Biomonitoring of firefighters' exposure to priority pollutant metal(loid)s during wildland fire combat missions: Impact on urinary levels and health risks.

Wildland firefighters are exposed to metal(loid)s released during wildfires through vegetation combustion, which also promotes remobilization of accumulated anthropogenic metal(loid)s. Studies biomonitoring metal(loid)s exposure promoted exclusively by wildfire suppression activities are lacking. This work aimed to characterize, for the first time, the impact of real-life wildland firefighting operations on urinary levels of priority pollutant metal(loid)s [14 included in ATSDR, 11 in USEPA, and 4 in Human Biomonitoring for Europe Initiative priority lists] in firefighters. Spot urines were sampled pre-exposure (105 non-smokers, 76 smokers) and post-exposure to firefighting activities (20 non-smokers, 25 smokers); among those, paired samples were collected from 14 non-smoking and 24 smoking firefighters. Smokers displayed significantly higher baseline levels of zinc (28 %), lithium (29 %), cadmium (55 %), rubidium (13 %), and copper (20 %) than non-smokers. Following wildfire suppression, the concentration of the WHO potentially toxic metal(loid)s rose from 2 % to 3 % in smokers and 2 % to 5 % in non-smokers (up to 4 % for all firefighters and up to 5 % in paired samples). Levels of nickel (33-53 %), antimony (45-56 %), and cesium (40-47 %) increased significantly post-exposure in non-smokers (in all firefighters and in paired samples), whose urinary concentrations were generally more impacted by wildfire emissions than those of smokers. Arsenic (80 %) displayed the only significant increase post-exposure in smokers, being the best discriminant of exposure to wildfire emissions in these subjects. Significant positive correlations were found for age and/or career length with cadmium, lead, barium, strontium, and mercury, and for body mass index with arsenic. The reference/guidance values were exceeded for arsenic, zinc, cesium, nickel, antimony, cadmium, lead, thallium, mercury, copper, and cobalt in 1-90 % of firefighters suggesting augmented health risks due to wildfire combating and emphasizing the need of mitigation strategies. This study also provides biomonitoring data to help setting reference values for the occupationally exposed part of population.

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.

INTRODUCTION: There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients. AIM: We intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that monitors mortality based on heavy metal exposure among hypertension patients. METHODS: Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2018). We developed 5 ML models for mortality prediction among hypertension patients by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, we chose the optimally performing model after parameter adjustment by genetic algorithm (GA) for prediction. Finally, in order to visualize the model's ability to make decisions, we used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 2347 participants in total. RESULTS: A best-performing eXtreme Gradient Boosting (XGB) with GA for mortality prediction among hypertension patients by 13 heavy metals was selected (AUC 0.959; 95% CI 0.953-0.965; accuracy 96.8%). According to sum of SHAP values, cadmium (0.094), cobalt (2.048), lead (1.12), tungsten (0.129) in urine, and lead (2.026), mercury (1.703) in blood positively influenced the model, while barium (- 0.001), molybdenum (- 2.066), antimony (- 0.398), tin (- 0.498), thallium (- 2.297) in urine, and selenium (- 0.842), manganese (- 1.193) in blood negatively influenced the model. CONCLUSIONS: Hypertension patients' mortality associated with heavy metal exposure was predicted by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Cadmium, cobalt, lead, tungsten in urine, and mercury in blood are positively correlated with mortality, while barium, molybdenum, antimony, tin, thallium in urine, and lead, selenium, manganese in blood is negatively correlated with mortality.

Frequencies of chromosomal aberrations and related biochemical parameters in welders.

