Diabetes MellitusNCT06645223
Correlation Between Gut Microbiota and Pancreatic Β-Cell Function in Diabetic Patients
An observational study of Diabetes Mellitus and Gut Microbiota, run by Peking University Third Hospital at 1 site in China.
Trial summary
The gut microbiota plays a crucial role in digestion, metabolism, nutrition, and immune regulation in the human body. In recent years, numerous studies have indicated that the gut microbiota and its metabolites are closely associated with metabolic diseases, including obesity, non-alcoholic fatty liver disease, and… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Diabetes Mellitus, Gut Microbiota
- How many people
- 160 participants
- Where
- 1 siteChina
- Study dates
- 20 Sep 2024to 1 Jul 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
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 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Young and elderly subjects with different glucose metabolism states will be recruited from the community or the outpatient department of Peking University Third Hospital between September 2024 and December 2025, meeting the following criteria.
Who may be able to join
- Age: Young (18 \< age \< 45 years) and elderly (age ≥ 65 years) subjects;
- Individuals with different glucose metabolism states, including normal glucose metabolism, pre-diabetes, and diabetes, without gastrointestinal diseases or a history of gastrointestinal surgery (such as active gastrointestinal inflammation or bleeding, inflammatory bowel disease, etc.), without cognitive impairment, without tumors, and without chronic respiratory diseases (such as chronic obstructive pneumonia, asthma, etc.), and not on a special diet (e.g., vegetarians).
Who cannot join
- Use of antibiotics or health supplements within the last month.
- Diarrhea within the last 2 weeks: bowel movements ≥3 times/24 hours, with changes in stool consistency.
- Constipation within the last 2 weeks (based on the Rome III criteria for functional constipation).
- Acute complications of diabetes, such as diabetic ketoacidosis, within the last 3 months.
- History of gastrointestinal diseases or gastrointestinal surgery.
- Cognitive impairment.
- History of tumors.
- Special diet (e.g., vegetarians).
Trial locations and contacts
1 site in 1 country.
About this study
The gut microbiota plays a crucial role in digestion, metabolism, nutrition, and immune regulation in the human body. In recent years, numerous studies have indicated that the gut microbiota and its metabolites are closely associated with metabolic diseases, including obesity, non-alcoholic fatty liver disease, and diabetes. China has rapidly entered an aging society, where aging is a major risk factor for abnormal glucose metabolism, manifested by decreased pancreatic function and increased insulin resistance. Concurrently, as the body ages, the composition of the gut microbiota also undergoes age-related changes, such as reduced microbial diversity, increased inter-individual variability, and a downregulation of the beneficial/harmful bacteria ratio. Therefore, direct modulation of the gut microbiota could become a potential therapeutic target for age-related metabolic diseases. Currently, some preclinical studies have transplanted fecal microbiota from young mice to aged mice to explore the improvement of age-related phenotypes, such as cognitive impairment, decreased immunity, and chronic inflammation.
The regulation of the gut microbiota is susceptible to changes caused by various factors, including age, diet, antibiotics, and psychological stress. Although mice and humans share high genetic homology, differences in diet structure, body size, and metabolic processes can result in significant diversity and compositional differences in their gut microbiota. Research indicates that the core microbiota of the mouse gut consists of 4 genera, while 90% of the European population comprises 9 genera, highlighting the differences in genus or species richness between mouse and human gut microbiota. Preliminary research by our group has shown that transplanting fecal microbiota from young mice to aged mice can increase postprandial plasma insulin levels in aged mice, suggesting that the restoration of gut microbiota diversity may be involved in age-related glucose metabolism abnormalities. However, due to interspecies differences in the gut microbiota, whether the differential microbiota between elderly and young humans can improve age-related glucose metabolism abnormalities remains to be explored.
Despite the abundance of human gut microbiota composition data in public databases, differences in sequencing methods, DNA extraction from specimens, and the nationality of subjects prevent standardization and integration of these data. Additionally, traditional 16s-rRNA sequencing methods lack sufficient precision in microbial classification and cannot annotate gene functions. These limitations have resulted in many studies on gut microbiota remaining at the level of exploring correlations with diseases, without establishing causality. The development of metagenomic sequencing technology can extend the definition of the human core gut microbiota to the species level and accurately annotate their gene functions. Combined with metabolomics detection, this technology can provide more comprehensive information on the dialogue between gut microbiota and the host. Therefore, this study aims to use multi-omics approaches (metagenomic sequencing and metabolomics detection) to analyze the differences in fecal microbiota and their metabolites between young and elderly populations under different glucose metabolism states. This will provide potential intervention targets for preventing age-related glucose metabolism abnormalities and offer new theoretical foundations for the molecular mechanisms of age-related metabolic diseases.
