Assessing the Risk of Heart Attack: A Bayesian Kernel Machine Regression Analysis of Heavy Metal Mixtures.

Background: The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods: This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results: Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions: This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

Associations between urinary rare Earth elements with renal function: Findings from a cross-sectional study in Guangxi, China.

BACKGROUND: With increased applications of rare earth elements (REEs) across various industries, evaluating the relationship between REEs exposure and potential health effects has become a public concern. In vivo experiments have established that REEs impact renal function. However, relevant epidemiological evidence on this relationship remains scarce. The objective of this study is to examine the impact of exposure to REEs on renal function. METHODS: In this cross-sectional study, 1052 participants were recruited from Guangxi, China. We measured urinary concentrations of 12 REEs using an inductively coupled plasma-mass spectrometer (ICP-MS). Multiple linear regression models were developed to explore the relationship between a single REEs exposure and the estimated glomerular filtration rate (eGFR), a marker of renal function. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to examine the combined effects of REE co-exposure on eGFR. RESULTS: In the multiple linear regression analysis, increasing the concentrations of lanthanum (La, ß: 8.22, 95% CI: 5.67-10.77), cerium (Ce, ß:6.61, 95% CI: 3.80-9.43), praseodymium (Pr, ß: 8.46, 95% CI: 5.85-11.07), neodymium (Nd, ß:8.75, 95% CI: 6.10-11.41), and dysprosium (Dy, ß:7.38, 95% CI: 4.85-9.91) significantly increased the eGFR. In the WQS regression model, the WQS index was significantly associated with eGFR (ß: 4.03, 95% CI: 2.46-5.60), with Pr having the strongest correlation with eGFR. Similar results were obtained in the BKMR model. Additionally, interactions between Pr and La, and Pr and Nd were observed. CONCLUSIONS: Co-exposure to REEs is positively associated with elevated eGFR. Pr is likely to have the most significant influence on increased eGFRs and this might be exacerbated when interacting with La and Nd. Mixed exposure to low doses of REEs had a protective effect on renal function, which can provide some evidence for the exposure threshold of REEs in the environment. TRIAL REGISTRATION: The study has been approved by the Guangxi Medical University Medical Ethics Committee (#20170206-1), and all participants provided written informed consent.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Associations between co-exposure to per- and polyfluoroalkyl substances and organophosphate esters and erythrogram in Chinese adults.

Erythrogram, despite its prevalent use in assessing red blood cell (RBC) disorders and can be utilized to evaluate various diseases, still lacks evidence supporting the effects of per- and polyfluoroalkyl substances (PFASs) and organophosphate esters (OPEs) on it. A cross-sectional study involving 467 adults from Shijiazhuang, China was conducted to assess the associations between 12 PFASs and 11 OPEs and the erythrogram (8 indicators related to RBC). Three models, including multiple linear regression (MLR), sparse partial least squares regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate both the individual and joint effects of PFASs and OPEs on the erythrogram. Perfluorohexane sulfonic acid (PFHxS) showed the strongest association with HGB (3.68%, 95% CI: 2.29%, 5.10%) when doubling among PFASs in MLR models. BKMR indicated that PFASs were more strongly associated with the erythrogram than OPEs, as evidenced by higher group posterior inclusion probabilities (PIPs) for PFASs. Within hemoglobin and hematocrit, PFHxS emerged as the most significant component (conditional PIP = 1.0 for both). Collectively, our study emphasizes the joint effect of PFASs and OPEs on the erythrogram and identified PFASs, particularly PFHxS, as the pivotal contributors to the erythrogram. Nonetheless, further investigations are warranted to elucidate the underlying mechanisms.

Association between brominated flame retardants (PBDEs and PBB153) exposure and hypertension in U.S. adults: results from NHANES 2005-2016.

