Weighted Quantile Sum Regression Research Articles

Association between multiple-heavy-metal exposures and systemic immune inflammation in a middle-aged and elderly Chinese general population

BackgroundExposure to heavy metals alone or in combination can promote systemic inflammation. The aim of this study was to investigate potential associations between multiple plasma heavy metals and markers of systemic immune inflammation.MethodsUsing a cross-sectional study, routine blood tests were performed on 3355 participants in Guangxi, China. Eight heavy metal elements in plasma were determined by inductively coupled plasma mass spectrometry. Immunoinflammatory markers were calculated based on peripheral blood WBC and its subtype counts. A generalised linear regression model was used to analyse the association of each metal with the immunoinflammatory markers, and the association of the metal mixtures with the immunoinflammatory markers was further assessed using weighted quantile sum (WQS) regression.ResultsIn the single-metal model, plasma metal Fe (log10) was significantly negatively correlated with the levels of immune-inflammatory markers SII, NLR and PLR, and plasma metal Cu (log10) was significantly positively correlated with the levels of immune-inflammatory markers SII and PLR. In addition, plasma metal Mn (log10 conversion) was positively correlated with the levels of immune inflammatory markers NLR and PLR. The above associations remained after multiple corrections. In the mixed-metal model, after WQS regression analysis, plasma metal Cu was found to have the greatest weight in the positive effects of metal mixtures on SII and PLR, while plasma metals Mn and Fe had the greatest weight in the positive effects of metal mixtures on NLR and LMR, respectively. In addition, blood Fe had the greatest weight in the negative effects of the metal mixtures for SII, PLR and NLR.ConclusionPlasma metals Cu and Mn were positively correlated with immunoinflammatory markers SII, NLR and PLR. While plasma metal Fe was negatively correlated with immunoinflammatory markers SII, NLR, and PLR.

Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants’ residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03–1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03–1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

Association of individual and combined exposures of 10 metals with periodontitis: Results from a large population-based study.

To systematically investigate the association between individual and combined metal exposure and periodontitis. Data encompassing complete periodontal examinations and metal detection in blood and urine samples were procured from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Three statistical methods, namely weighted logistic regression, Bayesian kernel machine regression (BKMR), and weighted quantile sum (WQS) regression, were used to evaluate the independent and combined associations between metals and periodontitis. Elevated concentrations of blood cadmium (odds ratio [OR]: 1.73, 95% confidence interval [CI]: 1.15-2.61) and blood lead (OR: 1.17, 95 %CI: 1.02-1.34) exhibited a positive association with periodontitis, even after adjusting for potential confounding factors. The BKMR and WQS regression suggested that the co-exposure of metals was also positively associated with periodontitis. Moreover, estradiol and albumin were identified as potential mediators in the relationship between the WQS index of the 10 metals in blood and periodontitis explaining 25.36% and 2.02% of the relationship, respectively. Furthermore, generally consistent patterns of associations between metals and periodontitis and mediating roles of estrogen and albumin were observed after a series of sensitivity analyses. This study provides evidence of positive associations between elevated levels of cadmium, lead or metal mixture and periodontitis, which may be partially mediated by sex hormones and oxidative stress indicators.

The association of urinary heavy metal exposure with frailty susceptibility and mortality in middle-aged and older adults: a population-based study

