Search Results (64)

Perfluoroalkyl substances (PFASs) possess immunosuppressive properties. However, their association with rheumatoid arthritis (RA) risk remains inconclusive across epidemiological studies. This study integrates population-based and mechanistic evidence to clarify the relationship between PFAS exposure and RA. We analyzed 8743 U.S. adults from the NHANES (2005–2018), assessing individual and mixed exposures to PFOA, PFOS, PFNA, and PFHxS using multivariable logistic regression, Bayesian kernel machine regression, quantile g-computation, and weighted quantile sum models. Network toxicology and molecular docking were utilized to identify core targets mediating immune disruption. The results showed that elevated PFOA (OR = 1.63, 95% CI: 1.41–1.89), PFOS (OR = 1.41, 1.25–1.58), and PFNA (OR = 1.40, 1.20–1.63) levels significantly increased RA risk. Mixture analyses indicated a positive joint effect (WQS OR = 1.06, 1.02–1.10; qgcomp OR = 1.26, 1.16–1.38), with PFOA as the primary contributor. Stratified analyses revealed stronger effects in females (PFOA Q4 OR = 3.75, 2.36–5.97) and older adults (≥60 years). Core targets included EGFR, SRC, TP53, and CTNNB1. PFAS mixtures increase RA risk, dominated by PFOA and modulated by sex/age. These findings help reconcile prior contradictions by identifying key molecular targets and vulnerable subpopulations, supporting regulatory attention to PFAS mixture exposure. Full article

Background: Environmental exposures, such as per- and polyfluoroalkyl substances (PFAS), in conjunction with social and behavioral factors, can significantly impact liver health. This research investigates the combined effects of PFAS (perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), alcohol consumption, smoking, income, and education on liver function among the U.S. population, utilizing data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). Methods: PFAS concentrations in blood samples were analyzed using online solid-phase extraction combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS), a highly sensitive and specific method for detecting levels of PFAS. Liver function was evaluated using biomarkers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), total bilirubin, and the fatty liver index (FLI). Descriptive statistics and multivariable linear regression analyses were employed to assess the associations between exposures and liver outcomes. Bayesian Kernel Machine Regression (BKMR) was utilized to explore the nonlinear and interactive effects of these exposures. To determine the relative influence of each factor on liver health, Posterior Inclusion Probabilities (PIPs) were calculated. Results: Linear regression analyses indicated that income and education were inversely associated with several liver injury biomarkers, while alcohol use and smoking demonstrated stronger and more consistent associations. Bayesian Kernel Machine Regression (BKMR) further highlighted alcohol and smoking as the most influential predictors, particularly for GGT and total bilirubin, with posterior inclusion probabilities (PIPs) close to 1.0. In contrast, PFAS showed weaker associations. Regression coefficients were small and largely non-significant, and PIPs were comparatively lower across most liver outcomes. Notably, education had a higher PIP for ALT and GGT than PFAS, suggesting a more protective role in liver health. People with higher education levels tend to live healthier lifestyles, have better access to healthcare, and are generally more aware of health risks. These factors can all help reduce the risk of liver problems. Overall mixture effects demonstrated nonlinear trends, including U-shaped relationships for ALT and GGT, and inverse associations for AST, FLI, and ALP. Conclusion: These findings underscore the importance of considering both environmental and social–behavioral determinants in liver health. While PFAS exposures remain a long-term concern, modifiable lifestyle and structural factors, particularly alcohol, smoking, income, and education, exert more immediate and pronounced effects on hepatic biomarkers in the general population. Full article

Information regarding the impact of polymetallic exposure on metabolic syndrome (MetS) among residents living near abandoned Pb-Zn mines is limited. Our objective was to investigate the impact of co-exposure to metal mixtures on the prevalence of MetS among residents. ICP-MS was used to measure the levels of 24 metals in the urine of 1744 participants, including 723 participants living near abandoned Pb-Zn mines, labeled as exposed area, and 1021 participants from other towns, labeled as reference area in the same city. Multivariable generalized linear regression, adaptive LASSO penalized regression, and BKMR were used to assess the associations between metals and MetS. The levels of eleven metals were higher, while those of nine metals were lower in the exposed area than those in the reference area. Mg, Cd, Ti, TI, Zn, Rb, and Pb were selected as important MetS predictors using LASSO regression. In exposed area, urinary Zn and TI were positively associated with MetS, whereas Mg was negatively associated with MetS. In the reference area, urinary Zn was positively associated with MetS, whereas Mg and Ti were negatively associated with MetS. The BKMR model indicates a statistically significant positive overall effect of the seven metal mixtures on MetS in the exposed area. Polymetallic exposure was positively associated with MetS risk in the abandoned Pb-Zn mining areas, suggesting that excessive Zn and TI may be associated with a higher MetS risk among residents living near abandoned Pb-Zn mines. Full article