Stainless steel welders are exposed to heavy filler metals. We evaluated the concentration of these metals in whole blood and urine, and the relevant biochemical parameters in relation to the total chromosomal aberrations (CAs), chromatid-type (CTA-type, CTAs) and chromosome-type (CSA-type, CSAs), in 117 welders and control individuals. Statistically higher concentrations of the total Cr, Ni and Mn were observed in whole blood and urine of welders, and the concentrations were higher in welders who smoked. On the contrary, concentrations of urinary heavy metals Cr and Mn adjusted for creatinine were significantly higher in the control groups. A statistically higher frequency of total CAs was observed in the whole group of welders, and also in the non-smoking welders, as compared to controls. The frequency of total CAs significantly correlated with the concentration of Cr, Ni and Mn in whole blood (R=0.61, P˂0.0001, R=0.33, P˂0.0001 and R=0.66, P˂0.0001, respectively), with urinary concentrations of Ni and Mn (R=0.27, P=0.003 and R=0.28, P=0.003, respectively) and with urinary concentrations of Cr, Ni and Mn adjusted for creatinine (R=0.22, P=0.029, R=0.26, P=0.005 and R=0.20, P=0.030, respectively). Likewise, the frequency of CTA-types significantly correlated with the concentration of Cr and Mn in whole blood (R=0.31, P=0.0007 and R=0.34, P=0.0002). The frequency of CSA-types significantly correlated with concentrations of Cr, Ni and Mn in whole blood (R=0.43, P˂0.0001, R=0.38, P˂0.0001 and R=0.46, P˂0.0001, respectively). The statistically higher values of serum creatinine and total bilirubin were detected in all welders, as well as in smokers when compared to the corresponding controls. The exposure to heavy metals in welders increased the frequencies of CAs and altered the balance between urinary excretion of heavy metals and their possible accumulation.

Associations of urinary heavy metals with age at menarche, age at menopause, and reproductive lifespan: A cross-sectional study in U.S. women.

Female reproductive timing and lifespan, with a close relation to long-term health outcomes, have been altered in U.S. women over the past decades. However, epidemiologic evidence of the potential causes was lacking. On the basis of 1981 naturally postmenopausal women from the National Health and Nutrition Examination Survey 1999-2020, this study aimed to investigate the associations of urinary heavy metals with age at menarche, age at menopause, and reproductive lifespan. Multivariate generalized linear regression and addictive models were used for single metal exposure analysis, and weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models were employed for mixed exposures. In the fully adjusted model, higher urinary antimony concentration was associated with earlier age at menarche of 0.137 years, while higher concentrations of cadmium, cesium, lead, antimony, and thallium were associated with delayed age at menopause of 0.396-0.687 years. Additionally, urinary barium, cesium, lead, antimony, and thallium levels were associated with longer reproductive lifespan ranging between 0.277 and 0.713 years. Both WQS and BKMR models showed significantly positive associations of metal mixtures with age at menopause (ß: 0.667, 95 % CI: 0.120-1.213) and reproductive lifespan (ß: 0.686, 95 % CI: 0.092-1.280), with cadmium and lead identified as principal contributors. In conclusion, heavy metal exposures were associated with reproductive timing and lifespan of U.S. women, highlighting the need for further prevention and intervention strategies.

Urinary metals and their associations with DNA oxidative damage among e-waste recycling workers in Hong Kong.

Recycling electronic waste (e-waste) poses risks of metal exposure, potentially leading to health impairments. However, no previous study has focused on this issue in Hong Kong. Therefore, from June 2021 to September 2022, this study collected urine samples from 101 e-waste workers and 100 office workers in Hong Kong to compare their urinary levels of metals using ICP-MS. Among the 15 included metals (with detection rates above the 70 % threshold), eight showed significantly higher urinary concentrations (unit: µg/g creatinine) in e-waste workers compared to office workers: Li (25.09 vs. 33.36), Mn (1.78 vs. 4.15), Ni (2.10 vs. 2.77), Cu (5.81 vs. 9.23), Zn (404.35 vs. 431.52), Sr (151.33 vs. 186.26), Tl (0.35 vs. 0.43), and Pb (0.69 vs. 1.16). E-waste workers in Hong Kong generally exhibited lower metal levels than those in developing regions but higher than their counterparts in developed areas. The urine level of 8-hydroxy-2-deoxyguanosine (8-OHdG) was determined by HPLC-MS/MS, and no significant difference was found between the two groups. Multiple linear regression models revealed no significant association between individual metal and urinary 8-OHdG concentrations. However, the metal mixture was identified to marginally elevate the 8-OHdG concentrations (1.12, 95 %CI: 0.04, 2.19) by quantile g­computation models, with Mn and Cd playing significant roles in such effect. In conclusion, while the metal levels among Hong Kong e-waste workers compared favorably with their counterparts in other regions, their levels were higher than those of local office workers. This underscores the need for policymakers to prioritize attention to this unique industry.

