Kernel Methods Research Articles

Associations between multi-metal joint exposure and decreased estimated glomerular filtration rate (eGFR) in solar greenhouse workers: A study of a unique farmer group

BackgroundSolar greenhouse workers, a unique farmer group, have been reported to have a higher risk of chronic kidney disease (CKD) compared to the general population, possible due to exposure to multiple metals. ObjectiveThis study aimed to investigate the associations between exposure to multiple metals and the estimated glomerular filtration rate (eGFR). MethodsA cross-sectional study was conducted in the Northwest China. Urine samples were tested for concentration of 14 metals, including chromium, manganese, iron et al. Blood creatinine was measured to calculate eGFR, which was to evaluate the kidney function. Linear model and the Bayesian Kernel Machine Regression (BKMR) models were used to evaluate the associations between metals exposure and eGFR. ResultThe study included 281 solar greenhouse workers, with 128 (45.6%) males and 153 (54.4%) females. The highest median concentrations of metals were zinc (418.55 μg/L), strontium (368.77 μg/L), and iron (55.73 μg/L), respectively. The linear model analysis showed that urinary levels of copper and zinc were negatively associated with eGFR [β = −0.021, 95% CI (−0.048, −0.007); β = −0.018, 95% CI (−0.068, −0.005)] considering a false discovery rate. BKMR results indicated a significant overall negative effect of 14 metals exposure on the eGFR when all metal levels were above the 50th percentile compared to the median value. ConclusionsThe decrease in eGFR among solar greenhouse workers was related to mixed metal exposure. Reducing exposure to the metals of copper, zinc, and lead could effectively protects kidney function. Further prospective studies are needed to resolve concerns about reverse causality.

Associations of aldehyde exposure with serum uric acid and hyperuricemia among U.S. adults from NHANES 2013-2014

BackgroundAldehyde is a kind of important environmental pollutant, which has been demonstrated to be associated with increased risks of various chronic diseases with the economic and social development. However, the effects of aldehydes on serum uric acid (SUA) and hyperuricemia remained inexplicit, and the potential mediating pathways for this relationship needed to be addressed. MethodsThis study investigated the associations of individual and mixed aldehydes with SUA and hyperuricemia among 1,588 U.S. adults from the National Health and Nutrition Examination Survey 2013-2014. The generalized linear regression model was applied to assess the effects of individual aldehydes and the Bayesian kernel machine regression were incorporated to examine the impacts of mixed aldehydes. Mediation analyses were performed to explore the roles of inflammation and oxidative stress indices in aldehyde-induced SUA and hyperuricemia. Moreover, we conducted subgroup analyses for demographic and physical factors to detect disparity between groups. ResultsPropanaldehyde, butyraldehyde, and hexanaldehyde were associated with higher SUA level and butyraldehyde was correlated with increased hyperuricemia prevalence after multiple correction. Positive relationships between aldehyde mixtures and SUA level were also observed and hexanaldehyde contributed most. In addition, lymphocyte count and gamma glutamyl transferase partially mediated the associations between propanaldehyde, butyraldehyde, hexanaldehyde and SUA level, with mediation proportions ranging from 19.105% to 27.316%. Subgroup analyses showed that SUA level of participants with obesity, hypercholesterolemia, and hypertension tended to be more sensitive to aldehyde exposure. ConclusionsOur results highlighted that multiple aldehydes mixtures exposure might increase SUA level, and revealed underlying mediating role of inflammation and oxidative stress. These findings provided crucial evidence for the impacts of environmental pollutants on human health and further prospective studies are still required to verify the findings.

