Effects of dynamic change in fetuin-A levels from the first to the second trimester on insulin resistance and gestational diabetes mellitus: a nested case-control study

[A prospective cohort study of association between early childhood body mass index trajectories and the risk of overweight]

OBJECTIVE

To compare the association between body mass index (BMI) trajectories determined by different methods and the risk of overweight in early childhood in a prospective cohort study, and to identify children with higher risk of obesity during critical growth windows of early childhood.

METHODS

A total of 1 330 children from Peking University Birth Cohort in Tongzhou (PKUBC-T) were included in this study. The children were followed up at birth, 1, 3, 6, 9, 12, 18, and 24 months and 3 years of age to obtain their height/length and weight data, and calculate BMI Z-score. Latent class growth mixture modeling (GMM) and longitudinal data-based k-means clustering algorithm (KML) were used to determine the grouping of early childhood BMI trajectories from birth to 24 mouths. Linear regression was used to compare the association between early childhood BMI trajectories determined by different methods and BMI Z-score at 3 years of age. The predictive performance of early childhood BMI trajectories determined by different methods in predicting the risk of overweight (BMI Z-score > 1) at 3 years was compared using the average area under the curve (AUC) of 5-fold cross-validation in Logistic regression models.

RESULTS

In the study population included in this research, the three-category trajectories determined using GMM were classified as low, medium, and high, accounting for 39.7%, 54.1%, and 6.2% of the participants, respectively. The two-category trajectories determined using the KML method were classified as low and high, representing 50. 3% and 49. 7% of the participants, respectively. The three-category trajectories determined using the KML method were classified as low, medium, and high, accounting for 31.1%, 47.4%, and 21.5% of the participants, respectively. There were certain differences in the growth patterns reflected by the early childhood BMI trajectories determined using different methods. Linear regression analysis found that after adjusting for maternal ethnicity, educational level, delivery mode, parity, maternal age at delivery, gestational week at delivery, children' s gender, and breastfeeding at 1 month of age, the association between the high trajectory group in the three-category trajectories determined by the KML method (manifested by a slightly higher BMI at birth, followed by rapid growth during infancy and a stable-high BMI until 24 months) and BMI Z-scores at 3 years was the strongest. Logistic regression analysis revealed that the three-category trajectory grouping determined by the KML method had the best predictive performance for the risk of overweight at 3 years. The results were basically consistent after additional adjustment for the high bound score of the child' s diet balanced index, average daily physical activity time, and screen time.

CONCLUSION

This study used different methods to identify early childhood BMI trajectories with varying characteristics, and found that the high trajectory group determined by the KML method was better able to identify children with a higher risk of overweight in early childhood. This provides scientific evidence for selecting appropriate methods to define early childhood BMI trajectories.

Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study

Early identification of children with neurodevelopmental abnormality is a major challenge, which is crucial for improving symptoms and preventing further decline in children with neurodevelopmental abnormality. This study focuses on developing a predictive model with maternal sociodemographic, behavioral, and medication-usage information during pregnancy to identify infants with abnormal neurodevelopment before the age of one. In addition, an interpretable machine-learning approach was utilized to assess the importance of the variables in the model. In this study, artificial neural network models were developed for the neurodevelopment of five areas of infants during the first year of life and achieved good predictive efficacy in the areas of fine motor and problem solving, with median AUC = 0.670 (IQR: 0.594, 0.764) and median AUC = 0.643 (IQR: 0.550, 0.731), respectively. The final model for neurodevelopmental abnormalities in any energy region of one-year-old children also achieved good prediction performance. The sensitivity is 0.700 (IQR: 0.597, 0.797), the AUC is 0.821 (IQR: 0.716, 0.833), the accuracy is 0.721 (IQR: 0.696, 0.739), and the specificity is 0.742 (IQR: 0.680, 0.748). In addition, interpretable machine-learning methods suggest that maternal exposure to drugs such as acetaminophen, ferrous succinate, and midazolam during pregnancy affects the development of specific areas of the offspring during the first year of life. This study established predictive models of neurodevelopmental abnormality in infants under one year and underscored the prediction value of medication exposure during pregnancy for the neurodevelopmental outcomes of the offspring.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

