Kernel Methods Research Articles

Association Between Heavy Metals Mixtures and Life's Essential 8 Score in General US Adults.

Heavy metals were toxic environmental pollutants capable of entering the human body, posing significant risks to human health. Life's Essential 8 (LE8) score is a new comprehensive index constructed for quantifying cardiovascular health (CVH). However, the association between heavy metals mixtures and LE8 appears ambiguous. To investigated the association between heavy metals and cardiovascular health in US population. Urinary heavy metals concentrations (barium, cadmium, cobalt, manganese, molybdenum, lead, antimony, strontium, thallium, tin, tungsten, uranium, cesium) were Ln-transformed and LE8 was consisted of eight metrics. Single and multivariate linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression models (BKMR) were utilized to assess the association between single and mixed exposure of thirteen heavy metals concentrations and LE8. In 4339 participants from National Health and Nutrition Examination Survey 2007-2018, single urinary heavy metals barium, cadmium, cobalt, lead, antimony, strontium, tin, tungsten, uranium and cesium showed a significant negative association with LE8. WQS models showed heavy metals mixture was negatively associated with LE8 (β = - 2.720, 95% CI - 3.660, - 1.790). BKMR analysis also demonstrated a downward trend of heavy metals mixture and LE8. Both WQS analyzed weights and the conditional posterior inclusion probabilities (condPIP) of BKMR showed that cadmium (37.78%, condPIP = 1.000), barium (24.56%, condPIP = 0.537) and uranium (14.71%, condPIP = 0.646) contributed most for these negative associations. Single and mixed heavy metals, especially cadmium, barium and uranium were negatively associated with LE8 score, a new comprehensive CVH index, predicting an increasing risk of cardiovascular diseases.

Quantum adversarial learning for kernel methods

We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defense strategies based on data augmentation with a few crafted perturbations can make the classifier robust against new attacks. Our results find applications in security-critical learning problems and in mitigating the effect of some forms of quantum noise, since the attacker can also be understood as part of the surrounding environment.

Prenatal exposure to metal mixture and birth weight; a Bayesian kernel machine regression analysis of two cohort studies in Japan and Iran

Prenatal exposure to metal mixture and birth weight; a Bayesian kernel machine regression analysis of two cohort studies in Japan and Iran

Association between non-persistent pesticides and hypertension in adults: insights from NHANES

ABSTRACT We investigated the relation between non-persistent pesticides and hypertension using data from 4260 adults in NHANES (2007–2014). Urinary concentrations of biomarkers for non-persistent pesticides were served as the level of pesticide exposure. The hypertension prevalence was 42.6%. The weighted logistic regression revealed a significant positive association between para-nitrophenol (PNP) and hypertension (OR: 1.11, 95% CI: 1.03–1.20, p = 0.010), while the 3,5,6-trichloropyridinol (TCPY), 3-phenoxybenzoic acid (3-PBA) and 2,4-dichlorophenoxyacetic acid (2,4-D) showed no significant association with hypertension. The bayesian kernel machine regression (BKMR) also supported the positive association of PNP with hypertension. The restricted cubic spline (RCS) confirmed the relation between PNP and hypertension was linear. The weighted quantile sum (WQS) demonstrated a positive association between combined exposure to the four pesticide biomarkers and hypertension (OR: 1.16, 95%CI: 1.02–1.32, p = 0.027), with PNP contributing the most (weighted 0.663). Our study emphasized the relationship between exposure to PNP and hypertension.

