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.

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.

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.

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.

Environmental pollutant risk factors for worse COVID-19 related clinical outcomes in predominately hispanic and latino populations.

BACKGROUND: Per- and poly-fluorinated compounds (PFAS) and heavy metals constitute two classes of environmental exposures with known immunotoxicant effects. In this pilot study, we aimed to evaluate the impact of exposure to heavy metals and PFAS on COVID-19 severity. We hypothesized that elevated plasma-PFAS concentrations and urinary heavy metal concentrations would be associated with increased odds of ICU admission in COVID-19 hospitalized individuals. METHODS: Using the University of Southern California Clinical Translational Sciences Institute (SC-CTSI) biorepository of hospitalized COVID-19 patients, urinary concentrations of 15 heavy metals and urinary creatinine were measured in n = 101 patients and plasma concentrations of 13 PFAS were measured in n = 126 patients. COVID-19 severity was determined based on whether a patient was admitted to the ICU during hospitalization. Associations of metals and PFAS with ICU admission were assessed using logistic regression models, controlling for age, sex, ethnicity, smoking status, and for metals, urinary dilution. RESULTS: The average age of patients was 55 ± 14.2 years. Among SC-CTSI participants with urinary measurement of heavy metals and blood measures of PFAS, 54.5% (n = 61) and 54.8% (n = 80) were admitted to the ICU, respectively. For heavy metals, we observed higher levels of Cd, Cr, and Cu in ICU patients. The strongest associations were with Cadmium (Cd). After accounting for covariates, each 1 SD increase in Cd resulted in a 2.00 (95% CI: 1.10-3.60; p = 0.03) times higher odds of admission to the ICU. When including only Hispanic or Latino participants, the effect estimates between cadmium and ICU admission remained similar. Results for PFAS were less consistent, with perfluorodecanesulfonic acid (PFDS) exhibiting a positive but non-significant association with ICU admission (Odds ratio, 95% CI: 1.50, 0.97-2.20) and perfluorodecanoic acid (PFDA) exhibiting a negative association with ICU admission (0.53, 0.31-0.88). CONCLUSIONS: This study supports the hypothesis that environmental exposures may impact COVID-19 severity.

Hormonal, liver, and renal function associated with electronic waste (e-waste) exposure in Dhaka, Bangladesh.

Electronic waste (e-waste) contains numerous metals and organic pollutants that have detrimental impacts on human health. We studied 199 e-waste recycling workers and 104 non-exposed workers; analyzed blood, urine, and hair samples to measure heavy metals, hormonal, liver, and renal function. We used quantile regression models to evaluate the impact of Pb, Cd, and Hg on hormonal, liver and renal function, and the role of DNA oxidative damage in mediating the relationship between exposures and outcomes. Exposed workers had higher blood lead (Pb) (median 11.89 vs 3.63 µg/dL), similar blood cadmium (Cd) (1.04 vs 0.99 µg/L) and lower total mercury (Hg) in hair (0.38 vs 0.57 ppm) than non-exposed group. Exposed workers also had elevated median concentrations of total triiodothyronine (TT3), aspartate aminotransferase (AST), alanine aminotransferase (ALT), urinary albumin, albumin creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) were significantly higher than non-exposed group (p≤0.05). Sex hormones including luteinizing hormone, follicle stimulating hormone, estrogen, progesterone and testosterone concentrations were not significantly different between exposed and non-exposed (all p≥0.05). The median concentration of ALT was 4.00 (95% CI: 0.23, 7.77), urinary albumin was 0.09 (95% CI: 0.06, 0.12) and ACR was 1.31 (95% CI: 0.57, 2.05) units higher in the exposed group compared to non-exposed group. Pb was associated with a 3.67 unit increase in the ALP (95% CI: 1.53, 5.80), 0.01 unit increase in urinary albumin (95% CI: 0.002, 0.01), and 0.07 unit increase in ACR (95% CI: 0.01, 0.13). However, no hormonal, renal, and hepatic parameters were associated with Cd or Hg. Oxidative DNA damage did not mediate exposure-outcome relationships (p≥0.05). Our data indicate e-waste exposure impairs liver and renal functions secondary to elevated Pb levels. Continuous monitoring, longitudinal studies to evaluate the dose-response relationship and effective control measure are required to protect workers from e-waste exposure.

