First-trimester multimarker prediction of gestational diabetes mellitus using targeted mass spectrometry

The effect of circulating adiponectin levels on incident gestational diabetes mellitus: systematic review and meta‑analysis

BACKGROUND

To quantitatively synthesize evidence from prospective observational studies regarding the mean levels of circulating adiponectin in patients with gestational diabetes mellitus (GDM) and the association between adiponectin levels and GDM risk.

METHODS

PubMed, EMBASE and Web of Science were searched from their inception until November 8th, 2022, for nested case-control studies and cohort studies. Random-effect models were applied to the synthesized effect sizes. The difference in circulating adiponectin levels between the GDM and control groups was measured using the pooled standardized mean difference (SMD) and 95% confidence interval (CI). The relationship between circulating adiponectin levels and GDM risk was examined using the combined odds ratio (OR) and 95% CI. Subgroup analyses were performed according to the study continent, GDM risk in the study population, study design, gestational weeks of circulating adiponectin detection, GDM diagnostic criteria, and study quality. Sensitivity and cumulative analyses were performed to evaluate the stability of the meta-analysis. Publication bias was assessed by funnel plots and Egger's test.

RESULTS

The 28 studies included 13 cohort studies and 15 nested case-control studies, containing 12,256 pregnant women in total. The mean adiponectin level in GDM patients was significantly lower than in controls (SMD = -1.514, 95% CI = -2.400 to -0.628, p = .001, I² = 99%). The risk of GDM was significantly decreased among pregnant women with increasing levels of circulating adiponectin (OR = 0.368, 95% CI = 0.271-0.500, p < .001, I²=83%). There were no significant differences between the subgroups.

CONCLUSIONS

Our findings indicate that increasing circulating adiponectin levels were inversely associated with the risk of GDM. Given the inherent heterogeneity and publication bias of the included studies, further well-designed large-scale prospective cohort or intervention studies are needed to confirm our finding.

Role of increased plasminogen activator inhibitor-1 and vitronectin in gestational diabetes mellitus

OBJECTIVE

The aim of this study was to analyze the second-trimester levels of vitronectin and plasminogen activator inhibitor-1 in gestational diabetes mellitus.

METHODS

This study was conducted between September 2020 and December 2020 at the University of Health Sciences, Bursa Yuksek Ihtisas Research and Training Hospital, Department of Obstetrics and Gynecology. A total of 30 pregnant women with gestational diabetes mellitus and 60 healthy controls between 24 and 27/6 weeks of gestation were included. The inclusion criteria were as follows: being between 18 and 45 years old and 24-27/6 gestational weeks, having singleton pregnancy, diagnosed with gestational diabetes mellitus by using a two-step challenge test. The exclusion criteria of this study were as follows: chronic inflammatory or infectious disease, fasting blood glucose>126 mg/dL, intolerance to glucose tolerance testing, abnormal liver or kidney function tests, as well as pregnancy with pre-gestational diabetes history of adverse perinatal outcomes. Serum vitronectin and plasminogen activator inhibitor-1 levels were measured using the enzyme-linked immunosorbent assay method.

RESULTS

Vitronectin and plasminogen activator inhibitor-1 levels were higher in the gestational diabetes mellitus group compared with controls [91.85 (23.08) vs. 80.10 (39.18) ng/mL, for vitronectin and 6.50 (1.05) vs. 4.35(1.0) ng/mL, for plasminogen activator inhibitor-1 (for both p<0.001)]. vitronectin >84.7 ng/mL was found to predict gestational diabetes mellitus with a sensitivity of 70% and specificity of 63.3%. Moreover, vitronectin had a significant positive correlation with fasting blood glucose (r=0.476, p<0.001), postprandial blood glucose (r=0.489, p<0.001), HbA1c (r=0.713, p<0.001), and plasminogen activator inhibitor-1 (r=0.586, p<0.001).

CONCLUSION

This study revealed that second-trimester vitronectin and plasminogen activator inhibitor-1 are increased in gestational diabetes mellitus and vitronectin could be a candidate for the prediction of gestational diabetes mellitus.

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Maternal circulating metabolic biomarkers and their prediction performance for gestational diabetes mellitus related macrosomia

INTRODUCTION

Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M.

METHODS

Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model.

