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Background Numerous studies have suggested significant associations between prenatal exposure to heavy metals and newborn anthropometric measures. However, little is known about the effect of various heavy metal mixtures at relatively low concentrations. Hence, this study aimed to investigate associations between prenatal exposures to a wide range of individual heavy metals and heavy metal mixtures with anthropometric measures of newborns. Methods We recruited 975 mother–term infant pairs from two major hospitals in Israel. Associations between eight heavy metals (arsenic, cadmium, chromium, mercury, nickel, lead, selenium, and thallium) detected in maternal urine samples on the day of delivery with weight, length, and head circumference at birth were estimated using linear and Bayesian kernel machine regression (BKMR) models. Results Most heavy metals examined in our study were observed in lower concentrations than in other studies, except for selenium. In the linear as well as the BKMR models, birth weight and length were negatively associated with levels of chromium. Birth weight was found to be negatively associated with thallium and positively associated with nickel. Conclusion By using a large sample size and advanced statistical models, we could examine the association between prenatal exposure to metals in relatively low concentrations and anthropometric measures of newborns. Chromium was suggested to be the most influential metal in the mixture, and its associations with birth weight and length were found negative. Head circumference was neither associated with any of the metals, yet the levels of metals detected in our sample were relatively low. The suggested associations should be further investigated and could shed light on complex biochemical processes involved in intrauterine fetal development.

Background: Impaired cognitive function following exposure to heavy metals has emerged as a significant global health concern. Nevertheless, the impact of combined exposure to multiple heavy metals on cognitive impairment remains unclear. Objective: This study aimed to explore the association between multiple heavy metals exposure and cognitive function to provide theoretical evidence to guide prevention strategies. Methods: The blood levels of lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), copper (Cu) and zinc (Zn) and the results of the cognitive function tests were extracted from 811 elderly Americans who completed the NHANES between 2011 and 2014. Quantile regression (QR), restricted cubic splines (RCS), and Bayesian kernel machine regression (BKMR) were used to explore the individual and joint association between heavy metals exposure and performance in 4 standardized cognitive tests; Item Response Theory (IRT), Delayed Recall Test (DRT), Animal Fluency Test (AFT) and Digit Symbol Substitution Test (DSST). Results: A negative association was noted between Cd levels and IRT ( p  = 0.048, 95%CI: -2.7, -0.1). Se concentrations ranging between 2.197 µg/L (95%CI: 0.004, 0.15) to 2.29 µg/L (95%CI: 2.56, 7.64) (log 10 Se) was postively associated with DSST ( p  = 0.001 ). Cu was negatively associated with DSST ( p  = 0.049, 95%CI: -37.75, -0.09), while Zn was positively associated with IRT ( p  = 0.022, 95%CI: 0.55, 11.73). Exposure to the 6 heavy metals combined showed a positive linear association with IRT, DRT, and a negative linear association with DSST. An interaction between Cd and the other heavy metals (excepted for Pb). Conclusion: Exposure to Pb, Cd, Hg, Se, Cu, and Zn was associated with cognitive function. Joint exposure to the 6 heavy metals showed a positive linear association with IRT, DRT, contrarily, a negative linear association with DSST.

Background Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals and are commonly found in everyday items. PFAS have been linked to disrupting glucose homeostasis, however, whether they are associated with gestational diabetes mellitus (GDM) risk remains inconclusive. We examined prospective associations of PFAS concentrations measured twice in pregnancy with GDM risk. Methods In the PETALS pregnancy cohort, a nested case–control study which included 41 GDM cases and 87 controls was conducted. PFAS analytes were measured in blood serum collected in both early and mid-pregnancy (mean [SD]: 13.9 [2.2] and 20.2 [2.2] gestational weeks, respectively), with cumulative exposure calculated by the area-under-the-curve (AUC) to integrate both the PFAS concentration and the timing of the exposure. Individual adjusted weighted unconditional logistic regression models examined seven PFAS in association with GDM risk. P-values were corrected using the false-discovery-rate (FDR). Mixture models were analyzed with Bayesian kernel machine regression (BKMR). Results PFDA, PFNA and PFOA were individually associated with higher GDM risk per interquartile range (IQR) in early pregnancy (OR [95% CI]: 1.23 [1.09, 1.38]), 1.40 [1.24, 1.58]), and 1.15 [1.04, 1.27], respectively), mid-pregnancy (1.28 [1.15, 1.43], 1.16 [1.05, 1.28], and 1.20 [1.09, 1.33], respectively), and with cumulative exposure (1.23 [1.09, 1.38], 1.21 [1.07, 1.37], and 1.19 [1.09, 1.31], respectively). PFOS in mid-pregnancy and with cumulative exposure was associated with increased GDM risk (1.41 [1.17, 1.71] and 1.33 [1.06, 1.58], respectively). PFUnDA in early pregnancy was associated with lower GDM risk (0.79 [0.64, 0.98]), whereas mid-pregnancy levels were associated with higher risk (1.49 [1.18, 1.89]). PFHxS was associated with decreased GDM risk in early and mid-pregnancy (0.48 [0.38, 0.60] and 0.48 [0.37, 0.63], respectively) and with cumulative exposure (0.49 [0.38,0.63]). PFPeA was not associated with GDM. Similar conclusions were observed in BKMR models; however, overall associations in these models were not statistically significant. Conclusions Higher risk of GDM was consistently observed in association with PFDA, PFNA, and PFOA exposure in both early and mid-pregnancy. Results should be corroborated in larger population-based cohorts and individuals of reproductive age should potentially avoid known sources of PFAS.

