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

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

Characteristics of simulated samplesOf the 5000 Qataris in the simulated 2020 sample, prevalences of T2DM, obesity, smoking, and physical inactivity were 19.2%, 40.7%, 16.4%, and 49.3%, respectively (Table S1 of Supplementary Material [SM]). Similarly, in 2030, the prevalences were 20.4%, 43.8%, 16.6%, and 51.2%, respectively, and in 2050, they were 24.4%, 48.4%, 18.3%, and 57.0%, respectively (Table S1 of SM).Yields of a diabetes testing programFigure 1 and Table S2 of SM show the yields of a T2DM testing program, for each targeted subpopulation stratum. In 2020, numbers of obese, smoking, and physically inactive women that needed to be tested to identify one T2DM case ranged from 26.3, 52.4, and 64.8, respectively, for those 15–19 years old, to 2.7, 4.8, and 3.8, respectively, for those 75–79 years old (Fig. 1A). Similarly, numbers of obese, smoking, and physically inactive men that needed to be tested to identify one T2DM case ranged from 11.0, 20.8, and 23.0, respectively, for those 15–19 years old, to 2.3, 3.7, and 3.2, respectively, for those 75–79 years old (Fig. 1B). For individuals with none of these risk factors, the testing yield for women and men ranged from 67.6 and 23.3, respectively, for those 15–19 years old, to 4.9 and 3.9, respectively, for those 75–79 years old (Fig. 1). The yields in 2030 were relatively similar to those in 2020, while the yields in 2050 were superior to those in 2020 (Table S2).Figure 1Yields of a screening program for diabetes mellitus (DM) targeting different subpopulation strata of (A) women and (B) men in 2020. The yield is defined as the number of individuals needed to be screened for DM to identify one DM case. The targeted subpopulations are stratified by age-group and obesity, smoking, and physical inactivity statuses.Univariable and multivariable logistic regressionTable 1 shows the univariable and multivariable logistic regression results for 2020, 2030, and 2050, and the specific risk score for each variable. All considered covariables were significantly associated with T2DM in univariable-level analyses and remained so at the multivariable level (Table 1).Table 1 Multivariable logistic regression of risk factors for diabetes mellitus at three different time points: (A) 2020, (B) 2030, and (C) 2050.Overall, in the multivariable analysis, age and obesity were the strongest predictors for T2DM and contributed most to the risk score (Table 1). Individuals aged ≥ 55 were at substantially higher risk of T2DM compared to younger individuals. The specific risk score for age decreased with time, while the specific risk score for sex, obesity, smoking, and physical inactivity remained largely stable (Table 1).For illustration, the 2020 Qatari diabetes risk score was expressed using the formula illustrated in Box 1.Box 1 Formula for the Qatari diabetes risk score for 2020.Performance of the Qatari diabetes risk scoreIn 2020, the AUC was 0.79 (95% confidence interval [CI] 0.77–0.80; Table 2 and Fig. 2). The optimal combination of sensitivity of 79.0% (95% CI 76.3–81.4%) and specificity of 66.8% (95% CI 65.3–68.2%) was obtained at a score cut-off value of 26.5 (Table 2). PPV and NPV were 36.1% (95% CI 34.1–38.2%) and 93.0% (95% CI 92.1–93.9%), respectively. With a cut-off value of 26.5, 42.0% (95% CI 40.6–43.4%) of Qataris aged 15–79 years old were at high risk of having undiagnosed T2DM (that is a risk score value above or equal the cut-off value), and therefore recommended for glycemia testing (Table 2).Table 2 Performance of the Qatari diabetes risk score at three different time points: 2020, 2030, and 2050.Figure 2Receiver operating characteristic curves showing the performance of the Qatari diabetes risk score in