Long- and Intermediate-Term Ambient Particulate Pollution Is Associated with Increased Osteoarthritis Risk: A Population-Based Prospective Analysis

Recent studies found the intrusion and retention of exogenous fine particles into joints, but epidemiological data for long- and intermediate-term exposure associations are scare. Here, all urban working, retired employee, and rural residents (16.78 million) in Beijing from January 1, 2011 to December 31, 2019 were included to investigate the effects of long- and intermediate-term ambient particulate exposure on development of osteoarthritis. We identified 1,742,067 participants as first-visit patients with osteoarthritis. For each interquartile range increase in annual PM2.5 (23.32 μg/m3) and PM10 (23.92 μg/m3) exposure concentration, the pooled hazard ratios were respectively 1.238 (95% CI: 1.228, 1.249) and 1.178 (95% CI: 1.168, 1.189) for first osteoarthritis outpatient visits. Moreover, age at first osteoarthritis outpatient visits significantly decreased by 4.52 (95% CI: 3.45 to 5.40) days per μg/m3 for annual PM2.5 exposure at below 67.85 μg/m3. Finally, among the six constituents analyzed, black carbon appears to be the most important component associated with the association between PM2.5 exposure and the three osteoarthritis-related outcomes.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

Exposure to PM2.5 and its five constituents is associated with the incidence of type 2 diabetes mellitus: a prospective cohort study in northwest China

Studies have demonstrated that fine particulate matter (PM2.5) is an underlying risk factor for type 2 diabetes mellitus (T2DM), but evidence exploring the relationship between PM2.5 chemical components and T2DM was extremely limited, to investigate the effects of long-term exposure to PM2.5 and its five constituents (sulfate [SO42-], nitrate [NO3-], ammonium [NH4+]), organic matter [OM] and black carbon [BC]) on incidence of T2DM. Based on the "Jinchang Cohort" platform, a total of 19,884 participants were selected for analysis. Daily average concentrations of pollutants were gained from Tracking Air Pollution in China (TAP). Cox proportional hazards regression models were utilized to estimate the hazard ratios (HR) and 95% confidence interval (CI) in single-pollutant models, restricted cubic splines functions were used to examine the dose-response relationships, and quantile g-computation (QgC) was applied to evaluate the combined effect of PM2.5 compositions on T2DM. Stratification analysis was also considered. A total of 791 developed new cases of T2DM were observed during a follow-up period of 45254.16 person-years. The concentrations of PM2.5, NO3-, NH4+, OM and BC were significantly associated with incidence of T2DM (P-trend < 0.05), with the HRs in the highest quartiles of 2.16 (95% CI 1.79, 2.62), 1.43 (95% CI 1.16, 1.75), 1.75 (95% CI 1.45, 2.11), 1.31 (95% CI 1.08, 1.59) and 1.79 (95% CI 1.46, 2.21), respectively. Findings of QgC model showed a noticeably positive joint effect of one quartile increase in PM2.5 constituents on increased T2DM morbidity (HR 1.27, 95% CI 1.09, 1.49), and BC (32.7%) contributed the most to the overall effect. The drinkers, workers and subjects with hypertension, obesity, higher physical activity, and lower education and income were generally more susceptible to PM2.5 components hazards. Long-term exposure to PM2.5 and its components (i.e., NO3-, NH4+, OM, BC) was positively correlated with T2DM incidence. Moreover, BC may be the most responsible for the association between PM2.5 constituents and T2DM. In the future, more epidemiological and experimental studies are needed to identify the link and potential biological mechanisms.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China

Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R2 of 0.84 and an RMSE of 4.9 μg/m3, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.

Long-Term Exposure to Fine Particulate Constituents and Vascular Damage in a Population with Metabolic Abnormality in China

AIM

To date, PM2.5-associated vascular damage in metabolic abnormalities has remained controversial. We knew little about the vascular damage of PM2.5 constituents. Thus, this study aimed to investigate the relationship between long-term exposure to PM2.5 and its constituents and vascular damage in metabolic abnormalities.