RESULTS

We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR <  0.05]. Based on the different clinical characteristics and Olink markers, a total of nine predictors, namely pre-pregnancy body mass index (BMI), weight gain at 24 gestational weeks (gw), parity, oral glucose tolerance test (OGTT) 2 h glucose at 24 gw, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw, were identified by LASSO regression. The model constructed using these 9 predictors displayed good prediction performance for GDM-M, with an area under the ROC of 0.970 (sensitivity = 0.955, specificity = 0.886), and was well calibrated (P _{Hosmer-Lemeshow test} = 0.897).

CONCLUSION

The Model included pre-pregnancy BMI, weight gain at 24 gw, parity, OGTT 2 h glucose at 24 gw, HDL and LDL at 24 gw, and plasma expression of CLUL1, VCAN and RNASE3 at 24 gw had good prediction performance for predicting macrosomia in women with GDM.

First trimester serum apolipoproteins in the prediction of late-onset preeclampsia

Late-onset preeclampsia occurring after 34 weeks of gestation is the most common form of preeclampsia, but little is known about either etiology or prevention. Current detection methods for preeclampsia in early pregnancy have not shown promising results in detecting late-onset preeclampsia. The aim of this study was to assess whether apolipoproteins in combination with maternal medical history and biophysical factors can be used as an early detection method for late-onset preeclampsia. This nested case-cohort study was based at Odense University Hospital, Denmark. Women attending their first trimester scan were invited to participate if they understood Danish or English, were above the age of 18, and had singleton pregnancies. Blood pressure, maternal medical history, uterine artery pulsatility indices, and blood samples were collected at inclusion. Outcome data were collected from participants' medical files postpartum, and cases were selected when preeclampsia diagnostics were present. Serum samples were analyzed by targeted mass spectrometry using a biomarker panel consisting of 12 apolipoproteins. Logistic regression analyses were performed and finally receiver operating curves were completed. The cohort consisted of 27 cases and 194 normotensive controls, randomized from 340 eligible participants. Significant differences were found between the two groups' baseline characteristics but none of the apolipoproteins showed significant difference (p < 0.05). The ROC-curve combining maternal characteristics, mean arterial pressure and two apolipoproteins showed the best sensitivity of 55.5% at a 10% false-positive rate and an area under the curve of 0.873. In conclusion, apolipoproteins did not improve the detection of late-onset preeclampsia in a combined screening model.

Association Of Blood Lipocalin-2 Levels with Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

Lipocalin-2 (LCN2) is becoming recognized as a pleiotropic mediator of metabolic disorders. However, the relationship between LCN2 and gestational diabetes mellitus (GDM) is not well understood. We performed a systematic review and meta-analysis to explore it. A systematic search of Cochrane Library, PubMed, Embase, Scopus, Web of Science, Chinese National Knowledge Infrastructure, and Wan-fang Database was done for relevant articles published up to September 29, 2021. Standardized mean difference (SMD) with 95% confidence intervals (CI) was calculated to explore the association of LCN2 levels with GDM using Revman 5.3 and Stata 15.1. Fifteen case-control studies were included in this meta-analysis. The patients with GDM had significantly higher levels of blood LCN2 than parturients with normal glucose tolerance (SMD=3.41, 95% CI=2.24 to 4.58). Meta-regression and subgroup analysis were conducted to investigate the source of heterogeneity. Likely sources of heterogeneity were age and testing methods. This study found that GDM showed higher blood LCN2 levels than controls. However, caution is warranted on the interpretation of these findings. Standardized LCN2 measurement methods and longitudinal studies are required to disentangle and better understand the relationships observed.

Cardiovascular Disease-Associated MicroRNAs as Novel Biomarkers of First-Trimester Screening for Gestational Diabetes Mellitus in the Absence of Other Pregnancy-Related Complications

