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Background There is growing concern of health effects of exposure to pollutant mixtures. We initially proposed an Environmental Risk Score (ERS) as a summary measure to examine the risk of exposure to multi-pollutants in epidemiologic research considering only pollutant main effects. We expand the ERS by consideration of pollutant-pollutant interactions using modern machine learning methods. We illustrate the multi-pollutant approaches to predicting a marker of oxidative stress (gamma-glutamyl transferase (GGT)), a common disease pathway linking environmental exposure and numerous health endpoints. Methods We examined 20 metal biomarkers measured in urine or whole blood from 6 cycles of the National Health and Nutrition Examination Survey (NHANES 2003–2004 to 2013–2014, n  = 9664). We randomly split the data evenly into training and testing sets and constructed ERS’s of metal mixtures for GGT using adaptive elastic-net with main effects and pairwise interactions (AENET-I), Bayesian additive regression tree (BART), Bayesian kernel machine regression (BKMR), and Super Learner in the training set and evaluated their performances in the testing set. We also evaluated the associations between GGT-ERS and cardiovascular endpoints. Results ERS based on AENET-I performed better than other approaches in terms of prediction errors in the testing set. Important metals identified in relation to GGT include cadmium (urine), dimethylarsonic acid, monomethylarsonic acid, cobalt, and barium. All ERS’s showed significant associations with systolic and diastolic blood pressure and hypertension. For hypertension, one SD increase in each ERS from AENET-I, BART and SuperLearner were associated with odds ratios of 1.26 (95% CI, 1.15, 1.38), 1.17 (1.09, 1.25), and 1.30 (1.20, 1.40), respectively. ERS’s showed non-significant positive associations with mortality outcomes. Conclusions ERS is a useful tool for characterizing cumulative risk from pollutant mixtures, with accounting for statistical challenges such as high degrees of correlations and pollutant-pollutant interactions. ERS constructed for an intermediate marker like GGT is predictive of related disease endpoints.

Research on the association of metal mixtures with glucose-insulin homeostasis is limited, and previous studies have typically focused on single metals. This study utilized data from 3110 adult subjects in the NHANES survey (2011–2018). Generalized linear models (GLM), logistic regression (LR), and restricted cubic splines (RCS) were employed to assess the associations of blood and urine metals with insulin resistance (IR) and glucose-insulin homeostasis. The Bayesian kernel machine regression (BKMR) and Bayesian weighted quantile sum (BWQS) models were further used to explore the independent and combined effects of metal exposures. In single-metal analyses, manganese (Mn) was positively correlated with insulin resistance (IR); cadmium(Cd), lead(Pb), mercury(Hg), and arsenic (As) were negatively correlated with the homeostasis model assessment of beta-cell function (HOMA-β); manganese and selenium (Se) were positively correlated with fasting plasma insulin (FPI); Se and cobalt (Co )were positively correlated with fasting plasma glucose (FPG); molybdenum (Mo) was positively correlated with HbA1c. In addition, both BWQS and BKMR models consistently showed that overall metal co-exposure had a positive effect on insulin resistance in the general population. Manganese was the most heavily weighted metal across all subgroups, with this association being more pronounced in males and individuals over 60 years of age. A negative association of metal mixtures with HOMA-β was observed in BWQS models. Furthermore, the analysis of BKMR models revealed possible interactions between insulin resistance and some components of metal mixtures in glucose homeostasis. The RCS model also identified nonlinear relationships between urinary Mo and HOMA-β, as well as between Co and both FPG and HbA1c. Our results suggest that metal mixtures may have adverse individual or combined effects on insulin resistance and glucose homeostasis in different population subgroups. These findings highlight the need for targeted interventions to mitigate the adverse effects of metal exposure on insulin-glucose homeostasis, which may provide new ideas for preventing and controlling the risk of type 2 diabetes due to metal exposure.

