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Assessment of Metabolic Profiles of Lower Extremity Arterial Disease in Patiens Withe Type 2 Diabetes

Assessment of Metabolic Profiles of Lower Extremity Arterial Disease in Patiens Withe Type 2 Diabetes Via LC-MS-based Nontargeted Metabolomic Approach

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05068895
Enrollment
74
Registered
2021-10-06
Start date
2021-06-01
Completion date
2021-10-15
Last updated
2021-10-06

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

Conditions

Diabetes Mellitus, Type 2, Diabetic Angiopathies

Keywords

Diabetes Mellitus, Type 2, Lower extremity artery disease, Metabolic Profiles, Metabolic biomarkers, Metabolomics, iquid chromatography-mass spectrometry

Brief summary

The prevalence of lower extremity arterial disease (LEAD) in patients with diabetes increases significantly and are characterized with obvious arteriosclerosis that are caused by multiple metabolic disorders. Metabolomics measures the metabolites in biological fluids or tissues that generated under certain conditions via rapidly evolving high-throughput technology. Herein, the investigators designed the study to characterize the serum metabolic profiles of LEAD patients and identify metabolic biomarkers using metabolomics. The serum of volunteers, type 2 diabetes mellitus(T2DM) patients with or without LEAD were collected and analyzed using liquid chromatography-mass spectrometry(LC-MS) coupled with a series of multivariate statistical analyses.

Interventions

OTHERliquid chromatography-mass spectrometry

Metabolomics is a rapidly evolving high-throughput technology that allows the measurement of the entire complement of metabolites generated by biochemical reactions under certain conditions in biological fluids or tissues. This technology has been used extensively to identify biomarkers in various cancers, nervous system diseases, cardiovascular diseases, pituitary diseases, and other diseases. The identification of biomarkers can be clinically useful for a more accurate diagnosis, prognosis, and treatment choice as well as disease monitoring. Among mass spectrometry (MS) methods, liquid chromatography-mass spectrometry (LC-MS) has been recognized as a robust metabolomics tool and has been widely applied in metabolite identification and quantification due to its high sensitivity, peak resolution, and repro- ducibility.

Sponsors

Zhiming Zhu
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes

Inclusion criteria

* age ranges from 18 to 100 years old * Signed informed consent and agreed to participate in this study * The diagnosis of T2DM is based on standard criteria recommended by WHO since 1999 * The diagnosis of T2DM patient with LEAD is based on standard criteria recommended by Chinese guideline on prevention and management of diabetic foot (2019 edition)(II).

Exclusion criteria

* younger than 18 years old or older than 100 years old * acute infection during the preceding 3 months * drugs or alcohol addicts * cancer * type 1 diabetes * patients with mental abnormality who are uncooperative with this study * pregnant or lactating women * refuse to sign informed consent

Design outcomes

Primary

MeasureTime frameDescription
Metabolic profiles of lower extremity artery disease4 monthsLC-MS analysis will be performed using a Q ExactiveTM HF-X liqiud chromatograph system coupled with a Thermo ScientificTM OrbitrapTM mass spectrometer according to a previously published procedure to detect the peak, identify the metabolites and perform the PCA and OPLS-DA analyses to better visualize the subtle similarities and differences among the complex datasets.

Secondary

MeasureTime frameDescription
Potential biomarker analysis for discrimination4 monthsScreening for potential biomarkers will be performed according to the VIP value (VIP \> 1.0) and significance test (P \< 0.05) from the OPLS-DA model.
Pathway analysis of differential metabolites4 monthsConducting pathway analysis for the significant metabolites identified by using MetaboAnalyst.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 13, 2026