Plasma proteomics identifies four candidate biomarkers associated with macrovascular complications in type 2 diabetes: evidence from a clinical cohort and an animal model
- 1Department of Endocrinology and Metabolism, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- 2Division of Life Sciences and Medicine, University of Science and Technology of China
Abstract
Objective: To identify and validate plasma protein biomarkers for T2DM macrovascular complications through proteomic screening, clinical validation, and animal models.
Methods: Plasma proteomics (4D-DIA, n = 6/group) was used to screen candidate proteins via differential expression, WGCNA, and PPI analyses. Candidates were validated in an independent clinical cohort (n = 162: 41 healthy controls, 35 T2DM alone, 86 T2DM with macrovascular complications) by ELISA for associations with metabolic parameters and diagnostic performance. A diabetic atherosclerosis mouse model (n = 10/group; C57BL/6J for control and diabetic groups, ApoE−/− for diabetic atherosclerosis, 24 weeks) was used to assess protein/mRNA expression and atherosclerotic lesions by Western blot, RT-qPCR, histology, and immunohistochemistry, with correlations to inflammatory and metabolic markers.
Results: In an exploratory proteomic discovery phase using a very small cohort (n = 6 per group), four candidate proteins were prioritized: adenylosuccinate lyase (ADSL), galectin-3 (LGALS3), mannosyl (α-1,3-)-glycoprotein β-1,2-N-acetylglucosaminyltransferase (MGAT1), and V-set and immunoglobulin domain-containing protein 4 (VSIG4). The four-protein panel showed good discrimination (AUC = 0.961). Adding the panel to a clinical model (age, disease duration, and HbA1c) significantly improved diagnostic performance (ΔAUC = 0.138, P = 0.008; NRI = 0.314; IDI = 0.261), with good calibration and internal stability. Firth penalized logistic regression identified ADSL (OR = 0.327, P = 0.0199) and MGAT1 (OR = 7.435, P = 2.71×10 –6 ) as independently associated with prevalent macrovascular disease. In animal models, ADSL, LGALS3, and VSIG4 correlated significantly with seven indicators, while MGAT1 correlated significantly with LDL-C, HDL-C, IL-6, and TNF-α after FDR. LGALS3 and VSIG4, though not independent in multivariable analysis, showed consistent upregulation and moderate discriminative ability (AUC = 0.736 and 0.686), supporting biological relevance.
Conclusion: ADSL and MGAT1 were independently associated with prevalent macrovascular complications.LGALS3 and VSIG4 may reflect cumulative metabolic and inflammatory burden rather than a lack of biological relevance; animal studies provide supportive evidence for this hypothesis.The four-protein panel improved diagnostic discrimination over clinical variables alone. These findings are exploratory and based on a small discovery cohort and internal validation; the four proteins are therefore best regarded as candidate diagnostic biomarkers associated with prevalent disease. Prospective external validation is warranted.
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BibTeX
@article{chen2026plasma,
title = {{Plasma proteomics identifies four candidate biomarkers associated with macrovascular complications in type 2 diabetes: evidence from a clinical cohort and an animal model}},
author = {Nuo Chen and Jingxuan Wang and Xiaojing Yuan and Wan Zhou},
journal = {Frontiers in Endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1955597},
url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1955597/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Plasma proteomics identifies four candidate biomarkers associated with macrovascular complications in type 2 diabetes: evidence from a clinical cohort and an animal model
AU - Nuo Chen
AU - Jingxuan Wang
AU - Xiaojing Yuan
AU - Wan Zhou
PY - 2026
DA - 2026/10/30
JO - Frontiers in Endocrinology
DO - 10.3389/fendo.2026.1955597
UR - https://www.frontiersin.org/articles/10.3389/fendo.2026.1955597/full
AB - Objective: To identify and validate plasma protein biomarkers for T2DM macrovascular complications through proteomic screening, clinical validation, and animal models. Methods: Plasma proteomics (4D-DIA, n = 6/group) was used to screen candidate proteins via differential expression, WGCNA, and PPI analyses. Candidates were validated in an independent clinical cohort (n = 162: 41 healthy controls, 35 T2DM alone, 86 T2DM with macrovascular complications) by ELISA for associations with metabolic parameters and diagnostic performance. A diabetic atherosclerosis mouse model (n = 10/group; C57BL/6J for control and diabetic groups, ApoE−/− for diabetic atherosclerosis, 24 weeks) was used to assess protein/mRNA expression and atherosclerotic lesions by Western blot, RT-qPCR, histology, and immunohistochemistry, with correlations to inflammatory and metabolic markers. Results: In an exploratory proteomic discovery phase using a very small cohort (n = 6 per group), four candidate proteins were prioritized: adenylosuccinate lyase (ADSL), galectin-3 (LGALS3), mannosyl (α-1,3-)-glycoprotein β-1,2-N-acetylglucosaminyltransferase (MGAT1), and V-set and immunoglobulin domain-containing protein 4 (VSIG4). The four-protein panel showed good discrimination (AUC = 0.961). Adding the panel to a clinical model (age, disease duration, and HbA1c) significantly improved diagnostic performance (ΔAUC = 0.138, P = 0.008; NRI = 0.314; IDI = 0.261), with good calibration and internal stability. Firth penalized logistic regression identified ADSL (OR = 0.327, P = 0.0199) and MGAT1 (OR = 7.435, P = 2.71×10 –6 ) as independently associated with prevalent macrovascular disease. In animal models, ADSL, LGALS3, and VSIG4 correlated significantly with seven indicators, while MGAT1 correlated significantly with LDL-C, HDL-C, IL-6, and TNF-α after FDR. LGALS3 and VSIG4, though not independent in multivariable analysis, showed consistent upregulation and moderate discriminative ability (AUC = 0.736 and 0.686), supporting biological relevance. Conclusion: ADSL and MGAT1 were independently associated with prevalent macrovascular complications.LGALS3 and VSIG4 may reflect cumulative metabolic and inflammatory burden rather than a lack of biological relevance; animal studies provide supportive evidence for this hypothesis.The four-protein panel improved diagnostic discrimination over clinical variables alone. These findings are exploratory and based on a small discovery cohort and internal validation; the four proteins are therefore best regarded as candidate diagnostic biomarkers associated with prevalent disease. Prospective external validation is warranted.
SN - 1664-2392
ER -