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September 23, 2025

Proteomic analysis for prediction of type 2 diabetes identifies cardiovascular disease-related proteins

Key Points

584 proteins significantly linked to type 2 diabetes were identified, with some proteins indicating increased or decreased risk.
The most significant pathway relates to lipid catabolic processes, highlighting lipid metabolism's role in type 2 diabetes.
Binary logistic regression analysis adjusted for sex, age, and comorbidities was used to determine protein associations.
Identifying these biomarkers may help in early cardiovascular risk mitigation for individuals with type 2 diabetes.

Abstract

Introduction and aim: Cardiovascular disease (CVD) and type 2 diabetes (T2D) are interconnected chronic conditions which cause significant global health challenges and mortality. T2D is a risk factor for CVD characterized by insulin resistance and beta-cell dysfunction. This study aims to investigate the association between the blood plasma proteome and T2D in the Trøndelag Health (HUNT) Study. Methods: The HUNT Study is a population-based cohort with four waves of enrollment beginning in 1984. 5,402 samples from HUNT3 were analysed with SomaScan including 3,221 on SomaScanv4.0 (~5,000 proteins) and 2,181 on SomaScanv4.1 (~7,000 proteins). T2D was defined based on self-reported disease from questionnaires. Binary logistic regression analysis was performed to test the associations of protein concentrations with T2D. Models included adjustment by sex, age, waist-hip-ratio (WHR), smoking status, and comorbidities. Results and Discussion: We identified 584 proteins that are significantly associated with T2D. From these proteins, CILP2 and MXRA8 are associated with decreased risk, while PLXB2 and NFASC are associated with increased risk for having T2D. The most significant pathway from GO enrichment analysis includes proteins related to lipid catabolic process (FDR adjusted p- value: 0.000054). Indeed, several proteins significantly associated with T2D are known for their role in lipid metabolism, for instance Adiponectin (OR 0.55; 95% CI 0.46 - 0.65), Apo A – IV (1.57;1.37 - 1.81), Apo B (0.63; 0.55 - 0.73), Apo C – I (0.73; 0.64 - 0.84), and Apo D (0.49;0.38 - 0.63). Conclusion: The proteins and pathways identified represent insights into the underlying molecular mechanism of T2D and may serve as potential biomarkers for risk prediction and prevention for CVD. Relevance for AtheroNET: The findings suggest that early identification of these proteins could mitigate cardiovascular risks in individuals with or at risk for T2D.

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