Kernel Methods Research Articles

Association between spectrum of mycotoxins and semen quality: A cross-sectional study in Beijing, China

Exposure to mycotoxins is unavoidable in daily life through ingestion, dermal, and inhalation routes. Toxicological studies found that exposure to mycotoxins might affect male reproductive function. However, there is still a lack of population evidence. We aimed to assess the association of individual and joint exposure to spectrum of mycotoxins with semen quality. The present study included 192 participants in Beijing, China. We measured conventional semen parameters and assessed semen quality. Sixty-seven traditional or emerging mycotoxins were determined to describe the spectrum of mycotoxins. The participants were widely exposed to multiple mycotoxins, and nearly half were simultaneously exposed to more than six mycotoxins. After adjusting potential confounders, logistic regression indicated that the number and concentration of plasma mycotoxin were correlated to the risk of low semen quality. Plasma beauvericin and citrinin concentrations were associated with lower semen quality. The least absolute shrinkage and selection operator regression showed similar results to logistic regression. Quantile-based g-computation and Bayesian kernel machine regression models found that the mixture of mycotoxins was harmful to semen quality, especially in sperm motility. In conclusion, both individual and mixture of mycotoxin exposure were correlated with lower semen quality. More regulations and measures should be taken to reduce mycotoxin contamination.

A prospective cohort study of multimetal exposure and risk of gestational diabetes mellitus

The relationship between co-exposure to multiple metals and gestational diabetes mellitus (GDM) and the mechanisms involved are poorly understood. In this nested case-control study, 228 GDM cases and 456 matched controls were recruited, and biological samples were collected at 12–14 gestational weeks. The urinary concentrations of 10 metals and 8-hydroxydeoxyguanosine (8-OHdG) as well as the serum levels of malondialdehyde (MDA) and advanced glycation end products (AGEs) were determined to assess the association of metals with GDM risk and the mediating effects of oxidative stress. Urinary Ti concentration was significantly and positively associated with the risk of GDM (odds ratio [OR]:1.45, 95 % confidence interval [CI]: 1.12, 1.88), while Mn and Fe were negatively associated with GDM risk (OR: 0.67, 95 % CI: 0.50, 0.91 or OR: 0.61, 95 % CI: 0.47, 0.80, respectively). A significant negative association was observed between Mo and GDM risk, specifically in overweight and obese pregnant women. Bayesian kernel machine regression showed a significant negative joint effect of the mixture of 10 metals on GDM risk. The adjusted restricted cubic spline showed a protective role of Mn and Fe in GDM risk (P < 0.05). A significant negative association was observed between essential metals and GDM risk in quantile g-computation analysis (P < 0.05). Mediation analyses showed a mediating effect of MDA on the association between Ti and GDM risk, with a proportion of 8.7 % (P < 0.05), and significant direct and total effects on Ti, Mn, and Fe. This study identified Ti as a potential risk factor and Mn, Fe, and Mo as potential protective factors against GDM, as well as the mediating effect of lipid oxidation.

Association between heavy metal mixed exposure and neonatal birth weight in pregnancy

