Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors
<p dir="ltr">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...
محفوظ في:
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2020
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| _version_ | 1864513512369291264 |
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| author | Md. Tawkat Islam Khondaker (16870107) |
| author2 | Junaed Younus Khan (16870110) Mahmoud Ahmed Refaee (16896423) Nady El Hajj (8552577) M. Sohel Rahman (12056885) Tanvir Alam (638619) |
| author2_role | author author author author author |
| author_facet | Md. Tawkat Islam Khondaker (16870107) Junaed Younus Khan (16870110) Mahmoud Ahmed Refaee (16896423) Nady El Hajj (8552577) M. Sohel Rahman (12056885) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Tawkat Islam Khondaker (16870107) Junaed Younus Khan (16870110) Mahmoud Ahmed Refaee (16896423) Nady El Hajj (8552577) M. Sohel Rahman (12056885) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2020-10-29T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/diagnostics10110883 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Obesity_in_Qatar_A_Case-Control_Study_on_the_Identification_of_Associated_Risk_Factors/26095063 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Medical biochemistry and metabolomics Nutrition and dietetics Information and computing sciences Machine learning obesity overweight BMI machine learning bone mineral composition bone mineral density Qatar Biobank (QBB) Qatar |
| dc.title.none.fl_str_mv | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/diagnostics10110883" target="_blank">https://dx.doi.org/10.3390/diagnostics10110883</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c928a71c30945909793d925b0deba263 |
| identifier_str_mv | 10.3390/diagnostics10110883 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26095063 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk FactorsMd. Tawkat Islam Khondaker (16870107)Junaed Younus Khan (16870110)Mahmoud Ahmed Refaee (16896423)Nady El Hajj (8552577)M. Sohel Rahman (12056885)Tanvir Alam (638619)Biological sciencesBioinformatics and computational biologyBiomedical and clinical sciencesMedical biochemistry and metabolomicsNutrition and dieteticsInformation and computing sciencesMachine learningobesityoverweightBMImachine learningbone mineral compositionbone mineral densityQatar Biobank (QBB)Qatar<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/diagnostics10110883" target="_blank">https://dx.doi.org/10.3390/diagnostics10110883</a></p>2020-10-29T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/diagnostics10110883https://figshare.com/articles/journal_contribution/Obesity_in_Qatar_A_Case-Control_Study_on_the_Identification_of_Associated_Risk_Factors/26095063CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260950632020-10-29T09:00:00Z |
| spellingShingle | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors Md. Tawkat Islam Khondaker (16870107) Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Medical biochemistry and metabolomics Nutrition and dietetics Information and computing sciences Machine learning obesity overweight BMI machine learning bone mineral composition bone mineral density Qatar Biobank (QBB) Qatar |
| status_str | publishedVersion |
| title | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| title_full | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| title_fullStr | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| title_full_unstemmed | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| title_short | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| title_sort | Obesity in Qatar: A Case-Control Study on the Identification of Associated Risk Factors |
| topic | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Medical biochemistry and metabolomics Nutrition and dietetics Information and computing sciences Machine learning obesity overweight BMI machine learning bone mineral composition bone mineral density Qatar Biobank (QBB) Qatar |