Bayesian Kernel Machine Regression Models Research Articles

The Associations between Exposure to Multiple Heavy Metals and Total Immunoglobulin E in U.S. Adults.

Immunoglobulin E (IgE) is a type of immunoglobulin, and elevated serum total IgE is often present in allergic diseases. Exposure to environmental heavy metals has been markedly linked to allergic diseases, leading to elevated total IgE levels. However, studies concerning the effects of multiple metal exposures on total IgE levels are limited. Therefore, the current study seeks to explore the correlation between heavy-metal co-exposure and total IgE levels based on the National Health and Nutrition Examination Survey (NHANES, 2005-2006). Participants possessed complete data on total IgE levels, 11 urinary metal concentrations and other covariates. The correlations between 11 metals and total IgE levels were analyzed using multiple linear regression, and total IgE levels were a continuous variable. Total IgE levels exceeding 150 kU/L were considered sensitized. Binary logistic regression analyses were employed to assess the correlation between metal exposure and the occurrence of an allergic state. Then, the association between co-exposure to the 11 metals and total IgE levels or the occurrence of sensitization status was further analyzed by Bayesian kernel machine regression (BKMR), a multi-contaminant model. There were 1429 adults with complete data included. Based on the median concentration, molybdenum (Mo) had the highest concentration (46.60 μg/L), followed by cesium (Cs), barium (Ba), lead (Pb), and mercury (Hg). And the median (interquartile range) for total IgE levels was 43.7 (17.3, 126.0) kU/L. Multiple linear regression results showed that Pb was significantly and positively associated with total IgE levels (β = 0.165; 95% CI: 0.046, 0.284). Binary logistic regression showed a significant positive correlation between urinary Pb (OR: 1.258; 95% CI: 1.052, 1.510) and tungsten (W) (OR: 1.251; 95% CI: 1.082, 1.447). Importantly, the BKMR model found a positive correlation between combined-metal exposure and total IgE levels and the occurrence of sensitization status. The mixed heavy-metal exposure was associated with increased total IgE levels, and this association may be driven primarily by the exposure of Pb and W. This study provides new insights into the relationship between heavy-metal exposure and allergic diseases. More research is needed to confirm these findings.

Risk of papillary thyroid carcinoma and nodular goiter associated with exposure to semi-volatile organic compounds: A multi-pollutant assessment based on machine learning algorithms

BackgroundExposure to semi-volatile organic compounds (SVOCs) may link to thyroid nodule risk, but studies of mixed-SVOCs exposure effects are lacking. Traditional analytical methods are inadequate for dealing with mixed exposures, while machine learning (ML) seems to be a good way to fill the gaps in the field of environmental epidemiology research. ObjectivesDifferent ML algorithms were used to explore the relationship between mixed-SVOCs exposure and thyroid nodule. MethodsA 1:1:1 age- and gender-matched case-control study was conducted in which 96 serum SVOCs were measured in 50 papillary thyroid carcinoma (PTC), 50 nodular goiters (NG), and 50 controls. Different ML techniques such as Random Forest, AdaBoost were selected based on their predictive power, and variables were selected based on their weights in the models. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were used to assess the mixed effects of the SVOCs exposure on thyroid nodule. ResultsForty-three of 96 SVOCs with detection rate >80 % were included in the analysis. ML algorithms showed a consistent selection of SVOCs associated with thyroid nodule. Fluazifop-butyl and fenpropathrin are positively associated with PTC and NG in single compound models (all P < 0.05). WQS model shows that exposure to mixed-SVOCs was associated with an increased risk of PTC and NG, with the mixture dominated by fenpropathrin, followed by fluazifop-butyl and propham. In the BKMR model, mixtures showed a significant positive association with thyroid nodule risk at high exposure levels, and fluazifop-butyl showed positive effects associated with PTC and NG. ConclusionThis study confirms the feasibility of ML methods for variable selection in high-dimensional complex data and showed that mixed exposure to SVOCs was associated with increased risk of PTC and NG. The observed association was primarily driven by fluazifop-butyl and fenpropathrin. The findings warranted further investigation.

Associations betweenco‑exposure toheavy metals and vertebral compression fracture, as well as femoral neck bone mineral density: A cross-sectional study from NHANES data.