Exposure-associated DNA methylation among people exposed to multiple industrial pollutants.

BACKGROUND: Current research on the epigenetic repercussions of exposure to a combination of pollutants is limited. This study aims to discern DNA methylation probes associated with exposure to multiple pollutants, serving as early effect markers, and single-nucleotide polymorphisms (SNPs) as surrogate indicators for population susceptibility. The investigation involved the analysis of urine exposure biomarkers for 11 heavy metals (vanadium, arsenic, mercury, cadmium, chromium, nickel, lead, manganese, copper, strontium, thallium), polycyclic aromatic hydrocarbon (PAHs) (1-hydroxypyrene), genome-wide DNA methylation sequencing, and SNPs array on all study participants. The data were integrated with metabolomics information and analyzed both at a community level based on proximity to home addresses relative to the complex and at an individual level based on exposure biomarker concentrations. RESULTS: On a community level, 67 exposure-related CpG probes were identified, while 70 CpG probes were associated with urine arsenic concentration, 2 with mercury, and 46 with vanadium on an individual level. These probes were annotated to genes implicated in cancers and chronic kidney disease. Weighted quantile sum regression analysis revealed that vanadium, mercury, and 1-hydroxypyrene contributed the most to cg08238319 hypomethylation. cg08238319 is annotated to the aryl hydrocarbon receptor repressor (AHRR) gene, and AHRR hypomethylation was correlated with an elevated risk of lung cancer. AHRR was further linked to deregulations in phenylalanine metabolism, alanine, aspartate, and glutamate metabolism, along with heightened oxidative stress. Additionally, three SNPs (rs11085020, rs199442, and rs10947050) corresponding to exposure-related CpG probes exhibited significant interaction effects with multiple heavy metals and PAHs exposure, and have been implicated in cancer progression and respiratory diseases. CONCLUSION: Our findings underscore the pivotal role of AHRR methylation in gene-environment interactions and highlight SNPs that could potentially serve as indicators of population susceptibility in regions exposed to multiple heavy metals and PAHs.

Exposure to four typical heavy metals induced telomere shortening of peripheral blood mononuclear cells in relevant with declined urinary aMT6s in rats.

Environmental heavy metals pollution have seriously threatened the health of human beings. An increasing number of researches have demonstrated that environmental heavy metals can influence the telomere length of Peripheral Blood Mononuclear Cells (PBMCs), which implicate biological aging as well as predicts diseases. Our previous study has shown that methylmercury (MeHg)-induced telomere shortening in rat brain tissue was associated with urinary melatonin metabolite 6-sulfatoxymelatonin (aMT6s) levels. Here, we aimed to further elucidate the impact of 4 typical heavy metals (As, Hg, Cd and Pb) on telomere length of PBMCs and their association with urinary aMT6s in rats. In this study, eighty-eight male Sprague-Dawley rats were randomized grouped into eleven groups. Among them, forty 3-month-old (young) and forty 12-month-old (middle-aged) rats were divided into young or middle-aged control groups as well as typical heavy metals exposed groups, respectively. Eight 24-month-old rats (old) was divided into aging control group. The results showed that MeHg exposure in young rats while sodium arsenite (iAs), MeHg, cadmium chloride (CdCl2), lead acetate (PbAc) exposure in middle-aged rats for 3 months significantly reduced the levels of and urinary aMT6s, as well as telomere length of PBMCs. In addition, they also induced abnormalities in serum oxidative stress (SOD, MDA and GPx) and inflammatory (IL-1ß, IL-6 and TNF-α) indicators. Notably, there was a significant positive correlation between declined level of urinary aMT6s and the shortening of telomere length in PBMCs in rats exposed to 4 typical heavy metals. These results suggested that 4 typical heavy metals exposure could accelerate the reduction of telomere length of PBMCs partially by inducing oxidative stress and inflammatory in rats, while ageing may be an important synergistic factor. Urinary aMT6s detection may be a alternative method to reflect telomere toxic effects induced by heavy metal exposure.