We assessed the diagnostic potential of cardiovascular disease-associated microRNAs for the early prediction of gestational diabetes mellitus (GDM) in singleton pregnancies of Caucasian descent in the absence of other pregnancy-related complications. Whole peripheral venous blood samples were collected within 10 to 13 weeks of gestation. This retrospective study involved all pregnancies diagnosed with only GDM (n = 121) and 80 normal term pregnancies selected with regard to equality of sample storage time. Gene expression of 29 microRNAs was assessed using real-time RT-PCR. Upregulation of 11 microRNAs (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-100-5p, miR-125b-5p, miR-126-3p, miR-181a-5p, miR-195-5p, miR-499a-5p, and miR-574-3p) was observed in pregnancies destinated to develop GDM. Combined screening of all 11 dysregulated microRNAs showed the highest accuracy for the early identification of pregnancies destinated to develop GDM. This screening identified 47.93% of GDM pregnancies at a 10.0% false positive rate (FPR). The predictive model for GDM based on aberrant microRNA expression profile was further improved via the implementation of clinical characteristics (maternal age and BMI at early stages of gestation and an infertility treatment by assisted reproductive technology). Following this, 69.17% of GDM pregnancies were identified at a 10.0% FPR. The effective prediction model specifically for severe GDM requiring administration of therapy involved using a combination of these three clinical characteristics and three microRNA biomarkers (miR-20a-5p, miR-20b-5p, and miR-195-5p). This model identified 78.95% of cases at a 10.0% FPR. The effective prediction model for GDM managed by diet only required the involvement of these three clinical characteristics and eight microRNA biomarkers (miR-1-3p, miR-20a-5p, miR-20b-5p, miR-100-5p, miR-125b-5p, miR-195-5p, miR-499a-5p, and miR-574-3p). With this, the model identified 50.50% of GDM pregnancies managed by diet only at a 10.0% FPR. When other clinical variables such as history of miscarriage, the presence of trombophilic gene mutations, positive first-trimester screening for preeclampsia and/or fetal growth restriction by the Fetal Medicine Foundation algorithm, and family history of diabetes mellitus in first-degree relatives were included in the GDM prediction model, the predictive power was further increased at a 10.0% FPR (72.50% GDM in total, 89.47% GDM requiring therapy, and 56.44% GDM managed by diet only). Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for GDM.

Apolipoprotein D and transthyretin are reduced in female adolescent offspring of women with type 1 diabetes: The EPICOM study

AIMS

Adolescent offspring exposed to maternal diabetes during intrauterine life show a less favourable metabolic profile than the background population. Here, we hypothesize that offspring of women with type 1 diabetes (T1D), possess sex-specific alterations in the serum profile of proteins involved in lipid, metabolic and transport processes and that these alterations are associated with lipid profile and indices of insulin sensitivity and secretion.

METHODS

A prospective nationwide follow-up study (EPICOM) in a Danish population. Blood samples were assessed from offspring of women with T1D (index offspring, n = 267, 13-20 years), and matched control offspring (n = 290). Serum proteins were analysed using a 25-plex cardio-metabolic targeted proteomics assay, which includes 12 apolipoproteins and 13 transport and inflammatory proteins.

RESULTS

Apolipoprotein D (ApoD) and transthyretin (TTR) were reduced in index females as compared to female controls (-8.1%, p < 0.001 and -6.1%, p = 0.006 respectively), but not in index males (2.2%, p = 0.476 and -2.4%, p = 0.731 respectively). Sex-dependent inverse associations between exposure to maternal T1D in utero and ApoD and TTR were significant after adjusting for age, BMI-SDS and Tanner stage (OR = 0.252 [95% CI 0.085, 0.745], p = 0.013 and OR = 0.149 [95% CI 0.040, 0.553], p = 0.004). ApoD correlated to indices of insulin sensitivity and secretion in a similar sex-specific pattern in crude and adjusted analyses.

CONCLUSIONS

Low ApoD may be regarded as an early risk marker of metabolic syndrome. A possible link between ApoD and cardiovascular disease needs further investigation.

Proteomics Studies in Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as pre-eclampsia and stillbirth. Further, women with GDM have approximately 10 times higher risk of diabetes later in life. Children born to mothers with GDM also face a higher risk of childhood obesity and diabetes later in life. Early prediction/diagnosis of GDM leads to early interventions such as diet and lifestyle, which could mitigate the maternal and fetal complications associated with GDM. However, no biomarkers identified to date have been proven to be effective in the prediction/diagnosis of GDM. Proteomic approaches based on mass spectrometry have been applied in various fields of biomedical research to identify novel biomarkers. Although a number of proteomic studies in GDM now exist, a lack of a comprehensive and up-to-date meta-analysis makes it difficult for researchers to interpret the data in the existing literature. Thus, we undertook a systematic review and meta-analysis on proteomic studies and GDM. We searched MEDLINE, EMBASE, Web of Science and Scopus from inception to January 2022. We searched Medline, Embase, CINHAL and the Cochrane Library, which were searched from inception to February 2021. We included cohort, case-control and observational studies reporting original data investigating the development of GDM compared to a control group. Two independent reviewers selected eligible studies for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020185951. Of 120 articles retrieved, 24 studies met the eligibility criteria, comparing a total of 1779 pregnant women (904 GDM and 875 controls). A total of 262 GDM candidate biomarkers (CBs) were identified, with 49 CBs reported in at least two studies. We found 22 highly replicable CBs that were significantly different (nine CBs were upregulated and 12 CBs downregulated) between women with GDM and controls across various proteomic platforms, sample types, blood fractions and time of blood collection and continents. We performed further analyses on blood (plasma/serum) CBs in early pregnancy (first and/or early second trimester) and included studies with more than nine samples (nine studies in total). We found that 11 CBs were significantly upregulated, and 13 CBs significantly downregulated in women with GDM compared to controls. Subsequent pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources found that these CBs were most strongly linked to pathways related to complement and coagulation cascades. Our findings provide important insights and form a strong foundation for future validation studies to establish reliable biomarkers for GDM.