Glycolysis mediates the association between phthalate exposure and the prevalence of childhood asthma: The National Health and Nutrition Examination Survey 2009–2018

Identified as a critical risk factor for childhood asthma, environmental pollution plays a pivotal role. However, research on the effects and mechanisms of phthalates mixture and their interactions in relation to childhood asthma is still lacking. In the National Health and Nutrition Examination Survey (NHANES) conducted from 2009 to 2018, our research explored the link between phthalates in urine and the prevalence of childhood asthma. In this study, which involved 810 participants, we used four different statistical analysis methods to investigate the association between urinary phthalate levels and childhood asthma. Additionally, we conducted a mediation analysis to explore whether the impact mechanism of phthalate exposure on childhood asthma operates through the glycolysis. Among the participants, 525 (64.81 %) individuals were diagnosed with asthma, with 330 (40.74 %) individuals undergoing testing for glycolytic markers. Through Spearman correlation analysis and weighted principal component analysis (W-PCA), it was found that mono-2-ethyl-5-carboxypentyl phthalate (MECPP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-(2-ethyl)-hexyl phthalate (MEHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP) are the four most highly correlated phthalates. In addition, comprehensive analysis by the weighted generalized linear models (W-GLM), weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models showed that phthalates mixture were positively associated with the prevalence of childhood asthma, especially MECPP, MEHHP and MEOHP. More importantly, glycolysis participated as a mediator in the relationship between MECPP, MEHHP and MEOHP exposure and the prevalence of childhood asthma, explaining 41.194 %, 38.322 % and 39.871 % of the effects respectively. Therefore, our study revealed that phthalate exposure is a risk factor for asthma in children, and glycolysis may be involved as a potential mediator in this process. This conclusion will be verified through more prospective studies in the future.

A fractional-derivative kernel learning strategy for predicting residual life of rolling bearings

In the field of mechanical equipment maintenance, accurately estimating the remaining useful life (RUL) of rolling bearings is crucial for ensuring reliable equipment operation. However, prevalent deep learning methods face challenges such as limited sample sizes, and “black-box” mechanisms. To enhance the accuracy and interpretability of rolling bearing RUL prediction, a novel fractional-derivative kernel mean p-power error filtering algorithm (FrKMPE) is introduced. A comprehensive analysis of convergence for this method in terms of both mean error and mean square error criteria is provided. By combining the memory properties of fractional-derivative with the adaptability of kernel method, it can effectively capture features of non-stationary signals and sensitively monitor changes of rolling bearing health states (HSs). The effectiveness of the FrKMPE is validated through its application to the prediction of RUL using the IEEE PHM 2012 challenge dataset and the XJTU-SY dataset. Experimental results demonstrate that the proposed FrKMPE outperforms existing kernel adaptive filtering and deep learning methods in rolling bearing RUL prediction. The proposed method has advantages in dealing with complex nonlinear data and improving prediction accuracy, and provides a new perspective and solution for rolling bearing RUL prediction.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Sound source localization via distance metric learning with regularization

Sound source localization (SSL) or simply direction of arrival (DOA) classification is an important ingredient in acoustic applications. Traditional model-based algorithms are susceptible to the effects of noise and reverberation, while data-driven deep learning algorithms maintain strong performance across a variety of acoustic circumstances, but typically require a large amount of labeled data. Nevertheless, the existing datasets for SSL are not sufficiently big and diverse to achieve the full potential of deep learning algorithms. Then, it is an imperative work to develop a non-data-hungry algorithm of SSL using small or medium data volume. To this end, we propose a regularized distance metric learning algorithm, that is, by means of the kernel method, we design a nonlinear feature transformation from two aspects: feature points and feature distributions. It transforms the data into a new feature space that brings features of the same class as close as possible and removes features of different classes as far away as possible, which can significantly improve the output of a DOA classifier that follows. Experimental results show that the proposed algorithm outperforms deep learning algorithms in diverse acoustic conditions.

Associations of Advanced Glycation End Products with Sleep Disorders in Chinese Adults.