Accumulating evidence showed that exposure to heavy metals was harmful to human health. Little is known regarding the mixing effects of multiple metal exposures on vertebral compression fracture (VCF) and femoral neck bone mineral density (BMD). This study aimed to explore the individual and joint effects of four heavy metals [manganese (Mn), lead (Pb), cadmium (Cd) and mercury (Hg)] on VCF risk and femoral neck BMD. This cross-sectional study included 1,007 eligible individuals with vertebral fractures from National Health and Nutrition Examination Survey 2013-2014. The outcome was the risk of VCF and femoral neck BMD. Weighted multivariate logistic regression was used to explore the individual effect of four heavy metals on the VCF risk, separately. Weighted multivariate linear regression was used to explore the individual effect of four heavy metals on the femoral neck BMD, separately. Adopted bayesian kernel machine regression (BKMR) model and quantile-based g computation (qgcomp) to examine the joint effects of four heavy metals on the VCF risk and femoral neck BMD. Among the population, 57 individuals developed VCF. After adjusting covariates, we found no statistical differences regarding the individual effects of four heavy metals on the risk of VCF. BKMR model and qgcomp indicated that there were no statistical differences regarding the joint effects between four heavy metals on the VCF risk. In addition, we found that Cd was associated with femoral neck BMD, and an increase in the mixture of heavy metal exposures was associated with a decreased risk of femoral neck BMD. No significant correlation was observed between co-exposure to Mn, Pb, Cd and Hg and VCF risk. But co-exposure to Mn, Pb, Cd and Hg may be associated with femoral neck BMD.

Association Between Multiple Heavy Metal Exposures and Cholesterol Levels in Residents Living Near a Smelter Plant in Korea.

Considering the interactions between heavy metals, a comprehensive evaluation of the effects of exposure to various types of co-interacting heavy metals on health is required. This study assessed the association between dyslipidemia markers and blood mercury, lead, cadmium, iron, zinc, and nickel levels in residents of an abandoned refinery plant. A total of 972 individuals (exposed group: 567, control group: 405) living near the Janghang refinery plant in the Republic of Korea were included. Blood mercury, lead, cadmium, iron, zinc, nickel, cholesterol, and triglyceride levels were measured. The combined effect of the six heavy metals on dyslipidemia markers was evaluated using a Bayesian kernel machine regression (BKMR) model and compared with the results of a linear regression analysis. The BKMR model results were compared using a stratified analysis of the exposed and control groups. In the BKMR model, the combined effect of the six heavy metals was significantly associated with total cholesterol (TC) levels both below the 45th percentile and above the 55th percentile in the total population. The combined effect range between the 25th and 75th percentiles of the six metals on TC levels was larger in the exposed group than that in the total population. In the control group, the combined effects of the changes in concentration of the six heavy metals on the TC concentration were not statistically significant. These results suggest that the cholesterol levels of residents around the Janghang refinery plant may be elevated owing to exposure to multiple heavy metals.

Role of alanine aminotransferase in the effects of urinary caffeine concentration and its primary metabolite concentration on cognitive function in older adults: Bayesian Kernel Machine regression analysis and mediation analysis.

This study aimed to explore the role of alanine aminotransferase (ALT) in the effects of urinary caffeine and its primary metabolites on cognitive function in elderly people. In this investigation, we meticulously curated a cohort from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) database. Animal fluency emerged as the pivotal metric for assessing cognitive function within our study population. In order to navigate the intricacies of mixture analysis and circumvent potential complexities, we harnessed the power of Bayesian kernel machine regression (BKMR) models. This method allowed us to dissect the nuanced impacts of caffeine and its primary urinary metabolites on cognitive function. While accounting for caffeine and its metabolites, we analyzed the relationship between ALT and cognitive function through non-linear dynamics. Lastly, employing structural equation modeling, we probed the intriguing question of whether ALT mediates the influence of 3,7-dimethylxanthine on cognitive function. This comprehensive approach has unveiled a deeper understanding of the multifaceted interplay among these variables, offering invaluable insights into the determinants of cognitive function within our cohort. After meticulous adjustment for various covariates, our linear regression analysis unveiled a noteworthy finding: 3,7-dimethylxanthine demonstrated a significant positive correlation with cognitive function (p < 0.05). Importantly, within the BKMR model employed, 3,7-dimethylxanthine emerged as the most influential factor within the compound, with posterior inclusion probabilities of 0.995 and 0.939. Furthermore, our single-exposure effect model confirmed its presence at the 25th, 50th, and 75th percentile concentrations of other components within the compound. Interestingly, bivariate concentration curves indicated no interaction within the compound, underscoring the prominent impact of 3,7-dimethylxanthine on cognitive function. Subsequently, through a test of Restricted Cubic Splines (RCS), we revealed a non-linear relationship between ALT and cognitive function at the 10th, 50th, and 90th percentiles (p < 0.05), indicating a heightened risk of diminished cognitive function in the low ALT group. Employing structural equation modeling, we meticulously examined the mediating role of ALT in relation to 3,7-dimethylxanthine and cognitive function. However, our study results did not yield significant evidence of a mediating effect. This comprehensive analysis elucidates the intricate interplay between these variables, unveiling the subtle mechanisms governing cognitive function. In this study, a noteworthy positive correlation was observed between 3,7-dimethylxanthine and cognitive function. Additionally, a non-linear relationship was identified between ALT and cognitive function, with lower levels of ALT associated with a decline in cognitive function. The RCS trend suggested that higher levels of ALT may similarly lead to diminished cognitive performance. However, in our pursuit to ascertain potential mediation, we regrettably found no significant evidence supporting mediation among these factors involving ALT. This underscores the need for more comprehensive investigations and expanded clinical explorations into the intricate associations among these three pivotal elements.