Heavy metals’ presence as environmental pollutants has a close link to adverse health effects. Frailty, a clinical syndrome hallmarked by elevated vulnerability to stressors, presents a substantial challenge in healthcare. However, the association between exposure to heavy metals and frailty largely remains unexplored. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2003–2018 and correlated with the U.S. National Death Index (NDI) from 2019, we investigated mortality outcomes. Logistic regression, Cox regression, Kaplan-Meier survival curves, weighted quantile-sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were employed to assess the association between heavy metal exposure and frailty incidence and mortality in the frail population. Eight metals were measured in urine using inductively coupled plasma mass spectrometry with values adjusted for urinary creatinine, which was used to reflect heavy metal exposure. The cohort incorporated 5370 female participants aged 45 and above, with 1518 diagnosed with frailty. The findings indicated a substantial correlation between exposure to specific heavy metals, namely tungsten (odds ratio [OR]: 1.94, 95% confidence interval [CI]: 1.31–2.89), cobalt (OR: 1.64, 95% CI: 1.40–1.93), cadmium (OR: 1.93, 95% CI: 1.52–2.43), and uranium (OR: 7.36, 95% CI: 1.53–35.28), and an elevated risk of frailty. WQS and BKMR regression models identified cadmium, cobalt, and tungsten as main contributors to frailty. Cox regression analysis, after adjustment for covariates, suggested that the higher the exposure levels to cadmium and lead, the higher the risk of death in frail patients, with associated hazard ratios (HR) of 95% CI: 1.96 (1.53, 2.52) and 1.30 (1.13, 1.49), respectively. Our study revealed a significant positive correlation between exposure to heavy metal mixtures and frailty onset in middle-aged and older adults, along with increased mortality in frail patients. Cobalt, cadmium, and tungsten emerged as prominent contributors to frailty, with cobalt and cadmium directly impacting the long-term life expectancy of frail patients.

Analysis of relationship between mixed heavy metal exposure and early renal damage based on a weighted quantile sum regression and Bayesian kernel machine regression model

BackgroundOccupation, environmental heavy metal exposure, and renal function impairment are closely related. The relationship between mixed metal exposure and chronic renal injury is inadequately described, and the interaction between each metal is poorly explored. ObjectiveThis cross-sectional study assessed mixed heavy metal exposure in the general population and their relationship with early renal impairment, as well as possible interactions between metals. MethodsThe study was conducted in two communities in Taiyuan City in northern China. Multiple linear regression, weighted quantile sum (WQS) and bayesian kernel machine regression (BKMR) regression were used to explore the relationship of mixed heavy metal exposure with indicators of early kidney injury (N-acetyl-β-D- glucosidase (UNAG), urinary albumin (UALB)). Meanwhile, BKMR was used to explore the possible interactions between mixed heavy metal and indicators of early kidney injury. ResultsBased on the WQS regression results, we observed adjusted WQS coefficient β (β-WQS) of 0.711 (95% CI: 0.543, 0.879). Notably, this change was primarily driven by As (35.6%) and Cd (22.5%). In the UALB model, the adjusted β-WQS was 0.657 (95% CI: 0.567, 0.747), with Ni (30.5%), Mn (22.1%), Cd (21.2%), and As (18.6%) exhibiting higher weights in the overall effect. The BKMR results showed a negative interaction between As and other metals in the UNAG and UALB models, a positive interaction between Mn and Ni and other metals. No significant pairwise interaction was observed in the association of metals with indicators of early kidney injury. ConclusionThrough multiple linear regression, WQS regression, and BKMR analyses, we found that exposure to mixed heavy metals such as Cd, Cr, Pb, Mn, As, Co and Ni was positively correlated with UNAG and UALB. Moreover, there are complex interactions between two or more heavy metals in more than one direction.

Combined exposure to multiple metals on abdominal aortic calcification: results from the NHANES study.

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 ≥ 25kg/m2, abdominal obesity, drinking, and smoking. Our study shows that exposure to multiple metals increases the risk of AAC in adults aged ≥ 40years 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.

Co-exposure to phthalates and polycyclic aromatic hydrocarbons and the risk of gestational hypertension in Chinese women