The impact of prenatal exposure to a mixture of heavy metals on birth weight in newborns has been a topic of ongoing interest. In this study, 258 mothers and infants from the New Hampshire Birth Cohort Study (NHBCS) were selected as the study subjects, and the concentrations of seven heavy metals in the placenta, including Aluminum (Al), Cobalt (Co), Chromium (Cr), Nickel (Ni), Plumbum (Pb), Selenium (Se) and Arsenic (As) were collected. And the birth weight of newborns, the relevant covariates of mothers and newborns were collected. Three analytical methods, Weighted Quantile Sum (WQS) regression, Quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR) were employed. After adjusting for maternal gestational age, pre-pregnancy BMI, smoking status, education level, parity, gestational age and newborn gender, the combined three methods showed that the total effect of mixed exposure of seven heavy metals on birth weight was negative. Specifically, the WQS analysis revealed that Se had the greatest impact on birth weight, followed by Al. The QGC results showed that the heavy metal associated with the reduction of birth weight was mainly Se and Al in female and male infants, respectively. The BKMR analysis demonstrated a negative combined effect of the seven heavy metals on birth weight in both male and female infants, with Se having the highest posterior inclusion probabilities (PIPs) for female infants (0.45), and Al having the highest PIPs for male infants (0.64) after stratification by gender. In summary, mixed exposure to heavy metals during pregnancy was associated with a decrease in newborn birth weight. Furthermore, there are gender effects with Se and Al associated with decreased birth weight in female and male infants, respectively. These findings provide a theoretical basis for the development of public health policies aimed at preventing adverse pregnancy outcomes and improving the health of newborns.

Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Prenatal EDC exposure, DNA Methylation, and early childhood growth: A prospective birth cohort study

BackgroundExposure to endocrine-disrupting chemicals (EDCs) has been found to be associated with growth and developmental abnormalities in children. However, the potential mechanisms by which exposure to EDCs during pregnancy increases the risk of obesity in children remain unclear. ObjectiveWe aimed to explore associations between prenatal EDC exposure and the body mass index (BMI) of children at age two, and to further explore the potential impact of DNA methylation (DNAm). MethodThis study included 285 mother–child pairs from a birth cohort conducted in Wuhan, China. The BMI of each child was assessed at around 24 months of age. The concentrations of sixteen EDCs at the 1st, 2nd, and 3rd trimesters were measured using ultra-high performance liquid chromatography coupled to a triple quadrupole mass spectrometer. The research utilized general linear models, weighted quantile sum regression, and Bayesian Kernel Machine Regression to assess the association between prenatal EDC exposure and childhood BMI z-scores (BMIz). Cord blood DNAm was measured using the Human Methylation EPIC BeadChip array. An epigenome-wide DNAm association study related to BMIz was performed using robust linear models. Mediation analysis was then applied to explore potential mediators of DNAm. ResultsUrinary concentrations of seven EDCs were positively associated with BMIz in the 1st trimester, which remained significant in the WQS model. A total of 641 differential DNAm positions were associated with elevated BMIz. Twelve CpG positions (annotated to DUXA, TMEM132C, SEC13, ID4, GRM4, C2CD2, PRAC1&PRAC2, TSPAN6 and DNAH10) mediated the associations between urine BP-3/BPS/MEP/TCS and elevated BMIz (P < 0.05). ConclusionOur results revealed that prenatal exposure to EDCs was associated with a higher risk of childhood obesity, with specific DNAm acting as a partial mediator.

Inflammation as a pathway for heavy metal-induced liver damage—Insights from a repeated-measures study in residents exposed to metals and bioinformatics analysis

BackgroundEpidemiological studies on heavy metal exposure and liver injury are predominantly cross-sectional, lacking longitudinal data and exploration of potential mechanisms. MethodWe conducted a repeated-measures study in Northeast China from 2016 to 2019, involving 322 participants. Linear mixed models (LMM) and Bayesian kernel machine regression (BKMR) were employed to explore the associations between individual and mixed blood metal concentrations [chromium (Cr), cadmium (Cd), vanadium (V), manganese (Mn), lead (Pb)] and liver function biomarkers [alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), globulin (GLB), total protein (TP)]. Mediation and enrichment analyses were used to determine whether the inflammatory response is a critical pathway for heavy metal-induced liver damage. ResultWe obtained a total of 958 observations. The results from LMM and BKMR indicated significant associations between individual and mixed heavy metals and liver function biomarkers. Longitudinal analysis revealed associations between Cd and the annual increase rate of ALT (β = 2.61; 95% CI: 0.97, 4.26), the annual decrease rate of ALB (β = −0.21; 95% CI: −0.39, −0.03), Mn and the annual increase rate of GLB (β = 0.38; 95% CI: 0.05, 0.72), and V and the annual decrease rate of ALB/GLB (β = −1.15; 95% CI: −2.00, −0.31). Mediation analysis showed that high-sensitivity C-reactive protein (hsCRP) mediated the associations between Cd and AST, TP, with mediation effects of 27.7% and 13.4%, respectively. Additionally, results from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses supported the role of inflammatory response pathways. ConclusionOur findings indicate that heavy metal exposure leads to liver damage, with the inflammatory response potentially serving as a crucial pathway in this process. This study offers a novel perspective on understanding heavy metal-induced liver injury and provides insights for preventive measures against the health damage caused by heavy metals.

