Do urinary metals associate with the homeostasis of inflammatory mediators? Results from the perspective of inflammatory signaling in middle-aged and older adults

Associations of Blood and Urinary Heavy Metals with Stress Urinary Incontinence Risk Among Adults in NHANES, 2003-2018

People come into contact with heavy metals in various ways in their daily lives. Accumulating evidence shows that toxic metal exposure is hazardous to human health. However, limited information is available regarding the impact of metal mixtures on stress urinary incontinence (SUI). Therefore, we used data from 10,622 adults from the 2003-2018 National Health and Nutrition Examination Survey (NHANES) to investigate the independent and comprehensive association between heavy metal co-exposure and SUI. Among them, 2455 (23.1%) had been diagnosed with SUI, while the rest had no SUI. We evaluated the independent and combined associations of 3 blood metals and 10 urinary metals with SUI risk, along with subgroup analyses according to age and gender. In the single-exposure model, blood cadmium (Cd), lead (Pb), mercury (Hg), urinary Cd, Pb, and cesium (Cs) were found to be positively connected with SUI risk. Moreover, weighted quantile sum (WQS) regression, quantile-based g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) consistently demonstrated blood and urinary metal-mixed exposure were positively associated with the risk of SUI, and emphasized that blood Pb and Cd and urinary Cd and Cs were the main positive drivers, respectively. This association was more pronounced in the young and middle-aged group (20-59 years old) and the female group. Nevertheless, further research is necessary to validate these significant findings.

Associations of toxic metals and their mixture with hyperuricemia in Chinese rural older adults

Previous studies have related single toxic metals (TMs) to hyperuricemia (HUA) among the general population, however, the association of the TM mixture with HUA, especially in older adults, remains poorly understood. We aimed to examine the relationships between individual TMs and their mixture and HUA in Chinese rural older adults. This study consisted of 2075 rural older adults aged 60 years or over. Blood concentrations of aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), gallium (Ga), mercury (Hg), lead (Pb), thallium (Tl), and uranium (U) were detected using inductively coupled plasma mass spectrometry. The associations of single TMs with HUA were assessed using logistic regression and restricted cubic spline (RCS) models, and the association of TM mixture with HUA was explored using the elastic net with environmental risk score (ENET-ERS), quantile g-computation (QGC), and Bayesian kernel machine regression (BKMR) models, respectively. Adjusted logistic regression model showed that Cs (OR = 1.65, 95% CI 1.37-1.99) and Pb (OR = 1.46, 95% CI 1.28-1.67) were positively related to HUA, and RCS model exhibited a positive linear association of Cs and Pb with HUA. ENET-ERS and QGC models quantified a positive correlation between the TM mixture and the odds of HUA, with estimated ORs of 1.15 (95% CI 1.11-1.19) and 1.84 (95% CI 1.37-2.47), respectively, and Cs and Pb had the most weight. BKMR model demonstrated a significant linear association between the TM mixture and increased odds of HUA, with the posterior inclusion probabilities (PIPs) of both Cs and Pb being 1.00. Moreover, we observed a positive interaction between Cs and Pb on HUA. The TM mixture is associated with increased odds of HUA in rural older adults, which may mainly be driven by Cs and Pb. Subsequent studies are warranted to confirm these findings and clarify the mechanisms linking multiple TMs with HUA.

Association of co-exposure to organophosphate esters and per- and polyfluoroalkyl substances and mixture with cardiovascular-kidney-liver-metabolic biomarkers among Chinese adults

BACKGROUND

Organophosphate esters (OPEs) and Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental contaminants with common exposure sources, leading to their widespread presence in human body. However, evidence on co-exposure to OPEs and PFAS and its impact on cardiovascular-kidney-liver-metabolic biomarkers remains limited.

METHODS

In this cross-sectional study, 467 adults were enrolled from January to May 2022 during physical visits in Shijiazhuang, Hebei province. Eleven types of OPEs and twelves types of PFAS were detected, among which eight OPEs and six PFAS contaminants were detected in more than 60% of plasma samples. Seventeen biomarkers were assessed to comprehensively evaluate the cardiovascular-kidney-liver-metabolic function. Multiple linear regression, multipollutant models with sparse partial least squares, and Bayesian kernel machine regression (BKMR) models were applied to examine the associations of individual OPEs and PFAS and their mixtures with organ function and metabolism, respectively.

