Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics.

BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING: This work was supported by Cancer Research UK (grant no. C8221/A29017).

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank.

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics.

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

Meat consumption and risk of ischemic heart disease: A systematic review and meta-analysis.

There is uncertainty regarding the association between unprocessed red and processed meat consumption and the risk of ischemic heart disease (IHD), and little is known regarding the association with poultry intake. The aim of this systematic review and meta-analysis was to quantitatively assess the associations of unprocessed red, processed meat, and poultry intake and risk of IHD in published prospective studies. We systematically searched CAB Abstract, MEDLINE, EMBASE, Web of Science, bioRxiv and medRxiv, and reference lists of selected studies and previous systematic reviews up to June 4, 2021. All prospective cohort studies that assessed associations between 1(+) meat types and IHD risk (incidence and/or death) were selected. The meta-analysis was conducted using fixed-effects models. Thirteen published articles were included (ntotal = 1,427,989; ncases = 32,630). Higher consumption of unprocessed red meat was associated with a 9% (relative risk (RR) per 50 g/day higher intake, 1.09; 95% confidence intervals (CI), 1.06 to 1.12; nstudies = 12) and processed meat intake with an 18% higher risk of IHD (1.18; 95% CI, 1.12 to 1.25; nstudies = 10). There was no association with poultry intake (nstudies = 10). This study provides substantial evidence that unprocessed red and processed meat, though not poultry, might be risk factors for IHD.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Describing a new food group classification system for UK biobank: analysis of food groups and sources of macro- and micronutrients in 208,200 participants.

PURPOSE: The UK Biobank study collected detailed dietary data using a web-based self-administered 24 h assessment tool, the Oxford WebQ. We aimed to describe a comprehensive food grouping system for this questionnaire and to report dietary intakes and key sources of selected nutrients by sex and education. METHODS: Participants with at least one valid 24-h questionnaire were included (n = 208,200). Dietary data were grouped based on the presence of nutrients as well as culinary use, processing, and plant/animal origin. For each food group, we calculated the contribution to energy intake, key macronutrients, and micronutrients. We also identified the top contributors to energy intake, free sugars and saturated fat by sex and education. RESULTS: From the 93 food groups, the top five contributors to energy intake (in descending order) were: desserts/cakes/pastries; white bread; white pasta/rice; bananas/other fruit; semi-skimmed milk. Wine, beer, and fruit juices were the top beverage contributors to overall energy intake. Biscuits, and desserts/cakes/pastries were the highest contributors to free sugars, total fat, and saturated fat intakes, but also contributed to the calcium and iron intakes. Top contributors to energy, saturated fat, and free sugars were broadly similar by sex and education category, with small differences in average nutrient intakes across the population. CONCLUSION: This new food classification system will support the growing interest in the associations between food groups and health outcomes and the development of food-based dietary guidelines. Food group variables will be available to all users of the UK Biobank WebQ questionnaire.

Recommended Definitions of Aggressive Prostate Cancer for Etiologic Epidemiologic Research.

BACKGROUND: In the era of widespread prostate-specific antigen testing, it is important to focus etiologic research on the outcome of aggressive prostate cancer, but studies have defined this outcome differently. We aimed to develop an evidence-based consensus definition of aggressive prostate cancer using clinical features at diagnosis for etiologic epidemiologic research. METHODS: Among prostate cancer cases diagnosed in 2007 in the National Cancer Institute's Surveillance, Epidemiology, and End Results-18 database with follow-up through 2017, we compared the performance of categorizations of aggressive prostate cancer in discriminating fatal prostate cancer within 10 years of diagnosis, placing the most emphasis on sensitivity and positive predictive value (PPV). RESULTS: In our case population (n = 55 900), 3073 men died of prostate cancer within 10 years. Among 12 definitions that included TNM staging and Gleason score, sensitivities ranged from 0.64 to 0.89 and PPVs ranged from 0.09 to 0.23. We propose defining aggressive prostate cancer as diagnosis of category T4 or N1 or M1 or Gleason score of 8 or greater prostate cancer, because this definition had one of the higher PPVs (0.23, 95% confidence interval = 0.22 to 0.24) and reasonable sensitivity (0.66, 95% confidence interval = 0.64 to 0.67) for prostate cancer death within 10 years. Results were similar across sensitivity analyses. CONCLUSIONS: We recommend that etiologic epidemiologic studies of prostate cancer report results for this definition of aggressive prostate cancer. We also recommend that studies separately report results for advanced category (T4 or N1 or M1), high-grade (Gleason score ≥8), and fatal prostate cancer. Use of this comprehensive set of endpoints will facilitate comparison of results from different studies and help elucidate prostate cancer etiology.

