Exploring Biomarkers in Type 2 Diabetes Mellitus versus Normoglycemia Identified through High-Throughput Proteomics: A Systematic Review and Meta-Analysis

García-Currás, Julia; Pérez-Lois, Raquel; Taboada
, Guillermo L.; Pata, María P.

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https://hdl.handle.net/2183/47900

Exploring Biomarkers in Type 2 Diabetes Mellitus versus Normoglycemia Identified through High-Throughput Proteomics: A Systematic Review and Meta-Analysis

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Taboada_GuillermoL_2026_Exploring_biomarkers_in_type_2_diabetes_mellitus.pdf (4.78 MB)

Identifiers

URI: https://hdl.handle.net/2183/47900

DOI: 10.1021/acs.jproteome.5c00773

Publication date

2025-11

Authors

García-Currás, Julia

Pérez-Lois, Raquel

Taboada , Guillermo L.

Pata, María P.

Bibliographic citation

J. García-Currás, R. Pérez-Lois, G. L. Taboada, and M. P. Pata, "Exploring Biomarkers in Type 2 Diabetes Mellitus versus Normoglycemia Identified through High-Throughput Proteomics: A Systematic Review and Meta-Analysis", Journal of Proteome Research, Vol. 25, núm. 1, 2026, pp. 4-20, DOI: 10.1021/acs.jproteome.5c00773

Abstract

[Abstract]: Recent advances in proteomics have enabled the identification of early protein biomarkers and metabolic disturbances associated with type 2 diabetes (T2D), a major global health challenge. This systematic review and meta-analysis synthesize evidence from 27 studies comparing proteomic profiles of individuals with T2D and normoglycemic controls, selected from 2,422 initial records. The QUADOMICS assessment showed good methodological reporting for sample handling and proteomic analysis (>70% of studies), but over 60% lacked information on confounding clinical factors and biomarker validation. A qualitative synthesis focused on 85 recurrently reported proteins (≥8 studies), which showed strong interconnectivity and were involved in immune response, lipid–protein organization, detoxification, proteolysis, and coagulation, key pathways implicated in T2D. An omics-based meta-analysis identified seven promising protein biomarkers for T2D related to lipid/glucose metabolism (Q12907_LMAN2, P02652_POA2, P07602_PSPA, P09622_DLD); cell binding/adhesion (P12109_COL6A1, P12830_CDH1); and translational regulation and mitochondrial function (P35232_PHB). Random-effects meta-analysis revealed variation in effect sizes across studies for previously highlighted biomarkers, but three of them (P02763_ORM1, P00738_HP, P25311_AZGP1) exhibited considerable consistency. To enhance accessibility and further exploration of findings, we provide the interactive web tool metaMarkersT2D: https://jgcurras.shinyapps.io/metaMarkersT2D/.

Description

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG Protein data sets were retrieved from the main text and Supporting Information of the included studies, except for Amorim et al., 2022 (PRIDE: PXD033101) and Wigger et al., 2021 (PRIDE: PXD022561), which were obtained directly from the PRIDE repository. All code used for the different steps of the systematic review and meta-analysis can be found on GitHub, including refinement of the search equation, preprocessing of protein lists, reanalysis of raw data, functional, interaction and semantic analysis, and meta-analysis execution: https://github.com/juliagcurras/metaanalysisT2D. Source code for the provided Shiny apps pubmedToCsv and metaMarkersT2D is also freely available on GitHub: https://github.com/juliagcurras/pubmedtocsv; https://github.com/juliagcurras/metaMarkersT2D.