Urine manganese, cadmium, lead, arsenic, and selenium among autism spectrum disorder children in Kuala Lumpur.

Background: The development of autism spectrum disorder (ASD) may stem from exposure to environmental pollutants such as heavy metals. The primary objective of this study is to determine the role of heavy metals of concern such as manganese (Mn), cadmium (Cd), lead (Pb), arsenic (As), and essential trace element selenium (Se) among ASD children in Kuala Lumpur, Malaysia. Method: A total of 155 preschoolers in Kuala Lumpur between the ages 3 to 6 participated in an unmatched case-control study, comprising ASD children (n = 81) recruited from an early intervention program for autism, and 74 children without autism who were recruited from public preschools. Urine samples were collected at home, delivered to the study site, and transported to the environmental lab within 24 hours. Inductively coupled plasma mass spectrometry (ICP-MS) was applied to measure the concentration of heavy metals in the samples. Data were analysed using bivariate statistical tests (Chi-square and T-test) and logistic regression models. Result: This study demonstrated that Cd, Pb, and As urine levels were significantly greater in children without autism relative to those affected with ASD (p < 0.05). No significant difference was in the levels of Se (p = 0.659) and Mn (p = 0.875) between children with ASD and the control group. The majority of children in both groups have urine As, Pb, and Cd values lower than 15.1 µg/dL, 1.0 µg/dL, and 1.0 µg/dL, respectively which are the minimal risk values for noncarcinogenic detrimental human health effect due to the heavy metal's exposure . Factors associated with having an ASD child included being a firstborn, male, and higher parental education levels (adjusted odds ratios (aOR) > 1, p < 0.05). Conclusion: Preschoolers in this study demonstrated low levels of heavy metals in their urine samples, which was relatively lower in ASD children compared to the healthy matched controls. These findings may arise from the diminished capacity to excrete heavy metals, especially among ASD children, thereby causing further accumulation of heavy metals in the body. These findings, including the factors associated with having an ASD child, may be considered by healthcare professionals involved in child development care, for early ASD detection. Further assessment of heavy metals among ASD children in the country and interventional studies to develop effective methods of addressing exposure to heavy metals will be beneficial for future reference.

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis.

Background and objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD. Methods: We analyzed data from the NHANES (2005-2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Results: Machine learning reveals correlation coefficients of -0.5059 and - 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively. Conclusion: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

Heavy metal concentrations and clinical symptoms in patients diagnosed with schizophrenia related to cigarette smoking.

