Complex interplay of heavy metals and renal injury: New perspectives from longitudinal epidemiological evidence.

BACKGROUND: Epidemiological studies have reported associations between heavy metals and renal function. However, longitudinal studies are required to further validate these associations and explore the interactive effects of heavy metals on renal function and their directional influence. METHOD: This study, conducted in Northeast China from 2016 to 2021, included a four-time repeated measures design involving 384 participants (1536 observations). Urinary concentrations of chromium (Cr), cadmium (Cd), manganese (Mn), and lead (Pb) were measured, along with renal biomarkers including urinary microalbumin (umAlb), urinary albumin-to-creatinine ratio (UACR), N-acetyl-ß-D-glucosaminidase (NAG), and ß2-microglobulin (ß2-MG) levels. Estimated glomerular filtration rate (eGFR) was calculated. A Linear Mixed Effects Model (LME) examined the association between individual metal exposure and renal biomarkers. Subsequently, Quantile g-computation and Bayesian Kernel Machine Regression (BKMR) models assessed the overall effects of heavy metal mixtures. Marginal Effect models examined the directional impact of metal interactions in the BKMR on renal function. RESULT: Results indicate significant impacts of individual and combined exposures of Cr, Cd, Pb, and Mn on renal biomarkers. Metal interactions in the BKMR model were observed, with synergistic effects of Cd-Cr on NAG, umAlb, UACR; Cd-Pb on NAG, UACR; Pb-Cr on umAlb, UACR, eGFR-MDRD, eGFR-EPI; and an antagonistic effect of Mn-Pb-Cr on UACR. CONCLUSION: Both individual and combined exposures to heavy metals are associated with renal biomarkers, with significant synergistic interactions leading to renal damage. Our findings elucidate potential interactions among these metals, offering valuable insights into the mechanisms linking multiple metal exposures to renal injury.

Joint and interactive effects of metal mixtures on liver damage: Epidemiological evidence from repeated-measures study.

BACKGROUND: The impact of heavy metals on liver function has been examined in numerous epidemiological studies. However, these findings lack consistency and longitudinal validation. METHODS: In this study, we conducted three follow-up surveys with 426 participants from Northeast China. Blood and urine samples were collected, along with questionnaire information. Urine samples were analyzed for concentrations of four metals (chromium [Cr], cadmium [Cd], lead [Pb], and manganese [Mn]), while blood samples were used to measure five liver function indicators (alanine aminotransferase [ALT], aspartate aminotransferase [AST], albumin [ALB], globulin [GLB], and total protein [TP]). We utilized a linear mixed-effects model (LME) to explore the association between individual heavy metal exposure and liver function. Joint effects of metal mixtures were investigated using quantile g-computation and Bayesian kernel machine regression (BKMR). Furthermore, we employed BKMR and Marginal Effect models to examine the interaction effects between metals on liver function. RESULTS: The LME results demonstrated a significant association between urinary heavy metals (Cr, Cd, Pb, and Mn) and liver function markers. BKMR results indicated positive associations between heavy metal mixtures and ALT, AST, and GLB, and negative associations with ALB and TP, which were consistent with the g-comp results. Synergistic effects were observed between Cd-Cr on ALT, Mn-Cr and Cr-Pb on ALB, while an antagonistic effect was found between Mn-Pb and Mn-Cd on ALB. Additionally, synergistic effects were observed between Mn-Cr on GLB and Cd-Cr on TP. Furthermore, a three-way antagonistic effect of Mn-Pb-Cr on ALB was identified. CONCLUSION: Exposure to heavy metals (Cr, Cd, Mn, Pb) is associated with liver function markers, potentially leading to liver damage. Moreover, there are joint and interaction effects among these metals, which warrant further investigation at both the population and mechanistic levels.

Threshold effect of urinary chromium on kidney function biomarkers: Evidence from a repeated-measures study.

Chronic kidney disease (CKD) is a public health concern worldwide, and chromium exposure may be a risk factor due to its potential nephrotoxicity. However, research on the association between chromium exposure and kidney function especially the potential threshold effect of chromium exposure is limited. A repeated-measures study involving 183 adults (641 observations) was conducted from 2017 to 2021 in Jinzhou, China. Urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) were measured as kidney function biomarkers. Generalized mixed models and two-piecewise linear spline mixed models were used to assess the dose-response relationship and potential threshold effect of chromium on kidney function, respectively. Temporal analysis was conducted by the latent process mixed model to depict the longitudinal change of kidney function over age. Urinary chromium was associated with CKD (odds ratio [OR] = 1.29; 95 % confidence interval [CI], 6.41, 14.06) and UACR (Percent change = 10.16 %; 95 % CI, 6.41 %, 14.06 %), and we did not find significant association between urinary chromium and eGFR (Percent change = 0.06 %; 95 % CI, -0.80 %, 0.95 %). The threshold analyses suggested the existence of threshold effects of urinary chromium, with inflection points at 2.74 µg/L for UACR and 3.95 µg/L for eGFR. Furthermore, we found that chromium exposure exhibited stronger kidney damage over age. Our study provided evidence for the threshold effects of chromium exposure on kidney function biomarkers and the heightened nephrotoxicity of chromium in older adults. More attention should be paid to the supervision of chromium exposure concentrations for preventing kidney damage, especially in older adults.

