Analysis of relationship between mixed heavy metal exposure and early renal damage based on a weighted quantile sum regression and Bayesian kernel machine regression model.

BACKGROUND: Occupation, environmental heavy metal exposure, and renal function impairment are closely related. The relationship between mixed metal exposure and chronic renal injury is inadequately described, and the interaction between each metal is poorly explored. OBJECTIVE: This cross-sectional study assessed mixed heavy metal exposure in the general population and their relationship with early renal impairment, as well as possible interactions between metals. METHODS: The study was conducted in two communities in Taiyuan City in northern China. Multiple linear regression, weighted quantile sum (WQS) and bayesian kernel machine regression (BKMR) regression were used to explore the relationship of mixed heavy metal exposure with indicators of early kidney injury (N-acetyl-ß-D- glucosidase (UNAG), urinary albumin (UALB)). Meanwhile, BKMR was used to explore the possible interactions between mixed heavy metal and indicators of early kidney injury. RESULTS: Based on the WQS regression results, we observed adjusted WQS coefficient ß (ß-WQS) of 0.711 (95% CI: 0.543, 0.879). Notably, this change was primarily driven by As (35.6%) and Cd (22.5%). In the UALB model, the adjusted ß-WQS was 0.657 (95% CI: 0.567, 0.747), with Ni (30.5%), Mn (22.1%), Cd (21.2%), and As (18.6%) exhibiting higher weights in the overall effect. The BKMR results showed a negative interaction between As and other metals in the UNAG and UALB models, a positive interaction between Mn and Ni and other metals. No significant pairwise interaction was observed in the association of metals with indicators of early kidney injury. CONCLUSION: Through multiple linear regression, WQS regression, and BKMR analyses, we found that exposure to mixed heavy metals such as Cd, Cr, Pb, Mn, As, Co and Ni was positively correlated with UNAG and UALB. Moreover, there are complex interactions between two or more heavy metals in more than one direction.

Integrated analysis of transcriptome-wide m6A methylation in a Cd-induced kidney injury rat model.

BACKGROUND: Accumulating evidence has demonstrated that N6-methyladenosine (m6A) plays important roles in a variety of diseases. However, the specific functions of m6A in CdCl2-induced kidney injury remain unclear. OBJECTIVE: Here, we investigate a transcriptome-wide map of m6A modifications and explore the effects of m6A on Cd-induced kidney injury. MATERIALS AND METHODS: The rat kidney injury model was constructed by subcutaneous injection of CdCl2 (0.5, 1.0, and 2.0 mg/kg). The m6A levels were measured by colorimetry. The level of expression of m6A-related enzymes were detected by reverse transcription quantitative real-time PCR analysis. Transcriptome-wide m6A methylome in CdCl2 (2.0 mg/kg) and the control group were profiled by methylated RNA immunoprecipitation sequencing (MeRIP-seq). Subsequently, the sequencing data were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), while gene set enrichment analysis (GSEA) confirmed the functional enrichment pathways of sequencing genes. In addition, a protein-protein interaction (PPI) network was applied to select hub genes. RESULTS: The levels of m6A and m6A regulators (METTL3, METTL14, WTAP, YTHDF2) were significantly increased in CdCl2 groups. We identified a total of 2615 differentially expressed m6A peaks, 868 differentially expressed genes and 200 genes with significant changes in both m6A modification and gene expression levels. GO, KEGG, and GSEA analyses indicated that these genes were mainly enriched in inflammation and metabolism-related pathways such as in IL-17 signaling and fatty acid metabolism. According the result of the conjoint analysis, we identified the top ten hub genes (Fos, Hsp90aa1, Gata3, Fcer1g, Cftr, Cspg4, Atf3, Cdkn1a, Ptgs2, and Npy) which may be regulated by m6A and involve in CdCl2-induced kidney damage. CONCLUSION: This study established a m6A transcriptional map in a CdCl2-induced kidney injury model and suggested that m6A may affect CdCl2 induced kidney injury via regulated the inflammation and metabolism related gene.

Exposure to perfluoroalkyl and polyfluoroalkyl substances and estimated glomerular filtration rate in adults: a cross-sectional study based on NHANES (2017-2018).

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) may be important environmental risk factors affecting renal function. This study aimed to investigate the relationships between PFASs and estimated glomerular filtration rate (eGFR) in univariate exposure and multivariate co-exposure models of PFASs. A total of 1700 people over 18 years from National Health and Nutrition Examination Survey (NHANES) in 2017-2018 were selected as subjects to explore the relationships between eGFR and six PFASs (perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFUA), perfluorodecanoic acid (PFDeA), and perfluorohexane sulfonate (PFHxS)). First, multiple linear regression was used to estimate the association of each PFAS with eGFR, and the joint effect of PFAS mixtures was evaluated by Bayesian kernel machine regression (BKMR). Multiple linear regression analysis showed PFOS (ß = - 0.246, p = 0.026) and PFHxS (ß = 0.538, p = 0.049) were significantly associated with eGFR in total population. In BKMR analysis, there was joint effect between PFOS and PFHxS for eGFR. And there were the joint effects of multiple PFAS on eGFR, especially the significant joint effect between PFHxS and PFDeA/PFNA/PFUA. Future cohort studies need to explore the association of multiple PFASs and health.

Urinary cadmium and peripheral blood telomere length predict the risk of renal function impairment: a study of 547 community residents of Shanxi, China.

Few reports have investigated the predictive value of urinary cadmium (UCd) and telomere length on renal function impairment. Therefore, we constructed nomogram models, using a cross-sectional survey to analyze the potential function of UCd and telomere length in renal function impairment risk. We randomly selected two community populations in Shanxi, China, and general information of the subjects was collected through face-to-face questionnaire surveys. Venous blood of subjects was collected to detect absolute telomere length (ATL) by real-time quantitative chain reaction (RT-PCR). Collecting urinary samples detected UCd and urinary N-acetyl-ß-d-glucosaminidase (UNAG). Estimated glomerular filtration rate (eGFR) was obtained based on serum creatinine (SCr). Nomogram models on risk prediction analysis of renal function impairment was constructed. After adjusting for other confounding factors, UCd (ß = 0.853, 95% confidence interval (CI): 0.739 ~ 0.986) and ATL (ß = 1.803, 95%CI: 1.017 ~ 1.154) were independent risk influencing factors for increased UNAG levels, and the risk factors for eGFR reduction were UCd (ß = 1.011, 95%CI: 1.187 ~ 1.471), age (ß = 1.630, 95%CI: 1.303 ~ 2.038), and sex (ß = 0.181, 95%CI: 0.105 ~ 0.310). Using UCd, ATL, sex, and age to construct the nomogram, and the C-statistics 0.584 (95%CI: 0.536 ~ 0.632) and 0.816 (95%CI: 0.781 ~ 0.851) were obtained by internal verification of the calibration curve, C-statistics revealed nomogram model validation was good and using decision curve analysis (DCA) confirmed a good predictive value of the nomogram models. In a nomogram model, ATL, UCd, sex, and age were detected as independent risk factors for renal function impairment, with UCd being the strongest predictor.