In our study, blood concentrations of lead (Pb), arsenic (As), and cadmium (Cd) and urine concentrations of thallium (Tl) were measured together with related symptoms of heavy metal poisoning in cigarette smoking volunteers diagnosed with schizophrenia, in cigarette smokers not diagnosed with schizophrenia, and in the control group of non-smokers and not diagnosed with schizophrenia volunteers. Our study was performed on 171 volunteers divided into the following subgroups: patients diagnosed with schizophrenia with at least 1 year of continuous cigarette smoking experience (56 participants), cigarette smokers not diagnosed with schizophrenia with at least one year of continuous smoking experience (58), and control group (not diagnosed with schizophrenia and non-smoking volunteers) (57). Smoking durations of cigarette smokers diagnosed with schizophrenia and cigarette smokers not diagnosed with schizophrenia are not similar (p = 0.431). Blood Pb, As, and Cd concentrations and urine Tl concentrations were the highest in the subgroup of cigarette smokers not diagnosed with schizophrenia, followed by the subgroup of cigarette smokers diagnosed with schizophrenia, and the control group. Only blood Pb concentrations were significantly higher (probability value p < 0.05) in the group of cigarette smokers not diagnosed with schizophrenia (5.16 µg/dL), comparing to the group of cigarette smokers diagnosed with schizophrenia (3.83 µg/dL) and to the control group (3.43 µg/dL). Blood Cd and As concentrations and urine Tl concentrations were significantly higher (p < 0.05) in cigarette smokers not diagnosed with schizophrenia than in the control group. The results revealed a statistically significant positive correlation (p < 0.001) in the cigarette smokers in the schizophrenia diagnosed group between blood Pb, blood As, and urine Tl concentrations and the duration of cigarette smoking.

Exposure to a mixture of metal(loid)s and sleep quality in pregnant women during early pregnancy: A cross-sectional study.

Biological characteristics of pregnant women during early pregnancy make them susceptible to both poor sleep quality and metal/metalloid exposure. However, the effects of metal(loid) exposure on sleep quality in pregnant women remain unknown and unexplored. We aimed to examine the relationship between exposure to a mixture of metal(loid)s and pregnant women's sleep quality during early pregnancy. We recruited 493 pregnant women in the first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected their spot urine samples. All urine specimens were assessed for eight metal(loid)s: arsenic (As), cadmium (Cd), iron (Fe), zinc (Zn), molybdenum (Mo), lead (Pb), selenium (Se), and mercury (Hg). We used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. Linear regression, logistic regression, generalized additive models (GAMs), quantile g-computation, and Bayesian kernel machine regression (BKMR) were applied to investigate the relationships between metal(loid) exposure and sleep quality. The results from single metal(loid) models, quantile g-computation models, and BKMR models consistently suggested that Fe was positively related to women's sleep quality. Moreover, in the quantile g-computation models, As was the most critical contributor to the negative effects of the metal(loid) mixture on sleep quality. In addition, we found significant As by Fe interaction for scores of PSQI and habitual sleep efficiency, Pb by Fe interaction for PSQI and sleep latency, and Hg by Fe interaction for PSQI, suggesting the interactive effects of As and Fe, Pb and Fe, Hg and Fe on sleep quality and specific sleep components. Our study provided the first-hand evidence of the effects of metal(loid) exposure on pregnant women's sleep quality. The underlying mechanisms need to be explored in the future.

Association of heavy metals exposure with lower blood pressure in the population aged 8-17 years: a cross-sectional study based on NHANES.

Background: The existing evidence regarding the joint effect of heavy metals on blood pressure (BP) in children and adolescents is insufficient. Furthermore, the impact of factors such as body weight, fish consumption, and age on their association remains unclear. Methods: The study utilized original data from the National Health and Nutrition Examination Survey, encompassing 2,224 children and adolescents with complete information on 12 urinary metals (barium, cadmium, cobalt, cesium, molybdenum, lead, antimony, thallium, tungsten, uranium, mercury and arsenic), BP, and core covariates. Various statistical methods, including weighted multiple logistic regression, linear regression, and Weighted Quantile Sum regression (WQS), were employed to evaluate the impact of mixed metal exposure on BP. Sensitivity analysis was conducted to confirm the primary analytical findings. Results: The findings revealed that children and adolescents with low-level exposure to lead (0.40 µg/L, 95%CI: 0.37, 0.42), mercury (0.38 µg/L, 95%CI: 0.35, 0.42) and molybdenum (73.66 µg/L, 95%CI: 70.65, 76.66) exhibited reduced systolic blood pressure (SBP) and diastolic blood pressure (DBP). Conversely, barium (2.39 µg/L, 95%CI: 2.25, 2.54) showed a positive association with increased SBP. A 25th percentile increase in the WQS index is significantly associated with a decrease in SBP of 0.67 mmHg (95%CI, -1.24, -0.10) and a decrease in DBP of 0.59 mmHg (95% CI, -1.06, -0.12), which remains statistically significant even after adjusting for weight. Furthermore, among individuals who consume fish, heavy metals have a more significant influence on SBP. A 25 percentile increase in the WQS index is significantly associated with a decrease of 3.30 mmHg (95% CI, -4.73, -1.87) in SBP, primarily attributed to mercury (27.61%), cadmium (27.49%), cesium (17.98%), thallium (8.49%). The study also identified a declining trend in SBP among children aged 10-17, whereas children aged 11-18 exhibited lower levels of systolic and diastolic blood pressure, along with a reduced risk of hypertension. Conclusion: Some heavy metals demonstrate an inverse association with the BP of children and adolescents, particularly notable in groups with fish consumption and older children and adolescents. Future studies are warranted to validate these findings and delve deeper into the interplay of heavy metals.

