Association between metabolic syndrome severity score and cardiovascular disease: results from a longitudinal cohort study on Chinese adults.

Objective: This study aimed to quantify the severity of metabolic syndrome(MetS) and investigate its association with cardiovascular disease(CVD) risk on Chinese adults. Methods: 13,500 participants from the Zhejiang Adult Chronic Disease Study were followed up between 2010 and 2021. A continuous MetS severity score derived from the five components of MetS was used to quantify MetS severity, and the association between MetS severity and the risk of incident CVD was assessed using Cox proportional hazard and restricted cubic spline regression. Results: Both the presence and severity of MetS were strongly associated with CVD risk. MetS was related to an increased risk of CVD (hazard ratio(HR):1.700, 95% confidence interval(CI): 1.380-2.094). Compared with the hazard ratio for CVD in the lowest quartile of the MetS severity score, that in the second, third, and highest quartiles were 1.812 (1.329-2.470), 1.746 (1.265-2.410), and 2.817 (2.015-3.938), respectively. A linear and positive dose-response relationship was observed between the MetS severity and CVD risk (P for non-linearity = 0.437). Similar results were found in various sensitivity analyses. Conclusion: The MetS severity score was significantly associated with CVD risk. Assessing MetS severity and further ensuring intervention measures according to the different severities of MetS may be more useful in preventing CVD.

Evaluation of graphical models for multi-group metabolomics data.

Gaussian graphical model is a strong tool for identifying interactions from metabolomics data based on conditional correlation. However, data may be collected from different stages or subgroups of subjects with heterogeneity or hierarchical structure. There are different integrating strategies of graphical models for multi-group data proposed by data scientists. It is challenging to select the methods for metabolism data analysis. This study aimed to evaluate the performance of several different integrating graphical models for multi-group data and provide support for the choice of strategy for similar characteristic data. We compared the performance of seven methods in estimating graph structures through simulation study. We also applied all the methods in breast cancer metabolomics data grouped by stages to illustrate the real data application. The method of Shaddox et al. achieved the highest average area under the receiver operating characteristic curve and area under the precision-recall curve across most scenarios, and it was the only approach with all indicators ranked at the top. Nevertheless, it also cost the most time in all settings. Stochastic search structure learning tends to result in estimates that focus on the precision of identified edges, while BEAM, hierarchical Bayesian approach and birth-death Markov chain Monte Carlo may identify more potential edges. In the real metabolomics data analysis from three stages of breast cancer patients, results were in line with that in simulation study.

Electro-acupuncture-induced neuroprotection is associated with activation of the IGF-1/PI3K/Akt pathway following adjacent dorsal root ganglionectomies in rats.

The aim of the present study was to investigate the putative role and underlying mechanisms of insulin­like growth factor 1 (IGF­1) in mediating neuroplasticity in rats subjected to partial dorsal root ganglionectomies following electro­acupuncture (EA) treatment. The rats underwent bilateral removal of the L1­L4 and L6 dorsal root ganglia (DRG), sparing the L5 DRG, and were subsequently subjected to 28 days of EA treatment at two paired acupoints, zusanli (ST 36)­xuanzhong (GB 39) and futu (ST 32)­sanyinjiao (SP 6), as the EA Model group. Rats that received partial dorsal root ganglionectomies without EA treatment served as a control (Model group). Subsequently, herpes simplex virus (HSV)­IGF­1, HSV­small interfering (si) RNA­IGF­1 and the associated control vectors were injected into the L5 DRG of rats in the EA Model group. HSV­IGF­1 transfection enhanced EA­induced neuroplasticity, which manifested as partial recovery in locomotor function, remission hyperpathia, growth of DRG­derived spared fibers, increased expression of phosphorylated (p­) phosphatidylinositol 3­kinase (PI3K) and Akt, and increased pPI3K/PI3K and pAkt/Akt expression ratios. By contrast, HSV­siRNA­IGF­1 treatment attenuated these effects induced by HSV­IGF­1 transfection. The results additionally demonstrated that HSV­IGF­1 transfection augmented the outgrowth of neurites in cultured DRG neurons, and interference of the expression of IGF­1 retarded neurite outgrowth. Co­treatment with a PI3K inhibitor or Akt siRNA inhibited the aforementioned effects induced by the overexpression of IGF­1. In conclusion, the results of the present study demonstrated the crucial roles of IGF­1 in EA­induced neuroplasticity following adjacent dorsal root ganglionectomies in rats via the PI3K/Akt signaling pathway.