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.
METHODS: Data on highest education attained were gathered for 459,170 participants (70% women) from 10 European countries. A relative index of inequality (RII) based on adult education was calculated for comparability across countries and generations. Cox regression models were applied to estimate relative inequality in pancreatic cancer risk, stratifying by age, gender, and center, and adjusting for known pancreatic cancer risk factors.
RESULTS: A total of 1,223 incident pancreatic cancer cases were included after a mean follow-up of 13.9 (±4.0) years. An inverse social trend was found in models adjusted for age, sex, and center for both sexes [HR of RII, 1.27; 95% confidence interval (CI), 1.02-1.59], which was also significant among women (HR, 1.42; 95% CI, 1.05-1.92). Further adjusting by smoking intensity, alcohol consumption, body mass index, prevalent diabetes, and physical activity led to an attenuation of the RII risk and loss of statistical significance.
CONCLUSIONS: The present reanalysis does not sustain the existence of an independent social inequality influence on pancreatic cancer risk in Western European women and men, using an index based on adult education, the most relevant social indicator linked to individual lifestyles, in a context of very low pancreatic cancer survival from (quasi) universal public health systems.
IMPACT: The results do not support an association between education and risk of pancreatic 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.
METHODS: Over half a million participants from 10 European countries were followed up for over 11 years, after which 865 newly diagnosed exocrine pancreatic cancer cases were identified. Adherence to the MD was estimated through an adapted score without the alcohol component (arMED) to discount alcohol-related harmful effects. Cox proportional hazards regression models, stratified by age, sex and centre, and adjusted for energy intake, body mass index, smoking status, alcohol intake and diabetes status at recruitment, were used to estimate hazard ratios (HRs) associated with pancreatic cancer and their corresponding 95% confidence intervals (CIs).
RESULTS: Adherence to the arMED score was not associated with risk of pancreatic cancer (HR high vs low adherence=0.99; 95% CI: 0.77-1.26, and HR per increments of two units in adherence to arMED=1.00; 95% CI: 0.94-1.06). There was no convincing evidence for heterogeneity by smoking status, body mass index, diabetes or European region. There was also no evidence of significant associations in analyses involving microscopically confirmed cases, plausible reporters of energy intake or other definitions of the MD pattern.
CONCLUSIONS: A high adherence to the MD is not associated with pancreatic cancer risk in the EPIC study.
METHODS: This study was conducted within the European Prospective Investigation into Nutrition and Cancer cohort, comprising male and female participants from 10 European countries. Between 1992 and 2000, there were 477,312 participants without cancer who completed a dietary questionnaire and were followed up to determine pancreatic cancer incidence. Coffee and tea intake was calibrated with a 24-hour dietary recall. Adjusted hazard ratios (HRs) were computed using multivariable Cox regression.
RESULTS: During a mean follow-up period of 11.6 y, 865 first incidences of pancreatic cancers were reported. When divided into fourths, neither total intake of coffee (HR, 1.03; 95% confidence interval [CI], 0.83-1.27; high vs low intake), decaffeinated coffee (HR, 1.12; 95% CI, 0.76-1.63; high vs low intake), nor tea were associated with risk of pancreatic cancer (HR, 1.22, 95% CI, 0.95-1.56; high vs low intake). Moderately low intake of caffeinated coffee was associated with an increased risk of pancreatic cancer (HR, 1.33; 95% CI, 1.02-1.74), compared with low intake. However, no graded dose response was observed, and the association attenuated after restriction to histologically confirmed pancreatic cancers.
CONCLUSIONS: Based on an analysis of data from the European Prospective Investigation into Nutrition and Cancer cohort, total coffee, decaffeinated coffee, and tea consumption are not related to the risk of pancreatic cancer.
OBJECTIVE: We aimed to evaluate the potential mediating roles of inflammatory, metabolic, liver injury, and iron metabolism biomarkers on the association between coffee intake and the primary form of liver cancer-hepatocellular carcinoma (HCC).
DESIGN: We conducted a prospective nested case-control study within the European Prospective Investigation into Cancer and Nutrition among 125 incident HCC cases matched to 250 controls using an incidence-density sampling procedure. The association of coffee intake with HCC risk was evaluated by using multivariable-adjusted conditional logistic regression that accounted for smoking, alcohol consumption, hepatitis infection, and other established liver cancer risk factors. The mediating effects of 21 biomarkers were evaluated on the basis of percentage changes and associated 95% CIs in the estimated regression coefficients of models with and without adjustment for biomarkers individually and in combination.
RESULTS: The multivariable-adjusted RR of having ≥4 cups (600 mL) coffee/d compared with <2 cups (300 mL)/d was 0.25 (95% CI: 0.11, 0.62; P-trend = 0.006). A statistically significant attenuation of the association between coffee intake and HCC risk and thereby suspected mediation was confirmed for the inflammatory biomarker IL-6 and for the biomarkers of hepatocellular injury glutamate dehydrogenase, alanine aminotransferase, aspartate aminotransferase (AST), γ-glutamyltransferase (GGT), and total bilirubin, which-in combination-attenuated the regression coefficients by 72% (95% CI: 7%, 239%). Of the investigated biomarkers, IL-6, AST, and GGT produced the highest change in the regression coefficients: 40%, 56%, and 60%, respectively.
CONCLUSION: These data suggest that the inverse association of coffee intake with HCC risk was partly accounted for by biomarkers of inflammation and hepatocellular injury.