Phthalates (PAEs) and polycyclic aromatic hydrocarbons (PAHs) are frequently detected in females of reproductive age. Many studies have found that environmental PAE and PAH levels are independent risk factors for gestational hypertension. However, exposure to both components is a more realistic scenario. To better assess the health effects of PAEs and PAHs in pregnant women, we explored the associations of exposure to both individual and combined PAEs and PAHs with gestational hypertension. This nested case–control study was a component of a prospective cohort study conducted in Beijing, China. We included 206 women with gestational hypertension and 214 pregnant controls. We used gas chromatography/tandem mass spectrometry (GC–MS/MS) to detect 8 PAEs and 13 PAHs in > 80 % of all collected hair samples. Multiple linear regression models were employed to test the individual associations between each component and gestational hypertension. A quantile-based g-computation (qgcomp) model and a weighted quantile sum (WQS) regression model were used to estimate whether exposure to both PAEs and PAHs increased the risk of gestational hypertension. The individual exposure analyses revealed that diethyl phthalate (DEP), diisobutyl phthalate (DIBP) (both PAEs), benzo(k)fluoranthene (BKF), anthracene, (ANT), and benzo(a)pyrene (BAP) (all PAHs) were positively associated with increased risk of gestational hypertension. In mixed-effect analyses, the qgcomp model indicated that co-exposure to PAEs and PAHs increased the risk of gestational hypertension (odds ratio = 2.01; 95 % confidence interval: 1.02, 3.94); this finding was verified by the WQS regression model. Our findings support earlier evidence that both PAEs and PAHs increase the risk of gestational hypertension, both individually and in combination. This suggests that reductions in exposure to endocrine system-disrupting chemicals such as PAEs and PAHs might reduce the risk of gestational hypertension.

The association between brominated flame retardants exposure with bone mineral density in US adults: A cross-sectional study of the national health and nutrition examination survey (NHANES) 2005–2014

Background and aimsExposure to brominated flame retardants (BFRs) has been widely confirmed to impair the normal functioning of the human body system. However, there is a paucity of study on the effects of serum BFRs on bone mineral density (BMD). This study aims to investigate the relationship between exposure to BFRs and BMD in a nationally representative sample of U.S. adults. Methods3079 participants aged between 20 and 80 years with complete data were included in the study. Serum levels of BFRs were measured using automated liquid-liquid extraction and subsequent sample clean-up. The BMD of all participants were assessed by DXA examinations. Generalize linear model, Restricted cubic spline (RCS), subgroup, weighted quantile sum (WQS) and bayesian kernel machine regression (BKMR) were used to estimate the association between serum BFRs and BMD. ResultsMultivariate linear regression analyses revealed that, after adjusting for covariates, PBB153 was significantly associated with TF-BMD (β = 0.0177, 95%CI: 0.0103–0.0252), FN-BMD (β = 0.009, 95%CI: 0.0036–0.0145), TS-BMD (β = 0.0081, 95%CI: 0.0013–0.015) and L1-BMD (β = 0.0144, 95%CI: 0.0075–0.0213). However, the associations lose their statistical significance after further adjustment for sex. BFRs exhibited S-shaped or line-plateau dose-response curves with BMD. In subgroup analyses, BFRs were significantly associated with BMD in participants who were younger than 55 years, female, overweight (BMI >25 kg/m2), and less alcohol consumption. In WQS and BKMR analyses, the effects of BFRs mixtures on BMD differed by sex, and PBDE153, PBDE209 and PBB153 had the highest weights in the WQS regression model. ConclusionThis study showed that serum BFRs negatively predicted BMD in men, but not in women or the general population. PBDE153, PBDE209, and PBB153 were significant BMD factors, especially in younger, overweight, and less alcohol consumption individuals.

Environmental and dietary exposure to 24 polycyclic aromatic hydrocarbons in a typical Chinese coking plant

