Plasma proteome analysis of rheumatic patients reveals differences in fingerprints based on cardiovascular history: a pilot study

• Published in: Proteome science Vol. 23; no. 1; pp. 4 - 12
• Main Authors: Hansildaar, Romy, van Velzen, Max, van der Vossen, Eduard W. J., Kramer, Gertjan, Nurmohamed, Michael T., Levels, Johannes H. M.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.04.2025; BMC
• Subjects: Blood plasma; Blood proteins; Cardiovascular diseases; Complications and side effects; Composition; Development and progression; Genetic aspects; Health aspects; Identification and classification; Machine learning; Measurement; Methods; Plasma proteomics; Proteomics; Rheumatoid arthritis; Risk factors; Netherlands; Machine learning; Plasma proteomics; RA
• ISSN: 1477-5956; 1477-5956
• DOI: 10.1186/s12953-025-00243-6

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is much higher than that in the general population. As its etiology is not fully understood, we performed a pilot study using a shotgun proteomic approach to investigate whether the plasma signature in RA patients with CVD might show an altered profile. Subjects with RA were compared to a group of RA patients with a previous cardiovascular event (CVE). The cohort consisted of an RA control group (n = 10) and a group (n = 10) of RA patients with a history of CVD. Samples were collected at least 6 months before the CVE and 3-6 months after the CVE. All subjects were matched to controls for age, sex, and medication use. Plasma depletion of the 14 most abundant proteins was followed by bottom-up shotgun proteomics analysis (LC‒MS/MS). Relative changes in protein/peptide abundance were investigated using classical statistical analyses with Perseus and XG-Boost machine learning to compare between groups and to determine the relative importance of identified proteins, respectively. Principal component analysis (PCA) revealed no difference in the global protein and peptide signatures between the control and CVE groups. A total of 150, 239 and 74 protein ID's showed in comparison between Post Event vs. controls, Event vs. no Event and Pre event vs. Post Event respectively a statistically difference in relative abundance (p < 0.05). Remarkedly a total of 236 proteins ID's showed a statistical significant difference in relative abundance in the PRE-Event group compared to the control group which could also be confirmed by XGboost machine learning. Here, we demonstrated potential differences in the plasma proteome signature of rheumatic patients with cardiovascular events. Interestingly, this signature may be present prior to CVE's. However the conclusions must be drawn with caution, since this is a pilot study and further investigation with larger cohorts is warranted to identify potential risk markers that may predict the relative risk of CVEs in rheumatic diseases.