The associations of prenatal exposure to PM2.5 and its constituents with fetal growth: A prospective birth cohort in Beijing, China

BACKGROUND

Limited studies investigated the association of prenatal exposure to PM_2.5 and fetal growth measured by ultrasound with inconsistent results. No study evaluated the effect of PM_2.5 constituents on fetal growth in utero. We aimed to investigated whether prenatal exposure to PM_2.5 and its constituents was associated with fetal growth measured by ultrasound.

METHODS

A total of 4319 eligible pregnant women in Peking University Birth Cohort in Tongzhou (PKUBC-T) were included in the study. Based on mothers' residential addresses, we estimated prenatal PM_2.5 concentrations with a satellite-based spatiotemporal model and PM_2.5 constituents concentrations with a modified Community Multiscale Air Quality model. Fetal growth parameters of abdominal circumference (AC), head circumference (HC), and femur length (FL) were measured by ultrasound and then estimated fetal weight (EFW) was calculated. We calculated sex and gestational age-specific fetal growth Z-score and then defined the corresponding fetal undergrowth. Generalized estimating equation was used to investigate the association of PM_2.5 and its constituents with fetal growth Z-score and fetal undergrowth.

RESULTS

Prenatal exposure to PM_2.5, OC, EC, SO₄^2-, NH₄⁺, or NO₃^- was consistently associated with decreased Z-scores of fetal growth parameters (AC, HC, FL, EFW). One IQR increase of PM_2.5, OC, EC, SO₄^2-, NH₄⁺, or NO₃^- was associated with -0.183 [95% confident interval (CI): -0.225, -0.141], -0.144 (95%CI: -0.181, -0.107), -0.123 (95%CI: -0.160, -0.085), -0.035 (95%CI: -0.055, -0.015), -0.095 (95%CI: -0.126, -0.064), and -0.124 (95%CI: -0.159, -0.088) decrease in EFW Z-score, respectively. Prenatal exposure to PM_2.5, OC, EC, SO₄^2-, NH₄⁺, or NO₃^- was also associated with higher risk of fetal AC, HC, FL or EFW undergrowth.

CONCLUSION

The study identified that prenatal exposure to PM_2.5 or its constituents was associated with impaired fetal growth. The findings provided evidence that control measures for PM_2.5 constituents should be implemented for further promoting fetal growth.

Serum Fetuin-A and Risk of Gestational Diabetes Mellitus: An Observational Study and Mendelian Randomization Analysis

CONTEXT

Fetuin-A was reported to be associated with risk of type 2 diabetes, but its association with incident gestational diabetes mellitus (GDM) was less studied.

OBJECTIVE

We aimed to examine the association between fetuin-A levels in early pregnancy and risk of incident GDM and to evaluate whether this association was causal.

METHODS

A total of 332 pregnant women with GDM and 664 matched controls were included in this nested case-control study. Multivariable conditional logistic regression was applied to investigate the prospective association between serum fetuin-A in early pregnancy and subsequent risk of GDM. Two-sample Mendelian randomization (MR) analysis was used to examine the causal association, using summary statistics from the CHARGE Consortium and the FinnGen consortium.

RESULTS

The mean age of the participants was 28.0 years, and the mean gestational age was 11.0 weeks (range 6-15) at enrollment. In the final model, the odds ratio (OR) for GDM comparing the extreme quartiles of fetuin-A levels was 1.78 (95% CI 1.06, 2.98; P for trend = 0.009), and the restricted cubic spline analysis indicated a linear association (P for nonlinearity = 0.83). This positive association was found in women with waist circumference <80 cm but not in those with waist circumference ≥80 cm (P for interaction = 0.04). However, MR analyses showed no evidence of a causal association with an OR of 0.91 (95% CI 0.67, 1.23) per unit increment of fetuin-A.

