Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking.

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

Epigenome-wide association study of adiposity and future risk of obesity-related diseases.

BACKGROUND: Obesity is an established risk factor for several common chronic diseases such as breast and colorectal cancer, metabolic and cardiovascular diseases; however, the biological basis for these relationships is not fully understood. To explore the association of obesity with these conditions, we investigated peripheral blood leucocyte (PBL) DNA methylation markers for adiposity and their contribution to risk of incident breast and colorectal cancer and myocardial infarction. METHODS: DNA methylation profiles (Illumina Infinium® HumanMethylation450 BeadChip) from 1941 individuals from four population-based European cohorts were analysed in relation to body mass index, waist circumference, waist-hip and waist-height ratio within a meta-analytical framework. In a subset of these individuals, data on genome-wide gene expression level, biomarkers of glucose and lipid metabolism were also available. Validation of methylation markers associated with all adiposity measures was performed in 358 individuals. Finally, we investigated the association of obesity-related methylation marks with breast, colorectal cancer and myocardial infarction within relevant subsets of the discovery population. RESULTS: We identified 40 CpG loci with methylation levels associated with at least one adiposity measure. Of these, one CpG locus (cg06500161) in ABCG1 was associated with all four adiposity measures (P = 9.07×10-8 to 3.27×10-18) and lower transcriptional activity of the full-length isoform of ABCG1 (P = 6.00×10-7), higher triglyceride levels (P = 5.37×10-9) and higher triglycerides-to-HDL cholesterol ratio (P = 1.03×10-10). Of the 40 informative and obesity-related CpG loci, two (in IL2RB and FGF18) were significantly associated with colorectal cancer (inversely, P < 1.6×10-3) and one intergenic locus on chromosome 1 was inversely associated with myocardial infarction (P < 1.25×10-3), independently of obesity and established risk factors. CONCLUSION: Our results suggest that epigenetic changes, in particular altered DNA methylation patterns, may be an intermediate biomarker at the intersection of obesity and obesity-related diseases, and could offer clues as to underlying biological mechanisms.

Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted 1H NMR Metabolic Profiling.

1H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled = 4 × 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled = 2 × 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled = 7 × 10-16 and 1 × 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled = 2 × 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled = 5 × 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity ≤ 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation.

Several studies have recently identified strong epigenetic signals related to tobacco smoking. However, an aspect that did not receive much attention is the evolution of epigenetic changes with time since smoking cessation. We conducted a series of epigenome-wide association studies to capture the dynamics of smoking-induced epigenetic changes after smoking cessation, using genome-wide methylation profiles obtained from blood samples in 745 women from 2 European populations. Two distinct classes of CpG sites were identified: sites whose methylation reverts to levels typical of never smokers within decades after smoking cessation, and sites remaining differentially methylated, even more than 35 years after smoking cessation. Our results suggest that the dynamics of methylation changes following smoking cessation are driven by a differential and site-specific magnitude of the smoking-induced alterations (with persistent sites being most affected) irrespective of the intensity and duration of smoking. Analyses of the link between methylation and expression levels revealed that methylation predominantly and remotely down-regulates gene expression. Among genes whose expression was associated with our candidate CpG sites, LRRN3 appeared to be particularly interesting as it was one of the few genes whose methylation and expression were directly associated, and the only gene in which both methylation and gene expression were found associated with smoking. Our study highlights persistent epigenetic markers of smoking, which can potentially be detected decades after cessation. Such historical signatures are promising biomarkers to refine individual risk profiling of smoking-induced chronic disease such as lung cancer.

R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses.