Prenatal exposure to polycyclic aromatic hydrocarbons and phthalate acid esters and gestational diabetes mellitus: A prospective cohort study

BackgroundPolycyclic aromatic hydrocarbons and phthalate acid esters (PAHs & PAEs), known as endocrine disrupting chemicals (EDCs), widely exist in daily life and industrial production. Previous studies have suggested that PAHs & PAEs may modify the intrauterine homeostasis and have adverse effects on fetal development. However, epidemiological evidence on the associations between PAHs & PAEs and gestational diabetes mellitus (GDM) is still limited. ObjectiveTo investigate the effects of prenatal PAHs &PAEs exposure on the risk of GDM and hyperglycemia in pregnant women. MethodsThe study population was a total of 725 pregnant women from a prospective birth cohort study conducted from December 2019 to December 2021. Blood glucose levels were collected by the hospital information system. Urinary PAHs & PAEs concentrations were determined by gas chromatography tandem mass spectrometry. The Poisson regression in a generalized linear model (GLM), multiple linear regression, quantile-based g-computation method (qgcomp), and Bayesian kernel machine regression (BKMR) were applied to explore and verify the individual and overall effects of PAHs & PAEs on glucose homeostasis. Potential confounders were adjusted in all statistical models. ResultsA total of 179 (24.69%) women were diagnosed with GDM. The Poisson regression suggested that a ln-unit increment of 4-OHPHE (4-hydroxyphenanthrene) (adjusted Risk Ratio (aRR) = 1.13; 1.02–1.26) was associated with the increased GDM risk. Mixed-exposure models showed similar results. We additionally found that MBZP (mono-benzyl phthalate) (aRR = 1.19; 1.02–1.39) was positively related to GDM risk in qgcomp model. Although neither model demonstrated that 2-OHNAP (2-hydroxynaphthalene) and 9-OHFLU (9-hydroxyfluorene) increased the risk of GDM, 2-OHNAP and 9-OHFLU exposure significantly increased blood glucose levels. BKMR model further confirmed that overall effects of PAHs & PAEs were significantly associated with the gestational hyperglycemia and GDM risk. ConclusionsOur study presents that environmental exposure to PAHs & PAEs was positively associated with gestational glucose levels and the risks of developing GDM. In particular, 2-OHNAP, 9-OHFLU, 4-OHPHE and MBZP may serve as important surveillance markers to prevent the development of GDM.

Association of urinary metal element with semen quality: a cross-sectional study from Eastern China.

The effect of metallic elements on semen quality remains controversial, with limited evidence on the effects of metal mixtures. We conducted a study involving 338 participants from multiple centers in Eastern China, measuring 17 urinary metals and semen quality parameters. Our analysis used various statistical models, including multivariate logistic and linear regression, Bayesian Kernel Machine Regression, and weighted quantile sum models, to examine the associations between metal levels and semen quality. Logistic regression showed that higher urinary lead was associated with increased risk of abnormal sperm concentration (OR = 1.86, p = 0.021), arsenic to higher abnormal progressive motility risk (OR = 1.49, p = 0.027), and antimony to greater abnormal total motility risk (OR = 1.37, p = 0.018). Conversely, tin was negatively correlated with the risk of abnormal progressive motility (OR = 0.76, p = 0.012) and total motility (OR = 0.74, p = 0.003), respectively. Moreover, the linear models showed an inverse association between barium and sperm count, even after adjusting for other metals (β = - 0.32, p < 0.001). Additionally, the WQS models showed that the metal mixture may increase the risk of abnormal total motility (βWQS = 0.55, p = 0.046). In conclusion, semen quality may be adversely affected by exposure to metals such as arsenic, barium, lead, and antimony. The combined effect of the metal mixture appears to be particularly impaired total motility.