Environmental exposures to heavy metals, polychlorinated biphenyls (PCBs), dioxins, and furans have been associated with adverse cardiovascular outcomes, yet their combined effects remain underexplored. This study examined the joint influence of these contaminants on cardiovascular risk indicators in a representative sample of U.S. adults from the 2003–2004 National Health and Nutrition Examination Survey (NHANES). Biomarkers of exposure included lead, cadmium, mercury, twelve PCB congeners, seven dioxins, and ten furans. Cardiovascular outcomes were assessed using blood pressure, Framingham Risk Score (FRS), and lipid profiles. Associations were analyzed using multivariable linear regression and Bayesian Kernel Machine Regression (BKMR), adjusting for age, sex, ethnicity, body mass index, smoking, alcohol consumption, and income. The results demonstrated that metals, particularly mercury, were strongly associated with increased blood pressure and altered HDL cholesterol. PCBs were predominantly linked to elevated systolic blood pressure and FRS, with PCB156 and PCB126 identified as principal contributors. Furans exhibited the strongest associations with dyslipidemia, including elevated LDL cholesterol, total cholesterol, and triglycerides. Combined exposure analysis revealed a complex pattern, with increasing pollutant burdens associated with rising blood pressure and risk scores but declining lipid levels. These findings underscore the outcome-specific effects of pollutant mixtures and suggest that chronic low-level exposure to multiple environmental contaminants may contribute to cardiovascular dysfunction in the general population. Further longitudinal research is needed to confirm these associations and guide risk reduction strategies. Full article

Per- and polyfluoroalkyl substances (PFASs) are ubiquitous environmental contaminants with potential endocrine-disrupting properties. This study examines the association between exposure to multiple PFASs and pan-cancers associated with sex hormones (PCSH) while accounting for potential non-linear relationships and interactions. We analyzed data from the National Health and Nutrition Examination Survey (NHANES), spanning two-year cycles from 1999 to 2012 and including 14,373 participants. Serum concentrations of six PFAS—perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorodecanoic acid (PFDE), perfluorononanoic acid (PFNA), and perfluoroundecanoic acid (PFUA)—were assessed for their relationship with PCSH. The statistical analyses included descriptive statistics, Spearman and Pearson correlation analyses, and both linear and logistic regression models. Additionally, Bayesian kernel machine regression (BKMR) was applied to capture potential nonlinear relationships and interactions. The initial t-tests showed a statistically significant difference in PFOS levels between individuals with and without PCSH (p = 0.0022), with higher mean PFOS levels in the PCSH group. Chi-square tests revealed a significant association between ethnicity and PCSH (p < 0.001). Linear and logistic regression analyses revealed significant associations for PFOS. BKMR analysis identified PFOA as having the highest posterior inclusion probability, indicating its importance in explaining PCSH risk. Univariate exposure-response analysis revealed limited individual PFAS effects. However, bivariate analysis indicated a complex U-shaped interaction pattern among many joint PFAS assessments. The overall exposure effect analysis suggested that the combined impact of all PFASs was more strongly associated with PCSH at exposure levels below the 0.5 quantile compared to higher levels. Single-variable interaction analyses highlighted PFOA and PFOS as the most interactive PFASs when evaluating their interaction with combined exposure to all other PFASs. In summary, while the initial findings suggested a positive association between PFOS and PCSH, the BKMR analysis revealed complex non-linear relationships and interactions among PFAS. These findings highlight the importance of evaluating PFASs as a mixture rather than as individual chemicals and using techniques that can capture non-linear relationships and interactions. Full article