diagnosing diabetes mellitus among Qataris at three time points: 2020, 2030, and 2050. The area under the curve (AUC) was 0.79 for the 2020 risk score, 0.78, for the 2030 risk score, and 0.76 for the 2050 risk score.In 2030, the AUC was 0.78 (95% CI 0.76–0.79; Table 2 and Fig. 2). The optimal combination of sensitivity of 77.5% (95% CI 74.9–80.0%) and specificity of 65.8% (95% CI 64.3–67.3%) was obtained at a score cut-off value of 24.5 (Table 2). PPV and NPV were 36.8% (95% CI 34.8–38.8%) and 92.0% (95% CI 90.9–92.9%), respectively. With a cut-off of 24.5, 43.0% (95% CI 41.6–44.4%) of Qataris aged 15–79 years old were at high risk of having undiagnosed T2DM (Table 2).In 2050, the AUC was 0.76 (95% CI 0.75–0.78; Table 2 and Fig. 2). The optimal combination of sensitivity of 74.4% (95% CI 71.9–76.8%) and specificity of 64.5% (95% CI 62.9–66.0%) was obtained at a cut-off of 25.5 (Table 2). PPV and NPV were 40.4% (95% CI 38.4–42.4%) and 88.7% (95% CI 87.4–89.8%), respectively. With a cut-off of 25.5, 45.0% (95% CI 43.6–46.4%) of Qataris aged 15–79 years old were at high risk of having undiagnosed T2DM (Table 2).In the sensitivity analysis in which the cut-off value was 34.5, a value chosen to achieve a specificity of 90%, 15.8% (95% CI 14.8–16.9%) of Qataris aged 15–79 years old were at high risk of having undiagnosed T2DM in 2020, and would therefore be recommended for glycemia testing. By maximizing the specificity, 59.1% of T2DM cases would be missed.In the sensitivity analysis, in which the cut-off was 18.5, a value chosen to achieve a sensitivity of 90%, 59.7% (95% CI 58.3–61.0%) of Qataris aged 15–79 years old were at high risk of having undiagnosed T2DM in 2020, and would therefore be recommended for glycemia testing. By maximizing sensitivity, only 10.0% of T2DM cases would be missed.Validation of the model-derived Qatari diabetes risk scoreTable S3 shows the data-derived risk score using the 2012 Qatar STEPwise Survey data. Table S4 shows also the 2012 model-derived risk score, as derived using the model outcomes.Table S5 shows performance of these two risk scores when both are applied to the 2012 STEPwise survey sample. For the model-derived risk score, the AUC was 0.69 (95% CI 0.66–0.72), similar to the AUC of the data-derived risk score of 0.70 (95% CI 0.68–0.73). Diagnostic performance was affirmed similar for both of these scores.Comparison with regional and international diabetes risk scoresTable 3 shows performance of regional (Emirati, Omani, and Saudi) and international (American, Danish, Dutch, Finnish, Taiwanese, and Thai) risk scores as applied to the 2020 Qatari sample. For all risk scores, the AUC ranged between 0.71 and 0.77; lower than the AUC of the Qatari risk score (0.79). Of the regional risk scores, the Emirati score had the largest AUC at 0.76 (95% CI 0.74–0.78) with a sensitivity of 62.5% (95% CI 59.4–65.5%) and a specificity of 77.4% (95% CI 76.1–78.7%). Of the international risk scores, the Danish score had the largest AUC at 0.77 (95% CI 0.76–0.79) with a sensitivity of 76.1% (95% CI 73.3–78.7%) and a specificity of 66.7% (95% CI 65.3–68.2%).Table 3 Performance of three regional and four international diabetes risk scores in predicting diabetes mellitus among Qataris in 2020.The Finnish, Taiwanese, and Thai risk scores showed very similar performance, and had the highest sensitivities at 84.8% (95% CI 82.4–86.9%), 84.3% (95% CI 81.8–86.5%), and 80.3% (95% CI 77.7–82.7%), respectively; but (predictably) had the lowest specificities at 55.5% (95% CI 53.9–57.0%), 56.8% (95% CI 55.3–58.4%), and 59.7% (95% CI 58.2–61.2%), respectively. Of all risk scores, the Omani risk score showed the lowest sensitivity at 56.7% (95% CI 53.6–59.8%), but the highest specificity at 79.6% (95% CI 78.3–80.8%).

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