Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation.

BACKGROUND: The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear. METHOD: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively. RESULT: PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP. CONCLUSION: Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

A Dataset on Population Activity Patterns in Typical Regions of North China.

This data article describes the "Typical Regional Activity Patterns" (TRAP) dataset, which is based on the Tackling Key Problems in Air Pollution Control Program. In order to explore the interaction between air pollution and physical activity, we collected activity patterns of 9,221 residents with different occupations and lifestyles for three consecutive days in typical regions (Jinan and Baoding) where air pollutant concentrations were higher than those in neighboring areas. The TRAP dataset consists of two aspects of information: demographic indicators (personal information, occupation, personal habits, and living situation) and physical activity pattern data (activity location and intensity); additionally, the exposure measures of physical activity patterns are included, which data users can match to various endpoints for their specific purpose. This dataset provides evidence for exploring the attributes of activity patterns of residents in northern China and for interdisciplinary researchers to develop strategies and measures for health education and health promotion.

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.

Metallomics analysis of metal exposure and cognitive function in older adults: A combined epidemiological and bioinformatics study.

Dementia is a significant cause of elderly disability and Alzheimer's disease (AD) is the most prevalent form of dementia. As an early stage of AD, the mechanism related to mild cognitive impairment (MCI) and heavy metals is still unclear. This study utilized a cross-sectional design and enrolled 514 older adults in Bejing, China. Cognitive function was assessed by the Mini-Mental State Examination (MMSE) and fourteen blood metals were measured by inductively coupled plasma mass spectrometry (ICP-MS). In the adjusted single-metal models, we observed that copper [Cu, ß (95% CI): 3.73 (-6.42, -1.03)] and lead [Pb, ß (95% CI): 0.79 (-1.26, -0.32)] demonstrated negative associations with cognitive function, while selenium [Se, ß (95% CI): 2.97 (1.23, 4.70)] was beneficial to cognition. Our findings were robust in secondary analysis using multi-metal models, which included generalized linear models (GLM), Bayesian kernel machine regression (BKMR), and quantile g-computation (qgcomp). Moreover, the toxic metal mixture (Cu and Pb) exhibited a significant negative association with MMSE scores and the inclusion of Se in the metal mixture attenuated the neurotoxicity of Cu-Pb mixture. The in silico analysis was used to examine the potential molecular mechanisms (genes, biological processes, pathways, and illnesses) of interaction among metal mixtures. We identified 20 cognition-related genes that are associated with both Cu-Pb and Se. Among these genes, eight (APOE, APP, BAX, BDNF, CASP3, HMOX1, TF, and TPP1) exhibited opposite effects on protein activity, mRNA expression, or protein expression in response to Se and Cu/Pb exposure, which could be the key genes accounting for the anti-neurotoxic effects of Se. Our findings support that Se can attenuate the neurotoxicity of exposure to single Cu or Pb, and Cu-Pb mixture. More research is needed to confirm our findings and gain knowledge about the molecular mechanisms of combined metal exposure on cognitive function.

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) [ßAs = -0.075, 95% confidence interval (CI): -0.129, -0.020, Padj = 0.04]. Similarly, selenium was negatively correlated with TT4 (ßSe = -0.134, 95% CI: -0.211, -0.058, Padj < 0.01) and peripheral deiodinase activity (GT) (ßSe = -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 (ßSr = 0.263, 95% CI: 0.112, 0.414, Padj = 0.01), and negative associations of strontium with TT4 (ßSr = -0.099, 95% CI: -0.150, -0.048, Padj < 0.01) and GT (ßSr = -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.

Serum cystatin C and mild cognitive impairment: The mediating role of glucose homeostasis.