Association between exposure to heavy metals and the risk of breast cancer: A case-control study in Tanzania.

Exposure to heavy metals is suspected to influence breast cancer development, although epidemiological evidence regarding this association remains controversial. This study investigated the association between urinary heavy metal levels and breast cancer risk through a hospital-based case-control design, involving 50 breast cancer patients and 40 healthy females. Heavy metals were quantified using an inductively coupled plasma atomic emission spectrophotometer (ICP-AES). The exposure levels were categorized into quartiles, and logistic regression was employed to evaluate the breast cancer risk associated with heavy metal exposure. The results revealed positive associations between risk of breast cancer and concentrations of Al, As, Ni, Cd, and Pb. In addition, the risk of breast cancer was high among participants in the 2nd, 3rd, and 4th quartiles of Al, As, Ni, Cd, and Pb. However, Co showed an inverse association with breast cancer. These results indicate that increased concentrations of heavy metals, with the exception of Co, are associated with the risk of breast cancer.

Independent and joint associations of multiple metals exposure with vital capacity index: a cross-sectional study in Chinese children and adolescents.

OBJECTIVE: The current study aimed to explore the relationships between urinary metals and vital capacity index (VCI) in 380 children and adolescents in Northeast China using a variety of statistical methods. METHODS: A cross-sectional survey was conducted among 380 children and adolescents in Liaoning Province, China. To assess the relationships between urinary metals and VCI, Elastic-net (ENET) regression, multivariate linear regression, weighted quantile sum (WQS), bayesian kernel machine regression (BKMR) and quantile-based g computation (qgcomp) were adopted. RESULTS: The ENET model selected magnesium (Mg), vanadium (V), manganese (Mn), arsenic (As), tin (Sn) and lead (Pb) as crucial elements. In multiple linear regression, we observed urinary Pb, Mn was negatively correlated with VCI individually in both total study population and adolescents (all p values < 0.05) in the adjustment model. The WQS indices were negatively related with VCI in total study population (ß=-3.19, 95%CI: -6.07, -0.30) and adolescents (ß=-3.46, 95%CI: -6.58, -0.35). The highest weight in total study population was Pb (38.80%), in adolescents was Mn (35.10%). In the qgcomp, Pb (31.90%), Mn (27.20%) were the major negative contributors to the association in the total population (ß=-3.51, 95%CI: -6.29, -0.74). As (42.50%), Mn (39.90%) were the main negative contributors (ß=-3.95, 95% CI: -6.68, -1.22) among adolescents. The results of BKMR were basically consistent with WQS and qgcomp analyses. CONCLUSIONS: Our results indicated that Pb and Mn were priority toxic materials on VCI. The cumulative effect of metals was negatively related to VCI, and this relationship was more pronounced in adolescents.

Exposure to heavy metals in utero and autism spectrum disorder at age 3: a meta-analysis of two longitudinal cohorts of siblings of children with autism.

