A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

Abstract We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Susanne F. Awad, Soha R. Dargham, Amine A. Toumi, Elsy M. Dumit, Katie G. El-Nahas, Abdulla O. Al-Hamaq, Julia A. Critchley, Jaakko Tuomilehto, Mohamed H. J. Al-Thani, Laith J. Abu-Raddad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7fb478e20e8f46c4b29a1b003c980897
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7fb478e20e8f46c4b29a1b003c980897
record_format	dspace
spelling	oai:doaj.org-article:7fb478e20e8f46c4b29a1b003c9808972021-12-02T10:49:22ZA diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes10.1038/s41598-021-81385-32045-2322https://doaj.org/article/7fb478e20e8f46c4b29a1b003c9808972021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81385-3https://doaj.org/toc/2045-2322Abstract We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.Susanne F. AwadSoha R. DarghamAmine A. ToumiElsy M. DumitKatie G. El-NahasAbdulla O. Al-HamaqJulia A. CritchleyJaakko TuomilehtoMohamed H. J. Al-ThaniLaith J. Abu-RaddadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Susanne F. Awad Soha R. Dargham Amine A. Toumi Elsy M. Dumit Katie G. El-Nahas Abdulla O. Al-Hamaq Julia A. Critchley Jaakko Tuomilehto Mohamed H. J. Al-Thani Laith J. Abu-Raddad A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
description	Abstract We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.
format	article
author	Susanne F. Awad Soha R. Dargham Amine A. Toumi Elsy M. Dumit Katie G. El-Nahas Abdulla O. Al-Hamaq Julia A. Critchley Jaakko Tuomilehto Mohamed H. J. Al-Thani Laith J. Abu-Raddad
author_facet	Susanne F. Awad Soha R. Dargham Amine A. Toumi Elsy M. Dumit Katie G. El-Nahas Abdulla O. Al-Hamaq Julia A. Critchley Jaakko Tuomilehto Mohamed H. J. Al-Thani Laith J. Abu-Raddad
author_sort	Susanne F. Awad
title	A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
title_short	A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
title_full	A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
title_fullStr	A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
title_full_unstemmed	A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
title_sort	diabetes risk score for qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7fb478e20e8f46c4b29a1b003c980897
work_keys_str_mv	AT susannefawad adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT sohardargham adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT amineatoumi adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT elsymdumit adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT katiegelnahas adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT abdullaoalhamaq adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT juliaacritchley adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT jaakkotuomilehto adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT mohamedhjalthani adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT laithjaburaddad adiabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT susannefawad diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT sohardargham diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT amineatoumi diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT elsymdumit diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT katiegelnahas diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT abdullaoalhamaq diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT juliaacritchley diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT jaakkotuomilehto diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT mohamedhjalthani diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes AT laithjaburaddad diabetesriskscoreforqatarutilizinganovelmathematicalmodelingapproachtoidentifyindividualsathighriskfordiabetes
_version_	1718396600781373440

A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

Ejemplares similares