P-591 Per- and Polyfluoroalkyl Substances Serum Concentrations and Polycystic Ovary Syndrome among Women Attending a Fertility Clinic

Abstract Study question What is the association between serum per- and poly-fluoroalkyl substances (PFAS) concentrations and polycystic ovary syndrome (PCOS) risk in populations with low-level PFAS exposure. Summary answer Higher serum concentrations of perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonate (PFHxS) were associated with elevated risk of PCOS among women attending a U.S. fertility clinic. What is known already Previous studies reported that exposure to high-levels of PFAS exposure was associated with elevated risk of PCOS. However, studies evaluating PCOS risk in populations with exposure to lower levels of PFAS are limited. Study design, size, duration A cohort study of 502 females who sought fertility evaluation for medically assisted reproduction in a U.S. fertility clinic and had similar PFAS exposure level as the general U.S. population. Participants/materials, setting, methods Nine PFAS were quantified in non-fasting serum samples collected at study entry. Diagnosis of PCOS was based on the Rotterdam criteria after study entry before treatment. We used logistic regression to examine the odds ratio (OR) of PCOS in relation to PFAS concentrations (continuous and by tertiles). We applied quantile g-computation (QGC) and Bayesian Kernel Machine Regression (BKMR) to examine the joint associations of PFAS mixture with PCOS. Main results and the role of chance A total of 38 (7.6%) females were diagnosed with PCOS. Doubling of serum PFOS and PFHxS concentrations were associated with higher odds of PCOS [OR (95%CI): 1.70 (1.06, 2.81) and 1.45 (1.02, 2.08), respectively]. There was a dose-response relationship of PFOS with PCOS risk (p of trend by tertiles = 0.07). Both QGC and BKMR identified PFOS as the most important contributor among the PFAS mixture to PCOS risk. No clear joint effect was found for other examined PFAS nor for PFAS as a total mixture on PCOS risk. Limitations, reasons for caution Findings in this subfertile population may not be directly generalizable to reproductive aged women in the general population. Wider implications of the findings Our findings are consistent with existing evidence from populations with higher concentrations of some of the evaluated PFAS and supports the potential adverse effects of PFAS on female reproductive function. Larger prospective studies are needed to validate the findings. Trial registration number NA

A general kernel machine regression framework using principal component analysis for jointly testing main and interaction effects: Applications to human microbiome studies

Abstract The effect of a treatment on a health or disease response can be modified by genetic or microbial variants. It is the matter of interaction effects between genetic or microbial variants and a treatment. To powerfully discover genetic or microbial biomarkers, it is crucial to incorporate such interaction effects in addition to the main effects. However, in the context of kernel machine regression analysis of its kind, existing methods cannot be utilized in a situation, where a kernel is available but its underlying real variants are unknown. To address such limitations, I introduce a general kernel machine regression framework using principal component analysis for jointly testing main and interaction effects. It begins with extracting principal components from an input kernel through the singular value decomposition. Then, it employs the principal components as surrogate variants to construct three endogenous kernels for the main effects, interaction effects, and both of them, respectively. Hence, it works with a kernel as an input without knowing its underlying real variants, and also detects either the main effects, interaction effects, or both of them robustly. I also introduce its omnibus testing extension to multiple input kernels, named OmniK. I demonstrate its use for human microbiome studies.