Combined Effects of Social and Behavioral Factors on Stress and Depression

Background: Chronic stress, driven by the persistent activation of the body’s stress response system—including the sympathetic nervous system and hypothalamic–pituitary–adrenal (HPA) axis—has far-reaching effects on both physical and mental health. This study examines the combined effects of social and behavioral factors on a latent variable consisting of stress and depressive symptoms, using a comprehensive framework to explore the complex interactions of these factors. Methods: Leveraging data from the United States Centers for Disease Control and Prevention’s (CDC’s) National Health and Nutrition Examination Survey (NHANES), we operationalized allostatic load—a measure of cumulative physiological stress—through 10 biomarkers spanning cardiovascular, inflammatory, and metabolic systems. Depressive symptoms were measured via the Patient Health Questionnaire-9 (PHQ-9), and a latent variable capturing the shared variance between stress and depressive symptoms was derived using factor analysis. To assess the influence of social (income and education) and behavioral (alcohol consumption and smoking) factors on this latent variable, we employed Bayesian Kernel Machine Regression (BKMR), allowing us to examine potential non-linear and interactive effects among these predictors. Results: Our results revealed a significant positive association between allostatic load and depressive symptoms across the sample, regardless of ethnic background. Alcohol consumption emerged as a key behavioral factor, with significant positive associations with stress. Conversely, education showed a protective effect, with higher education levels associated with decreased stress and depressive symptoms. Conclusions: These findings underscore the importance of addressing both social determinants and behavioral risk factors in mitigating the cumulative impacts of stress and depressive symptoms. By highlighting the roles of alcohol consumption and education, this study provides insights that can inform public health strategies aimed at promoting resilience and reducing stress-related health disparities.

Associations between the intake of single and multiple dietary vitamins and depression risk among populations with chronic kidney disease

BackgroundThe effects of multivitamin exposure on depression among patients with chronic kidney disease (CKD) have not been thoroughly explored. This study aimed to explore the effects of individual vitamin intakes and the joint effect of the intake of multiple vitamins (including vitamins A, B1, B2, B6, B12, C, D, E, and K) on depression risk in participants with CKD.MethodsA total of 3,123 participants with CKD (weighted n = 25,186,480) from the National Health and Nutrition Examination Survey database from 2007 to 2014 were included. Weighted multivariate logistic regression models were utilized to analyze the associations of individual dietary vitamin intakes with depression risk. Additionally, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were performed to evaluate the joint effect of the intake of the nine vitamins on depression risk.ResultsThe overall prevalence of depression was approximately 11.3% in the study participants. In the fully adjusted model, high intakes of vitamin A (OR: 0.54, 95% CI: 0.40–0.74), vitamin B1 (OR: 0.67, 95% CI: 0.48–0.95), vitamin B6 (OR: 0.70, 95% CI: 0.49–0.99), vitamin D (OR: 0.67. 95% CI: 0.48–0.94), and vitamin K (OR: 0.61, 95% CI: 0.44–0.85) were associated with a reduced likelihood of depression. BKMR and WQS regression showed that the joint effect of the intake of the nine dietary vitamins had a significant negative effect on depression, with vitamin A intake being the largest contributor in the two models. Lastly, WQS regression reflected the total mixed exposure effect of the nine vitamins (OR: 0.82, 95% CI: 0.69–0.99).ConclusionHigh intakes of vitamins A, B1, B6, D, and K are associated with low depression risk in patients with CKD. Furthermore, co-exposure to the nine dietary vitamins is a crucial factor contributing to low depression risk in this population.

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015–2018

BackgroundAlveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logistic regression and Bayesian kernel machine regression. Overfitting using kernel function, long calculation period, the definition of prior distribution and lack of rank of heavy metal will affect the performance of the statistical model. Optimal model on this topic still remains controversy. This study aimed: (1) to develop an algorithm for exploring the association between heavy metal exposure and ABL; (2) filter the actual causal variables and investigate how heavy metals were associated with ABL; and (3) identify the potential risk factors for ABL.MethodsData were collected from National Health and Nutrition Examination Survey (NHANES) between 2015 and 2018 to develop a machine learning (ML) model. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. The selected data were balanced using the Synthetic Minority Oversampling Technique (SMOTE) and divided into a training set and testing set at a 3:1 ratio. Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), and XGboost were used to construct the ML model. Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC), Precision, Recall, and F1 score were used to select the optimal model for further analysis. The contribution of the variables to the ML model was explained using the Shapley Additive Explanations (SHAP) method.ResultsRF showed the best performance in exploring the association between heavy metal exposure and ABL, with an AUC (0.88), accuracy (0.78), precision (0.76), recall (0.83), and F1 score (0.79). Age was the most important factor in the ML model (mean| SHAP value| = 0.09), and Cd was the primary contributor. Sex had little effect on the ML model contribution.ConclusionIn this study, RF showed superior performance compared with the other five algorithms. Among the 12 heavy metals, Cd was the most important factor in the ML model. The relationship of Co & Pb and ABL are weaker than that of Cd. Among all the independent variables, age was considered the most important factor for this model. As for PIR, low-income participants present association with ABL. Mexican American and Non-Hispanic White show low association with ABL compared to Non-Hispanic Black and other races. Gender feature demonstrates a weak association with ABL. In the future, more advanced algorithms should be developed to validate these results and related parameters can be tuned to improve the accuracy of the model.Clinical trial numbernot applicable.

Quantum delegated and federated learning via quantum homomorphic encryption

Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients’ private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.

Emerging PFAS Exposure Is More Potent in Altering Childhood Lipid Levels Mediated by Mitochondrial DNA Copy Number.

Per- and polyfluoroalkyl substances (PFAS) pose potential health risks to lipid metabolism, but the effects of emerging PFAS alternatives, particularly in children, remain unclear. This cross-sectional study investigated the association between emerging PFAS exposure and lipid levels in 294 Chinese children aged 7-10 years, analyzing blood samples for 14 PFAS and lipid profiles, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). Exposure to 6:2 Cl-PFESA, PFO4DA, and PFO5DoDA was associated with higher TC, TG, and LDL levels, with PFO4DA increasing the TC by 1.7% and PFO5DoDA increasing the TG by 10.7%. Weighted quantile sum (WQS) regression showed mixed PFAS exposure positively associated with TG (0.08, 95% CI: 0.007, 0.153). PFO4DA had the highest weight for TC (0.468), TG (0.327), LDL (0.57), ApoA1 (0.243), and ApoB (0.466), while PFMOAA had the highest weight for HDL (0.332). Bayesian Kernel Machine Regression (BKMR) analysis confirmed positive associations between the PFAS mixture and TC, TG, LDL, and ApoA1. Mediation analysis revealed that mtDNAcn significantly mediated PFAS exposure's effect on TG levels, explaining 27.2-74.2% of the total effect. These findings highlight the need for regulatory action to address the emerging PFAS risks.

Associations between phenol and paraben exposure and the risk of developing breast cancer in adult women: a cross-sectional study

Increasing evidence suggests that endocrine-disrupting chemicals (EDCs) have adverse effects on breast cancer (BC). The aim of this study was to assess the association between exposure to prevalent EDCs—phenols and parabens—and the risk of developing BC. Data on urinary bisphenol A (BPA), triclosan (TRS), benzophenone-3 (BP3), methyl paraben (MPB), ethyl paraben (EPB), propyl paraben (PPB), and butyl paraben (BUP) were obtained from the 2005–2014 National Health and Nutrition Examination Survey. A total of 4455 subjects were included in this cross-sectional study. The results from the weighted multivariable regression models indicated that exposure to elevated concentrations of TRS increased the risk of developing BC by 2.33 (Q2: 95% CI = 1.45–3.75, p < 0.001) and 1.94 times (Q3: 95% CI = 1.21–3.09, p = 0.006), respectively. The nonlinear association between TRS concentrations and the risk of developing BC was statistically significant (P nonlinear = 0.007), with the restricted cubic splines (RCS) curve exhibiting an inverted U shape. The association between TRS concentrations and the risk of developing BC was more pronounced among overweight individuals (BMI ≥ 25 kg/m2), those aged < 60 years, and white individuals. Weighted quantile sum (WQS) and Bayesian Kernel Machine Regression (BKMR) analysis revealed no significant overall association between mixtures of urinary phenol and paraben metabolites and BC risk. However, TRS exposure was the most influential, with higher TRS concentrations (both continuous and categorical) significantly associated with an increased BC risk, particularly in overweight individuals (BMI ≥ 25 kg/m2), those aged < 60 years, and white individuals.

Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels

Abstract. The cloud radiative kernel method is a popular approach to quantify cloud feedbacks and rapid cloud adjustments to increased CO2 concentrations and to partition contributions from changes in cloud amount, altitude, and optical depth. However, because this method relies on cloud property histograms derived from passive satellite sensors or produced by passive satellite simulators in models, changes in obscuration of lower-level clouds by upper-level clouds can cause apparent low-cloud feedbacks and adjustments, even in the absence of changes in lower-level cloud properties. Here, we provide a methodology for properly diagnosing the impact of changing obscuration on cloud feedbacks and adjustments and quantify these effects across climate models. Averaged globally and across global climate models, properly accounting for obscuration leads to weaker positive feedbacks from lower-level clouds and stronger positive feedbacks from upper-level clouds while simultaneously removing a mostly artificial anti-correlation between them. Given that the methodology for diagnosing cloud feedbacks and adjustments using cloud radiative kernels has evolved over several papers, and obscuration effects have only occasionally been considered in recent papers, this paper serves to establish recommended best practices and to provide a corresponding code base for community use.

Relation of volatile organic compounds to renal function in American adolescents: three statistical models

ABSTRACT This study was conducted to evaluate the relationship between 17 urinary metabolites of volatile organic compounds (mVOCs) in adolescents and renal function parameters (estimated glomerular filtration rate (eGFR), albumin-to-creatinine ratio (ACR), urinary albumin, serum uric acid (SUA), and blood urea nitrogen (BUN)). In adjusted generalised linear models (GLM), mVOCs were positively correlated with eGFR, urinary albumin, and BUN, and mVOCs were negatively correlated with ACR and SUA. Weighted Quartile Sum (WQS) index correlated with eGFR [β(95%CI): 0.040 (0.028, 0.052)], urine albumin [β(95%CI): 0.275 (0.203, 0.622)], SUA [β(95%CI): 0.040 (0.025, 0.055)] and BUN [β(95%CI): 0.102 (0.082, 0.122)]. In Bayesian Kernel Machine Regression (BKMR) model, total compound effect was positively correlated with eGFR, positive associations were observed in high concentration of the mixture with urine albumin and ACR. Findings suggest that single and mixed exposures to mVOCs may affect renal parameters in adolescents.

Association between the exposure to brominated flame retardants and psoriasis risk among U.S. adults: A population-based study.

Psoriasis risk can be affected by exposure to mixtures of environmental chemicals; however, the effect of brominated flame retardants (BFRs) exposure on self-reported psoriasis has not been assessed. Herein, we evaluated the association between the exposure to BFRs compounds of 6 chemicals with risk of psoriasis. Based on data from the National Health and Nutrition Examination Survey (NHANES) 2003-2006 and 2009-2014, this analysis was conducted among 6340 adults. Multivariable logistic regression, restricted cubic splines (RCS), weighted quantile sum (WQS) regression, quantile-based g computation (Qgcomp) regression, and Bayesian kernel machine regression (BKMR) models were performed to estimate the associations. Our results indicated that co-exposure to BFRs was associated with increased risk of psoriasis. Notably, PBB153 emerged as a significant factor contributing to the risk of psoriasis, and showed a non-linear relationship between the concentration of serum PBB153 and the prevalence of psoriasis. Moreover, the association between PBB153 and psoriasis risk remained stable across all subgroup analyses, including those stratified by gender. Our study presents robust evidence connecting BFRs exposure to the prevalence of psoriasis, emphasizing the necessity for continued research and policy interventions to address this environmental health issue. Because BFRs are extensively used and detected in various environmental sources and living organisms, future investigations should further elucidate the mechanisms underlying the observed associations between BFRs exposure and psoriasis risk.

Joint exposure to multiple air pollutants and residual cardiovascular risk in hypertension.

Despite the widespread availability of antihypertensive medications, residual cardiovascular risk of hypertension remained high. Limited studies have investigated the link between air pollution, particularly joint exposure to multiple air pollutants, with residual cardiovascular risk of hypertension. 1981 hypertensive patients (≥ 18 years) from an ongoing longitudinal cohort in China, were enrolled between 2013 and 2019. Using high-quality datasets from China, the ground-level air pollutants concentrations, including PM2.5, PM2.5-10, SO2, O3, CO and NO2, at each participant's residence were calculated. The relationships between individual and multiple air pollutants with the residual cardiovascular risk were assessed by Cox proportional hazards models, air pollution score analyses and Bayesian Kernel Machine Regression model. Over an average follow-up period of 2.24 years (SD, 1.25), 706 hypertensive patients developed cardiovascular disease. In the single-pollutant analysis, higher concentrations of PM2.5, PM2.5-10, SO2 and CO were linked to increased residual cardiovascular risk. The air pollution score analyses and Bayesian kernel machine regression suggested that combined exposure to multiple air pollutants had a positive association with the residual cardiovascular risk, and NO2 played a dominant role. With higher NO2 concentrations, the hazard ratio of individual pollutants to residual cardiovascular risk increased. Prolonged exposure to a mixture of various air pollutants is linked to elevated residual cardiovascular risk in individuals with hypertension. Apart from taking antihypertensive medication and adopting healthy lifestyle behaviors, hypertensive patients should lower air pollutant exposure to decrease residual cardiovascular risk.

Associations between co-exposure to multiple heavy metals and age-related macular degeneration: a cross-sectional study

Background & aimsAccumulating evidence suggests that exposure to single heavy metal can facilitate the progression of age-related macular degeneration (AMD). However, the effects of exposure to mixtures of heavy metals on AMD remain largely unexplored. This study aims to investigate both the joint and individual impacts of arsenic (As), mercury (Hg), cadmium (Cd), and lead (Pb) on AMD within a co-exposure framework. MethodsData from subjects participating the US National Health and Nutrition Examination Survey (NHANES, 2005-2008) were analyzed. Concentrations of As, Hg, Cd, and Pb were determined in urine by inductively coupled plasma dynamic reaction cell mass spectrometry (ICP-DRC-MS) for As and Hg, and inductively coupled plasma mass spectrometry (ICP-MS) for Cd and Pb. The weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models were employed to assess the effects of heavy metal mixtures on AMD risk. ResultsBoth WQS and BKMR analyses consistently revealed a significant overall association between heavy metal mixtures and the risk of all types of AMD. The combined effect was more evident among patients with early AMD compared to those with late AMD. Cd and Hg were the main contributors driving these combined effects within the context of metal mixtures. Elevated urinary levels of Cd were positively correlated with an increased risk for all types as well as early AMD. Higher exposure to Hg corresponded with an elevated risk for early AMD. Furthermore, BKMR analysis indicated that the influence of Cd on early AMD exhibited a non-linear pattern. ConclusionsOur findings suggest that co-exposure to As, Hg, Cd, and Pb is associated with an elevated risk for developing AMD, particularly in its early stages. Furthermore, excessive exposure to Cd and Hg has been identified as key contributing factors in this process.

Health effects of mixed metal exposure on accelerating aging among the elderly population.

Human exposure to heavy metals occurs through multiple pathways in daily life. However, the effects of mixed heavy metal exposure on accelerated aging in the elderly U.S. population remains unclear. This study aims to investigate the association between heavy metal concentrations in blood and urine and the onset of accelerated aging. A total of 2530 individuals were included in the blood metals analysis and 716 individuals in the urine metals analysis in this study. GrimAge acceleration (AgeAccelGrim) was calculated as the residuals from regressing DNA methylation GrimAge on chronological age. Weighted multivariable logistic regression models were applied to analyze the relationship between metal exposure with AgeAccelGrim. Bayesian kernel machine regression (BKMR) was performed to estimate the effects of individual metals or metals mixtures and AgeAccelGrim. We found that blood cadmium (Cd) concentration was positively associated with an increased risk of AgeAccelGrim in both females and males, with odds ratios (OR) of 5.73 (95 % confidence interval (95 % CI): 1.88-17.41, P trend = 0.008) and 14.25 (95 % CI: 4.88-41.62, P trend < 0.001), respectively. Moreover, these associations were more pronounced for urinary Cd compared to blood Cd. Blood lead (Pb) was also associated with a 3.52-fold (95 % CI: 1.21-10.24, P trend = 0.007) increase in AgeAccelGrim risk among males. BKMR confirmed the aforementioned findings, identifying Cd as the most significant contributor to accelerated aging. Exposure to heavy metals, either individually or in mixture, was positively associated with accelerated biological aging. These associations were more pronounced in males.

Association between plasma perfluoroalkyl substances and high-grade serous ovarian cancer overall survival: A nested case-control study.

Although evidence suggests that perfluoroalkyl and polyfluoroalkyl substances (PFASs) are positively correlated to several disease risks, no studies have proven if plasma PFASs are related to ovarian cancer survival. To explore the association between plasma PFASs and high-grade serous ovarian cancer (HGSOC) overall survival (OS) in the population who did not smoke. We conducted a nested case-control study within the Ovarian Cancer Follow-Up Study, matching 159 dead patients and 159 survival ones based on body mass index, sample date, and age at diagnosis. Nine plasma PFASs were extracted by solid phase extraction and measured using a liquid chromatography system coupled with tandem mass spectrometry. Baseline plasma concentrations of perfluorinated carboxylic acids (PFCAs) [perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), and perfluoroheptanoic acid (PFHpA)] and perfluorinated sulfonic acids (PFSAs) [perfluorooctane sulfonic acid (PFOS) and perfluorohexane sulfonic acid (PFHxS)] were calculated. Odds ratios (ORs) and corresponding 95 % confidence intervals (CIs) were calculated via conditional logistic regression models. To elucidate the combined effects, Bayesian kernel machine (BKMR), and regression quantile g-computation (QGC) models were utilized. In full-adjusted model, significant differences were observed between HGSOC survival and perfluorobutane sulfonic acid, PFHpA, PFHxS, PFOS, PFCA, and PFSA. ORs and 95 %CIs were 2.74 (1.41-5.31), 1.97 (1.03-3.76), 2.13 (1.15-3.95), 2.28 (1.16-4.47), 3.74 (1.78-7.85), and 2.56 (1.31-5.01), respectively for the highest tertile compared with the lowest tertile. The QGC and BKMR models indicated that elevated concentrations of PFAS mixtures were associated with poor OS in HGSOC. Both individual and mixed plasma PFASs may relate to poor OS of HGSOC. Further research is necessary to establish causality, and it is recommended to reinforce environmental risk mitigation strategies to minimize PFAS exposure.

Polycyclic aromatic hydrocarbons exposure in early pregnancy on child neurodevelopment.

The epidemiological evidence from studies on the impact of exposure to polycyclic aromatic hydrocarbons (PAHs) during pregnancy on child neurodevelopment is inconclusive. This study aimed to assess the associations of PAHs exposure in early pregnancy and neurodevelopmental outcomes in children aged 6-12 months in a prospective cohort. In this study, we included 172 mother-child pairs with complete data in Tianjin City, China. Ten PAH metabolites were determined in early-pregnancy urine using gas chromatography and tandem mass spectrometry (GC-MS/MS). Child neurodevelopment was measured using the Griffiths Development Scales-Chinese (GDS-C) when children were between 6 and 12 months old. We employed multivariable linear regression, Weighted Quantile Sum (WQS) regression, and Bayesian Kernel Machine Regression (BKMR) to assess the relationships of individual PAH metabolites and mixtures of these metabolites with child neurodevelopment. Multiplicative interactions were analyzed to examine effect modification by child sex. In the multivariable linear regression analysis, six PAH metabolites were found to be negatively associated with personal social scores, while three PAH metabolites showed a negative association with language scores. The WQS model revealed that the PAHs mixture was linked to decreased personal social scores (β=- 4.18, 95% CI=-7.56, - 0.80) and language scores (β=- 4.17, 95% CI=-7.98, -0.37). Furthermore, the BKMR models also indicated the negative associations between the PAHs mixture and personal social scores and language scores. Notably, three PAH metabolites (1-hydroxynaphthalene(1-OHNap), 3-hydroxyfluorene(3-OHFlu), 3+9-hydroxyphenanthrene(3+9-OHPhe)) were identified as important contributors to these associations. All analyses of interactions were null. Exposure to PAHs during early pregnancy, whether individually or as a mixture, demonstrated a negative association with child neurodevelopment outcomes.

