Serum N-glycomic profiling identifies candidate biomarker panels for assessing coronary artery stenosis severity

Abstract

Stenosis severity may escalate over the course of coronary artery disease (CAD), increasing the risk of death for the patient. Conventionally, the assessment of stenosis degree relies on invasive coronary angiography (ICA), an invasive examination unsuitable for patients in poor physical condition or those with contrast allergies and one that imposes a psychological burden on patients. Although abnormal serum N-glycan profiles have exhibited robust associations with various cardiovascular diseases, including CAD, their potential in diagnosing CAD stenosis remains to be determined. In this study, we performed a comprehensive analysis of serum N-glycome from 132 patients who underwent ICA and 27 healthy controls using MALDI-TOF-mass spectrometry. The patients who underwent ICA examination were categorized into four groups based on stenosis severity: no/mild/moderate/severe stenosis. Twenty-seven N-glycans were directly quantified, and 47 derived glycan traits were obtained. Notably, among these 74 glycan features, 18 exhibited variations across the study groups. Using a combination of least absolute shrinkage and selection operator and logistic regression analyses, we developed five diagnostic models for recognizing stenosis degree. Our results suggested that alterations in serum N-glycosylation modifications might be valuable for identifying stenosis degree and monitoring disease progression in individuals with CAD. It is expected to offer a noninvasive alternative for those who could not undergo ICA because of various reasons. However, the diagnostic potential of serum N-glycan panels as biomarkers requires multicenter, large cohort validation in the future.

Keywords: Biomarkers; Coronary artery stenosis; Mass spectrometry; N-glycomic profiling.

Fig. 1

Workflow of serum N-glycan profiling.

Fig. 2

Representative MALDI-TOF MS spectra showing the composition of serum N-glycans.

Fig. 3

Violin plots of altered serum N-glycan profiles among the H1, 0-CAD (no stenosis), 1-CAD (mild stenosis), 2-CAD (moderate stenosis), and 3-CAD (severe stenosis) groups. * indicates p-value <0.05, ** indicates p-value <0.01, *** indicates p-value <0.001, **** indicates p-value <0.0001.

Fig. 4

The recognition ability of N-glycome analysis for cases with or without coronary artery stenosis. (A) LASSO coefficient profile plot of the 18 differential glycan features against the log (lambda) sequence. (B) Penalty parameter (lambda) selection by LASSO adopted 10-fold cross-validation. (C) Forest plot of the logistic regression model. (D) Nomogram of the logistic regression model. (E) The receiver operating characteristics (ROC) curves of the nomogram with 95% confidence intervals of the area under the curve (AUC).

Fig. 5

The receiver operating characteristics (ROC) curves of coronary artery stenosis diagnostic models. 0-CAD indicates no stenosis, 1-CAD indicates mild stenosis, 2-CAD indicates moderate stenosis, and 3-CAD indicates severe stenosis.