Higher temperature and humidity exacerbate pollutant-associated lung dysfunction in the elderly

RationaleAir pollution and extreme temperature and humidity are risk factors for lung dysfunction, but their interactions are not clearly understood. ObjectivesTo assess the impact of exposure to air pollutants and meteorological factors on lung function, and the contribution of their interaction to the overall effect. MethodsThe peak expiratory flow rates of 135 participants were repeatedly measured during up to four visits. Two weeks before each visit, the concentrations of gaseous pollutants and 19 fine particle components, and the temperature and relative humidity, were continuously monitored in the community where they lived. A Bayesian Kernel machine regression model was used to explore the non-linear exposure-response relationships of the peak expiratory flow rate with pollutant exposure and meteorological factors, and their interactions. Measurements and main resultsIncreased temperature and relative humidity could exacerbate pollutant-associated decline in the peak expiratory flow rate, although their associations with lung dysfunction disappeared after adjustment for pollutant exposure. For example, declines of peak expiratory flow rate associated with interquartile range increase of 3-day cadmium exposure were −0.03 and −0.07 units, when temperature was at 0.1 and 19.5 °C, respectively. Decreased temperature were associated with declines of peak expiratory flow rate after adjustment for pollutant exposure, and had interaction with pollutant exposure on lung dysfunction. ConclusionsHigh temperature, low temperature, and high humidity were all high-risk factors for lung dysfunction, and their interactions with pollutant levels contributed greatly to the overall effects.

Association between combined exposure to dioxins and arthritis among US adults: a cross-sectional study.

Dioxins and dioxin-like compounds (DLCs) are common pollutants hazardous to human health. We applied 12 dioxins and DLCs data of 1851 participants (including 484 arthritis patients) from National Health Examination Survey (NHANES) 2001-2004 and quadrupled them into rank variables. Multivariate logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were used to explore the relationship between individual or mixed exposure to the pollutants and arthritis after adjusting for multiple covariates. In multivariable logistic regression with an individual dioxin or DLC, almost every chemical was significantly positively associated with arthritis, except PCB66 (polychlorinated biphenyl 66) and 1,2,3,4,6,7,8-heptachlorodibenzofuran (hpcdf). The WQS model indicated that the combined exposure to the 12 dioxins and DLCs was positively linked to arthritis (OR: 1.884, 95% CI: 1.514-2.346), with PCB156 (weighted 0.281) making the greatest contribution. A positive trend between combined exposure and arthritis was observed in the BKMR model, with a posterior inclusion probability (PIP) of 0.987 for PCB156, which was also higher than the other contaminants.

Associations between plasma metal/metalloid mixtures and the risk of central obesity: A prospective cohort study of Chinese adults

Central obesity has increased rapidly over the past decade and posed a substantial disease burden worldwide. Exposure to metals/metalloids has been acknowledged to be involved in the development of central obesity through regulation of cortisol, insulin resistance, and glucocorticoid receptor reduction. Despite the importance, it is lack of prospective study which comprehensively evaluate the relations between multiple metals exposure and central obesity. We explored the prospective associations of plasma metal concentrations with central obesity in a prospective study of the Dongfeng-Tongji cohort. The present study included 2127 participants with a 6.87-year mean follow-up duration. We measured 23 plasma metal/metalloid concentrations at baseline. The associations between metals and incident central obesity were examined utilizing the Cox proportional hazard regression in single and multiple metals models. Additionally, we applied elastic net (ENET), Bayesian kernel machine regression (BKMR), plasma metal score (PMS), and quantile-based g-computation (Qgcomp) models to explore the joint associations of metal mixtures with central obesity. After adjusting potential confounders, we found significant associations of plasma manganese (Mn) and thallium (Tl) concentrations with a higher risk of central obesity, whereas plasma rubidium (Rb) concentration was associated with a lower risk of central obesity both in single and multiple metals models (all FDR <0.05). The ENET and Qqcomp models verified similar metals (Mn, Rb, and Tl) as important predictors for central obesity. The results of both BKMR model and PMS suggested cumulative exposure to metal mixtures was associated with a higher risk of central obesity. Our findings suggested that co-exposure to metals was associated with a higher risk of central obesity. This study expands our knowledge that the management of metals/metalloids exposure may be beneficial for the prevention of new-onset central obesity, which may subsequently alleviate the disease burden of late-life health outcomes.