Advanced glycation end products (AGEs), a group of food processing byproducts, have been implicated in the development of various diseases. However, the relationship between circulating AGEs and sleep disorders remains uncertain. This cross-sectional study elucidated the association of plasma AGEs with sleep disorders among 1732 Chinese adults who participated in the initial visit (2019-2020) of the Tongji-Shenzhen Cohort (TJSZC). Sleep behavior was assessed using self-reported questionnaires and precise accelerometers. Plasma levels of AGEs, including Nε-(Carboxymethyl)lysine (CML), Nε-(Carboxyethyl)lysine (CEL), and Nδ-(5-hydro-5-methyl-4-imidazolone-2-yl)-ornithine (MG-H1), were quantified by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). In logistic regression, per IQR increment in individual AGEs was associated with an increased odds ratio of short sleep duration (CML: 1.11 [1.00, 1.23]; CEL: 1.16, [1.04, 1.30]), poor sleep quality (CML: 1.33 [1.10, 1.60]; CEL: 1.53, [1.17, 2.00]; MG-H1: 1.61 [1.25, 2.07]), excessive daytime sleepiness (CML: 1.33 [1.11, 1.60]; MG-H1: 1.39 [1.09, 1.77]), and insomnia (CML: 1.29 [1.05, 1.59]). Furthermore, in weighted quantile sum regression and Bayesian kernel machine regression analyses, elevated overall exposure levels of plasma AGEs were associated with an increased risk of sleep disorders, including short sleep duration, poor sleep quality, excessive daytime sleepiness, and insomnia, with CML being identified as the leading contributor. Insufficient vegetable intake and higher dietary fat intake was associated with an increase in plasma CEL. These findings support a significant association between plasma AGEs and sleep disorders, indicating that AGEs may adversely influence sleep health and reducing the intake of AGEs may facilitate preventing and ameliorating sleep disorders.

Relationship between phthalate exposure and kidney function in Taiwanese adults as determined through covariate-adjusted standardization and cumulative risk assessment

Few studies have investigated the associations between phthalate exposure and kidney function indicators in adults by simultaneously performing covariate-adjusted creatinine standardization, cumulative risk assessment, and mixture analysis. Thus, we applied these methods simultaneously to investigate the aforementioned associations in an adult population. This cross-sectional study analyzed data (N = 839) from a community-based arm of the Taiwan Biobank. The levels of 10 urinary phthalate metabolites were measured and calculated as the sum of the molar concentrations of the dibutyl phthalate metabolite (ΣDBPm) and di(2-ethylhexyl) phthalate (DEHP) metabolite (ΣDEHPm). The hazard index (HI) and daily intake (DI) were estimated by measuring the urinary levels of the phthalate metabolite. Kidney function biomarkers were assessed by measuring the following: blood urea nitrogen (BUN), uric acid, the albumin-to-creatinine ratio (ACR), and the estimated glomerular filtration rate (eGFR). Generalized linear models were implemented to examine the associations between exposure to individual phthalates, HI scores, and kidney function biomarkers. We also employed Bayesian kernel machine regression (BKMR) to analyze the relationships between exposure to various combinations of phthalates and kidney function. ΣDEHPm levels were significantly and positively associated with BUN and ACR levels, and ΣDBPm levels were positively associated with ACR levels. In addition, eGFR was negatively associated with ΣDBPm and ΣDEHPm levels. In the BKMR model, a mixture of 10 phthalate metabolites was significantly associated with BUN, uric acid, ACR, and eGFR results. Higher DIDEHP and higher DIDnBP values were significantly associated with lower eGFRs and higher ACRs, respectively. Higher DIDiBP and DIDEP values were significantly associated with higher uric acid levels. A higher HI was significantly associated with lower eGFRs and higher ACRs. Our results suggest that exposure to environmental phthalates is associated with impaired kidney function in Taiwanese adults.

Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis

Sex-Specific Effects of Combined Heavy Metal Exposure on Blood Pressure: A Bayesian Kernel Machine Regression Analysis

Prenatal and childhood exposure to endocrine-disrupting chemicals and early thelarche in 8-year-old girls: A prospective study using Bayesian kernel regression

BackgroundStudies on the combined effects of persistent and non-persistent endocrine-disrupting chemicals (EDCs) on puberty are insufficient. To date, no studies have analyzed breast development at age 8 years, a key criterion for determining precocious puberty. We investigated the relationship between prenatal or childhood exposure to EDC mixtures and early thelarche, defined as breast development before age 8 years in girls. MethodsThis prospective study included 211 girls with data on prenatal and 8-year-old exposure of cadmium (Cd), lead, mercury, bisphenol-A (BPA), 3-phenoxybenzoic acid, and three phthalate metabolites from the Environment and Development of Children cohort. Prenatal exposure was assessed through samples from pregnant women at 14–27th weeks of gestation. Tanner staging was assessed by a pediatric endocrinologist. The relationship between single and mixed chemical exposures and outcomes was assessed using logistic regression, generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) models. ResultsEarly thelarche was observed in 42 (19.9%) girls at age 8 years. In the logistic regression models, the risk of early thelarche increased with increased exposure to Cd in their mothers (adjusted odds ratio [aOR] per interquartile range [IQR] = 1.80, 95% confidence interval [CI] 1.23–2.65) but decreased with prenatal BPA exposure (aOR per IQR = 0.57, 95% CI 0.35–0.92). None of the 8-year-old chemical exposures was associated with early thelarche. In the GAM, early thelarche was positively correlated with prenatal Cd and inversely associated with prenatal BPA exposure (p = 0.004 for Cd and p = 0.036 for BPA). In the BKMR models, an increase in log-transformed prenatal Cd concentrations from the 25th to 75th percentile was associated with an increase in the estimated probability of early thelarche at age 8 years (risk difference: 0.46 [95% credible interval: 0.04–0.88]) when other chemicals were set at their median values. ConclusionsConsidering the combined effects of persistent and non-persistent chemical mixtures, maternal Cd exposure during the second trimester may be associated with early thelarche in 8-year-old girls.

Simulation modelling demonstrates differential performance of connectivity methods in their ability to predict genetic diversity in complex landscapes

ContextThere have been few evaluations of how well different connectivity modelling methods are able to predict the spatial genetic structure and genetic diversity of populations residing in complex landscapes. Given the wide application of connectivity modelling tools in applied conservation planning, it is crucial to broadly evaluate how these models perform across resistance, movement, and population structure conditions in predicting genetic diversity patterns. Such evaluations are critical to provide rigorous, biologically based guidance for conservation and management applications. ObjectivesOur goal was to investigate how the predictions of three connectivity models were related to spatial patterns of genetic diversity complex landscapes, considering factors such as population structure, resistance, genetic drift, genetic disequilibrium, and organism movement abilities. MethodsWe evaluated the performance of several connectivity methods across seven a priori landscape resistance surfaces to provide a broad assessment of their performance. We used CDPOP, an individual-based, spatially explicit population and genetic simulation model, to simulate genetic diversity across these resistance surfaces. This provided a pool of genetic diversity patterns that were the response factor in our simulation experiment. We then simulated landscape connectivity with several popular connectivity methods, including resistant kernels, Circuitscape, and Pathwalker, and evaluated how well they were able to predict spatial patterns of genetic diversity. ResultsResistant kernel outperformed other connectivity methods in predicting landscape patterns of genetic diversity. The strongest relationships occurred when the population process has created spatial structure but has not yet led to significant genetic diversity loss due to drift. The time lag disequilibrium was relatively short. Long simulation times resulted in severe reduction in prediction ability due to drift. ConclusionsResistant kernel predictions were much more strongly related to spatial patterns of genetic diversity than were predictions produced by Circuitscape and Pathwalker, across a large combination of population structures. Strong relationships exist between functional connectivity and genetic diversity, with clearer and stronger associations seen in spatial patterns of allelic richness compared to heterozygosity or spatial effective population size. Our results confirm the strong relationship between genetic diversity and population connectivity, and suggest that computationally efficient incidence function algorithms, such as resistant kernel methods, are well suited to predicting functional connectivity.

