BackgroundAs public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity.MethodsIn this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step.ResultsAmong the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large.ConclusionsThere is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.
Mounting evidence supports that fine particulate matter adversely impacts cardio-metabolic diseases particularly in susceptible individuals; however, health effects induced by the extreme concentrations within megacities in Asia is not well described. We enrolled 65 nonsmoking adults with metabolic syndrome and insulin resistance in the Beijing metropolitan area into a panel study of four repeated visits across four seasons since 2012. Daily ambient fine particulate matter (PM2.5) and personal black carbon (BC) levels ranged from 9.0 to 552.5 μg/m3 and 0.2 to 24.5 μg/m3, respectively, with extreme levels observed during January 2013. Cumulative PM2.5 exposure windows across the prior 1-7 days were significantly associated with systolic blood pressure (BP) elevations ranging from 2.0 (95% confidence interval 0.3-3.7) to 2.7 (0.6-4.8) mmHg per standard deviation increase [67.2 μg/m3]), while cumulative BC exposure during the previous 2-5 days were significantly associated with ranges in elevations in diastolic BP from 1.3 (0.0-2.5) to 1.7 (0.3-3.2) mmHg per standard deviation increase [3.6 μg/m3]). Both BC and PM2.5 were significantly associated with worsening insulin resistance (0.18 (0.01-0.36) and 0.22 (0.04-0.39) unit increase per standard deviation increase of personal-level BC, and 0.18 (0.02-0.34) and 0.22 (0.08-0.36) unit increase per standard deviation increase of ambient PM2.5 on lag days 4 and 5). These results provide important global public health warnings that air pollution may pose a risk to cardio-metabolic health even at the extremely high concentrations faced by billions of people in the developing world today.
BackgroundInflammation and oxidative stress play critical roles in the pathogenesis of inhaled air pollutant-mediated metabolic disease. Inflammation in the adipose tissues niches are widely believed to exert important effects on organ dysfunction. Recent data from both human and animal models suggest a role for inflammation and oxidative stress in epicardial adipose tissue (EAT) as a risk factor for the development of cardiovascular disease. We hypothesized that inhalational exposure to concentrated ambient fine particulates (CAPs) and ozone (O3) exaggerates inflammation and oxidative stress in EAT and perirenal adipose tissue (PAT).MethodsEight- week-old Male Sprague–Dawley rats were fed a normal diet (ND) or high fructose diet (HFr) for 8 weeks, and then exposed to ambient AIR, CAPs at a mean of 356 μg/m3, O3 at 0.485 ppm, or CAPs (441 μg/m3) + O3 (0.497 ppm) in Dearborn, MI, 8 hours/day, 5 days/week, for 9 days over 2 weeks.ResultsEAT and PAT showed whitish color in gross, and less mitochondria, higher mRNA expression of white adipose specific and lower brown adipose specific genes than in brown adipose tissues. Exposure to CAPs and O3 resulted in the increase of macrophage infiltration in both EAT and PAT of HFr groups. Proinflammatory genes of Tnf-α, Mcp-1 and leptin were significantly upregulated while IL-10 and adiponectin, known as antiinflammatory genes, were reduced after the exposures. CAPs and O3 exposures also induced an increase in inducible nitric oxide synthase (iNOS) protein expression, and decrease in mitochondrial area in EAT and PAT. We also found significant increases in macrophages of HFr-O3 rats. The synergetic interaction of HFr and dirty air exposure on the inflammation was found in most of the experiments. Surprisingly, exposure to CAPs or O3 induced more significant inflammation and oxidative stress than co-exposure of CAPs and O3 in EAT and PAT.ConclusionEAT and PAT are both white adipose tissues. Short-term exposure to CAPs and O3, especially with high fructose diet, induced inflammation and oxidative stress in EAT and PAT in rats. These findings may provide a link between air-pollution exposure and accelerated susceptibility to cardiovascular disease and metabolic complications.
Lupus develops when genetically predisposed people encounter environmental agents such as UV light, silica, infections and cigarette smoke that cause oxidative stress, but how oxidative damage modifies the immune system to cause lupus flares is unknown. We previously showed that oxidizing agents decreased ERK pathway signaling in human T cells, decreased DNA methyltransferase 1 and caused demethylation and overexpression of genes similar to those from patients with active lupus. The current study tested whether oxidant-treated T cells can induce lupus in mice. We adoptively transferred CD4+ T cells treated in vitro with oxidants hydrogen peroxide or nitric oxide or the demethylating agent 5-azacytidine into syngeneic mice and studied the development and severity of lupus in the recipients. Disease severity was assessed by measuring anti-dsDNA antibodies, proteinuria, hematuria and by histopathology of kidney tissues. The effect of the oxidants on expression of CD40L, CD70, KirL1 and DNMT1 genes and CD40L protein in the treated CD4+ T cells was assessed by Q-RT-PCR and flow cytometry. H2O2 and ONOO− decreased Dnmt1 expression in CD4+ T cells and caused the upregulation of genes known to be suppressed by DNA methylation in patients with lupus and animal models of SLE. Adoptive transfer of oxidant-treated CD4+ T cells into syngeneic recipients resulted in the induction of anti-dsDNA antibody and glomerulonephritis. The results show that oxidative stress may contribute to lupus disease by inhibiting ERK pathway signaling in T cells leading to DNA demethylation, upregulation of immune genes and autoreactivity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.