Background: This study explored the mediating role of glucose homeostasis indicators in the relationship between serum cystatin C and mild cognitive impairment (MCI). Methods: The present study used a cross-sectional design and included 514 participants aged ≥50 years in Beijing, China. The Mini-Mental State Examination was used to assess cognitive function. Serum cystatin C and a comprehensive set of glucose homeostasis indicators were detected, including fasting blood glucose (FBG), glycosylated albumin percentage (GAP), glycated hemoglobin (HbAlc), insulin, and homeostatic model assessment of insulin resistance (HOMA-IR), and beta cell function (HOMA-ß). Generalized linear models were used to investigate the associations among cystatin C, glucose homeostasis indicators, and cognitive function. Mediation analysis was conducted to explore potential mediator variables. Results: In this study of 514 participants, 76 (14.8%) had MCI. Those with cystatin C levels ≥1.09 mg/L had a 1.98-fold higher risk of MCI than those with levels <1.09 mg/L (95% CI, 1.05-3.69). FBG, GAP, and HbA1c increased the risk of MCI, while HOMA-ß decreased the risk. Notably, the associations between MCI risk and cystatin C or glucose homeostasis were only founded in diabetes patients. Serum cystatin C was found to be positively associated with HOMA-ß (beta (95% CI): 0.20 [0.06, 0.34]), HOMA-IR (0.23 [0.09, 0.36]), and insulin (0.22 [0.09, 0.34]) levels. Moreover, HOMA-ß was identified as playing a negative mediating role (proportion mediated: -16%) in the relationship between cystatin C and MCI. Conclusion: Elevated levels of cystatin C are associated with an increased risk of MCI. The glucose homeostasis indicator, HOMA-ß, plays a negative mediating role in the relationship between cystatin C and MCI risk.

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 (PM2.5), 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 NDVI1000 m rather than NDVI500 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.

Quantitative analysis of the minimum days of dietary survey to estimate dietary pesticide exposure: Implications for dietary pesticide sampling strategy.

Populations are exposed to pesticides through diet on a daily basis. However, there is no research guiding how to evaluate dietary pesticide exposure, and researchers used 1-day, 3-days, 7-days or even longer dietary survey to evaluate without any consensus. It is important for dietary pesticide evaluation to identify the minimum survey days. To increase knowledge of this, a data combination was applied between a two-wave consecutive repeated-measures study in Baoding City and the Fifth China Total Diet Study. Further policy consistency on pesticides were evaluated to explain its credibility. We computed the sensitivity and specificity to evaluate how well different days of dietary survey classify participants with high exposure, and calculated the minimum days required to estimate the participant-specific mean at different acceptable error range. With 1 day of dietary survey, the classification sensitivity was low (<0.6) for total HCH, endosulfan, chlordane, cyhalothrin, allethrin, and prallethrin; that for the other pesticides was high sensitivity (≥0.6). Sensitivity increased as the number of days increased, and the maximum marginal sensitivity increase (≥0.039) occurred from 1 to 2 days for all pesticides except phenothrin, whose maximum marginal sensitivity increase (0.042) occurred from 2 to 3 days. The specificity increased gradually from 0.8 to 0.9 from 1 to 7 days. Under the acceptable error range of 0.5%, 3-28 days were required for participant-specific mean estimation and 1-7 days were required when acceptable error range was shrunk in 1%. Only 1 day was enough if 5% error range was acceptable. In conclusion, 3 days in the study period was cost-effective to distinguish high exposure group, and it rose to 7 when estimating participant-specific mean from a conservative perspective. This study can serve as a reference to determine the minimum survey days for epidemiological studies employing dietary surveys.

Independent, combine and interactive effects of heavy metal exposure on dyslipidemia biomarkers: A cross-sectional study in northeastern China.

Dyslipidemia is a common disease in the older population and represents a considerable disease burden worldwide. Epidemiological and experimental studies have indicated associations between heavy metal exposure and dyslipidemia; few studies have investigated the effects of heavy metal mixture and interactions between metals on dyslipidemia. We recruited 1121 participants living in heavy metal-contaminated and control areas in northeast China from a cross-sectional survey (2017-2019). Urinary metals including chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) and dyslipidemia biomarkers, namely triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels, were measured. The generalized linear model (GLM) was used to explore the association of a single metal with dyslipidemia biomarkers. Bayesian kernel machine regression (BKMR) and multivariable linear regression were performed to explore the overall effect of metal mixture and the interaction between metals on dyslipidemia. Heavy metal mixture was positively associated with LDL-C, TC, and TG and negatively with HDL-C. In multivariable linear regression, Pb and Cd exhibited a synergistic association with LDL-C in the participants without hyperlipemia. Mn-Cd and Pb-Cr also showed a synergistic association with increasing the level of LDL-C in subjects without hyperlipemia. Cd-Cr showed an antagonistic association with HDL-C, respectively. Cr-Mn exhibited an antagonistic association with decreased HDL-C and TG levels. No significant interaction was noted among the three metals. Our study indicated that exposure to heavy metals is associated with dyslipidemia biomarkers and the presence of potential synergistic or antagonistic interactions between the heavy metals.

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 (ßCs = 0.14; 95% CI: 0.02, 0.26) whereas copper (Cu) was inversely related to TG (ßCu = -0.65; 95%CI: -1.14, -0.17) in adjusted models. Manganese (Mn) was mainly related to higher HDL-C (ßMn = 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.