Cardiometabolic multimorbidity (CMM) is caused by two or more of the diseases ischemic heart disease (CVD), type 2 diabetes mellitus (T2DM), and stroke, and therefore requires more hospitalization and healthcare costs. However, few studies have investigated fine particulate matter (PM 2.5 ) and its constituents and the risk of hospital admissions for CMM. We aimed to study these associations in Urumqi, a representative area in northwest China. The effect of PM 2.5 and its constituents on the hospital admissions for CMM was determined using the quantile-based g-computation (QBGC) and bayesian kernel machine regression (BKMR) method, in which the constituents with the greatest effect on the hospital admissions for CMM were ranked as NO 3 − > SO 4 2− > NH 4 + > BC > OM. Among all constituents, NO 3 − presented the highest risk, with the largest effect observed at lag 21-day at the maximum concentration (RR = 2.079, 95% CI: 1.396–3.097). Per IQR increase in NO 3 − had the significantly effect on hospital admissions for IHD (RR = 1.079, 95% CI: 1.028–1.132) and on hospital admissions for CMM (RR = 1.094, 95% CI: 1.039–1.152). Female patients hospitalized for CMM indicated heightened sensitivity to elevated NO 3 − levels (RR = 1.170, 95% CI: 1.077–1.271). The interaction between the high concentrations of PM 2.5 and its constituents with low temperature, high relative humidity (RH), and low sunshine duration (SSD) significantly affected hospital admissions for CMM. Additionally, cold waves, defined as the minimum temperature of below P 2.5 and sustained for 5 days (CW5), intensified the interaction with PM 2.5 and its constituents.

Evidence showing the association of perfluoroalkylated substance (PFAS) exposure with CVD risk is scarce. The objective of this study was to explore the relationships of CVD risk with mixed or individual serum PFAS levels among general adults. We analyzed combined data of 7904 adults who participated in the National Health and Nutrition Examination Survey 2003–2012 with a Bayesian kernel machine regression (BKMR) to examine the relationships of individual or mixed PFAS exposure with total CVD risk. A logistic regression model and restricted cubic spline (RCS) regression with multivariate adjustment were conducted to assess the relationships between individual serum PFAS levels and the risk of total CVD or its subtypes. A mediation model was applied to investigate how C-reactive protein (CRP) levels mediate the strength of the association. The BKMR results indicated a positive relationship between mixed PFAS exposure and total CVD risk; among the PFASs, perfluorooctane sulfonic acid (PFOS) had the highest posterior inclusion probability. As determined by logistic regression, a log-unit change in PFOS levels was positively related to a higher risk of heart attack and stroke in males (both P  < 0.05). A nonlinear relationship was found between PFOS levels and stroke risk ( P for nonlinearity = 0.04), as illustrated in the RCS plot. The mediation analysis showed that CRP levels mediated 8% and 1.2% of the relationship between serum PFOS and PFNA levels, respectively, and the prevalence of stroke. A significant relationship between higher serum PFAS concentrations and an increased risk of CVD was observed, mainly in males.

Background Exposure to aldehydes has been linked to adverse health outcomes such as inflammation and oxidative stress, but research on the effects of these compounds is limited. This study is aimed at assessing the association between aldehyde exposure and markers of inflammation and oxidative stress. Methods The study used data from the NHANES 2013–2014 survey ( n = 766) and employed multivariate linear models to investigate the relationship between aldehyde compounds and various markers of inflammation (alkaline phosphatase (ALP) level, absolute neutrophil count (ANC), and lymphocyte count) and oxidative stress (bilirubin, albumin, and iron levels) while controlling for other relevant factors. In addition to generalized linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) analyses were applied to examine the single or overall effect of aldehyde compounds on the outcomes. Results In the multivariate linear regression model, each 1 standard deviation (SD) change in propanaldehyde and butyraldehyde was significantly associated with increases in serum iron levels (beta and 95% confidence interval, 3.25 (0.24, 6.27) and 8.40 (0.97, 15.83), respectively) and the lymphocyte count (0.10 (0.04, 0.16) and 0.18 (0.03, 0.34), respectively). In the WQS regression model, a significant association was discovered between the WQS index and both the albumin and iron levels. Furthermore, the results of the BKMR analysis showed that the overall impact of aldehyde compounds was significantly and positively correlated with the lymphocyte count, as well as the levels of albumin and iron, suggesting that these compounds may contribute to increased oxidative stress. Conclusions This study reveals the close association between single or overall aldehyde compounds and markers of chronic inflammation and oxidative stress, which has essential guiding value for exploring the impact of environmental pollutants on population health.