Read the detailed description
- Research Objective: This study aims to explore the differences in gut microbiota and their metabolites, as well as gene function differences, between young and elderly individuals under different glucose metabolism states. The goal is to screen for specific bacterial species or metabolites with significant differences and examine their correlation with pancreatic β-cell function.
- Research Design
2.1 Study Subjects: Young and elderly subjects with different glucose metabolism states will be recruited from the community or the outpatient department of Peking University Third Hospital between September 2024 and December 2025, meeting the following criteria.
Inclusion Criteria:
- Age: Young (18 \< age \< 45 years) and elderly (age ≥ 65 years) subjects;
- Individuals with different glucose metabolism states, including normal glucose metabolism, pre-diabetes, and diabetes, without gastrointestinal diseases or a history of gastrointestinal surgery (such as active gastrointestinal inflammation or bleeding, inflammatory bowel disease, etc.), without cognitive impairment, without tumors, and without chronic respiratory diseases (such as chronic obstructive pneumonia, asthma, etc.), and not on a special diet (e.g., vegetarians).
Exclusion Criteria:
- Use of antibiotics or health supplements within the last month.
- Diarrhea within the last 2 weeks: bowel movements ≥3 times/24 hours, with changes in stool consistency.
- Constipation within the last 2 weeks (based on the Rome III criteria for functional constipation).
- Acute complications of diabetes, such as diabetic ketoacidosis, within the last 3 months.
- History of gastrointestinal diseases or gastrointestinal surgery.
- Cognitive impairment.
- History of tumors.
- Special diet (e.g., vegetarians).
2.2 Research Methods:
2.2.1 Fecal Sample Collection: Collect 2 grams of fecal tissue from selected participants and place it in sterile commercial test tubes. A total of 160 samples will be collected (40 from young individuals with normal glucose metabolism, 40 from young diabetic patients, 40 from elderly individuals with normal glucose metabolism, and 40 from elderly diabetic patients). Place the samples on ice immediately and transport them back to the laboratory within 1 hour. Store them at -80°C in a freezer.
2.2.2 Serum Sample Collection: Participants will undergo routine blood and urine tests, as well as blood biochemical and glucose metabolism tests, at the clinical laboratory of Peking University Third Hospital. During blood collection, an additional 5 mL of serum sample will be drawn. After centrifugation at 4°C, the supernatant will be aliquoted into 1 mL EP tubes and stored at -80°C in a freezer.
2.2.3 Metagenomic and Metabolomic Analysis: Metagenomic Sequencing: Extract microbial DNA from fecal samples using commercial kits. Fragment the DNA and prepare libraries for sequencing. Perform data quality control, metagenomic assembly, clustering for redundancy removal, and abundance analysis to obtain final sequencing fragments (Scaftigs). Annotate Scaftigs for species and predict gene functions, followed by standardized analysis across multiple samples, including abundance clustering, principal component analysis, and clustering analysis.
Metabolomic Analysis: Preprocess experimental samples to extract metabolites and perform detection on a metabolomics platform to obtain raw data. Use data processing software to convert the raw data into a data matrix suitable for further analysis, including information on metabolite mass-to-charge ratio, retention time, and peak area. Process and statistically analyze the dataset to identify differential metabolites. Finally, identify and screen metabolites associated with aging-related microbiota.
- Statistical Analysis: Statistical analysis of the data will be conducted using R 4.2.1. Quantitative data will undergo normality testing and will be expressed as mean ± standard deviation if they conform to a normal distribution. Inter-group comparisons of relevant indicators will be conducted using analysis of variance (ANOVA). Categorical data will be expressed as frequencies (%) and compared between groups using the chi-square test. A p-value \< 0.05 will be considered statistically significant.
- Conditions
- Diabetes Mellitus, Gut Microbiota
- Being tested
- No Interventions
- Medical subject headings
- Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionsDrug
no interventions
| Study group | Role | What they receive |
|---|---|---|
| Young Group | Not described Receives: Drug: No Interventions | |
| Elderly Group | Not described Receives: Drug: No Interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Metagenomic and Metabolomic Analysis of Gut Microbiota
Measured: From September 2024 to December 2025
Comparison of Pancreatic Beta-Cell Function Among Different Groups of Subjects
Measured: From September 2024 to December 2025
Comparison of Clinical Characteristics Among People with Different Glycemic Metabolic Status
Measured: From September 2024 to December 2025
Correlation Between Specific Bacterial Species and Pancreatic Beta-Cell Function Among Different Groups
Measured: From September 2024 to December 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2024
- Study start20 Sep 2024
- Primary completion31 Dec 2025 (estimated)
- Study completion1 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes MellitusStudies of the same condition, open and under way.
