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

  • Nornazliya Mohamad Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • Rose Iszati Ismet Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • MohdSalleh Rofiee Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • Zakaria Bannur Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • Thomas Hennessy Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia. Life Sciences & Diagnostics Group, Translational Research Institute, Brisbane, Australia
  • Manikandan Selvaraj Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • Aminuddin Ahmad Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
  • FadzilahMohd Nor Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
  • ThuhairahHasrah Abdul Rahman Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
  • Kamarudzaman Md.Isa Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • AdzroolIdzwan Ismail Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia
  • Lay Kek Teh Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia. Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Selangor 42300, Malaysia
  • Mohd Zaki Salleh Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300, Malaysia. Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Selangor 42300, Malaysia
DOI: 10.1186/s13336-015-0018-4

Abstract

Background: The 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.
Results: Fourteen (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.
Conclusions: The 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.

Keywords

Orang Asli, Metabolomics, Myocardial infarction, Predictive model, Phenotype
Published
2015-03-13
How to Cite
MOHAMAD, Nornazliya et al. Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice. Journal of Clinical Bioinformatics, [S.l.], v. 5, n. 1, mar. 2015. ISSN 2043-9113. Available at: <http://jclinbioinformatics.com/article/view/58>. Date accessed: 18 nov. 2017. doi: https://doi.org/10.1186/s13336-015-0018-4.