Current progress in metabolomics of gestational diabetes mellitus

Insulin resistance (IR) has been considered as the common pathological basis and developmental driving force for most metabolic diseases. Long noncoding RNAs (lncRNAs) have emerged as pivotal regulators in modulation of glucose and lipid metabolism. However, the comprehensive profile of lncRNAs in skeletal muscle cells under the insulin resistant status and the possible biological effects of them were not fully studied. In this research, using C2C12 myotubes as cell models in vitro, deep RNA-sequencing was performed to profile lncRNAs and mRNAs between palmitic acid-induced IR C2C12 myotubes and control ones. The results revealed that a total of 144 lncRNAs including 70 up-regulated and 74 down-regulated (|fold change| > 2, q < 0.05) were significantly differentially expressed in palmitic acid-induced insulin resistant cells. In addition, functional annotation analysis based on the Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases revealed that the target genes of the differentially expressed lncRNAs were significantly enriched in fatty acid oxidation, lipid oxidation, PPAR signaling pathway, and insulin signaling pathway. Moreover, Via qPCR, most of selected lncRNAs in myotubes and db/db mice skeletal muscle showed the consistent expression trends with RNA- sequencing. Co-expression analysis also explicated the key lncRNA-mRNA interactions and pointed out a potential regulatory network of candidate lncRNA ENSMUST00000160839. In conclusion, this study extended the skeletal muscle lncRNA database and provided novel potential regulators for future genetic and molecular studies on insulin resistance, which is helpful for prevention and treatment of the related metabolic diseases.

Background: The objective of the study was to explore the clinical significance of steroid hormones in the diagnosis of PCOS and PCOS‐related insulin resistance through liquid chromatography–mass spectrometry/mass spectrometry (LC–MS/MS) and chemiluminescent immunoassay (CLIA).Methods: The study included 114 patients with PCOS and 100 controls. Steroid hormone levels in serum were measured using LC–MS/MS and CLIA. The Bland–Altman method was used to check the consistency between the two methods. The diagnostic value of the LC–MS/MS method for female hyperandrogenemia and PCOS was evaluated.Results: Women with PCOS were younger than controls on average (p < 0.001). PCOS patients had higher luteal hormone (LH, p < 0.001), insulin (p = 0.002), estradiol (E2, p < 0.001), total testosterone (TT, p < 0.001), free androgen index (FAI, p = 0.021), dehydroepiandrosterone sulfate (DHEA, p = 0.021), insulin resistance index (HOMA‐IR) (p = 0.034), and fasting glucose (p = 0.017) levels than controls as measured by CLIA. The diagnostic value of TT was the best, and the area under the AUC curve was 0.766. Women with PCOS had higher androstenedione (A2, p < 0.001), FAI (p < 0.001), TT (p < 0.001), and 17‐hydroxyprogesterone (17‐OHP, p < 0.001) levels than controls as measured by LC–MS/MS. The ROC curve showed that the diagnostic efficacy of A2, TT, and 17‐OHP was 0.830, 0.851, and 0.714, respectively. The consistency of TT detected by LC–MS/MS and CLIA was poor according to the Bland–Altman method. Detected TT by LC–MS/MS had the highest diagnostic efficiency for PCOS. The diagnostic power of the LC–MS/MS results for PCOS‐related insulin resistance was analyzed. The results showed that the FAI had the highest diagnostic power, with an ROC curve of 0.798.Conclusion: LC–MS/MS is more sensitive and accurate than CLIA in the determination of serum TT and FAI. TT is more effective for the diagnosis of PCOS, whereas FAI is more valuable in the diagnosis of insulin resistance.

BACKGROUND The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants. AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA. METHODS The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was < 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value < 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses. RESULTS After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P < 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram’s prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit. CONCLUSION Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.