Screening for Type 2 Diabetes and Dysglycemia in Saudi Arabia: Development and Validation of Risk Scores

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

Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.

COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar

We developed a Coronavirus Disease 2019 (COVID-19) risk score to guide targeted RT-PCR testing in Qatar. The Qatar national COVID-19 testing database, encompassing a total of 2,688,232 RT-PCR tests conducted between February 5, 2020-January 27, 2021, was analyzed. Logistic regression analyses were implemented to derive the COVID-19 risk score, as a tool to identify those at highest risk of having the infection. Score cut-off was determined using the ROC curve based on maximum sum of sensitivity and specificity. The score’s performance diagnostics were assessed. Logistic regression analysis identified age, sex, and nationality as significant predictors of infection and were included in the risk score. The ROC curve was generated and the area under the curve was estimated at 0.63 (95% CI: 0.63–0.63). The score had a sensitivity of 59.4% (95% CI: 59.1%-59.7%), specificity of 61.1% (95% CI: 61.1%-61.2%), a positive predictive value of 10.9% (95% CI: 10.8%-10.9%), and a negative predictive value of 94.9% (94.9%-95.0%). The concept and utility of a COVID-19 risk score were demonstrated in Qatar. Such a public health tool can have considerable utility in optimizing testing and suppressing infection transmission, while maximizing efficiency and use of available resources.

Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

OBJECTIVES

This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).

DATA SOURCES

Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.

STUDY SELECTION

Articles published between December 2011 and October 2019 were considered.

DATA EXTRACTION

For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.

RESULTS

The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.

CONCLUSIONS

Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

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

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.

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

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.

The prevalence of undiagnosed type 2 diabetes and prediabetes in Eastern Mediterranean region (EMRO): A systematic review and meta-analysis

BACKGROUND

Previous studies of diabetes in Eastern Mediterranean Region (EMRO) did not assess the prevalence of either unknown diabetes or prediabetes. We conducted a systematic review and meta-analysis to estimate the prevalence of undiagnosed type 2 diabetes and prediabetes as well as variations by region in EMRO, using the relevant publications since 2000.

METHODS

We carried out a comprehensive electronic search on electronic databases from January 1, 2000 to March 1, 2018. We selected cross-sectional and cohort studies reporting the prevalence of undiagnosed type 2 diabetes, prediabetes, or both. Two independent reviewers initially screened the eligible articles; then, synthesized the target data from full papers. Random- or fixed-effect models, subgroup analysis on Human Development Index (HDI), and publication year and sensitivity analysis to minimize the plausible effect of outliers were used.

RESULTS

Amongst 849 identified citations, 55 articles were entered into meta-analysis, involving 567,025 individuals. The forest plots estimated 5.46% (confidence intervals [CI]: 4.77-6.14) undiagnosed diabetic and 12.19% (CI: 10.13-14.24) prediabetics in EMRO. Low HDI countries and high HDI countries had the highest (7.25%; CI: 4.59-9.92) and the lowest (3.98%; CI: 3.11-4.85) undiagnosed diabetes prevalence, respectively. Very high HDI countries and low HDI countries had the highest (13.50%; CI: 8.43-18.57) and the lowest (7.45%; 1.20-13.71) prediabetes prevalence, respectively. In addition, meta-regression analysis showed a statistically significant association between publication year and prevalence of prediabetes (Reg Coef = 0.059, P = 0.014). But such finding was not observed for undiagnosed diabetes and publication year (Reg Coef = 0.034, P = 0.124), prediabetes and HDI (Reg Coef = 0.128, P = 0.31) and undiagnosed diabetes and HDI (Reg Coef = - 0.04, P = 0.96).

CONCLUSION

The prevalence of undiagnosed diabetes and prediabetes was high and increasing. The notion of universal health coverage is a priority; that is the integration of the primary, secondary and tertiary health levels, as well as employing the available action plans. Therefore, future studies, using identical screening tool and diagnostic criteria, are warranted to make an accurate picture of diabetes in EMRO.

