Posterior Inclusion Probabilities Research Articles

Mediating role of systemic inflammation in the association between volatile organic compounds exposure and periodontitis: NHANES 2011–2014

BackgroundVolatile organic compounds (VOCs) are ubiquitous environmental pollutants which have been suggested to have adverse effects on human health. While the influence of environmental pollutant exposures on periodontitis has attracted elevating attention in recent years, the epidemiological evidence on the association between VOCs exposure and periodontitis was scarce. This study aimed to investigate the potential mediating role of systemic inflammation factors in the complex association between VOCs exposure and periodontitis.MethodsUtilizing data from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, we examined the impacts of VOCs exposure on periodontitis. Concentrations of urinary metabolites of VOCs (mVOCs) were measured using electrospray tandem mass spectrometry to evaluate internal VOCs exposure. Multivariable logistic regression, restricted cubic spline regression (RCS), Bayesian kernel machine regression (BKMR) and Quantile g-computation (QGC) models were performed to investigate the impacts of VOCs exposure on periodontitis. Mediation models were applied to assess the mediated effects of systemic inflammation on the association between mixed VOCs exposure and periodontitis. Besides, we analyzed the association between mixed VOCs exposure and periodontitis in stratified age, gender, and smoking status subgroups.Results1,551 participants were ultimately included for further analyses, of whom 45.20% suffering from periodontitis. Multivariable logistic regression and RCS identified positive associations between single urinary mVOCs and periodontitis (P < 0.05). Notably, BKMR and QGC models suggested that mixed VOCs exposure was significantly associated with periodontitis, with 2-Aminothiazoline-4-carboxylic acid (ATCA) contributing the most (conditional posterior inclusion probability = 0.997). Moreover, systemic inflammation markers (leukocyte and lymphocyte counts) were found to partly mediate the association between VOCs exposure and periodontitis (P < 0.05). No interaction effect was identified between mixed VOCs exposure and periodontitis in age, gender and smoking status subgroups (P > 0.05).ConclusionThis study demonstrated a positive association between VOCs exposure and periodontitis, which was potentially mediated by systemic inflammation factors. Further longitudinal researches are demanded to clarify the underlying mechanisms.

Associations of exposure to PM2.5 and its compounds with carotid intima-media thickness among middle-aged adults

BackgroundExposure to fine particulate matter (PM2.5) has been linked to an increased risk of atherosclerosis. However, it remains unclear whether specific compounds within PM2.5, rather than the overall mass, serve as a better indicator of adverse cardiovascular health outcomes associated with air pollution. MethodsIn this cross-sectional study, we included 3257 participants (aged 37–51 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Exposure to PM2.5 and its constituent compounds, black carbon (BC), ammonium, nitrate, organic matter, sulfate, mineral dust, and sea salt were included in the analyses. Carotid intima media thickness (cIMT; the average of common, bulb, and internal carotid) was measured by carotid ultrasonography. We assessed the cross-sectional associations of one-year exposure to PM2.5 and its compounds with mean cIMT using linear regression models adjusting for participants' demographics, individual- and neighborhood-level socioeconomic status, behavioral components, and health conditions. We also adopted Bayesian kernel machine regression (BKMR) models to investigate the association between the PM2.5 compound mixture and cIMT as well as the contribution of each compound to the association. ResultsGreater exposure to BC was associated with higher cIMT (mm) (β =0.034, 95 % CI = 0.019–0.049, per IQR increase [0.56 μg/m3] of BC) among participants with a mean age of 45.0, consisting of 45.9 % Black and 54.1 % White males and females. The association was generally consistent across participants' demographic characteristics. In our BKMR analysis, BC exhibited a dose-response association with cIMT with a high contribution to the association of cIMT with PM2.5 compound as a mixture (posterior inclusion probability [PIP]: 1.00). ConclusionsOur findings suggest that certain compounds of PM2.5, such as BC, may offer more reliable indications of the impact of air pollution on cardiovascular health.

