in China in 2021, and this number is projected to rise to 174.4 million by 2045.
Monogenic diabetes refers to diabetes mellitus caused by mutations in a single gene and accounts for approximately 1%-5% of all diabetes mellitus cases. Monogenic diabetes results from a single pathogenic defect in one of more than 40 genes. Since the type 2 diabetes-like presentation in young individuals was termed maturity-onset diabetes of the young (MODY) by Fajans and characterized by an autosomal dominant inheritance pattern, understanding of the phenotypic and genetic heterogeneity of monogenic diabetes has continued to expand. The main categories of monogenic diabetes include MODY, neonatal diabetes mellitus (NDM), and syndromic diabetes. In monogenic diabetes, high-penetrance variants predominantly cause severe impairment of β cell development and insulin secretion, leading to diabetes mellitus independent of other risk factors. In recent years, substantial progress has been made in elucidating the genetic defects underlying monogenic diabetes, improving diagnostic accuracy for rare subtypes, deepening understanding of patients' clinical courses, and contributing to the identification of optimal treatment strategies through precision medicine approaches. However, many aspects of this disease remain insufficiently characterized, including characteristic glycemic profiles and objective, quantifiable indicators applicable to clinical differential diagnosis. Therefore, further research is urgently needed.
Type 2 diabetes mellitus (T2DM) is a multifactorial disease resulting from the combined effects of genetic and environmental factors and accounts for approximately 96% of diabetes mellitus cases worldwide. The pathophysiology of T2DM is characterized by insulin resistance, pancreatic β cell dysfunction, and chronic inflammation. Hyperinsulinemia and insulin resistance may occur several years before the clinical onset of T2DM. Previous studies have demonstrated that more than 75% of individuals in the United States exhibit increased insulin secretion during oral glucose tolerance testing (OGTT) despite normal glucose clearance. This finding suggests that in a substantial proportion of the population, hyperinsulinemia may represent the earliest warning signal of metabolic disease risk, even in the presence of normal glucose tolerance. Targeted lifestyle interventions aimed at hyperinsulinemia, such as increased resistance training, nutritional strategies, and improved sleep, have been shown to produce immediate and sustained improvements in insulin resistance. However, within this gray zone spanning the progression from normal glucose tolerance to overt diabetes mellitus, characteristic glycemic profiles have not yet been clearly defined. Therefore, exploring glycemic variability characteristics is essential for elucidating the onset and progression of insulin resistance and type 2 diabetes mellitus, as well as for enabling early intervention.
The oral glucose tolerance test (OGTT), as the most widely used diagnostic gold standard for assessing glycemic characteristics, employs a standardized 75 g glucose load to evaluate early-phase and second-phase β cell secretory capacity following glucose stimulation. As an artificially constructed experimental simulation, OGTT does not reflect daily physiological conditions and can capture only short-term, single-day glycemic variability, thus failing to represent true blood glucose trajectories. Continuous glucose monitoring (CGM) can dynamically and continuously reflect interstitial fluid glucose levels in real time, providing critical information on the amplitude, frequency, and patterns of glycemic variability that cannot be obtained through traditional point blood glucose testing. CGM has become animportant tool for refined diabetes mellitus management. This technology provides technical support for delineating characteristic glycemic variability patterns under different insulin secretion states.
Accordingly, this study will use CGM to objectively and quantitatively compare glycemic variability parameters and patterns among four groups: patients with β cell dysfunction monogenic diabetes versus patients with type 2 diabetes mellitus, and patients with hyperinsulinemia versus healthy controls. This approach will reveal the effects of extreme β cell function on diurnal glycemic variability patterns and characterize distinctive dynamic glycemic profiles. In addition, this study will screen for biomarkers to facilitate early identification, diagnosis, and treatment of this specific type of diabetes mellitus, while simultaneously deepening understanding of glycemic characteristics in the early stages of insulin resistance and providing a theoretical basis for subsequent precise prevention and intervention.
Primary study objective: To evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes, including β-MND, hyperinsulinemia, and postprandial hypoglycemia phenotypes.
Secondary study objective:
- To assess differences in blood glucose profiles between individuals with extreme glucose metabolism phenotypes and healthy populations, as well as patients with type 2 diabetes mellitus (T2DM), particularly among comparable T2DM subgroups.
- To screen for biomarkers that enable more accurate identification of this disease.
