A model for predicting gestational diabetes mellitus in early pregnancy: a prospective study in Thailand

Fetal biometry measurements in diabetic pregnant women and neonatal outcomes

OBJECTIVE

In this study, we aimed to investigate how fetal head and abdominal circumferences are related to the incidence of neonatal complications in mothers with gestational diabetes mellitus (GDM) and pre-gestational diabetes mellitus (PGDM) compared to normal pregnancies.

METHODS

We retrospectively analyzed data of expectant mothers with GDM, PGDM, and normal pregnancies who delivered singleton full-term infants (≥37 weeks) at a tertiary center from January 2013 to December 2022. Ultrasonography-measured fetal weight, fetal head circumference, fetal abdominal circumference, difference between head and abdominal circumference, and head-to-abdominal circumference ratio were assessed. Neonatal outcomes were evaluated based on the rates of admission to the neonatal intensive care unit, intubation, and hypoglycemia. Statistical analyses, including univariate and multivariate analyses, were performed using the SPSS software (IBM Corp., Armonk, NY, USA).

RESULTS

Among the 473 participants, 175 (37.0%) were mothers with diabetes (DM). A head-to-abdominal circumference ratio <0.95 and a difference of ≥2.5 cm were significantly associated with neonatal hypoglycemia in all mothers with DM, with statistical significance noted only in the PGDM group. No significant association was observed in normal pregnancies.

CONCLUSION

Our findings indicate that a head-to-abdominal circumference ratio <0.95 and a ≥2.5 cm difference in circumferences are associated with neonatal hypoglycemia in mothers with DM.

Prediction of gestational diabetes mellitus using early-pregnancy data: a secondary analysis from a prospective cohort study in Iran

BACKGROUND

Early identification of gestational diabetes mellitus is essential for improving maternal and neonatal outcomes. While risk factors such as advanced maternal age, elevated pre-pregnancy body mass index, multiparity, and a history of gestational diabetes have been recognized, the role of serum biomarkers remains uncertain. This study explores the predictive value of early-pregnancy laboratory findings in conjunction with maternal demographic and clinical characteristics for gestational diabetes mellitus.

METHODS

Early-pregnancy data from the first pregnancy visits at 6-12 weeks of gestation from women in the Mothers and Children's Health cohort were collected. Comprehensive maternal demographic data (e.g., age and body mass index) and obstetrics history (e.g., gravidity, parity, miscarriage, intrauterine growth retardation, gestational diabetes mellitus, and preeclampsia) were recorded. Maternal blood samples were analyzed for complete blood count and biochemistry parameters. Gestational diabetes mellitus was diagnosed based on 75-g oral glucose tolerance test results between 24 and 28 weeks of gestation, following the International Association of Diabetes and Pregnancy Study Groups criteria. Multivariate logistic regression analysis assessed the predictive capacity of various variables. Receiver operating curve analysis was conducted to identify optimal predictive cut-offs for continuous variables.

RESULTS

1,565 pregnant women with a mean age of 32.6 ± 5.7 years, mean body mass index of 25.5 ± 4.9 kg/m², mean gravidity of 1.1 ± 1.1, and mean parity of 0.8 ± 0.8 were included. 297 pregnancies (19.0%) were complicated by gestational diabetes mellitus. In the multivariate analysis, higher maternal age (p < 0.001, odds ratio = 1.076 [1.035-1.118]), a history of gestational diabetes mellitus (p < 0.001, odds ratio = 3.007 [1.787-5.060]) and preeclampsia (p = 0.007, odds ratio = 2.710 [1.310-5.604]), and elevated early-pregnancy fasting blood sugar (p < 0.001, odds ratio = 1.062 [1.042-1.083]) emerged as independent predictors of gestational diabetes mellitus. Moreover, the receiver operating curve yielded an optimal cut-off of 89.5 mg/dL for early-pregnancy fasting blood sugar in predicting gestational diabetes mellitus.