The effect of sample size on polygenic hazard models for prostate cancer.

We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.

Sex hormone binding globulin and risk of breast cancer: a Mendelian randomization study.

BACKGROUND: There are observational data suggesting an inverse association between circulating concentrations of sex hormone binding globulin (SHBG) and risk of postmenopausal breast cancer. However, causality is uncertain and few studies have investigated this association by tumour receptor status. We aimed to investigate these associations under the causal framework of Mendelian randomization (MR). METHODS: We used summary association estimates extracted from published genome-wide association study (GWAS) meta-analyses for SHBG and breast cancer, to perform two-sample MR analyses. Summary statistics were available for 122 977 overall breast cancer cases, of which 69 501 were estrogen receptor positive (ER+ve) and 21 468 were ER-ve, and 105 974 controls. To control for potential horizontal pleiotropy acting via body mass index (BMI), we performed multivariable inverse-variance weighted (IVW) MR as the main analysis, with the robustness of this approach further tested in sensitivity analyses. RESULTS: The multivariable IVW MR analysis indicated a lower risk of overall (odds ratio [OR]: 0.94; 95% confidence interval [CI]: 0.90, 0.98; P: 0.006) and ER+ve (OR: 0.92; 95% CI: 0.87, 0.97; P: 0.003) breast cancer, and a higher risk of ER-ve disease (OR: 1.09; 95% CI: 1.00, 1.18; P: 0.047) per 25 nmol/L higher SHBG levels. Sensitivity analyses were consistent with the findings of the main analysis. CONCLUSIONS: We corroborated the previous literature evidence coming from observational studies for a potentially causal inverse association between SHBG concentrations and risk of ER+ve breast cancer, but our findings also suggested a potential novel positive association with ER-ve disease that warrants further investigation, given the low prior probability of being true.

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts.

OBJECTIVES: To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. DESIGN: Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. SETTING: Multiple institutions that were members of international PRACTICAL consortium. PARTICIPANTS: All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. MAIN OUTCOME MEASURES: Prediction with hazard score of age of onset of aggressive cancer in validation set. RESULTS: In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. CONCLUSIONS: Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Lifestyle factors and prostate-specific antigen (PSA) testing in UK Biobank: Implications for epidemiological research.

BACKGROUND: The central role of prostate-specific antigen (PSA) testing in the diagnosis of prostate cancer leads to the possibility that observational studies that report associations between risk factors and prostate cancer could be affected by detection bias. This study aims to investigate whether reported risk factors for prostate cancer are associated with PSA testing in a large middle-aged population-based cohort in the UK. METHODS: The cross-sectional association between a wide range of sociodemographic, lifestyle, dietary and health characteristics with PSA testing was examined in 212,039 men aged 40-69 years in UK Biobank. RESULTS: A total of 62,022 (29%) men reported they had ever had a PSA test. A wide range of factors was associated with a higher likelihood of PSA testing including age, height, education level, family history of prostate cancer, black ethnic origin, not being in paid/self-employment, living with a wife or partner, having had a vasectomy, being diagnosed with cancer or hypertension and having a high dietary intake of cereal, cooked and salad/raw vegetables, fresh fruit and tea. Conversely, socioeconomic deprivation, Asian ethnic origin, current smoking, low alcohol intake, high body-mass index, high coffee consumption and being diagnosed with diabetes, heart disease or stroke were associated with a lower likelihood of PSA testing. CONCLUSIONS: A variety of sociodemographic, lifestyle and health-related characteristics are associated with PSA testing, suggesting that observed associations of some of these traits with risk for prostate cancer in epidemiological studies may be, at least partially, due to detection bias.