BACKGROUND: Brominated Flame Retardants (BFRs) have attracted widespread concern due to their environmental persistence and potential toxicity. This study aims to examine the association between BFRs exposure and hypertension. METHODS: We used data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005 to 2016 for the cross-sectional analysis. To evaluate the individual and combined impacts of BFRs exposure on hypertension, we utilized multivariate models, including generalized additive models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models. RESULTS: 9882 individuals (48% male) aged ≥ 20 were included in the final analysis, of whom 4114 had hypertension. After controlling for potential covariates, higher serum concentrations of PBDE100 (OR: 1.26; 95% CI: 1.01, 1.57) and PBDE153 (OR: 1.50; 95% CI: 1.18, 1.88) were significantly associated with hypertension. A nonlinear relationship between PBDE28 and hypertension was observed (P = 0.03). Moreover, BFRs mixture were positively associated with the prevalence of hypertension in both the WQS (ß:1.09; 95% CI: 1.02, 1.17; P = 0.02) and BKMR models. CONCLUSION: Our study suggested that BFRs exposure is positively associated with hypertension in the general population. To confirm this association and elucidate the mechanisms, further research is required.

Associations between specific volatile organic chemical exposures and cardiovascular disease risks: insights from NHANES.

Introduction: An increasing body of research has demonstrated a correlation between pollutants from the environment and the development of cardiovascular diseases (CVD). However, the impact of volatile organic chemicals (VOC) on CVD remains unknown and needs further investigation. Objectives: This study assessed whether exposure to VOC was associated with CVD in the general population. Methods: A cross-sectional analysis was conducted utilizing data from five survey cycles (2005-2006, 2011-2012, 2013-2014, 2015-2016, and 2017-2018) of the National Health and Nutrition Examination Survey (NHANES) program. We analyzed the association between urinary VOC metabolites (VOCs) and participants by multiple logistic regression models, further Bayesian Kernel Machine Regression (BKMR) models and Weighted Quantile Sum (WQS) regression were performed for mixture exposure analysis. Results: Total VOCs were found to be positively linked with CVD in multivariable-adjusted models (p for trend = 0.025), independent of established CVD risk variables, such as hypertension, diabetes, drinking and smoking, and total cholesterol levels. Compared with the reference quartile of total VOCs levels, the multivariable-adjusted odds ratios in increasing quartiles were 1.01 [95% confidence interval (CI): 0.78-1.31], 1.26 (95% CI: 1.05-1.21) and 1.75 (95% CI: 1.36-1.64) for total CVD. Similar positive associations were found when considering individual VOCs, including AAMA, CEMA, CYMA, 2HPMA, 3HPMA, IPM3 and MHBMA3 (acrolein, acrylamide, acrylonitrile, propylene oxide, isoprene, and 1,3-butadiene). In BKMR analysis, the overall effect of a mixture is significantly related to VOCs when all chemicals reach or exceed the 75th percentile. Moreover, in the WQS models, the most influential VOCs were found to be CEMA (40.30%), DHBMA (21.00%), and AMCC (19.70%). Conclusion: The results of our study indicated that VOC was all found to have a significant association with CVD when comparing results from different models. These findings hold significant potential for public health implications and offer valuable insights for future research directions.

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.

Environmental exposures to organophosphorus flame retardants in early pregnancy and risks of gestational diabetes mellitus: a nested case-control study.

OPFRs are emerging environmental pollutants with reproductive and endocrine toxicity. This study aimed to examine the association between environmental exposure to OPFRs during early pregnancy and GDM. This nested case-control study was based on a birth cohort that was constructed at a maternal and child health hospital, including 74 cases of GDM among 512 pregnant women. The OPFRs, including TBP, TBEP, TCEP, TDCPP, TMCP, TOCP, and TPHP during 10-14 weeks of pregnancy were determined using GC-MS. The association between the OPFRs and GDM was assessed using WQS and BKMR models. The levels of OPFRs were significantly elevated in GDM patients (60) compared with the controls (90). The WQS analysis showed that mixtures of the OPFRs were significantly associated with GDM (OR 1.370, 95% CI 1.036-1.810, P = 0.027), and TBP, TPHP, and TMCP were the major contributors to the mixed exposure effect. In the BKMR model, individual exposure to TBP, TPHP, and TMCP, and the interaction of TMCP with TBP and TPHP were significantly associated with GDM. Environmental exposure to OPFRs is positively associated with GDM. These findings provide evidence for the adverse effects of OPFR exposure on the health of pregnant women.