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.

Predictors of urinary heavy metal concentrations among pregnant women in Jinan, China.

BACKGROUND: Toxic heavy metal exposure and insufficiency or excess of essential heavy metals may have negative effects on pregnant women's health and fetal growth. To date, the predictors of pregnant women's heavy metal exposure levels remain unclear and vary with different regions. The study intended to explore potential predictors of exposure to heavy metals individually and high co-exposure to heavy metal mixtures. METHODS: We recruited 298 pregnant women in first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected spot urine samples and questionnaire data on their demographic characteristics, lifestyle habits, consumption of food and dietary supplement, and residential environment. All urine samples were analyzed for seven heavy metals: cobalt (Co), molybdenum (Mo), strontium (Sr), arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg). RESULTS: Factors associated with single heavy metal concentration were as follows: a) urinary As, Sr and Cd increased with women's age respectively; b) pregnant women with higher monthly household income per capita had lower Sr and Mo levels; c) pregnant women with intermittent folic acid supplementation and those not taking tap water as domestic drinking water had lower Sr concentrations; d) Cd was positively linked with consumption frequency of rice; e) Hg was adversely related to consumption frequency of egg and the women who took purified water as domestic drinking water had lower Hg exposure. In addition, pregnant women's age was positively associated with odds of high co-exposure to Co, As, Sr, Mo, Cd and Pb; while those with an educational level of college had lower odds of high exposure to such a metal mixture compared with those whose educational levels were lower than high school. CONCLUSION: Predictors of single urinary heavy metal concentration included pregnant women's age (As, Sr and Cd), monthly household income per capita (Sr and Mo), folic acid supplementation (Sr), rice consumption frequency (Cd), egg consumption frequency (Hg) and the type of domestic drinking water (Sr and Hg). Pregnant women with older age, lower educational level tended to have high co-exposure to Co, As, Sr, Mo, Cd and Pb.

The association of cadmium heavy metal with growth failure in children: A systematic review and meta-analysis.

Introduction: Introduction: early exposure to cadmium toxic metal has been suggested to be associated with reduced infants/children growth; nevertheless, the available evidence is contradictory. Objective: this meta-analysis aimed to examine the association of cadmium exposure through biological samples to growth measurements of infants/children, including body weight, height, body mass index (BMI), BMI-for-age (BMI Z-score), weight-for-age (WAZ), height-for-age (HAZ), and weight-for-height (WHZ) z-scores. Methods: a systematic search in PubMed and Scopus was implemented to obtain the related studies. The standardized beta coefficients (ß) and 95 % confidence intervals (95 % CI) were used as effect sizes to test the associations using the random effects analysis. Results: a total of 15 studies with 6,181 participants were included in the meta-analysis. In the overall analysis, pooled analysis of available data revealed that cadmium exposure was inversely linked to height (ß = -0.06, 95 % CI = -0.12 to -0.01) and WAZ (ß = -0.01, 95 % CI = -0.02 to -0.003). These relationships were also supported by prospective cohort studies and urinary cadmium exposure. In the stratified analysis, cadmium exposure was negatively linked to the weight of children in prospective cohort studies, in studies that assessed urinary cadmium exposure. No significant association was detected between cadmium exposure and BMI, BMI Z-score, WHZ, and HAZ in the overall and subgroup analyses. Conclusions: this meta-analysis emphasized the importance of cadmium exposure as a risk factor for growth failure in infants/children.