CONCLUSIONS

Serum fetuin-A levels in early pregnancy were positively associated with risk of GDM, particularly in those with normal waist circumference. However, we found no genetic evidence for a causal association.

Fetuin-A expression in human umbilical vein endothelial cells and amnion cells of patients with gestational diabetes mellitus

OBJECTIVES

To elucidate the link between fetuin-A expression in human umbilical vein endothelial cells (HUVECs) and amnion cells (ACs) and clinicopathological changes in patients with gestational diabetes mellitus (GDM) and newborns.

METHODS

This retrospective cohort study included 82 pregnant patients (40 with GDM and 42 controls) between January 2019 and January 2022. The patients underwent a one-hour, 50 gram glucose challenge test (GCT) during the 24-28th weeks of pregnancy. Patients with positive GCTs immediately underwent a 3-hour, 100 gram oral glucose tolerance test. The expression level of fetuin-A in UVECs and ACs was evaluated by immunohistochemistry (IHC) and scored based on IHC staining in randomly selected slides. The IHC staining intensity was evaluated by the number of dots, which reflects the expression level of fetuin-A in both HUVECs and ACs.

RESULTS

The GDM group displayed significantly higher fetuin-A expression in both HUVECs (p<0.0001) and ACs (p=0.0001) when compared with the control group. Fetuin-A expression in HUVECs was correlated with fetal macrosomia, neonatal hypoglycemia, and placental weight. However, there was no correlation with fetuin-A expression in ACs.

CONCLUSION

There is a correlation between fetal macrosomia, neonatal hypoglycemia, placental weight, and fetuin-A expression of HUVECs in patients with GDM.

New Insights into Adipokines in Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is the most common metabolic disorder of pregnancy and has considerable short- and long-term consequences for the health of both the mother and the newborn. Within its pathophysiology, genetic, nutritional, epigenetic, immunological, and hormonal components have been described. Within the last two items, it is known that different hormones and cytokines secreted by adipose tissue, known collectively as adipokines, are involved in the metabolic alterations underlying GDM. Although the maternal circulating profile of adipokines in GDM has been extensively studied, and there are excellent reviews on the subject, it is in recent years that more progress has been made in the study of their expression in visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), placenta, and their concentrations in the umbilical circulation. Thus, this review compiles and organizes the most recent findings on the maternal and umbilical circulating profile and the levels of expression of adipokines in VAT, SAT, and placenta in GDM.

Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus 2.0

Gestational diabetes mellitus (GDM) has become a major public health problem and one of the most discussed issues in modern obstetrics. GDM is associated with serious adverse perinatal outcomes and long-term health consequences for both the mother and child. Currently, the importance and purposefulness of finding a biopredictor that will enable the identification of women with an increased risk of developing GDM as early as the beginning of pregnancy are highly emphasized. Both “older” molecules, such as adiponectin and leptin, and “newer” adipokines, including fatty acid-binding protein 4 (FABP4), have proven to be of pathophysiological importance in GDM. Therefore, in our previous review, we presented 13 novel biomolecules, i.e., galectins, growth differentiation factor-15, chemerin, omentin-1, osteocalcin, resistin, visfatin, vaspin, irisin, apelin, FABP4, fibroblast growth factor 21, and lipocalin-2. The purpose of this review is to present the potential and importance of another nine lesser known molecules in the pathogenesis of GDM, i.e., 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), angiopoietin-like protein-8 (ANGPTL-8), nesfatin-1, afamin, adropin, fetuin-A, zonulin, secreted frizzled-related proteins (SFRPs), and amylin. It seems that two of them, fetuin-A and zonulin in high serum levels, may be applied as biopredictors of GDM.