Association between pesticide exposure and thyroid function: analysis of Chinese and NHANES databases.

Background: Pesticides are widely used in agricultural activities. Although pesticide use is known to cause damage to the human body, its relationship with thyroid function remains unclear. Therefore, this study aimed to investigate the association between pesticide exposure and thyroid function. Methods: The Chinese database used included 60 patients with pyrethroid poisoning and 60 participants who underwent health checkups between June 2022 and June 2023. The NHANES database included 1,315 adults enrolled from 2007 to 2012. The assessed pesticide and their metabolites included 2,4-dichlorophenoxyacetic acid (2,4-D), 4-fluoro-3-phenoxybenzoic acid (4F3PB), para-nitrophenol (PN), 3-phenoxybenzoic acid (3P), and trans-dichlorovinyl-dimethylcyclopropane carboxylic acid (TDDC). The evaluated indicators of thyroid function were measured by the blood from the included population. The relationship between pesticide exposure and thyroid function indexes was investigated using linear regression, Bayesian kernel machine regression (BKMR), restricted cubic spline (RCS), and weighted quantile sum (WQS) models. Results: The Chinese data showed that pesticide exposure was negatively correlated with the thyroid function indicators FT4, TT4, TgAb, and TPOAb (all p < 0.05). The BKMR model analysis of the NHANES data showed that the metabolic mixture of multiple pesticides was negatively associated with FT4, TSH, and Tg, similar to the Chinese database findings. Additionally, linear regression analysis demonstrated positive correlations between 2,4-D and FT3 (p = 0.041) and 4F3PB and FT4 (p = 0.003), whereas negative associations were observed between 4F3PB and Tg (p = 0.001), 4F3PB and TgAb (p = 0.006), 3P and TgAB (p = 0.006), 3P and TPOAb (p = 0.03), PN and TSH (p = 0.003), PN and TT4 (p = 0.031), and TDDC and TPOAb (p < 0.001). RCS curves highlighted that most pesticide metabolites were negatively correlated with thyroid function indicators. Finally, WQS model analysis revealed significant differences in the weights of different pesticide metabolites on the thyroid function indexes. Conclusion: There is a significant negative correlation between pesticide metabolites and thyroid function indicators, and the influence weights of different pesticide metabolites on thyroid function indicators are significantly different. More research is needed to further validate the association between different pesticide metabolites and thyroid disease.

Associations of Urinary Heavy Metal Mixtures with High Remnant Cholesterol among US Adults: Evidence from the National Health and Nutrition Examination Survey (1998-2018).

The main objective of our study is to explore the associations between combined exposure to urinary heavy metals and high remnant cholesterol (HRC), a known cardiovascular risk factor. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we conducted a cross-sectional analysis of 5690 participants, assessing urinary concentrations of ten heavy metals. Ten heavy metals in urine were measured by inductively coupled plasma mass spectrometry (ICP-MS). Fasting residual cholesterol ≥0.8 mmol/L was defined as HRC (using blood samples). Statistical analyses included weighted multivariable logistic regression, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) to evaluate the associations of heavy metal exposure with HRC. Stratified analyses based on individual characteristics were also conducted. Multivariable logistic regression found that the four metals (OR Q4 vs. Q1: 1.33, 95% CI: 1.01-1.75 for barium (Ba); OR Q4 vs. Q1: 1.50, 95% CI: 1.16-1.94 for cadmium (Cd); OR Q4 vs. Q1: 1.52, 95% CI: 1.15-2.01 for mercury (Hg); OR Q4 vs. Q1: 1.35, 95% CI: 1.06-1.73 for lead (Pb)) were positively correlated with the elevated risk of HRC after adjusting for covariates. In addition, all three mixed models, including WQS (OR: 1.25; 95% CI: 1.07-1.46), qgcomp (OR: 1.17; 95% CI: 1.03-1.34), and BKMR, consistently showed a significant positive correlation between co-exposure to heavy metal mixtures and HRC, with Ba and Cd being the main contributors within the mixture. These associations were more pronounced in younger adults (20 to 59 years), males, and those with a higher body mass index status (≥25 kg/m2). Our findings reveal a significant relationship between exposure to the mixture of heavy metals and HRC among US adults, with Ba and Cd being the major contributors to the mixture's overall effect. Public health efforts aimed at reducing heavy metal exposure can help prevent HRC and, in turn, cardiovascular disease.

