A genome-wide association study provides insights into the genetic etiology of 57 essential and non-essential trace elements in humans.

Trace elements are important for human health but may exert toxic or adverse effects. Mechanisms of uptake, distribution, metabolism, and excretion are partly under genetic control but have not yet been extensively mapped. Here we report a comprehensive multi-element genome-wide association study of 57 essential and non-essential trace elements. We perform genome-wide association meta-analyses of 14 trace elements in up to 6564 Scandinavian whole blood samples, and genome-wide association studies of 43 trace elements in up to 2819 samples measured only in the Trøndelag Health Study (HUNT). We identify 11 novel genetic loci associated with blood concentrations of arsenic, cadmium, manganese, selenium, and zinc in genome-wide association meta-analyses. In HUNT, several genome-wide significant loci are also indicated for other trace elements. Using two-sample Mendelian randomization, we find several indications of weak to moderate effects on health outcomes, the most precise being a weak harmful effect of increased zinc on prostate cancer. However, independent validation is needed. Our current understanding of trace element-associated genetic variants may help establish consequences of trace elements on human health.

Associations of gene expression in blood with BMI and weight changes among women in the Norwegian Women and Cancer postgenome cohort.

OBJECTIVE: This study aimed to evaluate associations between blood gene expression profiles and (1) current BMI and (2) past weight changes (WCs) among women who had never been diagnosed with cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort. METHODS: This cross-sectional study (N = 1694) used gene expression profiles and information from three questionnaires: Q1 (baseline), Q2 (follow-up), and Q3 (blood collection). The authors performed gene-wise linear regression models to identify differentially expressed genes (DEGs) and functional enrichment analyses to identify their biological functions. RESULTS: When assessing BMIQ3 , the study observed 2394, 769, and 768 DEGs for the obesity-versus-normal weight, obesity-versus-overweight, and overweight-versus-normal weight comparisons, respectively. Up to 169 DEGs were observed when investigating WCQ3-Q1 (mean = 7 years, range = 5.5-14 years) and WCQ3-Q2 (mean = 1 year, range = <1 month-9 years) in interaction models with BMI categories, of which 1 to 169 genes were associated with WCs and 0 to 9 were associated with interaction effects of BMI and WCs. Biological functions of BMI-associated DEGs were linked to metabolism, erythrocytes, oxidative stress, and immune processes, whereas WC-associated DEGs were linked to signal transduction. CONCLUSIONS: Many BMI-associated but few WC-associated DEGs were identified in the blood of women in Norway. The biological functions of BMI-associated DEGs likely reflect systemic impacts of obesity, especially blood reticulocyte-erythrocyte ratio shifts.

Overall lifestyle changes in adulthood are associated with cancer incidence in the Norwegian Women and Cancer Study (NOWAC) - a prospective cohort study.

BACKGROUND: Cancer is a leading cause of premature death worldwide and incidence is expected to rise in the coming decades. Many cohort studies, measuring lifestyle factors at one time-point, have observed that overall healthy lifestyles were inversely related to cancer incidence. However, there is little knowledge on the impact of lifestyle modification within adulthood. METHODS: Using the Norwegian Women and Cancer study, two repeated self-reported assessments of lifestyle behaviours were used to calculate healthy lifestyle index scores at each time-point (N = 66 233). The associations between change in healthy lifestyle index score and lifestyle-related cancer incidence, including alcohol-, tobacco-, obesity-, and reproductive-related, and site-specific breast and colorectal cancer incidence were estimated using Cox proportional hazard regression models. To assess nonlinearity in the dose-response relationships, restricted cubic spline models were used. RESULTS: Independent of baseline lifestyle, positive lifestyle changes were inversely related to the incidence of overall lifestyle-related cancers, as well as alcohol-related, tobacco-related, obesity-related, and reproductive-related cancers, but not breast and colorectal site-specific cancers. An association between lifestyle worsening and cancer incidence compared to stable lifestyle was observed. CONCLUSIONS: This study provides evidence that overall lifestyle changes among cancer-free women between the ages of 41 and 76 impact the incidence of many cancer types. Regardless of baseline lifestyle, there was a negative dose-response relationship between magnitude of positive lifestyle change and the incidence of overall lifestyle-related cancers. We observed that underlying this trend was an especially clear association between lifestyle worsening and increased risk compared to stable lifestyle. For adult women, maintaining a stable healthy lifestyle and lifestyle improvement are important for preventing the occurrence of many cancer types.