Fetuin-A as a Marker of Insulin Resistance

Fetuin–A is a glycoprotein which helps in the regulation of metabolism. It is an early marker of insulin resistance (IR). The aim of this study was to evaluate the role of Fetuin–A as a predictive biomarker in cases of newly detected type 2 diabetes (NDD). The study involved 60 NDD and 60 Normal Healthy Controls (NHC). All the demographics and anthropological characteristics were noted. Fasting blood samples were drawn and various biochemical parameters were analyzed. The homeostatic model assessment of insulin resistance (HOMA-IR) and the quantitative insulin sensitivity check index (QUICKI) score was calculated. Chisquare, student T-test and Mann Whitney U tests were employed to associate and compare the mean and median between the NDD and NHC groups. Pearson's and Spearman’s correlation analysis were employed to examine the relationship of Fetuin–A levels with parametric and nonparametric variables. The independent predictors of Fetuin–A was determined by employing multiple forward linear regression. Fetuin–A was significantly high in NDD compared to NHC (1323 vs. 306.98 mcg/mL; p<0.001). Majority of NDD patients demonstrated IR based on the HOMA-IR (88.33% vs. 66.67%) and QUICKI score (96.67% vs. 85%). The multiple linear regression analysis showed that systolic blood pressure, age and QUICKI score were independently associated with Fetuin–A (p value <0.01). Fetuin–A may be used as a biomarker to detect NDD. Therefore, early detection of Fetuin–A levels in NDD gives an opportunity for suitable patient management.

Early markers of gestational diabetes mellitus: what we know and which way forward?

Women's metabolism during pregnancy undergoes numerous changes that can lead to gestational diabetes mellitus (GDM). The cause and pathogenesis of GDM, a heterogeneous disease, are not completely clear, but GDM is increasing in prevalence and is associated with the modern lifestyle. Most diagnoses of GDM are made via the guidelines from the International Association of Diabetes and Pregnancy Study Groups (IADSPG), which involve an oral glucose tolerance test (OGTT) between 24 and 28 weeks of pregnancy. Diagnosis in this stage of pregnancy can lead to short- and long-term implications for the mother and child. Therefore, there is an urgent need for earlier GDM markers in order to enable prevention and earlier treatment. Routine GDM biomarkers (plasma glucose, insulin, C-peptide, homeostatic model assessment of insulin resistance, and sex hormone-binding globulin) can differentiate between healthy pregnant women and those with GDM but are not suitable for early GDM diagnosis. In this article, we present an overview of the potential early biomarkers for GDM that have been investigated recently. We also present our view of future developments in the laboratory diagnosis of GDM.

Emerging Protein Biomarkers for the Diagnosis or Prediction of Gestational Diabetes-A Scoping Review

Introduction: Gestational diabetes (GDM), defined as hyperglycemia with onset or initial recognition during pregnancy, has a rising prevalence paralleling the rise in type 2 diabetes (T2DM) and obesity. GDM is associated with short-term and long-term consequences for both mother and child. Therefore, it is crucial we efficiently identify all cases and initiate early treatment, reducing fetal exposure to hyperglycemia and reducing GDM-related adverse pregnancy outcomes. For this reason, GDM screening is recommended as part of routine pregnancy care. The current screening method, the oral glucose tolerance test (OGTT), is a lengthy, cumbersome and inconvenient test with poor reproducibility. Newer biomarkers that do not necessitate a fasting sample are needed for the prompt diagnosis of GDM. The aim of this scoping review is to highlight and describe emerging protein biomarkers that fulfill these requirements for the diagnosis of GDM. Materials and Methods: This scoping review was conducted according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for scoping reviews using Cochrane Central Register of Controlled Trials (CENTRAL), the Cumulative Index to Nursing & Allied Health Literature (CINAHL), PubMed, Embase and Web of Science with a double screening and extraction process. The search included all articles published in the literature to July 2020. Results: Of the 3519 original database citations identified, 385 were eligible for full-text review. Of these, 332 (86.2%) were included in the scoping review providing a total of 589 biomarkers studied in relation to GDM diagnosis. Given the high number of biomarkers identified, three post hoc criteria were introduced to reduce the items set for discussion: we chose only protein biomarkers with at least five citations in the articles identified by our search and published in the years 2017-2020. When applied, these criteria identified a total of 15 biomarkers, which went forward for review and discussion. Conclusions: This review details protein biomarkers that have been studied to find a suitable test for GDM diagnosis with the potential to replace the OGTT used in current GDM screening protocols. Ongoing research efforts will continue to identify more accurate and practical biomarkers to take GDM screening and diagnosis into the 21st century.

