Diabetes research and clinical practice · 2025
Prediction of gestational diabetes mellitus using continuous glucose monitoring metrics.
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Chen Ling-Wei, Ku Chee Wai, Zheng Ruther Teo, Chan Jerry Kok Yen, Yap Fabian, Loy See Ling
Аннотация
AIMS: We evaluated continuous glucose monitoring (CGM)-derived metrics for predicting gestational diabetes mellitus (GDM).
METHODS: We analyzed data from 167 pregnant participants who had ≥ 3 days of CGM data at 18-24 weeks' gestation and who underwent 75-gram oral-glucose-tolerance-tests at 24-28 weeks in a multi-ethnic prospective cohort. Predictive performance of CGM metrics was assessed using the area-under-the-receiver-operating-characteristic-curve (AUROC) with 20 repetitions of 5-fold cross-validation; optimal cut-points were determined using Youden's index.
RESULTS: There were 30 (18 %) GDM cases. The strongest predictors were %time-above-7.8-mmol/L (%TA7.8) [AUROC (95 % CI): 0.862 (0.780, 0.945); cut-point: 1.23 %; sensitivity: 0.800; specificity: 0.847] and the hyperglycemia-component-of-the-Glycemic-Risk-Index (Hyper-GRI) [0.862 (0.779, 0.945); cut-point: 0.79; sensitivity: 0.767; specificity: 0.883]. J-index, standard deviation (SD), and mean-amplitude-of-glucose-excursions(MAGE) also achieved AUROCs > 0.80. The predictive performance of these metrics was stronger in women with BMI < 23 kg/m2 (n = 89; AUROC range: 0.813-0.882) than in those with BMI ≥ 23 kg/m2 (n = 78; AUROC range: 0.657-0.756). Among Chinese participants (n = 142), %TA7.8 and J-index had AUROC > 0.80; in non-Chinese participants (n = 25), SD performed best (AUROC: 0.845). Adding individual CGM metrics to a model including maternal age, pre-pregnancy BMI, job status, ethnicity, history of GDM, and family history of diabetes improved the AUROC from 0.642 to 0.895 (%TA7.8), 0.867 (Hyper-GRI), 0.877 (J-index), 0.868 (SD), and 0.848 (MAGE).
CONCLUSIONS: CGM-derived metrics show good performance in predicting GDM and potential for earlier detection of adverse pregnancy glycemic profiles.
METHODS: We analyzed data from 167 pregnant participants who had ≥ 3 days of CGM data at 18-24 weeks' gestation and who underwent 75-gram oral-glucose-tolerance-tests at 24-28 weeks in a multi-ethnic prospective cohort. Predictive performance of CGM metrics was assessed using the area-under-the-receiver-operating-characteristic-curve (AUROC) with 20 repetitions of 5-fold cross-validation; optimal cut-points were determined using Youden's index.
RESULTS: There were 30 (18 %) GDM cases. The strongest predictors were %time-above-7.8-mmol/L (%TA7.8) [AUROC (95 % CI): 0.862 (0.780, 0.945); cut-point: 1.23 %; sensitivity: 0.800; specificity: 0.847] and the hyperglycemia-component-of-the-Glycemic-Risk-Index (Hyper-GRI) [0.862 (0.779, 0.945); cut-point: 0.79; sensitivity: 0.767; specificity: 0.883]. J-index, standard deviation (SD), and mean-amplitude-of-glucose-excursions(MAGE) also achieved AUROCs > 0.80. The predictive performance of these metrics was stronger in women with BMI < 23 kg/m2 (n = 89; AUROC range: 0.813-0.882) than in those with BMI ≥ 23 kg/m2 (n = 78; AUROC range: 0.657-0.756). Among Chinese participants (n = 142), %TA7.8 and J-index had AUROC > 0.80; in non-Chinese participants (n = 25), SD performed best (AUROC: 0.845). Adding individual CGM metrics to a model including maternal age, pre-pregnancy BMI, job status, ethnicity, history of GDM, and family history of diabetes improved the AUROC from 0.642 to 0.895 (%TA7.8), 0.867 (Hyper-GRI), 0.877 (J-index), 0.868 (SD), and 0.848 (MAGE).
CONCLUSIONS: CGM-derived metrics show good performance in predicting GDM and potential for earlier detection of adverse pregnancy glycemic profiles.