Pre-diagnostic DNA methylation in blood leucocytes in cutaneous melanoma; a nested case-control study within the Norwegian Women and Cancer cohort.

The prognosis of cutaneous melanoma depends on early detection, and good biomarkers for melanoma risk may provide a valuable tool to detect melanoma development at a pre-clinical stage. By studying the epigenetic profile in pre-diagnostic blood samples of melanoma cases and cancer free controls, we aimed to identify DNA methylation sites conferring melanoma risk. DNA methylation was measured at 775,528 CpG sites using the Illumina EPIC array in whole blood in incident melanoma cases (n = 183) and matched cancer-free controls (n = 183) in the Norwegian Women and Cancer cohort. Phenotypic information and ultraviolet radiation exposure were obtained from questionnaires. Epigenome wide association (EWAS) was analyzed in future melanoma cases and controls with conditional logistic regression, with correction for multiple testing using the false discovery rate (FDR). We extended the analysis by including a public data set on melanoma (GSE120878), and combining these different data sets using a version of covariate modulated FDR (AdaPT). The analysis on future melanoma cases and controls did not identify any genome wide significant CpG sites (0.85 ≤ padj ≤ 0.99). In the restricted AdaPT analysis, 7 CpG sites were suggestive at the FDR level of 0.15. These CpG sites may potentially be used as pre-diagnostic biomarkers of melanoma risk.

Cigarette Smoking and Endometrial Cancer Risk: Observational and Mendelian Randomization Analyses.

BACKGROUND: Current epidemiologic evidence indicates that smoking is associated with a lower endometrial cancer risk. However, it is unknown if this association is causal or confounded. To further elucidate the role of smoking in endometrial cancer risk, we conducted complementary observational and Mendelian randomization (MR) analyses. METHODS: The observational analyses included 286,415 participants enrolled in the European Prospective Investigation into Cancer and Nutrition and 179,271 participants in the UK Biobank, and multivariable Cox proportional hazards models were used. In two-sample MR analyses, genetic variants robustly associated with lifetime amount of smoking (n = 126 variants) and ever having smoked regularly (n = 112 variants) were selected and their association with endometrial cancer risk (12,906 cancer/108,979 controls from the Endometrial Cancer Association Consortium) was examined. RESULTS: In the observational analysis, lifetime amount of smoking and ever having smoked regularly were associated with a lower endometrial cancer risk. In the MR analysis accounting for body mass index, a genetic predisposition to a higher lifetime amount of smoking was not associated with endometrial cancer risk (OR per 1-SD increment: 1.15; 95% confidence interval: 0.91-1.44). Genetic predisposition to ever having smoked regularly was not associated with risk of endometrial cancer. CONCLUSIONS: Smoking was inversely associated with endometrial cancer in the observational analyses, although unsupported by the MR. Additional studies are required to better understand the possible confounders and mechanisms underlying the observed associations between smoking and endometrial cancer. IMPACT: The results from this analysis indicate that smoking is unlikely to be causally linked with endometrial cancer risk.

Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort.

BACKGROUND: Metabolomics is a promising molecular tool for identifying novel etiological pathways leading to cancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones, we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite (acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine, phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2). METHODS: To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included as controls in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with 42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant associations were evaluated in a validation set (n = 786). RESULTS: For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total and saturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive association with age was observed. CONCLUSIONS: These associations may indicate possible mechanisms underlying associations between lifestyle and anthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestyle correlates of the metabolites investigated.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium.

BACKGROUND: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Combined Lifestyle Behaviors and the Incidence of Common Cancer Types in the Norwegian Women and Cancer Study (NOWAC).

INTRODUCTION: Only a small number of studies have examined the impact of combined lifestyle behaviors on cancer incidence, and never in a Norwegian population. PURPOSE: To examine linear and nonlinear associations of combined lifestyle factors, assessed through a healthy lifestyle index (HLI), with the incidence of postmenopausal breast, colorectal, lung, postmenopausal endometrial, postmenopausal ovarian, pancreatic, and kidney cancer among women in Norway. METHODS: This prospective study included 96,869 women enrolled in the Norwegian Women and Cancer (NOWAC) cohort. Baseline information on lifestyle factors was collected between 1996 and 2004. The HLI was constructed from five lifestyle factors: physical activity level, body mass index, smoking, alcohol consumption, and diet. Each factor contributed 0 to 4 points to the HLI score, which ranged from 0 to 20, with higher scores representing a healthier lifestyle. Multiple imputation was used to handle missing data. Cox proportional hazard regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI). Restricted cubic splines were used to examine nonlinearity in the associations. RESULTS: The HRs for a one-point increment on the HLI score were 0.97 (95% CI: 0.96-0.98) for postmenopausal breast cancer, 0.98 (0.96-1.00) for colorectal cancer, 0.86 (0.84-0.87) for lung cancer, 0.93 (0.91-0.95) for postmenopausal endometrial cancer, 0.99 (0.96-1.02) for postmenopausal ovarian cancer, 0.92 (0.89-0.95) for pancreatic cancer, and 0.94 (0.91-0.97) for kidney cancer. Nonlinearity was observed for the inverse associations between HLI score and the incidence of lung cancer and postmenopausal breast cancer. CONCLUSION: Based on our results, healthier lifestyle, as assessed by the HLI score, was associated with lower incidence of postmenopausal breast, colorectal, lung, postmenopausal endometrial, pancreatic, and kidney cancer among women, although the magnitude and linearity varied. Adoption of healthier lifestyle behaviors should be a public health priority to reduce the cancer burden among Norwegian women.

Novel Biomarkers of Habitual Alcohol Intake and Associations With Risk of Pancreatic and Liver Cancers and Liver Disease Mortality.

BACKGROUND: Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk. METHODS: Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies. RESULTS: Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log2) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log2) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC. CONCLUSIONS: 2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.

Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis.

Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case-control design. We employed several approaches to statistically analyze the data and the methods indicated that the case-control differences were subtle but most distinguishable in metastatic case-control pairs in the period 0-3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case-control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes.

Gene expression in blood reflects smoking exposure among cancer-free women in the Norwegian Women and Cancer (NOWAC) postgenome cohort.

Active smoking has been linked to modulated gene expression in blood. However, there is a need for a more thorough understanding of how quantitative measures of smoking exposure relate to differentially expressed genes (DEGs) in whole-blood among ever smokers. This study analysed microarray-based gene expression profiles from whole-blood samples according to smoking status and quantitative measures of smoking exposure among cancer-free women (n = 1708) in the Norwegian Women and Cancer postgenome cohort. When compared with never smokers and former smokers, current smokers had 911 and 1082 DEGs, respectively and their biological functions could indicate systemic impacts of smoking. LRRN3 was associated with smoking status with the lowest FDR-adjusted p-value. When never smokers and all former smokers were compared, no DEGs were observed, but LRRN3 was differentially expressed when never smokers were compared with former smokers who quit smoking ≤ 10 years ago. Further, LRRN3 was positively associated with smoking intensity, pack-years, and comprehensive smoking index score among current smokers; and negatively associated with time since cessation among former smokers. Consequently, LRRN3 expression in whole-blood is a molecular signal of smoking exposure that could supplant self-reported smoking data in further research targeting blood-based markers related to the health effects of smoking.

Pre- and post-diagnostic blood profiles of perfluoroalkyl acids in type 2 diabetes mellitus cases and controls.

BACKGROUND: Studies exploring the associations between perfluoroalkyl acids (PFAAs) and type 2 diabetes mellitus (T2DM) are rather limited and have reported conflicting results. All studies to date, including prospective ones, have relied on a single blood sample to study this association. Similarly, studies investigating how T2DM status may influence the longitudinal changes in PFAA concentrations have not been previously performed. As PFAA concentrations in humans have changed considerably over the last two decades, and as individuals diagnosed with T2DM usually undergo lifestyle changes that could influence these concentrations, a single blood sample may not necessarily reflect the life-time exposure to PFAA concentrations. Hence, repeated measurements from the same individuals will extend our understanding of how PFAAs are associated with T2DM. The present study, therefore, aimed to explore associations between pre- and post-diagnostic PFAA blood profiles and T2DM and assess factors associated with longitudinal changes in PFAAs in T2DM cases and controls. METHODS: Questionnaire data and blood samples from women participating in the Norwegian Women and Cancer study were used to conduct a nested case-control study among 46 T2DM cases matched to 85 non-diabetic controls. PFAAs were measured in blood samples collected prior to (2001/02) and after (2005/6) T2DM diagnosis. We investigated the association between PFAAs and incident and prevalent T2DM using conditional logistic regression. We assessed the longitudinal changes in PFAA concentrations within and between matched cases and controls using t-tests and linear regression models. RESULTS: We observed no significant associations between pre-diagnostic PFAA concentrations and T2DM incidence. Similar results were observed for the post-diagnostic PFAA concentrations and T2DM prevalence. Decrease over time in PFAA concentrations were observed for PFOA and ∑PFOS concentrations, whereas increase over time were observed for PFNA, PFDA and PFUnDA concentrations. Longitudinal trends in PFAA concentrations among T2DM cases were similar to the changes observed in controls. CONCLUSIONS: The study did not find evidence of association between PFAAs and incident or prevalent T2DM. The longitudinal changes in PFAAs concentrations were not influenced by T2DM status.