Technological advances in molecular biology over the past decade have given rise to high dimensional and complex datasets offering the possibility to investigate biological associations between a range of genomic features and complex phenotypes. The analysis of this novel type of data generated unprecedented computational challenges which ultimately led to the definition and implementation of computationally efficient statistical models that were able to scale to genome-wide data, including Bayesian variable selection approaches. While extensive methodological work has been carried out in this area, only few methods capable of handling hundreds of thousands of predictors were implemented and distributed. Among these we recently proposed GUESS, a computationally optimised algorithm making use of graphics processing unit capabilities, which can accommodate multiple outcomes. In this paper we propose R2GUESS, an R package wrapping the original C++ source code. In addition to providing a user-friendly interface of the original code automating its parametrisation, and data handling, R2GUESS also incorporates many features to explore the data, to extend statistical inferences from the native algorithm (e.g., effect size estimation, significance assessment), and to visualize outputs from the algorithm. We first detail the model and its parametrisation, and describe in details its optimised implementation. Based on two examples we finally illustrate its statistical performances and flexibility.

Differentially methylated microRNAs in prediagnostic samples of subjects who developed breast cancer in the European Prospective Investigation into Nutrition and Cancer (EPIC-Italy) cohort.

The crosstalk between microRNAs (miRNAs) and other epigenetic factors may lead to novel hypotheses about carcinogenesis identifying new targets for research. Because a single miRNA can regulate multiple downstream target genes, its altered expression may potentially be a sensitive biomarker to detect early malignant transformation and improve diagnosis and prognosis. In the current study, we tested the hypothesis that altered methylation of miRNA encoding genes, associated with deregulated mature miRNA expression, may be related to dietary and lifestyle factors and may contribute to cancer development. In a case-control study nested in a prospective cohort (EPIC-Italy), we analysed DNA methylation levels of miRNA encoding genes (2191 CpG probes related to 517 genes) that are present in the Infinium Human Methylation450 BeadChip array in prediagnostic peripheral white blood cells of subjects who developed colorectal cancer (CRC, n = 159) or breast cancer (BC, n = 166) and matched subjects who remained clinically healthy. In the whole cohort, several differentially methylated miRNA genes were observed in association with age, sex, smoking habits and physical activity. Interestingly, in the case-control study, eight differentially methylated miRNAs were identified in subjects who went on to develop BC (miR-328, miR-675, miR-1307, miR-1286, miR-1275, miR-1910, miR-24-1 and miR-548a-1; all Bonferroni-adjusted P < 0.05). No significant associations were found with CRC. Assuming that altered methylation of miRNAs detectable in blood may be present before diagnosis, it may represent a biomarker for early detection or risk of cancer and may help to understand the cascade of events preceding tumour onset.

Dynamics of the risk of smoking-induced lung cancer: a compartmental hidden Markov model for longitudinal analysis.

BACKGROUND: To account for the dynamic aspects of carcinogenesis, we propose a compartmental hidden Markov model in which each person is healthy, asymptomatically affected, diagnosed, or deceased. Our model is illustrated using the example of smoking-induced lung cancer. METHODS: The model was fitted on a case-control study nested in the European Prospective Investigation into Cancer and Nutrition study, including 757 incident cases and 1524 matched controls. Estimation was done through a Markov Chain Monte Carlo algorithm, and simulations based on the posterior estimates of the parameters were used to provide measures of model fit. We performed sensitivity analyses to assess robustness of our findings. RESULTS: After adjusting for its impact on exposure duration, age was not found to independently drive the risk of lung carcinogenesis, whereas age at starting smoking in ever-smokers and time since cessation in former smokers were found to be influential. Our data did not support an age-dependent time to diagnosis. The estimated time between onset of malignancy and clinical diagnosis ranged from 2 to 4 years. Our approach yielded good performance in reconstructing individual trajectories in both cases (sensitivity >90%) and controls (sensitivity >80%). CONCLUSION: Our compartmental model enabled us to identify time-varying predictors of risk and provided us with insights into the dynamics of smoking-induced lung carcinogenesis. Its flexible and general formulation enables the future incorporation of disease states, as measured by intermediate markers, into the modeling of the natural history of cancer, suggesting a large range of applications in chronic disease epidemiology.

