Next Article in Journal
Ultrasound Characterization of Patellar Tendon in Non-Elite Sport Players with Painful Patellar Tendinopathy: Absolute Values or Relative Ratios? A Pilot Study
Previous Article in Journal
Limited Diagnostic Utility of Chromogranin A Measurements in Workup of Neuroendocrine Tumors
Article

Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors

1
Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
2
College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar
3
Geriatric Department, Hamad Medical Corporation, Doha 3050, Qatar
4
Faculty of Medicine, Ain Shams University, Alabasia 38, Cairo, Egypt
5
College of Health and Life Sciences, Hamad Bin Khalifa University, Doha 34110, Qatar
*
Author to whom correspondence should be addressed.
Diagnostics 2020, 10(11), 883; https://doi.org/10.3390/diagnostics10110883
Received: 21 September 2020 / Revised: 20 October 2020 / Accepted: 23 October 2020 / Published: 29 October 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Obesity is an emerging public health problem in the Western world as well as in the Gulf region. Qatar, a tiny wealthy county, is among the top-ranked obese countries with a high obesity rate among its population. Compared to Qatar’s severity of this health crisis, only a limited number of studies focused on the systematic identification of potential risk factors using multimodal datasets. This study aims to develop machine learning (ML) models to distinguish healthy from obese individuals and reveal potential risk factors associated with obesity in Qatar. We designed a case-control study focused on 500 Qatari subjects, comprising 250 obese and 250 healthy individuals- the later forming the control group. We obtained the most extensive collection of clinical measurements for the Qatari population from the Qatar Biobank (QBB) repertoire, including (i) Physio-clinical Biomarkers, (ii) Spirometry, (iii) VICORDER, (iv) DXA scan composition, and (v) DXA scan densitometry readings. We developed several machine learning (ML) models to distinguish healthy from obese individuals and applied multiple feature selection techniques to identify potential risk factors associated with obesity. The proposed ML model achieved over 90% accuracy, thereby outperforming the existing state of the art models. The outcome from the ablation study on multimodal clinical datasets revealed physio-clinical measurements as the most influential risk factors in distinguishing healthy versus obese subjects. Furthermore, multiple feature ranking techniques confirmed known obesity risk factors (c-peptide, insulin, albumin, uric acid) and identified potential risk factors linked to obesity-related comorbidities such as diabetes (e.g., HbA1c, glucose), liver function (e.g., alkaline phosphatase, gamma-glutamyl transferase), lipid profile (e.g., triglyceride, low density lipoprotein cholesterol, high density lipoprotein cholesterol), etc. Most of the DXA measurements (e.g., bone area, bone mineral composition, bone mineral density, etc.) were significantly (p-value < 0.05) higher in the obese group. Overall, the net effect of hypothesized protective factors of obesity on bone mass seems to have surpassed the hypothesized harmful factors. All the identified factors warrant further investigation in a clinical setup to understand their role in obesity. View Full-Text
Keywords: obesity; overweight; BMI; machine learning; bone mineral composition; bone mineral density; Qatar; Qatar Biobank (QBB) obesity; overweight; BMI; machine learning; bone mineral composition; bone mineral density; Qatar; Qatar Biobank (QBB)
Show Figures

Graphical abstract

MDPI and ACS Style

Khondaker, M.T.I.; Khan, J.Y.; Refaee, M.A.; Hajj, N.E.; Rahman, M.S.; Alam, T. Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors. Diagnostics 2020, 10, 883. https://doi.org/10.3390/diagnostics10110883

AMA Style

Khondaker MTI, Khan JY, Refaee MA, Hajj NE, Rahman MS, Alam T. Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors. Diagnostics. 2020; 10(11):883. https://doi.org/10.3390/diagnostics10110883

Chicago/Turabian Style

Khondaker, Md. T.I., Junaed Y. Khan, Mahmoud A. Refaee, Nady E. Hajj, M. S. Rahman, and Tanvir Alam. 2020. "Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors" Diagnostics 10, no. 11: 883. https://doi.org/10.3390/diagnostics10110883

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop