Jessica A Grieger1,2, Shalem Y Leemaqz3,4,5, Emma J Knight6, Luke E Grzeskowiak3,4,5, Lesley M McCowan7, Gustaaf A Dekker3,4,8, Claire T Roberts3,4,5. 1. Robinson Research Institute, University of Adelaide, North Adelaide, SA, 5000, Australia. jessica.grieger@adelaide.edu.au. 2. Adelaide Medical School, University of Adelaide, Adelaide, SA, 5000, Australia. jessica.grieger@adelaide.edu.au. 3. Robinson Research Institute, University of Adelaide, North Adelaide, SA, 5000, Australia. 4. Adelaide Medical School, University of Adelaide, Adelaide, SA, 5000, Australia. 5. Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia. 6. School of Public Health, University of Adelaide, Adelaide, SA, Australia. 7. Department of Obstetrics and Gynaecology, University of Auckland, Auckland, New Zealand. 8. Women and Children's Division, Lyell McEwin Hospital, Adelaide, SA, Australia.
Abstract
PURPOSE: To assess the independent and joint contribution of the individual components of metabolic syndrome, and known risk factors for gestational diabetes (GDM), on risk of GDM. METHODS: Two thousand nine hundred and fifteen women from Australia and New Zealand, who participated in The Screening for Pregnancy Endpoints Study (SCOPE), were included. Using the SCOPE clinical data set and biomarkers obtained at 14-16 weeks' gestation, a logistic regression model was fitted to the binary outcome GDM, with individual metabolic syndrome components (waist circumference, blood pressure, glucose, HDL-C, triglycerides), recruitment site, and other established factors associated with GDM. Hierarchical partitioning was used to assess the relative contribution of each variable. RESULTS: Of the 2915 women, 103 women (3.5%) developed GDM. The deviance explained by the logistic regression model containing all variables was 18.65% and the AUC was 0.809. Seventy percent of the independent effect was accounted for by metabolic syndrome components. The highest independent relative contribution to GDM was circulating triglycerides (17 ± 3%), followed by waist circumference (13 ± 3%). Glucose and maternal BMI contributed 12 ± 2% and 12 ± 3%, respectively. The remaining factors had an independent relative contribution of < 10%. CONCLUSION: Triglyceride concentrations had the highest independent relative importance for risk of GDM. Increased focus for lowering triglycerides as an important risk factor for GDM is warranted.
PURPOSE: To assess the independent and joint contribution of the individual components of metabolic syndrome, and known risk factors for gestational diabetes (GDM), on risk of GDM. METHODS: Two thousand nine hundred and fifteen women from Australia and New Zealand, who participated in The Screening for Pregnancy Endpoints Study (SCOPE), were included. Using the SCOPE clinical data set and biomarkers obtained at 14-16 weeks' gestation, a logistic regression model was fitted to the binary outcome GDM, with individual metabolic syndrome components (waist circumference, blood pressure, glucose, HDL-C, triglycerides), recruitment site, and other established factors associated with GDM. Hierarchical partitioning was used to assess the relative contribution of each variable. RESULTS: Of the 2915 women, 103 women (3.5%) developed GDM. The deviance explained by the logistic regression model containing all variables was 18.65% and the AUC was 0.809. Seventy percent of the independent effect was accounted for by metabolic syndrome components. The highest independent relative contribution to GDM was circulating triglycerides (17 ± 3%), followed by waist circumference (13 ± 3%). Glucose and maternal BMI contributed 12 ± 2% and 12 ± 3%, respectively. The remaining factors had an independent relative contribution of < 10%. CONCLUSION: Triglyceride concentrations had the highest independent relative importance for risk of GDM. Increased focus for lowering triglycerides as an important risk factor for GDM is warranted.
Authors: Jessica A Grieger; Tina Bianco-Miotto; Luke E Grzeskowiak; Shalem Y Leemaqz; Lucilla Poston; Lesley M McCowan; Louise C Kenny; Jenny E Myers; James J Walker; Gus A Dekker; Claire T Roberts Journal: PLoS Med Date: 2018-12-04 Impact factor: 11.069