Stochastic Epigenetic Mutations Are Associated with Risk of Breast Cancer, Lung Cancer, and Mature B-cell Neoplasms.

BACKGROUND: Age-related epigenetic dysregulations are associated with several diseases, including cancer. The number of stochastic epigenetic mutations (SEM) has been suggested as a biomarker of life-course accumulation of exposure-related DNA damage; however, the predictive role of SEMs in cancer has seldom been investigated. METHODS: A SEM, at a given CpG site, was defined as an extreme outlier of DNA methylation value distribution across individuals. We investigated the association of the total number of SEMs with the risk of eight cancers in 4,497 case-control pairs nested in three prospective cohorts. Furthermore, we investigated whether SEMs were randomly distributed across the genome or enriched in functional genomic regions. RESULTS: In the three-study meta-analysis, the estimated ORs per one-unit increase in log(SEM) from logistic regression models adjusted for age and cancer risk factors were 1.25; 95% confidence interval (CI), 1.11-1.41 for breast cancer, and 1.23; 95% CI, 1.07-1.42 for lung cancer. In the Melbourne Collaborative Cohort Study, the OR for mature B-cell neoplasm was 1.46; 95% CI, 1.25-1.71. Enrichment analyses indicated that SEMs frequently occur in silenced genomic regions and in transcription factor binding sites regulated by EZH2 and SUZ12 (P < 0.0001 and P = 0.0005, respectively): two components of the polycomb repressive complex 2 (PCR2). Finally, we showed that PCR2-specific SEMs are generally more stable over time compared with SEMs occurring in the whole genome. CONCLUSIONS: The number of SEMs is associated with a higher risk of different cancers in prediagnostic blood samples. IMPACT: We identified a candidate biomarker for cancer early detection, and we described a carcinogenesis mechanism involving PCR2 complex proteins worthy of further investigations.

Lifetime Ultraviolet Radiation Exposure and DNA Methylation in Blood Leukocytes: The Norwegian Women and Cancer Study.

Ultraviolet radiation (UVR) exposure is a leading cause of skin cancers and an ubiquitous environmental exposure. However, the molecular mechanisms relating UVR exposure to melanoma is not fully understood. We aimed to investigate if lifetime UVR exposure could be robustly associated to DNA methylation (DNAm). We assessed DNAm in whole blood in three data sets (n = 183, 191, and 125) from the Norwegian Woman and Cancer cohort, using Illumina platforms. We studied genome-wide DNAm, targeted analyses of CpG sites indicated in the literature, global methylation, and accelerated aging. Lifetime history of UVR exposure (residential ambient UVR, sunburns, sunbathing vacations and indoor tanning) was collected by questionnaires. We used one data set for discovery and the other two for replication. One CpG site showed a genome-wide significant association to cumulative UVR exposure (cg01884057) (pnominal = 3.96e-08), but was not replicated in any of the two replication sets (pnominal ≥ 0.42). Two CpG sites (cg05860019, cg00033666) showed suggestive associations with the other UVR exposures. We performed extensive analyses of the association between long-term UVR exposure and DNAm. There was no indication of a robust effect of past UVR exposure on DNAm.

Predicted basal metabolic rate and cancer risk in the European Prospective Investigation into Cancer and Nutrition.

