A clinical diabetes risk prediction model for prediabetic women with prior gestational diabetes

• Published in: PloS one 2021-06, Vol.16 (6), p.e0252501-e0252501
• Main Authors: Man, Bernice, Schwartz, Alan, Pugach, Oksana, Xia, Yinglin, Gerber, Ben
• Format: Article
• Language: English
• Subjects: Biology and life sciences; Body mass index; Decision making; Demographic aspects; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diagnosis; Disease control; Fasting; Geriatrics; Gestational diabetes; Glucose; Health risks; Hemoglobin; Internal medicine; Medicine; Medicine and Health Sciences; Metformin; Physical Sciences; Placebos; Population; Prediabetic state; Prediction models; Pregnancy; Prevention; Regression analysis; Research and Analysis Methods; Risk factors; Risk management; Risk reduction; Secondary analysis; Womens health
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0252501

Without treatment, prediabetic women with a history of gestational diabetes mellitus (GDM) are at greater risk for developing type 2 diabetes compared with women without a history of GDM. Both intensive lifestyle intervention and metformin can reduce risk. To predict risk and treatment response, we developed a risk prediction model specifically for women with prior GDM. The Diabetes Prevention Program was a randomized controlled trial to evaluate the effectiveness of intensive lifestyle intervention, metformin (850mg twice daily), and placebo in preventing diabetes. Data from the Diabetes Prevention Program (DPP) was used to conduct a secondary analysis to evaluate 11 baseline clinical variables of 317 women with prediabetes and a self-reported history of GDM to develop a 3-year diabetes risk prediction model using Cox proportional hazards regression. Reduced models were explored and compared with the main model. Within three years, 82 (25.9%) women developed diabetes. In our parsimonious model using 4 of 11 clinical variables, higher fasting glucose and hemoglobin A1C were each associated with greater risk for diabetes (each hazard ratio approximately 1.4), and there was an interaction between treatment arm and BMI suggesting that metformin was more effective relative to no treatment for BMI [greater than or equal to] 35kg/m.sup.2 than BMI < 30kg/m.sup.2 . The model had fair discrimination (bias corrected C index = 0.68) and was not significantly different from our main model using 11 clinical variables. The estimated incidence of diabetes without treatment was 37.4%, compared to 20.0% with intensive lifestyle intervention or metformin treatment for women with a prior GDM. A clinical prediction model was developed for individualized decision making for prediabetes treatment in women with prior GDM.