Plasma Metabolites as Predictors of Warfarin Outcome in Atrial Fibrillation

Bawadikji AA 1 , Teh CH 2 , Sheikh Abdul Kader MAB 3 , Abdul Wahab MJB 3 , Syed Sulaiman SA 1 , Ibrahim B 4

Affiliations 

  • 1 School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia
  • 2 Bruker (Malaysia) Sdn Bhd, Bayan Lepas, Malaysia
  • 3 Department of Cardiology, Penang Hospital, George Town, Malaysia
  • 4 School of Pharmaceutical Sciences, Universiti Sains Malaysia, Gelugor, Malaysia. baharudin.ibrahim@usm.my
Am J Cardiovasc Drugs, 2020 Apr;20(2):169-177.
PMID: 31435902 DOI: 10.1007/s40256-019-00364-2

Abstract

BACKGROUND: Warfarin is prescribed as an oral anticoagulant to treat/prevent thromboembolism in conditions such as atrial fibrillation. As there is a narrow therapeutic window, treatment with warfarin is challenging. Pharmacometabonomics using nuclear magnetic resonance (NMR) spectroscopy may provide novel techniques for the identification of novel biomarkers of warfarin.

PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).

EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.

KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.

CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

MeSH terms
Similar publications