Decreased complexity of glucose time series index associated with adverse pregnancy outcomes in gestational diabetes mellitus.

AIM: We investigated the relationship between the complexity of the glucose time series index (CGI) during pregnancy and adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM). MATERIALS AND METHODS: In this retrospective cohort study, 388 singleton pregnant women with GDM underwent continuous glucose monitoring (CGM) at a median of 26.86 gestational weeks. CGI was calculated using refined composite multiscale entropy based on CGM data. The participants were categorized into tertiles according to their baseline CGI (CGI <2.32, 2.32-3.10, ≥3.10). Logistic regression was used to assess the association between CGI and composite adverse outcomes or large for gestational age (LGA). The discrimination performance of CGI was estimated using receiver operating characteristic analysis. RESULTS: Of the 388 participants, 71 (18.3%) had LGA infants and 63 (16.2%) had composite adverse outcomes. After adjustments were made for confounders, compared with those with a high CGI (CGI ≥3.10), participants with a low CGI (CGI <2.32) had a higher risk of composite adverse outcomes (odds ratio: 12.10, 95% confidence interval: 4.41-33.18) and LGA (odds ratio: 12.68, 95% confidence interval: 4.04-39.75). According to the receiver operating characteristic analysis, CGI was significantly better than glycated haemoglobin and conventional CGM indicators for the prediction of adverse pregnancy outcomes (all p < .05). CONCLUSION: A lower CGI during pregnancy was associated with composite adverse outcomes and LGA. CGI, a novel glucose homeostasis predictor, seems to be superior to conventional glucose indicators for the prediction of adverse pregnancy outcomes in women with GDM.

Machine Learning-Based Prediction of Large-for-Gestational Age Infants in Mothers with Gestational Diabetes Mellitus.

CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited. OBJECTIVE: We aimed to develop and validate an artificial intelligence (AI) based model to determine the probability of women with GDM giving birth to LGA infants during pregnancy using CGM measurements together with demographic data and metabolic indicators. METHODS: A total of 371 women with GDM from a prospective cohort at a university hospital were included. CGM was performed during 20-34 gestational weeks, and glycemic fluctuations were evaluated and visualized in women with GDM who gave birth to LGA and non-LGA infants. A convolutional neural network (CNN)-based fusion model was developed to predict LGA. Comparisons among the novel fusion model and three conventional models were made using the area under the receiver-operating characteristic curve (AUCROC) and accuracy. RESULTS: Overall, 76 (20.5%) out of 371 GDM women developed LGA neonates. The visualized 24-h glucose profiles differed at midmorning. This difference was consistent among subgroups categorized by pregestational BMI, therapeutic protocol and CGM administration period. The AI based fusion prediction model using 24-h CGM data and 15 clinical variables for LGA prediction (AUCROC 0.852, 95% CI 0.680-0.966, accuracy 84.4%) showed superior discriminative power compared with the three classic models. CONCLUSIONS: We demonstrated better performance in predicting LGA infants among women with GDM using the AI based fusion model. The characteristics of the CGM profiles allowed us to determine the appropriate window for intervention.

Effect of continuous glucose monitoring compared with self-monitoring of blood glucose in gestational diabetes patients with HbA1c<6%: a randomized controlled trial.

