Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practiceReportar como inadecuado




Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Journal of Clinical Bioinformatics

, 5:3

First Online: 13 March 2015Received: 26 December 2014Accepted: 27 February 2015DOI: 10.1186-s13336-015-0018-4

Cite this article as: Mohamad, N., Ismet, R.I., Rofiee, M. et al. J Clin Bioinform 2015 5: 3. doi:10.1186-s13336-015-0018-4

Abstract

BackgroundThe dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients cardiovascular were compared. A metabotype model was developed and validated using partial least square discriminant analysis PLSDA. Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently.

ResultsFourteen 14 metabolites were determined as potential biomarkers for cardiovascular risks with receiver operating characteristic of more than 0.7. They include 15S-HETE AUC = 0.997 and phosphorylcholine AUC = 0.995. Seven Orang Asli were clustered with the patients’ group and may have ongoing cardiovascular risks and problems. This is supported by biochemistry tests results that showed abnormalities in cholesterol, triglyceride, HDL and LDL levels.

ConclusionsThe disease prediction model based on metabolites is a useful diagnostic alternative as compared to the current single biomarker assays. The former is believed to be more cost effective since a single sample run is able to provide a more comprehensive disease profile, whilst the latter require different types of sampling tubes and blood volumes.

KeywordsOrang Asli Metabolomics Myocardial infarction Predictive model Phenotype AbbreviationsMIMyocardial infarction

HTHealthy volunteers

OAOrang Asli

CVDCardiovascular disease

PLSDAPartial least square discriminant analysis

PCAPrincipal component analysis

Electronic supplementary materialThe online version of this article doi:10.1186-s13336-015-0018-4 contains supplementary material, which is available to authorized users.

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Autor: Nornazliya Mohamad - Rose Iszati Ismet - MohdSalleh Rofiee - Zakaria Bannur - Thomas Hennessy - Manikandan Selvaraj - Aminu

Fuente: https://link.springer.com/



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