Influences of pregravid liver enzyme levels on the development of gestational diabetes mellitus

Diagnostic Approach to Elevated Liver Function Tests during Pregnancy: A Pragmatic Narrative Review

Liver disease is not uncommon during pregnancy and is associated with increased maternal and fetal/neonatal morbidity and mortality. Physiological changes during pregnancy, including a hyperestrogenic state, increase in circulating plasma volume and/or reduction in splanchnic vascular resistance, and hemostatic imbalance, may mimic or worsen liver disease. For the clinician, it is important to distinguish among the first presentation or exacerbation of chronic liver disease, acute liver disease non-specific to pregnancy, and pregnancy-specific liver disease. This last group classically includes conditions such as hyperemesis gravidarum, intrahepatic cholestasis of pregnancy, liver disorders associated with the pre-eclampsia spectrum, and an acute fatty liver of pregnancy. All of these disorders often share pathophysiological mechanisms, symptoms, and laboratory findings (such as elevated liver enzymes), but a prompt and correct diagnosis is fundamental to guide obstetric conduct, reduce morbidity and mortality, and inform upon the risk of recurrence or development of other chronic diseases later on in life. Finally, the cause of elevated liver enzymes during pregnancy is unclear in up to 30-40% of the cases, and yet, little is known on the causes and mechanisms underlying these alterations, or whether these findings are associated with worse maternal/fetal outcomes. In this narrative review, we aimed to summarize pragmatically the diagnostic work-up and the management of subjects with elevated liver enzymes during pregnancy.

Abnormal alanine aminotransferase levels in patients with moderate or severe ovarian hyperstimulation result in an increased risk of obstetric complications

OBJECTIVES

To explore the effect of abnormally elevated serum alanine aminotransferase (ALT) on pregnancy outcomes in patients with moderate and severe ovarian hyperstimulation syndrome (OHSS) at disease onset.

METHODS

This was a single-center retrospective cohort study conducted between January 1, 2014 and October 31, 2021. A total of 3550 fresh in vitro fertilization/intracytoplasmic sperm injection embryo transfer cycles were included, using Golan's three-degree, five-level classification to diagnose patients with OHSS. According to the patient's ALT level after diagnosis of OHSS, 123 (3.46%) patients with moderate-to-severe OHSS were divided into two groups. A control group included 3427 (96.54%) non-OHSS patients, and 91 (2.56%) abnormal ALT patients were matched with the control group for propensity scores.

RESULTS

There was no difference in baseline data between the abnormal ALT and matched control groups. The incidence of obstetric complications was significantly higher in the abnormal ALT group than in the matched control group (P < 0.05). After adjusting for confounding factors, the incidence of obstetric complications in the abnormal ALT group was still higher than that in the normal ALT group (P < 0.05).

CONCLUSION

In patients with moderate and severe OHSS, higher ALT levels resulted in an increased risk of obstetric and neonatal complications.

Predicting the Risk of Insulin-Requiring Gestational Diabetes before Pregnancy: A Model Generated from a Nationwide Population-Based Cohort Study in Korea

BACKGRUOUND

The severity of gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes. We aimed to generate a risk model for predicting insulin-requiring GDM before pregnancy in Korean women.

METHODS

A total of 417,210 women who received a health examination within 52 weeks before pregnancy and delivered between 2011 and 2015 were recruited from the Korean National Health Insurance database. The risk prediction model was created using a sample of 70% of the participants, while the remaining 30% were used for internal validation. Risk scores were assigned based on the hazard ratios for each risk factor in the multivariable Cox proportional hazards regression model. Six risk variables were selected, and a risk nomogram was created to estimate the risk of insulin-requiring GDM.

RESULTS

A total of 2,891 (0.69%) women developed insulin-requiring GDM. Age, body mass index (BMI), current smoking, fasting blood glucose (FBG), total cholesterol, and γ-glutamyl transferase were significant risk factors for insulin-requiring GDM and were incorporated into the risk model. Among the variables, old age, high BMI, and high FBG level were the main contributors to an increased risk of insulin-requiring GDM. The concordance index of the risk model for predicting insulin-requiring GDM was 0.783 (95% confidence interval, 0.766 to 0.799). The validation cohort's incidence rates for insulin-requiring GDM were consistent with the risk model's predictions.

CONCLUSION

A novel risk engine was generated to predict insulin-requiring GDM among Korean women. This model may provide helpful information for identifying high-risk women and enhancing prepregnancy care.

Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm

OBJECTIVE

To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.

METHODS

A case-control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer-Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models.

RESULTS

A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none.

CONCLUSIONS

The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.

