The Predictive Value of miR-16, -29a and -134 for Early Identification of Gestational Diabetes: A Nested Analysis of the DALI Cohort.
TL;DR: In this article, a nested case-control study of participants from the European multicenter 'Vitamin D and lifestyle intervention for GDM prevention (DALI)' trial using serum samples from obese pregnant women (BMI ≥ 29 kg/m2) entailed 82 GDM cases (early and late- GDM), and 41 age and BMI-matched women with normal glucose tolerance (NGT) throughout pregnancy (controls).
Abstract: Early identification of gestational diabetes mellitus (GDM) aims to reduce the risk of adverse maternal and perinatal outcomes. Currently, no circulating biomarker has proven clinically useful for accurate prediction of GDM. In this study, we tested if a panel of small non-coding circulating RNAs could improve early prediction of GDM. We performed a nested case-control study of participants from the European multicenter 'Vitamin D and lifestyle intervention for GDM prevention (DALI)' trial using serum samples from obese pregnant women (BMI ≥ 29 kg/m2) entailing 82 GDM cases (early- and late- GDM), and 41 age- and BMI-matched women with normal glucose tolerance (NGT) throughout pregnancy (controls). Anthropometric, clinical and biochemical characteristics were obtained at baseline (<20 weeks of gestation) and throughout gestation. Baseline serum microRNAs (miRNAs) were measured using quantitative real time PCR (qPCR). Elevated miR-16-5p, -29a-3p, and -134-5p levels were observed in women, who were NGT at baseline and later developed GDM, compared with controls who remained NGT. A combination of the three miRNAs could distinguish later GDM from NGT cases (AUC 0.717, p = 0.001, compared with fasting plasma glucose (AUC 0.687, p = 0.004)) as evaluated by area under the curves (AUCs) using Receiver Operator Characteristics (ROC) analysis. Elevated levels of individual miRNAs or a combination hereof were associated with higher odds ratios of GDM. Conclusively, circulating miRNAs early in pregnancy could serve as valuable predictive biomarkers of GDM.
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TL;DR: The role of miRNAs as a predictive factor that could potentially be useful in early diagnosis of gestational diabetes mellitus is emphasized, and changes in miRNA profile in the serum of GDM women may indicate significance in the pathophysiology of G DM.
Abstract: Introduction Circulating miRNAs are important mediators in epigenetic changes. These non-coding molecules regulate post-transcriptional gene expression by binding to mRNA. As a result, they influence the development of many diseases, such as gestational diabetes mellitus (GDM). Therefore, this study investigates the changes in the miRNA profile in GDM patients before hyperglycemia appears. Materials and Methods The study group consisted of 24 patients with GDM, and the control group was 24 normoglycemic pregnant women who were matched for body mass index (BMI), age, and gestational age. GDM was diagnosed with an oral glucose tolerance test between the 24th and 26th weeks of pregnancy. The study had a prospective design, and serum for analysis was obtained in the first trimester of pregnancy. Circulating miRNAs were measured using the NanoString quantitative assay platform. Validation with real time-polymerase chain reaction (RT-PCR) was performed on the same group of patients. Mann-Whitney U-test and Spearman correlation were done to assess the significance of the results. Results Among the 800 miRNAs, 221 miRNAs were not detected, and 439 were close to background noise. The remaining miRNAs were carefully investigated for their average counts, fold changes, p-values, and false discovery rate (FDR) scores. We selected four miRNAs for further validation: miR-16-5p, miR-142-3p, miR-144-3p, and miR-320e, which showed the most prominent changes between the studied groups. The validation showed up-regulation of miR-16-5p (p<0.0001), miR-142-3p (p=0.001), and miR-144-3p (p=0.003). Conclusion We present changes in miRNA profile in the serum of GDM women, which may indicate significance in the pathophysiology of GDM. These findings emphasize the role of miRNAs as a predictive factor that could potentially be useful in early diagnosis.
11 citations
TL;DR: MiR-29 plays an essential role in various organs relevant to intermediary metabolism and its upregulation contribute to impaired glucose metabolism, while it suppresses fibrosis development, so a correct balance of miR- 29a levels seems important for cellular and organ homeostasis in metabolism.
