Polycystic ovary syndrome (PCOS) severely affects women\'s fertility and accompanies serious metabolic disturbances, affecting 5%-20% of women of reproductive age globally. We previously found that exposure to toxic metals in the blood raised the risk of PCOS, but the association between exposure to toxic metals and the risk of PCOS in the follicular fluid, the microenvironment for oocyte growth and development in females, and its effect on metabolism has not been reported. This study aimed to evaluate the associations between the concentrations of cadmium (Cd), mercury (Hg), barium (Ba) and arsenic (As) in FF and the risk of PCOS, and to explore the mediating effect of metabolic markers in FF on the above relationship. We conducted a case-control study, including 557 women with PCOS and 651 controls. Ba, Cd, Hg and As levels in FF were measured by ICP-MS, metabolites levels in FF was measured by LC-MS/MS among 168 participants randomly selected from all the participants. Logistic regression models were used to assess the association of a single metal level with the PCOS risk, and linear regression models were used to assess the relationships of a single metal level with clinical phenotype parameters and metabolites levels. Combined effect of metals mixture levels on the risk of PCOS were assessed via weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR). Medication analysis was performed to explore the role of metabolic markers on the relationship of toxic metals levels with the risk of PCOS. The exposure levels of Cd, Hg, Ba and As in FF were all positively and significantly associated with the PCOS risk (with respect to the highest vs. lowest tertile group: OR = 1.57, 95% CI = 1.17 ~ 2.12 for Cd, OR = 1.69, 95% CI = 1.22 ~ 2.34 for Hg, OR = 1.76, 95% CI = 1.32 ~ 2.34 for Ba, OR = 1.42, 95% CI = 1.05 ~ 1.91 for As). In addition, levels of metal mixture also significantly correlated with the risk of PCOS, Cd level contributed most to it. Moreover, we observed significant positive relationships between Cd level and LH (β = 0.048, 95% CI = 0.002 ~ 0.094), T (β = 0.077, 95% CI = 0.029 ~ 0.125) and HOMA-IR value (β = 0.060, 95% CI = 0.012 ~ 0.107), as well as Hg level with LH, FSH/LH ratio and TC. Furthermore, we revealed that estrone sulfate, LysoPE 22:6 and N-Undecanoylglycine were significantly and positively mediating the association between Cd level and the risk of PCOS (with mediated proportion of 0.39, 0.24 and 0.35, respectively), and between Hg level and the risk of PCOS (with mediated proportion of 0.29, 0.20 and 0.46, respectively). These highly expressed metabolites significantly enriched in the fatty acid oxidation, steroid hormone biosynthesis and glycerophospholipids metabolism, which may explain the reason why the levels of Cd and Hg in FF associated with the phenotype of PCOS. Ba and As in FF was not found the above phenomenon. Our results suggested that exposure to multiple toxic metals (Cd, Hg, Ba and As) in FF associated with the increased risk of PCOS, Cd was a major contributor. Levels of Cd and Hg in FF significantly associated with the phenotype of PCOS. The above association may result from that Cd and Hg in FF related with the disturbance of fatty acid oxidation, steroid hormone biosynthesis and the glycerophospholipids metabolism.

The impact of exposure to metals on chronic kidney disease (CKD) has only been investigated in two-way or single metal interactions in previous studies. We investigated the associations between five single metals in blood and their mixed exposure and CKD by using the machine learning approach. Relevant data were extracted from the National Health and Nutrition Examination Survey (NHANES 2011-2020), and the level of five metals in blood detected by inductively coupled plasma mass spectrometry was considered as exposures, namely, cadmium (Cd), lead (Pb), total mercury (Hg), manganese (Mn), and selenium (Se). The correlations between individual metal and metal mixtures and CKD were then evaluated by survey-multivariable logistic regression (SMLR), generalized weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR). Altogether, our study included 12,412 participants representing 572.6 million non-institutionalized US adults. Several single metals with the high quartile of exposure showed a positive association with the CKD ratio including Cd [(AOR = 1.873, 95% CI: 1.537, 2.284), Q4], Pb [(AOR = 1.559, 95% CI: 1.295, 1.880), Q4], and total Hg [(AOR = 1.169, 95% CI: 1.018, 1.343), Q2], while Mn [(AOR = 0.796, 95% CI: 0.684, 0.927), Q2] and Se [(AOR = 0.805, 95% CI: 0.664, 0.976), Q4] were negatively associated with the CKD ratio. In light of the positive fit of the WQS regression model, a significantly positive correlation was found between mixed metals and CKD (AOR = 1.373, 95% CI: 1.224, 1.539) after full covariate adjustment, and a similar finding was also detected in the BKMR model. Our study revealed that each single metal including Cd, Pb, and total Hg might have a positive association with CKD while this association was negative for both Mn and Se. The five metals might have a positive joint effect on CKD.