This study assessed the relationship between environmental chemical mixtures—including metals, per- and polyfluoroalkyl substances (PFAS), phthalates, and plasticizers—and key cardiovascular health markers using data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES). The combined effects of these pollutants on cardiovascular markers were evaluated using Bayesian Kernel Machine Regression (BKMR), a flexible, non-parametric modeling approach that accommodates nonlinear and interactive relationships among exposures. BKMR was applied to assess both the joint and individual associations of the chemical mixture with systolic blood pressure (SBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), diastolic blood pressure (DBP), total cholesterol, and triglycerides. As part of the BKMR analysis, posterior inclusion probabilities (PIPs) were estimated to identify the relative importance of each exposure within the mixture. These results highlighted phthalates as major contributors to LDL, SBP, total cholesterol, HDL, and triglycerides while plasticizers were associated with LDL, SBP, HDL, and triglycerides. Metals and PFAS were most strongly linked to LDL, DBP, total cholesterol, and SBP. The overall mixture effect indicated that cumulative exposures were associated with lower LDL and SBP and elevated DBP, suggesting an increased cardiovascular risk. Triglycerides exhibited a complex quantile-dependent trend, with higher exposures associated with reduced levels. These findings underscore the importance of mixture-based risk assessments that reflect real-world exposure scenarios. Full article

Background: Cardiovascular disease (CVD) is a major global health burden influenced by genetic, behavioral, and environmental factors. Among these, exposure to per- and poly-fluoroalkyl substances (PFASs) and toxic metals has been increasingly implicated in adverse cardiovascular outcomes. However, the mediating role of dietary inflammation in these associations remains unclear. Objective: This study investigates the relationship between PFAS and metal exposures and CVD risk, focusing on the potential mediating role of diet, operationalized through the Dietary Inflammatory Index (DII). Additionally, this study examines age as an effect modifier in these associations. Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 cycle (n = 660), we assessed environmental exposures (lead, cadmium, mercury, perfluorooctanoic acid-PFOA, perfluorooctane sulfonate-PFOS), dietary inflammatory potential (DII), and cardiovascular markers (blood pressure, lipid profile, C-reactive protein). Statistical analyses included linear regression and Bayesian Kernel Machine Regression-Causal Mediation Analysis (BKMR-CMA) to estimate the direct, indirect (through DII), and total effects of exposure on CVD risk biomarkers. Results: Linear regression revealed significant associations between mercury and reduced systolic blood pressure (SBP) (p = 0.017) and cadmium with increased C-reactive protein (CRP) (p = 0.006). Mediation analysis suggested dietary inflammation may play a role, though estimates were imprecise. Conclusions: PFAS and metals may influence CVD risk through inflammatory pathways, with potential age-related differences. Future longitudinal studies are needed to clarify these complex interactions, reduce measurement error, and guide age-specific exposure regulations. Full article

Chronic kidney disease (CKD) is a noteworthy global health issue affecting 10% of the world’s populace. It is increasingly linked to environmental exposures; however, the interplay of toxic metals, per- and polyfluoroalkyl substances (PFAS), and essential elements has not been fully elucidated. This cross-sectional study analyzed 5800 out of the 9245 participants from the 2017–2018 NHANES dataset to evaluate the combined effect of PFAS, essential elements, and toxic metals on CKD using logistic regression and advanced environmental mixture models, namely, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), and Weighted Quantile Sum (WQS) regression. Our results showed cadmium (Cd) emerging as a significant contributor to CKD (OR = 2.16, p = 0.023) from the logistic regression analysis. Mercury (Hg) demonstrated the highest contribution in mixtures (posterior inclusion probability = 0.908) from our BKMR analysis, with a non-linear U-shaped dose–response relationship. Essential elements like selenium (Se) and manganese (Mn) exhibited protective correlations but complex non-linear interactions, moderating toxic metal effects from our qgcomp and WQS regression. Notably, antagonistic interactions between essential elements and some pollutants reduced the overall mixture impact on CKD, showing an overall decreasing joint effect of the combined PFAS, toxic metals, and essential elements on CKD, from the 25th to the 75th quantile. This study highlights the role of environmental co-exposures in CKD risk and highlights the need for advanced statistical and machine learning approaches in studying complex environmental mixture interactions on human health. Full article