Association of Gestational Diabetes Mellitus with Adverse Pregnancy Outcomes and Its Interaction with Maternal Age in Chinese Urban Women

BACKGROUND

The prevalence of gestational diabetes mellitus (GDM) has been dramatically increasing worldwide. The aims of this study were to examine associations of GDM with pregnancy outcomes in Chinese urban women and to evaluate the interaction between GDM and other major risk factors for the risk of adverse pregnancy outcomes.

METHODS

A retrospective analysis included 8844 women who delivered live singletons at ≥28 weeks of gestation between June 2012 and March 2013 among Chinese urban women. Structured questionnaires were used to collect information on demographic characteristics, lifestyle behavior, medical history, and pregnancy outcomes. The diagnosis of GDM was made between 24 and 28 gestational weeks according to the International Association of Diabetes and Pregnancy Study Groups criteria. Logistic regression models were used to assess the association of GDM with pregnancy outcomes and to examine the interaction between GDM and other major risk factors including maternal age, prepregnancy body mass index, and gestational weight gain for the risk of pregnancy outcomes.

RESULTS

13.9% of women were diagnosed with GDM. We found that GDM was associated with higher risk of cesarean delivery (odds ratio (OR) = 1.69, 95% CI (confidence interval): 1.48-1.92), preterm birth (OR = 1.32, 95% CI: 1.07-1.64), macrosomia (OR = 1.69, 95% CI: 1.34-2.13), and large for gestational age (LGA, OR = 1.43, 95% CI: 1.18-1.73) after adjustment for potential confounders. We also observed the interaction between GDM and maternal age for the risk of cesarean delivery (P for interaction = 0.025), and the OR of GDM for cesarean delivery was 1.71 (95% CI: 1.49-1.97) among women aged less than 35 years.

CONCLUSIONS

GDM was associated with an increased risk of cesarean delivery, preterm birth, macrosomia, and LGA in Chinese urban women, and there was an interaction between GDM and maternal age for the risk of cesarean delivery.

Effects of fetuin-A with diverse functions and multiple mechanisms on human health

Fetuin-A (Alfa 2-Heremans-Schmid) is a glycoprotein that is mainly synthesized by hepatocytes and then released into the bloodstream. While fetuin-A, a multifunctional protein, has inhibitory effects on health in the processes of calcification, mineralization, coronary artery calcification (CAC), and kidney stone formation by various mechanisms, it has such stimulatory effects as obesity, diabetes, and tumor progression processes. Fetuin-A produces these effects on the organism mainly by playing a role in the secretion levels of some inflammatory cytokines and exosomes, preventing unwanted calcification, inhibiting the autophosphorylation of tyrosine kinase, suppressing the release of adiponectin and peroxisome proliferator-activated receptor-γ (PPARγ), activating the toll-like receptor 4 (TLR-4), triggering the phosphatidylinositol 3 (PI3) kinase/Akt signaling pathway and cell proliferation, and mimicking the transforming growth factor-beta (TGF-β) receptor. In the present review, fetuin-A was examined in a wide perspective from the structure and release of fetuin-A to its effects on health.