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Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus.

ABSTRACT: BACKGROUND: Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222) were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222) were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1) expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2) in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization.

SUBMITTER: Zhao C

PROVIDER: S-EPMC3161072 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus.

Zhao Chun C Dong Jing J Jiang Tao T Shi Zhonghua Z Yu Bin B Zhu Yunlong Y Chen Daozhen D Xu Junrong J Huo Ran R Dai Juncheng J Xia Yankai Y Pan Shiyang S Hu Zhibin Z Sha Jiahao J

PloS one 20110824 8

<h4>Background</h4>Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM.<h4>Methodology/principal findings</h4>We systematically used the TaqMan ...[more]

PMID: 21887347

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Project description:BackgroundGestational diabetes mellitus (GDM) is glucose intolerance first recognised during pregnancy. Both modalities and thresholds of the GDM diagnostic test, the Oral Glucose Tolerance Test (OGTT), have varied widely over time and among countries. Additionally, OGTT limitations include inconsistency, poor patient tolerability, and questionable diagnostic reliability. Many biological parameters have been reported to be modified by GDM and could potentially be used as diagnostic indicators. This study aimed to 1) systematically explore biomarkers reported in the literature as differentiating GDM from healthy pregnancies 2) screen those indicators assessed against OGTT to propose OGTT alternatives.Main bodyA systematic review of GDM diagnostic indicators was performed according to PRISMA guidelines (PROSPERO registration CRD42020145499). Inclusion criteria were full-text, comprehensible English-language articles published January 2009-January 2021, where a biomarker (from blood, ultrasound, amniotic fluid, placenta) was compared between GDM and normal glucose tolerance (NGT) women from the second trimester onward to immediately postpartum. GDM diagnostic method had to be clearly specified, and the number of patients per study higher than 30 in total or 15 per group. Results were synthesised by biomarkers.ResultsOf 13,133 studies identified in initial screening, 174 studies (135,801 participants) were included. One hundred and twenty-nine studies described blood analytes, one amniotic fluid analytes, 27 ultrasound features, 17 post-natal features. Among the biomarkers evaluated in exploratory studies, Adiponectin, AFABP, Betatrophin, CRP, Cystatin-C, Delta-Neutrophil Index, GGT, TNF-A were those demonstrating statistically and clinically significant differences in substantial cohorts of patients (> 500). Regarding biomarkers assessed versus OGTT (i.e. potential OGTT alternatives) most promising were Leptin > 48.5 ng/ml, Ficolin3/adiponectin ratio ≥ 1.06, Chemerin/FABP > 0.71, and Ultrasound Gestational Diabetes Score > 4. These all demonstrated sensitivity and specificity > 80% in adequate sample sizes (> / = 100).ConclusionsNumerous biomarkers may differentiate GDM from normoglycaemic pregnancy. Given the limitations of the OGTT and the lack of a gold standard for GDM diagnosis, advanced phase studies are needed to triangulate the most promising biomarkers. Further studies are also recommended to assess the sensitivity and specificity of promising biomarkers not yet assessed against OGTT.Trial registrationPROSPERO registration number CRD42020145499.

Project description:BackgroundThe aim of this study was to evaluate whether soluble frizzled-related protein 4 (sFRP4) concentration in the first trimester of pregnancy is individually, or in combination with Leptin, Chemerin and/or Adiponectin, associated with the development of gestational diabetes (GDM).MethodsIn a nested case-control study, 50 women with GDM who spontaneously conceived and delivered a live-born infant were matched with a total of 100 uncomplicated singleton control pregnancies based on body mass index (± 2 kg/m2), gestational age at sampling (exact day) and maternal age (± 2 years). In serum samples, obtained between 70-90 days gestational age, sFRP4, Chemerin, Leptin and Adiponectin concentrations were determined by ELISA. Statistical comparisons were performed using univariate and multi-variate logistic regression analysis after logarithmic transformation of the concentrations. Discrimination of the models was assessed by the area under the curve (AUC).ResultsFirst trimester sFRP4 concentrations were significantly increased in GDM cases (2.04 vs 1.93 ng/ml; p<0.05), just as Chemerin (3.19 vs 3.15 ng/ml; p<0.05) and Leptin (1.44 vs 1.32 ng/ml; p<0.01). Adiponectin concentrations were significantly decreased (2.83 vs 2.94 ng/ml; p<0.01) in GDM cases. Further analysis only showed a weak, though significant, correlation of sFRP4 with Chemerin (R2 = 0.124; p<0.001) and Leptin (R2 = 0.145; p<0.001), and Chemerin with Leptin (R2 = 0.282; p<0.001) in the control group. In a multivariate logistic regression model of these four markers, only Adiponectin showed to be significantly associated with GDM (odds ratio 0.12, 95%CI 0.02-0.68). The AUC of this model was 0.699 (95%CI 0.605-0.793).ConclusionIn the first trimester of pregnancy, a multi-marker model with sFRP4, Leptin, Chemerin and Adiponectin is associated with the development of GDM. Therefore, this panel seems to be an interesting candidate to further evaluate for prediction of GDM in a prospective study.

Project description:Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24-28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

Project description:Gestational diabetes mellitus (GDM) is characterized by insulin resistance accompanied by low/absent beta-cell compensatory adaptation to the increased insulin demand. Although the molecular mechanisms and factors acting on beta-cell compensatory response during pregnancy have been partially elucidated and reported, those inducing an impaired beta-cell compensation and function, thus evolving in GDM, have yet to be fully addressed. MicroRNAs (miRNAs) are a class of small endogenous non-coding RNAs, which negatively modulate gene expression through their sequence-specific binding to 3'UTR of mRNA target. They have been described as potent modulators of cell survival and proliferation and, furthermore, as orchestrating molecules of beta-cell compensatory response and function in diabetes. Moreover, it has been reported that miRNAs can be actively secreted by cells and found in many biological fluids (e.g., serum/plasma), thus representing both optimal candidate disease biomarkers and mediators of tissues crosstalk(s). Here, we analyzed the expression profiles of circulating miRNAs in plasma samples obtained from n?=?21 GDM patients and from n?=?10 non-diabetic control pregnant women (24-33?weeks of gestation) using TaqMan array microfluidics cards followed by RT-real-time PCR single assay validation. The results highlighted the upregulation of miR-330-3p in plasma of GDM vs non-diabetics. Furthermore, the analysis of miR-330-3p expression levels revealed a bimodally distributed GDM patients group characterized by high or low circulating miR-330 expression and identified as GDM-miR-330high and GDM-miR-330low. Interestingly, GDM-miR-330high subgroup retained lower levels of insulinemia, inversely correlated to miR-330-3p expression levels, and a significant higher rate of primary cesarean sections. Finally, miR-330-3p target genes analysis revealed major modulators of beta-cell proliferation and of insulin secretion, such as the experimentally validated genes E2F1 and CDC42 as well as AGT2R2, a gene involved in the differentiation of mature beta-cells. In conclusion, we demonstrated that plasma miR-330-3p could be of help in identifying GDM patients with potential worse gestational diabetes outcome; in GDM, miR-330-3p may directly be transferred from plasma to beta-cells thus modulating key target genes involved in proliferation, differentiation, and insulin secretion.

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Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus.

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Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus.

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