Trace elements and Alzheimer dementia in population-based studies: A bibliometric and meta-analysis.

Alterations in the concentrations of trace elements may play a vital role in Alzheimer dementia progression. However, previous research results are inconsistent, and there is still a lack of review on the relationship between all the studied-trace elements and AD from various perspectives of population-based studies. In this study, we systematically reviewed previous population-based studies and identified the altered trace elements in AD patients. We searched the Web of Science Core Collection, PubMed, and Scopus database, and ultimately included 73 articles. A bibliometric analysis was conducted to explore the evolution of the field from an epidemiological perspective. Bibliometric data such as trace elements, biological materials, detection methods, cognitive tests, co-occurrence and co-citation statistics are all analyzed and presented in a quantitative manner. The 73 included studies analyzed 39 trace elements in total. In a further meta-analysis, standardized mean differences (SMDs) of 13 elements were calculated to evaluate their altered in AD patients, including copper, iron, zinc, selenium, manganese, lead, aluminum, cadmium, chromium, arsenic, mercury, cobalt, and manganese. We identified four trace elements-copper (serum), iron (plasma), zinc (hair), and selenium (plasma)-altered in AD patients, with SMDs of 0.37 (95% confidence interval [CI]: 0.10, 0.65), -0.68 (95% CI: -1.34, -0.02), -0.35 (95% CI: -0.62, -0.08), and -0.61 (95% CI: -0.97, -0.25), respectively. Finally, we formed a database of various trace element levels in AD patients and healthy controls. Our study can help future researchers gain a comprehensive understanding of the advancements in the field, and our results provide comprehensive population-based data for future research.

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, PM2.5, 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_PM2.5 = 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.

Associations and burdens of relative humidity with cause-specific mortality in three Chinese cities.

This study aimed to investigate the association between relative humidity (RH) and various cause of mortality, and then quantify the RH-related mortality fraction of low and high RH under the assumption that causal effects exist. Daily cause-specific mortality counts from 2008 to 2011, and contemporaneous meteorological data in three Chinese cities were collected. Distributed lag nonlinear models were adopted to quantify the nonlinear and delayed effects of RH on mortality risk. Low and high RH were defined as RH lower or higher than the minimum mortality risk RH (MMRH), respectively. Corresponding RH-related mortality fractions were calculated in the explanatory analysis. From the three cities, 736,301 deaths were collected. RH (mean ± standard deviation) were 50.9 ± 20.0 for Beijing, 75.5 ± 8.6 for Chengdu, and 70.8 ± 14.6 for Nanjing. We found that low RH in Beijing and high RH (about 80-90%) in Chengdu was associated with increased all-cause mortality risk. Both low and high RH may increase the CVD mortality risk in Beijing. Both low and high (about 80-85%) RH may increase the COPD mortality risk in Chengdu. Low RH (about < 45%) was associated with increased diabetes mortality risk in Nanjing. Effects of extreme low and extreme high RH were delayed in these cities, except that extreme low effects on COPD mortality appeared immediately in Chengdu. The effects of extreme low RH are higher than that of the extreme high RH in Beijing and Nanjing, while contrary in Chengdu. Finally, under the causal effect assumption, 6.80% (95% eCI: 2.90, 10.73) all-cause mortality and 12.48% (95% eCI: 7.17, 16.80) CVD deaths in Beijing, 9.59% (95% eCI: 1.38, 16.88) COPD deaths in Chengdu, and 23.79% (95% eCI: 0.92, 387.93) diabetes mortality in Nanjing were attributable to RH. Our study provided insights into RH-mortality risk, helped draw relative intervention policies, and is also significant for future predictions of climate change effects under different scenarios.

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 (PM2.5), 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 PM2.5, 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 PM2.5, 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 PM2.5 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.

Novel Strategies for Assessing Associations Between Selenium Biomarkers and Cardiometabolic Risk Factors: Concentration, Visit-to-Visit Variability, or Individual Mean? Evidence From a Repeated-Measures Study of Older Adults With High Selenium.