Urinary metals are associated with obesity in U.S. children and adolescents: A cross-sectional study

Heavy metals are pervasive in the environment, and exposure to these metals may contribute to obesity in children and adolescents. We hypothesized that metal exposures are associated with obesity in children and adolescents. Data were drawn from children and adolescents aged 6 to 19 years from the 2007 to 2018 National Health and Nutrition Examination Survey. We employed weighted multivariate logistic regression and restricted cubic spline to explore the effects of individual metal exposures on obesity, and weighted quantile sum regression, quantile g-computed regression, and Bayesian kernel machine regression to explore the effects of mixed metal exposures on obesity. Subgroup analyses by gender were also performed. All models were adjusted for age, gender, race, poverty to income ratio, and serum cotinine. Among the 3,650 children and adolescents studied, 21.04% had obesity. Logistic regression revealed positive associations between barium (OR = 1.23, 95% CI: 1.07-1.40) and thallium (OR = 1.55, 95% CI: 1.23-2.15) with obesity, while cadmium (OR = 0.74, 95% CI: 0.61-0.89), cobalt (OR = 0.51, 95% CI: 0.41-0.62), and lead (OR = 0.70, 95% CI: 0.57-0.86) were negatively associated with obesity. Restricted cubic spline indicated a nonlinear relationship between lead and thallium and obesity. Quantile g-computed regression demonstrated that mixed metal exposure was negatively associated with obesity (OR = 0.50, 95% CI: 0.42-0.59). Subgroup analyses revealed a gender-specific effect for mercury (P for interaction = 0.03), which was negatively associated with obesity in females (OR = 0.83, 95% CI: 0.69-0.99). In conclusion, metal exposures are associated with obesity in children and adolescents, with gender differences.

Elementomics of 32 elements in cord serum depicts the risk of orofacial clefts: A case-control study in Shanxi, China

Maternal exposure to various metallic and non-metallic elements has been linked to the occurrence of orofacial clefts (OFCs), yet there remains a dearth of comprehensive research on the potential ramifications of simultaneous exposure to multiple elements. In this study, we investigated the individual and combined effects of element exposure on OFCs in a cohort of 168 pregnant women (49 cases and 119 controls) in the Shanxi province of northern China from 2010 to 2015. Cord serum samples were obtained from all participants to analyze the levels of 32 elements using inductively coupled plasma-mass spectrometry. The study examined the independent correlation between element concentrations and OFCs using two machine screening models, Boruta and Least Absolute Shrinkage and Selection Operator. Bayesian kernel machine regression (BKMR) was utilized to determine the combined effects of key exposure elements on OFCs and to clarify the interaction between exposed elements through the generalized additive model (GAM). The screening models identified lead (Pb), tin (Sn), iron (Fe), and cesium (Cs) as the most significant risk factors for OFC development in offspring. In the BKMR model, the probability of OFCs increased with higher overall levels of these risk elements, with Pb emerging as the primary contributor to the combined effect of the mixture. The findings of the GAM indicated that the combined exposure to Pb and Sn had a synergistic effect on the risk of developing OFCs. Analysis of elemental exposure in umbilical cord serum suggested that Pb exposure may have detrimental effects on OFC development in offspring, which may be further intensified by a synergistic interaction between Sn and Pb in the occurrence of OFCs.

Functional partially linear single index models with measurement error in both functional and real covariates

In this paper, we investigate the functional partially linear single index models with contaminated functional and real covariates when the functional sample grid is dense. To eliminate the influence of measurement error on the estimations of the proposed models, a two-stage estimation procedure is introduced. In the first stage, we utilize a kernel presmoothing regression to denoise the corrupted functional curves. In the second stage, the fitted functional curves are applied to replace the original ones to obtain the predictor of unknown parameter in parametric component with the attenuation correction method and then we derive the predictor of unknown link function in nonparametric part with kernel method. Meanwhile, in order to improve computational efficiency, functional slice inverse regression method is employed to estimate the functional single index parameter. The asymptotic properties are also stated in this process at the situation of independent and identical distribution sample under some mild conditions. A simulation study and real data analysis are further explored to illustrate the good performance of our methodology.