Effects of bisphosphonate treatment on DNA methylation in osteonecrosis of the jaw.

Bisphosphonates are used in the treatment of hypocalcaemia, mainly in cancer and osteoporosis. Some patients experience adverse events, such as BP-related osteonecrosis of the jaw (BRONJ). DNA methylation plays a key role in gene regulation in many tissues, but its involvement in bone homeostasis is not well characterized, and no information is available regarding altered methylation in BRONJ. Using the Illumina Infinium HumanMethylation27 BeadChip assay, we performed an epigenome-wide association study in peripheral blood samples from 68 patients treated with nitrogenous BP, including 35 with BRONJ. Analysis of the estimated cumulative BP exposure distribution indicated that the exposure of the case group to BP was slightly higher than that of the control group; more severely affected cases (i.e., with BRONJ in both mandible and maxilla) were significantly more exposed to BP than were those with BRONJ only in the mandible or maxilla (one-sided Wilcoxon rank sum test, p=0.002). Logistic regression analysis confirmed the positive association between cumulative bisphosphonates exposure and risk of BRONJ (OR 1.015 per mg of cumulative exposure, 95% CI 1.004-1.032, p=0.036). Although no statistically significant differences were observed between case and control groups, methylation levels of probes mapping on three genes, ERCC8, LEPREL1 and SDC2, were strongly associated with cumulative BP exposure levels (p<1.31E-007). Enrichment analysis, combining differentially methylated genes with genes involved in the mevalonate pathway, showed that BP treatment can affect the methylation pattern of genes involved in extracellular matrix organization and inflammatory responses, leading to more frequent adverse effects such as BRONJ. Differences in DNA methylation induced by BP treatment could be involved in the pathogenesis of the bone lesion.

DNA methylation and associated gene expression in blood prior to lung cancer diagnosis in the Norwegian Women and Cancer cohort.

The majority of lung cancer is caused by tobacco smoking, and lung cancer-relevant epigenetic markers have been identified in relation to smoking exposure. Still, smoking-related markers appear to mediate little of the effect of smoking on lung cancer. Thus in order to identify disease-relevant markers and enhance our understanding of pathways, a wide search is warranted. Through an epigenome-wide search within a case-control study (131 cases, 129 controls) nested in a Norwegian prospective cohort of women, we found 25 CpG sites associated with lung cancer. Twenty-three were classified as associated with smoking (LC-AwS), and two were classified as unassociated with smoking (LC-non-AwS), as they remained associated with lung cancer after stringent adjustment for smoking exposure using the comprehensive smoking index (CSI): cg10151248 (PC, CSI-adjusted odds ratio (OR) = 0.34 [0.23-0.52] per standard deviation change in methylation) and cg13482620 (B3GNTL1, CSI-adjusted OR = 0.33 [0.22-0.50]). Analysis among never smokers and a cohort of smoking-discordant twins confirmed the classification of the two LC-non-AwS CpG sites. Gene expression profiles demonstrated that the LC-AwS CpG sites had different enriched pathways than LC-non-AwS sites. In conclusion, using blood-derived DNA methylation and gene expression profiles from a prospective lung cancer case-control study in women, we identified 25 CpG lung cancer markers prior to diagnosis, two of which were LC-non-AwS markers and related to distinct pathways.

DNA methylation and exposure to ambient air pollution in two prospective cohorts.