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.

Velocity divergence—Generalized adaptive probability density evolution method

AbstractThis study proposes a novel velocity divergence‐generalized adaptive probability density evolution method (VD‐GAPDEM) for calculating the probability density function of the stochastic response process of stochastic structures under stochastic dynamic loads. First, based on the principle of probability conservation, the velocity divergence‐generalized adaptive probability density evolution equation (VD‐GAPDEE) is derived for a stochastic system that can effectively consider the shape and location changes of the joint transitional probability density of representative points (RPs) in the stochastic response process. Second, a novel VD‐GAPDEM is proposed to solve the VD‐GAPDEE directly using the point selection technique based on the generalized F discrepancy and the second‐order Runge–Kutta method with a smoothing kernel method (Runge–Kutta‐SKFAM). Furthermore, the differences and connections between VD‐GAPDEM and the existing probability density evolution method are analyzed. Additionally, the high computational efficiency and accuracy of the proposed VD‐GAPDEM are demonstrated through three typical examples of stochastic response analysis, involving stochastic systems subjected to stochastic dynamic loads.

Learning With Incremental Instances and Features.

In many real-world applications, data may dynamically expand over time in both volume and feature dimensions. Besides, they are often collected in batches (also called blocks). We refer this kind of data whose volume and features increase in blocks as blocky trapezoidal data streams. Current works either assume that the feature space of data streams is fixed or stipulate that the algorithm receives only one instance at a time, and none of them can effectively handle the blocky trapezoidal data streams. In this article, we propose a novel algorithm to learn a classification model from blocky trapezoidal data streams, called learning with incremental instances and features (IIF). We attempt to design highly dynamic model update strategies that can learn from increasing training data with an expanding feature space. Specifically, we first divide the data streams obtained on each round and construct the corresponding classifiers for these different divided parts. Then, to realize the effective interaction of information between each classifier, we utilize a single global loss function to capture their relationship. Finally, we use the idea of ensemble to achieve the final classification model. Furthermore, to make this method more applicable, we directly transform it into the kernel method. Both theoretical analysis and empirical analysis validate the effectiveness of our algorithm.

Association between Personal Abiotic Airborne Exposures and Body Composition Changes among Healthy Adults (60-69 Years Old): A Combined Exposome-Wide and Lipidome Mediation Approach from the China BAPE Study.

Evidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults. Our research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome. From September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60-69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators. The ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropylphenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate ]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi--cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24:1)] mediated a significant portion, ranging from 12.27% to 26.03% (p-value ), of the observed increase in VFA. Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. https://doi.org/10.1289/EHP13865.

Nanoscale gold nanoparticle (GNP)-laden tumor cell model and its use for estimation of intracellular dose from GNP-induced secondary electrons.