Cardiovascular diseases (CVDs) are the leading cause of mortality globally, accounting for approximately one-third of all deaths. Exposure to toxic metals poses significant risks to cardiovascular health, contributing to the development of CVDs. Essential elements are crucial for maintaining cardiovascular function; however, imbalances or deficiencies in these elements can exacerbate the risk and progression of CVDs. Understanding the interactions between toxic metals and essential elements is crucial for elucidating their impact on cardiovascular health. This study aims to examine the individual and combined effects of toxic metals—lead (Pb), cadmium (Cd), and mercury (Hg)—along with essential elements—manganese (Mn), iron (Fe), and selenium (Se)—on CVDs. We explored the effects of toxic metals and essential elements using data from the National Health and Nutrition Examination Survey (NHANES, 2017–2018). We conducted descriptive analyses and applied advanced statistical methods, including Bayesian kernel machine regression (BKMR), weighted quantile sum regression (WQSR), and quantile g-computation, to assess the associations between these toxic metals and essential elements on key cardiovascular-related biomarkers. The results revealed distinct patterns of influence across the toxic metals and essential elements. Spearman correlation showed a stronger association among toxic metals than essential elements. Bayesian kernel machine regression (BKMR) and posterior inclusion probability (PIP) analysis identified lead, mercury, iron, and selenium as key contributors to CVD risk, with lead strongly linked to high-density lipoprotein (HDL), diastolic blood pressure (DBP), and systolic blood pressure (SBP). Selenium was linked to low-density lipoprotein (LDL) cholesterol and non-high-density lipoprotein (non-HDL) cholesterol. Univariate and bivariate analyses confirmed lead and mercury’s strong associations with triglycerides and blood pressure, while lead, selenium, and iron were linked to different cholesterol outcomes. Single-variable analysis revealed an interaction between individual exposures and combined exposures. The overall exposure effect assessing the impact of all exposures combined on CVD markers revealed a steady positive association with triglycerides, total cholesterol, LDL, non-HDL cholesterol, and DBP, with HDL and SBP increasing from the 65th percentile. Quantile g-computation and WQSR confirmed lead’s consistent positive association across all outcomes, with variations among other toxic metals and essential elements. In conclusion, our study suggests that toxic metals and essential elements are important factors in CVD outcomes, with different metals and elements associated with variations in specific biomarkers. Full article

Background: Humans are consistently and increasingly exposed to phthalate products, but the effect of the combined exposure to phthalates on myocardial injury remains largely unexplored. The present study aimed to explore the effect of the combined exposure to phthalates on myocardial injury. Methods: A total of 1237 male adults (aged ≥20) without coronary artery disease (CAD) from the National Health and Nutrition Examination Survey (NHANES) in 1999–2004 were included in the current study. Multiple linear regression, Bayesian kernel machine regression (BKMR), and a weighted quantile sum (WQS) model were employed to examine the associations of urinary phthalate metabolites with two cardiac injury biomarkers, including troponin T (TNT) and troponin I, using four highly sensitive assays (Abbott, Chicago, IL, USA; Siemens, Erlangen, Germany; and Ortho, Raritan, NJ, USA) (TNIA, TNIS, TNIO). Results: According to the linear regression analysis, mono-(3-carboxypropyl) phthalate (MCPP, a metabolite of di-n-octyl phthalate) was found to be positively associated with serum TNT; a positive association was found between mono-isobutyl phthalate (MiBP, a metabolite of di-isobutyl phthalate) and TNIA, as well as MiBP and TNIS. Mono-benzyl phthalate (MBzP, a metabolite of butyl benzyl phthalate) and MCPP were positively associated with serum TNIO. The BKMR analyses showed a positive overall relationship of serum TNT, TNIA, TNIS, and TNIO with increased concentrations of phthalate metabolites. The WQS model showed MCPP and MBzP were the top two contributors to being an increased risk for elevated TNT levels. MCPP and mono-ethyl phthalate (MEP, a metabolite of diethyl phthalate) were identified as the leading contributors to increased TNIA and TNIS. MCPP and MBzP were the dominant contributors to elevated TNIO. Conclusions: As a combined mixture, phthalate metabolites were positively associated with serum TNT and TNI among adults without CAD, indicating the potential toxic effect of phthalate exposure on cardiac injury. Full article

