Effect of individualised nutritional intervention on the postpartum nutritional status of patients with gestational diabetes mellitus and the growth and development of their offspring: a quasi-experimental study

Construction and validation of a line chart for gestational diabetes mellitus based on clinical indicators

BACKGROUND

Gestational diabetes mellitus (GDM) is a common complication of mid-to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers.

METHODS

Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves.

RESULTS

Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband- wife relationship, family history, and parental relationships as predictors. The Hosmer-Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ2 = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89-0.97), 0.72 (95% CI: 0.62-0.81), and 0.68 (95% CI: 0.53-0.83), respectively.

CONCLUSION

In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.

Changes in anthropometry in full-term breastfed newborns and associated factors for the first month

OBJECTIVE

We aim to assess the changes and associated factors in newborn anthropometry in the first month for full-term, healthy, and exclusive-breastfed infants.

METHODS

Neonatal anthropometric measurements were taken on day 5, day 15, and day 30 after delivery. Generalized estimating equations (GEE) analyzed the changes.

RESULTS

From 169 mother-newborn pairs, GEE showed that weight gain during the first month was influenced by maternal body mass index (BMI), delivery type, birth weight, and jaundice after adjusting confounding factors (p < .05). The neonatal length was affected by the smoking status of parents, gestational maternal health problems, maternal height, birth weight, and jaundice (p < .05). Neonatal head circumference was influenced by the smoking status of parents, gestational maternal health problems, maternal BMI, delivery type, maternal height, and birth weight.

CONCLUSION

Adverse perinatal factors including mother's smoke exposure, maternal obesity and diabetes, cesarean birth, and neonatal hyperbilirubinemia influence anthropometry in the first months of life.

Effect of individualized nutrition interventions on clinical outcomes of pregnant women with gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) can lead to excessive pregnancy weight gain (PWG), abnormal glucolipid metabolism, and delayed lactation. Therefore, it is necessary to provide appropriate and effective interventions for pregnant women with GDM.

AIM

To clarify the effects of individualized nutrition interventions on PWG, glucolipid metabolism, and lactation in pregnant women with GDM.

METHODS

The study population consisted of 410 pregnant women with GDM who received treatment at the Northern Jiangsu People's Hospital of Jiangsu Province and Yangzhou Maternal and Child Health Hospital between December 2020 and December 2022, including 200 who received routine in-terventions [control (Con) group] and 210 who received individualized nutrition interventions [research (Res) group]. Data on PWG, glucolipid metabolism [total cholesterol, (TC); triglycerides (TGs); fasting blood glucose (FPG); glycosylated hemoglobin (HbA1c)], lactation time, perinatal complications (cesarean section, premature rupture of membranes, postpartum hemorrhage, and pregnancy-induced hypertension), and neonatal adverse events (premature infants, fetal macrosomia, hypo-glycemia, and respiratory distress syndrome) were collected for comparative analysis.

RESULTS

The data revealed markedly lower PWG in the Res group vs the Con group, as well as markedly reduced TG, TC, FPG and HbA1c levels after the intervention that were lower than those in the Con group. In addition, obviously earlier lactation and statistically lower incidences of perinatal complications and neonatal adverse events were observed in the Res group.

CONCLUSION

Individualized nutrition interventions can reduce PWG in pregnant women with GDM, improve their glucolipid metabolism, and promote early lactation, which deserves clinical promotion.