Investigating the Interplay of Toxic Metals and Essential Elements in Liver Disease.

Liver diseases, including non-alcoholic fatty liver disease (NAFLD), are a growing global health issue. Environmental exposure to toxic metals can harm the liver, increasing the risk of NAFLD. Essential elements are vital for liver health, but imbalances or deficiencies can contribute to the development of NAFLD. Therefore, understanding the interplay between toxic metals and essential elements in liver disease is important. This study aims to assess the individual and combined effects of toxic metals (lead(Pb), cadmium (Cd), mercury (Hg)), and essential elements (manganese and selenium) on the risk of liver disease. Methods: We assessed the individual and combined effects of Pb, Cd, Hg, manganese (Mn), and selenium (Se) on liver disease risk using data from the National Health and Nutrition Examination Survey between 2017 and 2018. We performed descriptive statistics and linear regression analysis and then utilized Bayesian Kernel Machine Regression (BKMR) techniques such as univariate, bivariate, and overall effect analysis. BKMR enabled the assessment of non-linear exposure-response functions and interactions between metals and essential elements. Posterior Inclusion Probabilities (PIPs) were calculated to determine the importance of each metal and essential element in contributing to liver disease. Regarding our study results, the regression analysis of liver injury biomarkers ALT, AST, ALP, GGT, total bilirubin, and the FLI-an indicator of NAFLD-with toxic metals and essential elements, adjusting for covariates such as age, sex, BMI, alcohol consumption, ethnicity, income, and smoking status, demonstrated the differential effects of these contaminants on the markers of interest. Our BKMR analysis provided further insights. For instance, the PIP results underscored Pb's consistent importance in contributing to liver disease (PIP = 1.000), followed by Hg (PIP = 0.9512), Cd (PIP = 0.5796), Se (PIP = 0.5572), and Mn (PIP = 0.4248). Our univariate analysis showed a positive trend with Pb, while other exposures were relatively flat. Our analysis of the single-variable effects of toxic metals and essential elements on NAFLD also revealed that Pb significantly affected the risk of NAFLD. Our bivariate analysis found a positive (toxic) trend when Pb was combined with other metals and essential elements. For the overall exposure effect of exposure to all the contaminants together, the estimated risk of NAFLD showed a steady increase from the 60th to the 75th percentile. In conclusion, our study indicates that Pb exposure, when combined with other toxic metals and essential elements, plays a significant role in bringing about adverse liver disease outcomes.

Seasonal variation in urinary PAH metabolite levels and associations with neonatal birth outcomes.

