BACKGROUND: Stroke is the second leading cause of death for all human beings and poses a serious threat to human health. Environmental exposure to a mixture of metals may be associated with the occurrence and development of stroke, but the evidence in the Chinese population is not yet conclusive.
OBJECTIVE: This study evaluated the association between stroke risk and 13 metals METHODS: Metal concentrations in whole blood samples from 100 stroke cases and 100 controls were measured by ICP-MS. The cumulative impact of mixed metal on stroke risk was investigated by using three statistical models, BKMR, WQS and QGC.
RESULTS: The case group had higher concentrations of Mg, Mn, Zn, Se, Sn, and Pb than the control group (p<0.05). BKMR model indicated a correlation between the risk of stroke and exposure to mixed metals. WQS model showed that Mg (27.2 %), Se (25.1 %) and Sn (14.8 %) were positively correlated with stroke risk (OR=1.53; 95 %Cl: 1.03-2.37, p=0.013). The QGC model showed that Mg (49.2 %) was positively correlated with stroke risk, while Ti (31.7 %) was negatively correlated with stroke risk.
CONCLUSIONS: Mg may be the largest contributor to the cumulative effect of mixed metal exposure on stroke risk, and the interaction between metals requires more attention. These findings could provide scientific basis for effectively preventing stroke by managing metals in the environment.

With increasing prevalence rate of depression by years, more attention has been paid to the influence of environmental pollutants on depression, but relationship between exposure to volatile organic compounds (VOCs) and depression is rarely studied. Therefore, this cross-sectional study use the National Center for Health Statistics (NHANES) database (2013-2016 years) to explore association between exposure to multiple VOCs and depression in general population. Multiple linear and logistic regression models were used to analyze the association between urinary VOC metabolism (mVOCs) and depression. To further analyze effect of multiple mVOCs mixed exposure, Bayesian kernel machine regression (BKMR) models were performed. A total of 3240 participants and 16 mVOCs were included in the analysis. Results showed that 10 mVOCs exposure were positively correlated with depression by multiple linear and logistic regression models, especially CYMA and MHBMA3, which also showed significant positive association with depression in BKMR model. Mixed exposure of multiple mVOCs was significantly positively correlated with depression. Gender differences were existed in effects of some VOCs concentrations on depression. AAMA, CYMA and MA had significant positive correlations with depression by women, and DHBMA had significant positive correlations with depression by men. Hence, this study showed that exposing to VOCs might have negative impacts on depression, and impact of CYMA and MHBMA3 on depression may be more evident, which provide new ideas for prevention and control of depression. But further research and exploration are needed to clarify the mechanism and influence factors of this relationship, to demonstrate the reliability of these relationship.

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

An increasing number of studies suggest that environmental pollution may increase the risk of vitamin D deficiency (VDD). However, less is known about arsenic (As) exposure and VDD, particularly in Chinese pregnant women.
This study examines the correlations of different urinary As species with serum 25 (OH) D and VDD prevalence.
We measured urinary arsenite (As3+), arsenate (As5+), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) levels and serum 25(OH)D2, 25(OH)D3, 25(OH) D levels in 391 pregnant women in Tianjin, China. The diagnosis of VDD was based on 25(OH) D serum levels. Linear relationship, Logistic regression, and Bayesian kernel machine regression (BKMR) were used to examine the associations between urinary As species and VDD.
Of the 391 pregnant women, 60 received a diagnosis of VDD. Baseline information showed significant differences in As3+, DMA, and tAs distribution between pregnant women with and without VDD. Logistic regression showed that As3+ was significantly and positively correlated with VDD (OR: 4.65, 95% CI: 1.79, 13.32). Meanwhile, there was a marginally significant positive correlation between tAs and VDD (OR: 4.27, 95% CI: 1.01, 19.59). BKMR revealed positive correlations between As3+, MMA and VDD. However, negative correlations were found between As5+, DMA and VDD.
According to our study, there were positive correlations between iAs, especially As3+, MMA and VDD, but negative correlations between other As species and VDD. Further studies are needed to determine the mechanisms that exist between different As species and VDD.