METHODS

A total of 124,387 participants with metabolic abnormalities (defined as at least one metabolic disorder, such as obesity, elevated blood pressure, elevated triglyceride level, elevated fasting glucose level, or low HDL cholesterol level) were recruited in this study from 11 representative centers in China between January 2011 and December 2017. PM2.5 and its constituents (black carbon [BC], organic matter [OM], sulfate [SO42-], nitrate [NO3-], and ammonium salts [NH4＋]) were extracted. Elevated brachial-ankle pulse wave velocity (baPWV) (≥ 1,400 cm/s) and declined ankle-brachial index (ABI) (＜0.9) indicated vascular damage. Multivariable logistic regression and Quantile g-Computation models were utilized to explore the impact on outcomes.

RESULTS

Of the 124,387 participants (median age, 49 years), 87,870 (70.64%) were men. One-year lag exposure to PM2.5 and its constituents was significantly associated with vascular damage in single pollutant models. The adjusted odds ratios (OR) for each 1-µg/m3 increase in PM2.5 was 1.013 (95% CI, 1.012-1.015) and 1.031 (95% CI, 1.025-1.037) for elevated baPWV and decreased ABI, respectively. PM2.5 constituents were also associated with vascular damage in multi-pollutant models. Among the PM2.5 constituents, BC (47.17%), SO42- (33.59%), and NH4＋ (19.23%) have the highest contribution to elevated baPWV and NO3- (47.89%) and BC (23.50%) to declined ABI.

CONCLUSION

Chronic exposure to PM2.5 and PM2.5 constituents was related to vascular damage in the abnormal metabolic population in China. The heterogeneous contribution of different PM2.5 constituents to vessel bed damage is worthy of attention when developing targeted strategies.

Association of Fine Particulate Matter Constituents with the Predicted 10-Year Atherosclerotic Cardiovascular Disease Risk: Evidence from a Large-Scale Cross-Sectional Study

Little is known concerning the associations of fine particulate matter (PM2.5) and its constituents with atherosclerotic cardiovascular disease (ASCVD). A total of 31,162 participants enrolled from the Henan Rural Cohort were used to specify associations of PM2.5 and its constituents with ASCVD. Hybrid machine learning was utilized to estimate the 3-year average concentration of PM2.5 and its constituents (black carbon [BC], nitrate [NO3-], ammonium [NH4+], inorganic sulfate [SO42-], organic matter [OM], and soil particles [SOIL]). Constituent concentration, proportion, and residual models were utilized to examine the associations of PM2.5 constituents with 10-year ASCVD risk and to identify the most hazardous constituent. The isochronous substitution model (ISM) was employed to analyze the substitution effect between PM2.5 constituents. We found that each 1 μg/m3 increase in PM2.5, BC, NH4+, NO3-, OM, SO42-, and SOIL was associated with a 3.5%, 49.3%, 19.4%, 10.5%, 21.4%, 14%, and 28.5% higher 10-year ASCVD risk, respectively (all p < 0.05). Comparable results were observed in proportion and residual models. The ISM found that replacing BC with other constituents will generate the greatest health benefits. The results indicated that long-term exposure to PM2.5 and its constituents were associated with increased risks of ASCVD, with BC being the most attributable constituent.

Ambient PM2.5 and its components associated with 10-year atherosclerotic cardiovascular disease risk in Chinese adults

BACKGROUND

Exposure to particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) may increase the risk of 10-year atherosclerotic cardiovascular disease (ASCVD) risk. While PM2.5 is comprised of various components, the evidence on the correlation of its components with 10-year ASCVD risk and which component contributes most remains limited.

METHODS

Data were derived from the baseline assessments of China Multi-Ethnic Cohort (CMEC). In total, 69,722 individuals aged 35-74 years were included into this study. The annual average concentration of PM2.5 and its components (black carbon, ammonium, nitrate, sulfate, organic matter, soil particles, and sea salt) were estimated by satellite remote sensing and chemical transport models. The ASCVD risk of individuals was calculated by the equations from the China-PAR Project (prediction for ASCVD risk in China). The relationship between single exposure to PM2.5 and its components and predicted 10-year ASCVD risk was assessed using the logistic regression model. The effect of joint exposure was estimated, and the most significant contributor was identified using the weighted quantile sum approach.

