| Title: |
A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes. |
| Authors: |
Awad, SF; Dargham, SR; Toumi, AA; Dumit, EM; El-Nahas, KG; Al-Hamaq, AO; Critchley, JA; Tuomilehto, J; Al-Thani, MHJ; Abu-Raddad, LJ |
| Publisher Information: |
Nature Research |
| Publication Year: |
2021 |
| Collection: |
St George's University of London: Repository |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
2045-2322 |
| Relation: |
https://openaccess.sgul.ac.uk/id/eprint/112893/1/A%20diabetes%20risk%20score%20for%20Qatar%20utilizing%20a%20novel%20mathematical%20modeling%20approach%20to%20identify%20individuals%20at%20high%20risk%20for%20di.pdf; Awad, SF; Dargham, SR; Toumi, AA; Dumit, EM; El-Nahas, KG; Al-Hamaq, AO; Critchley, JA; Tuomilehto, J; Al-Thani, MHJ; Abu-Raddad, LJ (2021) A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes. Sci Rep, 11 (1). p. 1811. ISSN 2045-2322 https://doi.org/10.1038/s41598-021-81385-3 SGUL Authors: Critchley, Julia |
| Availability: |
https://openaccess.sgul.ac.uk/id/eprint/112893/; https://openaccess.sgul.ac.uk/id/eprint/112893/1/A%20diabetes%20risk%20score%20for%20Qatar%20utilizing%20a%20novel%20mathematical%20modeling%20approach%20to%20identify%20individuals%20at%20high%20risk%20for%20di.pdf |
| Rights: |
cc_by_4 |
| Accession Number: |
edsbas.31F55AA3 |
| Database: |
BASE |