Modulation effects of folic acid and vitamin D on the relationships between prenatal cumulative phthalate exposure and preschoolers' emotional and behavioral problems.

The results of current population-based studies exploring the effects of prenatal phthalate exposure on children's behavioral problems are not completely consistent. In addition, it is not clear whether vitamins improve phthalate-induced health risks. This study was based on the Ma'anshan Birth Cohort, which included 2910 mother-child pairs. To assess children's behavior, we used the Strengths and Difficulties Questionnaire (SDQ). A latent category growth model was used to model the scores of each subscale of the SDQ. Quantile g-computation and Bayesian kernel machine regression were applied to analyze the effect of prenatal cumulative phthalate exposure. Prenatal cumulative phthalate exposure was associated with the "borderline and abnormal emotional symptoms" trajectory (β=0.146, 95% CI=-0.0003, 0.292), affecting girls more in emotional areas, and boys in conduct areas. There was an interaction effect between folic acid supplementation, vitamin D levels and phthalate mixtures on the risk of abnormal emotional behavior in children, which was reflected mainly in prosocial behavior and conduct problems. Prenatal cumulative phthalate exposure increased the risk of abnormal emotional symptoms in children, and there were sex differences. The duration of folic acid supplementation and vitamin D level can affect the effects of cumulative phthalate exposure on emotion and behavior.

Gestational exposure to environmental chemical mixtures and cognitive abilities in children: A pooled analysis of two North American birth cohorts.

Gestational exposures to single toxic chemicals have been associated with cognitive deficits in children, but few studies have explored chemical mixtures. To evaluate the associations between gestational chemical biomarker mixtures and cognitive abilities in children from two prospective cohorts. This study includes 617 birthing parent-child pairs from the Health Outcomes and Measures of the Environment (HOME) and Maternal-Infant Research on Environmental Chemicals (MIREC) Studies. We measured 29 chemical biomarkers (metals, persistent organic pollutants, perfluoroalkyl substances, organophosphate esters, phenols, phthalates, organophosphate pesticides, and parabens) in pregnant individuals during early pregnancy and their children's cognitive abilities at ages 3 to 5years using Wechsler Intelligence Scales. We assessed linear associations using quantile g-computation and non-linear associations using Bayesian Kernel Machine Regression (BKMR) methods, adjusted for covariates. Using quantile g-computation, we observed overall null associations between the chemical biomarker mixture and cognitive outcomes among preschool-age children. Although statistical significance was not attained for child sex as an effect modifier, our stratified analysis unveiled a moderate divergence in association trends. We noted a marginal inverse trend between the chemical biomarker mixture and cognitive scores [Full-Scale Intelligence Quotient (FSIQ) & Performance Intelligence Quotient (PIQ)] among males. Using quantile g-computation and BKMR methods, we observed that PBDE47, PFHxS, and di-ethyl organophosphates commonly contributed towards a decline in FSIQ scores in males. Among males, a quartile increase in the chemical biomarker mixture was associated with a 0.64-point decrease (95% CI: -2.59, 1.31) in the FSIQ score and a 1.59-point decrease (95% CI: -3.72, 0.54) in the PIQ score. In this study, we observed a weak negative trend between the gestational chemical biomarker mixture and cognitive scores (FSIQ/PIQ) among males. Further studies are needed to confirm the findings between the longitudinal chemical biomarkers and child cognitive scores at school ages.