Maternal exposure to metals may pose a risk to the health of newborns, however, the underlying mechanisms remain ambiguous. Herein, we aimed to investigate the influence of metals exposure on birth outcomes and reveal the importance of metabolites in the exposure-outcomes association by using metabolomics methods.
In our study, 292 mother-pairs were included who were recruited from the affiliated hospitals of Nanjing Medical University between 2006 and 2011. We measured fifteen metals (mercury, lead, vanadium, arsenic, zinc, cadmium, rubidium, copper, cobalt, iron, molybdenum, strontium, thallium, magnesium and calcium) and metabolites in maternal second trimester serums by using inductively coupled plasma mass spectrometry and ultra-high performance liquid chromatography high resolution accurate mass spectrometry, respectively. A multi-step statistical analysis strategy including exposome-wide association study (ExWAS) model, variable selection models and multiple-exposure models were performed to systematically appraise the associations of individual and mixed metals exposure with birth outcomes. Furthermore, differential metabolites that associated with metals exposure and birth outcomes were identified using linear regression models.
Metal\'s levels in maternal serums ranged from 0.05 μg/L to 1864.76 μg/L. In the ExWAS model, maternal exposure to arsenic was negatively associated with birth weight (β = 188.83; 95% CI: -368.27, -9.39), while maternal mercury exposure showed a positive association (β = 533.65; 95%CI: 179.40, 887.90) with birth weight. Moreover, each unit increase in mercury (1 ng/mL-log transformed) was associated with a 1.82 week-increase (95%CI: 0.85, 2.79) in gestational age. These findings were subsequently validated by variable selection models and multiple exposure models. Metabolomic analysis further revealed the significant role of 3-methyladenine in the relationship between arsenic exposure and birth weight.
This study provides new epidemiological evidence indicating the associations of metals exposure and neonatal birth outcomes, and emphasizes the potential role of metabolite biomarkers and their importance in monitoring adverse birth outcomes.

Aging is related to a progressive decline in physiological functions and is affected by environmental factors. Metal exposures are linked with many health effects, but have poorly understood associations with aging. In this study, a total of 33,916 participants from the Dongfeng-Tongji cohort were included to establish biological age (BA) predictors by using recent advanced algorithms, Klemera and Doubal method (KDM) and Mahalanobis distance. Two biological aging indexes (BAIs), recorded as KDM-accel [the residual from regressing KDM-BA on chronological age] and physiological dysregulation (PD), were separately defined and tested on their associations with mortality by using Cox proportional hazard models. Among 3320 subjects with laboratory determinations of 23 metals in plasma, the individual and overall associations between these metals and BAIs were evaluated by using multiple-linear regression and weighted quantile sum (WQS) models. Both BAIs were prospectively associated with all-cause mortality among the whole participants [KDM-accel: HR(95%CI) = 1.23(1.18, 1.29); PD: HR(95%CI) = 1.37(1.31, 1.42)]. Each 1-unit increment in ln-transformed strontium and molybdenum were cross-sectionally associated with a separate 0.71- and 0.34-year increase in KDM-accel, and each 1 % increment in copper, rubidium, strontium, cobalt was cross-sectionally associated with a separate 0.10 %, 0.10 %, 0.09 %, 0.02 % increase in PD (all FDR < 0.05). The WQS models observed mixture effects of multi-metals on aging, with a 0.20-year increase in KDM-accel and a 0.04 % increase in PD for each quartile increase in ln-transformed concentrations of all metals [KDM-accel: β(95%CI) = 0.20(0.08, 0.32); PD: β(95%CI) = 0.04(0.02, 0.06)]. Our findings revealed that plasma strontium, molybdenum, copper, rubidium and cobalt were associated with accelerated aging. Multi-metals exposure showed mixture effects on the aging process, which highlights potential preventative interventions.