Long-term exposure to air pollution has been associated with several adverse health effects including cardiovascular, respiratory diseases and cancers. However, underlying molecular alterations remain to be further investigated. The aim of this study is to investigate the effects of long-term exposure to air pollutants on (a) average DNA methylation at functional regions and, (b) individual differentially methylated CpG sites. An assumption is that omic measurements, including the methylome, are more sensitive to low doses than hard health outcomes. This study included blood-derived DNA methylation (Illumina-HM450 methylation) for 454 Italian and 159 Dutch participants from the European Prospective Investigation into Cancer and Nutrition (EPIC). Long-term air pollution exposure levels, including NO2, NOx, PM2.5, PMcoarse, PM10, PM2.5 absorbance (soot) were estimated using models developed within the ESCAPE project, and back-extrapolated to the time of sampling when possible. We meta-analysed the associations between the air pollutants and global DNA methylation, methylation in functional regions and epigenome-wide methylation. CpG sites found differentially methylated with air pollution were further investigated for functional interpretation in an independent population (EnviroGenoMarkers project), where (N=613) participants had both methylation and gene expression data available. Exposure to NO2 was associated with a significant global somatic hypomethylation (p-value=0.014). Hypomethylation of CpG island's shores and shelves and gene bodies was significantly associated with higher exposures to NO2 and NOx. Meta-analysing the epigenome-wide findings of the 2 cohorts did not show genome-wide significant associations at single CpG site level. However, several significant CpG were found if the analyses were separated by countries. By regressing gene expression levels against methylation levels of the exposure-related CpG sites, we identified several significant CpG-transcript pairs and highlighted 5 enriched pathways for NO2 and 9 for NOx mainly related to the immune system and its regulation. Our findings support results on global hypomethylation associated with air pollution, and suggest that the shores and shelves of CpG islands and gene bodies are mostly affected by higher exposure to NO2 and NOx. Functional differences in the immune system were suggested by transcriptome analyses.

A life course approach to explore the biological embedding of socioeconomic position and social mobility through circulating inflammatory markers.

Lower socioeconomic position (SEP) has consistently been associated with poorer health. To explore potential biological embedding and the consequences of SEP experiences from early life to adulthood, we investigate how SEP indicators at different points across the life course may be related to a combination of 28 inflammation markers. Using blood-derived inflammation profiles measured by a multiplex array in 268 participants from the Italian component of the European Prospective Investigation into Cancer and Nutrition cohort, we evaluate the association between early life, young adulthood and later adulthood SEP with each inflammatory markers separately, or by combining them into an inflammatory score. We identified an increased inflammatory burden in participants whose father had a manual occupation, through increased plasma levels of CSF3 (G-CSF; ß = 0.29; P = 0.002), and an increased inflammatory score (ß = 1.96; P = 0.029). Social mobility was subsequently modelled by the interaction between father's occupation and the highest household occupation, revealing a significant difference between "stable Non-manual" profiles over the life course versus "Manual to Non-manual" profiles (ß = 2.38, P = 0.023). Low SEP in childhood is associated with modest increase in adult inflammatory burden; however, the analysis of social mobility suggests a stronger effect of an upward social mobility over the life course.

Biological marks of early-life socioeconomic experience is detected in the adult inflammatory transcriptome.

Consistent evidence is accumulating to link lower socioeconomic position (SEP) and poorer health, and the inflammatory system stands out as a potential pathway through which socioeconomic environment is biologically embedded. Using bloodderived genome-wide transcriptional profiles from 268 Italian participants of the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we evaluated the association between early life, young and later adulthood SEP and the expression of 845 genes involved in human inflammatory responses. These were examined individually and jointly using several inflammatory scores. Our results consistently show that participants whose father had a manual (as compared to nonmanual) occupation exhibit, later in life, a higher inflammatory score, hence indicating an overall increased level of expression for the selected inflammatory-related genes. Adopting a life course approach, these associations remained statistically significant upon adjustment for later-in-life socioeconomic experiences. Sensitivity analyses indicated that our findings were not affected by the way the inflammatory score was calculated, and were replicated in an independent study. Our study provides additional evidence that childhood SEP is associated with a sustainable upregulation of the inflammatory transcriptome, independently of subsequent socioeconomic experiences. Our results support the hypothesis that early social inequalities impacts adult physiology.

Epigenetic signatures of internal migration in Italy.