- More trials run by Peking University Third HospitalThe 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
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 160 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yangjin, Peking University Third Hospital
- Investigators
- Yang Jin, Principal investigator, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06645223
- Sponsor's protocol ID
- M2024784
- Status last verified
- Sep 2024
- First submitted
- 28 Sep 2024
- Last updated
- 16 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 16 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Correlation Between Gut Microbiota and Pancreatic Β-Cell Function in Diabetic Patients
- ClinicalTrials.gov ID
- NCT06645223
- Acronym / sponsor ID
- M2024784
- Conditions
- Diabetes Mellitus, Gut Microbiota
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 160 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 20 Sep 2024 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 1 Jul 2026 (estimated)
- First posted
- 16 Oct 2024 (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.
The gut microbiota plays a crucial role in digestion, metabolism, nutrition, and immune regulation in the human body. In recent years, numerous studies have indicated that the gut microbiota and its metabolites are closely associated with metabolic diseases, including obesity, non-alcoholic fatty liver disease, and diabetes. China has rapidly entered an aging society, where aging is a major risk factor for abnormal glucose metabolism, manifested by decreased pancreatic function and increased insulin resistance. Concurrently, as the body ages, the composition of the gut microbiota also undergoes age-related changes, such as reduced microbial diversity, increased inter-individual variability, and a downregulation of the beneficial/harmful bacteria ratio. Therefore, direct modulation of the gut microbiota could become a potential therapeutic target for age-related metabolic diseases. Currently, some preclinical studies have transplanted fecal microbiota from young mice to aged mice to explore the improvement of age-related phenotypes, such as cognitive impairment, decreased immunity, and chronic inflammation.
The regulation of the gut microbiota is susceptible to changes caused by various factors, including age, diet, antibiotics, and psychological stress. Although mice and humans share high genetic homology, differences in diet structure, body size, and metabolic processes can result in significant diversity and compositional differences in their gut microbiota. Research indicates that the core microbiota of the mouse gut consists of 4 genera, while 90% of the European population comprises 9 genera, highlighting the differences in genus or species richness between mouse and human gut microbiota. Preliminary research by our group has shown that transplanting fecal microbiota from young mice to aged mice can increase postprandial plasma insulin levels in aged mice, suggesting that the restoration of gut microbiota diversity may be involved in age-related glucose metabolism abnormalities. However, due to interspecies differences in the gut microbiota, whether the differential microbiota between elderly and young humans can improve age-related glucose metabolism abnormalities remains to be explored.
Despite the abundance of human gut microbiota composition data in public databases, differences in sequencing methods, DNA extraction from specimens, and the nationality of subjects prevent standardization and integration of these data. Additionally, traditional 16s-rRNA sequencing methods lack sufficient precision in microbial classification and cannot annotate gene functions. These limitations have resulted in many studies on gut microbiota remaining at the level of exploring correlations with diseases, without establishing causality. The development of metagenomic sequencing technology can extend the definition of the human core gut microbiota to the species level and accurately annotate their gene functions. Combined with metabolomics detection, this technology can provide more comprehensive information on the dialogue between gut microbiota and the host. Therefore, this study aims to use multi-omics approaches (metagenomic sequencing and metabolomics detection) to analyze the differences in fecal microbiota and their metabolites between young and elderly populations under different glucose metabolism states. This will provide potential intervention targets for preventing age-related glucose metabolism abnormalities and offer new theoretical foundations for the molecular mechanisms of age-related metabolic diseases.
Detailed description
- Research Objective: This study aims to explore the differences in gut microbiota and their metabolites, as well as gene function differences, between young and elderly individuals under different glucose metabolism states. The goal is to screen for specific bacterial species or metabolites with significant differences and examine their correlation with pancreatic β-cell function.
- Research Design
2.1 Study Subjects: Young and elderly subjects with different glucose metabolism states will be recruited from the community or the outpatient department of Peking University Third Hospital between September 2024 and December 2025, meeting the following criteria.
Inclusion Criteria:
- Age: Young (18 \< age \< 45 years) and elderly (age ≥ 65 years) subjects;
- Individuals with different glucose metabolism states, including normal glucose metabolism, pre-diabetes, and diabetes, without gastrointestinal diseases or a history of gastrointestinal surgery (such as active gastrointestinal inflammation or bleeding, inflammatory bowel disease, etc.), without cognitive impairment, without tumors, and without chronic respiratory diseases (such as chronic obstructive pneumonia, asthma, etc.), and not on a special diet (e.g., vegetarians).
Exclusion Criteria:
- Use of antibiotics or health supplements within the last month.
- Diarrhea within the last 2 weeks: bowel movements ≥3 times/24 hours, with changes in stool consistency.
- Constipation within the last 2 weeks (based on the Rome III criteria for functional constipation).