Polycyclic aromatic hydrocarbons (PAHs), known for their health risks, are prevalent in the environment, with the coking industry being a major source of their emissions. To bridge the knowledge gap concerning the relationship between environmental and dietary PAH exposure, we explore this complex interplay by investigating the dietary exposure characteristics of 24 PAHs within a typical Chinese coking plant and their association with environmental pollution. Our research revealed Nap and Fle as primary dietary contaminants, emphasizing the significant influence of soil and atmospheric pollution on PAH exposure. We subjected our data to non-metric multidimensional scaling (NMDS), Spearman correlation analysis, Lasso regression, and Weighted Quantile Sum (WQS) regression to delve into this multifaceted phenomenon. NMDS reveals that dietary PAH exposure, especially within the high molecular weight (HMW) group, is common both within and around the coking plant. This suggests that meals prepared within the plant may be contaminated, posing health risks to coking plant workers. Furthermore, our assessment of dietary exposure risk highlights Nap and Fle as the primary dietary contaminants, with BaP and DahA raising concerns due to their higher carcinogenic potential. Our findings indicate that dietary exposure often exceeds acceptable limits, particularly for coking plant workers. Correlation analyses uncover the dominant roles of soil and atmospheric pollution in shaping dietary PAH exposure. Soil contamination significantly impacts specific PAHs, while atmospheric pollution contributes to others. Additionally, WQS regression emphasizes the substantial influence of soil and drinking water on dietary PAHs. In summary, our study sheds light on the dietary exposure characteristics of PAHs in a typical Chinese coking plant and their intricate interplay with environmental factors. These findings underscore the need for comprehensive strategies to mitigate PAH exposure so as to safeguard both human health and the environment in affected regions.

Environmental exposure to per- and polyfluoroalkyl substances mixture and asthma in adolescents.

Previous epidemiological studies about the relationship between per- and polyfluoroalkyl substances (PFAS) concentrations and adolescent asthma have typically examined single PFAS, without considering the mixtures effects of PFAS. Using data from the 2013-2018 National Health and Nutrition Examination Survey (NHANES), 886 adolescents aged 12-19years were included in this study. We explored the association between PFAS mixture concentrations and adolescent asthma using weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) models, respectively. After adjusting for confounders, the results of the WQS regression and BKMR models were consistent, with mixed exposure to the five PFAS not significantly associated with asthma in all adolescents. The association remained nonsignificant in the subgroup analysis by sex. Our study demonstrated no significant association between mixed exposure to PFAS and adolescent asthma, and more large cohort studies are needed to confirm this in the future.

Association between urinary organophosphate ester metabolite exposure and thyroid disease risk among US adults: National Health and Nutrition Examination Survey 2011-2014.

Organophosphate esters (OPEs) may interfere with thyroid function, but the relationship between OPEs and thyroid disease remains unclear. This study aims to elucidate the relationship between OPEs exposure and thyroid disease risk in the general population in the United States. Data were obtained from the 2011-2014 National Health and Nutrition Examination Survey cycle. All participants were tested for seven OPE metabolites in their urine and answered questions about whether they had thyroid disease through questionnaires. Logistic regression was employed to analyze the association between exposure to individual OPE metabolites and thyroid disease. Weighted Quantile Sum (WQS) regression modeling was utilized to assess exposure to mixed OPE metabolites and risk of thyroid disease. Bayesian kernel machine regression(BKMR) models to analyze the overall mixed effect of OPE metabolites. A total of 2,449 participants were included in the study, 228 of whom had a history of thyroid disease. Bis(1,3-dichloro-2-propyl) phos (BDCPP), Diphenyl phosphate (DPHP) and Bis(2-chloroethyl) phosphate (BCEP) were the top three metabolites with the highest detection rates of 91.75%, 90.77% and 86.57%, respectively. In multivariate logistic regression models, after adjustment for confounding variables, individuals with the highest tertile level of BCEP were significantly and positively associated with increased risk of thyroid disease (OR=1.57, 95% CI=1.04-2.36), using the lowest tertile level as reference. In the positive WQS regression model, after correcting for confounding variables, mixed exposure to OPE metabolites was significantly positively associated with increased risk of thyroid disease (OR=1.03, 95% CI=1.01-1.06), with BCEP and DPHP having high weights. In the BKMR model, the overall effect of mixed exposure to OPE metabolites was not statistically significant, but univariate exposure response trends showed that the risk of thyroid disease decreased and then increased as BCEP exposure levels increased. The study revealed a significant association between exposure to OPE metabolites and an increased risk of thyroid disease, with BCEP emerging as the primary contributor. The risk of thyroid disease exhibits a J-shaped pattern, whereby the risk initially decreases and subsequently increases with rising levels of BCEP exposure. Additional studies are required to validate the association between OPEs and thyroid diseases.

