Background: The atherogenic index of plasma (AIP) always remains in a potential association with the arterial stiffness, however, in large hypertensive patient populations, this relation is not fully discovered and needs to be studied in depth. The present analysis thus sought to further explore the association that exists between AIP and arterial stiffness in patients diagnosed with arterial hypertension in China.Methods: This cross-sectional study analyzed 4744 Chinese individuals with essential hypertension. AIP was defined as the base 10 logarithm of the ratio of plasma of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-c) levels indicates as in molar concentrations. Measurement of arterial stiffness was carried out via brachial-ankle pulse wave velocity (baPWV).Results: Data were adjusted for potential confounding variables, after which a multivariate linear regression analysis revealed AIP to be positively correlated with baPWV (β = 1.34, 95% CI: 0.96 to 1.72, P < 0.001). When AIP was instead treated as a categorical variable divided into quartiles, this same relationship was observed (P for trend < 0.001). We additionally found AIP and baPWV had a stronger positive association in individuals with a body mass index (BMI) < 24 kg/m2 (P for interaction < 0.05).Conclusion: AIP and arterial stiffness were positively correlated in essential hypertension patients in China, especially in those with a BMI < 24 kg/m2.
Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies.Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR.Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model.Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.
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