- Acute complications of diabetes, such as diabetic ketoacidosis, within the last 3 months.
- History of gastrointestinal diseases or gastrointestinal surgery.
- Cognitive impairment.
- History of tumors.
- Special diet (e.g., vegetarians).
2.2 Research Methods:
2.2.1 Fecal Sample Collection: Collect 2 grams of fecal tissue from selected participants and place it in sterile commercial test tubes. A total of 160 samples will be collected (40 from young individuals with normal glucose metabolism, 40 from young diabetic patients, 40 from elderly individuals with normal glucose metabolism, and 40 from elderly diabetic patients). Place the samples on ice immediately and transport them back to the laboratory within 1 hour. Store them at -80°C in a freezer.
2.2.2 Serum Sample Collection: Participants will undergo routine blood and urine tests, as well as blood biochemical and glucose metabolism tests, at the clinical laboratory of Peking University Third Hospital. During blood collection, an additional 5 mL of serum sample will be drawn. After centrifugation at 4°C, the supernatant will be aliquoted into 1 mL EP tubes and stored at -80°C in a freezer.
2.2.3 Metagenomic and Metabolomic Analysis: Metagenomic Sequencing: Extract microbial DNA from fecal samples using commercial kits. Fragment the DNA and prepare libraries for sequencing. Perform data quality control, metagenomic assembly, clustering for redundancy removal, and abundance analysis to obtain final sequencing fragments (Scaftigs). Annotate Scaftigs for species and predict gene functions, followed by standardized analysis across multiple samples, including abundance clustering, principal component analysis, and clustering analysis.
Metabolomic Analysis: Preprocess experimental samples to extract metabolites and perform detection on a metabolomics platform to obtain raw data. Use data processing software to convert the raw data into a data matrix suitable for further analysis, including information on metabolite mass-to-charge ratio, retention time, and peak area. Process and statistically analyze the dataset to identify differential metabolites. Finally, identify and screen metabolites associated with aging-related microbiota.
- Statistical Analysis: Statistical analysis of the data will be conducted using R 4.2.1. Quantitative data will undergo normality testing and will be expressed as mean ± standard deviation if they conform to a normal distribution. Inter-group comparisons of relevant indicators will be conducted using analysis of variance (ANOVA). Categorical data will be expressed as frequencies (%) and compared between groups using the chi-square test. A p-value \< 0.05 will be considered statistically significant.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Young Group | – | Drug: No Interventions |
| Elderly Group | – | Drug: No Interventions |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Young and elderly subjects with different glucose metabolism states will be recruited from the community or the outpatient department of Peking University Third Hospital between September 2024 and December 2025, meeting the following criteria.
Key inclusion criteria (2 listed)
- Age: Young (18 \< age \< 45 years) and elderly (age ≥ 65 years) subjects;
- Individuals with different glucose metabolism states, including normal glucose metabolism, pre-diabetes, and diabetes, without gastrointestinal diseases or a history of gastrointestinal surgery (such as active gastrointestinal inflammation or bleeding, inflammatory bowel disease, etc.), without cognitive impairment, without tumors, and without chronic respiratory diseases (such as chronic obstructive pneumonia, asthma, etc.), and not on a special diet (e.g., vegetarians).
Key exclusion criteria (8 listed)
- Use of antibiotics or health supplements within the last month.
- Diarrhea within the last 2 weeks: bowel movements ≥3 times/24 hours, with changes in stool consistency.
- Constipation within the last 2 weeks (based on the Rome III criteria for functional constipation).
- Acute complications of diabetes, such as diabetic ketoacidosis, within the last 3 months.
- History of gastrointestinal diseases or gastrointestinal surgery.
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 |
|---|---|---|
| No Interventionsno interventions | Drug | Elderly Group, Young Group |
Primary outcome
Metagenomic and Metabolomic Analysis of Gut Microbiota
Time frame: From September 2024 to December 2025
Comparison of Pancreatic Beta-Cell Function Among Different Groups of Subjects
Time frame: From September 2024 to December 2025
Comparison of Clinical Characteristics Among People with Different Glycemic Metabolic Status
Time frame: From September 2024 to December 2025
Correlation Between Specific Bacterial Species and Pancreatic Beta-Cell Function Among Different Groups
Time frame: From September 2024 to December 2025
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Oct 2024.
- 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. This study is not recruiting at the moment.
Yang Jin principal investigator
Peking University Third Hospital
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 Diabetes Mellitus and Gut Microbiota, run by Peking University Third Hospital 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 2 inclusion and 8 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: status not confirmed, as of the sponsor's last update on 16 Oct 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Metagenomic and Metabolomic Analysis of Gut Microbiota (time frame: From September 2024 to December 2025). 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?
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 16 Oct 2024.