13 results found
Jain P, Vineis P, Liquet B, et al., 2018, A multivariate approach to investigate the combined biological effects of multiple exposures., J Epidemiol Community Health
Epidemiological studies provide evidence that environmental exposures may affect health through complex mixtures. Formal investigation of the effect of exposure mixtures is usually achieved by modelling interactions, which relies on strong assumptions relating to the identity and the number of the exposures involved in such interactions, and on the order and parametric form of these interactions. These hypotheses become difficult to formulate and justify in an exposome context, where influential exposures are numerous and heterogeneous. To capture both the complexity of the exposome and its possibly pleiotropic effects, models handling multivariate predictors and responses, such as partial least squares (PLS) algorithms, can prove useful. As an illustrative example, we applied PLS models to data from a study investigating the inflammatory response (blood concentration of 13 immune markers) to the exposure to four disinfection by-products (one brominated and three chlorinated compounds), while swimming in a pool. To accommodate the multiple observations per participant (n=60; before and after the swim), we adopted a multilevel extension of PLS algorithms, including sparse PLS models shrinking loadings coefficients of unimportant predictors (exposures) and/or responses (protein levels). Despite the strong correlation among co-occurring exposures, our approach identified a subset of exposures (n=3/4) affecting the exhaled levels of 8 (out of 13) immune markers. PLS algorithms can easily scale to high-dimensional exposures and responses, and prove useful for exposome research to identify sparse sets of exposures jointly affecting a set of (selected) biological markers. Our descriptive work may guide these extensions for higher dimensional data.
van Veldhoven K, Keski-Rahkonen P, Barupal DK, et al., 2018, Effects of exposure to water disinfection by-products in a swimming pool: A metabolome-wide association study, ENVIRONMENT INTERNATIONAL, Vol: 111, Pages: 60-70, ISSN: 0160-4120
Demetriou CA, van Veldhoven K, Relton C, et al., 2015, Biological embedding of early-life exposures and disease risk in humans: a role for DNA methylation, EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, Vol: 45, Pages: 303-332, ISSN: 0014-2972
Stringhini S, Polidoro S, Sacerdote C, et al., 2015, Life-course socioeconomic status and DNA methylation of genes regulating inflammation, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 44, Pages: 1320-1330, ISSN: 0300-5771
van Veldhoven K, Polidoro S, Baglietto L, et al., 2015, Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis, CLINICAL EPIGENETICS, Vol: 7, ISSN: 1868-7083
Tsilidis KK, Capothanassi D, Allen NE, et al., 2014, Metformin Does Not Affect Cancer Risk: A Cohort Study in the UK Clinical Practice Research Datalink Analyzed Like an Intention-to-Treat Trial, DIABETES CARE, Vol: 37, Pages: 2522-2532, ISSN: 0149-5992
van Veldhoven K, Rahman S, Vineis P, 2014, Epigenetics and epidemiology: models of study and examples., Pages: 241-255
Epidemiological studies have successfully identified several environmental causes of disease, but often these studies are limited by methodological problems (e.g. lack of sensitivity and specificity in exposure assessment; confounding). Proposed approaches to improve observational studies of environmental associations are Mendelian randomization and the meet-in-the-middle (MITM) approach. The latter uses signals from the growing field of -omics as putative intermediate biomarkers in the pathogenetic process that links exposure with disease. The first part of this approach consists in the association between exposure and disease. The next step consists in the study of the relationship between (biomarkers of) exposure and intermediate -omic biomarkers of early effect; thirdly, the relation between the disease outcome and intermediate -omic biomarkers is assessed. We propose that when an association is found in all three steps it is possible that there is a casual association. One of the associations that have been investigated extensively in the recent years but is not completely understood is that between environmental endocrine disruptors and breast cancer. Here we present an example of how the "meet-in-the-middle" approach can be used to address the role of endocrine disruptors, by reviewing the relevant literature.
Demetriou CA, Chen J, Polidoro S, et al., 2013, Methylome Analysis and Epigenetic Changes Associated with Menarcheal Age, PLOS ONE, Vol: 8, ISSN: 1932-6203
Herceg Z, Lambert M-P, van Veldhoven K, et al., 2013, Towards incorporating epigenetic mechanisms into carcinogen identification and evaluation, CARCINOGENESIS, Vol: 34, Pages: 1955-1967, ISSN: 0143-3334
Shenker NS, Polidoro S, van Veldhoven K, et al., 2013, Epigenome-wide association study in the European Prospective Investigation into Cancer and Nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking, HUMAN MOLECULAR GENETICS, Vol: 22, Pages: 843-851, ISSN: 0964-6906
Shenker NS, Ueland PM, Polidoro S, et al., 2013, DNA Methylation as a Long-term Biomarker of Exposure to Tobacco Smoke, EPIDEMIOLOGY, Vol: 24, Pages: 712-716, ISSN: 1044-3983
Vineis P, van Veldhoven K, Chadeau-Hyam M, et al., 2013, Advancing the application of omics-based biomarkers in environmental epidemiology, ENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Vol: 54, Pages: 461-467, ISSN: 0893-6692
van Veldhoven CM, Khan AE, Teucher B, et al., 2011, Physical activity and lymphoid neoplasms in the European Prospective Investigation into Cancer and nutrition (EPIC), EUROPEAN JOURNAL OF CANCER, Vol: 47, Pages: 748-760, ISSN: 0959-8049
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