Skip to content

Research on Potential Biomarkers of Prediabetes and Diabetes Based on MALDI-TOF MS Platform.

Research on Potential Biomarkers of Prediabetes and Diabetes Based on MALDI-TOF MS Platform.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06097065
Enrollment
2860
Registered
2023-10-24
Start date
2022-05-20
Completion date
2026-12-31
Last updated
2023-10-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetes Mellitus, Gestational Diabetes Mellitus, Prediabetes, Proteomics

Brief summary

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.

Detailed description

Prediabetes is a stage of abnormal glucose metabolism between normal blood glucose level and diabetes, which is a gray zone between normal and abnormal, including impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or both. It is a very important high-risk group of diabetes. Diabetes is a group of metabolic diseases characterized by hyperglycemia caused by a variety of causes. Gestational diabetes mellitus refers to varying degrees of abnormal glucose metabolism that occur during pregnancy. This project aims to detect differential feature peaks through MALDI-TOF MS technology 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 and to establish a clinical predictive diagnostic model based on differences, and to evaluate the model.

Interventions

DIAGNOSTIC_TESTOral Glucose Tolerance Test

Grouping based on detected oral glucose tolerance test results without any other intervention.

Sponsors

Zhujiang Hospital
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
OTHER

Eligibility

Sex/Gender
ALL
Healthy volunteers
Yes

Inclusion criteria

1. Inclusion criteria for cases: Non pregnant people: the remaining fasting serum/plasma and urine samples of prediabetes/diabetes patients (prediabetes: IFG: FPG 6.1-6.9mmol/L, Blood glucose 2h after meal\<7.8mmol/L(WHO); IGT: FPG\<7.0mmol/L, Blood glucose 2h after meal 7.8-11.1mmol/L(WHO); diabetes: Typical symptoms of diabetes, FPG \>= 7.0mmol/L or 75g OGTT 2h blood glucose \>= 11.1mmol/L). Pregnant people: the remaining fasting serum/plasma and urine samples of gestational diabetes patients (75g OGTT test FPG \>= 5.1mmol/L or 1h blood glucose \>= 10.0mmol/L or 2h blood glucose \>= 8.5mmol/L(IADPSG; ADA)). 2. Inclusion criteria of the controls were as follows: Non pregnant people: the remaining fasting serum/plasma and urine samples of normal population for glucose tolerance test (FPG 3.9-6.1mmol/L,75g OGTT test 1h blood glucose 6.7-11.1mmol/L,75g OGTT test 2h blood glucose 3.6-7.8mmol/L). Pregnant people: the remaining fasting serum/plasma and urine samples of people who do not meet the diagnostic criteria for gestational diabetes (3.9\<=75g OGTT test FPG\<5.1mmol/L,6.7 \<= 1h blood glucose\<10.0mmol/L,3.6\<=2h blood glucose\<8.5mmol/L). \-

Exclusion criteria

Common

Design outcomes

Primary

MeasureTime frameDescription
Number and types of proteins/peptides differential characteristic peaksone yearObtain the number and types of proteins/peptides differential characteristic peaks between the case group and the control group through data analysis.

Secondary

MeasureTime frameDescription
ROC curve and area under curve AUC of clinical predictive diagnostic modelone yearConstruct 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.

Countries

China

Contacts

Primary ContactNianyi Zeng
zengny1@i.smu.edu.cn13928801657
Backup ContactHongwei Zhou, Professor
hzhou@smu.edu.cn18688489622

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026