- Conditions
- Monogenic Diabetes, Hyperinsulinemia
- Medical subject headings
- Hyperinsulinism
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group A : diabetes mellitus group | Group A1 (patients with β cell dysfunction monogenic diabetes): 60 cases; Group A2 (patients with type 2 diabetes mellitus): 60 cases | |
| Group B : normal glucose tolerance group | Group B1 (normal glucose tolerance with fasting / postprandial hyperinsulinemia): 60 cases; Group B2 (normal glucose tolerance with normal insulin levels): 60 cases |
What is being measured
The primary outcome is the main question the study is designed to answer.
mean blood glucose in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glucose management indicator (GMI) in %
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
highest glucose values in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
lowest glucose values in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
coefficient of variation (CV)
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
mean amplitude of glycemic excursions (MAGE) in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
standard deviation of blood glucose (SDBG) in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
mean of daily differences (MODD) in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
average daily risk range (ADRR)
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
largest amplitude of glycemic excursions (LAGE) in mmol/L
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
high blood glucose index (HBGI)
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
low blood glucose index (LBGI)
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
time in range (TIR) in %
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
time above range (TAR) in %
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
time below range (TBR) in %
glycemic variability data
Measured: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
Secondary outcomes (2)
weight in kilograms
weight and height will be combined to report BMI in kg/m^2
Measured: The study is a cross-sectional study, and the above indicators were measured only once at the initial enrollment.
height in meters
weight and height will be combined to report BMI in kg/m^2
Measured: The study is a cross-sectional study, and the above indicators were measured only once at the initial enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 May 2026
- Study start25 May 2026 (estimated)
- Primary completion25 Dec 2026 (estimated)
- Study completion9 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Monogenic DiabetesStudies 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
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ren qian, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07564518
- Sponsor's protocol ID
- 2026PHB206-001
- Status last verified
- May 2026
- First submitted
- 14 Apr 2026
- Last updated
- 2 Jun 2026
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of FreeStyle Libre 2 for Evaluating Glycemic Variability Characteristics in Patients With Extreme Glucose Metabolism Phenotypes
- ClinicalTrials.gov ID
- NCT07564518
- Acronym / sponsor ID
- 2026PHB206-001
- Conditions
- Monogenic Diabetes, Hyperinsulinemia
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 25 May 2026 (estimated)
- Primary completion
- 25 Dec 2026 (estimated)
- Study completion
- 9 Jan 2027 (estimated)
- First posted
- 4 May 2026 (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 cross-sectional study aims to further subdivide diabetes mellitus into more homogeneous subgroups by focusing on extreme glucose metabolism phenotypes, including monogenic diabetes with β cell dysfunction, hyperinsulinemia caused by excessive β cell secretion, and postprandial hypoglycemia phenotypes. By utilizing continuous glucose monitoring (CGM) technology and the FreeStyle Libre 2 glucose monitoring device, this study will evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes and perform comparative analyses using existing CGM data from healthy populations and patients with type 2 diabetes in our center's database. The study aims to address current gaps in understanding glycemic variability characteristics under extreme β cell functional states, provide novel dynamic monitoring evidence to support early identification, precise classification, and personalized management of these special metabolic states, and simultaneously screen for biomarkers to enable more accurate disease identification, thereby offering potential avenues for improving personalized treatment of diabetes mellitus.
Detailed description
Diabetes mellitus is one of the leading causes of death and disability worldwide, affecting individuals regardless of race, sex, or age. Over the past decade, the prevalence of diabetes mellitus in China has increased markedly. Statistics indicate that there were 140.9 million adults with diabetes mellitus in China in 2021, and this number is projected to rise to 174.4 million by 2045.
Monogenic diabetes refers to diabetes mellitus caused by mutations in a single gene and accounts for approximately 1%-5% of all diabetes mellitus cases. Monogenic diabetes results from a single pathogenic defect in one of more than 40 genes. Since the type 2 diabetes-like presentation in young individuals was termed maturity-onset diabetes of the young (MODY) by Fajans and characterized by an autosomal dominant inheritance pattern, understanding of the phenotypic and genetic heterogeneity of monogenic diabetes has continued to expand. The main categories of monogenic diabetes include MODY, neonatal diabetes mellitus (NDM), and syndromic diabetes. In monogenic diabetes, high-penetrance variants predominantly cause severe impairment of β cell development and insulin secretion, leading to diabetes mellitus independent of other risk factors. In recent years, substantial progress has been made in elucidating the genetic defects underlying monogenic diabetes, improving diagnostic accuracy for rare subtypes, deepening understanding of patients' clinical courses, and contributing to the identification of optimal treatment strategies through precision medicine approaches. However, many aspects of this disease remain insufficiently characterized, including characteristic glycemic profiles and objective, quantifiable indicators applicable to clinical differential diagnosis. Therefore, further research is urgently needed.
Type 2 diabetes mellitus (T2DM) is a multifactorial disease resulting from the combined effects of genetic and environmental factors and accounts for approximately 96% of diabetes mellitus cases worldwide. The pathophysiology of T2DM is characterized by insulin resistance, pancreatic β cell dysfunction, and chronic inflammation. Hyperinsulinemia and insulin resistance may occur several years before the clinical onset of T2DM. Previous studies have demonstrated that more than 75% of individuals in the United States exhibit increased insulin secretion during oral glucose tolerance testing (OGTT) despite normal glucose clearance. This finding suggests that in a substantial proportion of the population, hyperinsulinemia may represent the earliest warning signal of metabolic disease risk, even in the presence of normal glucose tolerance. Targeted lifestyle interventions aimed at hyperinsulinemia, such as increased resistance training, nutritional strategies, and improved sleep, have been shown to produce immediate and sustained improvements in insulin resistance. However, within this gray zone spanning the progression from normal glucose tolerance to overt diabetes mellitus, characteristic glycemic profiles have not yet been clearly defined. Therefore, exploring glycemic variability characteristics is essential for elucidating the onset and progression of insulin resistance and type 2 diabetes mellitus, as well as for enabling early intervention.