Inflammation mediates the relationship between diet quality assessed by healthy eating index-2015 and metabolic syndrome.

Metabolic syndrome is a cluster of metabolic disorders, including obesity, hypertension, hyperglycemia, and abnormal lipid levels. However, researches on the association between overall dietary quality measured by the Healthy Eating Index-2015 (HEI-2015) and the risk of metabolic syndrome is still lacking. This study utilized data from four cycles (2011-2018) of the National Health and Nutrition Examination Survey (NHANES) database, including 17,582 participants. Logistic regression analysis was employed to explore the correlation between HEI and the risk of metabolic syndrome. Additionally, mediation analysis was conducted to examine the effects of Systemic Immune-Inflammation Index (SII) and serum uric acid as potential mediators between HEI and metabolic syndrome risk. Weighted quantile sum (WQS) regression evaluated the composite exposure impact of the 13 components of the HEI on metabolic syndrome, as well as the proportion of their weights. Higher dietary quality measured by HEI-2015 (at the 75th percentile) was negatively correlated with the risk of metabolic syndrome (OR=0.80, 95%CI=0.72-0.89, P=0.003). Higher SII and serum uric acid levels were identified as risk factors for metabolic syndrome (P for trend<0.001). Approximately 37.5% of the effect of HEI on metabolic syndrome occurrence was mediated by SII (Indirect effect=-0.002, 95%CI (-0.003,-0.001), Direct effect=-0.022, 95%CI (-0.0273,-0.015)). Additionally, 25% of the effect of HEI on metabolic syndrome occurrence was mediated by serum uric acid levels (Indirect effect=-0.006, 95%CI (-0.010,-0.012), Direct effect=-0.024, 95%CI (-0.041,-0.009)). WQS regression analysis revealed the highest weighted proportions for seafood and plant proteins (25.20%) and sodium (17.79%), while the weight for whole fruit was the lowest (0.25%). Better dietary quality measured by HEI-2015 was associated with a lower likelihood of metabolic syndrome. Higher SII and serum uric acid levels were identified as risk factors for metabolic syndrome and potential mediators.

Cumulative residual cholesterol predicts the risk of cardiovascular disease in the general population aged 45 years and older

BackgroundNumerous studies have affirmed a robust correlation between residual cholesterol (RC) and the occurrence of cardiovascular disease (CVD). However, the current body of literature fails to adequately address the link between alterations in RC and the occurrence of CVD. Existing studies have focused mainly on individual RC values. Hence, the primary objective of this study is to elucidate the association between the cumulative RC (Cum-RC) and the morbidity of CVD.MethodsThe changes in RC were categorized into a high-level fast-growth group (Class 1) and a low-level slow-growth group (Class 2) by K-means cluster analysis. To investigate the relationship between combined exposure to multiple lipids and CVD risk, a weighted quantile sum (WQS) regression analysis was employed. This analysis involved the calculation of weights for total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), which were used to effectively elucidate the RC.ResultsAmong the cohort of 5,372 research participants, a considerable proportion of 45.94% consisted of males, with a median age of 58. In the three years of follow-up, 669 participants (12.45%) had CVD. Logistic regression analysis revealed that Class 2 individuals had a significantly reduced risk of developing CVD compared to Class 1. The probability of having CVD increased by 13% for every 1-unit increase in the Cum-RC according to the analysis of continuous variables. The restricted cubic spline (RCS) analysis showed that Cum-RC and CVD risk were linearly related (P for nonlinearity = 0.679). The WQS regression results showed a nonsignificant trend toward an association between the WQS index and CVD incidence but an overall positive trend, with the greatest contribution from TC (weight = 0.652), followed by LDL (weight = 0.348).ConclusionCum-RC was positively and strongly related to CVD risk, suggesting that in addition to focusing on traditional lipid markers, early intervention in patients with increased RC may further reduce the incidence of CVD.