Ancient viral DNA in the human genome linked to neurodegenerative diseases

BackgroundHuman endogenous retroviruses (HERVs) are sequences in the human genome that originated from infections with ancient retroviruses during our evolution. Previous studies have linked HERVs to neurodegenerative diseases, but defining their role in aetiology has been challenging. Here, we used a retrotranscriptome-wide association study (rTWAS) approach to assess the relationships between genetic risk for neurodegenerative diseases and HERV expression in the brain, calculated with genomic precision. MethodsWe analysed genetic association statistics pertaining to Alzheimer’s disease, amyotrophic lateral sclerosis, multiple sclerosis, and Parkinson’s disease, using HERV expression models calculated from 792 cortical samples. Robust risk factors were considered those that survived multiple testing correction in the primary analysis, which were also significant in conditional and joint analyses, and that had a posterior inclusion probability above 0.5 in fine-mapping analyses. ResultsThe primary analysis identified 12 HERV expression signatures associated with neurodegenerative disease susceptibility. We found one HERV expression signature robustly associated with amyotrophic lateral sclerosis on chromosome 12q14 (MER61_12q14.2) and one robustly associated with multiple sclerosis on chromosome 1p36 (ERVLE_1p36.32a). A co-expression analysis suggested that these HERVs are involved in homophilic cell adhesion via plasma membrane adhesion molecules. ConclusionsWe found HERV expression profiles robustly associated with amyotrophic lateral sclerosis and multiple sclerosis susceptibility, highlighting novel risk mechanisms underlying neurodegenerative disease, and offering potential new targets for therapeutic intervention.

BEATRICE: Bayesian fine-mapping from summary data using deep variational inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer's. BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.

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.

DNAm scores for serum GDF15 and NT-proBNP levels associate with a range of traits affecting the body and brain

BackgroundPlasma growth differentiation factor 15 (GDF15) and N‐terminal proB‐type natriuretic peptide (NT‐proBNP) are cardiovascular biomarkers that associate with a range of diseases. Epigenetic scores (EpiScores) for GDF15 and NT-proBNP may provide new routes for risk stratification.ResultsIn the Generation Scotland cohort (N ≥ 16,963), GDF15 levels were associated with incident dementia, ischaemic stroke and type 2 diabetes, whereas NT-proBNP levels were associated with incident ischaemic heart disease, ischaemic stroke and type 2 diabetes (all PFDR < 0.05). Bayesian epigenome-wide association studies (EWAS) identified 12 and 4 DNA methylation (DNAm) CpG sites associated (Posterior Inclusion Probability [PIP] > 95%) with levels of GDF15 and NT-proBNP, respectively. EpiScores for GDF15 and NT-proBNP were trained in a subset of the population. The GDF15 EpiScore replicated protein associations with incident dementia, type 2 diabetes and ischaemic stroke in the Generation Scotland test set (hazard ratios (HR) range 1.36–1.41, PFDR < 0.05). The EpiScore for NT-proBNP replicated the protein association with type 2 diabetes, but failed to replicate an association with ischaemic stroke. EpiScores explained comparable variance in protein levels across both the Generation Scotland test set and the external LBC1936 test cohort (R2 range of 5.7–12.2%). In LBC1936, both EpiScores were associated with indicators of poorer brain health. Neither EpiScore was associated with incident dementia in the LBC1936 population.ConclusionsEpiScores for serum levels of GDF15 and Nt-proBNP associate with body and brain health traits. These EpiScores are provided as potential tools for disease risk stratification.

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE allele, a commonly associated variant of Alzheimer's. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

Association of combined lead, cadmium, and mercury with systemic inflammation.

Exposure to environmental metals has been increasingly associated with systemic inflammation, which is implicated in the pathogenesis of various chronic diseases, including those with neurodegenerative aspects. However, the complexity of exposure and response relationships, particularly for mixtures of metals, has not been fully elucidated. This study aims to assess the individual and combined effects of lead, cadmium, and mercury exposure on systemic inflammation as measured by C-reactive protein (CRP) levels, using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. We employed Bayesian Kernel Machine Regression (BKMR) to analyze the NHANES 2017-2018 data, allowing for the evaluation of non-linear exposure-response functions and interactions between metals. Posterior Inclusion Probabilities (PIP) were calculated to determine the significance of each metal's contribution to CRP levels. The PIP results highlighted mercury's significant contribution to CRP levels (PIP = 1.000), followed by cadmium (PIP = 0.6456) and lead (PIP = 0.3528). Group PIP values confirmed the importance of considering the metals as a collective group in relation to CRP levels. Our BKMR analysis revealed non-linear relationships between metal exposures and CRP levels. Univariate analysis showed a flat relationship between lead and CRP, with cadmium having a positive relationship. Mercury exhibited a U-shaped association, indicating both low and high exposures as potential risk factors for increased inflammation. Bivariate analysis confirmed this relationship when contaminants were combined with lead and cadmium. Analysis of single-variable effects suggested that cadmium and lead are associated with higher values of the h function, a flexible function that takes multiple metals and combines them in a way that captures the complex and potentially nonlinear relationship between the metals and CRP. The overall exposure effect of all metals on CRP revealed that exposures below the 50th percentile exposure level are associated with an increase in CRP levels, while exposures above the 60th percentile are linked to a decrease in CRP levels. Our findings suggest that exposure to environmental metals, particularly mercury, is associated with systemic inflammation. These results highlight the need for public health strategies that address the cumulative effects of metal exposure and reinforce the importance of using advanced statistical methods to understand the health impact of environmental contaminants. Future research should focus on the mechanistic pathways of metal-induced inflammation and longitudinal studies to ascertain the long-term effects of these exposures.