BACKGROUND: Autism spectrum disorder (ASD) is a prevalent and heterogeneous neurodevelopmental disorder. Risk is attributed to genetic and prenatal environmental factors, though the environmental agents are incompletely characterized. METHODS: In Early Autism Risk Longitudinal Investigation (EARLI) and Markers of Autism Risk in Babies Learning Early Signs (MARBLES), two pregnancy cohorts of siblings of children with ASD, urinary metals concentrations during two pregnancy time periods (< 28 weeks and ≥ 28 weeks of gestation) were measured using inductively coupled plasma mass spectrometry. At age three, clinicians assessed ASD with DSM-5 criteria. In an exposure-wide association framework, using multivariable log binomial regression, we examined each metal for association with ASD status, adjusting for gestational age at urine sampling, child sex, age at pregnancy, race/ethnicity and education. We meta-analyzed across the two cohorts. RESULTS: In EARLI (n = 170) 17% of children were diagnosed with ASD, and 44% were classified as having non-neurotypical development (Non-TD). In MARBLES (n = 231), 21% were diagnosed with ASD, and 14% classified as Non-TD. During the first and second trimester period (< 28 weeks), having cadmium concentration over the level of detection was associated with 1.69 (1.08, 2.64) times higher risk of ASD, and 1.29 (0.95, 1.75)times higher risk of Non-TD. A doubling of first and second trimester cesium concentration was marginally associated with 1.89 (0.94, 3.80) times higher risk of ASD, and a doubling of third trimester cesium with 1.69 (0.97, 2.95) times higher risk of ASD. CONCLUSION: Exposure in utero to elevated levels of cadmium and cesium, as measured in urine collected during pregnancy, was associated with increased risk of developing ASD.

Metal concentrations and KIM-1 levels in school-aged children: a cross-sectional study.

Environmental exposure to heavy metals and metalloids, originating from sources such as mining and manufacturing activities, has been linked to adverse renal effects. This cross-sectional study assessed children's exposure to these elements and its association with urinary kidney injury molecule-1 (KIM-1). We analyzed data from 99 school-aged children residing in nine localities within the state of Colima, Mexico, during the latter half of 2023. Levels of 23 metals/metalloids and urinary KIM-1 were measured using inductively coupled plasma mass spectrometry (ICP-MS) and enzyme-linked immunosorbent assay, respectively. Detectable levels of these contaminants were found in over 91% of participants, with varied exposure profiles observed across locations ( p = 0.019). After adjusting for confounding factors like gender, age, and locality, higher levels of six metals/metalloids (boron, cadmium, cesium, lithium, selenium, zinc) were significantly associated with increased KIM-1 levels. Tailored mitigation efforts are crucial to protect children from regional pollutant burdens. However, limitations exist, as our study did not capture all potential factors influencing heavy metal/metalloid and KIM-1 levels.

Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach.

Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.

Associations of mixed metal exposure with chronic kidney disease from NHANES 2011-2018.

Metals have been proved to be one of risk factors for chronic kidney disease (CKD) and diabetes, but the effect of mixed metal co-exposure and potential interaction between metals are still unclear. We assessed the urine and whole blood levels of cadmium (Cd), manganese (Mn), lead (Pb), mercury (Hg), and renal function in 3080 adults from National Health and Nutrition Survey (NHANES) (2011-2018) to explore the effect of mixed metal exposure on CKD especially in people with type 2 diabetes mellitus (T2DM). Weighted quantile sum regression model and Bayesian Kernel Machine Regression model were used to evaluate the overall exposure impact of metal mixture and potential interaction between metals. The results showed that the exposure to mixed metals was significantly associated with an increased risk of CKD in blood glucose stratification, with the risk of CKD being 1.58 (1.26,1.99) times in urine and 1.67 (1.19,2.34) times in whole blood higher in individuals exposed to high concentrations of the metal mixture compared to those exposed to low concentrations. The effect of urine metal mixture was elevated magnitude in stratified analysis. There were interactions between urine Pb and Cd, Pb and Mn, Pb and Hg, Cd and Mn, Cd and Hg, and blood Pb and Hg, Mn and Cd, Mn and Pb, Mn and Hg on the risk of CKD in patients with T2DM and no significant interaction between metals was observed in non-diabetics. In summary, mixed metal exposure increased the risk of CKD in patients with T2DM, and there were complex interactions between metals. More in-depth studies are needed to explore the mechanism and demonstrate the causal relationship.