Using Bayesian and weighted regression to evaluate the association of idiopathic oligoastenoteratozoospermia with seminal plasma metal mixtures

Idiopathic oligoastenoteratozoospermia (iOAT) affects 30% of infertile men of reproductive age. However, the associations between Cr, Fe, Cu, Se or Co levels and iOAT risk have not been determined. This research aimed to assess the associations between Cr, Fe, Cu, Se and Co levels as well as their mixtures in seminal plasma and the risk of iOAT and severe iOAT. Therefore, a case‒control study including 823 participants (416 iOAT patients and 407 controls) recruited from October 2021 to August 2022 at the reproductive medicine center of the First Affiliated Hospital of Anhui Medical University was conducted in Anhui, China. The concentrations of Cr, Fe, Cu, Se and Co in seminal plasma were detected via inductively coupled plasma‒mass spectrometry. Binary logistic regression models were used to assess the associations between the levels of Cr, Fe, Cu, Se and Co and the risk of iOAT and severe iOAT; additionally, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regressions were performed to evaluate the joint effect of seminal plasma levels of Cr, Fe, Cu, Se and Co on the risk of iOAT and explore which elements contributed most to the relationship. We found significant associations between the concentrations of Fe, Cu and Se in seminal plasma and iOAT risk after adjusting for covariates (Fe, lowest tertile vs. second tertile: aOR = 1.86, 95% CI = 1.31, 2.64; Cu, lowest tertile vs. second tertile: aOR = 1.95, 95% CI = 1.37, 2.76; Se, lowest tertile vs. second tertile: aOR = 1.65, 95% CI = 1.17, 2.35). A lower Se concentration in seminal plasma (lowest tertile vs. second tertile: aOR = 1.84, 95% CI = 1.10, 3.10) was positively associated with the risk of severe iOAT. Additionally, we also observed an association between the concentration of Cr in seminal plasma and the risk of iOAT before adjusting for covariates (Cr, third tertile vs. lowest tertile: OR=1.44, 95% CI: 1.03, 2.02). According to the BKMR analyses, the risk of iOAT increased when the overall concentrations were less than the 25th percentile. The results from the WQS regression indicated that a negative WQS index was significantly associated with the iOAT risk, while a positive WQS index was not. Se and Fe had significant weights in the negative direction. In conclusion, lower Cu, Fe and Se levels in seminal plasma were positively associated with iOAT risk, while higher Cr levels in seminal plasma were positively associated with iOAT risk according to the single element model, and lower levels of Se were related to a greater risk of severe iOAT; when comprehensively considering all the results from BKMR and WQS regression, Fe, Se and Cr levels contributed most to this relationship.

Inverse association between maternal serum concentrations of trace elements and risk of spontaneous preterm birth: a nested case-control study in China.

Few studies have evaluated the joint effect of trace elements on spontaneous preterm birth (SPTB). This study aimed to examine the relationships between the individual or mixed maternal serum concentrations of Fe, Cu, Zn, Se, Sr and Mo during pregnancy, and risk of SPTB. Inductively coupled plasma MS was employed to determine maternal serum concentrations of the six trace elements in 192 cases with SPTB and 282 controls with full-term delivery. Multivariate logistic regression, weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR) were used to evaluate the individual and joint effects of trace elements on SPTB. The median concentrations of Sr and Mo were significantly higher in controls than in SPTB group (P < 0·05). In multivariate logistic regression analysis, compared with the lowest quartile levels of individual trace elements, the third- and fourth-quartile Sr or Mo concentrations were significantly associated with reduced risk of SPTB with adjusted OR (aOR) of 0·432 (95 CI < 0·05). In multivariate logistic regression analysis, compared with the lowest quartile levels of individual trace elements, the third- and fourth-quartile Sr or Mo concentrations were significantly associated with reduced risk of SPTB with adjusted aOR of 0·432 (95 % CI 0·247, 0·756), 0·386 (95 % CI 0·213, 0·701), 0·512 (95 % CI 0·297, 0·883) and 0·559 (95 % CI 0·321, 0·972), respectively. WQSR revealed the inverse combined effect of the trace elements mixture on SPTB (aOR = 0·368, 95 % CI 0·228, 0·593). BKMR analysis confirmed the overall mixture of the trace elements was inversely associated with the risk of SPTB, and the independent effect of Sr and Mo was significant. Our findings suggest that the risk of SPTB decreased with concentrations of the six trace elements, with Sr and Mo being the major contributors.

