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Background <p>Preclinical atherosclerosis and prediabetes are key targets of preventive medicine as their prevalence rises. Therefore, it is crucial to identify early processes and limit confounders such as lipid-lowering or antidiabetic therapy and advanced atherosclerosis. Proteomics enables the identification of biomarkers and molecular pathways related to atherogenesis in prediabetes.</p> Purpose <p>To investigate the relationship between prediabetes and preclinical atherosclerosis in apparently healthy individuals using a comprehensive proteomic approach.</p> Methods <p>This cross-sectional, population-based study included 389 participants (mean age 49 ± 10&#xa0;years; 47% males) from the Białystok PLUS cohort in Poland. Individuals with known diabetes, major cardiovascular, inflammatory, or malignant diseases, or those receiving steroidal or lipid-lowering therapy were excluded. Carotid ultrasound was used to assess preclinical atherosclerosis, and prediabetes was defined as impaired fasting glucose, impaired glucose tolerance, or HbA1c 5.7–6.4%. Proteomic profiling was performed using the Olink® Reveal platform, enabling deep profiling of 1050 proteins with the Proximity Extension Assay and next-generation sequencing readout, yielding log2-scaled NPX (Normalized Protein eXpression) values. In preliminary analyses, we identified proteins associated with prediabetes and then linked them to early atherosclerotic lesions.</p> Results <p>A block-sPLS-DA model integrating clinical and proteomic data revealed clear separation between participants with and without prediabetes. The clinical block comprised eight variables reflecting cardiometabolic status, whereas the proteomic block retained 45 proteins across two components.&#xa0;The heatmap shows pairwise Pearson correlations between selected serum proteins and clinical variables (Fig.&#xa0;<InternalRef RefID="Fig1">1</InternalRef>). Vascular and age measures cluster together and share correlation patterns distinct from those of BMI and glycaemic parameters. A protein module including the ectodysplasin A2 receptor (EDA2R) and leiomodin 1 (LMOD1) correlates positively with age and vascular parameters, and inversely with GFR and HDL-C. Multivariable linear regression analyses were performed with selected vascular parameters as dependent variables and clinical covariates, together with proteins identified in Component 2, which are weakly related to clinical parameters and thus may represent novel biomarkers associated with prediabetes (Fig.&#xa0;<InternalRef RefID="Fig2">2</InternalRef>). Expression of EDA2R (B = 0.05; <i>P</i> = 0.001), C16orf89 (B = 0.05; <i>P</i> = 0.001), and LMOD1 (B = 0.039; <i>P</i> = 0.016) was associated with increased IMT. Functional enrichment analysis of selected proteins revealed significant overrepresentation of proteins associated with synapse maturation.</p> Conclusion <p>An integrative block-sPLS-DA approach separated individuals with prediabetes from those without and revealed a proteomic signature independent of clinical covariates. Within this signature, the expression of LMOD1, EDA2R, and C16orf89 showed robust associations with atherosclerosis-related vascular traits. Enrichment analyses highlighted proteins involved in neuronal processes as candidate pathways linking early glucose disturbances with preclinical atherosclerosis.</p>

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Multiblock proteomic profiling identifies novel biomarkers linking prediabetes with preclinical atherosclerosis in apparently healthy adults

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  • 发表日期：

    2026年3月28日

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  • Natalia Zieleniewska,
  • Jacek Jamiołkowski,
  • Marcin Kondraciuk,
  • Małgorzata Chlabicz,
  • Marlena Duabtówka,
  • Irina Kowalska,
  • Karol Kamiński

摘要

Background

Preclinical atherosclerosis and prediabetes are key targets of preventive medicine as their prevalence rises. Therefore, it is crucial to identify early processes and limit confounders such as lipid-lowering or antidiabetic therapy and advanced atherosclerosis. Proteomics enables the identification of biomarkers and molecular pathways related to atherogenesis in prediabetes.

Purpose

To investigate the relationship between prediabetes and preclinical atherosclerosis in apparently healthy individuals using a comprehensive proteomic approach.

Methods

This cross-sectional, population-based study included 389 participants (mean age 49 ± 10 years; 47% males) from the Białystok PLUS cohort in Poland. Individuals with known diabetes, major cardiovascular, inflammatory, or malignant diseases, or those receiving steroidal or lipid-lowering therapy were excluded. Carotid ultrasound was used to assess preclinical atherosclerosis, and prediabetes was defined as impaired fasting glucose, impaired glucose tolerance, or HbA1c 5.7–6.4%. Proteomic profiling was performed using the Olink® Reveal platform, enabling deep profiling of 1050 proteins with the Proximity Extension Assay and next-generation sequencing readout, yielding log2-scaled NPX (Normalized Protein eXpression) values. In preliminary analyses, we identified proteins associated with prediabetes and then linked them to early atherosclerotic lesions.

Results

A block-sPLS-DA model integrating clinical and proteomic data revealed clear separation between participants with and without prediabetes. The clinical block comprised eight variables reflecting cardiometabolic status, whereas the proteomic block retained 45 proteins across two components. The heatmap shows pairwise Pearson correlations between selected serum proteins and clinical variables (Fig. 1). Vascular and age measures cluster together and share correlation patterns distinct from those of BMI and glycaemic parameters. A protein module including the ectodysplasin A2 receptor (EDA2R) and leiomodin 1 (LMOD1) correlates positively with age and vascular parameters, and inversely with GFR and HDL-C. Multivariable linear regression analyses were performed with selected vascular parameters as dependent variables and clinical covariates, together with proteins identified in Component 2, which are weakly related to clinical parameters and thus may represent novel biomarkers associated with prediabetes (Fig. 2). Expression of EDA2R (B = 0.05; P = 0.001), C16orf89 (B = 0.05; P = 0.001), and LMOD1 (B = 0.039; P = 0.016) was associated with increased IMT. Functional enrichment analysis of selected proteins revealed significant overrepresentation of proteins associated with synapse maturation.

Conclusion

An integrative block-sPLS-DA approach separated individuals with prediabetes from those without and revealed a proteomic signature independent of clinical covariates. Within this signature, the expression of LMOD1, EDA2R, and C16orf89 showed robust associations with atherosclerosis-related vascular traits. Enrichment analyses highlighted proteins involved in neuronal processes as candidate pathways linking early glucose disturbances with preclinical atherosclerosis.