Previous studies have demonstrated that exposure to polycyclic aromatic hydrocarbons (PAHs) can affect maternal and infant health. However, the conclusions regarding the effects of seasonal PAH exposure on maternal and infant health have been inconsistent. To further elucidate this issue, this study included data from 2282 mother-infant pairs in the Zuni birth cohort. The objective was to investigate the association between maternal late-pregnancy urinary PAH metabolite concentrations and neonatal birth outcomes during the heating and non-heating seasons. The results demonstrated that PAH exposure in Zunyi was primarily dominated by 2-OHNAP and 1-OHNAP and that the concentrations of PAH metabolites were significantly higher during the heating season. Furthermore, PAH metabolite exposure was found to affect neonatal birth weight, birth length, and parity index with seasonal differences. Further dose-effect analyses revealed nonlinear relationships and seasonal differences between PAH metabolites and neonatal birth weight, birth length, and parity index. Bayesian kernel mechanism regression modeling demonstrated that the inverted U-shaped relationship between PAH metabolites and neonatal birth weight and parity index was exclusive to the heating season. Consequently, it can be posited that maternal exposure to PAH metabolites during late pregnancy exerts a detrimental influence on neonatal growth and development, which is further compounded by the use of heating fuels. This highlights the necessity to either control or alter the use of heating fuels during pregnancy.

Associations between exposure to pesticides mixture and semen quality among the non-occupationally exposed males: Four statistical models.

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, ß-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of ß-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (ß: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality.

Effects of exposure to multiple metallic elements in the first trimester of pregnancy on the risk of preterm birth.

Exposure to certain heavy metals has been demonstrated to be associated with a higher risk of preterm birth (PTB). However, studies focused on the effects of other metal mixtures were limited. A nested case‒control study enrolling 94 PTB cases and 282 controls was conducted. Metallic elements were detected in maternal plasma collected in the first trimester using inductively coupled plasma‒mass spectrometry. The effect of maternal exposure on the risk of PTB was investigated using logistic regression, least absolute shrinkage and selection operator, restricted cubic spline (RCS), quantile g computation (QGC) and Bayesian kernel machine regression (BKMR). Vanadium (V) and arsenic (As) were positively associated with PTB risk in the logistic model, and V remains positively associated in the multi-exposure logistic model. QGC analysis determined V (69.42%) and nickel (Ni) (70.30%) as the maximum positive and negative contributors to the PTB risk, respectively. BKMR models further demonstrated a positive relationship between the exposure levels of the mixtures and PTB risk, and V was identified as the most important independent variable among the elements. RCS analysis showed an inverted U-shape effect of V and gestational age, and plasma V more than 2.18 µg/L was considered a risk factor for shortened gestation length. Exposure to metallic elements mixtures consisting of V, As, cobalt, Ni, chromium and manganese in the first trimester was associated with an increased risk of PTB, and V was considered the most important factor in the mixtures in promoting the incidence of PTB.

Urinary volatile organic compound metabolites and COPD among US adults: mixture, interaction and mediation analysis.

BACKGROUND: Volatile organic compounds (VOCs) encompass hundreds of high production volume chemicals and have been reported to be associated with adverse respiratory outcomes such as chronic obstructive pulmonary disease (COPD). However, research on the combined toxic effects of exposure to various VOCs on COPD is lacking. We aimed to assess the effect of VOC metabolite mixture on COPD risk in a large population sample. METHODS: We assessed the effect of VOC metabolite mixture on COPD risk in 5997 adults from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020 (pre-pandemic) using multivariate logistic regression, Bayesian weighted quantile sum regression (BWQS), quantile-based g-Computation method (Qgcomp), and Bayesian kernel machine regression (BKMR). We explored whether these associations were mediated by white blood cell (WBC) count and total bilirubin. RESULTS: In the logistic regression model, we observed a significantly increased risk of COPD associated with 9 VOC metabolites. Conversely, N-acetyl-S-(benzyl)-L-cysteine (BMA) and N-acetyl-S-(n-propyl)-L-cysteine (BPMA) showed insignificant negative correlations with COPD risk. The overall mixture exposure demonstrated a significant positive relationship with COPD in both the BWQS model (adjusted odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.06, 1.58) and BKMR model, and with marginal significance in the Qgcomp model (adjusted OR = 1.22, 95% CI: 0.98, 1.52). All three models indicated a significant effect of the VOC metabolite mixture on COPD in non-current smokers. WBC count mediated 7.1% of the VOC mixture associated-COPD in non-current smokers. CONCLUSIONS: Our findings provide novel evidence suggesting that VOCs may have adverse associations with COPD in the general population, with N, N- Dimethylformamide and 1,3-Butadiene contributing most. These findings underscore the significance of understanding the potential health risks associated with VOC mixture and emphasize the need for targeted interventions to mitigate the adverse effects on COPD risk.