J-shaped association of dietary catechins intake with the prevalence of osteoarthritis and moderating effect of physical activity: an American population-based cohort study.

Catechins are a class of natural compounds with a variety of health benefits, The relationship between catechins and the prevalence of osteoarthritis (OA) is unknown. This study investigated the associations between daily intake of catechins and the prevalence of OA among American adults and assessed the moderating effect of physical activity (PA). This study included 10,039 participants from the National Health and Nutrition Examination Survey (2007-2010,2017-2018). The logistic regression, weighted quantile sum (WQS) regression, and restricted cubic spline (RCS) regression models were conducted to explore the associations between daily intake of catechins and the prevalence of OA. Moreover, interaction tests were performed to assess the moderating effect of PA. After multivariable adjustment, the weighted multivariable logistic regression and RCS regression analyses revealed significant J-shaped non-linear correlations between intakes of epigallocatechin and epigallocatechin 3-gallate had significant associations with the prevalence of OA among in U.S. adults. WQS regression analysis showed that excessive epigallocatechin intake was the most significant risk factor for OA among all subtypes of catechins. In the interaction assay, PA showed a significant moderating effect in the relationship between epigallocatechin intake and OA prevalence. The intake of gallocatechin and gallocatechin 3-gallate had a significant negative correlation with the prevalence of OA and the dose-response relationship was J-shaped.PA below 150 MET-min/week and the threshold intakes of 32.70mg/d for epigallocatechin and 76.24mg/d for epigallocatechin 3-gallate might be the targets for interventions to reduce the risk of developing OA.

Association between polycyclic aromatic hydrocarbons and periodontitis: Results from a large population-based study.

To explore the association between polycyclic aromatic hydrocarbons (PAHs) (measured using urinary metabolites) and periodontitis using data from the National Health and Nutrition Examination Survey 2009-2014. Weighted binary logistic regression, Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) regression were used to evaluate independent and joint associations between the six urinary monohydroxylated metabolites of PAHs (OH-PAHs) and periodontitis. In all, 3413 participants were included in this study. All six urinary OH-PAHs were present at higher levels in the periodontitis group compared with the non-periodontitis group (p < .001). Fully adjusted multivariable logistic regressions showed positive associations between the six urinary OH-PAHs and periodontitis (p < .05). Higher concentrations of OH-PAHs were also positively associated with attachment loss, periodontal pocket depth (PPD) and the number of tooth loss. BKMR and WQS regression yielded similar positive associations between OH-PAH mixtures and periodontitis. PAHs and their mixture are positively associated with periodontitis, which may provide novel insights into periodontitis prevention from an environmental exposure perspective.

Exposure to organochlorine pesticides and polychlorinated biphenyls, adherence to an ideal cardiovascular health, and arterial stiffness among Chinese adults.

This study aimed to assess the relationships between exposure to individual organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and their mixture and arterial stiffness and explore whether adherence to an ideal cardiovascular health (CVH) could mitigate these associations. The cross-sectional study enrolled 1437 Chinese adults between March and May 2019 in Wuhan, China. OCPs and PCBs concentrations were measured using solid phase extraction coupled with gas chromatography-tandem mass spectrometry. Arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). CVH was determined by three behavioral and four biological metrics and categorized as ideal, intermediate, and poor CVH. We applied generalized linear model and weighted quantile sum (WQS) regression to evaluate the associations of exposure to individual OCPs or PCBs and their mixture with baPWV, respectively. We found that participants with detectable levels of heptachlor epoxide, PCB-153, and PCB-180 had higher baPWV (β: 34.25, 95% CI 14.28-54.22; β: 27.64, 95% CI 7.90-47.38; and β: 30.51, 95% CI 10.68-50.35) than those with undetectable levels. In WQS regression, the mixture of OCPs and PCBs was related to a higher baPWV (β: 24.93, 95% CI 2.70-47.15). Compared with participants with ideal CVH and undetectable OCPs or PCBs levels, those with poor CVH and detectable OCPs or PCBs levels had the highest increase in baPWV (heptachlor epoxide: β: 147.94, 95% CI 112.52-183.55; PCB-153: β: 150.22, 95% CI 115.40-185.04; PCB-180: β: 147.02, 95% CI 111.66-182.38). Our findings suggested that individual OCPs, PCBs, and their mixture exposure were positively associated with arterial stiffness, and adherence to an ideal CVH may mitigate the adverse effect.

