Exploration of Potential Biomarkers for Type 2 Diabetes by UPLC-QTOF-MS and WGCNA of Skin Surface Lipids.

BACKGROUND:Diabetes has become popular and has become one of the most important global health care challenges. Patients with diabetes have a high incidence of skin diseases. Cell and animal models are often used to study the skin conditions of people with diabetes. METHODS:In this study, a volunteer questionnaire survey, skin lipomics analysis based on ultra-high performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry (UPLC-QTOF-MS), and weighted gene co-expression network analysis (WGCNA) were used to study the differences in skin conditions and skin lipids of participants with type 2 diabetes mellitus (Group D) versus healthy individuals (Group H) and the correlation between these groups. The questionnaire was used to investigate personal basic, diabetes, and facial skin status information of 77 female volunteers aged 55-65 years old from the Peking University Shougang Hospital. The facial skin lipids of all volunteers were analysed by UPLC-QTOF-MS technique; the differential lipids between groups D and H were analysed by partial least-squared discriminant and univariate analysis. RESULTS:In total, 23 kinds of differential lipids were identified, all of which belonged to sphingolipids. The use of WGCNA combined clinical information with lipid analysis to study the relationship between glycosylated haemoglobin, skin pigmentation/non-pigmentation, and skin lipids. Two types of lipids were identified to distinguish between hub lipids of high and low glycosylated haemoglobin; 12 types of lipids were identified that could distinguish between the hub lipids of pigmented and non-pigmented participants (PLS-DA). CONCLUSION:The experimental results not only provide a reference for the diagnosis and classification of diabetes via analysing the skin lipids of patients, but also provides a theoretical basis for further study on the effects of diabetes on the skin of patients.