Background Quercetin and H19 can promote osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). However, whether quercetin regulates H19 expression to promote osteogenic differentiation of BMSCs is unclear. Methods BMSC proliferation, matrix mineralization, and alkaline phosphatase (ALP) activity were assessed using the Cell Counting Kit-8, ALP assay kit, and alizarin red staining kit, respectively. Expression of H19, miR-625-5p, BMP-2, osteocalcin, and RUNX2 were measured by qRT-PCR; β-catenin protein level was measured by western blotting. Results Quercetin promoted BMSC proliferation, enhanced ALP activity, and upregulated the expression of BMP-2, osteocalcin, and RUNX2 mRNAs, suggesting that it promoted osteogenic differentiation of BMSCs. Moreover, quercetin increased H19 expression, while the effect of quercetin on BMSCs was reversed by silencing H19 expression. Additionally, miR-625-5p, interacted with H19, was downregulated during quercetin-induced BMSC osteogenic differentiation, which negatively correlated with H19 expression. Silencing miR-625-5p expression promoted BMSC proliferation and osteogenic differentiation, whereas miR-625-5p overexpression weakened the effect of quercetin on BMSCs. Finally, quercetin treatment or downregulation of miR-625-5p expression increased β-catenin protein level in BMSCs. Upregulation or downregulation of miR-625-5p or H19 expression, respectively, inhibited β-catenin protein level in quercetin treated-BMSCs. Conclusion H19 promotes, while miR-625-5p inhibits BMSC osteogenic differentiation. Quercetin activates the Wnt/β-catenin pathway and promotes BMSC osteogenic differentiation via the H19/miR-625-5p axis.

Background Although there is numerous evidence on the epidemiological risk factors for insulin resistance (IR)-related metabolic diseases, there is still insufficient evidence to explore the non-linear association of Atherogenic Index of Plasma (AIP) with IR. Therefore, we aimed to elucidate the non-linear relationship between AIP and IR and type 2 diabetes (T2D). Methods This cross-sectional study was conducted in the National Health and Nutrition Survey (NHANES) from 2009 to 2018. A total of 9,245 participants were included in the study. The AIP was calculated as log10 (triglycerides/high-density lipoprotein cholesterol). The outcome variables included IR and T2D defined by the 2013 American Diabetes Association guidelines. The weighted multivariate linear regression, weighted multivariate logistic regression, subgroup analysis, generalized additive model, smooth fitting curve and two-part logistic regression were adopted to reveal the relationship of AIP with IR and T2D. Results After adjustment for age, gender, race, education level, smoking status, alcohol consumption, vigorous/moderate physical activity, body mass index, waist circumference and hypertension, we found that AIP was positively associated with fasting blood glucose (β = 0.08, 95% CI: 0.06, 0.10), glycosylated hemoglobin (β = 0.04, 95% CI: 0.39, 0.58), fasting serum insulin (β = 4.26, 95% CI: 3.73, 4.79), and homeostasis model assessment of insulin resistance (β = 0.22, 95% CI: 0.18, 0.25). Further studies found that AIP was associated with increased risk of IR (OR = 1.29, 95% CI: 1.26–1.32) and T2D (OR = 1.18, 95% CI: 1.15–1.22). However, the positive association between AIP and IR or T2D was more significant in female than in male (IR: P for interaction = 0.0135; T2D: P for interaction = 0.0024). A non-linear and inverse L-shaped association was found between AIP and IR, while a J-shaped association was found between AIP and T2D. In patients with − 0.47 < AIP < 0.45, increased AIP was significantly associated with increased risk of IR and T2D. Conclusions AIP showed an inverse L-shaped association with IR and a J-shaped association with T2D, indicating that AIP should be reduced to a certain level to prevent IR and T2D.

Background Atherogenic index of plasma (AIP) plays an important role in predicting the occurrence of cardiovascular events and metabolic diseases. However, the relationship between AIP and insulin resistance (IR) are limited and controversial. Therefore, we aimed to clarify the relationship of AIP with IR and type 2 diabetes (T2D). Methods This cross-sectional study Based on the data of the National Health and Nutrition Survey (NHANES) from 2009 to 2018. Weighted multivariate linear regression, weighted multivariate logistic regression, subgroup analysis, generalized additive model, smooth curve fitting and two-part logistic regression were adopted to reveal the relationship between AIP and IR, T2D and its risk markers. Results A total of 9,245 patients were enrolled. After adjusting the potential confounders, AIP was positively correlated with FBG [β = 0.85 (95%CI: 0.66, 1.05)], HbA1c [β = 0.48 (95%CI: 0.39, 0.58)], FSI [β = 47.74 (95%CI: 41.42, 54.07)] and HOMA-IR [β = 2.39 (95%CI: 1.97, 2.82)]. Overall, there was a significant positive association between the AIP and IR [OR = 15.80 (95%CI: 11.44, 21.80)] and T2D [OR = 7.18 (95%CI: 5.21, 9.917)]. However, there was a significant interaction in the gender subgroup (IR: P for interaction = 0.0135; T2D: P for interaction = 0.0024) and smoking subgroup (IR: P for interaction = 0.0130). A reverse L-shaped association was found between AIP and IR, with a turning point of 0.45. Before the turning point, the OR (95% CI) was 13.15 (10.45, 16.55). A J-shaped association was found between AIP and T2D, with a turning point of -0.47. Before the turning point, the OR (95%CI) was 5.39(4.21, 6.89). Conclusions This study indicated that AIP exhibited an positive correlation with the risk markers of T2D (FBG, HbA1c, FSI and HOMA-IR). AIP is related to the increased risk of IR, and this relationship follows a reverse L-shaped curve. AIP is related to the increased risk of T2D, and this relationship follows a J-shaped curve.