Abstract: The microRNA-29a family members miR-29a-3p, miR-29b-3p and miR-29c-3p are ubiquitously expressed and consistently increased in various tissues and cell types in conditions of metabolic disease; obesity, insulin resistance and type 2 diabetes. In pancreatic beta cells, miR-29a is required for normal exocytosis, but increased levels are associated with impaired beta cell function. Similarly, in liver miR-29 species are higher in models of insulin resistance and type 2 diabetes, and either knock-out or depletion using a microRNA inhibitor improves hepatic insulin resistance. In skeletal muscle, miR-29 upregulation is associated with insulin resistance and altered substrate oxidation, and similarly, in adipocytes over-expression of miR-29a leads to insulin resistance. Blocking miR-29a using nucleic acid antisense therapeutics show promising results in preclinical animal models of obesity and type 2 diabetes, although the widespread expression pattern of miR-29 family members complicates the exploration of single target tissues. However, in fibrotic diseases, such as in late complications of diabetes and metabolic disease (diabetic kidney disease, non-alcoholic steatohepatitis), miR-29 expression is suppressed by TGFβ allowing increased extracellular matrix collagen to form. In the clinical setting circulating levels of miR-29a and miR-29b are consistently increased in type 2 diabetes and in gestational diabetes, and are also possible prognostic markers for deterioration of glucose tolerance. In conclusion, miR-29 plays an essential role in various organs relevant to intermediary metabolism and its upregulation contribute to impaired glucose metabolism, while it suppresses fibrosis development. Thus, a correct balance of miR-29a levels seems important for cellular and organ homeostasis in metabolism.
9 citations
TL;DR: In this paper, the authors showed that transgenic overexpression of miR-29a under control of osteocalcin promoter in osteoblasts attenuated HFD-induced body overweight, hyperglycemia, and hypercholesterolemia.
Abstract: Skeletal tissue involves systemic adipose tissue metabolism and energy expenditure. MicroRNA signaling controls high-fat diet (HFD)-induced bone and fat homeostasis dysregulation remains uncertain. This study revealed that transgenic overexpression of miR-29a under control of osteocalcin promoter in osteoblasts (miR-29aTg) attenuated HFD-mediated body overweight, hyperglycemia, and hypercholesterolemia. HFD-fed miR-29aTg mice showed less bone mass loss, fatty marrow, and visceral fat mass together with increased subscapular brown fat mass than HFD-fed wild-type mice. HFD-induced O2 underconsumption, respiratory quotient repression, and heat underproduction were attenuated in miR-29aTg mice. In vitro, miR-29a overexpression repressed transcriptomic landscapes of the adipocytokine signaling pathway, fatty acid metabolism, and lipid transport, etc., of bone marrow mesenchymal progenitor cells. Forced miR-29a expression promoted osteogenic differentiation but inhibited adipocyte formation. miR-29a signaling promoted brown/beige adipocyte markers Ucp-1, Pgc-1α, P2rx5, and Pat2 expression and inhibited white adipocyte markers Tcf21 and Hoxc9 expression. The microRNA also reduced peroxisome formation and leptin expression during adipocyte formation and downregulated HFD-induced leptin expression in bone tissue. Taken together, miR-29a controlled leptin signaling and brown/beige adipocyte formation of osteogenic progenitor cells to preserve bone anabolism, which reversed HFD-induced energy underutilization and visceral fat overproduction. This study sheds light on a new molecular mechanism by which bone integrity counteracts HFD-induced whole-body fat overproduction.
9 citations
TL;DR: In this article , the authors summarized the role of exosomal miRNAs and circulating miRNA in Gestational Diabetes mellitus (GDM) diagnosis and treatment and found that the changes in the expression of certain miRNA genes could be used to diagnose GDM.
Abstract: Gestational diabetes mellitus (GDM) is defined as glucose intolerance that occurs during the second or third trimester of pregnancy. As the incidence of GDM rises, so does the risk of maternal and fetal complications with short- and long-term consequences. As a result, early diagnosis and treatment of this condition are important to avoiding adverse pregnancy outcomes. Exosomes are tiny vesicles secreted by living cells which contain a variety of bioactive substances. They are released by cells to facilitate cell-to-cell communication and regulate a variety of biological processes such as cellular immune response, inflammatory response, and apoptosis, among others. Many studies have recently confirmed that changes in the expression and secretion of exosomal miRNAs can be used as novel markers for the diagnosis, prognosis, and treatment of GDM. In this review, we summarized the various roles of exosomal miRNAs and circulating miRNAs in GDM. We found that the changes in the expression of certain miRNAs could be used to diagnosing GDM. Exosomal miRNAs target metabolic pathways, resulting in insulin resistance. We also highlighted the potential for miRNAs and exosomal miRNAs to be used as biomarkers for diagnosis or therapeutic agents.
9 citations
TL;DR: Cardiovascular disease-associated microRNAs represent promising early biomarkers to be implemented into routine first-trimester screening programs with a very good predictive potential for gestational diabetes mellitus.
Abstract: 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.
8 citations
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13 Aug 2009
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Abstract: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, its easy to: produce handsome, publication-quality plots, with automatic legends created from the plot specification superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements, and that can easily be applied to multiple plots approach your graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems,and youll find it easy to get graphics out of your head and on to the screen or page.
29,504 citations
TL;DR: The correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
Abstract: The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
29,217 citations
TL;DR: It is shown that exosomes contain both mRNA and microRNA, which can be delivered to another cell, and can be functional in this new location, and it is proposed that this RNA is called “exosomal shuttle RNA” (esRNA).
