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- Beelen, Rob, et al.
(författare)
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Natural-Cause Mortality and Long-Term Exposure to Particle Components : An Analysis of 19 European Cohorts within the Multi-Center ESCAPE Project
- 2015
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Ingår i: Journal of Environmental Health Perspectives. - : Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 123:6, s. 525-533
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Forskningsöversikt (refereegranskat)abstract
- Background: Studies have shown associations between mortality and long-term exposure to particulate matter air pollution. Few cohort studies have estimated the effects of the elemental composition of particulate matter on mortality. Objectives: Our aim was to study the association between natural-cause mortality and long-term exposure to elemental components of particulate matter. Methods: Mortality and confounder data from 19 European cohort studies were used. Residential exposure to eight a priori-selected components of particulate matter ( PM) was characterized following a strictly standardized protocol. Annual average concentrations of copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM size fractions <= 2.5 mu m (PM2.5) and <= 10 mu m (PM10) were estimated using land-use regression models. Cohort-specific statistical analyses of the associations between mortality and air pollution were conducted using Cox proportional hazards models using a common protocol followed by meta-analysis. Results: The total study population consisted of 291,816 participants, of whom 25,466 died from a natural cause during follow-up (average time of follow-up, 14.3 years). Hazard ratios were positive for almost all elements and statistically significant for PM2.5 sulfur (1.14; 95% CI: 1.06, 1.23 per 200ng/m(3)). In a two-pollutant model, the association with PM2.5 sulfur was robust to adjustment for PM2.5 mass, whereas the association with PM2.5 mass was reduced. Conclusions: Long-term exposure to PM2.5 sulfur was associated with natural-cause mortality. This association was robust to adjustment for other pollutants and PM2.5.
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- Wessel, Jennifer, et al.
(författare)
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Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
- 2015
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF = 1.4%) with lower FG (beta = -0.09 +/- 0.01 mmol l(-1), P = 3.4 x 10(-12)), T2D risk (OR[95% CI] = 0.86[0.76-0.96], P = 0.010), early insulin secretion (beta = -0.07 +/- 0.035 pmol(insulin) mmol(glucose)(-1), P = 0.048), but higher 2-h glucose (beta = 0.16 +/- 0.05 mmol l(-1), P = 4.3 x 10(-4)). We identify a gene-based association with FG at G6PC2 (p(SKAT) = 6.8 x 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF = 20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (beta = 0.02 +/- 0.004 mmol l(-1), P = 1.3 x 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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- Viallon, Vivian, et al.
(författare)
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A new pipeline for the normalization and pooling of metabolomics data
- 2021
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Ingår i: Metabolites. - : MDPI. - 2218-1989 .- 2218-1989. ; 11:9
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Tidskriftsartikel (refereegranskat)abstract
- Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
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- Xun, Wei Wei, et al.
(författare)
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Single-nucleotide polymorphisms (5p15.33, 15q25.1, 6p22.1, 6q27 and 7p15.3) and lung cancer survival in the European Prospective Investigation into Cancer and Nutrition (EPIC).
- 2011
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Ingår i: Mutagenesis. - : Oxford University Press (OUP). - 0267-8357 .- 1464-3804. ; 26:5, s. 657-666
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Tidskriftsartikel (refereegranskat)abstract
- The single-nucleotide polymorphisms (SNPs) rs402710 (5p15.33), rs16969968 and rs8034191 (15q25.1) have been consistently identified by genome-wide association studies (GWAS) as significant predictors of lung cancer risk, while rs4324798 (6p22.1) was previously found to influence survival time in small-cell lung cancer (SCLC) patients. Using the same population of one of the original GWAS, we investigated whether the selected SNPs and 31 others (also identified in GWAS) influence survival time, assuming an additive model. The effect of each polymorphism on all cause survival was estimated in 1094 lung cancer patients, and lung cancer-specific survival in 763 patients, using Cox regression adjusted for a priori confounders and competing causes of death where appropriate. Overall, after 1558 person-years of post-diagnostic follow-up, 874 deaths occurred from all causes, including 690 from lung cancer. In the lung cancer-specific survival analysis (1102 person-years), only rs7452888 (6q27) and rs2710994 (7p15.3) modified survival, with adjusted hazard ratios of 1.19 (P = 0.009) and 1.32 (P = 0.011) respectively, taking competing risks into account. Some weak associations were identified in subgroup analysis for rs16969968 and rs8034191 (15q25.1) and rs4324798 (6p22.1) and survival in never-smokers, as well as for rs402710 in current smokers and SCLC patients. In conclusion, rs402710 (5p15.33), rs16969968 and rs8034191 (both 15q25.1) and rs4324798 (6p22.1) were found to be unrelated to survival times in this large cohort of lung cancer patients, regardless of whether the cause of death was from lung cancer or not. However, rs7452888 (6q27) was identified as a possible candidate SNP to influence lung cancer survival, while stratified analysis hinted at a possible role for rs8034191, rs16969968 (15q25.1) and rs4324798 (6p22.1) in influencing survival time in lung cancer patients who were never-smokers, based on a small sample.
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