Background Triglyceride–glucose (TyG) index, a simple surrogate marker of insulin resistance, has been reported to be associated with arterial stiffness. However, previous studies were limited by the cross-sectional design. The purpose of this study was to explore the longitudinal association between TyG index and progression of arterial stiffness. Methods A total of 6028 participants were derived from the Kailuan study. TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Arterial stiffness was measured using brachial-ankle pulse wave velocity (baPWV). Arterial stiffness progression was assessed by the annual growth rate of repeatedly measured baPWV. Multivariate linear regression models were used to estimate the cross-sectional association of TyG index with baPWV, and Cox proportional hazard models were used to investigate the longitudinal association between TyG index and the risk of arterial stiffness. Results Multivariate linear regression analyses showed that each one unit increase in the TyG index was associated with a 39 cm/s increment (95%CI, 29–48 cm/s, P < 0.001) in baseline baPWV and a 0.29 percent/year increment (95%CI, 0.17–0.42 percent/year, P < 0.001) in the annual growth rate of baPWV. During 26,839 person-years of follow-up, there were 883 incident cases with arterial stiffness. Participants in the highest quartile of TyG index had a 58% higher risk of arterial stiffness (HR, 1.58; 95%CI, 1.25–2.01, P < 0.001), as compared with those in the lowest quartile of TyG index. Additionally, restricted cubic spline analysis showed a significant dose–response relationship between TyG index and the risk of arterial stiffness (P non-linearity = 0.005). Conclusion Participants with a higher TyG index were more likely to have a higher risk of arterial stiffness. Subjects with a higher TyG index should be aware of the following risk of arterial stiffness progression, so as to establish lifestyle changes at an early stage.

Rhenium is one of the most valuable elements found in nature, and its capture and recycle are highly desirable for resource recovery. However, the effective and efficient collection of this material from industrial waste remains quite challenging. Herein, a tetraphenylmethane‐based cationic polymeric network (CPN‐tpm) nanotrap is designed, synthesized, and evaluated for ReO4− recovery. 3D building units are used to construct imidazolium salt‐based polymers with positive charges, which yields a record maximum uptake capacity of 1133 mg g−1 for ReO4− collection as well as fast kinetics ReO4− uptake. The sorption equilibrium is reached within 20 min and a kd value of 8.5 × 105 mL g−1 is obtained. The sorption capacity of CPN‐tpm remains stable over a wide range of pH values and the removal efficiency exceeds 60% for pH levels below 2. Moreover, CPN‐tpm exhibits good recyclability for at least five cycles of the sorption–desorption process. This work provides a new route for constructing a kind of new high‐performance polymeric material for rhenium recovery and rhenium‐contained industrial wastewater treatment.

Background Metabolic syndrome severity, expressed by the continuous metabolic syndrome risk score (MetS score), has been demonstrated to be able to predict future health conditions. However, little is known about the association between MetS score and renal function. Methods A total of 22,719 participants with normal renal function abstracted from the Kailuan Study were followed from 2006 to 2016. The new onset of chronic kidney disease (CKD) was defined as eGFR <60 ml/min per 1.73 m2 and/or proteinuria >300 mg/dl. Progressive decline in renal function was defined as an annual change rate of eGFR below the 10th percentile of the whole population. Results In the multivariate‐adjusted model, we found that the risk of progressive decline in renal function increased consistently with the MetS score, with an odds ratio of 1.49 (95% CI, 1.28, 1.73) for those subjects>75th percentile compared with those <25th percentile. Additionally, a high MetS score was found to be associated with an increased risk of CKD, with a hazard ratio of 1.53 (95% CI, 1.33, 1.78) for subjects >75th percentile compared with those <25th percentile. Conclusions Our findings suggested that the MetS score was associated with an increased risk of a progressive decline in renal function and was also a strong and independent risk factor for the development of CKD. These findings provide evidence of the potential clinical utility of the MetS score for assessing metabolic syndrome severity to detect the risk of decreased renal function and CKD.

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BackgroundAir pollutants are considered as non-negligible risk factors of idiopathic pulmonary fibrosis (IPF). However, the relationship between long-term air pollution and the incidence of IPF is unknown.ObjectiveTo explore the associations of air pollutants with IPF risk and further assess modification effect of genetic susceptibility.MethodsLand-use regression model estimated concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter (PM2.5 and PM10). The polygenic risk score (PRS) was constructed using 13 independent SNPs. The Cox proportional hazard models were used to evaluate the associations of air pollutants with IPF risk and further investigate the modification effect of genetic susceptibility. Additionally, absolute risk was calculated.ResultsAmong 433 738 participants from the UK Biobank, the incidence of IPF was 27.45/1 00 000 person-years during a median follow-up of 11.78 years. The adjusted hazard ratios (HRs) [95% confidence interval (CIs)] of IPF for each interquartile range increase in NO2, NOx, and PM2.5 were 1.11 (1.03, 1.19), 1.07 (1.01, 1.13), and 1.09 (1.02, 1.17), respectively. PM2.5 had the highest population attribution risk, followed by NOx and NO2. There were additive interactions between NO2, NOx and PM2.5 and genetic susceptibility. Participants with high PRS and high air pollution had the highest risk of incident IPF compared to those with low PRS and low air pollution [NO2: 3.94 (2.77, 5.60); NOx: 3.08 (2.21, 4.27); PM2.5: 3.65 (2.60, 5.13); PM10: 3.23 (2.32, 4.50)].ConclusionLong-term air pollutants exposures may elevate the risk of incident IPF. There are additive effects of air pollutants and genetic susceptibility on IPF risk.