Exposure to Multiple Metal(loid)s and Hypertension in Chinese Older Adults.

Evidence about effects of metal(loid)s on hypertension among adults is insufficient. The aim of our study was to evaluate the individual and joint associations between seven selected metal(loid)s and hypertension, including lead (Pb), manganese (Mn), nickel (Ni), arsenic (As), cadmium (Cd), chromium (Cr), and vanadium (V)) in Chinese older adults. This study included 1009 older adults, and the blood concentrations of seven metal(loid)s were evaluated by inductively coupled plasma mass spectrometry (ICP-MS). The following conditions were considered as hypertension: (1) either systolic blood pressure ≥ 140mm Hg or diastolic blood pressure ≥ 90mm Hg, (2) a self-reported history of hypertension, or (3) currently taking antihypertensive medications. Logistic regression was utilized to investigate the association between individual metal(loid) and hypertension, while Bayesian kernel machine regression (BKMR) was employed to investigate the association of the metal(loid) mixture with hypertension. Adjusted single-metal(loid) model showed a significant positive association between Pb and hypertension (OR = 1.24, 95%CI = 1.03-1.50). This significant association still existed in multi-metal(loid) model (OR = 1.22, 95%CI = 1.01-1.47). BKMR further indicated a positive linear association of Pb with hypertension. The metal(loid) mixture was positively associated with hypertension in older adults, although not significant. Within the mixture, Pb had the highest posterior inclusion probabilities value (PIP = 0.9192). There were multiplicative interactions of Pb and Mn on hypertension. In addition, Pb and Mn had additive effects on the association of other blood metal(loid)s with hypertension. The associations of multiple metal(loid)s with hypertension are dependent on diabetes, areas, age, and BMI. The metal(loid) mixture exposure may contribute to hypertension in Chinese older adults, mainly driven by Pb and interactions of Pb and Mn. Reducing exposure to these metal(loid)s may prevent hypertension among older adults, which is especially true for those living with diabetes.

Association between urinary mixture metal levels and olfactory function in coal miners.

Exposure to occupational metallic mixtures has a potential impact on olfactory function. However, research evidence is limited on the potential impact of exposure to metallic mixtures and olfactory dysfunction. Furthermore, the coal dust generated contains multiple various metals during coal mining, and no study yet has focus on the olfactory dysfunction of coal miners. In this study, we evaluate the association between urinary metallic mixtures and olfactory function in coal miners, while also exploring the potential applicability of plasma olfactory marker protein (OMP) as a biomarker for assessing olfaction. From July to October 2023, coal workers from seven different coal mining enterprises were recruited for the survey when they come for the employee health checkup. Ultimately, 376 participants were met the inclusion criteria and, respectively, determined with the concentrations of urine (16 metals) and plasma (OMP). Meanwhile, applying UPSIT to access their olfactory function. Binary logistic regression and restricted cubic spline (RCS) model were used to estimate the association of individual metals with olfactory function. Bayesian kernel machine regression (BKMR) and Quantile g-computation (QG-C) regression were employed to assess the overall association between metal mixtures and olfactory function and identify the major contributing elements. In a single-metal model, two metals in urine were found to be significantly associated with olfactory function. RCS analysis further revealed that the association between Iron (Fe) and olfactory function was linear, while Lead (Pb) exhibited a non-linear. The BKMR model demonstrated a significant positive association between metal mixture concentration and olfactory function. Combined QG-C regression analysis suggested that metals Cr, Fe, Se, Sb, and Pb could impact the performance of the olfactory test (UPSIT), with Pb being identified as the most influential contributor. The correlation between plasma OMP protein levels and urinary metal concentrations was weak. Multiple metals are associated with olfactory function in the coal miners. A significant positive association was observed between metal mixture concentrations and olfactory function, with Pb being the most important contributor. In this study, plasma OMP has not been demonstrated to serve as a biomarker for olfactory function.

