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Updated: Jul 11, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
NMR-based metabolomics in giant cell arteritis and polymyalgia rheumatica sequential sera differentiates active and
Aikaterini Iliou1, Ourania D Argyropoulou2, Dimitris-Anastasios Palamidas2,3
1Department of Pharmaceutical Chemistry, School of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece.
Objectives:
GCA is an inflammatory disease following a chronic, relapsing course. The metabolic alterations related to the intense inflammatory process during the active phase and the rapid impact of steroid treatment remain unknown. This study aims to investigate the serum metabolome in active and inactive disease states.
Methods:
A total of 110 serum samples from 50 patients (33 GCA and 17 PMR) at three time points-0 (V1: active disease), 1 and 6 months (V2 and V3: remission)-of treatment with glucocorticoids (GCs) were subjected to NMR-based metabolomic analysis. Multi- and univariate statistical analyses were utilized to unveil metabolome alterations following treatment.
Results:
Distinct metabolic profiles were identified between activity and remission, independent of disease type. N-acetylglycoproteins and cholines of bound phospholipids emerged as predictive markers of disease activity. Altered levels of 4 of the 21 small molecules were also observed, including increased levels of phenylalanine and decreased glutamine, alanine and creatinine in active disease. Metabolic fingerprinting discriminated GCA from PMR in remission. GCA and PMR patients exhibited characteristic lipid alterations as a response and/or adverse effect of GC treatment. Correlation analysis showed that several identified biomarkers were further associated with acute phase reactants, CRP and ESR.
Conclusion:
The NMR profile of serum metabolome could identify and propose sensitive biomarkers of inflammation. Metabolome alterations, following GC treatment, could provide predictors for future steroid-induced side effects.
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