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Background: Metabolic syndrome (MS) is one of the major causes of coronary artery diseases (CAD). Gut microbiome diversity and its natural fermentation products are not only correlated with MS and CAD, but their correlations also appear to be stronger than the associations with traditional risk factors. Therefore, the aim of this study was to provide a new potential pathway for the natural fermentation product butyrate to improve MS and to examine whether it is associated with serum metabolic profiles and gut flora composition. Methods: C57BL/6J mice fed a high-fat diet (HFD) were treated with 400 mg/kg of sodium butyrate for 16 weeks. Blood and fecal samples were collected, and the metabolite concentrations and 16s rRNA were measured with liquid chromatography–MS and Illumina platform, respectively. The plasma differential metabolites and gut microbiome composition were analyzed with XCMS online and QIIME 2, respectively. Results: Gut microbiome-derived butyrate reduced glucose intolerance and insulin resistance, resisting HFD-induced increase in the relative abundance of f_Lachnospiraceae, f_Rikenellaceae, and f_Paraprevotellaceae. Meanwhile, sodium butyrate increased the levels of α-linolenate, all-trans-retinal, resolvin E1, and leukotriene in the plasma, and the differential pathways showed enrichment in mainly resolvin E biosynthesis, histidine degradation, lipoxin biosynthesis, and leukotriene biosynthesis. Moreover, sodium butyrate increased the levels of phosphorylated-adenosine 5′-monophosphate-activated protein kinase (p-AMPK) and facilitated glucose transporter member 4 (GLUT4) in the adipose tissue. Conclusion: Butyrate can induce AMPK activation and GLUT4 expression in the adipose tissue, improving cardiovascular disease (CVD)-related metabolic disorder, resisting HFD-induced gut microbiome dysbiosis, and promoting resolvin E1 and lipoxin biosynthesis. Oral supplement of the natural fermentation product butyrate can be a potential strategy for preventing CVD.

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The Savitzky–Golay (SG) method and moving-window waveband screening are applied to a coupling model of principal component (PCA) and linear discriminant analyses (LDA).

Endothelial dysfunction is caused by many factors, such as dyslipidemia, endoplasmic reticulum (ER) stress, and inflammation. It has been demonstrated that endothelial dysfunction is the initial process of atherosclerosis. AMP-activated protein kinase (AMPK) is an important metabolic switch that plays a crucial role in lipid metabolism and inflammation. However, recent evidence indicates that AMPK could be a target for atherosclerosis by improving endothelial function. For instance, activation of AMPK inhibits the production of reactive oxygen species induced by mitochondrial dysfunction, ER stress, and NADPH oxidase. Moreover, activation of AMPK inhibits the production of pro-inflammatory factors induced by dyslipidemia and hyperglycemia and restrains production of perivascular adipose tissue-released adipokines. AMPK activation prevents endothelial dysfunction by increasing the bioavailability of nitric oxide. Therefore, we focused on the primary risk factors involved in endothelial dysfunction, and summarize the features of AMPK in the protection of endothelial function, by providing signaling pathways thought to be important in the pathological progress of risk factors.

Equidistant combination multiple linear regression (EC-MLR) for the quasi-continuous wavelength selection of spectroscopic analysis was proposed and successfully applied to the reagent-free determination of soil organic matter with near-infrared spectroscopy. For comparison, the continuous-mode moving window partial least squares (MWPLS) and the discrete-mode successive projections algorithm (SPA) were improved by considering the stability and applied to the same analysis object as well. All methods exhibited good effect, but the modeling accuracy, stability, and validation effect of EC-MLR were better than that of the other two methods. Compared with MWPLS, the optimal EC-MLR model contained only 16 wavelengths, and method complexity was substantially reduced. Compared with SPA-MLR, the optimal EC-MLR model could easily undergo spectral preprocessing to improve predictive capability. Moreover, appropriate equidistant discrete wavelength combination with EC-MLR corresponded to the spectral absorption band with proper resolution and can effectively overcome co-linearity interruption for the MLR model. Thus, the EC-MLR method has great potential in practical application and instrument design.

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