Background: Environmental exposures to heavy metals/metalloids such as arsenic, cadmium, and mercury have been implicated in adverse cardiovascular health outcomes. Using data from the All of Us research program, we investigated the associations between these metals/metalloids and six cardiovascular-related biomarkers: systolic blood pressure (SBP), HDL cholesterol, LDL cholesterol, C-reactive protein (CRP), total cholesterol, and triglycerides. Methods: This study explored the relationship between outcome cardiovascular variables (SBP, CRP, LDL, HDL, triglycerides, and total cholesterol) and predictor metal/metalloid variables (cadmium, mercury, and arsenic) among 136 participants (53.4 percent women). We initially conducted linear regression to determine the association between variables of interest. Bayesian Kernel Machine Regression (BKMR) analysis was subsequently performed to capture potential non-linear relationships, as well as interactions among metal/metalloid exposures. In the BKMR analysis, posterior inclusion probabilities (PIPs) quantified the contribution of each metal/metalloid to the outcomes, with higher PIP values indicating a greater likelihood of a specific exposure being a key predictor for a given cardiovascular biomarker. Within the BKMR framework, univariate, bivariate, and overall exposure–response analyses provided insights into the individual and combined effects of metal/metalloid exposures. These analyses identified the factors with the strongest associations and highlighted interactions between exposures. Results: In this study, the average age of male participants was 58.2 years, while female participants had an average age of 55.6 years. The study population included 104 individuals identifying as White (mean age: 57.5 years), 10 as Black or African American (mean age: 63.2 years), 7 as Hispanic (mean age: 48.2), 3 as Asian (mean age: 49.7 years), and 12 as Other race (mean age: 48.8 years). In our study, men exhibited higher levels of SBP, triglycerides, mercury, and arsenic, while women had higher levels of CRP, LDL cholesterol, HDL cholesterol, total cholesterol, and cadmium. Black people exhibited higher levels and greater variability in markers of cardiovascular risk and inflammation (e.g., blood pressure and CRP), Asians consistently showed the lowest levels across most biomarkers, while White people, Hispanics, and the “Other” group demonstrated moderate levels with some variability. In linear regression, we identified significant positive associations between mercury and HDL cholesterol, arsenic and triglycerides, and arsenic and total cholesterol. In BKMR analysis, PIP results revealed that mercury had the highest predictive contribution for SBP, HDL cholesterol, and triglycerides; cadmium for CRP; and arsenic for LDL and total cholesterol. Univariate and bivariate exposure–response analyses in BKMR demonstrated non-linear exposure–response patterns, including U-shaped and inverted U-shaped patterns for cadmium, particularly CRP and total cholesterol. Traditional linear regression techniques would have missed these patterns. Conclusion: Our study results highlight the influence of environmental metal/metalloid exposures on cardiovascular biomarkers, providing evidence of non-linear and interactive effects that warrant further investigation to understand their role in cardiovascular disease risk better. Full article

Background: Chronic stress, driven by the persistent activation of the body’s stress response system—including the sympathetic nervous system and hypothalamic–pituitary–adrenal (HPA) axis—has far-reaching effects on both physical and mental health. This study examines the combined effects of social and behavioral factors on a latent variable consisting of stress and depressive symptoms, using a comprehensive framework to explore the complex interactions of these factors. Methods: Leveraging data from the United States Centers for Disease Control and Prevention’s (CDC’s) National Health and Nutrition Examination Survey (NHANES), we operationalized allostatic load—a measure of cumulative physiological stress—through 10 biomarkers spanning cardiovascular, inflammatory, and metabolic systems. Depressive symptoms were measured via the Patient Health Questionnaire-9 (PHQ-9), and a latent variable capturing the shared variance between stress and depressive symptoms was derived using factor analysis. To assess the influence of social (income and education) and behavioral (alcohol consumption and smoking) factors on this latent variable, we employed Bayesian Kernel Machine Regression (BKMR), allowing us to examine potential non-linear and interactive effects among these predictors. Results: Our results revealed a significant positive association between allostatic load and depressive symptoms across the sample, regardless of ethnic background. Alcohol consumption emerged as a key behavioral factor, with significant positive associations with stress. Conversely, education showed a protective effect, with higher education levels associated with decreased stress and depressive symptoms. Conclusions: These findings underscore the importance of addressing both social determinants and behavioral risk factors in mitigating the cumulative impacts of stress and depressive symptoms. By highlighting the roles of alcohol consumption and education, this study provides insights that can inform public health strategies aimed at promoting resilience and reducing stress-related health disparities. Full article

