SUBJECTS/METHODS: Nested within the European Prospective Investigation into Cancer and Nutrition (EPIC-IBD), incident UC and CD cases and matched controls where included. At recruitment, participants completed validated food frequency and lifestyle questionnaires. Alcohol consumption was classified as either: non-use, former, light (⩽0.5 and 1 drink per week), below the recommended limits (BRL) (⩽1 and 2 drinks per day), moderate (⩽2.5 and 5 drinks per day), or heavy use (>2.5 and >5 drinks per day) for women and men, respectively; and was expressed as consumption at enrolment and during lifetime. Conditional logistic regression was applied adjusting for smoking and education, taking light users as the reference.
RESULTS: Out of 262 451 participants in six countries, 198 UC incident cases/792 controls and 84 CD cases/336 controls were included. At enrolment, 8%/27%/32%/23%/11% UC cases and 7%/29%/40%/19%/5% CD cases were: non-users, light, BRL, moderate and heavy users, respectively. The corresponding figures for lifetime non-use, former, light, BRL, moderate and heavy use were: 3%/5%/23%/44%/19%/6% and 5%/2%/25%/44%/23%/1% for UC and CD cases, respectively. There were no associations between any categories of alcohol consumption and risk of UC or CD in the unadjusted and adjusted odds ratios.
CONCLUSION: There was no evidence of associations between alcohol use and the odds of developing either UC or CD.
METHODS: Studying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case-control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).
RESULTS: We observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case-control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.
CONCLUSIONS: Taken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.
METHODS: To gain a more comprehensive picture on how these markers can modulate BC risk, alone or in conjunction, we performed simultaneous measurements of LTL and mtDNA copy number in up to 570 BC cases and 538 controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. As a first step, we measured LTL and mtDNA copy number in 96 individuals for which a blood sample had been collected twice with an interval of 15 years.
RESULTS: According to the intraclass correlation (ICC), we found very good stability over the time period for both measurements, with ICCs of 0.63 for LTL and 0.60 for mtDNA copy number. In the analysis of the entire study sample, we observed that longer LTL was strongly associated with increased risk of BC (OR 2.71, 95% CI 1.58-4.65, p = 3.07 × 10- 4 for highest vs. lowest quartile; OR 3.20, 95% CI 1.57-6.55, p = 1.41 × 10- 3 as a continuous variable). We did not find any association between mtDNA copy number and BC risk; however, when considering only the functional copies, we observed an increased risk of developing estrogen receptor-positive BC (OR 2.47, 95% CI 1.05-5.80, p = 0.04 for highest vs. lowest quartile).
CONCLUSIONS: We observed a very good correlation between the markers over a period of 15 years. We confirm a role of LTL in BC carcinogenesis and suggest an effect of mtDNA copy number on BC risk.
PATIENTS AND METHODS: A nested case-control study was conducted with the European Prospective Investigation into Cancer and Nutrition (EPIC) with 1871 cases and 1871 matched controls. Conditional logistic regression analysis was used to investigate the association of pre-diagnostic circulating MSP with risk of incident prostate cancer overall and by tumour subtype. EPIC-derived estimates were combined with published data to calculate an MR estimate using two-sample inverse-variance method.
RESULTS: Plasma MSP concentrations were inversely associated with prostate cancer risk after adjusting for total prostate-specific antigen concentration [odds ratio (OR) highest versus lowest fourth of MSP = 0.65, 95% confidence interval (CI) 0.51-0.84, Ptrend = 0.001]. No heterogeneity in this association was observed by tumour stage or histological grade. Plasma MSP concentrations were 66% lower in rs10993994 TT compared with CC homozygotes (per allele difference in MSP: 6.09 ng/ml, 95% CI 5.56-6.61, r2=0.42). MR analyses supported a potentially causal protective association of MSP with prostate cancer risk (OR per 1 ng/ml increase in MSP for MR: 0.96, 95% CI 0.95-0.97 versus EPIC observational: 0.98, 95% CI 0.97-0.99). Limitations include lack of complete tumour subtype information and more complete information on the biological function of MSP.
CONCLUSIONS: In this large prospective European study and using MR analyses, men with high circulating MSP concentration have a lower risk of prostate cancer. MSP may play a causally protective role in prostate cancer.
METHODS: We investigated overall obesity and abdominal adiposity in relation to SIC in the European Prospective Investigation into Cancer and Nutrition (EPIC), a large prospective cohort of approximately half a million men and women from ten European countries. Overall obesity and abdominal obesity were assessed by body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR). Multivariate Cox proportional hazards regression modeling was performed to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs). Stratified analyses were conducted by sex, BMI, and smoking status.
RESULTS: During an average of 13.9 years of follow-up, 131 incident cases of SIC (including 41 adenocarcinomas, 44 malignant carcinoid tumors, 15 sarcomas and 10 lymphomas, and 21 unknown histology) were identified. WC was positively associated with SIC in a crude model that also included BMI (HR per 5-cm increase = 1.20, 95 % CI 1.04, 1.39), but this association attenuated in the multivariable model (HR 1.18, 95 % CI 0.98, 1.42). However, the association between WC and SIC was strengthened when the analysis was restricted to adenocarcinoma of the small intestine (multivariable HR adjusted for BMI = 1.56, 95 % CI 1.11, 2.17). There were no other significant associations.