RESULTS

Of the over 400 exposure-outcome associations tested when modelling, we observed robust results across three models that perfluorohexanoic acid (PFHxS) was significantly positively associated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), and indirect bilirubin (IBIL). Perfluorononanoic acid was significantly associated with decreased AST/ALT and increased very-low-density lipoprotein cholesterol levels. Besides, perfluorodecanoic acid was correlated with increased high lipoprotein cholesterol and perfluoroundecanoic acid was consistently associated with lower glucose level. BKMR analysis showed that OPEs and PFAS mixtures were positively associated with IBIL and TBIL, among which PFHxS was the main toxic chemicals.

CONCLUSIONS

Our findings suggest that exposure to OPEs and PFAS, especially PFHxS and PFNA, may disrupt organ function and metabolism in the general population, providing insight into the potential pathophysiological mechanisms of OPEs and PFAS co-exposure and chronic diseases.

Association between plasma metal exposure and health span in very elderly adults: a prospective cohort study with mixture statistical approach

BACKGROUND

Metals have been linked to a diverse spectrum of age-related diseases; however, the effects of metal exposure on health span remains largely unknown. This cohort study aims to determine the association between plasma metal and health span in elder adults aged ≥ 90 years.

METHODS

The plasma concentrations of seven metals were measured at baseline in 300 elder adults. The end of the health span (EHS) was identified as the occurrence of one of eight major morbidities or mortality events. We used Cox regression to assess hazard ratios (HR). The combined effects of multiple metal mixtures were estimated using grouped-weighted quantile sum (GWQS), quantile g-computation (Q-gcomp), and Bayesian kernel machine regression (BKMR) methods.

RESULTS

The estimated HR for EHS with an inter-quartile range (IQR) increment for selenium (Se) was 0.826 (95% confidence interval [CI]: 0.737-0.926); magnesium (Mg), 0.806 (95% CI: 0.691-0.941); iron (Fe), 0.756 (95% CI: 0.623-0.917), and copper (Cu), 0.856 (95% CI: 0.750-0.976). The P for trend of Se, Mg, and Fe were all < 0.05. In the mixture analyses, Q-gcomp showed a negative correlation with EHS (P = 0.904), with the sum of the negative coefficients being -0.211.

CONCLUSION

Higher plasma Se, Mg, and Fe reduced the risk of premature end of health span, suggesting that essential metal elements played a role in health maintenance in elder adults.

Short-term effects of ambient PM1, PM2.5, and PM10 on internal metal/metalloid profiles in older adults: A distributed lag analysis in China

There is limited evidence linking exposure to ambient particulate matter (PM) with internal doses of metals and metalloids (metal(loid)s). This study aimed to evaluate the effects of short-term exposure to ambient PM on urine metal(loid)s among Chinese older adults. Biological monitoring data of 15 urine metal(loid)s collected in 3, 970 community-dwelling older adults in Fuyang city, Anhui Province, China, from July to September 2018, were utilized. PMs with an aerodynamic diameter ≤ 1 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10) up to eight days before urine collection were estimated by space-time extremely randomized trees (STET) model. Residential greenness was reflected by Normalized Difference Vegetation Index (NDVI). We used generalized additive model (GAM) combined with distributed lag linear/non-linear models (DLMs/DLNMs) to estimate the associations between short-term PM exposure and urine metal(loid)s. The results suggested that the cumulative exposures to PM1, PM2.5, or PM10 over two days (lag0-1 days) before urine collection were associated with elevated urine metal(loid)s in DLMs, while exhibited linear or "inverted U-shaped" relationships with seven urine metal(loid)s in DLNMs, including Gallium (Ga), Arsenic (As), Aluminum (Al), Magnesium (Mg), Calcium (Ca), Uranium (U), and Barium (Ba). Aforementioned results indicated robust rather than spurious associations between PMs and these seven metal(loid)s. After standardizations for three PMs, PM1 was the greatest contributor to U, PM2.5 made the greatest contributions to Ga, As, Al, and Ba, and PM10 contributed the most to Mg and Ca. Furthermore, the effects of three PMs on urine Ga, As, Al, Mg, Ca, and Ba were reduced when exposed to higher levels of NDVI. Overall, short-term exposures to ambient PMs contribute to elevated urinary metal(loid) levels in older adults, and three PMs exhibit various contributions to different urine metal(loid)s. Moreover, residential greenness may attenuate the effects of PMs on urine metal(loid)s.

