Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

The association between ambient air pollution exposure and hospitalization for cardiovascular events has been reported in several studies with conflicting results. A case-crossover design was used to investigate the effects of air pollution in 660 first-time myocardial infarction cases in Stockholm in 1993–1994, interviewed shortly after diagnosis using a standard protocol. Air pollution data came from central urban background monitors. No associations were observed between the risk for onset of myocardial infarction and two-hour or 24-hour air pollution exposure. No evidence of susceptible subgroups was found. This study provides no support that moderately elevated air pollution levels trigger first-time myocardial infarction.

Background

Evidence suggests that cardiovascular effects of air pollution are mediated by inflammation and that air pollution can induce genetic expression of the interleukin-6 gene (IL6).

Objectives

We investigated whether IL6 and fibrinogen gene variants can affect plasma IL-6 responses to air pollution in patients with cardiovascular disease.

Methods

We repeatedly determined plasma IL-6 in 955 myocardial infarction survivors from six European cities (n = 5,539). We conducted city-specific analyses using additive mixed models adjusting for patient characteristics, time trend, and weather to assess the impact of air pollutants on plasma IL-6. We pooled city-specific estimates using meta-analysis methodology. We selected three IL6 single-nucleotide polymorphisms (SNPs) and one SNP each from the fibrinogen α-chain gene (FGA) and β-chain gene (FGB) for gene–environment analyses.

Results

We found the most consistent modifications for variants in IL6 rs2069832 and FBG rs1800790 after exposure to carbon monoxide (CO; 24-hr average; p-values for interaction, 0.034 and 0.019, respectively). Nitrogen dioxide effects were consistently modified, but p-values for interaction were larger (0.09 and 0.19, respectively). The strongest effects were seen 6–11 hr after exposure, when, for example, the overall effect of a 2.2% increase in IL-6 per 0.64 mg/m3 CO was modified to a 10% (95% confidence interval, 4.6–16%) increase in IL-6 (p-value for interaction = 0.002) for minor homozygotes of FGB rs1800790.

Conclusions

The effect of gaseous traffic-related air pollution on inflammation may be stronger in genetic subpopulations with ischemic heart disease. This information could offer an opportunity to identify postinfarction patients who would benefit more than others from a cleaner environment and antiinflammatory treatment.

Background

Air pollutants may induce airway inflammation and sensitization due to generation of reactive oxygen species. The genetic background to these mechanisms could be important effect modifiers.

Objective

Our goal was to assess interactions between exposure to air pollution and single nucleotide polymorphisms (SNPs) in the β2-adrenergic receptor (ADRB2), glutathione S-transferase P1 (GSTP1), and tumor necrosis factor (TNF) genes for development of childhood allergic disease.

Methods

In a birth cohort originally of 4,089 children, we assessed air pollution from local traffic using nitrogen oxides (traffic NOx) as an indicator based on emission databases and dispersion modeling and estimated individual exposure through geocoding of home addresses. We measured peak expiratory flow rates and specific IgE for inhalant and food allergens at 4 years of age, and selected children with asthma symptoms up to 4 years of age (n = 542) and controls (n = 542) for genotyping.

Results

Interaction effects on allergic sensitization were indicated between several GSTP1 SNPs and traffic NOx exposure during the first year of life (pnominal < 0.001–0.06). Children with Ile105Val/Val105Val genotypes were at increased risk of sensitization to any allergen when exposed to elevated levels of traffic NOx (for a difference between the 5th and 95th percentile of exposure: odds ratio = 2.4; 95% confidence interval, 1.0–5.3). In children with TNF-308 GA/AA genotypes, the GSTP1–NOx interaction effect was even more pronounced. We observed no conclusive interaction effects for ADRB2.

Conclusion

The effect of air pollution from traffic on childhood allergy appears to be modified by GSTP1 and TNF variants, supporting a role of genes controlling the antioxidative system and inflammatory response in allergy.

Background

Short-term fluctuations of ambient air pollution have been associated with exacerbation of cardiovascular disease. A multi-city study was designed to assess the probability of recurrent hospitalization in a cohort of incident myocardial infarction survivors in five European cities. The objective of this paper is to discuss the methods for analyzing short-term health effects in a cohort study based on a case-series.

Methods

Three methods were considered for the analyses of the cohort data: Poisson regression approach, case-crossover analyses and extended Cox regression analyses. The major challenge of these analyses is to appropriately consider changes within the cohort over time due to changes in the underlying risk following a myocardial infarction, slow time trends in risk factors within the population, dynamic cohort size and seasonal variation.

Results

Poisson regression analyses, case-crossover analyses and Extended Cox regression analyses gave similar results. Application of smoothing methods showed the capability to adequately model the complex time trends.

Conclusion

From a practical point of view, Poisson regression analyses are less time-consuming, and therefore might be used for confounder selection and most of the analyses. However, replication of the results with Cox models is desirable to assure that the results are independent of the analytical approach used. In addition, extended Cox regression analyses would allow a joint estimation of long-term and short-term health effects of time-varying exposures.

Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as “genetically matched controls” for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify “axes” of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study. Genet. Epidemiol. 34: 319–326, 2010. © 2010 Wiley-Liss, Inc.