CONCLUSION: WC, rather than BMI, may be positively associated with adenocarcinomas but not carcinoid tumors of the small intestine.
IMPACT: Abdominal obesity is a potential risk factor for adenocarcinoma in the small intestine.
METHODS: Within the prospective EPIC (European Prospective Investigation into Cancer) study, we set up a nested matched case-control study among 366,351 participants with inflammatory bowel disease data, including 256 incident cases of UC and 117 of CD, and 4 matched controls per case. Dietary intake was recorded at baseline from validated food frequency questionnaires. Incidence rate ratios of developing UC and CD were calculated for quintiles of the Mediterranean diet score and a posteriori dietary patterns produced by factor analysis.
RESULTS: No dietary pattern was associated with either UC or CD risks. However, when excluding cases occurring within the first 2 years after dietary assessment, there was a positive association between a "high sugar and soft drinks" pattern and UC risk (incidence rate ratios for the fifth versus first quintile, 1.68 [1.00-2.82]; Ptrend = 0.02). When considering the foods most associated with the pattern, high consumers of sugar and soft drinks were at higher UC risk only if they had low vegetables intakes.
CONCLUSIONS: A diet imbalance with high consumption of sugar and soft drinks and low consumption of vegetables was associated with UC risk. Further studies are needed to investigate whether microbiota alterations or other mechanisms mediate this association.
METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.
RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.
CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
MATERIALS AND METHODS: We assessed the association between plasma phospholipid fatty acids and breast cancer risk in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition study. Sixty fatty acids were measured by gas chromatography in pre-diagnostic plasma phospholipids from 2982 incident breast cancer cases matched to 2982 controls. Conditional logistic regression models were used to estimate relative risk of breast cancer by fatty acid level. The false discovery rate (q values) was computed to control for multiple comparisons. Subgroup analyses were carried out by estrogen receptor (ER) and progesterone receptor expression in the tumours.
RESULTS: A high level of palmitoleic acid [odds ratio (OR) for the highest quartile compared with the lowest OR (Q4-Q1) 1.37; 95% confidence interval (CI), 1.14-1.64; P for trend = 0.0001, q value = 0.004] as well as a high desaturation index (DI16) (16:1n-7/16:0) [OR (Q4-Q1), 1.28; 95% C, 1.07-1.54; P for trend = 0.002, q value = 0.037], as biomarkers of de novo lipogenesis, were significantly associated with increased risk of breast cancer. Levels of industrial trans-fatty acids were positively associated with ER-negative tumours [OR for the highest tertile compared with the lowest (T3-T1)=2.01; 95% CI, 1.03-3.90; P for trend = 0.047], whereas no association was found for ER-positive tumours (P-heterogeneity =0.01). No significant association was found between n-3 polyunsaturated fatty acids and breast cancer risk, overall or by hormonal receptor.
CONCLUSION: These findings suggest that increased de novo lipogenesis, acting through increased synthesis of palmitoleic acid, could be a relevant metabolic pathway for breast tumourigenesis. Dietary trans-fatty acids derived from industrial processes may specifically increase ER-negative breast cancer risk.
MATERIALS & METHODS: Here, we examined the potential of DNA methylation changes in 910 prediagnostic peripheral blood samples as a marker of exposure to tobacco smoke in a large multinational cohort.
RESULTS: We identified 748 CpG sites that were differentially methylated between smokers and nonsmokers, among which we identified novel regionally clustered CpGs associated with active smoking. Importantly, we found a marked reversibility of methylation changes after smoking cessation, although specific genes remained differentially methylated up to 22 years after cessation.
CONCLUSION: Our study has comprehensively cataloged the smoking-associated DNA methylation alterations and showed that these alterations are reversible after smoking cessation.
METHODS: Biomarkers of internal exposure were measured in red blood cells (collected at baseline) by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) . In this cross-sectional analysis, four dependent variables were evaluated: HbAA, HbGA, sum of total adducts (HbAA + HbGA), and their ratio (HbGA/HbAA). Simple and multiple regression analyses were used to identify determinants of the four outcome variables. All dependent variables (except HbGA/HbAA) and all independent variables were log-transformed (log2) to improve normality. Median (25th-75th percentile) HbAA and HbGA adduct levels were 41.3 (32.8-53.1) pmol/g Hb and 34.2 (25.4-46.9) pmol/g Hb, respectively.
RESULTS: The main food group determinants of HbAA, HbGA, and HbAA + HbGA were biscuits, crackers, and dry cakes. Alcohol intake and body mass index were identified as the principal determinants of HbGA/HbAA. The total percent variation in HbAA, HbGA, HbAA + HbGA, and HbGA/HbAA explained in this study was 30, 26, 29, and 13 %, respectively.
CONCLUSIONS: Dietary and lifestyle factors explain a moderate proportion of acrylamide adduct variation in non-smoking postmenopausal women from the EPIC cohort.