Gold nanoparticles (GNPs) accumulated within tumor cells have been shown to sensitize tumors to radiotherapy. From a physics point of view, the observed GNP-mediated radiosensitization is due to various downstream effects of the secondary electron (SE) production from internalized GNPs such as GNP-mediated dose enhancement. Over the years, numerous computational investigations on GNP-mediated dose enhancement/radiosensitization have been conducted. However, such investigations have relied mostly on simple cellular geometry models and/or artificial GNP distributions. Thus, it is at least desirable, if not necessary, to conduct further investigations using cellular geometry models that properly reflect realistic cell morphology as well as internalized GNP distributions at the nanoscale. The primary aim of this study was to develop a nanometer-resolution geometry model of a GNP-laden tumor cell for computational investigations of GNP-mediated dose enhancement/radiosensitization. The secondary aim was to demonstrate the utility of this model by quantifying GNP-induced SE tracks/dose distribution at sub-cellular levels for further validation of a nanoscopic dose point kernel (nDPK) method against full-fledged Geant4 Monte Carlo (MC) simulation. A transmission electron microscopy (TEM) image of a single cell showing cytoplasm, cellular nucleus, and internalized GNPs in the cellular endosome was segmented into sub-cellular levels based on pixel value thresholding. A corresponding material density was allocated to each pixel, and, by adding a thickness, each pixel was transformed to a geometric voxel and imported as a Geant4-acceptable input geometry file. In Geant4-Penelope MC simulation, a clinical 6MV photon beam was applied, vertically or horizontally to the cell surface, and energy deposition to the cellular nucleus and cytoplasm, due to SEs emitted by internalized GNPs, was scored. Next, nDPK calculations were performed by generating virtual electron tracks from each GNP voxel to all nucleus and cytoplasm voxels. Subsequently, another set of Geant4 simulation was performed with both Penelope and DNA physics models under the geometry closely mimicking in vitro cell irradiation with a clinical 6MV photon beam, allowing for derivation of nDPK specific to this geometry and further comparison between Gean4 simulation and nDPK method. The Geant4-calculated SE tracks and associated energy depositions showed significant dependence on photon incidence angle. For perpendicular incidence, nDPK results showed good agreement (average percentage pixel-to-pixel difference of 0.4% for cytoplasm and 0.5% for nucleus) with Geant4 results, while, for parallel incidence, the agreement became worse (-1.7%-0.7% for cytoplasm and -5.5%-0.8% for nucleus). Under the 6MV cell irradiation geometry, nDPK results showed reasonable agreement (pixel-to-pixel Pearson's product moment correlation coefficient of 0.91 for cytoplasm and 0.98 for nucleus) with Geant4 results. The currently developed TEM-based model of a GNP-laden cell offers unprecedented details of realistic intracellular GNP distributions for nanoscopic computational investigations of GNP-mediated dose enhancement/radiosensitization. A benchmarking study performed with this model showed reasonable agreement between Geant4- and nDPK-calculated intracellular dose deposition by SEs emitted from internalized GNPs, especially under perpendicular incidence - a popular cell irradiation geometry and when the Geant4-Penelope physics model was used.

Association of metals and bisphenols exposure with lipid profiles and dyslipidemia in Chinese adults: Independent, combined and interactive effects

BackgroundAlthough studies have assessed the association of metals and bisphenols with lipid metabolism, the observed results have been controversial, and limited knowledge exists about the combined and interactive effects of metals and bisphenols exposure on lipid metabolism. MethodsPlasma metals and serum bisphenols concentrations were evaluated in 888 participants. Multiple linear regression and logistic regression models were conducted to assess individual associations of 18 metals and 3 bisphenols with 5 lipid profiles and dyslipidemia risk, respectively. The dose-response relationships of targeted contaminants with lipid profiles and dyslipidemia risk were captured by applying a restriction cubic spline (RCS) function. The bayesian kernel machine regression (BKMR) model was used to assess the overall effects of metals and bisphenols mixture on lipid profiles and dyslipidemia risk. The interactive effects of targeted contaminants on interested outcomes were explored by constructing an interaction model. ResultsSingle-contaminant analyses revealed that exposure to iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), strontium (Sr), and tin (Sn) was associated with elevated lipid levels. Cobalt (Co) showed a negative association with high density lipoprotein cholesterol (HDL-C). Bisphenol A (BPA) and bisphenol AF (BPAF) were associated with decreased HDL-C levels, with nonlinear associations observed. Vanadium (V), lead (Pb), and silver (Ag) displayed U-shaped dose-response relationships with most lipid profiles. Multi-contaminant analyses indicated positive trends between contaminants mixture and total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). The interaction analyses showed that Se-Fe exhibited synergistic effects on LDL-C and non-HDL-C, and Se-Sn showed a synergistic effect on HDL-C. ConclusionsOur study suggested that exposure to metals and bisphenols was associated with changes in lipid levels, and demonstrated their combined and interactive effects.