Background Perfluoroalkyl substances (PFASs) are a large family of synthetic chemicals, some of which are mammary toxicants and endocrine disruptors. Recent studies have implicated exposure to PFASs as a risk factor for breast cancer in Europe and America. Little is known about the role of PFASs with respect to breast cancer in the Chinese population. Methods Participants who were initially diagnosed with breast cancer at Tianjin Medical University Cancer Institute and Hospital between 2012 and 2016 were recruited as cases. The controls were randomly selected from the participants with available blood samples in the Chinese National Breast Cancer Screening Program (CNBCSP) cohort. Ultimately, we enrolled 373 breast cancer patients and 657 controls. Plasma PFASs were measured by an ultra-performance liquid chromatography (UPLC) system coupled to a 5500 Q-Trap triple quadrupole mass spectrometer. A logistic regression model with least absolute shrinkage and selection operator (LASSO) regularization was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to assess the relationships between PFASs and breast cancer. The three most predictive variables in the LASSO model were selected from 17 PFASs, which was based on the optimal penalty coefficient (λ = 0.0218) identified with the minimum criterion. Additionally, Bayesian kernel machine regression (BKMR) and quantile g-computation models were applied to evaluate the associations between separate and mixed exposure to PFASs and breast cancer. Results Perfluorooctanesulfonic acid (PFOS) exhibited the highest concentration in both the cases and controls. Perfluorooctanoic acid (PFOA) and perfluoro-n-decanoic acid (PFDA) were positively associated with breast cancer, and perfluoro-n-tridecanoic acid (PFTrDA) was negatively associated with breast cancer according to both the continuous-PFASs and the quartile-PFASs logistic regression models. Of note, PFOA was associated with the occurrence of estrogen receptor (ER)-, progesterone receptor (PR)-, and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (OR ER+  = 1.47, 95% CI: 1.19, 1.80; OR PR+  = 1.36, 95% CI: 1.09, 1.69; OR HER2  = 1.62, 95% CI: 1.19, 2.21). Conclusions Overall, we observed that PFASs were associated with breast cancer in Chinese women. Prospective cohort studies and mechanistic experiments are warranted to elucidate whether these associations are causal.

Mechanisms underlying abnormal uric acid (UA) levels from exposure to toxic metals/metalloids have not been not fully elucidated, especially in the context of mixtures. The aim was to identify major toxic metals/metalloids that affected UA levels with a mixture exposure concept in the association model. From 2007–2016 National Health and Nutrition Examination Survey (NHANES), 4794 adults were involved. Serum UA (SUA) and SUA to serum creatinine ratio (SUA/SCr) were used to estimate the UA levels, and cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) in the blood and/or urine were evaluated in the study. We assessed the associations between toxic metals and UA levels using linear regression and Bayesian kernel machine regression (BKMR). The median [ P 25 , P 75 ] SUA/SCr and SUA level were 6.22 [5.27, 7.32] and 0.83 [0.72, 0.98], respectively. There was no difference for SUA/SCr by gender (men, 6.25 [5.39, 7.29]; women, 6.17 [5.17, 7.36], P  = 0.162), but men had higher SUA than women (men, 0.95 [0.85, 1.05]; women, 0.72 [0.64, 0.82], P  < 0.001). Blood Pb ( β men  = 0.651 and β women  = 1.014) and urinary Cd ( β men  = 0.252 and β women  = 0.613) were positively associated with SUA/SCr, and urinary Pb ( β men  =  − 0.462 and β women  =  − 0.838) was inversely associated with SUA/SCr in multivariate linear regression analysis. However, urinary As ( β men  = 0.351) was positively associated with SUA/SCr only in men. BKMR showed that higher concentrations of exposure to a mixture of toxic metals were positively associated with higher UA levels, where Cd, Pb, and urinary As contributed most to the overall mixture effect in men, while Pb and urinary Cd in women. Our study provided the first evidence that mixtures of metals are associated with the UA levels. Increased concentrations of metals, mainly blood Pb, urinary Cd, and As (only in men) may increase the level of UA.

ObjectivesThree distinct models were utilized to investigate the combined impacts of serum aldehyde exposure and periodontitis.Materials and methodsWe performed a cross-sectional analysis using data from 525 participants in the 2013–2014 National Health and Nutrition Examination Survey (NHANES). The directed acyclic graphs (DAG) were used to select a minimal sufficient adjustment set of variables (MSAs). To investigate the relationship between aldehydes and periodontitis, we established three models including multiple logistic regression model, restricted cubic spline (RCS) model, and Bayesian kernel machine regression (BKMR) model.ResultsAfter taking all covariates into account, the multiple logistic regression model revealed that elevated concentrations of isopentanaldehyde and propanaldehyde were strongly associated with periodontitis (isopentanaldehyde: OR: 2.38, 95% CI: 1.34–4.23; propanaldehyde: OR: 1.51, 95% CI: 1.08–2.13). Furthermore, the third tertile concentration of isopentanaldehyde was associated with a 2.04-fold increase in the incidence of periodontitis (95% CI: 1.05–3.95) compared to the first tertile concentration, with a P for trend = 0.04. RCS models showed an “L”-shaped relationship between isopentanaldehyde and periodontitis (P for nonlinear association < 0.01), with inflection point of 0.43 ng/mL. BKMR identified a strong connection between mixed aldehydes and periodontitis, with isopentanaldehyde exhibiting the greatest posterior inclusion probability (PIP) with 0.901 and propanaldehyde exhibiting a PIP with 0.775.ConclusionsIsopentanaldehyde and propanaldehyde are positively associated with the risk of periodontitis.Clinical relevancePeriodontitis may be associated with exposure to mixed aldehyde. This study emphasizes the important role of aldehydes in primary prevention of periodontitis.

