ObjectivesOur study aimed to distinguish the ability of anthropometric indices to assess the risk of metabolic syndrome (MetS).DesignProspective cohort study.SettingShenyang, China.ParticipantsA total of 379 residents aged between 40 and 65 were enrolled. 253 of them were free of MetS and had been followed up for 4.5 years.MethodsAt baseline, all the participants underwent a thorough medical examination. A variety of anthropometric parameters were measured and calculated, including waist circumference (WC), body mass index (BMI), a body shape index (ABSI), abdominal volume index (AVI), body adiposity index, body roundness index, conicity index, waist-to-hip ratio and visceral adiposity index (VAI). After 4.5 year follow-up, we re-examined whether participants were suffering from MetS. A receiver operating characteristic (ROC) curve was applied to examine the potential of the above indices to identify the status and risk of MetS.OutcomesOccurrence of MetS.ResultsAt baseline, 33.2% participants suffered from MetS. All of the anthropometric indices showed clinical significance, and VAI was superior to the other indices as it was found to have the largest area under the ROC curve. After a 4.5 year follow-up, 37.8% of men and 23.9% of women developed MetS. ROC curve analysis suggested that baseline BMI was the strongest predictor of MetS for men (0.77 (0.68–0.85)), and AVI was the strongest for women (0.72 (0.64–0.79)). However, no significant difference was observed between WC and both indices. In contrast, the baseline ABSI did not predict MetS in both genders.ConclusionsThe present study indicated that these different indices derived from anthropometric parameters have different discriminatory abilities for MetS. Although WC did not have the largest area under the ROC curve for diagnosing and predicting MetS, it may remain a better index of MetS status and risk because of its simplicity and wide use.

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

Context Ferulic acid (FA) has antioxidative and anti-inflammatory effects, and is a promising drug to treat sepsis. Objective To study the therapeutic effect of FA in sepsis-induced acute lung injury (ALI) and its underlying mechanisms. Materials and methods The caecal ligation and puncture (CLP) manoeuvre was applied to establish a murine model of sepsis-induced ALI, and female BALB/c mice (6 mice per group) were subjected to 100 mg/kg FA or 0.8 mg/kg ferrostatin-1 (Fer-1, ferroptosis inhibitor) treatment to clarify the role of FA in preserving alveolar epithelial barrier function and inhibiting ferroptosis. Lipopolysaccharide (LPS; 500 ng/mL)-induced cell models were prepared and subjected to FA (0.1 μM), sh-Nrf2, and Fe (Fe-citrate, ferroptosis inducer; 5 M) treatment to study the in vitro effect of FA on LPS-induced alveolar epithelial cell injury and the role of the Nrf2/HO-1 pathway. Results We found that FA decreased the lung injury score (48% reduction), lung wet/dry weight ratio (33% reduction), and myeloperoxidase activity (58% reduction) in sepsis-induced ALI. Moreover, FA inhibited ferroptosis of alveolar epithelial cells and improved alveolar epithelial barrier dysfunction. The protective role of FA against alveolar epithelial barrier dysfunction could be reversed by the ferroptosis inducer Fe-citrate, suggesting that FA alleviates alveolar epithelial barrier dysfunction by inhibiting ferroptosis. Mechanistically, we found that FA inhibited ferroptosis of alveolar epithelial cells by activating the Nrf2/HO-1 pathway. Conclusion Collectively, our data highlighted the alleviatory role of ferulic acid in sepsis-induced ALI by activating the Nrf2/HO-1 pathway and inhibiting ferroptosis, offering a new basis for sepsis treatment.