Prenatal co-exposure to phthalate metabolites and bisphenols among non-syndromic cleft lip and/or palate in offspring

Phthalate metabolites and bisphenols can cause adverse pregnancy outcomes. However, there is no study to evaluate the associations of prenatal exposure to phthalate metabolites and bisphenols with non-syndromic cleft lip and/or palate (NSCL/P) risk in offspring. A population-based case-control study was conducted in a multicenter setting from 2005 to 2021, enrolling 448 pregnant women. Seven phthalate metabolites and six bisphenols were quantified in placenta using liquid chromatography-tandem mass spectrometry. In the logistic regression analysis, high levels of mono-ethyl phthalate, mono-cyclohexyl phthalate, mono-octyl phthalate, bisphenol A, bisphenol AF, bisphenol AP, and fluorene-9-bisphenol were associated with increased NSCL/P risk with odds ratios (95% confidence intervals) of 1.86(1.07,3.25), 6.56(3.47,12.39), 8.49(4.44,16.24), 8.34(4.32,16.08), 3.19(1.81,5.62), 2.78(1.59,4.86), and 5.16(2.82,9.44). The Bayesian kernel machine regression model revealed that co-exposure to phthalate metabolites and bisphenols was associated with increased NSCL/P risk. Similarly, quantile-based g-computation analysis indicated that each quantile increase in mixture concentration was positively related to higher risk for NSCL/P [odds ratio (95% confidence interval) = 2.98(1.97,4.51)]. This study provides novel evidence that prenatal single and co-exposure to phthalate metabolites and bisphenols were associated with increased NSCL/P risk, suggesting that exposure to phthalate metabolites and bisphenols during pregnancy should be minimized to reduce the incidence of NSCL/P in offspring.

Associations between single and combined exposures to environmental phenols and ulcerative colitis in American adults

ObjectiveThe etiology of ulcerative colitis (UC) is complex and involves multiple factors, with exposure to environmental toxins potentially contributing greatly to its pathogenesis. Therefore, this study was carried out with the purpose of delving into the associations between single and combined exposures to environmental phenols and UC among American adults. MethodsSurvey data from the 2009–2010 National Health and Nutrition Examination Survey were selected for our research. The associations between single and combined exposures to environmental phenols and the prevalence of UC were analyzed using weighted multivariate logistic regression models as well as Bayesian kernel machine regression (BKMR). ResultsA total of 1,422 adults aged 20 years old and above were included in this study, 17 of whom had UC. The correlation matrix showed strong associations between 2,4-dichlorophenol (2,4-DCP) and 2,5-dichlorophenol (2,5-DCP) (R = 0.81), as well as between 2,4,5-trichlorophenol (2,4,5-TCP) and 2,4,6-trichlorophenol (2,4,6-TCP) (R = 0.73). The logistic regression model revealed that, after adjusting for confounders, exposure to environmental phenols was positively associated with the prevalence of UC, with 2,4,6-TCP showing a significant association (OR = 2.37, 95 % CI = 1.10, 5.09, P = 0.037). The BKMR analysis indicated an upward trend in the overall effect of combined exposures to environmental phenols on UC. All five phenols contributed to this effect, with 2,4,6-TCP exhibiting the most pronounced effect. When other compounds were fixed at the 50th percentile, the impact of the five phenols on UC demonstrated a positive association, without any noteworthy interaction among the compounds. ConclusionOur findings suggested that exposure to environmental phenols may contribute to the occurrence of UC among American adults.

Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model.