RESULTS

Totally 69,722 participants were included, of which 95.8 % and 4.2 % had low and high 10-year ASCVD risk, respectively. Per standard deviation increases in the 3-year average concentration of PM2.5 mass (odds ratio [OR] 1.23, 95 % confidence interval [CI]: 1.12-1.35), black carbon (1.21, 1.11-1.33), ammonium (1.21, 1.10-1.32), nitrate (1.25, 1.14-1.38), organic matter (1.29, 1.18-1.42), sulfate (1.17, 1.07-1.28), and soil particles (1.15, 1.04-1.26) were related to high 10-year ASCVD risk. The overall effect (1.19, 1.11-1.28) of the PM2.5 components was positively associated with 10-year ASCVD risk, and organic matter had the most contribution to this relationship. Female participants were more significantly impacted by PM2.5, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles compared to others.

CONCLUSION

Long-term exposure to PM2.5 mass, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles were positively associated with high 10-year ASCVD risk, while sea salt exhibited a protective effect. Moreover, the organic matter might take primary responsibility for the relationship between PM2.5 and 10-year ASCVD risk. Females were more susceptible to the adverse effect.

Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China

BACKGROUND

PM2.5 is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the CT scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5 exposure and pulmonary nodules was limited. &#xD;Objective: To examine potential associations of exposures to PM2.5 and its major chemical constituents with the prevalence of pulmonary nodules.&#xD;Methods: 16865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5 and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5 and its components on the risk of pulmonary nodules, respectively. &#xD;Results: Each 1mg/m3 increase in PM2.5 (OR 1.011 (95%CI: 1.007-1.014)) was positively associated with pulmonary nodules. Among five PM2.5 components, in single-pollutant effect models, every 1 μg/m3 increase in OM, BC, and NO3- elevated the risk of pulmonary nodule prevalence by 1.040 (95%CI: 1.025-1.055), 1.314 (95%CI: 1.209-1.407) and 1.021 (95%CI: 1.007-1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5 components was 1.076 (95%CI: 1.023-1.133) fold. Notably, NO3- BC and OM contributed higher risks of pulmonary nodules than other PM2.5 components. And the NO3- particles were identified to have the highest contribution. The impacts of PM2.5 components on pulmonary nodules were consistent across gender and age.&#xD;Conclusion: These findings provide important evidence for the positive correlation between exposure to PM2.5 and pulmonary nodules in China and identify that NO3- particles have the highest contribution to the risk.&#xD.

Long-term effects of ambient PM2.5 constituents on metabolic syndrome in Chinese children and adolescents

Metabolic syndrome (MetS) is considered a main public health issue as it remarkably adds the risk of cardiovascular disease, leading to a heavy burden of disease. There is growing evidence linking fine particulate matter (PM_2.5) exposure to MetS. However, the influences of PM_2.5 constituents, especially in children and adolescents, remain unclear. Our study was according to a national analysis among Chinese children and adolescents to examine the associations between long-term exposure to PM_2.5 main constituents and MetS. A total of 10,066 children and adolescents aged 10-18 years were recruited in 7 provinces in China, with blood tests, health exams, and questionnaire surveys. We estimated long-term exposures to PM_2.5 mass and its five constituents, containing black carbon (BC), organic matter (OM), inorganic nitrate (NO₃^-), sulfate (SO₄^2-), and soil particles (SOIL) from multi-source data fusion models. Mixed-effects logistic regression models were used with the adjustment of a variety of covariates. In the surveyed populations, 2.9% were classified as MetS. From the single-pollutant models, we discovered that long-term exposures to PM_2.5 mass, BC, OM, NO₃^-, as well as SO₄^2-, were significantly associated with the prevalence of MetS, with odds ratios (ORs) per 1 μg/m³ that were 1.02 (95% confidence interval (CI): 1.01, 1.03) for PM_2.5 mass, 1.24 (95% CI: 1.14, 1.35) for BC, 1.07 (95% CI: 1.04, 1.11) for OM, 1.09 (95% CI: 1.04, 1.13) for NO₃^-, and 1.14 (95% CI:1.04, 1.24) for SO₄^2-. The influence of BC on the prevalence of MetS was robust in both the multi-pollutant model and the PM_2.5-constituent joint model. The paper indicates long-term exposure to PM_2.5 mass and specific PM_2.5 constituents, particularly for BC, was significantly associated with a higher MetS prevalence among children and adolescents in China. Our results highlight the significance of establishing further regulations on PM_2.5 constituents.