Introducción: Introducción: se ha sugerido que la exposición temprana al metal tóxico cadmio se asocia a un crecimiento reducido de los bebés y niños; sin embargo, la evidencia disponible es contradictoria. Objetivo: este metanálisis tuvo como objetivo examinar la asociación de la exposición al cadmio a través de muestras biológicas con las mediciones de crecimiento de bebés/niños, incluidos el peso corporal, la altura, el índice de masa corporal (IMC), el IMC para la edad (puntuación Z del IMC) y las puntuaciones z de peso para la edad (WAZ), altura para la edad (HAZ) y peso para la altura (WHZ). Métodos: se implementó una búsqueda sistemática en PubMed y Scopus para obtener los estudios relacionados. Los coeficientes beta estandarizados (ß) y los intervalos de confianza del 95 % (IC 95 %) se utilizaron como tamaños del efecto para probar las asociaciones mediante el análisis de efectos aleatorios. Resultados: se incluyeron en el metanálisis un total de 15 estudios, con 6.181 participantes. En el análisis general, el análisis conjunto de los datos disponibles reveló que la exposición al cadmio estaba inversamente relacionada con la altura (ß = -0,06, IC del 95 % = -0,12 a -0,01) y WAZ (ß = -0,01, IC del 95 % = -0,02 a -0,003). Estas relaciones también fueron respaldadas por estudios de cohortes prospectivos y exposición al cadmio en orina. En el análisis estratificado, la exposición al cadmio se relacionó negativamente con el peso de los niños en estudios de cohortes prospectivos, en estudios que evaluaron la exposición al cadmio en la orina. No se detectó ninguna asociación significativa entre la exposición al cadmio y el IMC, la puntuación Z del IMC, el WHZ y el HAZ en los análisis generales y de subgrupos. Conclusiones: este metanálisis enfatizó la importancia de la exposición al cadmio como factor de riesgo del retraso del crecimiento en lactantes/niños.

Atherogenic Index as a Cardiovascular Biomarker in Mexican Workers from Marginalized Urban Areas Occupationally Exposed to Metals.

BACKGROUND: Cardiovascular disease (CVD) is one of the main causes of death and disability worldwide. The etiology of CVD is often associated with multiple risk factors, with environmental factors receiving considerable attention. Individuals with precarious jobs are among the groups most affected by chronic exposure to environmental pollutants. AIM: This study aimed to evaluate occupational exposure to heavy metals among individuals in precarious job settings and investigate atherogenic indices as biomarkers of cardiovascular risk. METHODS: A total of 137 workers participated in this cross-sectional study conducted in three work environments in San Luis Potosi, Mexico. Urine and blood samples were collected to assess metal exposure and biochemical profiles, including atherogenic indices. RESULTS: The results showed that workers in the brick sector exhibited the highest levels of metal exposure, particularly arsenic (44.06 µg/L), followed by stonecutters and garbage collectors (24.7 and 16.9 µg/L, respectively). Similarly, Castelli risk index (CRI) and the atherogenic index of plasma (AIP) were higher in brickmakers (3.883 and 0.499) compared to stonecutters (3.285 and 0.386) and garbage collectors (3.329 and 0.367). CONCLUSIONS: Evidence of exposure to heavy metals was observed in the three populations, in addition to the fact that individuals with greater exposure to arsenic also exhibited higher CRI and AIP.

Heavy metals and metalloids exposure and liver function in Chinese adults - A nationally representative cross-sectional study.