Background and Aims: Previous studies have focused only on the cardiometabolic effects of selenium concentrations. We explored whether selenium levels and their visit-to-visit variability (VVV) and individual mean (IM) are independently associated with cardiometabolic risk factors. Methods: A three-wave repeated-measures study of older adults with high selenium (n = 201) was conducted in Beijing from 2016 to 2018. Whole blood selenium and urinary selenium concentrations were measured. VVV and IM were used to profile the homeostasis of the selenium biomarkers. Four indicators, namely standard deviation, coefficient of variation, average real variability, and variability independent of the mean, were employed to characterize VVV. We considered 13 cardiometabolic factors: four lipid profile indicators, three blood pressure indices, glucose, uric acid, waistline, hipline, waist-hip ratio, and sex-specific metabolic syndrome score. Linear mixed-effects regression models with random intercepts for the participants were employed to explore the associations of the selenium concentrations, VVV, and IM with the cardiometabolic factors. Results: The geometric mean whole blood and urinary selenium levels were 134.30 and 18.00 µg/L, respectively. Selenium concentrations were significantly associated with numerous cardiometabolic factors. Specifically, whole blood selenium was positively associated with total cholesterol [0.22, 95% confidence interval (CI): 0.12, 0.33], low-density lipoprotein cholesterol (LDL-C; 0.28, 95% CI: 0.13, 0.42), glucose (0.22, 95% CI: 0.10, 0.34), and uric acid (0.16, 95% CI: 0.04, 0.28). After adjustment for VVV, the IM of whole blood selenium was positively correlated with total cholesterol (0.002, 95% CI: 0.001, 0.004), triglycerides (0.007, 95% CI: 0.004, 0.011), and LDL-C (0.002, 95% CI: 0.000, 0.004). However, we did not observe any robust associations between the VVV of the selenium biomarkers and cardiometabolic risk factors after adjustment for IM. Conclusion: Our findings suggest that selenium concentrations and their IMs are significantly associated with cardiometabolic risk factors among older adults with high selenium. Longer repeated-measures studies among the general population are required to validate our findings and elucidate the relevant underlying mechanisms.

Association of Metals and Metalloids With Urinary Albumin/Creatinine Ratio: Evidence From a Cross-Sectional Study Among Elderly in Beijing.

Background: Environmental exposure to toxic elements contributes to the pathogenesis of chronic kidney disease (CKD). Few studies focus on the association of urinary metals and metalloids concentrations with the urinary albumin/creatinine ratio (UACR) among elderly, especially in areas and seasons with severe air pollution. Objective: We aimed to evaluate the associations of urinary metals and metalloids concentration with UACR, which is an early and sensitive indicator of CKD. Method: We conducted a cross-sectional study among 275 elderly people in Beijing from November to December 2016, which has experienced the most severe air pollution in China. We measured 15 urinary metals and metalloids concentration and estimated their association with UACR using a generalized linear model (GLM). Bayesian kernel machine regression (BKMR) and quantile g-computation (qgcomp) models were also conducted to evaluate the combined effect of metal and metalloid mixtures concentration. Results: Of the 275 elderly people included in the analysis, we found that higher urinary Cu concentration was positively associated with UACR using GLM (ß = 0.36, 95% CI: 0.25, 0.46). Using the BKMR model, we found that the change in UACR was positively associated with a change in urinary Cu concentration from its 25th to 75th percentile value with all other metals and metalloids concentration fixed at their 25th, 50th, or 75th percentile levels. Urinary Cu concentration had the most significant positive contribution (59.15%) in the qgcomp model. Our finding was largely robust in three mixture modeling approaches: GLM, qgcomp, and BKMR. Conclusion: This finding suggests that urinary Cu concentration was strongly positively associated with UACR. Further analyses in cohort studies are required to corroborate this finding.

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

OBJECTIVE: We aimed to investigate whether urinary metal mixtures are associated with the homeostasis of inflammatory mediators in middle-aged and older adults. METHODS: A four-visit repeated-measures study was conducted with 98 middle-aged and older adults from five communities in Beijing, China. Only one person was lost to follow-up at the third visit. Ultimately, 391 observations were included in the analysis. The urinary concentrations of 10 metals were measured at each visit using inductively coupled plasma mass spectrometry (ICP-MS) with a limit of detection (LOD) ranging from 0.002 to 0.173 µg/L, and the detection rates were all above 84%. Similarly, 14 serum inflammatory mediators were measured using a Beckman Coulter analyzer and the Bio-Plex MAGPIX system. A linear mixed model (LMM), LMM with least absolute shrinkage and selection operator regularization (LMMLASSO), and Bayesian kernel machine regression (BKMR) were adopted to explore the effects of urinary metal mixtures on inflammatory mediators. RESULTS: In LMM, a two-fold increase in urinary cesium (Cs) and chromium (Cr) was statistically associated with -35.22% (95% confidence interval [CI]: -53.17, -10.40) changes in interleukin 6 (IL-6) and -11.13% (95 %CI: -20.67, -0.44) in IL-8. Urinary copper (Cu) and selenium (Se) was statistically associated with IL-6 (88.10%, 95%CI: 34.92, 162.24) and tumor necrosis factor-alpha (TNF-α) (22.32%, 95%CI: 3.28, 44.12), respectively. Similar results were observed for the LMMLASSO and BKMR. Furthermore, Cr, Cs, Cu, and Se were significantly associated with other inflammatory regulatory network mediators. For example, urinary Cs was statistically associated with endothelin-1, and Cr was statistically associated with endothelin-1 and intercellular adhesion molecule 1 (ICAM-1). Finally, the interaction effects of Cu with various metals on inflammatory mediators were observed. CONCLUSION: Our findings suggest that Cr, Cs, Cu, and Se may disrupt the homeostasis of inflammatory mediators, providing insight into the potential pathophysiological mechanisms of metal mixtures and chronic diseases.

