Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus
Parkhi, Durga Periyathambi, Nishanthi Ghebremichael-Weldeselassie, Yonas Patel, Vinod Sukumar, Nithya Siddharthan, Rahul Narlikar, Leelavati Saravanan, Ponnusamy
Parkhi, Durga
Periyathambi, Nishanthi
Ghebremichael-Weldeselassie, Yonas
Patel, Vinod
Sukumar, Nithya
Siddharthan, Rahul
Narlikar, Leelavati
Saravanan, Ponnusamy
Affiliation
University of Warwick, Coventry; George Eliot Hospital NHS Trust, Nuneaton; The Open University, Milton Keynes; The Institute of Mathematical Sciences, Chennai, India; Indian Institute of Science Education and Research, Pune, India
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Publication date
2023-09-09
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Abstract
Early onset of type 2 diabetes and cardiovascular disease are common complications for women diagnosed with gestational diabetes. Prediabetes refers to a condition in which blood glucose levels are higher than normal, but not yet high enough to be diagnosed as type 2 diabetes. Currently, there is no accurate way of knowing which women with gestational diabetes are likely to develop postpartum prediabetes. This study aims to predict the risk of postpartum prediabetes in women diagnosed with gestational diabetes. Our sparse logistic regression approach selects only two variables - antenatal fasting glucose at OGTT and HbA1c soon after the diagnosis of GDM - as relevant, but gives an area under the receiver operating characteristic curve of 0.72, outperforming all other methods. We envision this to be a practical solution, which coupled with a targeted follow-up of high-risk women, could yield better cardiometabolic outcomes in women with a history of GDM.
Keywords: Computational bioinformatics; Endocrinology; Female reproductive endocrinology; Reproductive medicine.
Citation
Parkhi D, Periyathambi N, Ghebremichael-Weldeselassie Y, Patel V, Sukumar N, Siddharthan R, Narlikar L, Saravanan P. Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus. iScience. 2023 Sep 9;26(10):107846. doi: 10.1016/j.isci.2023.107846.
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Article