Main nutrient patterns are associated with prospective weight change in adults from 10 European countries.

PURPOSE: Various food patterns have been associated with weight change in adults, but it is unknown which combinations of nutrients may account for such observations. We investigated associations between main nutrient patterns and prospective weight change in adults. METHODS: This study includes 235,880 participants, 25-70 years old, recruited between 1992 and 2000 in 10 European countries. Intakes of 23 nutrients were estimated from country-specific validated dietary questionnaires using the harmonized EPIC Nutrient DataBase. Four nutrient patterns, explaining 67 % of the total variance of nutrient intakes, were previously identified from principal component analysis. Body weight was measured at recruitment and self-reported 5 years later. The relationship between nutrient patterns and annual weight change was examined separately for men and women using linear mixed models with random effect according to center controlling for confounders. RESULTS: Mean weight gain was 460 g/year (SD 950) and 420 g/year (SD 940) for men and women, respectively. The annual differences in weight gain per one SD increase in the pattern scores were as follows: principal component (PC) 1, characterized by nutrients from plant food sources, was inversely associated with weight gain in men (-22 g/year; 95 % CI -33 to -10) and women (-18 g/year; 95 % CI -26 to -11). In contrast, PC4, characterized by protein, vitamin B2, phosphorus, and calcium, was associated with a weight gain of +41 g/year (95 % CI +2 to +80) and +88 g/year (95 % CI +36 to +140) in men and women, respectively. Associations with PC2, a pattern driven by many micro-nutrients, and with PC3, a pattern driven by vitamin D, were less consistent and/or non-significant. CONCLUSIONS: We identified two main nutrient patterns that are associated with moderate but significant long-term differences in weight gain in adults.

Prediction of individual genetic risk to prostate cancer using a polygenic score.

BACKGROUND: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

Prediagnostic Sex Steroid Hormones in Relation to Male Breast Cancer Risk.

PURPOSE: Although previous studies have implicated a variety of hormone-related risk factors in the etiology of male breast cancers, no previous studies have examined the effects of endogenous hormones. PATIENTS AND METHODS: Within the Male Breast Cancer Pooling Project, an international consortium comprising 21 case-control and cohort investigations, a subset of seven prospective cohort studies were able to contribute prediagnostic serum or plasma samples for hormone quantitation. Using a nested case-control design, multivariable unconditional logistic regression analyses estimated odds ratios and 95% CIs for associations between male breast cancer risk and 11 individual estrogens and androgens, as well as selected ratios of these analytes. RESULTS: Data from 101 cases and 217 matched controls were analyzed. After adjustment for age and date of blood draw, race, and body mass index, androgens were found to be largely unrelated to risk, but circulating estradiol levels showed a significant association. Men in the highest quartile had an odds ratio of 2.47 (95% CI, 1.10 to 5.58) compared with those in the lowest quartile (trend P = .06). Assessment of estradiol as a ratio to various individual androgens or sum of androgens showed no further enhancement of risk. These relations were not significantly modified by either age or body mass index, although estradiol was slightly more strongly related to breast cancers occurring among younger (age < 67 years) than older men. CONCLUSION: Our results support the notion of an important role for estradiol in the etiology of male breast cancers, similar to female breast cancers.

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans.

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.

Tinned fruit consumption and mortality in three prospective cohorts.