Association of plasma trans fatty acid concentrations with blood pressure and hypertension in U.S. adults.

Objective: The present study aimed to evaluate the association of plasma trans fatty acids (TFAs) biomarkers with the risk of hypertension. Methods: Using data from the National Health and Nutrition Examination Surveys (NHANES 2009-2010), we conducted a thorough analysis using both the traditional regression model and the Bayesian Kernel Machine Regression (BKMR) model to investigate the associations of individual TFAs and their mixtures with systolic blood pressure (SBP), diastolic blood pressure (DBP), and the risk of hypertension in a sample of 1,970 American adults. Results: The concentrations of TFAs were natural logarithms (ln) transformed to approximate a normal distribution. Multivariate linear regression models showed that each 1-unit increase in ln-transformed plasma concentrations of palmitelaidic, elaidic, vaccenic, and linolelaidic acids was associated with separate 2.94-, 3.60-, 2.46- and 4.78-mm Hg and 2.77-, 2.35-, 2.03-, and 3.70- mm Hg increase in SBP and DBP, respectively (P < 0.05). The BKMR model showed positive associations between the four TFAs mixtures and SBP and DBP. In addition, linolelaidic acid contributed the most to an increased blood pressure. Similar results were observed with the threshold of hypertension (≥130/80 mm Hg). Conclusion: Our findings provide preliminary evidence that plasma TFA concentrations are associated with increased blood pressure and the risk of hypertension in US adults. This study also suggests that linolelaidic acid might exhibit more deleterious effects on hypertension than other TFAs. Further studies should be conducted to validate these results.

Associations of single and multiple vitamin exposure with childhood eczema: data from the national health and nutrition examination survey.

Aim: The association between vitamins and eczema has garnered attention, yet few studies have evaluated the effects of co-exposure to multiple vitamins on this condition. This study aims to assess the association of vitamin mixtures with eczema in children. Methods: This cross-sectional study analyzed data from 2,244 children aged 6-17 years from the National Health and Nutrition Examination Surveys. Eczema served as the primary outcome. Six serum vitamins, namely, vitamins A, B6, B12, C, D, and E, were the main variables. Weighted multivariate logistic regression was adopted to analyze the association between each serum vitamin and eczema. Odds ratios (OR) with a 95% confidence interval (CI) were calculated. Bayesian kernel machine regression (BKMR) analysis and the quantile g-computation (qgcomp) model were used to evaluate the association of co-exposure to multiple vitamins with eczema. Results: In total, 10.83% of children (n = 243) developed eczema. After adjusting for confounding factors, we observed that compared with the reference group (vitamin B12 with second quartile), the OR for eczema was 0.604 (95% CI: 0.373-0.978, P = 0.041) for the first quartile of vitamin B12. Both BKMR analysis and the qgcomp model consistently showed that co-exposure to the six vitamins was positively correlated with the risk of eczema, with vitamin B6 contributing most to the overall effect. In BKMR analyses, we observed an interaction between vitamins B6 and B12 concerning eczema risk. Conclusion: Co-exposure to vitamins A, C, B6, B12, D, and E was found to be associated with an increased risk of eczema in children, with vitamin B6 as the greatest positive contributor driving the overall effect.

Association between concentrations of rare earth elements in chorionic villus and risk for unexplained spontaneous abortion.

