Establishing reference ranges of serum lipid level during pregnancy and evaluating its association with perinatal outcomes: A cohort study

Elevated serum cholesterol levels during pregnancy as predictors for postpartum hypercholesterolemia: A prospective cohort study

OBJECTIVE

To identify the associations between gestational cholesterol levels and the risk of postpartum hypercholesterolemia, and to establish trimester-specific reference values.

METHODS

Serum lipids at gestational weeks 6-8, 16, 24, and 36, and 42 days postpartum were derived from 905 pregnant women of a prospective cohort. The major outcome was postpartum hypercholesterolemia. Logistic regression and restricted cubic splines were conducted to estimate the associations between cholesterol levels at specific gestational ages and postpartum hypercholesterolemia. Associations of the trend of changes in cholesterol levels during pregnancy with postpartum hypercholesterolemia were evaluated by linear mixed-effect model and linear or logistic regression. Reference values were computed by the receiver operating characteristic curves.

RESULTS

Serum concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and the ratios of TC/HDL-C and LDL-C/HDL-C all increased during pregnancy and decreased at 42 days postpartum. The elevated levels of TC and LDL-C, decreased levels of HDL-C in pregnancy, and their rapid change rates were positively associated with higher risks of postpartum hypercholesterolemia. The established reference values from the first to the third trimester were below 5.47, 6.35, and 7.22 mmol/L for TC; below 2.83, 3.82, and 4.21 mmol/L for LDL-C; and more than 1.50, 1.55, and 1.50 mmol/L for HDL-C, respectively.

CONCLUSION

Maternal cholesterol levels and their trend of change during pregnancy were predictors of postpartum hypercholesterolemia. Trimester-specific reference values were established in a Chinese population.

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

OBJECTIVES

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM).

DESIGN

We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets.

PATIENTS AND MEASUREMENTS

Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks.

RESULTS

The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk.

CONCLUSION

This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Association between maternal blood lipids and neonatal hypoglycaemia in pregnancy with gestational diabetes mellitus: a cohort study

BACKGROUND

Gestational diabetes mellitus (GDM) prevalence is on the rise globally. Offspring of diabetic mothers face increased risk of neonatal hypoglycaemia (NH), and women with GDM have abnormal lipid profiles. However, there is no consensus on the link between maternal blood lipids and NH in infants from mothers with GDM. This study aimed to explore how maternal blood lipids affect NH.

METHODS

A retrospective cohort study was conducted at the First Affiliated Hospital of Sun Yat-sen University. Information on participants' baseline characteristics and maternal metabolic profiles of glucose and lipids was collected. Significant variables from the univariate analysis were included in logistic regression, which was used to construct the predictive model for NH. A nomogram was constructed for visualizing the model and assessed using the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Neonatal capillary blood glucose (CBG) decreased rapidly in the first hour after birth, increased gradually from the first to the second hour, and then remained stable. In the NH group, 86.11% (502/583) of hypoglycaemia cases occurred within the first two hours after birth. Multivariate logistic regression suggested that the lipid indices of maternal apoprotein B/apoprotein A1 (Apo-B/Apo-A1) (odds ratio (OR) = 1.36, 95% confidence intervals (CIs): 1.049-1.764, P = 0.02) and apoprotein E (Apo-E) (OR = 1.014, 95% CIs: 1.004-1.024, P = 0.004) were positively associated with NH in neonates from mothers with GDM. Triglycerides (TGs) (OR = 0.883, 95% CIs: 0.788-0.986, P = 0.028) were inversely associated with NH. Maternal glycated haemoglobin (HbA1c), age, twin pregnancy and caesarean delivery also had predictive value of NH. The AUC of the nomogram derived from these factors for the prediction model of NH was 0.657 (95% CIs: 0.630-0.684).

CONCLUSIONS

The present study revealed that the Apo-B/Apo-A1 and Apo-E levels were associated with an increased risk of NH. A nomogram was developed to forecast the risk of NH in babies born to mothers with GDM, incorporating maternal blood lipids, HbA1c, age, twin pregnancy, and caesarean section. The trajectory of glycaemia for neonates indicates the need for intensive CBG monitoring within 2 h of birth for neonates from mothers with GDM.

Serum lipid reference values recommended during a twin pregnancy and evaluating its association with perinatal outcomes

BACKGROUND

Maternal lipid metabolism fluctuations have been shown to increase the risk of adverse pregnancy outcomes. However, there is no consensus over what constitutes normal maternal lipid values during twin pregnancy. Therefore, the aim of this study was to establish a serum lipid reference range for a twin pregnancy.

