Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06645223
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
None
First Post:
2024-09-28
Brief Title:
Correlation Between Gut Microbiota and Pancreatic Β-Cell Function in Diabetic Patients
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Correlation Between Gut Microbiota and Pancreatic Β-Cell Function in Diabetic Patients
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 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 beneficialharmful 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
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:
1 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
2 Research Design
21 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
1 Age Young 18 lt age lt 45 years and elderly age 65 years subjects
2 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 eg vegetarians
Exclusion Criteria
1 Use of antibiotics or health supplements within the last month
2 Diarrhea within the last 2 weeks bowel movements 3 times24 hours with changes in stool consistency
3 Constipation within the last 2 weeks based on the Rome III criteria for functional constipation
4 Acute complications of diabetes such as diabetic ketoacidosis within the last 3 months
5 History of gastrointestinal diseases or gastrointestinal surgery
6 Cognitive impairment
7 History of tumors
8 Special diet eg vegetarians
22 Research Methods
221 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 -80C in a freezer
222 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 4C the supernatant will be aliquoted into 1 mL EP tubes and stored at -80C in a freezer
223 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
3 Statistical Analysis Statistical analysis of the data will be conducted using R 421 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 lt 005 will be considered statistically significant
2 Research Design
21 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
1 Age Young 18 lt age lt 45 years and elderly age 65 years subjects
2 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 eg vegetarians
Exclusion Criteria
1 Use of antibiotics or health supplements within the last month
2 Diarrhea within the last 2 weeks bowel movements 3 times24 hours with changes in stool consistency
3 Constipation within the last 2 weeks based on the Rome III criteria for functional constipation
4 Acute complications of diabetes such as diabetic ketoacidosis within the last 3 months
5 History of gastrointestinal diseases or gastrointestinal surgery
6 Cognitive impairment
7 History of tumors
8 Special diet eg vegetarians
22 Research Methods
221 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 -80C in a freezer
222 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 4C the supernatant will be aliquoted into 1 mL EP tubes and stored at -80C in a freezer
223 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
3 Statistical Analysis Statistical analysis of the data will be conducted using R 421 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 lt 005 will be considered statistically significant
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None