Early Identification of the Maternal, Placental and Fetal Dialog in Gestational Diabetes and Its Prevention

Gestational diabetes mellitus (GDM) complicates between 5 and 12% of pregnancies, with associated maternal, fetal, and neonatal complications. The ideal screening and diagnostic criteria to diagnose and treat GDM have not been established and, currently, diagnostic use with an oral glucose tolerance test occurs late in pregnancy and produces poor reproducibility. Therefore, in recent years, significant research has been undertaken to identify a first-trimester biomarker that can predict GDM later in pregnancy, enable early intervention, and reduce GDM-related adverse pregnancy outcomes. Possible biomarkers include glycemic markers (fasting glucose and hemoglobin A1c), adipocyte-derived markers (adiponectin and leptin), pregnancy-related markers (pregnancy-associated plasma protein-A and the placental growth factor), inflammatory markers (C-reactive protein and tumor necrosis factor-α), insulin resistance markers (sex hormone-binding globulin), and others. This review summarizes current data on first-trimester biomarkers, the advantages, and the limitations. Large multi-ethnic clinical trials and cost-effectiveness analyses are needed not only to build effective prediction models but also to validate their clinical use.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

The Lipocalin Apolipoprotein D Functional Portrait: A Systematic Review

Apolipoprotein D is a chordate gene early originated in the Lipocalin protein family. Among other features, regulation of its expression in a wide variety of disease conditions in humans, as apparently unrelated as neurodegeneration or breast cancer, have called for attention on this gene. Also, its presence in different tissues, from blood to brain, and different subcellular locations, from HDL lipoparticles to the interior of lysosomes or the surface of extracellular vesicles, poses an interesting challenge in deciphering its physiological function: Is ApoD a moonlighting protein, serving different roles in different cellular compartments, tissues, or organisms? Or does it have a unique biochemical mechanism of action that accounts for such apparently diverse roles in different physiological situations? To answer these questions, we have performed a systematic review of all primary publications where ApoD properties have been investigated in chordates. We conclude that ApoD ligand binding in the Lipocalin pocket, combined with an antioxidant activity performed at the rim of the pocket are properties sufficient to explain ApoD association with different lipid-based structures, where its physiological function is better described as lipid-management than by long-range lipid-transport. Controlling the redox state of these lipid structures in particular subcellular locations or extracellular structures, ApoD is able to modulate an enormous array of apparently diverse processes in the organism, both in health and disease. The new picture emerging from these data should help to put the physiological role of ApoD in new contexts and to inspire well-focused future research.

Adipose-Derived Lipid-Binding Proteins: The Good, the Bad and the Metabolic Diseases

Adipose tissue releases a large range of bioactive factors called adipokines, many of which are involved in inflammation, glucose homeostasis and lipid metabolism. Under pathological conditions such as obesity, most of the adipokines are upregulated and considered as deleterious, due to their pro-inflammatory, pro-atherosclerotic or pro-diabetic properties, while only a few are downregulated and would be designated as beneficial adipokines, thanks to their counteracting properties against the onset of comorbidities. This review focuses on six adipose-derived lipid-binding proteins that have emerged as key factors in the development of obesity and diabetes: Retinol binding protein 4 (RBP4), Fatty acid binding protein 4 (FABP4), Apolipoprotein D (APOD), Lipocalin-2 (LCN2), Lipocalin-14 (LCN14) and Apolipoprotein M (APOM). These proteins share structural homology and capacity to bind small hydrophobic molecules but display opposite effects on glucose and lipid metabolism. RBP4 and FABP4 are positively associated with metabolic syndrome, while APOD and LCN2 are ubiquitously expressed proteins with deleterious or beneficial effects, depending on their anatomical site of expression. LCN14 and APOM have been recently identified as adipokines associated with healthy metabolism. Recent findings on these lipid-binding proteins exhibiting detrimental or protective roles in human and murine metabolism and their involvement in metabolic diseases are also discussed.

Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Background

Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.

Methods

Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.

Results

This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.

Conclusions

Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-021-00606-8.

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning

CONTEXT

Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.

OBJECTIVES

This work aimed to establish effective models to predict early GDM.

METHODS

Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.

RESULTS

A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).

CONCLUSIONS

We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

MiRNAs in Gestational Diabetes Mellitus: Potential Mechanisms and Clinical Applications

Gestational diabetes mellitus (GDM) is a common pregnancy complication which is normally diagnosed in the second trimester of gestation. With an increasing incidence, GDM poses a significant threat to maternal and offspring health. Therefore, we need a deeper understanding of GDM pathophysiology and novel investigation on the diagnosis and treatment for GDM. MicroRNAs (miRNAs), a class of endogenic small noncoding RNAs with a length of approximately 19-24 nucleotides, have been reported to exert their function in gene expression by binding to proteins or being enclosed in membranous vesicles, such as exosomes. Studies have investigated the roles of miRNAs in the pathophysiological mechanism of GDM and their potential as noninvasive biological candidates for the management of GDM, including diagnosis and treatment. This review is aimed at summarizing the pathophysiological significance of miRNAs in GDM development and their potential function in GDM clinical diagnosis and therapeutic approach. In this review, we summarized an integrated expressional profile and the pathophysiological significance of placental exosomes and associated miRNAs, as well as other plasma miRNAs such as exo-AT. Furthermore, we also discussed the practical application of exosomes in GDM postpartum outcomes and the potential function of several miRNAs as therapeutic target in the GDM pathological pathway, thus providing a novel clinical insight of these biological signatures into GDM therapeutic approach.

Molecular pathophysiology of diabetes mellitus during pregnancy with antenatal complications

Gestational diabetes mellitus is a daunting problem accompanied by severe fetal development complications and type 2 diabetes mellitus in postpartum. Diagnosis of diabetic conditions occurs only in the second trimester, while associated antenatal complications are typically revealed even later. We acquired an assay of peripheral and cord blood samples of patients with different types of diabetes mellitus who delivered either healthy newborns or associated with fetopathy complications. Obtained data were handled with qualitative and quantitative analysis. Pathways of molecular events involved in diabetes mellitus and fetopathy were reconstructed based on the discovered markers and their quantitative alteration. Plenty of pathways were integrated to differentiate the type of diabetes and to recognize the impact of the diabetic condition on fetal development. The impaired triglycerides transport, glucose uptake, and consequent insulin resistance are mostly affected by faulted lipid metabolism (APOM, APOD, APOH, APOC1) and encouraged by oxidative stress (CP, TF, ORM2) and inflammation (CFH, CFB, CLU) as a secondary response accompanied by changes in matrix architecture (AFM, FBLN1, AMBP). Alterations in proteomes of peripheral and cord blood were expectedly unequal. Both up- and downregulated markers were accommodated in the cast of molecular events interconnected with the lipid metabolism, RXR/PPAR-signaling pathway, and extracellular architecture modulation. The obtained results congregate numerous biological processes to molecular events that underline diabetes during gestation and uncover some critical aspects affecting fetal growth and development.

Gestational Diabetes Mellitus Screening and Diagnosis

An ideal screening test for gestational diabetes should be capable of identifying not only women with the disease but also the women with a high risk of developing gestational diabetes mellitus (GDM). Screening and diagnosis are the main steps leading to the way of management. There is a lack of consensus among healthcare professionals regarding the screening methods worldwide. Different study groups advocate a variety of screening methods with the support of evidence-based comprehensive data. Some of the organizations suggest screening for high risk or all pregnant women, while others prefer to offer definitive testing without screening. Glycemic thresholds are also not standardized to decide GDM among different guidelines. Prevalence rates of GDM vary between populations and with the choice of glucose thresholds for both screening and definitive tests. One-step or two-step methods have been used for GDM diagnosis. However, screening includes selecting patients with historical risk factors, 50 g 1-h glucose challenge test, fasting plasma glucose, random plasma glucose, and hemoglobin A1c with different cutoffs. In this chapter, screening and diagnosis methods of GDM accepted by different study groups will be discussed which will be followed by the evaluation of different glycemic thresholds. Then the advantages and disadvantages of used methods will be explained and the chapter will finish with an evaluation of the current international guidelines.