Independent, combine and interactive effects of heavy metal exposure on dyslipidemia biomarkers: A cross-sectional study in northeastern China.

Dyslipidemia is a common disease in the older population and represents a considerable disease burden worldwide. Epidemiological and experimental studies have indicated associations between heavy metal exposure and dyslipidemia; few studies have investigated the effects of heavy metal mixture and interactions between metals on dyslipidemia. We recruited 1121 participants living in heavy metal-contaminated and control areas in northeast China from a cross-sectional survey (2017-2019). Urinary metals including chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) and dyslipidemia biomarkers, namely triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels, were measured. The generalized linear model (GLM) was used to explore the association of a single metal with dyslipidemia biomarkers. Bayesian kernel machine regression (BKMR) and multivariable linear regression were performed to explore the overall effect of metal mixture and the interaction between metals on dyslipidemia. Heavy metal mixture was positively associated with LDL-C, TC, and TG and negatively with HDL-C. In multivariable linear regression, Pb and Cd exhibited a synergistic association with LDL-C in the participants without hyperlipemia. Mn-Cd and Pb-Cr also showed a synergistic association with increasing the level of LDL-C in subjects without hyperlipemia. Cd-Cr showed an antagonistic association with HDL-C, respectively. Cr-Mn exhibited an antagonistic association with decreased HDL-C and TG levels. No significant interaction was noted among the three metals. Our study indicated that exposure to heavy metals is associated with dyslipidemia biomarkers and the presence of potential synergistic or antagonistic interactions between the heavy metals.

Association between urine metals and liver function biomarkers in Northeast China: A cross-sectional study.

BACKGROUND: After heavy metals enter the body, they affect a variety of organs, particularly the main metabolic organ, the liver. Moreover, people are more likely to be exposed to multiple metals than to a single metal. We explored the associations between exposure to a heavy metal mixture and liver function biomarkers. METHODS: This study involved 1171 residents living in areas with or without heavy metal exposure in northeast China. Urine concentrations of chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) were measured. Total protein (TP), albumin (ALB), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were used as biomarkers of liver function. A generalized linear model (GLM), quantile g-computation, and Bayesian kernel machine regression (BKMR) were used to explore the associations between the four metals and liver function. RESULTS: GLM analysis revealed that Cr level was negatively associated with TP (ß = - 0.57; 95% CI: - 0.89, - 0.26) and ALB (ß = - 0.27; 95% CI: - 0.47, - 0.07) levels, and Cd level was positively associated with AST (ß = 1.04; 95% CI: 0.43, 1.65) and ALT (ß = 0.94; 95% CI: 0.08, 1.79) levels. ALB (ß = 0.26; 95% CI: 0.10, 0.41) and ALT (ß = 0.52; 95% CI: 0.02, 1.02) levels were positively associated with urine Mn concentration. The quantile g-computation indicated that exposure to a mixture of the four metals was significantly associated with TP (ß = - 0.56; 95% CI: - 0.94, - 0.18) and ALT (ß = 0.84; 95% CI: 0.04, 1.63) levels. Among the metals, Cr had the strongest effect on TP and Cd had that on AST. The BKMR model indicated that mixed metal exposure was negatively associated with TP and ALB levels and positively associated with ALT and AST levels. CONCLUSION: Exposure to mixtures of heavy metals may influence liver function. Cr and Cd may be the largest contributors.

Association of environmental exposure to chromium with differential DNA methylation: An epigenome-wide study.

Introduction: Previous studies have reported that chromium (Cr)-induced epigenetic alterations and DNA methylation play a vital role in the pathogenesis of diseases induced by chromium exposure. Epigenomic analyses have been limited and mainly focused on occupational chromium exposure; their findings are not generalizable to populations with environmental Cr exposure. Methods: We identified the differential methylation of genes and regions to elucidate the mechanisms of toxicity related to environmental chromium exposure. DNA methylation was measured in blood samples collected from individuals in Cr-contaminated (n = 10) and unexposed areas (n = 10) by using the Illumina Infinium HumanMethylation850K array. To evaluate the relationship between chromium levels in urine and CpG methylation at 850 thousand sites, we investigated differentially methylated positions (DMPs) and differentially methylated regions (DMRs) by using linear models and DMRcate method, respectively. The model was adjusted for biologically relevant variables and estimated cell-type compositions. Results: At the epigenome-wide level, we identified five CpGs [cg20690919 (p FDR =0.006), cg00704664 (p FDR =0.024), cg10809143 (p FDR =0.043), cg27057652 (p FDR =0.047), cg05390480 (p FDR =0.024)] and one DMR (chr17: 19,648,718-19,648,972), annotated to ALDH3A1 genes (p < 0.05) as being significantly associated with log2 transformed urinary chromium levels. Discussion: Environmental chromium exposure is associated with DNA methylation, and the significant DMPs and DMR being annotated to cause DNA damage and genomic instability were found in this work. Research involving larger samples is required to further explore the epigenetic effect of environmental chromium exposure on health outcomes through DNA methylation.