Rare earth elements (REEs) exposure during pregnancy may increase the risk of unexplained spontaneous abortion. However, the association between REEs intrauterine exposure and unexplained spontaneous abortion had yet to be studied. In order to conduct this large case-control study, we thus collected chorionic villus from 641 unexplained spontaneous abortion and 299 control pregnant women and detected the concentrations of 15 REEs by inductively coupled plasma mass spectrometer (ICP-MS). Because the detection rates of 10 REEs were less than 80%, the remaining 5 REEs, which were lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd) and yttrium (Y), underwent to further analysis. The association between 5 REEs and unexplained spontaneous abortion was assessed by using the logistic regression, bayesian kernel regression (BKMR) and weighted quantile sum regression (WQS) models. In the adjusted logistic regression model, Pr, Nd and Y enhanced the incidence of unexplained spontaneous abortion in a dose-dependent way and Ce increased the risk only at high concentration group. The result of BKMR model demonstrated that the risk of unexplained spontaneous abortion increased as the percentile of five mixed REEs increased. Y and Nd were both significantly associated with an increased incidence of unexplained spontaneous abortion, but La was correlated with a decrease in the risk of unexplained spontaneous abortion. Pr was substantially associated with an increase in the risk of unexplained spontaneous abortion when other REEs concentrations were fixed at the 25th and 50th percentiles. According to WQS regression analysis, the WQS index was significantly associated with unexplained spontaneous abortion (OR = 3.75, 95% CI:2.40-5.86). Y had the highest weight, followed by Nd and Pr, which was consistent with the analysis results of our other two models. In short, intrauterine exposure to REEs was associated with an increased risk of unexplained spontaneous abortion, with Y, Nd and Pr perhaps playing an essential role.

Alkali and alkaline earth elements in follicular fluid and the likelihood of diminished ovarian reserve in reproductive-aged women: a case‒control study.

BACKGROUND: Imbalances in alkali elements (AEs) and alkaline earth elements (AEEs) cause reproductive disorders. However, it remains unclear whether AEs/AEEs in follicular fluid have a relationship with the serious reproductive disorder known as diminished ovarian reserve (DOR). METHODS: A nested case‒control study was carried out in China. Follicular fluid samples from 154 DOR patients and 154 controls were collected and assessed for nine AEs/AEE levels. Both the mixed and single effects of the elements on DOR were estimated with a Bayesian kernel machine (BKMR) and logistic regressions. RESULTS: The DOR group had higher median concentrations of Li, Na, and K in follicular fluid (all P values < 0.05). The logistic regression showed that compared with their lowest tertile, the high tertiles of K [OR:2.45 (1.67-4.43)], Li [OR: 1.89 (1.06-3.42)], and Cs [OR: 1.97 (1.10-3.54)] were significantly associated with the odds of DOR. The BKMR model reported that the DOR likelihood increased linearly across the 25th through 75th percentiles of the nine-AE/AEE mixture, while the AE group contributed more to the overall effect. CONCLUSION: This study revealed an association in which the likelihood of DOR increased with higher overall concentrations of AE/AEEs in follicular fluid. Among the nine detected elements, K, Li, and Cs exhibited significant individual associations with DOR. We provide new clues for the environmental factors on female fertility decline. TRIAL REGISTRATION: Retrospectively registered.

Comparative mathematical modeling of causal association between metal exposure and development of chronic kidney disease.

Background: Previous studies have identified several genetic and environmental risk factors for chronic kidney disease (CKD). However, little is known about the relationship between serum metals and CKD risk. Methods: We investigated associations between serum metals levels and CKD risk among 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyzed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Metal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to assess associations and predict CKD risk based on serum metals. A chained mediation model was applied to investigate how interventions with different heavy metals influence renal function indicators (creatinine and cystatin C) and their impact on diagnosing and treating renal impairment. Results: Serum potassium (K), sodium (Na), and calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybdenum (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30th to 45th percentiles compared to the median, but the opposite was observed for the 55th to 60th percentiles. For example, a change in serum K concentration from the 25th to the 75th percentile was associated with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25th, 50th and 75th percentiles, respectively. Conclusions: Cumulative metal exposures, especially double-exposure to serum K and Se may impact CKD risk. Machine learning methods validated the external relevance of the metal factors. Our study highlights the importance of employing diverse methodologies to evaluate health effects of metal mixtures.