Differential effects of PM2.5 and its carbon components on blood pressure in hypertensive and non-hypertensive populations: a panel study in Beijing

Published literature considering the association between ambient air pollution and blood pressure is highly inconsistent, which may be explained by the different proportions of susceptible subpopulations. We hypothesized that hypertensive patients are more sensitive to air pollution due to the disruption of neurohumoral system. The study aimed to reveal the association between PM2.5 and its carbon components and blood pressure, and whether this association is modified by hypertension status. We conducted a panel study in Beijing, China. Four repeated measurements were performed from 2016 to 2018. Linear mixed-effects models and generalized additive mixed models were performed to investigate the associations between PM2.5 and its carbon components and blood pressure. Subgroup analyses were performed by hypertension status to reveal potential effect modification. Among hypertensive patients, for every 1 μg/m3 increment of PM2.5, TC, OC, and EC in 1-day to 2-day MA, SBP increased from 0.16 mmHg (95% CI, 0.03 to 0.29) to 6.75 mmHg (95% CI, 2.82 to 10.68), and PP increased from 0.14 mmHg (95% CI, 0.02 to 0.26) to 6.03% (95% CI, 2.46 to 9.59%), but no significant association was observed among non-hypertensive subjects. The p values for the interaction between pollutants and hypertension status in 1-day to 2-day MA were less than 0.05. These findings suggest that hypertensive patients may be more susceptible to the adverse effects of air pollution than non-hypertensive subjects, which might provide guidance to hypertensive patients living in areas with high levels of particle pollution.

Associations of mixed urinary metals exposure with metabolic syndrome in the US adult population

BACKGROUND

Metals are harmful to human health in many ways. However, the association between metals and metabolic syndrome (MetS) remains unclear. Aims of this study is to discuss the relationship between urinary metal and MetS.

METHODS

This study included 3419 adult participants from the National Health and Nutrition Examination Survey (NHANES) (2005-2018). Logistic regression analysis, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and restricted cubic spline (RCS) were used to explore the associations of nine urinary metal and MetS.

RESULTS

BKMR and WQS showed the effects of combined nine urinary metal were negatively correlated with MetS. Logistic regression analysis, WQS, and BKMR all suggested that cesium (Cs) and lead (Pb) were negatively correlated with MetS (all PFDCR <0.05). And RCS suggested log2-transformed Cs (χ2 = 20, P < 0.001) and log2-transformed Pb (χ2 = 19.9, P < 0.001) were negatively and linearly associated with MetS.

CONCLUSION

Existing evidence suggests that urine metal content is related to MetS. Cs and Pb are negatively related to MetS. It is still necessary to study and further discuss the causal relationship and mechanism.

Exploring the association of metal mixture in blood to the kidney function and tumor necrosis factor alpha using machine learning methods

This research aimed to approach relationships between metal mixture in blood and kidney function, tumor necrosis factor alpha (TNF-α) by machine learning. Metals levels were measured by Inductively Couple Plasma Mass Spectrometry in blood from 421 participants. We applied K Nearest Neighbor (KNN), Naive Bayes classifier (NB), Support Vector Machines (SVM), random forest (RF), Gradient Boosting Decision Tree (GBDT), Categorical boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Whale Optimization-based XGBoost (WXGBoost) to identify the effect of plasma metals, TNF-α, and estimated glomerular filtration rate (eGFR by CKD-EPI equation). We conducted not only toxic metals, lead (Pb), arsenic (As), cadmium (Cd) but also included trace essential metals, selenium (Se), copper (Cu), zinc (Zn), cobalt (Co), to predict the interaction of TNF-α, TNF-α/white blood count, and eGFR. The high average TNF-α level group was observed among subjects with higher Pb, As, Cd, Cu, and Zn levels in blood. No associations were shown between the low and high TNF-α level group in blood Se and Co levels. Those with lower eGFR group had high Pb, As, Cd, Co, Cu, and Zn levels. The crucial predictor of TNF-α level in metals was blood Pb, and then Cd, As, Cu, Se, Zn and Co. The machine learning revealed that As was the major role among predictors of eGFR after feature selection. The levels of kidney function and TNF-α were modified by co-exposure metals. We were able to acquire highest accuracy of over 85% in the multi-metals exposure model. The higher Pb and Zn levels had strongest interaction with declined eGFR. In addition, As and Cd had synergistic with prediction model of TNF-α. We explored the potential of machine learning approaches for predicting health outcomes with multi-metal exposure. XGBoost model added SHAP could give an explicit explanation of individualized and precision risk prediction and insight of the interaction of key features in the multi-metal exposure.