Dioxins are ubiquitous endocrine-disrupting substances, but determining the effects and benchmark doses in situations of coexposure is highly challenging. The objective of this study was to assess the relationship between dioxin andgestational diabetes mellitus (GDM), calculate the benchmark dose (BMD) of dioxin in coexposure scenarios, and derive a daily exposure threshold using an optimized physiologically based toxicokinetic (PBTK) model. Based on a nested case-control study including 77 cases with GDM and 154 controls, serum levels of 29 dioxin-like compounds (DLCs) along with 10 perfluoroalkyl acids (PFAAs), seven polybrominated diphenyl ethers (PBDEs), and five non-dioxin-like polychlorinated biphenyls (ndl-PCBs) were measured at 9-16 weeks of gestation. Bayesian machine kernel regression (BKMR) was employed to identify significant chemicals, and probit and logistic models were used to calculate BMD adjusted for significant chemicals. A physiologically based toxicokinetic (PBTK) model was optimized using polyfluorinated dibenzo-p-dioxins and dibenzofurans (PFDD/Fs) data by the Bayesian-Monte Carlo Markov chain method and was used to determine the daily dietary exposure threshold. The median serum level of total dioxin toxic equivalent (TEQ) was 7.72 pg TEQ/g fat. Logistic regression analysis revealed that individuals in the fifth quantile of total TEQ level had significantly higher odds of developing GDM compared to those in the first quantile (OR, 8.87; 95% CI 3.19, 27.58). The BKMR analysis identified dioxin TEQ and BDE-153 as the compounds with the greatest influence. The binary logistic and probit models showed that the BMD10 (benchmark dose corresponding to a 10% extra risk) and BMDL10 (lower bound on the BMD10) were 3.71 and 3.46 pg TEQ/g fat, respectively, when accounting for coexposure to BDE-153 up to the 80% level. Using the optimized PBTK model and modifying factor, it was estimated that daily exposure should be below 4.34 pg TEQ kg-1 bw week-1 in order to not reach a harmful serum concentration for GDM. Further studies should utilize coexposure statistical methods and physiologically based pharmacokinetic (PBTK) models in reference dose calculation.

Revealing the links between hair metal(loids) and alterations in blood pressure among children in e-waste recycling areas through urinary metabolomics

Hypertension is prevalent in e-waste recycling areas, and elevated blood pressure in children significantly increases the risk of hypertension in adulthood. However, the associations and toxic pathways between chronic exposure to metal(loids) and elevated blood pressure are rarely investigated. In this study, we measured the levels of 29 hair metal(loids) (chronic exposure biomarkers) and blood pressure in 667 susceptible children from an e-waste recycling area to explore their relationships. Paired urine metabolomics analysis was also performed to interpret potential mechanistic pathways. Results showed that the hypertension prevalence in our recruited children (13.0 %) exceeded the average rate (9.5 %) for Chinese children aged 6–17 years. The top five abundant metal(loids), including lead, strontium, barium, and zinc, demonstrated the most profound associations with elevated systolic blood pressure. Quantile g-computation, weighted quantile sum, and Bayesian kernel machine regression analysis jointly demonstrated a significant association between chronic exposure to metal(loids) mixture and systolic blood pressure. Interestingly, selenium showed significant antagonistic interactions with these four metals, suggesting that supplementing selenium may help children resist the elevated blood pressure induced by metal(loids) exposure. Increased metal(loids) and blood pressure levels were significantly linked to changes in urine metabolomics. Structural equation model indicated that androsterone glucuronide and N-Acetyl-1-aspartylglutamic acid were the significant mediators of the associations between metal(loids) and blood pressure, with mediation effects of 77.4 % and 29.0 %, respectively, suggesting that androsterone glucuronide and N-Acetyl-1-aspartylglutamic acid may be involved in the development of metal-induced blood pressure elevating effect. Girls were more vulnerable to metal(loids)-induced hormonal imbalance, especially androsterone glucuronide, than boys. Chronic exposure to metal(loids) at e-waste recycling sites may contribute to elevated blood pressure in children through disrupting various metabolism pathways, particularly hormonal balance. Our study provides new insights into potential mechanistic pathways of metal(loids)-induced changes in children's blood pressure.