BACKGROUND: Bisphenols (BPs) have been shown to exhibit developmental toxicities. Epidemiological evidence on prenatal BPs exposure and infant growth primarily confined scopes to specific BPs and birth outcomes, with few studies focusing on infant growth and reporting inconsistent findings. The joint effect of prenatal exposure to BPs mixture on infant growth was rarely studied.
OBJECTIVE: This study examined associations of prenatal exposure to individual bisphenol A (BPA) and its analogues (bisphenol F [BPF], bisphenol S [BPS], bisphenol AF [BPAF], and tetrachlorobisphenol A [TCBPA]) and their mixture with infant growth.
METHODS: Urinary concentrations of BPs in pregnant women were quantified. Weight, body mass index, skinfold thickness, and circumference measurements of infants were collected at birth, 6 and 12 months of age, rapid growth and overweight were further defined. Multiple linear regression models and Bayesian kernel machine regression models (BKMR) were used to analyze associations of exposure to individual BPs and BPs mixture with infants\' anthropometric measurements, and to identify the important components among mixture. The risks for rapid growth and overweight of each BP were determined using modified Poisson regression models.
RESULTS: A general profile of higher prenatal BPs exposure (mainly BPA, BPF, and BPS) associated with higher anthropometric measurements and higher risks of overweight during infancy was found. We also observed higher risks of rapid growth in infants following prenatal BPs exposure, with risk ratios ranging from 1.46 to 1.91. The joint effect of BPs mixture and single effect of each BP from the BKMR models were consistent with findings from the linear regression models, further suggesting that associations in girls were generally driven by BPA, BPF, or BPS, while in boys mainly by BPF.
CONCLUSIONS: Prenatal exposure to BPs and their mixture could increase anthropometric measurements of offspring during infancy, with implications of altered growth trajectory in future.

Per- and poly-fluoroalkyl substances (PFAS) have been reported to have hepatotoxic effects. However, it is unclear whether they are linked to non-alcoholic fatty liver disease (NAFLD). This nested case-control study focused on the epidemiological links between PFAS and the prevalence of NAFLD. We selected 476 new cases of NAFLD and 952 age- and sex-matched controls from the Jinchang cohort population between 2014 and 2019. Serum concentrations of PFAS were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Only PFAS with a detection rate of ≥90 % were included for analysis, which included PFPeA, PFOA, PFNA, PFHxS, PFOS, and 9Cl-PF3ONS. The relationship between single and co-exposure to PFAS and the occurrence of NAFLD was evaluated using conditional logistic regression, Quantile g-computation (QgC), and Bayesian kernel machine regression (BKMR) model. Logistic regression indicated that PFPeA, PFOA, and 9Cl-PF3ONS were positive correlation with the incidence of NAFLD after adjusting for confounders, with odds ratios (OR) and 95 % confidence interval (CI) of 3.13 (95 % CI: 2.53, 3.86), 1.39 (95 % CI: 1.12, 1.73), and 1.41 (95 % CI: 1.20, 1.66), respectively. PFNA, PFHxS, and PFOS were nonlinearly and negatively associated with the incidence of NAFLD, with OR (95 % CI) of 0.53 (0.46, 0.62), 0.83 (0.73, 0.95), and 0.52 (0.44, 0.61), respectively. QgC showed a significant joint effect of PFAS mixture on NAFLD onset (OR: 1.52, 95 % CI: 1.24, 1.88). BKMR showed a weak positive trend between PFAS mixtures and NAFLD incidence. Positive correlations were primarily driven by PFPeA and 9Cl-PF3ONS, while negative correlations were mainly influenced by PFNA and PFOS. The BKMR model also suggested that there was an interaction between PFOS and PFNA and other four PFAS compounds. In conclusion, our findings suggest that individual and co-exposure to PFAS is associated with a risk of NAFLD onset.

