Simple risk score to screen for prediabetes: A cross‐sectional study from the Qatar Biobank cohort

Abstract

Aims/Introduction: The progression from prediabetes to type 2 diabetes is preventable by lifestyle intervention and/or pharmacotherapy in a large fraction of individuals with prediabetes. Our objective was to develop a risk score to screen for prediabetes in the Middle East, where diabetes prevalence is one of the highest in the world. Materials and Methods: In this cross-sectional, case-control study, we used data of 4,895 controls and 2,373 prediabetic adults obtained from the Qatar Biobank cohort. Significant risk factors were identified by logistic regression and other machine learning methods. The receiver operating characteristic was used to calculate the area under curve, cutoff point, sensitivity, specificity, positive and negative predictive values. The prediabetes risk score was developed from data of Qatari citizens, as well as long-term (≥15 years) residents. Results: The significant risk factors for the Prediabetes Risk Score in Qatar were age, sex, body mass index, waist circumference and blood pressure. The risk score ranges from 0 to 45. The area under the curve of the score was 80% (95% confidence interval 78-83%), and the cutoff point of 16 yielded sensitivity and specificity of 86.2% (95% confidence interval 82.7-89.2%) and 57.9% (95% confidence interval 65.5-71.4%), respectively. Prediabetes Risk Score in Qatar performed equally in Qatari nationals and long-term residents. Conclusions: Prediabetes Risk Score in Qatar is the first prediabetes screening score developed in a Middle Eastern population. It only uses risk factors measured non-invasively, is simple, cost-effective, and can be easily understood by the general public and health providers. Prediabetes Risk Score in Qatar is an important tool for early detection of prediabetes, and can help tremendously in curbing the diabetes epidemic in the region.

Prediabetes in Syria and Its Associated Factors: A Single-Center Cross-Sectional Study

“…Additionally, prevalence in our study was lower than the prevalence in Ain Shams University in Egypt (36%) [17], Oman (44.2%) [18], and Kuwait (47.9%) [19]. Prevalence of prediabetes in our study was also lower than Qatar (32.6%) [20], Saudi Arabia (27.6%) [21], and Germany, where it was (33%) using IFG, (16%) using IGT, and (26%) using HbA1C [22]. However, prevalence in Syria was higher than that in Ethiopia (15.7%) [23] and UAE where it was between 8 and (17%) depending on ethnicity [24].…”

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review

“…The majority of the studies were conducted in Asia (37.5%) [ 7 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], six in the Middle East and North Africa (25.0%) [ 18 , 19 , 20 , 21 , 22 , 23 ], five in Europe [ 24 , 25 , 26 , 27 , 28 ], three in North America [ 29 , 30 , 31 ], and one in South America [ 32 ]. For the data source of the study sample, about half of the studies (54.2%) developed their risk prediction tools using retrospective population-level health data [ 7 , 11 , 12 , 15 , 16 , 17 , 18 , 22 , 25 , 26 , 28 , 29 , 31 ], while the remaining studies sampled their data from community-based health surveys [ 10 , 13 , 14 , 19 , 20 , 21 , 23 , 24 , 27 , 30 , 32 ]. The median development sample size was 2073 and ranged from 308 to 40,381.…”

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire

“…However, prediabetes presents overlapping pathophysiology of impaired insulin sensitivity and secretion [25,26]. Although screening tools for prediabetes have been developed [14,[16][17][18][19][20], this is the first study to develop a model to identify the glucose metabolism status of individuals without diabetes. This model encourages individuals to understand their glucose metabolism status and learn how they should change their lifestyle to prevent diabetes.…”

“…Unlike diabetics who need to take their medications, nondiabetic people have no strong motivation or coercion to take screening tests. Clinical measurement values, such as fasting plasma glucose and abdominal circumference, are valid predictors of glucose metabolism status [14,[17][18][19][20]. However, the need to link a tool to clinical laboratory data may limit their scope of use.…”

“…However, well-trained interviewers were needed to obtain dietary information such as GDSQ. Abbas et al [17] reported a risk score for screening individuals with high HbA1c. They used only noninvasively measured factors, including age, sex, body mass index (BMI), waist circumference, and blood pressure.…”