Associations of urinary metal concentrations with anemia: A cross-sectional study of Chinese community-dwelling elderly

BackgroundAnemia seriously affects the health and quality of life of the older adult population and may be influenced by various types of environmental metal exposure. Current studies on metals and anemia are mainly limited to single metals, and the association between polymetals and their mixtures and anemia remains unclear. MethodsWe determined 11 urinary metal concentrations and hemoglobin levels in 3781 participants. Binary logistic regression and restricted cubic spline (RCS) model were used to estimate the association of individual metals with anemia. We used Bayesian kernel machine regression (BKMR) and Quantile g-computation (Q-g) regression to assess the overall association between metal mixtures and anemia and identify the major contributing elements. Stratified analyses were used to explore the association of different metals with anemia in different populations. ResultsIn a single-metal model, nine urinary metals significantly associated with anemia. RCS analysis further showed that the association of arsenic (As) and copper (Cu) with anemia was linear, while cobalt, molybdenum, thallium, and zinc were non-linear. The BKMR model revealed a significant positive association between the concentration of metal mixtures and anemia. Combined Q-g regression analysis suggested that metals such as Cu, As, and tellurium (Te) were positively associated with anemia, with Te as the most significant contributor. Stratified analyses showed that the association of different metals with anemia varied among people of different sexes, obesity levels, lifestyle habits, and blood pressure levels. ConclusionsMultiple metals are associated with anemia in the older adult population. A significant positive association was observed between metal mixture concentrations and anemia, with Te being the most important factor. The association between urinary metal concentrations and anemia is more sensitive in the non-hypertensive populations.

Associations of maternal exposure to multiple plasma trace elements with the prevalence of fetal congenital heart defects: A nested case-control study

BackgroundScanty knowledge prevails regarding the combined impact of multiple plasma trace elements and main contributors on the prevalence of congenital heart defects (CHDs) in offspring. Thus, we performed a nested case-control analysis in a neonates cohort to investigate this important public health issue. MethodsWe selected 164 pairs of cases and non-malformed controls from live births registered in the parent cohort (n = 11,578) at the same hospital. Plasma levels of 14 trace elements were determined by inductively coupled plasma-mass spectrometry. The odds ratios (ORs) of exposure were compared between cases and controls. Bayesian Kernel Machine Regression (BKMR) and Quantile g-Computation (QgC) models were employed to assess the cumulative effect of exposure to trace elements. ResultsWe found positive associations and linear dose-response relationships between plasma Pb and Sn and CHD. BKMR models indicated that the overall effect of the trace element mixture was associated with CHDs below the 45th percentile or above the 50th percentile, and the combined effect was primarily attributed to Sn and Pb. The QgC model indicated significantly increased odds of CHD with simultaneous exposure to all studied trace elements (OR: 2.19, 95%CI: 1.44–3.33). ConclusionsThis study is the first to report an association between elevated levels of mixed trace elements in maternal plasma with an increased prevalence of fetal CHDs, particularly in the case of Pb and Sn. Findings from this study provide further evidence of the important of heavy metal pollution to human health, and can help stakeholders prioritize policies and develop interventions to target the leading contributors to human exposure.

Urinary concentrations of bisphenol A, parabens and benzophenone-type ultra violet light filters in relation to sperm DNA fragmentation in young men: A chemical mixtures approach

People are daily exposed to multiple endocrine disruptor compounds (EDCs) that may interfere with different molecular and cellular processes, promoting a potential estrogenic, androgenic, or anti-androgenic state. However, most epidemiological studies attempting to establish relationships between EDCs exposure and health effects are still considering individual compounds. A few studies have shown associations between exposure to individual non-persistent EDCs and sperm DNA fragmentation (SDF) in different male populations. Thus, the aim of this study was to investigate associations between combined exposure to non-persistent EDCs and SDF index in young men. A cross-sectional study was conducted with 158 healthy university students from Southeaster Spain. The participants provided spot urine and semen samples on the same day. The concentrations of urinary bisphenol A (BPA), benzophenones [2,4-dihydroxybenzophenone (BP-1); 2,2′,4,4′-tetrahydroxybenzophenone (BP-2), 2-hydroxy-4-methoxybenzophenone (BP-3), 2,2′-dihydroxy-4-methoxybenzophenone (BP-8), 4-hydroxybenzophenone (4OHBP)], and parabens (methylparaben, ethylparaben, propylparaben, butylparaben) were measured by dispersive liquid-liquid microextraction and ultrahigh-performance liquid chromatography with tandem mass spectrometry detection. SDF was analysed using a Sperm Chromatin Dispersion test. Statistical analyses were carried out using Bayesian Kernel Machine Regression models to evaluate associations between combined exposure to these compounds and SDF index while adjusting by relevant covariates. The increase in urinary concentration of 4OHBP was found to be the most important contributor to the negative association between urinary EDCs concentrations and SDF index, being of −5.5 % [95 % CI: −10.7, −0.3] for those in percentile 50, and − 5.4 % [95 % CI: −10.8, −0.1] for those in percentile 75. No significant associations were observed between other EDCs and SDF index. Our findings show that urinary 4OHBP levels may be associated with a decrease in the SDF index. Nonetheless, the effects we observed were likely to be small and of uncertain clinical significance. Further research is needed to replicate our findings in other male populations.

