alexa Application of High-Dimensional Statistics and Network Based Visualization Techniques on Arab Diabetes and Obesity Data
ISSN: 2157-7420

Journal of Health & Medical Informatics
Open Access

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Research Article

Application of High-Dimensional Statistics and Network Based Visualization Techniques on Arab Diabetes and Obesity Data

Raghvendra Mall1#, Reda Rawi1,2#, Ehsan Ullah1, Khalid Kunji1, Abdelkrim Khadir3, Ali Tiss3, Jehad Abubaker3, Michal A Kulinski5, Mohammad M Ramzi5, Mohammed Dehbi4* and Halima Bensmail1*

1Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

2Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA

3Dasman Diabetes Institute, Kuwait City, Kuwait

4Diabetes Research Centre, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar

5Hamad Medical Corporation, Doha, Qatar

#Authors contributed equally

*Corresponding Author:
Mohammed Dehbi
Qatar Biomedical Research Institute
Hamad Bin Khalifa University
Doha, Qatar
Tel: +974-44546339
E-mail: [email protected]
Halima Bensmail
Qatar Computing Research Institute
Hamad Bin Khalifa University
Doha, Qatar
Tel: +(974) 4454 0195
E-mail: [email protected]

Received Date: March 23, 2017; Accepted Date: April 05, 2017; Published Date: April 12, 2017

Citation: Mall R, Rawi R, Ullah E, Kunji K, Khadir A, et al. (2017) Application of High-Dimensional Statistics and Network Based Visualization Techniques on Arab Diabetes and Obesity Data. J Health Med Informat 8:257. doi: 10.4172/2157-7420.1000257

Copyright: © 2017 Mall R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Background: Obesity and its co-morbidities are characterized by a chronic low-grade in amatory state, uncontrolled expression of metabolic measurements and dis-regulation of various forms of stress response. However, the contribution and correlation of in ammation, metabolism and stress responses to the disease are not fully elucidated. In this paper a cross-sectional case study was conducted on clinical data comprising 117 human male and female subjects with and without Type 2 Diabetes (T2D). Characteristics such as anthropometric, clinical and biochemical measurements were collected. Methods: Association of these variables with T2D and BMI were assessed using penalized hierarchical linear and logistic regression. In particular, elastic net, hdi and glinternet were used as regularization models to distinguish between cases and controls. Differential network analysis using closed-form approach was performed to identify pairwise-interaction of variables that influence prediction of the phenotype. Results: For the 117 participants, physical variables such as PBF, HDL and TBW had absolute coefficients 0.75, 0.65 and 0.34 using the glinternet approach, biochemical variables such as MIP, ROS and RANTES were identified as determinants of obesity with some interaction between inflammatory markers such as IL-4, IL-6, MIP, CSF, Eotaxin and ROS. Diabetes was associated with a significant increase in Thiobarbituric Acid Reactive Substances (TBARS) which are considered as an index of endogenous lipid peroxidation and an increase in two inflammatory markers, MIP-1 and RANTES. Furthermore, we obtained 13 pairwise effects. The pairwise effects include pairs from and within physical, clinical and biochemical features, in particular metabolic, inflammatory, and oxidative stress markers. Conclusion: We showcase those markers of oxidative stress (derived from lipid peroxidation) such as MIP-1 and RANTES participate in the pathogenesis of diseases such as diabetes and obesity in the Arab population.


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