Negatively interactive effect of chromium and cadmium on obesity: Evidence from adults living near ferrochromium factory.

BACKGROUND: Researchers have reported that chromium (Cr) exposure may be associated with metabolism of glucose and lipids in residents living in a long-term Cr polluted area. Previous statistical analysis is mainly focused on individual chromium exposure. Furtherly, we aim to investigated the independent, combined, and interaction effects of the co-exposure of urine Cr (UCr) with cadmium (UCd), lead (UPb) and manganese (UMn) on body mass index (BMI), waist circumference, and the risk of overweight and abdominal obesity. METHOD: We enrolled 1187 participants from annual surveys between 2017 and 2019. Heavy metal concentrations in urine were standardized using covariate-adjusted urine creatinine levels. Multiple linear/logistic regression models were applied to measure the single effect of urine heavy metal concentration on the outcomes. The quantile-based g-computation (g-comp) model was used to evaluate the combined effect of metal mixture on the outcomes and to compare the contribution of each metal. Both additive and multiplicative interactions were measured for UCr with UCd, UPb, UMn on the outcomes. Analysis was performed on the overall population and stratified by smoking habit. RESULTS: For the overall study population, UCr was positively associated with BMI (p trend = 0.023) and waist circumference (p trend = 0.018). For smoking participants, the g-comp model demonstrated that the metal mixture was negatively associated with BMI, with UCr and UCd contributing the most in the positive and negative direction. A negative additive interaction was observed between UCr and UCd on BMI and abdominal obesity. We did not observe a significant interaction effect of UCr with UPb or UMn. CONCLUSION: Our study indicated that Cr and Cd exposure may be associated with BMI and waist circumference, with combined and interaction effects of the heavy metals noted. Further epidemiological and experimental researches could simultaneously consider single and complex mixed exposure to verify the findings and biological mechanisms.

Association between urine metals and liver function biomarkers in Northeast China: A cross-sectional study.

BACKGROUND: After heavy metals enter the body, they affect a variety of organs, particularly the main metabolic organ, the liver. Moreover, people are more likely to be exposed to multiple metals than to a single metal. We explored the associations between exposure to a heavy metal mixture and liver function biomarkers. METHODS: This study involved 1171 residents living in areas with or without heavy metal exposure in northeast China. Urine concentrations of chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) were measured. Total protein (TP), albumin (ALB), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were used as biomarkers of liver function. A generalized linear model (GLM), quantile g-computation, and Bayesian kernel machine regression (BKMR) were used to explore the associations between the four metals and liver function. RESULTS: GLM analysis revealed that Cr level was negatively associated with TP (ß = - 0.57; 95% CI: - 0.89, - 0.26) and ALB (ß = - 0.27; 95% CI: - 0.47, - 0.07) levels, and Cd level was positively associated with AST (ß = 1.04; 95% CI: 0.43, 1.65) and ALT (ß = 0.94; 95% CI: 0.08, 1.79) levels. ALB (ß = 0.26; 95% CI: 0.10, 0.41) and ALT (ß = 0.52; 95% CI: 0.02, 1.02) levels were positively associated with urine Mn concentration. The quantile g-computation indicated that exposure to a mixture of the four metals was significantly associated with TP (ß = - 0.56; 95% CI: - 0.94, - 0.18) and ALT (ß = 0.84; 95% CI: 0.04, 1.63) levels. Among the metals, Cr had the strongest effect on TP and Cd had that on AST. The BKMR model indicated that mixed metal exposure was negatively associated with TP and ALB levels and positively associated with ALT and AST levels. CONCLUSION: Exposure to mixtures of heavy metals may influence liver function. Cr and Cd may be the largest contributors.