Abstract
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.
Highlights
Obesity is a chronic, multifactorial disease associated with multiple comorbidities including diabetes, cardiovascular disease, stroke, hypertension as well as different types of cancers [1,2,3].Diabetes has the strongest association with obesity [4], where more than 80% of type 2 diabetes cases are either overweight or obese [5]
The objective of our study is to develop a new machine learning (ML) model to distinguish obese individuals based on 236 clinical measurements, collected from Qatar Biobank (QBB) [23,24] and to identify obesity-associated risk factors in the Qatari population
When we stratified the participants based on age, we found 116 (23%), 208
Summary
Multifactorial disease associated with multiple comorbidities including diabetes, cardiovascular disease, stroke, hypertension as well as different types of cancers [1,2,3].Diabetes has the strongest association with obesity [4], where more than 80% of type 2 diabetes cases are either overweight or obese [5]. Obesity is considered as one of the risk factors for osteoarthritis [7,8,9], ischemic stroke [10], and atrial fibrillation [11]. Lipoprotein abnormalities, such as a change in high-density lipoprotein cholesterol, are closely related to obesity [12]. Obesity and its related comorbidities have a negative impact on the health care systems in several countries In this regard, Qatar is not an exception as reported by Mandeya et al [13], where the negative impact of obesity on the community, healthcare services, and economy of the country is described
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