BackgroundType 2 (T2DM) is believed to be common in Saudi Arabia, but data are limited. In this population survey, we determined the prevalence of T2DM and prediabetes.Materials and MethodsA representative sample among residents aged ≥ 18 years of the city of Jeddah was obtained comprising both Saudi and non-Saudi families (N = 1420). Data on dietary, clinical and socio-demographic characteristics were collected and anthropometric measurements taken. Fasting plasma glucose and glycated hemoglobin (HbA1c) were used to diagnose diabetes and prediabetes employing American Diabetes Association criteria. Multiple logistic regression analysis was used to identify factors associated with T2DM.ResultsAge and sex standardized prevalence of prediabetes was 9.0% (95% CI 7.5–10.5); 9.4% (7.1–11.8) in men and 8.6% (6.6–10.6) in women. For DM it was 12.1% (10.7–13.5); 12.9% (10.7–13.5) in men and 11.4% (9.5–13.3) in women. The prevalence based on World Population as standard was 18.3% for DM and 11.9% for prediabetes. The prevalence of DM and prediabetes increased with age. Of people aged ≥50 years 46% of men and 44% of women had DM. Prediabetes and DM were associated with various measures of adiposity. DM was also associated with and family history of dyslipidemia in women, cardiovascular disease in men, and with hypertension, dyslipidemia and family history of diabetes in both sexes.DiscussionAge was the strongest predictor of DM and prediabetes followed by obesity. Of people aged 50 years or over almost half had DM and another 10–15% had prediabetes leaving only a small proportion of people in this age group with normoglycemia. Since we did not use an oral glucose tolerance test the true prevalence of DM and prediabetes is thus likely to be even higher than reported here. These results demonstrate the urgent need to develop primary prevention strategies for type 2 diabetes in Saudi Arabia.

Background: Obesity is a risk factor for many chronic diseases, and its prevalence and trends vary among populations. Saudi Arabia shows a greater rise in prevalence than many other countries. We aimed to study the association between several chronic disorders, demographic, and lifestyle factors with increased body mass index (BMI) in the adult population of Jeddah. Methods: Data were obtained from a door-to-door cross sectional study. A three-stage stratified cluster sampling technique was adopted. Individuals in selected households agreeing to participate were interviewed to complete a predesigned questionnaire covering demographic and lifestyle variables, medical history, and family history of chronic diseases. This was followed by anthropometric and blood pressure measurements. A random capillary plasma glucose (RPG) was measured, followed by further testing using fasting plasma glucose and glycated hemoglobin (HbA1c) to verify whether participants were normal, diabetic, or prediabetic. Multiple logistic regression analyses were used to adjust for confounding factors. Results: A total of 1419 individuals were included in the study: 667 men and 752 women. The prevalence of overweight and obesity was 35.1 and 34.8%, respectively, in men, and 30.1% and 35.6%, respectively, in women. Both overweight and obesity increased in prevalence to 60 years of age, and decreased in the oldest age group in both sexes. After adjusting for age, risk of obesity in men was increased with having a postgraduate degree [odds ratio (OR), 95%CI = 2.48, 1.1–5.61] and decreased with increased physical activity (OR, 95%CI = 0.49, 0.26–0.91). Risk of prediabetes and diabetes was increased in obese women (OR, 95%CI = 2.94, 1.34–6.44, and 3.61, 1.58–8.26 respectively), that of hypertension in obese men (OR, 95%CI =2.62, 1.41–4.87), and that of dyslipidemia in both sexes (OR, 95%CI = 2.60, 1.40–4.83 in men, and 2.0, 1.01–3.85 in women). A family history of dyslipidemia was associated with reduced risk of obesity among women (OR, 95%CI = 0.33, 0.12–0.92), whereas, in people with above normal weight (BMI ⩾25), there was increased risks of prediabetes, diabetes, and dyslipidemia among women (OR, 95%CI = 2.50, 1.21–5.17; 3.20, 1.45–7.03, and 1.88, 1.02–3.49, respectively ), and of hypertension among men (OR, 95%CI = 1.80, 1.00–3.23). Conclusions: The prevalence of overweight and obesity in the Saudi population remain high, indicating ineffectiveness or lack of preventive measures. Risk of prediabetes, diabetes, dyslipidemia, and hypertension increased with increasing BMI, with some sex differences in these associations.

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 twostage 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.

Diet and other lifestyle habits have been reported to contribute to the development of dyslipidemia in various populations. Therefore, this study investigated the association between dyslipidemia and dietary and other lifestyle practices among Saudi adults. Data were collected from adults (≥20 years) not previously diagnosed with diabetes in a cross-sectional design. Demographic, anthropometric, and clinical characteristics, as well as lifestyle and dietary habits were recorded using a predesigned questionnaire. Fasting blood samples were drawn to estimate the serum lipid profile. Out of 1385 people, 858 (62%) (491 men, 367 women) had dyslipidemia. After regression analysis to adjust for age, body mass index, and waist circumference, an intake of ≥5 cups/week of Turkish coffee, or carbonated drinks was associated with increased risk of dyslipidemia in men (OR (95% CI), 2.74 (1.53, 4.89) p = 0.001, and 1.53 (1.04, 2.26) p = 0.03 respectively), while the same intake of American coffee had a protective effect (0.53 (0.30, 0.92) p = 0.025). Sleep duration <6 h, and smoking were also associated with increased risk in men (1.573 (1.14, 2.18) p = 0.006, and 1.41 (1.00, 1.99) p = 0.043 respectively). In women, an increased intake of fresh vegetables was associated with increased risk (2.07 (1.09, 3.94) p = 0.026), which could be attributed to added salad dressing. Thus, there are sex differences in response to dietary and lifestyle practices.

Introduction: Early detection and treatment of dysglycemia including diabetes and prediabetes is demonstrated to improve disease outcomes and prevent complications. Objective: To assess the association of prediabetes with lipid metabolism disorders to clarify whether systematic screening for prediabetes should be proposed for individuals with dyslipidemia. Material and Methods: A cross-sectional study design, employing a stratified two-stage cluster sampling method recruited non-diabetic adults (age ≥18 years) from attendees of Primary Health Care (PHC) centers in Jeddah. Anthropomorphic measurements, demographic and clinical information were taken, and blood pressure was measured. Fasting blood sample was obtained for the measurement of plasma glucose (FPG), glycated hemoglobin (HbA1C), and lipid profile. Plasma glucose was estimated 1 hr after the ingestion of 50 g glucose (1h-OGTT). Prediabetes and dyslipidemia were defined according to international guidelines. Demographic and clinical factors of subjects with prediabetes, and those with normoglycemia were compared. Multiple logistic regression analysis was used to adjust for confounding factors. Results: A total of 613 individuals were included with a mean age (±SD) of 32±11.8years, and 54.8% being female. Prediabetes was detected in 28.7%, and dyslipidemia in 54.2% of participants. After adjusting for age, an association was found for high low-density lipoproteincholesterol (LDL-C) and prediabetes based on any definition. After adjustment for body mass index (BMI), the association was retained for any type of dyslipidemia and in particular high LDL-C. After adjusting for both age and BMI, a significant association was found only between high LDL-C and prediabetes based on any definition (OR, 95% CI=1.50,1.02-2.19, P= 0.037). Conclusion: Even though high LDL-C is associated with an increased probability of prediabetes, a recommendation for universal screening of dyslipidemic patients requires further cohort studies.