Emerging evidence suggests that a metabolic profile associated with obesity may be a more relevant risk factor for some cancers than adiposity per se. Basal metabolic rate (BMR) is an indicator of overall body metabolism and may be a proxy for the impact of a specific metabolic profile on cancer risk. Therefore, we investigated the association of predicted BMR with incidence of 13 obesity-related cancers in the European Prospective Investigation into Cancer and Nutrition (EPIC). BMR at baseline was calculated using the WHO/FAO/UNU equations and the relationships between BMR and cancer risk were investigated using multivariable Cox proportional hazards regression models. A total of 141,295 men and 317,613 women, with a mean follow-up of 14 years were included in the analysis. Overall, higher BMR was associated with a greater risk for most cancers that have been linked with obesity. However, among normal weight participants, higher BMR was associated with elevated risks of esophageal adenocarcinoma (hazard ratio per 1-standard deviation change in BMR [HR1-SD ]: 2.46; 95% CI 1.20; 5.03) and distal colon cancer (HR1-SD : 1.33; 95% CI 1.001; 1.77) among men and with proximal colon (HR1-SD : 1.16; 95% CI 1.01; 1.35), pancreatic (HR1-SD : 1.37; 95% CI 1.13; 1.66), thyroid (HR1-SD : 1.65; 95% CI 1.33; 2.05), postmenopausal breast (HR1-SD : 1.17; 95% CI 1.11; 1.22) and endometrial (HR1-SD : 1.20; 95% CI 1.03; 1.40) cancers in women. These results indicate that higher BMR may be an indicator of a metabolic phenotype associated with risk of certain cancer types, and may be a useful predictor of cancer risk independent of body fatness.

Prospective analysis of circulating metabolites and breast cancer in EPIC.

BACKGROUND: Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. METHODS: A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. RESULTS: Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. CONCLUSIONS: These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.

Appraising the causal relevance of DNA methylation for risk of lung cancer.

BACKGROUND: DNA methylation changes in peripheral blood have recently been identified in relation to lung cancer risk. Some of these changes have been suggested to mediate part of the effect of smoking on lung cancer. However, limitations with conventional mediation analyses mean that the causal nature of these methylation changes has yet to be fully elucidated. METHODS: We first performed a meta-analysis of four epigenome-wide association studies (EWAS) of lung cancer (918 cases, 918 controls). Next, we conducted a two-sample Mendelian randomization analysis, using genetic instruments for methylation at CpG sites identified in the EWAS meta-analysis, and 29 863 cases and 55 586 controls from the TRICL-ILCCO lung cancer consortium, to appraise the possible causal role of methylation at these sites on lung cancer. RESULTS: Sixteen CpG sites were identified from the EWAS meta-analysis [false discovery rate (FDR) < 0.05], for 14 of which we could identify genetic instruments. Mendelian randomization provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites plays a causal role in lung cancer development (FDR > 0.05), including for cg05575921-AHRR where methylation is strongly associated with both smoke exposure and lung cancer risk. CONCLUSIONS: The results contrast with previous observational and mediation analysis, which have made strong claims regarding the causal role of DNA methylation. Thus, previous suggestions of a mediating role of methylation at sites identified in peripheral blood, such as cg05575921-AHRR, could be unfounded. However, this study does not preclude the possibility that differential DNA methylation at other sites is causally involved in lung cancer development, especially within lung tissue.

Global test for high-dimensional mediation: Testing groups of potential mediators.

We address the problem of testing whether a possibly high-dimensional vector may act as a mediator between some exposure variable and the outcome of interest. We propose a global test for mediation, which combines a global test with the intersection-union principle. We discuss theoretical properties of our approach and conduct simulation studies that demonstrate that it performs equally well or better than its competitor. We also propose a multiple testing procedure, ScreenMin, that provides asymptotic control of either familywise error rate or false discovery rate when multiple groups of potential mediators are tested simultaneously. We apply our approach to data from a large Norwegian cohort study, where we look at the hypothesis that smoking increases the risk of lung cancer by modifying the level of DNA methylation.

Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis.

Differences in health status by socioeconomic position (SEP) tend to be more evident at older ages, suggesting the involvement of a biological mechanism responsive to the accumulation of deleterious exposures across the lifespan. DNA methylation (DNAm) has been proposed as a biomarker of biological aging that conserves memory of endogenous and exogenous stress during life.We examined the association of education level, as an indicator of SEP, and lifestyle-related variables with four biomarkers of age-dependent DNAm dysregulation: the total number of stochastic epigenetic mutations (SEMs) and three epigenetic clocks (Horvath, Hannum and Levine), in 18 cohorts spanning 12 countries.The four biological aging biomarkers were associated with education and different sets of risk factors independently, and the magnitude of the effects differed depending on the biomarker and the predictor. On average, the effect of low education on epigenetic aging was comparable with those of other lifestyle-related risk factors (obesity, alcohol intake), with the exception of smoking, which had a significantly stronger effect.Our study shows that low education is an independent predictor of accelerated biological (epigenetic) aging and that epigenetic clocks appear to be good candidates for disentangling the biological pathways underlying social inequalities in healthy aging and longevity.

Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins.

Importance: There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. Objective: To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. Design, Setting, and Participants: Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). Main Outcomes and Measures: Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). Results: In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. Conclusions and Relevance: This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.