Is the placental proteome impaired in well-controlled gestational diabetes?

In pregnancy complicated by gestational diabetes mellitus (GDM), the human placenta shows several pathological functional and structural changes, but the extent to which maternal glycemic control contributes to placental abnormalities remains unclear. The aim of this study was to profile and compare the proteome of placentas from healthy pregnant women and those with GDM, to investigate the placenta-specific protein composition and possible changes of its function in presence of GDM. Quantitative proteomic analysis, based on LC-MS^E approach, revealed that higher (approximately 15% increase) levels of galectin 1 and collagen alpha-1 XIV chain (although the difference regarding the latter was at the limit of significance) were present in GDM samples, while heat shock 70 kDa protein 1A/1B was less abundant in GDM placental tissue. These data seem to indicate that GDM, when well controlled, did not markedly affect the placental proteome.

First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus

Background

Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse outcomes including preeclampsia, caesarean section, macrosomia, neonatal morbidity and future development of type 2 diabetes in both mother and child. Current selective screening strategies rely on clinical risk factors such as age, family history of diabetes, macrosomia or GDM in a previous pregnancy, and they possess a relatively low specificity. Here we hypothesize that novel first trimester protein predictors of GDM can contribute to the current selective screening strategies for early and accurate prediction of GDM, thus allowing for timely interventions.

Methods

A proteomics discovery approach was applied to first trimester sera from obese (BMI ≥27 kg/m²) women (n = 60) in a nested case-control study design, utilizing tandem mass tag labelling and tandem mass spectrometry. A subset of the identified protein markers was further validated in a second set of serum samples (n = 210) and evaluated for their contribution as predictors of GDM in relation to the maternal risk factors, by use of logistic regression and receiver operating characteristic analysis.

Results

Serum proteomic profiling identified 25 proteins with significantly different levels between cases and controls. Three proteins; afamin, serum amyloid P-component and vitronectin could be further confirmed as predictors of GDM in a validation set. Vitronectin was shown to contribute significantly to the predictive power of the maternal risk factors, indicating it as a novel independent predictor of GDM.

Conclusions

Current selective screening strategies can potentially be improved by addition of protein predictors.

Maternal serum level of resistin is associated with risk for gestational diabetes mellitus: A meta-analysis

BACKGROUND

Resistin is most likely involved in the pathogenesis of gestational diabetes mellitus (GDM), but the existing findings are inconsistent.

AIM

To review the literature investigating the associations of the risk of GDM with serum level of resistin.

METHODS

A systematic literature search was performed using MEDLINE, EMBASE, and Web of Science (all databases). This meta-analysis included eligible studies that: (1) investigated the relationship between the risk of GDM and serum resistin; (2) included GDM cases and controls without GDM; (3) diagnosed GDM according to the oral glucose-tolerance test; (4) were performed in humans; (5) were published as full text articles in English; and (6) provided data with median and quartile range, median and minimum and maximum values, or mean and standard deviation. The pooled standardized mean difference (SMD) and 95% confidence interval (CI) were calculated to estimate the association between the risk of GDM and serum resistin. To analyze the potential influences of need for insulin in GDM patients and gestational age at blood sampling, we performed a subgroup analysis. Meta-regression with restricted maximum likelihood estimation was performed to assess the potentially important covariate exerting substantial impact on between-study heterogeneity.

RESULTS

The meta-analysis for the association between serum resistin level and GDM risk included 18 studies (22 comparisons) with 1041 cases and 1292 controls. The total results showed that the risk of GDM was associated with higher serum resistin level (SMD = 0.250, 95%CI: 0.116, 0.384). The “after 28 wk” subgroup, “no need for insulin” subgroup, and “need for insulin” subgroup indicated that higher serum resistin level was related to GDM risk (“after 28 wk” subgroup: SMD = 0.394, 95%CI: 0.108, 0.680; “no need for insulin” subgroup: SMD = 0.177, 95%CI: 0.018, 0.336; “need for insulin” subgroup: SMD = 0.403, 95%CI: 0.119, 0.687). The “before 14 wk” subgroup, “14-28 wk” subgroup, and “no information of need for insulin” subgroup showed a nonsignificant association between serum resistin level and GDM risk (“before 14 wk” subgroup: SMD = 0.087, 95%CI: -0.055, 0.230; “14-28 wk” subgroup: SMD = 0.217, 95%CI: -0.003, 0.436; “no information of need for insulin” subgroup: SMD = 0.356, 95%CI: -0.143, 0.855). The postpartum subgroup included only one study and showed that higher serum resistin level was related to GDM risk (SMD = 0.571, 95%CI: 0.054, 1.087) The meta-regression revealed that no need for insulin in GDM patients, age distribution similar between cases and controls, and ELISA all had a significant impact on between-study heterogeneity.