Incident dementia and long-term exposure to constituents of fine particle air pollution: A national cohort study in the United States

Growing evidence suggests that fine particulate matter (PM_2.5) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM_2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO₃^-), sulfate (SO₄^2-), ammonium (NH₄⁺), and soil dust (DUST). To investigate the impact of long-term exposure to PM_2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM_2.5 and its major constituents. An interquartile range increase in PM_2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM_2.5 constituents, associations remained significant for BC, OM, SO₄^2-, and NH₄⁺ for both end points (even after adjustments of other constituents), among which BC and SO₄^2- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM_2.5 is significantly associated with higher rates of incident dementia and AD and that SO₄^2-, BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

PM2.5 Constituents and Onset of Gestational Diabetes Mellitus: Identifying Susceptible Exposure Windows

Fine particulate matter (PM_2.5) has been linked to gestational diabetes mellitus (GDM). However, PM_2.5 is a complex mixture with large spatiotemporal heterogeneities, and women with early-onset GDM (i.e., diagnosed before 24^th gestation week) have distinct maternal characteristics and a higher risk of worse health outcomes compared with those with late-onset GDM (i.e., diagnosed in or after 24^th gestation week). We aimed to examine differential impacts of PM_2.5 and its constituents on early- vs. late-onset GDM, and to identify corresponding susceptible exposure windows. We leveraged statewide linked electronic health records and birth records data in Florida in 2012-2017. Exposures to PM_2.5 and its constituents (i.e., sulfate [SO₄ ^2-], ammonium [NH₄ ⁺], nitrate [NO₃ ^-], organic matter [OM], black carbon [BC], mineral dust [DUST], and sea-salt [SS]) were spatiotemporally linked to pregnant women based on their residential histories. Cox proportional hazards models and multinomial logistic regression were used to examine the associations of PM_2.5 and its constituents with GDM and its onsets. Distributed non-linear lag models were implemented to identify susceptible exposure windows. Exposures to PM_2.5, SO₄ ^2-, NH₄ ⁺, and BC were statistically significantly associated with higher hazards of GDM. Exposures to PM_2.5 during weeks 1-12 of gestation were positively associated with GDM. Associations of early-onset GDM with PM_2.5 in the 1^st and 2^nd trimesters, SO₄ ^2- in the 1^st and 2^nd trimesters, and NO₃ ^- in the preconception and 1^st trimester were considerably stronger than observations for late-onset GDM. Our findings suggest there are differential associations of PM_2.5 and its constituents with early- vs. late-onset GDM, with different susceptible exposure windows. This study helps better understand the impacts of air pollution on GDM accounting for its physiological heterogeneity.

Short-term exposure to fine particulate matter constituents and mortality: case-crossover evidence from 32 counties in China

A growing number of studies associated increased mortality with exposures to specific fine particulate (PM_2.5) constituents, while great heterogeneity exists between locations. In China, evidence linking PM_2.5 constituents and mortality was extensively sparse. This study primarily aimed to quantify short-term associations between PM_2.5 constituents and non-accidental mortality among the Chinese population. We collected daily mortality records from 32 counties in China between January 1, 2011, and December 31, 2013. Daily concentrations of main PM_2.5 constituents (organic carbon (OC), elemental carbon (EC), nitrate (NO₃^-), sulfate (SO₄^2-), and ammonium (NH₄⁺)) were estimated using the modified Community Multiscale Air Quality model. Time-stratified case-crossover design with conditional logistic regression models was adopted to estimate mortality risks associated with short-term exposures to PM_2.5 mass and its constituents. Stratification analyses were done by sex, age, and season. A total of 116,959 non-accidental deaths were investigated. PM_2.5 concentrations on the day of death were averaged at 75.7 µg m^-3 (control day: 75.6 µg m^-3), with an interquartile range (IQR) of 65.2 µg m^-3. Per IQR rise in PM_2.5, EC, OC, NO₃^-, SO₄^2-, and NH₄⁺ at lag-04 day was associated with an increase in non-accidental mortality of 2.4% (95% confidence interval, (1.0-3.7), 1.7% (0.8-2.7), 2.9% (1.6-4.3), 2.1% (0.4-3.9), 1.0% (0.2-1.9), and 1.6% (0.3-2.9), respectively. Both PM_2.5 mass and its constituents were strongly associated with elevated cardiovascular mortality risks, but only PM_2.5, EC, and OC were positively associated with respiratory mortality at lag-3 day. PM_2.5 mass and its constituents associated effects on mortality varied among sex- and age-specific subpopulations. Differences in the seasonal pattern of associations exist among PM_2.5 constituents, with stronger effects related to EC and NO₃^- in warm months but SO₄^2- and NH₄⁺ in cold months. Short-term exposures to PM_2.5 compositions were positively associated with increased risks of mortality, particularly those constituents from combustion-related sources.

