Forskning ved Københavns Universitet - Københavns Universitet


1H NMR spectroscopy-based interventional metabolic phenotyping: a cohort study of rheumatoid arthritis patients

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

  • Michael B Lauridsen
  • Bliddal, Henning
  • Robin Christensen
  • Bente Danneskiold-Samsøe
  • Robert Bennett
  • Hector Keun
  • John C Lindon
  • Jeremy K Nicholson
  • Mikkel H Dorff
  • Jerzy W Jaroszewski
  • Steen Honore Hansen
  • Cornett, Claus
1H NMR spectroscopy-based metabolic phenotyping was used to identify biomarkers in the plasma of patients with rheumatoid arthritis (RA). Forty-seven patients with RA (23 with active disease at baseline and 24 in remission) and 51 healthy subjects were evaluated during a one-year follow-up with assessments of disease activity (DAS-28) and 1H NMR spectroscopy of plasma samples. Discriminant analysis provided evidence that the metabolic profiles predicted disease severity. Cholesterol, lactate, acetylated glycoprotein, and lipid signatures were found to be candidate biomarkers for disease severity. The results also supported the link between RA and coronary artery disease. Repeated assessment using mixed linear models showed that the predictors obtained from metabolic profiles of plasma at baseline from patients with active RA were significantly different from those of patients in remission (P=0.0007). However, after 31 days of optimized therapy, the two patient groups were not significantly different (P=0.91). The metabolic profiles of both groups of RA patients were different from the healthy subjects. 1H NMR-based metabolic phenotyping of plasma samples in patients with RA is well suited for discovery of biomarkers and may be a potential approach for disease monitoring and personalized medication for RA therapy.
TidsskriftJournal of Proteome Research
Udgave nummer9
Sider (fra-til)4545-4553
StatusUdgivet - 3 sep. 2010

Bibliografisk note

Keywords: metabonomics, phenotyping, NMR, rheumatoid arthritis, chemometrics, disease monitoring, personalized medicine

ID: 33967511