Diabetes Risk among Medical Students in Tabuk City, Saudi Arabia

<b><i>Introduction:</i></b> Diabetes risk estimation is essential for the implementation of preventive measures. <b><i>Objectives:</i></b> We aimed to assess the diabetes risk among medical students in Tabuk, Saudi Arabia. <b><i>Methods:</i></b> This cross-sectional study was conducted among 169 medical students in the Medical College, University of Tabuk, Saudi Arabia, from October 2017 to April 2018. Participants signed a written informed consent and then responded to a questionnaire modified from the Finnish and the ARABRISK diabetes score. The questionnaire consisted of eight components inquiring about age, BMI, central adiposity, fruit and vegetable consumption, physical activity if found to have high blood pressure or blood sugar, and family history of diabetes mellitus. The Statistical Package for Social Sciences (SPSS) was used for data analysis. <b><i>Results:</i></b> Out of 169 students (68% with a family history of diabetes), obesity and overweight were found in 21.3 and 26.6%, respectively, 45.6% had central adiposity, more than half were not practicing exercise daily, and 60.4% were not consuming fruits and vegetables daily. A significant percentage was found to have high blood sugar (9.5%) and high blood pressure (4.7%). The diabetes risk score was high or moderate in 16% of the students. <b><i>Conclusion:</i></b> Medical students in Tabuk City were at high risk for diabetes mellitus. Obesity, overweight, central adiposity, physical inactivity, and less consumption of fruits and vegetables substantially contributed to the risk. Measures to prevent obesity, improving fruit and vegetable consumption, and exercise are needed.

Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitus

OBJECTIVE

The objective of this study was to develop a simple non-invasive risk score, specific to the United Arab Emirates (UAE) citizens, to identify individuals at increased risk of having undiagnosed type 2 diabetes mellitus.

RESEARCH DESIGN AND METHODS

A retrospective analysis of the UAE National Diabetes and Lifestyle data was conducted. The data included demographic and anthropometric measurements, and fasting blood glucose. Univariate analyses were used to identify the risk factors for diabetes. The risk score was developed for UAE citizens using a stepwise forward regression model.

RESULTS

A total of 872 UAE citizens were studied. The overall prevalence of diabetes in the UAE adult citizens in the Northern Emirates was 25.1%. The significant risk factors identified for diabetes were age (≥35 years), a family history of diabetes mellitus, hypertension, body mass index ≥30.0 and waist-to-hip ratio ≥0.90 for males and ≥0.85 for females. The performance of the model was moderate in terms of sensitivity (75.4%, 95% CI 68.3 to 81.7) and specificity (70%, 95% CI 65.8 to 73.9). The area under the receiver-operator characteristic curve was 0.82 (95% CI 0.78 to 0.86).

CONCLUSIONS

A simple, non-invasive risk score model was developed to help to identify those at high risk of having diabetes among UAE citizens. This score could contribute to the efficient and less expensive earlier detection of diabetes in this high-risk population.

Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting

Objective. To develop and validate a risk score for detecting cases of undiagnosed diabetes in a resource-constrained country. Methods. Two population-based studies in Peruvian population aged ≥35 years were used in the analysis: the ENINBSC survey (n = 2,472) and the CRONICAS Cohort Study (n = 2,945). Fasting plasma glucose ≥7.0 mmol/L was used to diagnose diabetes in both studies. Coefficients for risk score were derived from the ENINBSC data and then the performance was validated using both baseline and follow-up data of the CRONICAS Cohort Study. Results. The prevalence of undiagnosed diabetes was 2.0% in the ENINBSC survey and 2.9% in the CRONICAS Cohort Study. Predictors of undiagnosed diabetes were age, diabetes in first-degree relatives, and waist circumference. Score values ranged from 0 to 4, with an optimal cutoff ≥2 and had a moderate performance when applied in the CRONICAS baseline data (AUC = 0.68; 95% CI: 0.62-0.73; sensitivity 70%; specificity 59%). When predicting incident cases, the AUC was 0.66 (95% CI: 0.61-0.71), with a sensitivity of 69% and specificity of 59%. Conclusions. A simple nonblood based risk score based on age, diabetes in first-degree relatives, and waist circumference can be used as a simple screening tool for undiagnosed and incident cases of diabetes in Peru.