Association between mixed dioxin exposure and hyperuricemia in U.S. adults: A comparison of three statistical models

Association between exposure to a mixture of organochlorine pesticides and hyperuricemia in U.S. adults: A comparison of four statistical models

The association between the exposure of organochlorine pesticides (OCPs) and serum uric acid (UA) levels remained uncertain. In this study, to investigate the combined effects of OCP mixtures on hyperuricemia, we analyzed serum OCPs and UA levels in adults from the National Health and Nutrition Examination Survey (2005-2016). Four statistical models including weighted logistic regression, weighted quantile sum (WQS), quantile g-computation (QGC), and bayesian kernel machine regression (BKMR) were used to assess the relationship between mixed chemical exposures and hyperuricemia. Subgroup analyses were conducted to explore potential modifiers. Among 6,529 participants, the prevalence of hyperuricemia was 21.15%. Logistic regression revealed a significant association between both hexachlorobenzene (HCB) and trans-nonachlor and hyperuricemia in the fifth quintile (OR: 1.54, 95% CI: 1.08-2.19; OR: 1.58, 95% CI: 1.05-2.39, respectively), utilizing the first quintile as a reference. WQS and QGC analyses showed significant overall effects of OCPs on hyperuricemia, with an OR of 1.25 (95% CI: 1.09-1.44) and 1.20 (95% CI: 1.06-1.37), respectively. BKMR indicated a positive trend between mixed OCPs and hyperuricemia, with HCB having the largest weight in all three mixture analyses. Subgroup analyses revealed that females, individuals aged 50 years and above, and those with a low income were more vulnerable to mixed OCP exposure. These results highlight the urgent need to protect vulnerable populations from OCPs and to properly evaluate the health effects of multiple exposures on hyperuricemia using mutual validation approaches.

Exploring the association between two groups of metals with potentially opposing renal effects and renal function in middle-aged and older adults: Evidence from an explainable machine learning method

BACKGROUND

Machine learning models have promising applications in capturing the complex relationship between mixtures of exposures and outcomes.

OBJECTIVE

Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults.

METHODS

This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR).

RESULTS

After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the "trigger point" at which the risk of CKD abruptly changed.

CONCLUSION

Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.

Association between combined exposure to dioxins and arthritis among US adults: a cross-sectional study

Dioxins and dioxin-like compounds (DLCs) are common pollutants hazardous to human health. We applied 12 dioxins and DLCs data of 1851 participants (including 484 arthritis patients) from National Health Examination Survey (NHANES) 2001-2004 and quadrupled them into rank variables. Multivariate logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were used to explore the relationship between individual or mixed exposure to the pollutants and arthritis after adjusting for multiple covariates. In multivariable logistic regression with an individual dioxin or DLC, almost every chemical was significantly positively associated with arthritis, except PCB66 (polychlorinated biphenyl 66) and 1,2,3,4,6,7,8-heptachlorodibenzofuran (hpcdf). The WQS model indicated that the combined exposure to the 12 dioxins and DLCs was positively linked to arthritis (OR: 1.884, 95% CI: 1.514-2.346), with PCB156 (weighted 0.281) making the greatest contribution. A positive trend between combined exposure and arthritis was observed in the BKMR model, with a posterior inclusion probability (PIP) of 0.987 for PCB156, which was also higher than the other contaminants.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BACKGROUND

Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods.

OBJECTS

We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data.

METHODS

Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA.

RESULTS

The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA.

CONCLUSIONS

Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

Exposure to ambient air pollutants is associated with an increased incidence of hyperuricemia: A longitudinal cohort study among Chinese government employees

BACKGROUND

It is widely recognized that ambient air pollution can induce various detrimental health outcomes. However, evidence linking ambient air pollutants and hyperuricemia incidence is scarce.

OBJECTIVES

To assess the association between long-term air pollution exposure and the risk of hyperuricemia.

METHODS

In this study, a total of 5854 government employees without hyperuricemia were recruited and followed up from January 2018 to June 2021 in Hunan Province, China. Hyperuricemia was defined as serum uric acid (SUA) level of >420 μmol/L for men and >360 μmol/L for women or use of SUA-lowering medication or diagnosed as hyperuricemia during follow-up. Data from local air quality monitoring stations were used to calculate individual exposure levels of PM10, PM2.5, SO2 and NO2 by inverse distance weightingn (IDW) method. Cox proportional hazard model was applied to evaluate the causal relationships between air pollutant exposures and the risk of hyperuricemia occurrence after adjustment for potential confounders and meanwhile, restricted cubic spline was used to explore the dose-response relationships.

RESULTS

The results indicated that exposures to PM10 (hazard ratio, HR = 1.042, 95% conficence interal, 95% CI: 1.028, 1.057), PM2.5 (HR = 1.204, 95% CI: 1.141, 1.271) and NO2 (HR = 1.178, 95% CI: 1.125,1.233) were associated with an increased HR of hyperuricemia. In addition, a nonlinear dose-response relationship was found between PM10 exposure level and the HR of hyperuricemia (p for nonlinearity = 0.158) with a potential threshold of 50.11 μg/m3. Subgroup analysis demonstrated that participants usually waking up at night and using natural ventilation were more vulnerable to the exposures of PM10, PM2.5, NO2, and SO2.

CONCLUSION

Long-term exposures to ambient PM10, PM2.5 and NO2 are associated with an increased incidence of hyperuricemia among Chinese government employees.

Associations of polychlorinated biphenyl and organochlorine pesticide exposure with hyperuricemia: modification by lifestyle factors

Recent research has reported positive associations of exposure to polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) with hyperuricemia. However, most of these studies have primarily focused on the individual effects of PCB/OCP exposure. We aimed to explore the associations of both individual and combined PCB/OCP exposure with hyperuricemia and examine whether such associations could be modified by lifestyle factors. The cross-sectional study recruited 2032 adults between March and May 2019 in Wuhan, China. Logistic regression and weighted quantile sum (WQS) regression were applied to explore the relationship of individual and combined PCB/OCP exposure with hyperuricemia, while considering the modified effects of lifestyle factors. Of the 2032 participants, 522 (25.7%) had hyperuricemia. Compared with the non-detected group, the detected groups of PCB153 and PCB180 exhibited a positive association with hyperuricemia, with OR (95% CIs) of 1.52 (1.22, 1.91) and 1.51 (1.20, 1.90), respectively. WQS regression showed that PCB/OCP mixture was positively associated with hyperuricemia (OR: 1.31, 95% CI: 1.08, 1.58). PCB153/PCB180 exposure, combined with an unhealthy lifestyle, has a significant additive effect on hyperuricemia. Overall, PCB/OCP mixture and individual PCB153/PCB180 exposure were positively associated with hyperuricemia. Adherence to a healthy lifestyle may modify the potential negative impact of PCBs/OCPs on hyperuricemia.