Dataset Information

Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women.

ABSTRACT: BACKGROUND:Exposures to ambient gaseous pollutants have been linked to cardiovascular diseases (CVDs), but the biological mechanisms remain uncertain. OBJECTIVES:This study examined the changes in CVD marker levels resulting from elevated exposure to ambient gaseous pollutants in midlife women. METHODS:Annual repeated measurements of several inflammatory, hemostatic and lipid makers were obtained from 2306 midlife women enrolled in the longitudinal Study of Women's Health Across the Nation (SWAN) between 1999 and 2004. Ambient carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) data were assigned to each woman based on proximity of the monitoring station to her residential address. Short- and long-term exposures were calculated, and their associations with markers were examined using linear mixed-effects regression models, adjusted for demographic, health and other factors. RESULTS:Short-term CO exposure was associated with increased fibrinogen, i.e., every interquartile increase of average prior one-week exposure to CO was associated with 1.3% (95% CI: 0.6%, 2.0%) increase in fibrinogen. Long-term exposures to NO2 and SO2 were associated with reduced high-density lipoproteins and apolipoprotein A1, e.g., 4.0% (1.7%, 6.3%) and 4.7% (2.8%, 6.6%) decrease per interquartile increment in prior one-year average NO2 concentration, respectively. Fine particle (PM2.5) exposure confounded associations between CO/NO2 and inflammatory/hemostatic markers, while associations with lipoproteins were generally robust to PM2.5 adjustment. CONCLUSIONS:Exposures to these gas pollutants at current ambient levels may increase thrombotic potential and disrupt cholesterol metabolism, contributing to greater risk of CVDs in midlife women. Caution should be exercised in evaluating the confounding by PM2.5 exposure.

SUBMITTER: Wu XM

PROVIDER: S-EPMC5584622 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women.

Wu Xiangmei May XM Basu Rupa R Malig Brian B Broadwin Rachel R Ebisu Keita K Gold Ellen B EB Qi Lihong L Derby Carol C Green Rochelle S RS

Environment international 20170726

<h4>Background</h4>Exposures to ambient gaseous pollutants have been linked to cardiovascular diseases (CVDs), but the biological mechanisms remain uncertain.<h4>Objectives</h4>This study examined the changes in CVD marker levels resulting from elevated exposure to ambient gaseous pollutants in midlife women.<h4>Methods</h4>Annual repeated measurements of several inflammatory, hemostatic and lipid makers were obtained from 2306 midlife women enrolled in the longitudinal Study of Women's Health A ...[more]

PMID: 28732305

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Project description:BackgroundVulnerability theories propose that suboptimal levels of lipid markers and proinflammatory proteins predict future heightened depression. Scar models posit the reverse association. However, most studies that tested relationships between non-specific immune/endocrine markers and depression did not separate temporal inferences between people and within-person and how different immunometabolism markers related to unique depression symptoms. We thus used cross-lagged prospective network analyses (CLPN) to investigate this topic.MethodsCommunity midlife women (n = 2224) completed the Center for Epidemiologic Studies-Depression scale and provided biomarker samples across five time-points spanning 9 years. CLPN identified significant relations (edges) among components (nodes) of depression (depressed mood, somatic symptoms, interpersonal issues), lipid markers [insulin, fasting glucose, triglycerides, low-density lipoprotein-cholesterol (LDL), high-density lipoprotein-cholesterol (HDL)], and proinflammatory proteins [C-reactive protein (CRP), fibrinogen], within and across time-points. All models adjusted for age, estradiol, follicle-stimulating hormone, and menopausal status.ResultsIn within-person temporal networks, higher CRP and HDL predicted all three depression components (d = 0.131-2.112). Increased LDL preceded higher depressed mood and interpersonal issues (v. somatic symptoms) (d = 0.251-0.327). Elevated triglycerides predicted more somatic symptoms (v. depressed mood and interpersonal problems) (d = 0.131). More interpersonal problems forecasted elevated fibrinogen and LDL levels (d = 0.129-0.331), and stronger somatic symptoms preceded higher fibrinogen levels (d = 0.188).ConclusionsResults supported both vulnerability and scar models. Long-term dysregulated immunometabolism systems, social disengagement, and related patterns are possible mechanistic accounts. Cognitive-behavioral therapies that optimize nutrition and physical activity may effectively target depression.

Project description:Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R2 and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

Dataset Information

Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women.

Publications

Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women.

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