Alveolar Type II Cell Damage and Nrf2-SOD1 Pathway Downregulation Are Involved in PM2.5-Induced Lung Injury in Rats

The general toxicity of fine particulate matter (PM_2.5) has been intensively studied, but its pulmonary toxicities are still not fully understood. To investigate the changes of lung tissue after PM_2.5 exposure and identify the potential mechanisms of pulmonary toxicity, PM_2.5 samples were firstly collected and analyzed. Next, different doses of PM_2.5 samples (5 mg/kg, 10 mg/kg, 20 mg/kg) were intratracheally instilled into rats to simulate lung inhalation of polluted air. After instillation for eight weeks, morphological alterations of the lung were examined, and the levels of oxidative stress were detected. The data indicated that the major contributors to PM_2.5 mass were organic carbon, elemental carbon, sulfate, nitrate, and ammonium. Different concentrations of PM_2.5 could trigger oxidative stress through increasing reactive oxygen species (ROS) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) levels, and decreasing expression of antioxidant-related proteins (nuclear factor erythroid 2-related factor 2 (Nrf2), superoxide dismutase 1 (SOD1) and catalase). Histochemical staining and transmission electron microscopy displayed pulmonary inflammation, collagen deposition, mitochondrial swelling, and a decreasing number of multilamellar bodies in alveolar type II cells after PM_2.5 exposure, which was related to PM_2.5-induced oxidative stress. These results provide a basis for a better understanding of pulmonary impairment in response to PM_2.5.

Associations between Individual Exposure to Fine Particulate Matter Elemental Constituent Mixtures and Blood Lipid Profiles: A Panel Study in Chinese People Aged 60-69 Years

Dyslipidemia may be a potential mechanism linking fine particulate matter (PM_2.5) to adverse cardiovascular outcomes. However, inconsistent associations between PM_2.5 and blood lipids have resulted from the existing research, and the joint effect of PM_2.5 elemental constituents on blood lipid profiles remains unclear. We aimed to explore the overall associations between PM_2.5 elemental constituents and blood lipid profiles and to identify the significant PM_2.5 elemental constituents in this association. Sixty-nine elderly people were recruited between September 2018 and January 2019. Each participant completed a survey questionnaire, 3 days of individual exposure monitoring, health examination, and biological sample collection at each follow-up visit. Bayesian kernel machine regression (BKMR) models were used to identify the joint effects of the 17 elemental constituents on blood lipid profiles. Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) levels were significantly increased in older adults when exposed to the mixture of PM_2.5 elemental constituents. Copper and titanium had higher posterior inclusion probabilities than other constituents, ranging from 0.76 to 0.90 (Cu) and 0.74 to 0.94 (Ti). Copper and titanium in the PM_2.5 elemental constituent mixture played an essential role in changes to blood lipid levels. This study highlights the importance of identifying critical hazardous PM_2.5 constituents that may cause adverse cardiovascular outcomes in the future.

Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults

Fine particulate matter (PM_2.5) was reported to be associated with metabolic syndrome (MetS), but how PM_2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM_2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects (n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM_2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM_2.5 constituents-MetS" associations. Results indicated PM_2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO₄^2-), calcium ion (Ca²⁺), magnesium ion (Mg²⁺), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM_2.5 and some constituents (SO₄^2-, Ca²⁺, Mg²⁺, Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM_2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO₄^2-, Ca²⁺, Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM_2.5 constituents and MetS risk in China.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.