Screening for diabetes and impaired glucose metabolism in Qatar: Models’ development and validation

Published:October 26, 2021DOI:https://doi.org/10.1016/j.pcd.2021.10.002

      Highlights

      • The prevalence of diabetes in Qatar (15.6%) is one of highest worldwide.
      • We propose models screening for diabetes and impaired glucose metabolism in Qatar.
      • The models performed well with area under the curve ranging from .774 to .870.
      • The models are based on demographics, past history and anthropometric measurements.
      • The proposed models can be used for primary prevention of diabetes in Qatar.

      Abstract

      Aim

      To establish two scoring models for identifying individuals at risk of developing Impaired Glucose Metabolism (IGM) or Type two Diabetes Mellitus (T2DM) in Qatari.

      Materials and methods

      A sample of 2000 individuals, from Qatar BioBank, was evaluated to determine features predictive of T2DM and IGM. Another sample of 1000 participants was obtained for external validation of the models. Several scoring models screening for T2DM were evaluated and compared to the model proposed by this study.

      Results

      Age, gender, waist-to-hip-ratio, history of hypertension and hyperlipidemia, and levels of educational were statistically associated with the risk of T2DM and constituted the Qatar diabetes mellitus risk score (QDMRISK). Along with, the 6 aforementioned variables, the IGM model showed that BMI was statistically significant. The QDMRISK performed well with area under the curve (AUC) 0.870 and .815 in the development and external validation cohorts, respectively. The QDMRISK showed overall better accuracy and calibration compared to other evaluated scores. The IGM model showed good accuracy and calibration, with AUCs .796 and .774 in the development and external validation cohorts, respectively.

      Conclusions

      This study developed Qatari-specific diabetes and IGM risk scores to identify high risk individuals and can guide the development of a nationwide primary prevention program.

      Keywords

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      References

        • Federation I.D.
        IDF Diabetes Atlas.
        9th edn. Brussels, Belgium2019
        • American Diabetes, A
        Diagnosis and classification of diabetes mellitus.
        Diabetes Care. 2014; 37: S81-90
        • American Diabetes, A
        Economic costs of diabetes in the U.S. in 2017.
        Diabetes Care. 2018; 41: 917-928
        • Echouffo-Tcheugui J.B.
        • et al.
        Screening for type 2 diabetes and dysglycemia.
        Epidemiol. Rev. 2011; 33: 63-87
        • Schulze M.B.
        • et al.
        An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes.
        Diabetes Care. 2007; 30: 510-515
        • Chen L.
        • et al.
        AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.
        Med. J. Aust. 2010; 192: 197-202
        • Glümer C.
        • et al.
        A Danish Diabetes Risk Score for Targeted Screening. The Inter99 study.
        Diabetes Care. 2004; 27: 727-733
        • Lindstrom J.
        • Tuomilehto J.
        The Diabetes Risk Score: a practical tool to predict type 2 diabetes risk.
        Diabetes Care. 2003; 26: 725-731
        • Al-Lawati J.A.
        • Tuomilehto J.
        Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East.
        Diabetes Res. Clin. Pract. 2007; 77: 438-444
        • Sulaiman N.
        • et al.
        Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitus.
        BMJ Open Diabetes Res. Care. 2018; 6: e000489
        • Chien K.
        • et al.
        A prediction model for type 2 diabetes risk among Chinese people.
        Diabetologia. 2008; 52: 443
        • Wilson P.F.
        • et al.
        Prediction of incident diabetes mellitus in middle-aged adults: the framingham offspring study.
        Arch. Intern. Med. 2007; 167: 1068-1074
        • Wang A.
        • et al.
        Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study.
        Sci. Rep. 2016; 6: 26548
        • Khalaf M.
        • et al.
        Screening for Diabetes in Kuwait and Evaluation of Risk Scores. Vol. 16. 2010: 725-731
        • Kahn H.S.
        • et al.
        Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years.
        Ann. Intern. Med. 2009; 150: 741-751
        • Balkau B.
        • et al.
        Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR).
        Diabetes Care. 2008; 31: 2056-2061
        • Aekplakorn W.
        • et al.
        A risk score for predicting incident diabetes in the Thai population.
        Diabetes Care. 2006; 29: 1872-1877
        • Mohan V.
        • et al.
        A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects.
        J. Assoc. Physicians India. 2005; 53: 759-763
        • Al Kuwari H.
        • et al.
        The Qatar biobank: background and methods.
        BMC Public Health. 2015; 15: 1208
        • Al Thani A.
        • et al.
        Qatar biobank cohort study: study design and first results.
        Am. J. Epidemiol. 2019; 188: 1420-1433
      1. Qatar Biobank for Medical Research. January 20, 2021; Available from: http://www.qatarbiobank.org.qa/home.

        • Engelgau M.M.
        • et al.
        Screening for diabetes mellitus in adults. The utility of random capillary blood glucose measurements.
        Diabetes Care. 1995; 18: 463-466
        • Organization., W.H
        Obesity and Overweight Fact Sheet.
        2017
        • Glümer C.
        • et al.
        A Danish diabetes risk score for targeted screening: the Inter99 study.
        Diabetes Care. 2004; 27: 727-733
        • Awad S.F.
        • et al.
        A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes.
        Sci. Rep. 2021; 11: 1811
        • Hosmer D.W.
        • Hjort N.L.
        Goodness-of-fit processes for logistic regression: simulation results.
        Stat. Med. 2002; 21: 2723-2738
        • Halter J.B.
        Aging and insulin secretion.
        in: Edward S.N.A. Masoro J. Handbook of the Biology of Aging. Elsevier Inc., 2011: 373-384
        • Association, A.D
        Standards of medical care in diabetes—2013.
        Diabetes Care. 2013; 36: S11-S66
        • Diabetes Prevention Program Research, G
        • et al.
        10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study.
        Lancet. 2009; 374: 1677-1686
        • Tuomilehto J.
        • et al.
        Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.
        N. Engl. J. Med. 2001; 344: 1343-1350
        • Knowler W.C.
        • et al.
        Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.
        N. Engl. J. Med. 2002; 346: 393-403
        • American Diabetes, A
        Economic costs of diabetes in the U.S. In 2012.
        Diabetes Care. 2013; 36: 1033-1046
        • Diabetes Prevention Program Research, G
        The 10-year cost-effectiveness of lifestyle intervention or metformin for diabetes prevention: an intent-to-treat analysis of the DPP/DPPOS.
        Diabetes Care. 2012; 35: 723-730
        • Rich S.S.
        Mapping genes in diabetes: genetic epidemiological perspective.
        Diabetes. 1990; 39: 1315-1319
        • Kekalainen P.
        • et al.
        Hyperinsulinemia cluster predicts the development of type 2 diabetes independently of family history of diabetes.
        Diabetes Care. 1999; 22: 86-92