Thyroid disrupting effects of multiple metals exposure: Comprehensive investigation from the thyroid parenchyma to hormonal function in a prospective cohort study

This study aimed to investigate the thyroid disrupting effects of multiple metals exposure with comprehensive investigation from the thyroid parenchyma to hormonal function. In this prospective cohort study of in-service staff of the Baoding Power Supply, we found that arsenic was negatively associated with total thyroxine (TT4) [βAs = -0.075, 95% confidence interval (CI): -0.129, -0.020, Padj = 0.04]. Similarly, selenium was negatively correlated with TT4 (βSe = -0.134, 95% CI: -0.211, -0.058, Padj < 0.01) and peripheral deiodinase activity (GT) (βSe = -0.133, 95% CI: -0.210, -0.056, Padj = 0.01). With respect to strontium, there were positive associations of strontium with thyroid-stimulating hormone (βSr = 0.263, 95% CI: 0.112, 0.414, Padj = 0.01), and negative associations of strontium with TT4 (βSr = -0.099, 95% CI: -0.150, -0.048, Padj < 0.01) and GT (βSr = -0.102, 95% CI: -0.153, -0.050, Padj < 0.01). We also observed negative associations of metal mixtures with TT4 and GT and potential interactions. Increased risks of thyroid nodule associated with aluminum, cobalt and nickel were also observed. Our findings suggest that multiple metals exposure leads to a multi-pronged assault to thyroid from the thyroid parenchyma to hormonal function. Future large-scale prospective cohort studies of general population and experimental studies were warranted.

Association of per- and polyfluoroalkyl substances with hepatic steatosis and metabolic dysfunction-associated fatty liver disease among patients with acute coronary syndrome

Etiology of hepatic steatosis and metabolic dysfunction-associated fatty liver disease (MAFLD) among acute coronary syndrome (ACS) remains unclear. Existing studies suggested the potential role of per- and polyfluoroalkyl substances (PFAS) in comorbidity of hepatic steatosis among ACS patients. Therefore, we conducted a cross-sectional study based on the ACS inpatients to assess the associations of plasma PFAS congeners and mixtures with hepatic steatosis and MAFLD. This study included 546 newly diagnosed ACS patients. Twelve PFAS were quantified using ultra-high-performance liquid chromatography-tandem mass spectrometry. Hepatic steatosis was defined by hepatic steatosis index (HSI). MAFLD was defined as the combination of hepatic steatosis based on the risk factor calculation with metabolic abnormalities. Generalized linear model was used to examine the associations of PFAS congeners with HSI and MAFLD. Adaptive elastic net (AENET) was further used for PFAS congeners selection. Mixture effects were also assessed with Bayesian kernel machine regression model (BKMR). Congeners analysis observed significant greater percent change of HSI for each doubling in PFOS (1.82%, 95% CI: 0.87%, 2.77%), PFHxS (1.17%, 95% CI: 0.46%, 1.89%) and total PFAS (1.84%, 95% CI: 0.56%, 3.14%). Moreover, each doubling in PFOS (OR=1.42, 95% CI: 1.13, 1.81), PFHxS (OR=1.31, 95% CI: 1.09, 1.59) and total PFAS (OR=1.43, 95% CI: 1.06, 1.94) was associated with increased risk of MAFLD. In AENET regression, only PFOS presented significant positive associations with HSI. Mixture analysis indicated significant positive associations between PFAS mixtures and HSI. This is the first study to demonstrate associations of PFAS congeners and mixtures with hepatic steatosis and MAFLD among ACS patients, which provides hypothesis into the mechanisms behind comorbidity of hepatic steatosis among ACS patients, as well as tertiary prevention of ACS.