BACKGROUND: Observational studies have suggested that the risks of non-communicable diseases in voluntary migrants become similar to those in the host population after one or more generations, supporting the hypothesis that these diseases have a predominantly environmental (rather than inherited) origin. However, no study has been conducted thus far to identify alterations at the molecular level that might mediate these changes in disease risk after migration. METHODS: Using genome-wide DNA methylation profiles from more than 1000 Italian participants, we conducted an epigenome-wide association study (EWAS) to identify differences between south-to-north migrants and their origin (southern natives) and host (north-western natives) populations. RESULTS: We identified several differentially methylated CpG loci, in particular when comparing south-to-north migrants with north-western natives. We hypothesise that these alterations may underlie an adaptive response to exposure differentials that exist between origin and host populations. CONCLUSIONS: Our study is the first large agnostic investigation of DNA methylation changes linked to migratory processes, and shows the potential of EWAS to investigate their biological effects.

Life-course socioeconomic status and DNA methylation of genes regulating inflammation.

BACKGROUND: In humans, low socioeconomic status (SES) across the life course is associated with greater diurnal cortisol production, increased inflammatory activity and higher circulating antibodies for several pathogens, all suggesting a dampened immune response. Recent evidence suggests that DNA methylation of pro-inflammatory genes may be implicated in the biological embedding of the social environment. METHODS: The present study examines the association between life-course SES and DNA methylation of candidate genes, selected on the basis of their involvement in SES-related inflammation, in the context of a genome-wide methylation study. Participants were 857 healthy individuals sampled from the EPIC Italy prospective cohort study. RESULTS: Indicators of SES were associated with DNA methylation of genes involved in inflammation. NFATC1, in particular, was consistently found to be less methylated in individuals with low vs high SES, in a dose-dependent manner. IL1A, GPR132 and genes belonging to the MAPK family were also less methylated among individuals with low SES. In addition, associations were found between SES and CXCL2 and PTGS2, but these genes were consistently more methylated among low SES individuals. CONCLUSIONS: Our findings support the hypothesis that the social environment leaves an epigenetic signature in cells. Although the functional significance of SES-related DNA methylation is still unclear, we hypothesize that it may link SES to chronic disease risk.

Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts.

DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case-control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31-0.54, P-value=3.3 × 10(-11)) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31-0.56, P-value=3.9 × 10(-10)), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case-control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.

Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation.

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study.