CONCLUSION

This meta-analysis supports that the maternal serum resistin level is associated with GDM risk.

The role of mass spectrometry in studies of glycation processes and diabetes management

In the last decade, mass spectrometry has been widely employed in the study of diabetes. This was mainly due to the development of new, highly sensitive, and specific methods representing powerful tools to go deep into the biochemical and pathogenetic processes typical of the disease. The aim of this review is to give a panorama of the scientifically valid results obtained in this contest. The recent studies on glycation processes, in particular those devoted to the mechanism of production and to the reactivity of advanced glycation end products (AGEs, AGE peptides, glyoxal, methylglyoxal, dicarbonyl compounds) allowed to obtain a different view on short and long term complications of diabetes. These results have been employed in the research of effective markers and mass spectrometry represented a precious tool allowing the monitoring of diabetic nephropathy, cardiovascular complications, and gestational diabetes. The same approaches have been employed to monitor the non-insulinic diabetes pharmacological treatments, as well as in the discovery and characterization of antidiabetic agents from natural products. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 38:112-146, 2019.

Unbiased and targeted mass spectrometry for the HDL proteome

PURPOSE OF REVIEW

Mass spectrometry is an ever evolving technology that is equipped with a variety of tools for protein research. Some lipoprotein studies, especially those pertaining to HDL biology, have been exploiting the versatility of mass spectrometry to understand HDL function through its proteome. Despite the role of mass spectrometry in advancing research as a whole, however, the technology remains obscure to those without hands on experience, but still wishing to understand it. In this review, we walk the reader through the coevolution of common mass spectrometry workflows and HDL research, starting from the basic unbiased mass spectrometry methods used to profile the HDL proteome to the most recent targeted methods that have enabled an unprecedented view of HDL metabolism.

RECENT FINDINGS

Unbiased global proteomics have demonstrated that the HDL proteome is organized into subgroups across the HDL size fractions providing further evidence that HDL functional heterogeneity is in part governed by its varying protein constituents. Parallel reaction monitoring, a novel targeted mass spectrometry method, was used to monitor the metabolism of HDL apolipoproteins in humans and revealed that apolipoproteins contained within the same HDL size fraction exhibit diverse metabolic properties.

SUMMARY

Mass spectrometry provides a variety of tools and strategies to facilitate understanding, through its proteins, the complex biology of HDL.

Early Pregnancy Biochemical Predictors of Gestational Diabetes Mellitus

PURPOSE OF REVIEW

Universal oral glucose tolerance-based screening is employed to identify pregnant women with gestational diabetes mellitus (GDM), as treatment of this condition decreases the risk of associated complications. A simple and accurate blood test which identifies women at low or high risk for GDM in the first trimester would have the potential to decrease costs and improve outcomes through prevention or treatment. This review summarizes published data on early pregnancy biomarkers which have been tested as predictors of GDM.

RECENT FINDINGS

A large number of first-trimester biochemical predictors of GDM have been reported, mostly in small case-control studies. These include glycemic markers (fasting glucose, post-load glucose, hemoglobin A1C), inflammatory markers (C-reactive protein, tumor necrosis factor-alpha), insulin resistance markers (fasting insulin, sex hormone-binding globulin), adipocyte-derived markers (adiponectin, leptin), placenta-derived markers (follistatin-like-3, placental growth factor, placental exosomes), and others (e.g., glycosylated fibronectin, soluble (pro)renin receptor, alanine aminotransferase, ferritin). A few large studies suggest that first-trimester fasting glucose or hemoglobin A1C may be useful for identifying women who would benefit from early GDM treatment. To translate the findings from observational studies of first-trimester biomarkers for GDM to clinical practice, trials or cost-effectiveness analyses of screening and treatment strategies based on these novel biomarkers are needed.