Association of Long-term exposure to air pollution and residential greenness with lipid profile: Mediating role of inflammation

Lipidemic effect of air pollutants are still inconsistent and their joint effects are neglected. Meanwhile, identified inflammation pathways in animal have not been applied in epidemiological studies, and beneficial effect of residential greenness remained unclear. Therefore, we used data from typically air-polluted Chinese cities to answer these questions. Particulate matter (PM) with a diameter of ≤ 1 µm (PM₁), PM with a diameter of ≤ 2.5 µm (PM_2.5), PM with a diameter of ≤ 10 µm (PM₁₀), sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) were predicted by space-time extremely randomized trees model. Residential greenness was reflected by Normalized Difference Vegetation Index (NDVI). Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured, and atherogenic coefficient (AC) and TG/HDL-C (TGH) ratio were calculated to indicate lipid metabolism. Generalized additive mixed model and quantile g-computation were respectively conducted to investigate individual and joint lipidemic effect of air pollutants. Covariates including demographical characteristics, living habits, meteorological factors, time trends, and disease information were considered to avoid confounding our results. Complement C3 and high-sensitivity C-reactive protein (hsCRP) were analyzed as potential mediators. Finally, association between NDVI and lipid markers were explored. We found that long-term air pollutants exposure were positively associated with lipid markers. Complement C3 mediated 54.72% (95% CI: 0.30, 63.10) and 72.53% (95% CI: 0.65, 77.61) of the association between PM₁ and TC and LDL-C, respectively. We found some significant associations of lipid markers with NDVI_1000 m rather than NDVI_500 m. BMI, disease status, smoke/drink habits are important effect modifiers. Results are robust in sensitive analysis. Our study indicated that air pollutants exposure may detriment lipid metabolism and inflammation may be the potential triggering pathways, while greenness may exert beneficial effects. This study provided insights for the lipidemic effects of air pollution and greenness.

Associations of metals and metals mixture with lipid profiles: A repeated-measures study of older adults in Beijing

Metals inevitably and easily enter into human bodies and can induce a series of pathophysiological changes, such as oxidative stress damage and lipid peroxidation, which then may further induce dyslipidemia. However, the effects of metals and metals mixture on the lipid profiles are still unclear, especially in older adults. A three-visits repeated measurement of 201 older adults in Beijing was conducted from November 2016 to January 2018. Linear Mixed Effects models and Bayesian kernel machine regression models were used to estimate associations of eight blood metals and metals mixture with lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), Castelli risk indexes I (CRI-1), Castelli risk indexes II (CRI-2), atherogenic coefficient (AC), and non-HDL cholesterol (NHC). Cesium (Cs) was positively associated with TG (β_Cs = 0.14; 95% CI: 0.02, 0.26) whereas copper (Cu) was inversely related to TG (β_Cu = -0.65; 95%CI: -1.14, -0.17) in adjusted models. Manganese (Mn) was mainly related to higher HDL-C (β_Mn = 0.14; 95% CI: 0.07, 0.21) whereas molybdenum showed opposite association. Metals mixture was marginally positive associated with HDL-C, among which Mn played a crucial role. Our findings suggest that the effects of single metal on lipid profiles may be counteracted in mixtures in the context of multiple metal exposures; however, future studies with large sample size are still needed to focus on the detrimental effects of single metals on lipid profiles as well as to identify key components.

Association of long-term air pollution exposure with the risk of prediabetes and diabetes: Systematic perspective from inflammatory mechanisms, glucose homeostasis pathway to preventive strategies

BACKGROUND

Limited evidence suggests the association of air pollutants with a series of diabetic cascades including inflammatory pathways, glucose homeostasis disorder, and prediabetes and diabetes. Subclinical strategies for preventing such pollutants-induced effects remain unknown.

METHODS

We conducted a cross-sectional study in two typically air-polluted Chinese cities in 2018-2020. One-year average PM₁, PM_2.5, PM₁₀, SO₂, NO₂, and O₃ were calculated according to participants' residence. GAM multinomial logistic regression was performed to investigate the association of air pollutants with diabetes status. GAM and quantile g-computation were respectively performed to investigate individual and joint effects of air pollutants on glucose homeostasis markers (glucose, insulin, HbA1c, HOMA-IR, HOMA-B and HOMA-S). Complement C3 and hsCRP were analyzed as potential mediators. The ABCS criteria and hemoglobin glycation index (HGI) were examined for their potential in preventive strategy.