BACKGROUND: Indian Asians, who make up a quarter of the world's population, are at high risk of developing type 2 diabetes. We investigated whether DNA methylation is associated with future type 2 diabetes incidence in Indian Asians and whether differences in methylation patterns between Indian Asians and Europeans are associated with, and could be used to predict, differences in the magnitude of risk of developing type 2 diabetes. METHODS: We did a nested case-control study of DNA methylation in Indian Asians and Europeans with incident type 2 diabetes who were identified from the 8-year follow-up of 25 372 participants in the London Life Sciences Prospective Population (LOLIPOP) study. Patients were recruited between May 1, 2002, and Sept 12, 2008. We did epigenome-wide association analysis using samples from Indian Asians with incident type 2 diabetes and age-matched and sex-matched Indian Asian controls, followed by replication testing of top-ranking signals in Europeans. For both discovery and replication, DNA methylation was measured in the baseline blood sample, which was collected before the onset of type 2 diabetes. Epigenome-wide significance was set at p<1 × 10(-7). We compared methylation levels between Indian Asian and European controls without type 2 diabetes at baseline to estimate the potential contribution of DNA methylation to increased risk of future type 2 diabetes incidence among Indian Asians. FINDINGS: 1608 (11·9%) of 13 535 Indian Asians and 306 (4·3%) of 7066 Europeans developed type 2 diabetes over a mean of 8·5 years (SD 1·8) of follow-up. The age-adjusted and sex-adjusted incidence of type 2 diabetes was 3·1 times (95% CI 2·8-3·6; p<0·0001) higher among Indian Asians than among Europeans, and remained 2·5 times (2·1-2·9; p<0·0001) higher after adjustment for adiposity, physical activity, family history of type 2 diabetes, and baseline glycaemic measures. The mean absolute difference in methylation level between type 2 diabetes cases and controls ranged from 0·5% (SD 0·1) to 1·1% (0·2). Methylation markers at five loci were associated with future type 2 diabetes incidence; the relative risk per 1% increase in methylation was 1·09 (95% CI 1·07-1·11; p=1·3 × 10(-17)) for ABCG1, 0·94 (0·92-0·95; p=4·2 × 10(-11)) for PHOSPHO1, 0·94 (0·92-0·96; p=1·4 × 10(-9)) for SOCS3, 1·07 (1·04-1·09; p=2·1 × 10(-10)) for SREBF1, and 0·92 (0·90-0·94; p=1·2 × 10(-17)) for TXNIP. A methylation score combining results for the five loci was associated with future type 2 diabetes incidence (relative risk quartile 4 vs quartile 1 3·51, 95% CI 2·79-4·42; p=1·3 × 10(-26)), and was independent of established risk factors. Methylation score was higher among Indian Asians than Europeans (p=1 × 10(-34)). INTERPRETATION: DNA methylation might provide new insights into the pathways underlying type 2 diabetes and offer new opportunities for risk stratification and prevention of type 2 diabetes among Indian Asians. FUNDING: The European Union, the UK National Institute for Health Research, the Wellcome Trust, the UK Medical Research Council, Action on Hearing Loss, the UK Biotechnology and Biological Sciences Research Council, the Oak Foundation, the Economic and Social Research Council, Helmholtz Zentrum Munchen, the German Research Center for Environmental Health, the German Federal Ministry of Education and Research, the German Center for Diabetes Research, the Munich Center for Health Sciences, the Ministry of Science and Research of the State of North Rhine-Westphalia, and the German Federal Ministry of Health.

Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers.

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.

Methylome analysis and epigenetic changes associated with menarcheal age.

Reproductive factors have been linked to both breast cancer and DNA methylation, suggesting methylation as an important mechanism by which reproductive factors impact on disease risk. However, few studies have investigated the link between reproductive factors and DNA methylation in humans. Genome-wide methylation in peripheral blood lymphocytes of 376 healthy women from the prospective EPIC study was investigated using LUminometric Methylation Assay (LUMA). Also, methylation of 458877 CpG sites was additionally investigated in an independent group of 332 participants of the EPIC-Italy sub-cohort, using the Infinium HumanMethylation 450 BeadChip. Multivariate logistic regression and linear models were used to investigate the association between reproductive risk factors and genome wide and CpG-specific DNA methylation, respectively. Menarcheal age was inversely associated with global DNA methylation as measured with LUMA. For each yearly increase in age at menarche, the risk of having genome wide methylation below median level was increased by 32% (OR:1.32, 95%CI:1.14-1.53). When age at menarche was treated as a categorical variable, there was an inverse dose-response relationship with LUMA methylation levels (OR(12-14 vs. ≤11 yrs):1.78, 95%CI:1.01-3.17 and OR(≥15 vs. ≤11 yrs):4.59, 95%CI:2.04-10.33; P for trend<0.0001). However, average levels of global methylation as measured by the Illumina technology were not significantly associated with menarcheal age. In locus by locus comparative analyses, only one CpG site had significantly different methylation depending on the menarcheal age category examined, but this finding was not replicated by pyrosequencing in an independent data set. This study suggests a link between age at menarche and genome wide DNA methylation, and the difference in results between the two arrays suggests that repetitive element methylation has a role in the association. Epigenetic changes may be modulated by menarcheal age, or the association may be a mirror of other important changes in early life that have a detectable effect on both methylation levels and menarcheal age.