Prenatal exposure to per- and polyfluoroalkyl substances and early childhood adiposity and cardiometabolic health in the Healthy Start study.

Observational and experimental studies have suggested that prenatal exposure to per- and polyfluoroalkyl substances (PFAS) can increase childhood adiposity and cardiometabolic disruption. However, most previous studies have used weight-based measures that cannot distinguish between fat mass and lean mass. We evaluated associations of prenatal PFAS exposure with precisely measured body composition and cardiometabolic biomarkers in early childhood. 373 eligible mother-infant pairs in the Healthy Start longitudinal cohort. We used multiple linear regression and Bayesian kernel machine regression models to estimate associations between five PFAS in maternal mid-pregnancy serum, and early childhood adiposity via air displacement plethysmography. Secondary outcomes included body mass index, waist circumference, and fasting serum lipids, glucose, insulin and adipokines. Models were adjusted for potential confounders and effect modification by child sex was evaluated. The median age of children at assessment was 4.6 years. Prenatal concentration of perfluorooctanoate (PFOA) was positively associated with percent fat mass (0.89% per log2-unit increase, 95% CI: 0.15, 1.64), while perfluorononanoate (PFNA) was positively associated with fat mass index and body mass index. Cardiometabolic markers in blood were generally not associated with prenatal PFAS in this population. Mixture models confirmed the importance of PFNA and PFOA in predicting percent fat mass, while PFNA was most important for fat mass index, body mass index, and waist circumference. There were no significant effects of the five PFAS as a mixture, potentially due to opposing effects of different PFAS. Our results agree with previous studies showing that prenatal serum concentrations of certain PFAS are positively associated with early childhood adiposity. Notably, associations were stronger for measures incorporating precisely measured fat mass compared to measures of body size or weight. Early life increases in adiposity may precede the development of adverse cardiometabolic health outcomes in children exposed to PFAS during gestation.

Prenatal exposure to Per- and polyfluoroalkyl substances and adiposity measures of children at 4 and 6 years: A prospective birth cohort in China

There is growing evidence that prenatal exposure to Per- and polyfluoroalkyl substances (PFAS) was associated with childhood obesity, but evidence on multiple adiposity measures including arm circumference (AC), and waist circumference (WC) among Chinese children is limited. We investigated the associations of prenatal exposure to PFAS with adiposity measures of children at 4 and 6 years of age in the Shanghai-Minhang Birth Cohort Study. A total of 573 mother-child pairs with maternal PFAS concentrations and at least one measurement of adiposity measures of children were included in the present study. Eleven PFAS were assessed in maternal fasting blood samples. Information on children's weight, height, AC, and WC was collected at follow-ups. Weight for age Z score (WAZ), body mass index for age Z score (BMIz), and children overweight were calculated based on the World Health Organization Child Growth Standards. Multivariate linear regression, Poisson regression with robust error variance, and Bayesian Kernel Machine Regression (BKMR) models were used to examine the associations of prenatal exposure to PFAS with children's adiposity measures. Eight PFAS with detection rates above 85 % were included in the analyses. In the multivariate linear regression models, maternal PFNA concentrations were associated with a greater AC (β = 0.29, 95 % Confidence Interval (CI): 0.04–0.55) in 4-year-old children and with an increase in WAZ (β = 0.26, 95 % CI: 0.06–0.46), BMIz (β = 0.31, 95 % CI: 0.09–0.53), AC (β = 0.49, 95 % CI: 0.08–0.90), and WC (β = 1.47, 95 % CI: 0.41–2.52) in 6-year-old children. We also observed the associations of maternal concentrations of PFOS, PFNA, PFUdA, and PFTrDA with the increased risk of children overweight in 6-year-old children. BKMR models further supported the findings from multivariate linear regression and Poisson regression models, and identified PFNA as the most important contributor. Moreover, the associations described above were generally more pronounced in girls. In conclusion, prenatal exposure to PFAS was associated with an increased risk of children's adiposity with a sex-specific manner, and PFNA contributed most to the associations after controlling for the effect of co-exposure to other PFAS compounds, especially among girls at 6 years of age.

