Early diabetes screening in pregnancy

Evaluation of the value of fasting plasma glucose in the first trimester for the prediction of adverse pregnancy outcomes

AIMS

To evaluate the importance and usefulness of fasting plasma glucose (FPG) in the first trimester in predicting adverse pregnancy outcomes.

METHODS

A retrospective study of 22,398 singleton pregnancies was conducted. Participants were divided into subgroups according to first-trimester FPG (low FPG, FPG < 5.1 mmol/L; medium FPG, 5.1 mmol/L ≤ FPG < 5.6 mmol/L; high FPG, 5.6 ≤ FPG < 7.0 mmol/L) and oral glucose tolerance test(OGTT) results (normal and abnormal) during pregnancy. Patient characteristics and risk of adverse pregnancy outcomes were compared. Then, the whole population of women with abnormal OGTT served as a reference, and the relative risks of maternal and neonatal complications in normal OGTT women were analyzed by categorical analyses and logistic regression. Subgroup analyses were performed according to pre-pregnancy body mass index (BMI).

RESULTS

The frequency of adverse pregnancy outcomes increased with increasing FPG levels during the first trimester, regardless of OGTT results. High FPG + Abnormal OGTT had the worst outcome. Compared to the whole population of women with abnormal OGTT, Normal OGTT + Medium FPG showed the same risk of PIH and macrosomia. Normal OGTT + High FPG showed the same risk of PIH, macrosomia as well as LGA and preterm birth. Additionally, Normal OGTT + Medium FPG + BMI ≥ 24 kg/m² showed significantly higher risk of PIH (OR = 1.867, 1.245-2.800), macrosomia (OR = 1.748, 1.304-2.344) and LGA (OR = 1.274, 1.019-1.593). Furthermore, the OR value for PIH was 3.759 (1.680-8.412) in Normal OGTT + High FPG + BMI ≥ 24 kg/m² compared to women with abnormal OGTT.

CONCLUSIONS

First-trimester FPG values can help identify women at increased risk for adverse pregnancy outcomes. Increased attention and management should be given to women with early pregnancy FPG ≥ 5.10 mmol/L despite a normal OGTT, especially if their BMI ≥ 24 kg/m².

Hemoglobin A1c and Early Gestational Diabetes

Background: Screening for diabetes in early pregnancy is recommended for high-risk women, however, the optimal test for the diagnosis of early gestational diabetes mellitus (GDM) is unknown. Thus, the objective of this study was to evaluate hemoglobin A_1c (HbA_1c) as a diagnostic test for early GDM compared with two-step testing. Materials and Methods: Retrospective cohort of women with prior GDM or obesity who had HbA_1c and two-step testing <21 weeks' gestation. Early GDM was diagnosed by 1 hour, 50 g oral glucose challenge test (GCT) ≥135 mg/dL and ≥2 abnormal values on 3 hour, 100 g oral glucose tolerance test or GCT >200 mg/dL. The area under the receiver operating characteristic curve (AUC) evaluated HbA_1c for diagnosis of early GDM. Results: Of 243 women, 14 (5.8%) had early GDM by two-step testing. Median HbA_1c levels were higher among women with GDM versus those without GDM (5.8% vs. 5.3%, p < 0.001). The AUC for HbA_1c compared with two-step testing was 0.80 (95% CI 0.69-0.91). The optimal HbA_1c threshold was 5.6% (64% sensitivity, 84% specificity). Conclusions: HbA_1c is moderately predictive of early GDM compared with two-step testing, and a threshold lower than that used for diabetes diagnosis among nonpregnant adults is justified.

Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

BACKGROUND

Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression.

OBJECTIVE

The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions.

METHODS

We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down's syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics.

RESULTS

In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%).

CONCLUSION

In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM.

Difference in the prevalence of gestational diabetes mellitus according to gestational age at 75-g oral glucose tolerance test in Japan: The Japan Assessment of Gestational Diabetes Mellitus Screening trial

AIMS/INTRODUCTION

To evaluate the differences in the results of 75-g oral glucose tolerance tests (OGTTs) according to gestational age in Japan.

MATERIALS AND METHODS

In this prospective cohort study, 2,578 pregnant women were divided into three categories based on their gestational age during the 75-g OGTT: <14 weeks' gestation, 14-23 weeks' gestation and 24-32 weeks' gestation. The association between gestational age and the results of the 75-g OGTT were evaluated using multivariable analysis.

RESULTS

Early gestational age was associated with high fasting plasma glucose levels at the time of the 75-g OGTT, and low corresponding 1-h and 2-h plasma glucose levels. Compared with women with a gestational age of 24-32 weeks, women who had undergone the 75-g OGTT at <14 weeks' gestation had significantly higher odds of gestational diabetes mellitus diagnosis based on the currently used criteria in Japan (adjusted odds ratio 1.42, 95% confidence interval 1.07-1.90).

CONCLUSIONS

The results of the 75-g OGTT varied by gestational age. The use of the same 75-g OGTT cut-off values for the diagnosis of gestational diabetes mellitus, regardless of gestational age, might lead to increases in the prevalence of gestational diabetes mellitus diagnosis in Japan.