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.

Remnant cholesterol (RC) is a known contributor to cardiovascular disease, but its role in gestational diabetes mellitus (GDM) remains insufficiently characterized. To examine the association between first-trimester RC levels and GDM risk, and to quantify the mediating effects of prepregnancy body mass index (BMI) and insulin sensitivity. This prospective cohort study included 877 pregnant women in Southeastern China. RC was calculated from first-trimester fasting lipid profiles. GDM was diagnosed via a 75 g oral glucose tolerance test at 24 to 28 weeks. Binary logistic regression, restricted cubic splines, and serial mediation models were employed for analysis. Among all participants, 160 (18.2%) developed GDM. After full adjustment for confounders, women in the highest quartile of first-trimester RC had a significantly higher risk of GDM (odds ratio = 2.850, 95% CI: 1.679-4.836) compared with those in the lowest quartile. A significant positive dose-response relationship was observed (P for overall < .001). Serial mediation analysis indicated that the association between RC and GDM was partially mediated through the sequential pathway of prepregnancy BMI → Matsuda index, accounting for 13.50% of the total effect. RC also showed modest predictive performance for GDM (area under the curve = 0.673) compared with conventional lipid parameters. Elevated first-trimester RC is independently associated with increased GDM risk. This association is partially mediated by prepregnancy BMI and subsequent insulin resistance. Measuring RC in early pregnancy may improve the identification of women at high risk for GDM.

Background: Macrosomia is a major adverse outcome associated with gestational diabetes mellitus (GDM), primarily driven by insulin resistance (IR). The triglyceride-glucose (TyG) index is a well-established surrogate marker for insulin resistance; however, its predictive value when combined with obesity indicators remains incompletely defined. This study aimed to compare the predictive performance of the TyG index and its derived indices during the second trimester for the risk of macrosomia in GDM. Methods: The TyG index, TyG body mass index (TyG-BMI), TyG waist circumference (TyG-WC), and TyG waist-to-height ratio (TyG-WHtR) were assessed at 24– 28 weeks of gestation. Their associations with macrosomia were analyzed using binary logistic regression and restricted cubic spline (RCS) analysis. The predictive performance of these indices was compared using receiver operating characteristic (ROC) curves, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). The associations between TyG and its derived indices and the risk of delivering a macrosomic infant were further explored across different subgroups. Results: Logistic regression revealed that GDM women in the highest tertile had a 3.458- and 3.718-fold higher risks of delivering macrosomia compared to the lowest tertile for TyG-BMI and TyG-WC, respectively. RCS analysis showed a dose-response relationship (all P for overall < 0.001). Compared with the conventional TyG index, which had an area under the curve (AUC) of 0.596, both TyG-BMI (AUC = 0.682) and TyG-WC (AUC = 0.681) demonstrated significantly better performance (P < 0.01); furthermore, their superiority was confirmed by significant IDI and NRI values (P < 0.01). The association was more pronounced in primiparous women aged < 35 years (P < 0.05). Conclusion: Elevated second-trimester TyG index and its derived indices are associated with an increased risk of macrosomia in GDM women. TyG-BMI and TyG-WC outperformed the conventional TyG index and may serve as valuable supplementary markers for early risk stratification in GDM.