A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Urinary concentrations of phthalate/DINCH metabolites and body mass index among European children and adolescents in the HBM4EU Aligned Studies: A cross-sectional multi-country study

BackgroundPhthalates are ubiquitous in the environment. Despite short half-lives, chronic exposure can lead to endocrine disruption. The safety of phthalate substitute DINCH is unclear. ObjectiveTo evaluate associations between urinary concentrations of phthalate/DINCH metabolites and body mass index (BMI) z-score among children and adolescents. MethodWe used Human Biomonitoring for Europe Aligned Studies data from 2876 children (12 studies, 6–12 years, 2014–2021) and 2499 adolescents (10 studies, 12–18 years, 2014–2021) with up to 14 phthalate/DINCH urinary metabolites. We used multilevel linear regression to assess associations between phthalate/DINCH concentrations and BMI z-scores, testing effect modification by sex. In a subset, Bayesian kernel machine regression (BKMR) and quantile-based g-computation assessed important predictors and mixture effects. ResultsIn children, we found few associations in single pollutant models and no interactions by sex (p-interaction > 0.1). BKMR detected no relevant exposures (posterior inclusion probabilities, PIPs < 0.25), nor joint mixture effect. In adolescent single pollutant analysis, mono-ethyl phthalate (MEP) concentrations were associated with higher BMI z-score in males (β = 0.08, 95 % CI: 0.001,0.15, per interquartile range increase in ln-transformed concentrations, p-interaction = 0.06). Conversely, mono-isobutyl phthalate (MiBP) was associated with a lower BMI z-score in both sexes (β = -0.13, 95 % CI: −0.19, −0.07, p-interaction = 0.74), as was sum of di(2-ethylhexyl) phthalate (∑DEHP) metabolites in females only (β = -0.08, 95 % CI: −0.14, −0.02, p-interaction = 0.01). In BKMR, higher BMI z-scores were predicted by MEP (PIP=0.90) and MBzP (PIP=0.84) in males. Lower BMI z-scores were predicted by MiBP (PIP=0.999), OH-MIDP (PIP=0.88) and OH-MINCH (PIP=0.72) in both sexes, less robustly by DEHP (PIP=0.61) in females. In quantile g-computation, the overall mixture effect was null for males, and trended negative for females (β = -0.11, 95 % CI: −0.25, 0.03, per joint exposure quantile). ConclusionIn this large Europe-wide study, we found age/sex-specific differences between phthalate metabolites and BMI z-score, stronger in adolescents. Longitudinal studies with repeated phthalate measurements are needed.

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.

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.

Adverse impact of phthalate and polycyclic aromatic hydrocarbon mixtures on birth outcomes: A metabolome Exposome-Wide association study

It has been well-investigating that individual phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affect public health. However, there is still a gap that the mixture of PAEs and PAHs impacts birth outcomes. Through innovative methods for mixtures in epidemiology, we used a metabolome Exposome-Wide Association Study (mExWAS) to evaluate and explain the association between exposure to PAEs and PAHs mixtures and birth outcomes. Exposure to a higher level of PAEs and PAHs mixture was associated with lower birth weight (maximum cumulative effect: 143.5 g) rather than gestational age. Mono(2-ethlyhexyl) phthalate (MEHP) (posterior inclusion probability, PIP = 0.51), 9-hydroxyphenanthrene (9-OHPHE) (PIP = 0.53), and 1-hydroxypyrene (1-OHPYR) (PIP = 0.28) were identified as the most important compounds in the mixture. In mExWAS, we successfully annotated four overlapping metabolites associated with both MEHP/9-OHPHE/1-OHPYR and birth weight, including arginine, stearamide, Arg-Gln, and valine. Moreover, several lipid-related metabolism pathways, including fatty acid biosynthesis and degradation, alpha-linolenic acid, and linoleic acid metabolism, were disturbed. In summary, these findings may provide new insights into the underlying mechanisms by which PAE and PAHs affect fetal growth.

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.

Predicting future inflation in Indonesia using Dynamic Model Averaging (DMA)

The features of Indonesia's inflation data, which make it extremely susceptible to shocks like those felt in 2005 and 2008, as well as extensive potential influencing factors, lead to problems in forecasting inflation. These problems include time variation in coefficients, models that can change over time, and many predictors to consider. Dynamic Model Averaging (DMA) solves these problems since it has evolved coefficients and models that change over time. This study uses DMA to predict future inflation by involving eight macroeconomic indicators as exogenous variables. The results of the in-sample analysis show that six predictors are significant in forecasting inflation, with posterior inclusion probability (PIP) being above 40%. Although the remaining predictors have PIP means below 40%, they can still be considered important. The out-of-sample results suggest that DMA performs better than dynamic model selection and models that don’t include exogenous variables, such as autoregressive models. The forecast results indicate a consistent pattern over the 12 months studied. The attempt to control inflation can be achieved by prioritizing the money supply factor, which has the highest PIP value, indicating that it is the most important factor.