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.

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.

Background Environmental exposure to toxic brominated flame retardants (BFRs) has been confirmed to have detrimental effects on human health. The impact of serum BFRs on hyperlipidemia risk has not been sufficiently examined. Our objective is to identify both the individual and combined effects of serum BFRs on hyperlipidemia and to further investigate the most influential chemicals. Methods We included 7,009 individuals with complete details on 9 types of serum BFRs, hyperlipidemia, and other covariates from the NHANES in 2007–2016. Multivariate logistic regression was conducted to evaluate the individual impact of BFRs exposure on hyperlipidemia risk. We assessed the cumulative effect of BFRs on hyperlipidemia risk through weighted quantile sum (WQS) regression, quantile g-computation (QGC), and Bayesian kernel machine regression models. Results PBDE 28, PBDE 47, PBDE 85, PBDE 99, PBDE 100, PBDE 154, PBDE 209, and PBB153 were found to be positively associated with hyperlipidemia risk. The results of WQS and QGC revealed consistent positive correlation. PBDE209 emerged as the most significant chemicals exerting influence. The restricted cubic splines regression further identified significant dose-response relationship. Conclusion Exposure to individual and combined serum BFRs has been associated with an increased risk of hyperlipidemia. The causal relationship still requires confirmation through large-scale cohort studies.

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.

Perchlorates, nitrates, and thiocyanates are prevalent environmental chemicals. Their potential association with arthritis remains unexplored. This study aimed to investigate the link between perchlorate, nitrate, and thiocyanate exposure and arthritis, as well as the potential role of inflammation in this context. Utilizing the National Health and Nutrition Examination Survey (NHANES) data spanning from 2005 to 2016, the study enrolled 6597 participants aged 20-59 (young and middle-aged), of which 1045 had arthritis. Employing multivariate logistic regression modeling, multiple linear regression models, restricted cubic spline analysis, Bayesian kernel machine regression (BKMR) modeling, and mediation analysis, we assessed these relationships. There was a significant positive association between elevated urinary thiocyanate levels and arthritis risk [1.19 (1.11, 1.28)]. This association held true across subgroups of osteoarthritis (OA) [1.24 (1.10, 1.40)] and rheumatoid arthritis (RA) [1.33 (1.15, 1.55)]. Thiocyanate levels displayed a dose-dependent relationship with arthritis risk, showing a linear trend (nonlinear P > 0.05). Conversely, perchlorate and nitrate did not exhibit associations with arthritis risk. BKMR outcomes highlighted a positive correlation between a mixture of perchlorate, nitrate, and thiocyanate and arthritis risk, with thiocyanate being the predominant predictors. Moreover, BKMR and generalized linear model analyses unveiled no significant synergistic effect of urinary perchlorate, nitrate, and thiocyanate on arthritis risk. Furthermore, thiocyanate exposure has been linked to elevated levels of inflammatory indicators (white blood cell, neutrophils, lymphocytes, and systemic immune-inflammatory index (SII)). Heightened thiocyanate exposure may be linked to elevated arthritis risk, either single or in combined effects. Additionally, thiocyanate exposure is associated with heightened inflammation levels.

Previous studies have related single essential metal elements (EMEs) to sleep quality among older adults, however, the association of the EME mixture with sleep quality remained poorly understood. This study aimed to investigate the relationships between single EMEs and the EME mixture and sleep quality in older adults living in Chinese communities. This study consisted of 3957 older adults aged 60 years or over. Urinary concentrations of cobalt (Co), vanadium (V), selenium (Se), molybdenum (Mo), strontium (Sr), calcium (Ca), and magnesium (Mg) were detected using inductively coupled plasma mass spectrometry. Sleep quality was evaluated using Pittsburgh Sleep Quality Index (PSQI). The associations of single EMEs and EME mixture with sleep quality were assessed using logistic regression and Bayesian kernel machine regression (BKMR) models, respectively. Adjusted single-element logistic regression models showed that Mo ( OR  = 0.927, 95% CI :0.867–0.990), Sr ( OR  = 0.927, 95% CI :0.864–0.994), and Mg ( OR  = 0.934 , 95% CI :0.873–0.997) were negatively related to poor sleep quality. BKMR models exhibited similar results. Also, higher levels of the EME mixture in urine were inversely related to the odds of poor sleep quality after adjustment for covariates, and Mo had the largest conditional posterior inclusion probability (condPIP) value in the mixture. Mo, Sr, and Mg were negatively related to poor sleep quality, separately and as the mixture. The EME mixture in urine was associated with decreased odds of poor sleep quality in older adults, and Mo was the greatest contributor within the mixture. Additional cohort research is warranted to clarify the relationship of multiple EMEs with sleep quality.

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.