Dysglycemia risk score in Saudi Arabia: A tool to identify people at high future risk of developing type 2 diabetes

• Published in: Journal of diabetes investigation 2020-07, Vol.11 (4), p.844-855
• Main Authors: Bahijri, Suhad, Al‐Raddadi, Rajaa, Ajabnoor, Ghada, Jambi, Hanan, Al Ahmadi, Jawaher, Borai, Anwar, Barengo, Noël C, Tuomilehto, Jaakko
• Format: Article
• Language: English
• Subjects: Adult; Age; Age Factors; Aged; Antihypertensives; Asymptomatic; Blood pressure; Body mass index; Cardiovascular disease; Cluster Analysis; Cross-Cultural Comparison; Cross-Sectional Studies; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - diagnosis; Diabetes Mellitus, Type 2 - epidemiology; Diabetes risk score; Dysglycemia; Family medical history; Fasting; Female; Finland - epidemiology; Glucose; Glucose tolerance; Glucose Tolerance Test; Hemoglobin; Humans; Hyperglycemia; Hypertension; Laboratories; Lifestyles; Logistic Models; Male; Middle Aged; Original; Physical activity; Plasma; Population; Prediabetic State - diagnosis; Prediabetic State - epidemiology; Prevalence; Questionnaires; Regression analysis; Risk Assessment - standards; Risk Factors; ROC Curve; Sample size; Saudi Arabia - epidemiology; Saudi population; Sensitivity and Specificity; Sex Factors; Sleep; Statistical analysis; Variables; Waist Circumference; Womens health; Young Adult
• ISSN: 2040-1116; 2040-1124
• DOI: 10.1111/jdi.13213

Aims/Introduction To develop a non‐invasive risk score to identify Saudis having prediabetes or undiagnosed type 2 diabetes. Methods Adult Saudis without diabetes were recruited randomly using a stratified two‐stage cluster sampling method. Demographic, dietary, lifestyle variables, personal and family medical history were collected using a questionnaire. Blood pressure and anthropometric measurements were taken. Body mass index was calculated. The 1‐h oral glucose tolerance test was carried out. Glycated hemoglobin, fasting and 1‐h plasma glucose were measured, and obtained values were used to define prediabetes and type 2 diabetes (dysglycemia). Logistic regression models were used for assessing the association between various factors and dysglycemia, and Hosmer–Lemeshow summary statistics were used to assess the goodness‐of‐fit. Results A total of 791 men and 612 women were included, of whom 69 were found to have diabetes, and 259 had prediabetes. The prevalence of dysglycemia was 23%, increasing with age, reaching 71% in adults aged ≥65 years. In univariate analysis age, body mass index, waist circumference, use of antihypertensive medication, history of hyperglycemia, low physical activity, short sleep and family history of diabetes were statistically significant. The final model for the Saudi Diabetes Risk Score constituted sex, age, waist circumference, history of hyperglycemia and family history of diabetes, with the score ranging from 0 to 15. Its fit based on assessment using the receiver operating characteristic curve was good, with an area under the curve of 0.76 (95% confidence interval 0.73–0.79). The proposed cut‐point for dysglycemia is 5 or 6, with sensitivity and specificity being approximately 0.7. Conclusion The Saudi Diabetes Risk Score is a simple tool that can effectively distinguish Saudis at high risk of dysglycemia. We aimed to develop a non‐ invasive risk score to identify Saudi individuals with prediabetes or undiagnosed type 2 diabetes. Adult Saudis without diabetes were recruited randomly from the Saudi population. Various blood tests were used to define prediabetes and type 2 diabetes (dysglycemia). The final model for the Saudi Diabetes Risk Score constituted sex, age, waist circumference, history of hyperglycemia and family history of diabetes, with the score ranging from 0 to 15, and the cut‐point for dysglycemia being 5 or 6.