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Population-centric risk prediction modeling for gestational diabetes mellitus: A machine learning approach

  • Mukkesh Kumar
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore and National University Health System, Singapore, Republic of Singapore
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  • Li Chen
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Karen Tan
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Li Ting Ang
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Cindy Ho
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Gerard Wong
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Shu E Soh
    Affiliations
    Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
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  • Kok Hian Tan
    Affiliations
    Division of Obstetrics and Gynecology, KK Women’s and Children’s Hospital, Republic of Singapore

    Obstetrics and Gynecology Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Republic of Singapore
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  • Jerry Kok Yen Chan
    Affiliations
    Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore

    Department of Reproductive Medicine, KK Women’s and Children’s Hospital, Republic of Singapore

    Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Republic of Singapore
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  • Keith M Godfrey
    Affiliations
    MRC Lifecourse Epidemiology Unit & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, UK
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  • Shiao-yng Chan
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
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  • Mary Foong Fong Chong
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore and National University Health System, Singapore, Republic of Singapore
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  • John E Connolly
    Affiliations
    Institute of Molecular and Cell Biology, Agency for Science Technology and Research, Singapore, Republic of Singapore
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  • Yap Seng Chong
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
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  • Author Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Johan G Eriksson
    Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Department of Obstetrics and Gynaecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore

    Department of General Practice and Primary Health Care, University of Helsinki, Finland

    Folkhälsan Research Center, Helsinki, Finland
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  • Author Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Mengling Feng
    Correspondence
    Corresponding authors.
    Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Affiliations
    Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore and National University Health System, Singapore, Republic of Singapore
    Search for articles by this author
  • Author Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Neerja Karnani
    Correspondence
    Corresponding authors.
    Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
    Affiliations
    Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Bioinformatics Institute, Agency for Science Technology and Research, Singapore, Republic of Singapore

    Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
    Search for articles by this author
  • Author Footnotes
    1 Joint Senior Authors: Johan G Eriksson, Mengling Feng, Neerja Karnani
Published:February 03, 2022DOI:https://doi.org/10.1016/j.diabres.2022.109237

      Highlights

      • UK NICE guidelines may be insufficient to assess GDM risk in Asian women.
      • Higher blood pressure during first trimester is a top risk factor for GDM.
      • Non-invasive AI model can be leveraged as a rapid GDM risk stratification tool.
      • AI model is robust when using a modified two-point IADPSG 2018 GDM criteria.
      • AI model may be a cost effective alternative strategy to universal GDM screening.

      Abstract

      Aims

      The heterogeneity in Gestational Diabetes Mellitus (GDM) risk factors among different populations impose challenges in developing a generic prediction model. This study evaluates the predictive ability of existing UK NICE guidelines for assessing GDM risk in Singaporean women, and used machine learning to develop a non-invasive predictive model.

      Methods

      Data from 909 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study, Growing Up in Singapore Towards healthy Outcomes (GUSTO), was used for predictive modeling. We used a CatBoost gradient boosting algorithm, and the Shapley feature attribution framework for model building and interpretation of GDM risk attributes.

      Results

      UK NICE guidelines showed poor predictability in Singaporean women [AUC:0.60 (95% CI 0.51, 0.70)]. The non-invasive predictive model comprising of 4 non-invasive factors: mean arterial blood pressure in first trimester, age, ethnicity and previous history of GDM, greatly outperformed [AUC:0.82 (95% CI 0.71, 0.93)] the UK NICE guidelines.

      Conclusions

      The UK NICE guidelines may be insufficient to assess GDM risk in Asian women. Our non-invasive predictive model outperforms the current state-of-the-art machine learning models to predict GDM, is easily accessible and can be an effective approach to minimize the economic burden of universal testing & GDM associated healthcare in Asian populations.

      Keywords

      Abbreviations:

      AI (Artificial Intelligence), AUC (Area under the Receiver Operating Characteristic Curve), BMI (Body Mass Index), GCT (Glucose Challenge Test), GDM (Gestational Diabetes Mellitus), GUSTO (Growing Up in Singapore Towards healthy Outcomes), HbA1c (Hemoglobin A1C), HEI-SGP (Healthy Easting Index for Pregnant women in Singaporev), IADPSG (International Association of Diabetes and Pregnancy Study Groups), IDF (International Diabetes Federation), IGF1 (Insulin-like Growth Factor 1), KKH (KK Women’s and Children’s Hospital), NICE (National Institute for Health and Care Excellence), OGTT (Oral Glucose Tolerance Test), SHAP (SHapley Additive exPlanations), WHO (World Health Organization), (Mathematical symbol delta (change in))
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