Associations between Phthalate Metabolite Concentrations in Follicular Fluid and Reproductive Outcomes among Women Undergoing in Vitro Fertilization/Intracytoplasmic Sperm Injection Treatment.

Phthalates have been reported to impair fertility in various studies. However, evidence exploring the associations between phthalate metabolites in follicular fluid (FF) and reproductive outcomes is lacking. To investigate the associations between phthalate metabolite concentrations in FF and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes among women recruited from a fertility clinic. We included 641 women undergoing IVF/ICSI treatment from December 2018 to January 2020. The levels of eight phthalate metabolites, including monoethyl phthalate (MEP), mono-isobutyl phthalate (MiBP), mono--butyl phthalate (MBP), monobenzyl phthalate (MBzP), mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), and mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), were quantified in FF collected on the oocyte retrieval day. Associations between quartiles of individual phthalate metabolite concentrations and nine IVF/ICSI outcomes, including oocyte yield, mature oocyte number, two distinct pronuclei (2PN) zygote number, fertilization rate, blastocyst formation rate, implantation, clinical pregnancy, miscarriage, and live birth, were estimated with generalized linear models. The effects of phthalate mixtures on IVF/ICSI outcomes were assessed using Bayesian kernel machine regression (BKMR) models. After adjusting for relevant confounders, elevated quartiles of MBzP, MEHHP, and MEHP in FF were inversely associated with the numbers of retrieved oocytes, mature oocytes, and 2PN zygotes (all for trends ). In comparison with the lowest quartile, the highest quartile of molar sum of di(2-ethylhexyl) phthalate metabolites () was associated with a reduction of 9.1% [95% confidence interval (CI): , ] and 10.3% (95% CI: , ) in yielded oocyte and mature oocyte numbers, respectively. Furthermore, the BKMR models revealed inverse associations between phthalate mixtures and the numbers of retrieved oocytes and mature oocytes. We generally found null results for implantation, clinical pregnancy, miscarriage, and live birth. Certain phthalate metabolites in FF are inversely associated with the numbers of retrieved oocytes, mature oocytes, and 2PN zygotes among women undergoing IVF/ICSI treatment. https://doi.org/10.1289/EHP11998.

Association between vitamin levels and obesity in the national health and nutrition examination surveys 2017 to 2018.

In recent years, the rapidly increasing incidence of obesity is becoming a worldwide public health problem. Obesity is a chronic disease which may have a major negative effect on the people's quality of life. Previous studies on the comprehensive effects of multivitamins on central obesity and general obesity are relatively few. The aim of this study was to evaluate association of vitamins exposure with obesity risk and obesity-related indicators. We fitted three statistical models (linear regression model, logistic regression model, and Bayesian kernel machine regression model) to evaluate the correlation between vitamin levels and obesity in the study population. The vitamin score represents the overall level of vitamin in serum, which was mutually verified with the results obtained from statistical model. The vitamin (A, C, and D) levels were significantly higher among non-obesity group compared to the obesity group. Using the lowest quartile of vitamin level as a referent, vitamin A, C, and D levels showed significantly negative correlation with the obesity risk in both adjusted and unadjusted models. When considering all vitamin as a mixed exposure, we found a generally negative relationship between vitamin mixtures with binary outcome (obesity) and continuous outcome (BMI, waist circumference, and hsCRP). Reduced levels of vitamins (A, C and D) increased the risk of obesity. Increased levels of vitamin mixtures can significantly reduce obesity risk and obesity-related indicators. Vitamins may reduce the risk of obesity by suppressing inflammatory responses.

Repeated measurements and mixture effects of urinary bisphenols, parabens, polycyclic aromatic hydrocarbons, and other chemicals on biomarkers of oxidative stress in pre- and postpartum women

