METHODS: A cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups.
RESULTS: The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSIONS: The applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.
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.
METHODS: Warfarin relies on regular monitoring of International Normalized Ratio which is a standardized test to measure prothrombin time and appropriate dose adjustment. Pharmacometabonomics is a novel scientific field which deals with identification and quantification of the metabolites present in the metabolome using spectroscopic techniques such as Nuclear Magnetic Resonance (NMR). Pharmacometabonomics helps to indicate perturbation in the levels of metabolites in the cells and tissues due to drug or ingestion of any substance. NMR is one of the most widely-used spectroscopic techniques in metabolomics because of its reproducibility and speed.
RESULTS: There are many factors that influence the metabolism of warfarin, making changes in drug dosage common, and clinical factors like drug-drug interactions, dietary interactions and age explain for the most part the variability in warfarin dosing. Some studies have showed that pharmacogenetic testing for warfarin dosing does not improve health outcomes, and around 26% of the variation in warfarin dose requirements remains unexplained yet.
CONCLUSION: Many recent pharmacometabonomics studies have been conducted to identify novel biomarkers of drug therapies such as paracetamol, aspirin and simvastatin. Thus, a technique such as NMR based pharmacometabonomics to find novel biomarkers in plasma and urine might be useful to predict warfarin outcome.