Background Volatile organic compounds (VOCs) encompass hundreds of high production volume chemicals and have been reported to be associated with adverse respiratory outcomes such as chronic obstructive pulmonary disease (COPD). However, research on the combined toxic effects of exposure to various VOCs on COPD is lacking. We aimed to assess the effect of VOC metabolite mixture on COPD risk in a large population sample. Methods We assessed the effect of VOC metabolite mixture on COPD risk in 5997 adults from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020 (pre-pandemic) using multivariate logistic regression, Bayesian weighted quantile sum regression (BWQS), quantile-based g-Computation method (Qgcomp), and Bayesian kernel machine regression (BKMR). We explored whether these associations were mediated by white blood cell (WBC) count and total bilirubin. Results In the logistic regression model, we observed a significantly increased risk of COPD associated with 9 VOC metabolites. Conversely, N-acetyl-S-(benzyl)-L-cysteine (BMA) and N-acetyl-S-(n-propyl)-L-cysteine (BPMA) showed insignificant negative correlations with COPD risk. The overall mixture exposure demonstrated a significant positive relationship with COPD in both the BWQS model (adjusted odds ratio (OR) = 1.30, 95% confidence interval (CI): 1.06, 1.58) and BKMR model, and with marginal significance in the Qgcomp model (adjusted OR = 1.22, 95% CI: 0.98, 1.52). All three models indicated a significant effect of the VOC metabolite mixture on COPD in non-current smokers. WBC count mediated 7.1% of the VOC mixture associated-COPD in non-current smokers. Conclusions Our findings provide novel evidence suggesting that VOCs may have adverse associations with COPD in the general population, with N, N- Dimethylformamide and 1,3-Butadiene contributing most. These findings underscore the significance of understanding the potential health risks associated with VOC mixture and emphasize the need for targeted interventions to mitigate the adverse effects on COPD risk.

Volatile organic compounds (VOCs) are prevalent in daily life, yet the relationship between VOCs exposure and the atherogenic index of plasma (AIP) remains inadequately explored, especially in populations with high levels of exposure. This study aims to investigate the non-linear association between VOCs exposure and AIP in the U.S. adult population. Data from the National Health and Nutrition Examination Survey (NHANES) collected between 2011 and 2018 were analyzed. A range of statistical techniques, including Spearman’s correlation analysis, weighted quantile sum (WQS), multivariate logistic regression, restricted cubic splines (RCS), stratified threshold models, and bayesian kernel machine regression (BKMR), were systematically employed to assess the relationship between high-dose VOCs exposure and AIP in U.S. adults. The study included 6,027 participants, with an average age of 37 (18–59), and 50.46% were male. Of these, 3,011 had elevated AIP levels. The Mann-Whitney U test compared VOCs exposure across quartiles (Q1–Q4). Spearman models revealed strong joint exposure effects between VOCs like IPMA3 and HMPMA ( ρ  = 0.97). WQS regression showed a positive association between VOCs and total cholesterol (TC) ( β  = 5.45, 95% CI  = 5.42–5.58, P  = 0.04) and high-density lipoprotein cholesterol (HDL-C) ( β  = 1.07, 95% CI  = 1.03–1.10, P  = 0.02). After adjusting for confounders, logistic regression revealed that VOCs such as 3-4MHA, 34DMA, AAMA, ATCA, CYMA, HEMA, and SBMA were linked to higher AIP. RCS analysis indicated a nonlinear association between VOCs and AIP. Stratified modeling found that ATCA was significantly and positively associated with AIP ( OR  = 1.60, 95% CI  = 1.20–2.14, p  < 0.01), and that when ATCA levels exceeded 128.60 ng/mL, there was a 60% increased risk of elevated AIP. Higher urinary VOCs levels, particularly ATCA, are significantly associated with increased AIP, offering new insights into the potential link between VOCs exposure and cardiovascular disease.