(1) Background: Neoplasms of the central nervous system (CNS) encompass a cluster of malignant diseases originating from tissues or structures within the CNS. Environmental factors, including heavy metals, may contribute to their development. Therefore, this research was to investigate the association between heavy metal exposure and CNS tumor susceptibility using single and muti-metal models. (2) Methods: 63 CNS tumor patients and 71 controls were included. Urine samples from the CNS tumor patients and controls were analyzed for 47 metals using inductively coupled plasma-mass spectrometry in this study. Statistical analyses included conditional Wilcoxon rank-sum tests, logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Bayesian Kernel Machine Regression (BKMR). (3) Results: In the single metal model, higher levels of seventeen metals might be associated with a lower incidence of CNS tumor, while higher exposure levels of five metals are associated with a higher incidence of tumor. LASSO regression selected nine metals for further BKMR analysis. The joint effects showed decreased tumor risk with increased metal mixture concentration. The level of the metals Ge, As, Rb, Zr, and Sn may be related to the incidence of meningiomas and gliomas. (4) Conclusions: This study explored the association between various metals and CNS tumors, providing ideas for future prospective cohort studies and laboratory studies, and providing a foundation for new ideas in the prevention and treatment of CNS tumors. Full article

Background: While current epidemiological studies have documented associations between environmental metals and renal dysfunction, the majority have concentrated on plasma metal levels. The relationship between urinary metal exposure and chronic kidney disease (CKD) remains contentious, particularly within specific demographic groups. Methods: This cross-sectional study included 2919 rural Chinese adults recruited between 2018 and 2019. Urine metals were measured by ICP-MS. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify metals significantly associated with CKD. Then, we used binary logistic regression, along with restricted cubic spline (RCS) models, to assess the individual exposure effects of specific metals on CKD. Quantile g-computation, weighted quantile sum regression, and Bayesian kernel machine regression (BKMR) models were applied to evaluate combined effects of metal exposures on CKD. Gender-stratified analyses were also conducted to explore these associations. Results: LASSO identified seven metals (V, Cu, Rb, Sr, Ba, W, Pb) with significant impacts on CKD. In single-metal models, Cu and W exhibited a positive correlation with CKD, whereas V, Rb, Sr, Ba, and Pb showed significant negative correlations (all p < 0.05). RCS analysis revealed nonlinear associations between V, Cu, Ba, Pb, and CKD (all p-nonlinear < 0.05). In the multi-metal model, quantile-based g-computation demonstrated a collective negative association with CKD risk for the seven mixed urinary metal exposures (OR (95% CI) = −0.430 (−0.656, −0.204); p < 0.001), with V, Rb, Sr, Ba, and Pb contributing to this effect. The WQS model analysis further confirmed this joint negative association (OR (95% CI): −0.885 (−1.083, −0.899); p < 0.001), with V as the main contributor. BKMR model analysis indicated an overall negative impact of the metal mixture on CKD risk. Interactions may exist between V and Cu, as well as Cu and Sr and Pb. The female subgroup in the BKMR model demonstrated consistency with the overall association. Conclusions: Our study findings demonstrate a negative association between the urinary metal mixture and CKD risk, particularly notable in females. Joint exposure to multiple urinary metals may involve synergistic or antagonistic interactions influencing renal function. Further research is needed to validate these observations and elucidate underlying mechanisms. Full article

Volatile organic compounds (VOCs) are associated with obesity health risks, while the association of mixed VOCs with visceral adiposity indicators remains unclear. In this study, a total of 2015 adults from the National Health and Nutrition Examination Survey (NHANES) were included. Weighted generalized linear models, restricted cubic spline (RCS), weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR) were adopted to assess the association of VOC metabolites (mVOCs) with the visceral adiposity index (VAI) and lipid accumulation product (LAP). Multiple mVOCs were positively associated with the VAI and LAP in the single-exposure model, especially N-acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA) and N-acetyl-S-(N-methylcarbamoyl)-L-cysteine (AMCC). The associations of mVOCs with VAI and LAP were more significant in <60-year-old and non-obese individuals, with interactions of CEMA with age and AMCC with obesity status. Nonlinear relationships between certain mVOCs and the VAI or the LAP were also observed. In the WQS model, co-exposure to mVOCs was positively correlated with the VAI [β (95%CI): 0.084 (0.022, 0.147)]; CEMA (25.24%) was the major contributor. The result of the BKMR revealed a positive trend of the association between mixed mVOCs and the VAI. Our findings suggest that VOC exposure is strongly associated with visceral obesity indicators. Further large prospective investigations are necessary to support our findings. Full article