METHODS: We examined associations of body mass index (BMI), waist circumference (WC), and waist-hip ratio (WHR) with lung cancer risk among 1.6 million Americans, Europeans, and Asians. Cox proportional hazard regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment for potential confounders. Analyses for WC/WHR were further adjusted for BMI. The joint effect of BMI and WC/WHR was also evaluated.
RESULTS: During an average 12-year follow-up, 23 732 incident lung cancer cases were identified. While BMI was generally associated with a decreased risk, WC and WHR were associated with increased risk after controlling for BMI. These associations were seen 10 years before diagnosis in smokers and never smokers, were strongest among blacks, and varied by histological type. After excluding the first five years of follow-up, hazard ratios per 5 kg/m2 increase in BMI were 0.95 (95% CI = 0.90 to 1.00), 0.92 (95% CI = 0.89 to 0.95), and 0.89 (95% CI = 0.86 to 0.91) in never, former, and current smokers, and 0.86 (95% CI = 0.84 to 0.89), 0.94 (95% CI = 0.90 to 0.99), and 1.09 (95% CI = 1.03 to 1.15) for adenocarcinoma, squamous cell, and small cell carcinoma, respectively. Hazard ratios per 10 cm increase in WC were 1.09 (95% CI = 1.00 to 1.18), 1.12 (95% CI = 1.07 to 1.17), and 1.11 (95% CI = 1.07 to 1.16) in never, former, and current smokers, and 1.06 (95% CI = 1.01 to 1.12), 1.20 (95% CI = 1.12 to 1.29), and 1.13 (95% CI = 1.04 to 1.23) for adenocarcinoma, squamous cell, and small cell carcinoma, respectively. Participants with BMIs of less than 25 kg/m2 but high WC had a 40% higher risk (HR = 1.40, 95% CI = 1.26 to 1.56) than those with BMIs of 25 kg/m2 or greater but normal/moderate WC.
CONCLUSIONS: The inverse BMI-lung cancer association is not entirely due to smoking and reverse causation. Central obesity, particularly concurrent with low BMI, may help identify high-risk populations for lung cancer.
OBJECTIVE: To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer.
DESIGN, SETTING, AND PARTICIPANTS: SNPs implicated in any phenotype other than prostate cancer (p≤10(-7)) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24,534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated.
RESULTS AND LIMITATIONS: A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p=1.6×10(-6)), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p=3.22×10(-18)). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p=2.5×10(-6)). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p=4.67×10(-5)); it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL.
CONCLUSIONS: We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology.
PATIENT SUMMARY: We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified.
RESULTS: Higher total neopterin concentrations were associated with reduced HDLC (9.7 %, p
METHODS: A case-control study was nested in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. A total of 2008 incident invasive breast cancer cases (estrogen receptor (ER)+, n = 1622; ER-, n = 386), matched 1:1 to controls, were included in the analysis. Women were predominantly postmenopausal at blood collection (77%); postmenopausal women included users and non-users of postmenopausal hormone therapy (HT). Serum OPG was quantified with an electrochemiluminescence assay. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression.
RESULTS: The associations between OPG and ER+ and ER- breast cancer differed significantly. Higher concentrations of OPG were associated with increased risk of ER- breast cancer (top vs. bottom tertile RR = 1.93 [95% CI 1.24-3.02]; p trend = 0.03). We observed a suggestive inverse association for ER+ disease overall and among women premenopausal at blood collection. Results for ER- disease did not differ by menopausal status at blood collection (p het = 0.97), and we observed no heterogeneity by HT use at blood collection (p het ≥ 0.43) or age at breast cancer diagnosis (p het ≥ 0.30).
CONCLUSIONS: This study provides the first prospective data on OPG and breast cancer risk by hormone receptor subtype. High circulating OPG may represent a novel risk factor for ER- breast cancer.
METHODS: We analysed the relationship between pre-diagnostic prolactin levels and the risk of in situ breast cancer overall, and by menopausal status and use of postmenopausal hormone therapy (HT) at blood donation. Conditional logistic regression was used to assess this association in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, including 307 in situ breast cancer cases and their matched control subjects.
RESULTS: We found a significant positive association between higher circulating prolactin levels and risk of in situ breast cancer among all women [pre-and postmenopausal combined, ORlog2=1.35 (95% CI 1.04-1.76), Ptrend=0.03]. No statistically significant heterogeneity was found between prolactin levels and in situ cancer risk by menopausal status (Phet=0.98) or baseline HT use (Phet=0.20), although the observed association was more pronounced among postmenopausal women using HT compared to non-users (Ptrend=0.06 vs Ptrend=0.35). In subgroup analyses, the observed positive association was strongest in women diagnosed with in situ breast tumors<4 years compared to ≥4 years after blood donation (Ptrend=0.01 vs Ptrend=0.63; Phet=0.04) and among nulliparous women compared to parous women (Ptrend=0.03 vs Ptrend=0.15; Phet=0.07).
CONCLUSIONS: Our data extends prior research linking prolactin and invasive breast cancer to the outcome of in situ breast tumours and shows that higher circulating prolactin is associated with increased risk of in situ breast cancer.