The association between oxidative stress and exposure to bisphenols, parabens, phenols, polycyclic aromatic hydrocarbons (PAH), and volatile organic compounds (VOCs) has been investigated by many in vitro and in vivo studies. However, most of these findings are based on cross-sectional studies, as a result of which the combined effects of these compounds have been rarely analyzed. In this study, our objective was to assess urinary bisphenols, parabens, PAHs, and VOCs, in relation to oxidative stress during pre-and postpartum periods, analyze the association between these chemicals and oxidative stress via repeated measurements using a linear mixed model (LMM), and evaluate the combined effects exerted by these chemicals on oxidative stress using Bayesian Kernel Machine Regression (BKMR). A total 529 urine samples were collected from 242 pregnant women during the 1st and 2nd trimesters, as well as postpartum follow-ups. Three bisphenols, four parabens, benzopheone-3 (BP-3), triclosan (TCS), four PAHs, two VOCs, and 3- phenoxy-benzoic acid (3-PBA) were analyzed. We also measured 8-hydroxydeoxyguanosine (8-OHdG) and malondialdehyde (MDA), which serve as oxidative stress biomarkers in maternal urine samples. During this period, 8-OHdG decreased steadily, whereas MDA increased during pregnancy and decreased after childbirth. LMM indicated that Bisphenol A, Prophyl-paraben, BP-3, and 1-hydroxypyrene (1-OHP) showed a significant association with increased MDA levels. The BKMR models revealed that the mixture effect exerted by these 16 chemicals had changed MDA levels, which indicate oxidative stress, and that both Butyl Paraben (BP) and 1-hydroxypyrene (1-OHP) had contributed to such oxidative stress. Mixtures of each subgroup (bisphenols, parabens, and PAHs) were associated with increased MDA levels. These findings suggest that exposure to some phenols and PAHs during pre- and post-partum stages may cause oxidative stress, and that exposure to these chemicals should be minimized during this period.

Individual and combined association between nutritional trace metals and the risk of preterm birth in a recurrent pregnancy loss cohort

BackgroundRecurrent pregnancy loss (RPL) was associated with an elevated risk of pregnancy complications, particularly preterm birth (PTB). However, the risk factors associated with PTB in RPL remained unclear. Emerging evidence indicated that maternal exposure to metals played a crucial role in the development of PTB. The objective of our study was to investigate the individual and combined associations of nutritional trace metals (NTMs) during pregnancy with PTB in RPL.MethodsUsing data from a recurrent pregnancy loss cohort (n = 459), propensity score matching (1:3) was performed to control for covariates. Multiple logistic regression and multiple linear regression were employed to identify the individual effects, while elastic-net regularization (ENET) and Bayesian kernel machine regression (BKMR) were used to examine the combined effects on PTB in RPL.ResultsThe logistic regression model found that maternal exposure to copper (Cu) (quantile 4 [Q4] vs. quantile 1 [Q1], odds ratio [OR]: 0.21, 95% confidence interval [CI]: 0.05, 0.74) and zinc (Zn) (Q4 vs. Q1, OR: 0.19, 95%CI: 0.04, 0.77) was inversely associated with total PTB risk. We further constructed environmental risk scores (ERSs) using principal components and interaction terms derived from the ENET model to predict PTB accurately (p &amp;lt; 0.001). In the BKMR model, we confirmed that Cu was the most significant component (PIP = 0.85). When other metals were fixed at the 25th and 50th percentiles, Cu was inversely associated with PTB. In addition, we demonstrated the non-linear relationships of Zn with PTB and the potential interaction between Cu and other metals, including Zn, Ca, and Fe.ConclusionIn conclusion, our study highlighted the significance of maternal exposure to NTMs in RPL and its association with PTB risk. Cu and Zn were inversely associated with PTB risk, with Cu identified as a crucial factor. Potential interactions between Cu and other metals (Zn, Ca, and Fe) further contributed to the understanding of PTB etiology in RPL. These findings suggest opportunities for personalized care and preventive interventions to optimize maternal and infant health outcomes.

The association between levels of samarium, hafnium, tungsten and rhenium in seminal plasma and the risk of idiopathic oligo-astheno-teratozoospermia in men of childbearing age.

Oligo-astheno-teratozoospermia (OAT) is a global public health problem, which affects 30% men of childbearing age. Meanwhile, with the rapid development of industry and economy, the contents of rare earth elements (REEs) in the environment are increasing. However, little is known about the associations between REEs levels and OAT risk. To evaluate the associations between the levels of four REEs (samarium (Sm), hafnium (Hf), tungsten (W), rhenium (Re)) in seminal plasma and OAT risk, from October 2021 to November 2022, semen samples from 924 men of childbearing age (460 controls and 464 cases) were collected from the reproductive center of the First Affiliated Hospital of Anhui Medical University. Inductively coupled plasma-mass spectrometry (ICP-MS) was used to measure the levels of Sm, Hf, Re and W in seminal plasma. Bayesian kernel machine regression (BKMR) was conducted to explore the joint effects of levels of four REEs in seminal plasma on the risk of OAT and select the one exerting a major role; generalized linear regression models (GLM) with log link function were employed to investigate the association of every REE level in seminal plasma and OAT risk; sankey diagram and linear regression models were utilized to describe the associations between the levels of four REEs and the indexes of sperm quality. The levels of four REEs in seminal plasma were higher in the case group than levels in the control group (pSm = 0.011, pHf = 0.040, pW = 0.062, pRe = 0.001, respectively). In BKMR analysis, theOAT risk increased when the overall levels of four REEs were higher than their 55th percentile compared to all of them at their 50th percentile, and Re level played a major role in the association. Additionally, Re level in seminal plasma was positively associated with theOAT risk in the single element model after adjustment of covariates (medium vs. low: OR (95% CI) = 1.55 (1.10, 2.18); high vs. low: OR (95% CI) = 1.69 (1.18, 2.42)). Lastly, the sankey diagram and linear regression models revealed that Sm level was negatively associated with the PR%, total sperm count and total progressively motile sperm count; Hf level was negatively associated with the PR%; W and Re levels were negatively associated with the PR% and total motility, and Re level was positively associated with abnormal morphology rate. Men of childbearing age with OAT had higher levels of Sm, Hf and Re in seminal plasma than those in the control group. An increasing trend for theOAT risk was observed with an increase in mixture levels of Sm, Hf, W and Re, and Re exposure level played a major role in the association whether in BKMR model or single element model. Additionally, the levels of these four REEs were negatively associated with the indexes of sperm quality.

