Plasma Metabolites as Predictors of Warfarin Outcome in Atrial Fibrillation

Autor: Chin-Hoe Teh, Syed Azhar Syed Sulaiman, Baharudin Ibrahim, Abdulkader Ahmad Bawadikji, Mohamed Jahangir Bin Abdul Wahab, Muhamad Ali Bin Sheikh Abdul Kader
Rok vydání: 2019
Předmět:
Adult
Male
Warfarin side effects
medicine.medical_specialty
Magnetic Resonance Spectroscopy
Multivariate analysis
Adolescent
Administration
Oral
030204 cardiovascular system & hematology
Logistic regression
Sensitivity and Specificity
Young Adult
03 medical and health sciences
0302 clinical medicine
Thromboembolism
Internal medicine
Atrial Fibrillation
medicine
Pharmacometabolomics
Humans
Metabolomics
heterocyclic compounds
Pharmacology (medical)
In patient
International Normalized Ratio
cardiovascular diseases
030212 general & internal medicine
Aged
Models
Statistical
business.industry
Warfarin
Anticoagulants
Atrial fibrillation
General Medicine
Middle Aged
medicine.disease
Cross-Sectional Studies
Cardiology
Oral anticoagulant
Female
Cardiology and Cardiovascular Medicine
business
Biomarkers
medicine.drug
Zdroj: American Journal of Cardiovascular Drugs. 20:169-177
ISSN: 1179-187X
1175-3277
DOI: 10.1007/s40256-019-00364-2
Popis: 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. 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). 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. 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%. 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.
Databáze: OpenAIRE
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