The particulate matter with diameter of less than 2.5 µm (PM2.5) is an important risk factor for respiratory infectious diseases, such as scarlet fever, tuberculosis, and similar diseases. However, it is not clear which component of PM2.5 is more important for respiratory infectious diseases. Based on data from 31 provinces in mainland China obtained between 2013 and 2019, this study investigated the effects of different PM2.5 components, i.e., sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and organic matter (OM), and black carbon (BC), on respiratory infectious diseases incidence [pulmonary tuberculosis (PTB), scarlet fever (SF), influenza, hand, foot, and mouth disease (HFMD), and mumps]. Geographical probes and the Bayesian kernel machine regression (BKMR) model were used to investigate correlations, single-component effects, joint effects, and interactions between components, and subgroup analysis was used to assess regional and temporal heterogeneity. The results of geographical probes showed that the chemical components of PM2.5 were associated with the incidence of respiratory infectious diseases. BKMR results showed that the five components of PM2.5 were the main factors affecting the incidence of respiratory infectious diseases (PIP>0.5). The joint effect of influenza and mumps by co-exposure to the components showed a significant positive correlation, and the exposure-response curve for a single component was approximately linear. And single-component modelling revealed that OM and BC may be the most important factors influencing the incidence of respiratory infections. Moreover, respiratory infectious diseases in southern and southwestern China may be less affected by the PM2.5 component. This study is the first to explore the relationship between different components of PM2.5 and the incidence of five common respiratory infectious diseases in 31 provinces of mainland China, which provides a certain theoretical basis for future research.

Exposure to metals increases the risk of many diseases and has become a public health concern. However, few studies have focused on the effect of metal on abdominal aortic calcification (AAC), especially the combined effects of metal mixtures. In this study, we aim to investigate the combined effect of metals on AAC risk and determine the key components in the multiple metals. We tried to investigate the relationship between multiple metal exposure and AAC risk. Fourteen urinary metals were analyzed with five statistical models as follows: generalized linear regression, weighted quantile sum regression (WQS), quantile g-computation (Qgcomp), and Bayesian kernel machine regression (BKMR) models. A total of 838 participants were involved, of whom 241 (28.8%) had AAC. After adjusting for covariates, in multiple metal exposure logistic regression, cadmium (Cd) (OR = 1.364, 95% CI = 1.035-1.797) was positively associated with AAC risk, while cobalt (Co) (OR = 0.631, 95% CI = 0.438-0.908) was negatively associated with AAC risk. A significant positive effect between multiple metal exposure and AAC risk was observed in WQS (OR = 2.090; 95% CI = 1.280-3.420, P < 0.01), Qgcomp (OR = 1.522, 95% CI = 1.012-2.290, P < 0.05), and BKMR models. It was found that the positive association may be driven primarily by Cd, lead (Pb), uranium (U), and tungsten (W). Subgroups analysis showed the association was more significant in participants with BMI ≥ 25 kg/m2, abdominal obesity, drinking, and smoking. Our study shows that exposure to multiple metals increases the risk of AAC in adults aged ≥ 40 years in the USA and that Cd, Pb, U, and W are the main contributors. The association is stronger in participants who are obese, smoker, or drinker.

Metal pollution can cause a decline in female fertility, however, previous studies have focused more on the effect of a single metal on fertility. In this study, we evaluated the effect of metal mixtures on female fertility based on nested case-control samples. The plasma levels of 22 metal elements from 180 women were determined by an inductively coupled plasma mass spectrometer (ICP-MS). Minimum absolute contraction and selection operator (LASSO) penalty regression selected metals with the greatest influence on clinical outcome. Logistic regression was used to analyze the correlation between single metals and fertility while a Bayesian kernel function regression (BKMR) model was used to analyze the effect of mixed metals. Eight metals (Calcium (Ca), Chromium (Cr), Cobalt (Co), Copper (Cu), Zinc (Zn), Rubidium (Rb), Strontium (Sr) and Zirconium (Zr)) were selected by LASSO regression for subsequent analysis. After adjusting for covariates, the logistic model showed that Cu (Odds Ratio(OR):0.33, 95% CI: 0.13 - 0.84) and Co (OR:0.38, 95% CI: 0.15 -0.94) caused a significant reduction in fertility, and identified the protective effect of Zn (OR: 2.96, 95% CI:1.21 -7.50) on fertility. Trend tests showed that increased Cr, Cu, and Rb levels were associated with reduced fertility. The BKMR model showed that Cr, Co, Cu, and Rb had a nonlinear relationship with fertility decline when controlling for the concentrations of other metals and suggested that Cu and Cr might exert an influence on fertility. Analysis showed a negative correlation between Cu, Cr, Co, Rb, and fertility, and a positive correlation between Zn and fertility. Furthermore, we found evidence for the interaction between Cu and Cr. Our findings require further validation and may identify new mechanisms in the future.

