Novel metabolic marker Afamin: A predictive factor for Large-for-Gestational-Age (LGA) fetus estimation in pregnancies with gestational diabetes mellitus?

Novel serum biomarkers for early diagnosis of gestational diabetes mellitus-a review

Gestational diabetes mellitus (GDM) affects 9-25% of pregnancies. Undiagnosed or poorly managed GDM is associated with both short- and long-term complications in the fetus and mother. The pathogenesis of GDM is complex and has not yet been fully elucidated. Several biomarkers found in maternal serum have the potential for the early diagnosis of GDM. The aim of this narrative review was to explore novel biomarkers that have not been comprehensively described in previous reviews. We believe these biomarkers may allow for the detection of GDM in the early stages of pregnancy, enabling timely proper treatment and potentially preventing complications for both the mother and the fetus.

An intelligent adverse delivery outcomes prediction model based on the fusion of multiple obstetric clinical data

Adverse delivery outcomes is a major re-productive health problem that affects the physical and mental health of pregnant women. Obviously, obstetric clinical data has periodically time series characteristics. This paper proposed a three stage adverse delivery outcomes prediction model via the fusion of multiple time series clinical data. The first stage is data aggregation, in which the data set is collected from the obstetric clinical data and divided based on time series features. In the second stage, a multi-channel gated cycle unit is used to solve the calculation error caused by irregular sampling of time series data. The hidden layer feature vector is connected with the fully connected layer, reshaped into a new one-dimensional feature, and fused with the non-time series data into a new data set. The third stage is the prediction stage of adverse delivery outcomes. By connecting the multi-channel gated cycle unit with the extreme gradient lift, the data transmitted in the corresponding channel is used in the feature extraction stage, in which the weighted entropy-based feature extraction is adopted. With the help of the extracted features, a hybrid artificial neural network architecture (MGRU-XGB) was developed to predict adverse delivery outcomes. The experimental results showed that the hybrid model had the best prediction performance for adverse delivery outcomes compared with other single models in terms of sensitivity, specificity, AUC and other evaluation indexes.

Comparison of Afamin Values in Umbilical Cord Blood After Delivery in Pregnancies With and Without Gestational Diabetes Mellitus

OBJECTIVE

Afamin is a protein that increases in gestational diabetes but its concentration in neonates hasn't been investigated. Our objective is to compare cord blood afamin levels in neonates born to mothers with and without diabetes, and to explore its relationship with maternal and neonatal variables.

METHODS

In this case control study, umbilical cord blood was collected for afamin measurement in pregestational/gestational diabetic pregnancies (n = 40) and healthy pregnancies (n = 45) after delivery. Correlation analysis was conducted to examine the relationship between afamin levels and maternal BMI, age, HbA1c, fasting and postprandial blood glucose, gestational age, birth weight.

RESULTS

The diabetic group had a higher median afamin level (p < 0.001). Afamin concentrations did not differ significantly between diabetic subgroups. The concentration of afamin in cord blood was independent of maternal BMI, age, HbA1c, blood glucose, gestational age, birth weight.

CONCLUSION

The concentration of afamin in cord blood of diabetic pregnancies is significantly higher, irrespective of other clinical factors.

Investigation of serum afamin concentration in pregnant women diagnosed with late fetal growth restriction or small for gestational age fetus

OBJECTIVE

To investigate the afamin concentration in the serum of pregnant women diagnosed with late fetal growth restriction (FGR) or small for gestational age (SGA) in the third trimester.

METHODS

This prospective case-control study was conducted on 126 pregnant women, 42 of whom were diagnosed with late FGR in the third trimester, 43 were SGA, and 41 were healthy controls. The groups were compared in terms of maternal serum afamin concentrations.

RESULTS

Three groups were similar in terms of demographic characteristics and gestational age at blood sampling for afamin (p < .05). The median afamin concentration was determined as 199 ng/mL in the late FGR group, 153 ng/mL in the SGA group, and 108 ng/mL in the control group (p = .000). In the post-hoc analysis, while maternal serum afamin concentrations were found to be significantly higher in the late FGR group and SGA group compared to the control group but, this significance could not be shown between the FGR group and the SGA group (p = .00001, p = .005, p = .137, respectively). In the ROC analysis, the optimal cutoff value of serum afamin concentration to predict late FGR was determined as 141 ng/mL, with a sensitivity of 66.6% and a specificity of 85.3%.

CONCLUSIONS

The serum afamin concentration in the third trimester was found to be higher in pregnant women with late FGR compared to the SGA and control groups. Although afamin is seen as a promising molecule in the clinical prediction of late FGR, this needs to be supported by large series of studies.

Clinical Significance of Neuregulin 4, Afamin, and SERPINB1 in Gestational Diabetes Mellitus and Their Relationship with Insulin Resistance

Objective

This study aims to explore the serum levels of neuregulin 4 (NRG4), afamin (AFM), and serpin family B member 1 (SERPINB1) in gestational diabetes mellitus (GDM) patients and their relationship with insulin resistance.

Method

Serum levels of AFM, SERPINB1, and NRG4 were measured in GDM (n = 58), and non-GDM women (n = 60) using enzyme-linked immunosorbent assay (ELISA) kits. Besides, the serum insulin and glucose levels were also measured followed by calculating the homeostatic model assessment of insulin resistance (HOMA-IR). The correlation was performed using the Pearson analysis.

Results

The increased serum levels of AFM and SERPINB1 were revealed in GDM patients as compared with non-GDM women, accompanied by the lower NRG4 serum level. ROCs for AFM concentrations showed an AUC of 0.629 (95% CI: 0.527∼0.731), 0.832 (95% CI: 0.754∼0.909) for the SERPINB1 serum level, and 0.626 (95% CI: 0.524∼0.728) for the NRG4 serum level. The threshold was 108.05 mg/L, 8.75 ng/mL, and 96.25 ng/mL of AFM, SERPINB1, and NRG4. Moreover, the combined ROC of AFM, SERPINB1, and NRG4 serum levels showed higher sensitivity (72.41%) and specificity (85.00%) for the diagnosis of GDM (AUC = 0.839; 95% CI: 0.764∼0.913). In GDM patients, the Pearson analysis revealed a significant correlation between AFM and SERPINB1 (r = 0.776), AFM and NRG4 (r = -0.799), as well as SERPINB1 and NRG4 (r = -0.783). Moreover, AFM and SERPINB1 serum concentrations in GDM patients were positively related to insulin levels, fasting glucose levels, and HOMA-IR values. However, the SERPINB1 serum level was negatively correlated with serum insulin and glucose levels and HOMA-IR.

Conclusion

Abnormal serum levels of NRG4, AFM, and SERPINB1, as highly sensitive diagnostic tools, are closely related to insulin resistance in GDM patients.

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.