Association between Combined Metals and PFAS Exposure with Dietary Patterns: A Preliminary Study.

The global burden of chronic diseases has been increasing, with evidence suggesting that diet and exposure to environmental pollutants, such as per- and polyfluoroalkyl substances (PFAS) and heavy metals, may contribute to their development. The Dietary Inflammatory Index (DII) assesses the inflammatory potential of an individual's diet. However, the complex interplay between PFAS, heavy metals, and DII remains largely unexplored. The goal of this cross-sectional study was to investigate the associations between diet operationalized as the DII with individual and combined lead, cadmium, mercury, perfluorooctanoic acid (PFOA), and perfluorooctanesulfonic acid (PFOS) exposures using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. Descriptive statistics, a correlational analysis, and linear regression were initially used to assess the relationship between the variables of interest. We subsequently employed Bayesian kernel Machine regression (BKMR) to analyze the data to assess the non-linear, non-additive, exposure-response relationships and interactions between PFAS and metals with the DII. The multi-variable linear regression revealed significant associations between the DII and cadmium and mercury. Our BKMR analysis revealed a complex relationship between PFAS, metal exposures, and the DII. In our univariate exposure-response function plot, cadmium and mercury exhibited a positive and negative linear relationship, respectively, which indicated a positive and negative relationship across the spectrum of exposures with the DII. In addition, the bivariate exposure-response function between two exposures in a mixture revealed that cadmium had a robust positive relationship with the DII for different quantiles of lead, mercury, PFOA, and PFOS, indicating that increasing levels of cadmium are associated with the DII. Mercury's bivariate plot demonstrated a negative relationship across all quantiles for all pollutants. Furthermore, the posterior inclusion probability (PIP) results highlighted the consistent importance of cadmium and mercury with the inflammatory potential of an individual's diet, operationalized as the DII in our study, with both showing a PIP of 1.000. This was followed by PFOS with a PIP of 0.8524, PFOA at 0.5924, and lead, which had the lowest impact among the five environmental pollutants, with a PIP of 0.5596. Our study suggests that exposures to environmental metals and PFAS, particularly mercury and cadmium, are associated with DII. These findings also provide evidence of the intricate relationships between PFAS, heavy metals, and the DII. The findings underscore the importance of considering the cumulative effects of multi-pollutant exposures. Future research should focus on elucidating the mechanistic pathways and dose-response relationships underlying these associations in a study that examines causality, which will enable a deeper understanding of the dietary risks associated with environmental pollutants.

Associations of toxic metals and their mixture with hyperuricemia in Chinese rural older adults.

Previous studies have related single toxic metals (TMs) to hyperuricemia (HUA) among the general population, however, the association of the TM mixture with HUA, especially in older adults, remains poorly understood. We aimed to examine the relationships between individual TMs and their mixture and HUA in Chinese rural older adults. This study consisted of 2075 rural older adults aged 60years or over. Blood concentrations of aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), gallium (Ga), mercury (Hg), lead (Pb), thallium (Tl), and uranium (U) were detected using inductively coupled plasma mass spectrometry. The associations of single TMs with HUA were assessed using logistic regression and restricted cubic spline (RCS) models, and the association of TM mixture with HUA was explored using the elastic net with environmental risk score (ENET-ERS), quantile g-computation (QGC), and Bayesian kernel machine regression (BKMR) models, respectively. Adjusted logistic regression model showed that Cs (OR = 1.65, 95% CI 1.37-1.99) and Pb (OR = 1.46, 95% CI 1.28-1.67) were positively related to HUA, andRCS model exhibited a positive linear association of Cs and Pb with HUA. ENET-ERS and QGC models quantified a positive correlation between the TM mixture and the odds of HUA, with estimated ORs of 1.15 (95% CI 1.11-1.19) and 1.84 (95% CI 1.37-2.47), respectively, and Cs and Pb had the most weight. BKMR model demonstrated a significant linear association between the TM mixture and increased odds of HUA, with the posterior inclusion probabilities (PIPs) of both Cs and Pb being 1.00. Moreover, we observed a positive interaction between Cs and Pb on HUA. The TM mixture is associated with increased odds of HUA in rural older adults, which may mainly be driven by Cs and Pb. Subsequent studies are warranted to confirm these findings and clarify the mechanisms linking multiple TMs with HUA.

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

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

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

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

Methylome-wide studies of six metabolic traits.

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised βrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.