BACKGROUND: Endometriosis is a common gynecological disease that affects approximately 5 %∼10 % of reproductive-aged women. Zinc (Zn), selenium (Se), copper (Cu), cobalt (Co) and molybdenum (Mo) are essential trace elements and are very important for human health. However, studies on the relationship between mixtures of essential trace elements and the risk of endometriosis are limited and inconsistent. In particular, studies confirming the association via different sample types are limited.
OBJECTIVE: This study aimed to investigate the associations between Zn, Se, Cu, Co and Mo concentrations in blood and follicular fluid (FF) and endometriosis risk in a Chinese population.
METHODS: A total of 609 subjects undergoing in vitro fertilization (IVF) were recruited; 836 samples were analyzed, including 451 blood samples (234 controls and 217 cases) and 385 FF samples (203 controls and 182 cases). In addition, 227 subjects provided both blood and FF samples. Zn, Se, Cu, Co and Mo concentrations in blood and FF were quantified via inductively coupled plasma-mass spectrometry (ICP-MS). The associations between the levels of Zn, Se, Cu, Co and Mo and the risk of endometriosis were assessed using single-element models (logistic regression models), and the combined effect of the trace elements on endometriosis risk was assessed using multielement models (Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression).
RESULTS: Based on the single-element models, significant associations of Zn concentrations in blood (high-level vs. low-level group: aOR = 14.17, 95 % CI: 7.31, 27.50) and FF (first tertile vs. second tertile group: aOR = 0.34, 95 % CI: 0.16, 0.71; third tertile vs. second tertile group: aOR = 2.32, 95 % CI: 1.38, 3.91, respectively) and Co concentrations in blood (first tertile vs. second tertile group, aOR = 0.24, 95 % CI: 0.12, 0.48) and FF (third tertile vs. second tertile group: aOR = 3.87, 95 % CI: 2.19, 6.84) with endometriosis risk were found after adjustment for all confounders. In FF, Cu and Mo levels were significantly greater among the cases than among the controls, with a positive association with endometriosis risk (Cu (first tertile vs. second tertile group: aOR = 0.39, 95 % CI: 0.19, 0.81; third tertile vs. second tertile group: aOR = 2.73, 95 % CI: 1.61, 4.66, respectively) and Mo (high-level vs. low-level group: aOR = 14.93, 95 % CI: 7.16, 31.12)). However, similar associations between blood Cu and Mo levels and endometriosis risk were not found. In addition, the levels of these five essential trace element mixtures in blood and in FF were significantly and positively associated with endometriosis risk according to the BKMR analyses; the levels of Zn and Cu in blood and the levels of Mo in FF were significantly related to the risk of endometriosis, and the posterior inclusion probabilities (PIPs) were 1.00, 0.99 and 1.00 for Zn and Cu levels in blood and Mo levels in FF, respectively. Furthermore, Zn and Mo were the highest weighted elements in blood and FF, respectively, according to WQS analyses.
CONCLUSIONS: The risk of endometriosis was associated with elevated levels of several essential trace elements (Zn, Cu and Co). Elevated levels of these elements may be involved in the pathomechanism of endometriosis. However, further studies with larger sample sizes will be necessary to confirm these associations.