Value of PAPP-A combined with BMI in predicting the prognosis of gestational diabetes mellitus: an observational study

The aim of this study was to investigate the potential of pregnancy-associated plasma protein A (PAPP-A) and clinical data in predicting gestational diabetes mellitus (GDM). Clinical data of 318 pregnant women with GDM and 200 healthy pregnant women were retrospectively analysed. The age, BMI and caesarean section in GDM were significantly higher than in normal group. Serum and placental levels of PAPP-A were significantly lower in GDM than in normal group. Pearson's correlation analysis showed that serum levels of PAPP-A were negatively correlated with BMI and blood glucose level. Binary logistic regression analysis displayed that PAPP-A were the potential factors influencing GDM. The area under the ROC curve (AUC) for PAPP-A combined with BMI in predicting GDM was 0.941, significantly higher than that of the single one. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.Impact statementWhat is already known on this subject? GDM not only increases the risk of perinatal morbidity, but also results in an increased risk of long-term sequelae for both mother and child including diabetes, cardiovascular disease obesity. Previous data indicate that besides glycemic control in the second trimester, interventions initiated early in pregnancy can reduce the rate of GDM in pregnant women. The expression of PAPP-A in serum of GDM pregnant women was decreased in the first trimester. Whereas, whether PAPP-A can be as an early predictor of GDM is not clear.What do the results of this study add? The present study shows that PAPP-A MoM was less than 0.6757 in the first trimester of pregnancy is more prone to GDM. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.What are the implications of these findings for clinical practice and/or further research? Early GDM prediction is crucial for prevention and management of GDM, to cope with the rising prevalence of GDM and reduce later life chronic disease of both mother and child. Based on the level of PAPP-A MoM and BMI, interventions such as lifestyle changes initiated early in pregnancy shouldbeenabledin pregnant women.

Metabolic Profiling of Pregnant Women with Obesity: An Exploratory Study in Women at Greater Risk of Gestational Diabetes

Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0−18+6 weeks) and at timepoint 2 (23+2−30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.

Association between pregravid liver enzyme levels and gestational diabetes in twin pregnancies: a secondary analysis of national cohort study

Multiple pregnancies are prone to gestational diabetes mellitus (GDM). This study investigated the association between pregravid liver enzyme levels and the development of GDM in a twin pregnancy. Women who had the National Health Screening Examination and delivered their twin babies within one year were enrolled. Pregravid liver enzyme levels were divided into high and low level. Risks for developing GDM by high levels of liver enzymes were analyzed, in subgroups by pregravid obesity or metabolic syndrome. Among the 4348 twin pregnancies, 369 women (8.5%) developed GDM not requiring insulin treatment (GDM - IT), and 119 women (2.7%) developed GDM requiring insulin treatment(GDM + IT). High levels of pregravid GGT and ALT were related to risks of GDM + IT not only in women with obesity or metabolic syndrome (odds ratio[OR] 6.348, 95% confidence interval [CI] 2.579-15.624 and OR 6.879, 95% CI 2.232-21.204, respectively), but also in women without obesity (OR 3.05, 95% CI 1.565-5.946) or without metabolic syndrome (OR 3.338, 95% CI 1.86-5.992), compared to in women with low levels of those. However, there were no significant associations in the pregravid ALT and GGT levels and risks for development of GDM - IT, unrelated to pregravid obesity or metabolic syndrome. Therefore, this study suggests that women with high levels of pregravid GGT and ALT need to recognize their increased risk of GDM + IT, regardless of pregravid obesity or MetS, when they get pregnant twin.

Nonalcoholic fatty liver disease and the risk of insulin-requiring gestational diabetes

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases; however, there has been little research into its impact on gestational diabetes mellitus (GDM).

METHODS

This study included 308,095 women registered in the Korean National Health Insurance Service database, who delivered between 2011 and 2015 and received a health examination within 52 weeks before pregnancy. Insulin-requiring GDM was defined as no insurance claims for diabetes mellitus and a fasting blood glucose level of < 126 mg/dL before pregnancy, and initiation of insulin treatment during pregnancy. A fatty liver index (FLI) was calculated using body mass index, waist circumference, and blood triglyceride and γ-glutamyl transferase levels. FLI scores < 30 ruled out hepatic steatosis, while FLI scores ≥ 60 indicated NAFLD.

RESULTS

The prevalence of NAFLD was 0.8% (2355/308,095) and 1984 (0.6%) subjects developed insulin-requiring GDM. FLIs of 30-59 and ≥ 60 were significantly associated with increased risk of insulin-requiring GDM (odds ratio [OR] 3.50; 95% confidence interval [CI] 2.99-4.10; OR 4.19; 95% CI 3.37-5.23), respectively. Further exploration of the association of FLI with GDM across FLI decile categories revealed a steady increase in OR across the categories. The association was more prominent among those without metabolic syndrome.

CONCLUSION

NAFLD in women is an independent risk factor for insulin-requiring GDM.