Abstract: Exosomes are vesicles of endocytic origin released by many cells. These vesicles can mediate communication between cells, facilitating processes such as antigen presentation. Here, we show that exosomes from a mouse and a human mast cell line (MC/9 and HMC-1, respectively), as well as primary bone marrow-derived mouse mast cells, contain RNA. Microarray assessments revealed the presence of mRNA from approximately 1300 genes, many of which are not present in the cytoplasm of the donor cell. In vitro translation proved that the exosome mRNAs were functional. Quality control RNA analysis of total RNA derived from exosomes also revealed presence of small RNAs, including microRNAs. The RNA from mast cell exosomes is transferable to other mouse and human mast cells. After transfer of mouse exosomal RNA to human mast cells, new mouse proteins were found in the recipient cells, indicating that transferred exosomal mRNA can be translated after entering another cell. In summary, we show that exosomes contain both mRNA and microRNA, which can be delivered to another cell, and can be functional in this new location. We propose that this RNA is called "exosomal shuttle RNA" (esRNA).
10,484 citations
TL;DR: It is shown that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical.
Abstract: Proteins are built by using the information contained in molecules of messenger RNA (mRNA). Cells have several ways of controlling the amounts of different proteins they make. For example, a so-called ‘microRNA’ molecule can bind to an mRNA molecule to cause it to be more rapidly degraded and less efficiently used, thereby reducing the amount of protein built from that mRNA. Indeed, microRNAs are thought to help control the amount of protein made from most human genes, and biologists are working to predict the amount of control imparted by each microRNA on each of its mRNA targets. All RNA molecules are made up of a sequence of bases, each commonly known by a single letter—‘A’, ‘U’, ‘C’ or ‘G’. These bases can each pair up with one specific other base—‘A’ pairs with ‘U’, and ‘C’ pairs with ‘G’. To direct the repression of an mRNA molecule, a region of the microRNA known as a ‘seed’ binds to a complementary sequence in the target mRNA. ‘Canonical sites’ are regions in the mRNA that contain the exact sequence of partner bases for the bases in the microRNA seed. Some canonical sites are more effective at mRNA control than others. ‘Non-canonical sites’ also exist in which the pairing between the microRNA seed and mRNA does not completely match. Previous work has suggested that many non-canonical sites can also control mRNA degradation and usage. Agarwal et al. first used large experimental datasets from many sources to investigate microRNA activity in more detail. As expected, when mRNAs had canonical sites that matched the microRNA, mRNA levels and usage tended to drop. However, no effect was observed when the mRNAs only had recently identified non-canonical sites. This suggests that microRNAs primarily bind to canonical sites to control protein production. Based on these results, Agarwal et al. further developed a statistical model that predicts the effects of microRNAs binding to canonical sites. The updated model considers 14 different features of the microRNA, microRNA site, or mRNA—including the mRNA sequence around the site—to predict which sites within mRNAs are most effectively targeted by microRNAs. Tests showed that Agarwal et al.'s model was as good as experimental approaches at identifying the effective target sites, and was better than existing computational models. The model has been used to power the latest version of a freely available resource called TargetScan, and so could prove a valuable resource for researchers investigating the many important roles of microRNAs in controlling protein production.
5,365 citations
TL;DR: A novel estimate of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the oral glucose tolerance test (OGTT).
Abstract: OBJECTIVE: Several methods have been proposed to evaluate insulin sensitivity from the data obtained from the oral glucose tolerance test (OGTT). However, the validity of these indices has not been rigorously evaluated by comparing them with the direct measurement of insulin sensitivity obtained with the euglycemic insulin clamp technique. In this study, we compare various insulin sensitivity indices derived from the OGTT with whole-body insulin sensitivity measured by the euglycemic insulin clamp technique. RESEARCH DESIGN AND METHODS: In this study, 153 subjects (66 men and 87 women, aged 18-71 years, BMI 20-65 kg/m2) with varying degrees of glucose tolerance (62 subjects with normal glucose tolerance, 31 subjects with impaired glucose tolerance, and 60 subjects with type 2 diabetes) were studied. After a 10-h overnight fast, all subjects underwent, in random order, a 75-g OGTT and a euglycemic insulin clamp, which was performed with the infusion of [3-3H]glucose. The indices of insulin sensitivity derived from OGTT data and the euglycemic insulin clamp were compared by correlation analysis. RESULTS: The mean plasma glucose concentration divided by the mean plasma insulin concentration during the OGTT displayed no correlation with the rate of whole-body glucose disposal during the euglycemic insulin clamp (r = -0.02, NS). From the OGTT, we developed an index of whole-body insulin sensitivity (10,000/square root of [fasting glucose x fasting insulin] x [mean glucose x mean insulin during OGTT]), which is highly correlated (r = 0.73, P < 0.0001) with the rate of whole-body glucose disposal during the euglycemic insulin clamp. CONCLUSIONS: Previous methods used to derive an index of insulin sensitivity from the OGTT have relied on the ratio of plasma glucose to insulin concentration during the OGTT. Our results demonstrate the limitations of such an approach. We have derived a novel estimate of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the OGTT.
4,988 citations