METHODS: We analysed data from 264,906 European adults from the EPIC prospective cohort study, aged between 40 and 70 years at the time of recruitment. Flexible parametric survival models were used to model risk of death conditional on risk factors, and survival functions and attributable fractions (AF) for deaths prior to age 70 years were calculated based on the fitted models.
RESULTS: We identified 11,930 deaths which occurred before the age of 70. The AF for premature mortality for smoking was 31 % (95 % confidence interval (CI), 31-32 %) and 14 % (95 % CI, 12-16 %) for poor diet. Important contributions were also observed for overweight and obesity measured by waist-hip ratio (10 %; 95 % CI, 8-12 %) and high blood pressure (9 %; 95 % CI, 7-11 %). AFs for physical inactivity and excessive alcohol intake were 7 % and 4 %, respectively. Collectively, the AF for all six risk factors was 57 % (95 % CI, 55-59 %), being 35 % (95 % CI, 32-37 %) among never smokers and 74 % (95 % CI, 73-75 %) among current smokers.
CONCLUSIONS: While smoking remains the predominant risk factor for premature death in Europe, poor diet, overweight and obesity, hypertension, physical inactivity, and excessive alcohol consumption also contribute substantially. Any attempt to minimise premature deaths will ultimately require all six factors to be addressed.
OBJECTIVE: This study evaluated the associations of plasma carotenoid, retinol, tocopherol, and vitamin C concentrations and risk of breast cancer.
DESIGN: In a nested case-control study within the European Prospective Investigation into Cancer and Nutrition cohort, 1502 female incident breast cancer cases were included, with an oversampling of premenopausal (n = 582) and estrogen receptor-negative (ER-) cases (n = 462). Controls (n = 1502) were individually matched to cases by using incidence density sampling. Prediagnostic samples were analyzed for α-carotene, β-carotene, lycopene, lutein, zeaxanthin, β-cryptoxanthin, retinol, α-tocopherol, γ-tocopherol, and vitamin C. Breast cancer risk was computed according to hormone receptor status and age at diagnosis (proxy for menopausal status) by using conditional logistic regression and was further stratified by smoking status, alcohol consumption, and body mass index (BMI). All statistical tests were 2-sided.
RESULTS: In quintile 5 compared with quintile 1, α-carotene (OR: 0.61; 95% CI: 0.39, 0.98) and β-carotene (OR: 0.41; 95% CI: 0.26, 0.65) were inversely associated with risk of ER- breast tumors. The other analytes were not statistically associated with ER- breast cancer. For estrogen receptor-positive (ER+) tumors, no statistically significant associations were found. The test for heterogeneity between ER- and ER+ tumors was statistically significant only for β-carotene (P-heterogeneity = 0.03). A higher risk of breast cancer was found for retinol in relation to ER-/progesterone receptor-negative tumors (OR: 2.37; 95% CI: 1.20, 4.67; P-heterogeneity with ER+/progesterone receptor positive = 0.06). We observed no statistically significant interaction between smoking, alcohol, or BMI and all investigated plasma analytes (based on tertile distribution).
CONCLUSION: Our results indicate that higher concentrations of plasma β-carotene and α-carotene are associated with lower breast cancer risk of ER- tumors.
OBJECTIVES: First, to summarize the main design features of a prospective case-control study -nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort- on plasma concentrations of persistent organic pollutants (POPs) and pancreatic cancer risk. And second, to assess the main methodological challenges posed by associations among characteristics and habits of study participants, fasting status, time from blood draw to cancer diagnosis, disease progression bias, basis of cancer diagnosis, and plasma concentrations of lipids and POPs. Results from etiologic analyses on POPs and pancreatic cancer risk, and other analyses, will be reported in future articles.
METHODS: Study subjects were 1533 participants (513 cases and 1020 controls matched by study centre, sex, age at blood collection, date and time of blood collection, and fasting status) enrolled between 1992 and 2000. Plasma concentrations of 22 POPs were measured by gas chromatography - triple quadrupole mass spectrometry (GC-MS/MS). To estimate the magnitude of the associations we calculated multivariate-adjusted odds ratios by unconditional logistic regression, and adjusted geometric means by General Linear Regression Models.
RESULTS: There were differences among countries in subjects' characteristics (as age, gender, smoking, lipid and POP concentrations), and in study characteristics (as time from blood collection to index date, year of last follow-up, length of follow-up, basis of cancer diagnosis, and fasting status). Adjusting for centre and time of blood collection, no factors were significantly associated with fasting status. Plasma concentrations of lipids were related to age, body mass index, fasting, country, and smoking. We detected and quantified 16 of the 22 POPs in more than 90% of individuals. All 22 POPs were detected in some participants, and the smallest number of POPs detected in one person was 15 (median, 19) with few differences by country. The highest concentrations were found for p,p'-DDE, PCBs 153 and 180 (median concentration: 3371, 1023, and 810 pg/mL, respectively). We assessed the possible occurrence of disease progression bias (DPB) in eight situations defined by lipid and POP measurements, on one hand, and by four factors: interval from blood draw to index date, tumour subsite, tumour stage, and grade of differentiation, on the other. In seven of the eight situations results supported the absence of DPB.
CONCLUSIONS: The coexistence of differences across study centres in some design features and participant characteristics is of relevance to other multicentre studies. Relationships among subjects' characteristics and among such characteristics and design features may play important roles in the forthcoming analyses on the association between plasma concentrations of POPs and pancreatic cancer risk.
METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.
RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.
CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.
METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).
RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.
CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.