Dietary recommendations to promote health include fresh, frozen and tinned fruit, but few studies have examined the health benefits of tinned fruit. We therefore studied the association between tinned fruit consumption and mortality. We followed up participants from three prospective cohorts in the United Kingdom: 22,421 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort (1993-2012), 52,625 participants from the EPIC-Oxford cohort (1993-2012), and 7440 participants from the Whitehall II cohort (1991-2012), all reporting no history of heart attack, stroke, or cancer when entering these studies. We estimated the association between frequency of tinned fruit consumption and all cause mortality (primary outcome measure) using Cox regression models within each cohort, and pooled hazard ratios across cohorts using random-effects meta-analysis. Tinned fruit consumption was assessed with validated food frequency questionnaires including specific questions about tinned fruit. During 1,305,330 person years of follow-up, 8857 deaths occurred. After adjustment for lifestyle factors and risk markers the pooled hazard ratios (95% confidence interval) of all cause mortality compared with the reference group of tinned fruit consumption less often than one serving per month were: 1.05 (0.99, 1.12) for one to three servings per month, 1.10 (1.03, 1.18) for one serving per week, and 1.13 (1.04, 1.23) for two or more servings per week. Analysis of cause-specific mortality showed that tinned fruit consumption was associated with mortality from cardiovascular causes and from non-cardiovascular, non-cancer causes. In a pooled analysis of three prospective cohorts from the United Kingdom self-reported tinned fruit consumption in the 1990s was weakly but positively associated with mortality during long-term follow-up. These findings raise questions about the evidence underlying dietary recommendations to promote tinned fruit consumption as part of a healthy diet.

General and abdominal obesity and risk of esophageal and gastric adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition.

General obesity, as reflected by BMI, is an established risk factor for esophageal adenocarcinoma (EAC), a suspected risk factor for gastric cardia adenocarcinoma (GCC) and appears unrelated to gastric non-cardia adenocarcinoma (GNCC). How abdominal obesity, as commonly measured by waist circumference (WC), relates to these cancers remains largely unexplored. Using measured anthropometric data from 391,456 individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC) study and 11 years of follow-up, we comprehensively assessed the association of anthropometric measures with risk of EAC, GCC and GNCC using multivariable proportional hazards regression. One hundred twenty-four incident EAC, 193 GCC and 224 GNCC were accrued. After mutual adjustment, BMI was unrelated to EAC, while WC showed a strong positive association (highest vs. lowest quintile HR = 1.19; 95% CI, 0.63-2.22 and HR = 3.76; 1.72-8.22, respectively). Hip circumference (HC) was inversely related to EAC after controlling for WC, while WC remained positively associated (HR = 0.35; 0.18-0.68, and HR=4.10; 1.94-8.63, respectively). BMI was not associated with GCC or GNCC. WC was related to higher risks of GCC after adjustment for BMI and more strongly after adjustment for HC (highest vs. lowest quintile HR = 1.91; 1.09-3.37, and HR = 2.23; 1.28-3.90, respectively). Our study demonstrates that abdominal, rather than general, obesity is an indisputable risk factor for EAC and also provides evidence for a protective effect of gluteofemoral (subcutaneous) adipose tissue in EAC. Our study further shows that general obesity is not a risk factor for GCC and GNCC, while the role of abdominal obesity in GCC needs further investigation.

Healthy lifestyle and risk of breast cancer among postmenopausal women in the European Prospective Investigation into Cancer and Nutrition cohort study.

Breast cancer is the most common cancer among women and prevention strategies are needed to reduce incidence worldwide. A healthy lifestyle index score (HLIS) was generated to investigate the joint effect of modifiable lifestyle factors on postmenopausal breast cancer risk. The study included 242,918 postmenopausal women from the multinational European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, with detailed information on diet and lifestyle assessed at baseline. The HLIS was constructed from five factors (diet, physical activity, smoking, alcohol consumption and anthropometry) by assigning scores of 0-4 to categories of each component, for which higher values indicate healthier behaviours. Hazard ratios (HR) were estimated by Cox proportional regression models. During 10.9 years of median follow-up, 7,756 incident breast cancer cases were identified. There was a 3% lower risk of breast cancer per point increase of the HLIS. Breast cancer risk was inversely associated with a high HLIS when fourth versus second (reference) categories were compared [adjusted HR = 0.74; 95% confidence interval (CI): 0.66-0.83]. The fourth versus the second category of the HLIS was associated with a lower risk for hormone receptor double positive (adjusted HR = 0.81, 95% CI: 0.67-0.98) and hormone receptor double negative breast cancer (adjusted HR = 0.60, 95% CI: 0.40-0.90). Findings suggest having a high score on an index of combined healthy behaviours reduces the risk of developing breast cancer among postmenopausal women. Programmes which engage women in long term health behaviours should be supported.

A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer.

Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of > 10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P < 5 × 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.