Health effect of multiple air pollutant mixture on sarcopenia among middle-aged and older adults in China

BackgroundAs the global aging process accelerates, the health challenges posed by sarcopenia among middle-aged and older adults are becoming increasingly prominent. However, the available evidence on the adverse effects of air pollution on sarcopenia is limited, particularly in the Western Pacific region. This study aimed to explore relationships of multiple air pollutants with sarcopenia and related biomarkers using the nationally representative database. MethodsTotally, 6585 participants aged over 45 years were enrolled from the China Health and Retirement Longitudinal Study (CHARLS) in 2011 and 3443 of them were followed up until 2015. Air pollutants were estimated from high-resolution satellite-based spatial-temporal models. In the cross-sectional analysis, we used generalized linear regression, unconditional logistic regression analytical and restricted cubic spline (RCS) methods to assess the single-exposure and non-linear effects of multiple air pollutants on sarcopenia and related surrogate biomarkers (serum creatinine and cystatin C). Several popular mixture analysis techniques such as Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) regression, and quantile-based g-computation (Qgcomp) were further used to examinate the combined effects of multiple air pollutants. Logistic regression was used to further analyze the longitudinal association between air pollution and sarcopenia. ResultsEach interquartile range increase in PM2.5, PM10 and NO2 was significantly associated with an increased risk of sarcopenia, with adjusted odds ratios (aORs) of 1.09 [95 % confidence interval (CI): 1.01, 1.20], 1.24 (95 % CI: 1.14, 1.35) and 1.18 (95 % CI: 1.08, 1.28), respectively. Our findings also showed that five air pollutants were significantly associated with the sarcopenia index. In addition, employing a mixture analysis approach, we confirmed significant combined effects of air pollution mixtures on sarcopenia risk and associated biomarkers, with PM10 and PM2.5 identified as major contributors to the combined effect. The results of the exposure-response (E-R) relationships, subgroup analysis, longitudinal analysis and sensitivity analysis all showed the unfavorable impact of air pollution on sarcopenia risk and related vulnerable populations. ConclusionsSingle-exposure and co-exposure to multiple air pollutants were positively associated with sarcopenia among middle-aged and older adults in China. Our study provided new evidence that air pollution mixture was significantly associated with sarcopenia related biomarkers.

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 (β: 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 (β: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.

Association of metals with early postnatal gut microbiota among infants admitted to the neonatal intensive care unit

The gut microbiota is closely related to infant health. However, the impact of environmental factors on the gut microbiota has not been widely investigated, particularly in vulnerable populations such as infants admitted to the neonatal intensive care unit (NICU). This study investigated the association between exposure to 12 metals and the composition of the gut microbiota in infants admitted to the NICU. Metal concentrations were determined in serum samples obtained from 107 infants admitted to the NICU at Hunan Children's hospital, China. Gut microbiota data were derived from 16S rRNA sequencing using stool samples. Generalized linear regression (GLR) models and Bayesian kernel machine regression (BKMR) analyses were used to estimate the associations between metals and both alpha-diversity indices and bacterial taxa. The GLR models showed that tin correlated negatively with the Shannon index (β = −0.55, 95% conficence interval [CI]: −0.79, −0.30, PFDR< 0.001) and positively with the Simpson index (β = 0.26, 95% CI: 0.13, 0.39, PFDR< 0.001). The BKMR analysis yielded similar results, showing that tin had the largest posterior inclusion probability for both the Shannon (0.986) and the Simpson (0.796) indices. Tin, cadmium, mercury, lead, and thallium were associated with changes in one or more taxa at the genus level. The BKMR analysis also revealed a negative correlation between metal mixtures and Clostridium_sensu_stricto, and tin contibuted mostly to the negative correlation. Early postnatal exposure to metals were associated with differences in the microbiome among infants admitted to the NICU. However, as the study was cross-sectional, these relationships must be confirmed in further studies.

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

AbstractExposure 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.