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Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The [email protected] Study

  • Author Footnotes
    1 These authors contributed equally to this work.
    Sergio Martínez-Hervás
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Department of Medicine, University of Valencia, Avenida Blasco Ibañez 15, Valencia 46010, Spain

    Service of Endocrinology and Nutrition, Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain

    INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain

    CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain
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  • Author Footnotes
    1 These authors contributed equally to this work.
    María M. Morales-Suarez-Varela
    Footnotes
    1 These authors contributed equally to this work.
    Affiliations
    Department of Preventive Medicine, Unit of Public Health and Environmental Care, University of Valencia, Vicente Andres Estelles Avenue, Burjassot, Valencia 46100, Spain

    CIBER of Epidemiology and Public Health (CIBERESP), Monforte de Lemos 3-5, Madrid 28029, Spain
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  • Irene Andrés-Blasco
    Affiliations
    Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
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  • Francisco Lara-Hernández
    Affiliations
    Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
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  • Isabel Peraita-Costa
    Affiliations
    Department of Preventive Medicine, Unit of Public Health and Environmental Care, University of Valencia, Vicente Andres Estelles Avenue, Burjassot, Valencia 46100, Spain

    CIBER of Epidemiology and Public Health (CIBERESP), Monforte de Lemos 3-5, Madrid 28029, Spain
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  • José T. Real
    Correspondence
    CIBER of Diabetes and Associated metabolic Diseases (CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain), INCLIVA Biomedical Research Institute, Menendez Pelayo 4cc, Valencia 46010, Spain (A-B.G-G and J.T.R) and Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain (J.T.R)
    Affiliations
    Department of Medicine, University of Valencia, Avenida Blasco Ibañez 15, Valencia 46010, Spain

    Service of Endocrinology and Nutrition, Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain

    INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain

    CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain
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  • Ana-Bárbara García-García
    Correspondence
    CIBER of Diabetes and Associated metabolic Diseases (CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain), INCLIVA Biomedical Research Institute, Menendez Pelayo 4cc, Valencia 46010, Spain (A-B.G-G and J.T.R) and Valencia University Clinical Hospital, Avenida Blasco Ibañez 17, Valencia 46010, Spain (J.T.R)
    Affiliations
    CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain

    Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
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  • F. Javier Chaves
    Affiliations
    CIBER of Diabetes and Associated Metabolic Diseases CIBERDEM, Monforte de Lemos 3-5, Madrid 28029, Spain

    Genomic and Diabetes Unit, INCLIVA Biomedical Research Institute, Menendez Pelayo 4acc, Valencia 46010, Spain
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  • Author Footnotes
    1 These authors contributed equally to this work.

      Highlights

      • We have developed a simple model for T2DM prediction based on basic parameters.
      • Higher FPG levels represent greater risk of incident T2DM in the next years. In intermediate FPG values, age determines T2DM risk and BMI in a second step.
      • In individuals with lower FPG, higher FTGs increases risk of developing T2DM. The present model correctly classified 93.5% of individuals.

      Abstract

      Aims

      To develop a simple multivariate predictor model of incident type 2 diabetes in general population.

      Methods

      Participants were recruited from the Spanish [email protected] cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model.

      Results

      156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92–106 mg/dL (OR = 3.76, 95%CI = 2.36–6.00), > 106 mg/dL (OR = 13.21; 8.26–21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89–43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs  = 97–180 mg/dL, OR = 3.12; 1.05–9.24). This model correctly classified 93.5% of individuals.

      Conclusions

      The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.

      Keywords

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