Kaat Beunen1, Astrid Neys2, Paul Van Crombrugge3, Carolien Moyson4, Johan Verhaeghe5, Sofie Vandeginste6, Hilde Verlaenen6, Chris Vercammen7, Toon Maes7, Els Dufraimont8, Nele Roggen8, Christophe De Block9, Yves Jacquemyn10, Farah Mekahli11, Katrien De Clippel12, Annick Van Den Bruel13, Anne Loccufier14, Annouschka Laenen15, Roland Devlieger5, Chantal Mathieu4, Katrien Benhalima4. 1. Department of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. kaat.beunen@kuleuven.be. 2. KU Leuven, Herestraat 49, 3000, Leuven, Belgium. 3. Department of Endocrinology, OLV Ziekenhuis Aalst-Asse-Ninove, Moorselbaan 164, 9300, Aalst, Belgium. 4. Department of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. 5. Department of Obstetrics and Gynecology, University Hospital Gasthuisberg, KU Leuven, Herestraat 49, 3000, Leuven, Belgium. 6. Department of Obstetrics and Gynecology, OLV Ziekenhuis Aalst-Asse-Ninove, Moorselbaan 164, 9300, Aalst, Belgium. 7. Department of Endocrinology, Imelda Ziekenhuis, Imeldalaan 9, 2820, Bonheiden, Belgium. 8. Department of Obstetrics and Gynecology, Imelda Ziekenhuis, Imeldalaan 9, 2820, Bonheiden, Belgium. 9. Department of Endocrinology-Diabetology-Metabolism, Antwerp University Hospital, Drie Eikenstraat 655, 2650, Edegem, Belgium. 10. Department of Obstetrics and Gynecology, Antwerp University Hospital, Drie Eikenstraat 655, 2650, Edegem, Belgium. 11. Department of Endocrinology, Kliniek St-Jan Brussel, Kruidtuinlaan 32, 1000, Brussel, Belgium. 12. Department of Obstetrics and Gynecology, Kliniek St-Jan Brussel, Kruidtuinlaan 32, 1000, Brussel, Belgium. 13. Department of Endocrinology, AZ St Jan Brugge, Ruddershove 10, 8000, Brugge, Belgium. 14. Department of Obstetrics and Gynecology, AZ St Jan Brugge, Ruddershove 10, 8000, Brugge, Belgium. 15. Center of Biostatics and Statistical Bioinformatics, KU Leuven, Kapucijnenvoer 35 bloc d, box 7001, 3000, Leuven, Belgium.
Abstract
AIMS: To determine the fasting plasma glucose (FPG) level at which an oral glucose tolerance test (OGTT) could be avoided to screen for gestational diabetes (GDM) and to evaluate the characteristics of women across this FPG threshold. METHODS: A multi-centric prospective cohort study with 1843 women receiving universal screening for GDM with a 75 g OGTT. RESULTS: In the total population, GDM prevalence was 12.5% (231). A FPG < 78 mg/dL was the cut-off with best trade-off to limit the number of missed GDM cases [44 (19.0%)] with a negative predictive value of 97.3% (95% CI 96.5-98.0) for GDM, while avoiding 52.2% OGTTs. Compared to GDM with FPG ≥ 78 mg/dL [187 (81.0%)], GDM women with FPG < 78 mg/dL had a significantly lower BMI (27.1 ± 4.5 vs. 29.6 ± 5.2 kg/m2, p = 0.003), less insulin resistance [Matsuda: 0.4 (0.4-0.7) vs. 0.3 (0.2-0.5), p < 0.001] and better β-cell function [ISSI-2: 0.13 (0.08-0.25) vs. 0.09 (0.04-0.15), p = 0.004]. Compared to NGT women (1612) with FPG ≥ 78 mg/dL [846 (52.5%)], NGT with FPG < 78 mg/dL [766 (47.5%)] had a significantly lower BMI (26.0 ± 3.9 vs. 27.8 ± 4.7 kg/m2, p < 0.001), less insulin resistance [Matsuda: 0.7 (0.5-0.9) vs. 0.5 (0.4-0.7), p < 0.001], better β-cell function [ISSI-2: 0.17 (0.10-0.30) vs. 0.12 (0.07-0.21), p < 0.001], and less often large-for-gestational age infants [9.2 (70) vs. 16.2% (136), p < 0.001]. CONCLUSIONS: FPG < 78 mg/dL can be used to limit the number of OGTTs when screening for GDM. Women with FPG < 78 mg/dL had a better metabolic profile and in NGT women also less fetal overgrowth.
AIMS: To determine the fasting plasma glucose (FPG) level at which an oral glucose tolerance test (OGTT) could be avoided to screen for gestational diabetes (GDM) and to evaluate the characteristics of women across this FPG threshold. METHODS: A multi-centric prospective cohort study with 1843 women receiving universal screening for GDM with a 75 g OGTT. RESULTS: In the total population, GDM prevalence was 12.5% (231). A FPG < 78 mg/dL was the cut-off with best trade-off to limit the number of missed GDM cases [44 (19.0%)] with a negative predictive value of 97.3% (95% CI 96.5-98.0) for GDM, while avoiding 52.2% OGTTs. Compared to GDM with FPG ≥ 78 mg/dL [187 (81.0%)], GDM women with FPG < 78 mg/dL had a significantly lower BMI (27.1 ± 4.5 vs. 29.6 ± 5.2 kg/m2, p = 0.003), less insulin resistance [Matsuda: 0.4 (0.4-0.7) vs. 0.3 (0.2-0.5), p < 0.001] and better β-cell function [ISSI-2: 0.13 (0.08-0.25) vs. 0.09 (0.04-0.15), p = 0.004]. Compared to NGT women (1612) with FPG ≥ 78 mg/dL [846 (52.5%)], NGT with FPG < 78 mg/dL [766 (47.5%)] had a significantly lower BMI (26.0 ± 3.9 vs. 27.8 ± 4.7 kg/m2, p < 0.001), less insulin resistance [Matsuda: 0.7 (0.5-0.9) vs. 0.5 (0.4-0.7), p < 0.001], better β-cell function [ISSI-2: 0.17 (0.10-0.30) vs. 0.12 (0.07-0.21), p < 0.001], and less often large-for-gestational age infants [9.2 (70) vs. 16.2% (136), p < 0.001]. CONCLUSIONS: FPG < 78 mg/dL can be used to limit the number of OGTTs when screening for GDM. Women with FPG < 78 mg/dL had a better metabolic profile and in NGT women also less fetal overgrowth.
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