NMR-based metabolomics in giant cell arteritis and polymyalgia rheumatica sequential sera differentiates active and inactive disease.

OBJECTIVES: Giant Cell Arteritis-(GCA) is an inflammatory disease following a chronic, relapsing course. The metabolic alterations related to the intense inflammatory process during the active phase and to the rapid impact of steroid treatment, remain unknown. The study aims to investigate the serum metabolome in active and inactive disease state. METHODS: 110 serum samples from 50 patients [33-GCA and 17-Polymyalgia rheumatica-(PMR)] at 3 time points, 0-(V1: active disease), 1 and 6 months-(V2 and V3: remission) of treatment with glucocorticosteroids (GCs), were subjected to Nuclear Magnetic Resonance (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, independently to disease type. N-acetylglycoproteins and cholines of bound phospholipids, emerged as predictive markers of disease activity. Altered levels of 4 out of the 21 small molecules were also observed, including increased levels of phenylalanine, and decreased of 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 GCs treatment. Correlation analysis showed that several identified biomarkers were further associated with acute phase reactants, C-Reactive Protein and Erythrocyte Sedimentation Rate. CONCLUSION: The NMR profile of serum metabolome could identify and propose sensitive biomarkers of inflammation. Metabolome alterations, following GCs treatment, could provide predictors for future steroid-induced side effects.

Congruence and Complementarity of Differential Mobility Spectrometry and NMR Spectroscopy for Plasma Lipidomics.

The lipid composition of lipoprotein particles is determinative of their respective formation and function. In turn, the combination and correlation of nuclear magnetic resonance (NMR)-based lipoprotein measurements with mass spectrometry (MS)-based lipidomics is an appealing technological combination for a better understanding of lipid metabolism in health and disease. Here, we developed a combined workflow for subsequent NMR- and MS-based analysis on single sample aliquots of human plasma. We evaluated the quantitative agreement of the two platforms for lipid quantification and benchmarked our combined workflow. We investigated the congruence and complementarity between the platforms in order to facilitate a better understanding of patho-physiological lipoprotein and lipid alterations. We evaluated the correlation and agreement between the platforms. Next, we compared lipid class concentrations between healthy controls and rheumatoid arthritis patient samples to investigate the consensus among the platforms on differentiating the two groups. Finally, we performed correlation analysis between all measured lipoprotein particles and lipid species. We found excellent agreement and correlation (r > 0.8) between the platforms and their respective diagnostic performance. Additionally, we generated correlation maps detailing lipoprotein/lipid interactions and describe disease-relevant correlations.