Objective: This study evaluated the effect of continuous glucose monitoring (CGM) versus self-monitored blood glucose (SMGB) in gestational diabetes mellitus (GDM) with hemoglobin A1c (HbA1c) <6%. Methods: From January 2019 to February 2021, 154 GDM patients with HbA1c<6% at 24-28 gestational weeks were recruited and assigned randomly to either SMBG only or CGM in addition to SMBG, with 77 participants in each group. CGM was used in combination with fingertip blood glucose monitoring every four weeks until antepartum in the CGM group, while in the SMBG group, fingertip blood glucose monitoring was applied. The CGM metrics were evaluated after 8 weeks, HbA1c levels before delivery, gestational weight gain (GWG), adverse pregnancy outcomes and CGM medical costs were compared between the two groups. Results: Compared with patients in the SMBG group, the CGM group patients had similar times in range (TIRs) after 8 weeks (100.00% (93.75-100.00%) versus 99.14% (90.97-100.00%), p=0.183) and HbA1c levels before delivery (5.31 ± 0.06% versus 5.35 ± 0.06%, p=0.599). The proportion with GWG within recommendations was higher in the CGM group (59.7% versus 40.3%, p=0.046), and the newborn birth weight was lower (3123.79 ± 369.58 g versus 3291.56 ± 386.59 g, p=0.015). There were no significant differences in prenatal or obstetric outcomes, e.g., cesarean delivery rate, hypertensive disorders, preterm births, macrosomia, hyperbilirubinemia, neonatal hypoglycemia, respiratory distress, and neonatal intensive care unit admission >24 h, between the two groups. Considering glucose monitoring, SMBG group patients showed a lower cost than CGM group patients. Conclusions: For GDM patients with HbA1c<6%, regular SMBG is a more economical blood glucose monitoring method and can achieve a similar performance in glycemic control as CGM, while CGM is beneficial for ideal GWG.

A Novel Nomogram for Predicting Gestational Diabetes Mellitus During Early Pregnancy.

Objective: Gestational diabetes mellitus (GDM) is a serious threat to maternal and child health. However, there isn't a standard predictive model for the disorder in early pregnancy. This study is to investigate the association of blood indexes with GDM and establishes a practical predictive model in early pregnancy for GDM. Methods: This is a prospective cohort study enrolling 413 pregnant women in the department of Obstetrics and Gynecology in Shanghai General Hospital from July 2020 to April 2021.A total of 116pregnantwomen were diagnosed with GDM during the follow-up. Blood samples were collected at early trimester (gestational weeks 12-16) and second trimester(gestational weeks 24-26 weeks). A predictive nomogram was established based on results of the multivariate logistic model and 5-fold cross validation. We evaluate the nomogram by the area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analysis (DCAs). Results: Significant differences were observed between the GDM and normal controls among age, pre-pregnancy BMI, whether the pregnant women with complications, the percentage of B lymphocytes, fasting plasma glucose (FPG), HbA1c, triglyceride and the level of progesterone in early trimester. Risk factors used in nomogram included age, pre-pregnancy BMI, FPG, HbA1c, the level of IgA, the level of triglyceride, the percentage of B lymphocytes, the level of progesterone and TPOAb in early pregnancy. The AUC value was 0.772, 95%CI (0.602,0.942). The calibration curves for the probability of GDM demonstrated acceptable agreement between the predicted outcomes by the nomogram and the observed values. DCA curves showed good positive net benefits in the predictive model. Conclusions: A novel predictive nomogram was developed for GDM in our study, which could do help to patient counseling and management during early pregnancy in clinical practice.

B Lymphocytes Are Predictors of Insulin Resistance in Women with Gestational Diabetes Mellitus.

Backgroud: The present study aimed to investigate the association between immune cells and gestational diabetes mellitus (GDM) and identify a reasonable predictor of insulin resistance in women with GDM. OBJECTIVE: The clinical and biochemical characteristics of 124 women with GDM and 168 healthy pregnant women were compared. METHODS: The percentage of immune cells in the blood of the subjects was analyzed by flow cytometry. Pearson's correlation analysis revealed the correlation between the percentage of B lymphocytes and insulin resistance. A cutoff point was determined for the percentage of B lymphocytes, based on insulin resistance, using receiver operating characteristic (ROC) curves. RESULTS: Compared to the healthy pregnant women, the percentages of B lymphocytes and IgA produced by B-cells were significantly different in women with GDM. The percentage of B lymphocytes was positively related to insulin resistance.The number of 14.05% of B lymphocytes was an optimal cutoff point that predicted the insulin resistance in women with GDM. CONCLUSION: The percentage of B lymphocytes was positively associated with insulin resistance, and hence, might serve as an appropriate predictor of insulin resistance in women with GDM.