RESULTS

Long-term air pollutants exposure was associated with the risk of prediabetes [Prevalence ratio for O₃ (PR_O₃) = 1.96 (95% CI: 1.24, 3.03)] and diabetes [PR_PM₁ = 1.18 (95% CI: 1.05, 1.32); PR_PM_2.5 = 1.08 (95% CI: 1.00, 1.16); PR_O₃ = 1.35 (95% CI: 1.03, 1.74)]. PM₁, PM₁₀, SO₂ or O₃ exposure was associated with glucose-homeostasis disorder. For example, O₃ exposure was associated with increased levels of glucose [7.67% (95% CI: 1.75, 13.92)], insulin [19.98% (95% CI: 4.53, 37.72)], HOMA-IR [34.88% (95% CI: 13.81, 59.84)], and decreased levels of HOMA-S [-25.88% (95% CI: -37.46, -12.16)]. Complement C3 and hsCRP played mediating roles in these relationships with proportion mediated ranging from 6.95% to 60.64%. Participants with HGI ≤ -0.53 were protected from the adverse effects of air pollutants.

CONCLUSION

Our study provides comprehensive insights into air pollutant-associated diabetic cascade and suggests subclinical preventive strategies.

Residential greenness attenuated association of long-term air pollution exposure with elevated blood pressure: Findings from polluted areas in Northern China

Background

Evidence on the hypertensive effects of long-term air pollutants exposure are mixed, and the joint hypertensive effects of air pollutants are also unclear. Sparse evidence exists regarding the modifying role of residential greenness in such effects.

Methods

A cross-sectional study was conducted in typically air-polluted areas in northern China. Particulate matter with diameter < 1 μm (PM₁), particulate matter with diameter < 2.5 μm (PM_2.5), particulate matter with diameter < 10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) were predicted by space-time extremely randomized trees model. We used the Normalized Difference Vegetation Index (NDVI) to reflect residential green space. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were examined. We also calculated the pulse pressure (PP) and mean arterial pressure (MAP). Generalized additive model and quantile g-computation were, respectively, conducted to investigate individual and joint effects of air pollutants on blood pressure. Furthermore, beneficial effect of NDVI and its modification effect were explored.

Results

Long-term air pollutants exposure was associated with elevated DBP and MAP. Specifically, we found a 10-μg/m³ increase in PM_2.5, PM₁₀, and SO₂ were associated with 2.36% (95% CI: 0.97, 3.76), 1.51% (95% CI: 0.70, 2.34), and 3.54% (95% CI: 1.55, 5.56) increase in DBP; a 10-μg/m³ increase in PM_2.5, PM₁₀, and SO₂ were associated with 1.84% (95% CI: 0.74, 2.96), 1.17% (95% CI: 0.52, 1.83), and 2.43% (95% CI: 0.71, 4.18) increase in MAP. Air pollutants mixture (one quantile increase) was positively associated with increased values of DBP (8.22%, 95% CI: 5.49, 11.02) and MAP (4.15%, 95% CI: 2.05, 6.30), respectively. These identified harmful effect of air pollutants mainly occurred among these lived with low NDVI values. And participants aged ≥50 years were more susceptible to the harmful effect of PM_2.5 and PM₁₀ compared to younger adults.

Conclusions

Our study indicated the harmful effect of long-term exposure to air pollutants and these effects may be modified by living within higher green space place. These evidence suggest increasing residential greenness and air pollution control may have simultaneous effect on decreasing the risk of hypertension.

The effect of urinary essential and non-essential elements on serum albumin: Evidence from a community-based study of the elderly in Beijing

Background & aims

Few epidemiological studies have investigated the relationships of urinary essential and non-essential elements with serum albumin, an indicator of nutritional status, especially for the elderly in China.

Methods

A community-based study among elderly participants (n = 275) was conducted in Beijing from November to December 2016. We measured 15 urinary elements concentrations and serum albumin levels. Three statistical methods including the generalized linear model (GLM), quantile g-computation model (qgcomp) and bayesian kernel machine regression (BKMR) were adapted.

Results

In GLM analysis, we observed decreased serum albumin levels associated with elevated urinary concentrations of aluminum, arsenic, barium, cobalt, chromium, copper, iron, manganese, selenium, strontium, and zinc. Compared with the lowest tertile, the highest tertile of cadmium and cesium was also negatively associated with serum albumin. Urinary selenium concentration had the most significant negative contribution (30.05%) in the qgcomp analysis. The negative correlations of element mixtures with serum albumin were also observed in BKMR analysis.

Conclusions

Our findings suggested the negative associations of essential and non-essential elements with serum albumin among the elderly. Large-scare cohort studies among the general population are required to validate our findings and elucidate the relevant underlying mechanisms.