METHODS

A retrospective survey was conducted, from 2011 to 2021, at the Peking University Third Hospital. A total of 881 twin pregnancies, with lipid data from early and middle pregnancies, were included. After excluding those with adverse pregnancy outcomes, we performed a descriptive analysis of total cholesterol (TC), triglycerides (TG), high-density lipid cholesterol (HDL-C), and low-density lipid cholesterol (LDL-C) levels, using the mean and standard deviation to determine appropriate percentiles. We later determined the lipid reference range in early and middle pregnancy based on the initial results. We evaluated Inappropriate lipid levels associations with pregnancy outcomes, including gestational diabetes, pregnancy-induced hypertension, small for gestational age.

RESULTS

(1) Serum levels of TC, TG, LDL-C, and HDL-C increased significantly from early to late pregnancy, where the greatest increase was observed in TG. (2) Based on the results, we recommend that TC, TG, and LDL-C serum reference values during early and middle pregnancy should be less than the 95th percentile. On the other hand, HDL-C should be greater than the 5th percentile. During early pregnancy, the values recommended are TC < 5.31 mmol/L, TG < 2.25 mmol/L, HDL > 1.02 mmol/L and LDL < 3.27 mmol/L, and those during middle pregnancy are TC < 8.74 mmol/L, TG < 4.89 mmol/L, HDL > 1.25 mmol/L and LDL < 5.49 mmol/L, while the values during late pregnancy are TC < 9.11 mmol/L, TG < 6.70 mmol/L, HDL > 1.10 mmol/L and LDL < 5.81 mmol/L. Higher levels of blood lipids were associated with GDM, PE, SGA.

CONCLUSIONS

We suggested a reference ranges for blood lipids during the twin pregnancy in a Chinese population. The reference ranges recommended by this study can be used to identify women with twin pregnancies using unfavorable lipid values. Higher levels of blood lipids were associated with adverse pregnancy outcomes.

Development and validation of risk prediction models for large for gestational age infants using logistic regression and two machine learning algorithms

BACKGROUND

Large for gestational age (LGA) is one of the adverse outcomes during pregnancy that endangers the life and health of mothers and offspring. We aimed to establish prediction models for LGA at late pregnancy.

METHODS

Data were obtained from an established Chinese pregnant women cohort of 1285 pregnant women. LGA was diagnosed as >90th percentile of birth weight distribution of Chinese corresponding to gestational age of the same-sex newborns. Women with gestational diabetes mellitus (GDM) were classified into three subtypes according to the indexes of insulin sensitivity and insulin secretion. Models were established by logistic regression and decision tree/random forest algorithms, and validated by the data.

RESULTS

A total of 139 newborns were diagnosed as LGA after birth. The area under the curve (AUC) for the training set is 0.760 (95% confidence interval [CI] 0.706-0.815), and 0.748 (95% CI 0.659-0.837) for the internal validation set of the logistic regression model, which consisted of eight commonly used clinical indicators (including lipid profile) and GDM subtypes. For the prediction models established by the two machine learning algorithms, which included all the variables, the training set and the internal validation set had AUCs of 0.813 (95% CI 0.786-0.839) and 0.779 (95% CI 0.735-0.824) for the decision tree model, and 0.854 (95% CI 0.831-0.877) and 0.808 (95% CI 0.766-0.850) for the random forest model.

CONCLUSION

We established and validated three LGA risk prediction models to screen out the pregnant women with high risk of LGA at the early stage of the third trimester, which showed good prediction power and could guide early prevention strategies.

The Influence of Maternal Aerobic Exercise, Blood DHA and EPA Concentrations on Maternal Lipid Profiles

Exercise and polyunsaturated fatty acid (PUFA) supplementation independently improve lipid profiles. The influence of both exercise and PUFAs on lipids during pregnancy remains unknown. This study evaluated exercise, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) concentrations on lipids during pregnancy. Participants were randomized to aerobic exercise or control groups. From 16 weeks gestation until delivery, groups met 3x/week; exercisers performed moderate-intensity aerobic activity, controls performed low-intensity stretching and breathing. At 16 and 36 weeks’ gestation, maternal blood was analyzed for lipids (total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG)), DHA and EPA. In intent-to-treat analysis, the aerobic group (n = 20), relative to controls (n = 10), exhibited a higher HDL change across gestation (p = 0.03). In a per protocol analysis, the aerobic group, relative to controls, exhibited 21.2% lower TG at 36 weeks (p = 0.04). After controlling for 36-week DHA and EPA, exercise dose predicts 36 weeks’ TG (F (1,36) = 6.977, p = 0.012, r² = 0.16). Aerobic exercise normalizes late pregnancy TG. During pregnancy, exercise dose controls the rise in TG, therefore maintaining normal levels. DHA and EPA do not have measurable effects on lipids. Regardless of PUFA levels, exercise at recommended levels maintains appropriate TG levels in pregnant women. Normal TG levels are critical for pregnancy outcomes, and further studies are warranted to investigate this association in broader populations.