Research on Potential Biomarkers of Prediabetes and Diabetes Based on MALDI-TOF MS Platform.
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorZhujiang Hospital
About this trial
Through the MALDI-TOF MS platform, explore the proteomics and peptidomics differences of fasting serum/plasma and urine between non pregnant people with normal glucose tolerance test and prediabetes /diabetes patients, pregnant people with normal glucose tolerance test and pregnant diabetes patients respectively; To explore the role of its proteomics and peptidomics differences in the diagnosis of prediabetes and diabetes, and to establish a new method of differential diagnosis by using the omics data and key characteristic peaks to find potential new diagnostic markers.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
None
Disqualifiers
None
Trial population
(1) cases: Non pregnant people: the remaining fasting serum/plasma and urine samples of prediabetes/diabetes patients; Pregnant people: the remaining fasting serum/plasma and urine samples of gestational diabetes patients. (2) controls: (Non)pregnant people: the remaining fasting serum/plasma and urine samples of normal population.
Trial design
Design model
Case-control
Time perspective
Other
Treatments tested in this trial
Oral Glucose Tolerance Test
Diagnostic testGrouping based on detected oral glucose tolerance test results without any other intervention.
Treatment groups
2,860 Participants
are divided into 4 treatment groupsGroup A: Non pregnant people with prediabetes or diabetes1 intervention
Group B: Non pregnant people with normal glucose tolerance1 intervention
Group C: pregnant people with gestational diabetes mellitus1 intervention
Group D: pregnant people with normal glucose tolerance1 intervention
Trial outcomes
Primary outcomes
1
Number and types of proteins/peptides differential characteristic peaks
Obtain the number and types of proteins/peptides differential characteristic peaks between the case group and the control group through data analysis.
Time frame
one yearSecondary outcomes
1
ROC curve and area under curve AUC of clinical predictive diagnostic model
Construct a clinical predictive diagnostic model based on the obtained proteins/peptides differential feature peaks and calculate the area under the ROC curve AUC to evaluate the predictive effect of the model.
Time frame
one yearSponsors and contacts
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Source ClinicalTrials.gov