CONCLUSIONS

Our findings demonstrated that, in addition to established risk factors, a history of preeclampsia and elevated early-pregnancy fasting blood glucose are independent predictors of gestational diabetes mellitus. Therefore, close monitoring of pregnant women with these risk factors in early pregnancy is warranted to facilitate timely diagnostic and therapeutic interventions, reducing the burden of gestational diabetes.

TRIAL REGISTRATION

Not applicable.

Incidence of excessive gestational weight gain among overweight and obese women

OBJECTIVE

To determine the incidence of excessive gestational weight gain (GWG) among overweight and obese pregnant women, its associated factors, and pregnancy outcomes.

METHODS

A total of 355 overweight or obese singleton pregnant women who were included. Obstetric characteristics, weight gain, and pregnancy outcomes, were extracted from medical records. GWG was categorized according to the Institute of Medicine recommendation. Comparisons were made between individuals with inadequate, normal, and excessive GWG. Logistic regression analysis was performed to determine independent associated factors for excessive GWG.

RESULTS

Majority of the women were overweight (68.7%), 38.9% were nulliparous, and mean pre-pregnancy body mass index was 28.9 kg/m2. Excessive GWG was observed in 53% of the women. Women with excessive GWG had significantly higher weight gain in every trimester. Risk of excessive GWG increased in women ≤30 years, while gestational diabetes (GDM) significantly decreased the risk. Women with excessive GWG had a significantly higher primary cesarean section rate. Both women with normal and excessive GWG showed higher rate of having large for gestational age (LGA) infants (P=0.003). Maternal age of ≤30 years significantly increased the risk of excessive GWG (adjusted odds ratio [aOR], 1.91; 95% confidence interval [CI], 1.11-3.27) and GDM significantly decreased this risk (aOR, 0.40; 95% CI, 0.24-0.67).

CONCLUSION

The incidence of excessive GWG among overweight and obese women was 53%. Maternal age of ≤30 years significantly increased this risk while women with GDM were significantly decreased risk. Primary cesarean section and fetal LGA significantly increased in women with excessive GWG.

Urinary metabolite biomarkers of pregnancy complications associated with maternal exposure to particulate matter

Particulate matter 2.5 (PM2.5) is associated with reproductive health and adverse pregnancy outcomes. However, studies evaluating biological markers of PM2.5 are lacking, and identifying biomarkers for estimating prenatal exposure to prevent pregnancy complications is essential. Therefore, we aimed to explore urine metabolites that are easy to measure as biomarkers of exposure. In this matched case-control study based on the PM2.5 exposure, 30 high PM2.5 group (>15 μg/m3) and 30 low PM2.5 group (<15 μg/m3) were selected from air pollution on pregnancy outcome (APPO) cohort study. We used a time-weighted average model to estimate individual PM exposure, which used indoor PM2.5 and outdoor PM2.5 concentrations by atmospheric measurement network based on residential addresses. Clinical characteristics and urine samples were collected from participants during the second trimester of pregnancy. Urine metabolites were quantitatively measured using gas chromatography-mass spectrometry following multistep chemical derivatization. Statistical analyses were conducted using SPSS version 21 and MetaboAnalyst 5.0. Small for gestational age and gestational diabetes (GDM) were significantly increased in the high PM2.5 group, respectively (P = 0.042, and 0.022). Fifteen metabolites showed significant differences between the two groups (P < 0.05). Subsequent pathway enrichment revealed that four pathways, including pentose and glucuronate interconversion with three pentose sugars (ribose, arabinose, and xylose; P < 0.05). The concentration of ribose increased preterm births (PTB) and GDM (P = 0.044 and 0.049, respectively), and the arabinose concentration showed a tendency to increase in PTB (P = 0.044). Therefore, we identified urinary pentose metabolites as biomarkers of PM2.5 and confirmed the possibility of their relationship with pregnancy complications.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.