BACKGROUND: In China, the effects of heavy metals and metalloids (HMMs) on liver health are not consistently documented, despite their prevalent environmental presence. OBJECTIVE: Our research assessed the association between HMMs and liver function biomarkers in a comprehensive sample of Chinese adults. METHODS: We analyzed data from 9445 participants in the China National Human Biomonitoring survey. Blood and urine were evaluated for HMM concentrations, and liver health was gauged using serum albumin (ALB), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) metrics. Various statistical methods were employed to understand the relationship between 11 HMMs and liver function, adjusting for multiple factors. We also explored interactions with alcohol intake, gender, and age. RESULTS: Among HMMs, selenium in blood [weighted geometric mean (GM) = 95.56 µg/L] and molybdenum in urine (GM = 46.44 µg/L) showed the highest concentrations, while lead in blood (GM = 21.92 µg/L) and arsenic in urine (GM = 19.80 µg/L) had the highest levels among risk HMMs. Manganese and thallium consistently indicated potential risk factor to liver in both sample types, while selenium displayed potential liver protection. Blood HMM mixtures were negatively associated with ALB (ß = -0.614, 95% CI: -0.809, -0.418) and positively with AST (ß = 0.701, 95% CI: 0.290, 1.111). No significant associations were found in urine HMM mixtures. Manganese, tin, nickel, and selenium were notable in blood mixture associations, with selenium and cobalt being significant in urine. The relationship of certain HMMs varied based on alcohol consumption. CONCLUSION: This research highlights the complex relationship between HMM exposure and liver health in Chinese adults, particularly emphasizing metals like manganese, thallium, and selenium. The results suggest a need for public health attention to low dose HMM exposure and underscore the potential benefits of selenium for liver health. Further studies are essential to establish causality.

Reduction of haemoglobin is related to metal mixtures exposure in Chinese preschoolers: Joint effect models.

Heavy metal exposure is a known risk factor for hematologic disorders in children, yet the impact of co-exposure to multiple metals remains underexplored. This cross-sectional study investigates the relationship between urinary levels of 23 metals and haemoglobin (Hb) in 1460 Chinese preschoolers. The concentrations of the 23 urinary metals were quantified using an inductively coupled plasma mass spectrometer, while Hb levels were assessed through finger prick blood samples. To evaluate the co-exposure effects, we employed three approaches: Generalized linear regression model, joint effect models including Quantile g-Computation and Bayesian Kernel Machine Regression (BKMR). From the generalized linear regression and Quantile g-computation, urinary uranium, thallium, aluminium, iron and tungsten were correlated negatively with Hb, while urinary barium was correlated positively (all P < 0.05). Moreover, significant negative associations between metal mixtures exposure with Hb were identified in both Quantile g-computation [ß (95% CI): -0.083 (-0.132, -0.033), P = 0.0012] and BKMR [90th percentile vs. 50th percentile ß (95% CI): -0.238 (-0.368, -0.107), P < 0.001] with aluminium emerging as the primary contributor to this joint effect (weight in Quantile g-computation = 0.399, PIPs in BKMR = 0.896). These findings provide a potential explanation for environmental exposure to metals and Hb-related disease in preschoolers.

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.

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.

There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013-2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best-performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772-0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (-0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA-XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

Associations of exposure to heavy metal mixtures with kidney stone among U.S. adults: A cross-sectional study.

Assessing the effects of heavy metals (HMs) on kidney stone is often limited to analyzing individual metal exposures, with studies on the effects of exposure to mixtures of HMs being scarce. To comprehensively evaluate the relationship between exposure to mixed HMs and kidney stones, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2007-2016, which included 7809 adults. We used multiple statistical methods, including multiple logistic regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp) and bayesian kernel machine regression (BKMR), to assess the association between single HM and mixed exposure to HMs and kidney stones. Firstly, in single exposure analysis, urinary cadmium (Cd) and cobalt (Co) demonstrated a positive association with the risk of kidney stones. Secondly, various other approaches consistently revealed that mixed exposure to HMs exhibited a positive association with kidney stone risk, primarily driven by Cd, Co, and barium (Ba) in urine, with these associations being particularly notable among the elderly population. Finally, both BKMR and survey-weighted generalized linear models consistently demonstrated a significant synergistic effect between urinary Co and urinary uranium (Ur) in elevating the risk of kidney stones. Overall, this study provides new epidemiological evidence that mixed exposure to HMs is associated with an increased risk of kidney stones. Further prospectively designed studies are needed to confirm these findings.