Sex-specific associations of urinary mixed-metal concentrations with femoral bone mineral density among older people: an NHANES (2017-2020) analysis.

Background: Heavy metal exposure is an important cause of reduced bone mineral density (BMD). Epidemiological studies focusing on the effects of mixed heavy metal exposure on BMD in middle-aged and older people are scarce. In single-metal studies, men and women have shown distinct responses of BMD to environmental metal exposure. This study therefore aimed to elucidate the association between mixed heavy metal exposure and BMD and to investigate whether it is sex-specific. Methods: Data from the 2017-2020 National Health and Nutrition Examination Survey were selected for this cross-sectional study. The study used three statistical methods, i.e., linear regression, Bayesian kernel machine regression (BKMR) modeling, and weighted quartiles (WQS) regression, to explore the association between the urinary concentrations of 11 metals (barium, cadmium, cobalt, cesium, manganese, molybdenum, lead, antimony, tin, thallium, and Tungsten), either individually or as a mixture, and total femoral BMD. Results: A total of 1,031 participants were included in this study. Femoral BMD was found to be higher in men than women. A significant negative correlation between the urinary concentrations of the 10 metals and femoral BMD was found in the overall cohort. Further gender sub-stratified analyses showed that in men, urinary metal concentrations were negatively correlated with femoral BMD, with cobalt and barium playing a significant and non-linear role in this effect. In women, although urinary metal concentrations negatively modulated femoral BMD, none of the correlations was statistically significant. Antimony showed sex-specific differences in its effect. Conclusion: The urinary concentrations of 10 mixed heavy metals were negatively correlated with femoral BMD in middle-aged and older participants, and this effect showed gender differences. These findings emphasize the differing role of mixed metal exposure in the process of BMD reduction between the sexes but require further validation by prospective studies.

Interaction between depression and non-essential heavy metals (Cd, Pb, and Hg) on metabolic diseases.

OBJECTIVES: Metal exposure and depression have each been associated with adverse metabolic diseases, but no study has examined the potential interaction between them. We examined the interaction of depression on the association between metals and metabolic diseases among adults. STUDY DESIGN: The interaction of depression in the relationship between metal and metabolic disease in adults was investigated using NHANES, a cross-sectional survey design. METHODS: By employing data from the NHANES database spanning the years 2007-2018, regression models were employed to investigate the independent impacts of heavy metals (cadmium, lead, and mercury) and depression on metabolic diseases (type 2 diabetes, hypertension, hyperlipidemia, metabolic syndrome). Subsequently, the association between metals and metabolic diseases was explored stratified by depression, and the interaction between heavy metals and depression was explored. Because of the complex NHANES design, statistical evaluations were adjusted through weighting to represent the populace of the United States. RESULTS: We found log transformed-urinary lead was significantly associated with type 2 diabetes (OR: 2.33; 95 % CI: 1.23, 4.41) in adults with depression. Log transformed-urinary lead was not associated with type 2 diabetes (OR: 0.84; 95 % CI: 0.56, 1.27) in adults without depression. The interaction between Pb and depression in type 2 diabetes was significant (P for interaction = 0.033). Log transformed-urinary lead * depression was significantly associated with type 2 diabetes (OR: 1.82; 95 % CI: 1.01, 3.34) in adults. There was no significant interaction between cadmium and mercury exposure and depression in patients with type 2 diabetes, hypertension, hyperlipidemia, and metabolic syndrome (P for interaction > 0.05). CONCLUSIONS: The presence of depression positively modified the adverse associations between urinary lead and type 2 diabetes.