Higher Chinese Healthy Eating Index Scores are Associated with a Lower Risk of Hyperuricemia: A Large Cross-Sectional Survey Based in China

Hyperuricemia (HUA) is a metabolic disease with an increasing prevalence in China and worldwide. Few studies have focused on the relationship between daily dietary quality and hyperuricemia in China. Data were obtained from the China Health and Nutrition Survey (CHNS). Adults with complete dietary survey and uric acid data were selected for the study, along with complete data on covariates about demographics, lifestyle, and history of disease. The Chinese Healthy Eating Index (CHEI) was used to reflect the quality of daily diet. The logistic regression model and weighted quantile sum (WQS) regression model were used to explore the relationship between CHEI score and risk of HUA. WQS regression models can also further assess the contribution of the various dietary components of CHEI in reducing the risk of HUA. There was a not-significant trend in protective effect between higher CHEI scores and HUA risk in the logistic regression model. In the WQS model, higher CHEI scores showed a significant protective effect on the HUA (OR: .601,0.298-.907). Fruits, tubers, and dairy were top contributor in the protective effect of CHEI (26.81%, 14.34%, and 12.95%, respectively). Higher CHEI associated with lower HUA risk in Chinese adults, and more attention should be paid to fruits, tubers, and dairy.

Decreased DNA repair capacity caused by exposure to metal mixtures is modulated by the PARP1 rs1136410 variant in newborns from a polluted metropolitan area

BackgroundDNA damage caused by exposure to metal mixtures and the potential modulating role of genes involved in DNA repair and the antioxidant response have not been evaluated in newborns. AimThe aim was to evaluate the association between prenatal exposure to metal mixtures and DNA repair capacity (DRC) in newborns from the Metropolitan Area of Mexico City (MAMC), a heavily polluted area, and the impact of variants in genes involved in DNA repair and the antioxidant response on this association. MethodsWe analyzed cord blood samples obtained at delivery from 125 healthy newborns from the MAMC. Twenty-four elements were determined by inductively coupled plasma mass spectrometry (ICP‒MS), but only 12 (Cu, I, Se, Zn, As, Ba, Cs, Mn, Sb, Sr, Pb, and Ti) were quantified in most samples. DRC was assessed by the challenge-comet assay, and OGG1, PARP1, and NFE2L2 genotyping was performed with TaqMan probes. Metal mixtures were identified and analyzed using principal component analysis (PCA) and weighted quantile sum (WQS) regression. Independent adjusted linear regression models were used to evaluate the associations. ResultsA null DRC was observed in 46% of newborns. The metals with the highest concentrations were Mn, Sr, Ti, and Pb. Essential elements showed normal levels. Only the mixture characterized by increased As, Cs, Cu, Se, and Zn levels was inversely associated with DRC. As was the principal contributor (37.8%) in the negative direction in the DRC followed by Ba and Sb, according to the WQS regression. Newborns carrying of the derived (G) allele of the PARP1 rs1136410 variant showed decreased DRC by exposure to some potentially toxic metals (PTMs) (As, Cs, and Ba). ConclusionPrenatal exposure to metal mixtures negatively affected DRC in newborns, and the PARP1 rs1136410 variant had a modulating role in this association.