The oral glucose tolerance test (OGTT), as the most widely used diagnostic gold standard for assessing glycemic characteristics, employs a standardized 75 g glucose load to evaluate early-phase and second-phase β cell secretory capacity following glucose stimulation. As an artificially constructed experimental simulation, OGTT does not reflect daily physiological conditions and can capture only short-term, single-day glycemic variability, thus failing to represent true blood glucose trajectories. Continuous glucose monitoring (CGM) can dynamically and continuously reflect interstitial fluid glucose levels in real time, providing critical information on the amplitude, frequency, and patterns of glycemic variability that cannot be obtained through traditional point blood glucose testing. CGM has become animportant tool for refined diabetes mellitus management. This technology provides technical support for delineating characteristic glycemic variability patterns under different insulin secretion states.
Accordingly, this study will use CGM to objectively and quantitatively compare glycemic variability parameters and patterns among four groups: patients with β cell dysfunction monogenic diabetes versus patients with type 2 diabetes mellitus, and patients with hyperinsulinemia versus healthy controls. This approach will reveal the effects of extreme β cell function on diurnal glycemic variability patterns and characterize distinctive dynamic glycemic profiles. In addition, this study will screen for biomarkers to facilitate early identification, diagnosis, and treatment of this specific type of diabetes mellitus, while simultaneously deepening understanding of glycemic characteristics in the early stages of insulin resistance and providing a theoretical basis for subsequent precise prevention and intervention.
Primary study objective: To evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes, including β-MND, hyperinsulinemia, and postprandial hypoglycemia phenotypes.
Secondary study objective:
- To assess differences in blood glucose profiles between individuals with extreme glucose metabolism phenotypes and healthy populations, as well as patients with type 2 diabetes mellitus (T2DM), particularly among comparable T2DM subgroups.
- To screen for biomarkers that enable more accurate identification of this disease.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group A : diabetes mellitus group | – | –Group A1 (patients with β cell dysfunction monogenic diabetes): 60 cases; Group A2 (patients with type 2 diabetes mellitus): 60 cases |
| Group B : normal glucose tolerance group | – | –Group B1 (normal glucose tolerance with fasting / postprandial hyperinsulinemia): 60 cases; Group B2 (normal glucose tolerance with normal insulin levels): 60 cases |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- this study will use CGM to objectively and quantitatively compare glycemic variability parameters and patterns among four groups: patients with β cell dysfunction monogenic diabetes versus patients with type 2 diabetes mellitus, and patients with hyperinsulinemia versus healthy controls. This approach will reveal the effects of extreme β cell function on diurnal glycemic variability patterns and characterize distinctive dynamic glycemic profiles. 60 patients with β cell dysfunction monogenic diabetes and 60 patients with hyperinsulinemia and normal glucose tolerance who meet the inclusion criteria outlined will be recruited from the endocrinology outpatient clinic of our hospital. Groups A1 and A2, as well as Groups B1 and B2, will be matched at a 1:1 ratio by age, sex, and BMI. In addition, Groups A1 and A2 will be matched at a 1:1 ratio by HbA1c.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
mean blood glucose in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
glucose management indicator (GMI) in %
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
highest glucose values in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
lowest glucose values in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
coefficient of variation (CV)
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
mean amplitude of glycemic excursions (MAGE) in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
standard deviation of blood glucose (SDBG) in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
mean of daily differences (MODD) in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
average daily risk range (ADRR)
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
largest amplitude of glycemic excursions (LAGE) in mmol/L
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
high blood glucose index (HBGI)
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
low blood glucose index (LBGI)
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
time in range (TIR) in %
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
time above range (TAR) in %
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
time below range (TBR) in %
Time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.
glycemic variability data
2 secondary outcomes
weight in kilograms
Time frame: The study is a cross-sectional study, and the above indicators were measured only once at the initial enrollment.
height in meters
Time frame: The study is a cross-sectional study, and the above indicators were measured only once at the initial enrollment.
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 Jun 2026.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Monogenic Diabetes and Hyperinsulinemia, run by Peking University People's Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
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: opening soon, as of the sponsor's last update on 2 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
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 Qian Ren, Doctorate (010-88324371, qianren_xuan@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
mean blood glucose in mmol/L (time frame: The study is a cross-sectional study, patients wil wear a CGM for 14 days after enrollment, and will not wear it afterwards.). The study lists 15 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 Jun 2026.