Exposure to metal mixtures and young children’s growth and development: A biomonitoring-based study in Eastern China

Exposure to metal mixtures may affect children's health but the conclusions are controversial. We aimed to investigate the associations of metal mixture exposure with children’s physical and behavioral development. 15 metals were detected in the urine samples of 278 preschoolers aged 3–6 years from eastern China. Multiple linear models and restricted cubic splines were used to evaluate dose-response relationships between single metal and children’s physical and behavioral development. The Bayesian Kernel Machine Regression (BKMR) models, the weighted quantile sum (WQS) models and Quantile G-Computation were applied to evaluate the joint effects of metal mixtures. The results showed that arsenic (As) was negatively associated with z score of height for age (HAZ) in individual-metal models [β (95%CI): − 0.22 (−0.38, −0.06), P = 0.006]. Concerning children’s behavioral development, multiple-metal models demonstrated a negative association with strontium (Sr) [β (95%CI): − 0.82 (−1.38, −0.26), P = 0.004], and a positive association with tin (Sn) [β (95%CI): 0.69 (0.16, 1.21), P = 0.010]. Notably, these associations remained significant or suggestive even after adjustments for multiple tests, sensitivity analyses, and application of different statistical models, including BKMR, WQS, and Quantile G-Computation. Furthermore, the study identified a negative joint effect of the metal mixture on HAZ, as demonstrated by BKMR and Quantile G-Computation models, with As playing an irreplaceable role in this observed impact. In summary, exposure to As appears to have adverse effects on HAZ, while exposure to Sn may hinder children’s behavioral development. Conversely, exposure to Sr may have a protective effect on children’s behavioral development. Additionally, the combined impact of metal mixtures is implicated in potentially impairing children’s physical development, particularly in terms of HAZ.

Association between polycyclic aromatic hydrocarbon exposure and blood lipid levels: the indirect effects of inflammation and oxidative stress.

Although previous studies have indicated polycyclic aromatic hydrocarbons (PAHs) as cardiovascular health risk factors, evidence linking exposure to PAHs and blood lipids is still lacking, and the mechanism remains largely unknown. In this study, we evaluated the association between human internal exposure to PAHs and blood lipid levels in adults, as well as the indirect effects of inflammation and oxidative stress. The internal exposure of PAHs was assessed by determining serum PAHs and their hydroxylated metabolites (OH-PAHs) in the paired urine samples.Multivariable linear regression results demonstrated significant positive associations of individual PAHs and OH-PAHs with blood lipid biomarkers.The Bayesian kernel machine regression model revealed positive joint effects of PAH internal exposure on the fasting blood glucose, low-density lipoprotein cholesterol, total cholesterol, and total triglyceride, as well as an increased ratio of apolipoprotein B to apolipoprotein A1. In evaluating individual effects, serum phenanthrene played the most significant role in the association of increased PAH exposure with elevated fasting blood glucose. Quantile g-computation demonstrated the significant change in the levels of apolipoprotein B, ratio of apolipoprotein B to apolipoprotein A1, low-density lipoprotein cholesterol, and total cholesterol per quartile increase in PAH internal exposure. The restricted cubic spline analysis demonstrated the non-linear relationship between individual PAHs and OH-PAHs on blood lipid biomarkers. The mediation analysis indicated that PAH exposure may affect blood lipids not directly, but rather indirectly through intermediate inflammation and oxidative stress. The results demonstrated a significant association between increased PAH exposure levels and elevated blood lipids, highlighting the indirect effects of inflammation and oxidative stress.