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Background Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer. Methods Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression. For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium. Results In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD = 1.09: 95% CI 1.07, 1.11), aggressive (1.09: 1.03, 1.16) and possibly early-onset disease (1.11: 1.00, 1.24); associations were similar in MR analyses (OR per 1 SD = 1.07: 1.00, 1.15; 1.10: 1.01, 1.20; and 1.13; 0.98, 1.30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1.06: 1.02, 1.11) and IGFBP-3 (1.08: 1.04, 1.11) had higher risks of overall prostate cancer, whereas higher IGFBP-1 was associated with a lower risk (0.95: 0.91, 0.99); these associations were attenuated following adjustment for IGF-I. Conclusions These findings support the role of IGF-I in the development of prostate cancer, including for aggressive disease.

Background The global prevalence of diabetes is high and rapidly increasing. Some previous studies have found that vegetarians might have a lower risk of diabetes than non-vegetarians. Objective We examined the association between vegetarianism and risk of hospitalisation or death with diabetes in a large, prospective cohort study of British adults. Methods The analysed cohort included participants from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford study who were diabetes free at recruitment (1993–2001), with available dietary intake data at baseline, and linked hospital admissions and death data for diabetes over follow-up (n = 45,314). Participants were categorised as regular meat eaters (≥50 g per day: n = 15,181); low meat eaters (Results Over a mean of 17.6 years of follow-up, 1224 incident cases of diabetes were recorded. Compared with regular meat eaters, the low meat eaters, fish eaters, and vegetarians were less likely to develop diabetes (hazard ratio (HR) = 0.63, 95% confidence interval (CI) 0.54–0.75; HR = 0.47, 95% CI 0.38–0.59; and HR = 0.63, 95% CI 0.54–0.74, respectively). These associations were substantially attenuated after adjusting for body mass index (BMI) (low meat eaters: HR = 0.78, 95% CI 0.66–0.92; fish eaters: HR = 0.64, 95% CI 0.51–0.80; and vegetarians: HR = 0.89, 95% CI 0.76–1.05). Conclusions Low meat and non-meat eaters had a lower risk of diabetes, in part because of a lower BMI.

Background: It has been proposed that night shift work could increase breast cancer incidence. A 2007 World Health Organization review concluded, mainly from animal evidence, that shift work involving circadian disruption is probably carcinogenic to humans. We therefore aimed to generate prospective epidemiological evidence on night shift work and breast cancer incidence. Methods: Overall, 522 246 Million Women Study, 22 559 EPIC-Oxford, and 251 045 UK Biobank participants answered questions on shift work and were followed for incident cancer. Cox regression yielded multivariable-adjusted breast cancer incidence rate ratios (RRs) and 95% confidence intervals (CIs) for night shift work vs no night shift work, and likelihood ratio tests for interaction were used to assess heterogeneity. Our meta-analyses combined these and relative risks from the seven previously published prospective studies (1.4 million women in total), using inverse-variance weighted averages of the study-specific log RRs. Results: In the Million Women Study, EPIC-Oxford, and UK Biobank, respectively, 673, 28, and 67 women who reported night shift work developed breast cancer, and the RRs for any vs no night shift work were 1.00 (95% CI = 0.92 to 1.08), 1.07 (95% CI = 0.71 to 1.62), and 0.78 (95% CI = 0.61 to 1.00). In the Million Women Study, the RR for 20 or more years of night shift work was 1.00 (95% CI = 0.81 to 1.23), with no statistically significant heterogeneity by sleep patterns or breast cancer risk factors. Our meta-analysis of all 10 prospective studies included 4660 breast cancers in women reporting night shift work; compared with other women, the combined relative risks were 0.99 (95% CI = 0.95 to 1.03) for any night shift work, 1.01 (95% CI = 0.93 to 1.10) for 20 or more years of night shift work, and 1.00 (95% CI = 0.87 to 1.14) for 30 or more years. Conclusions: The totality of the prospective evidence shows that night shift work, including long-term shift work, has little or no effect on breast cancer incidence.

Additional file 1 Methods 1. Assessment of dietary intake for meat. Methods 2. Assessment of health outcomes. Methods 3. Covariates. Table 1. Disease outcome definition and exclusion criteria. Table 2. Baseline characteristics of participants by total meat intake in UK Biobank (n = 467,384). Table 3. Baseline characteristics of participants by unprocessed red meat intake in UK Biobank (n = 468,328). Table 4. Baseline characteristics of participants by processed meat intake in UK Biobank (n = 472,844). Table 5. Baseline characteristics of participants by poultry meat intake in UK Biobank (n = 473,011). Table 6. Risk of 25 common conditions by total meat intake in UK Biobank. Table 7. Risk of 25 common conditions by unprocessed red and processed meat intake in UK Biobank. Table 8. Risk of 25 common conditions by unprocessed red meat intake in UK Biobank. Table 9. Risk of 25 common conditions by processed meat intake in UK Biobank. Table 10. Risk of 25 common conditions by poultry meat intake in UK Biobank. Figure 1. Participant flow chart of the study. Figure 2. Risk of 25 common conditions by higher daily intake of total meat excluding the first 4 years of follow-up, in never smokers. Figure 3. Risk of 25 common conditions by higher daily intake of unprocessed red and processed meat excluding the first 4 years of follow-up, in never smokers. Figure 4. Risk of 25 common conditions by higher daily intake of unprocessed red meat excluding the first 4 years of follow-up, in never smokers. Figure 5. Risk of 25 common conditions by higher daily intake of processed meat excluding the first 4 years of follow-up, in never smokers. Figure 6. Risk of 25 common conditions by higher daily intake of poultry meat excluding the first 4 years of follow-up, in never smokers. Figure 7. Risk of 25 common conditions per 100 g/day higher daily intake of total meat.

BACKGROUND: Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer. METHODS: In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure. RESULTS: Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62-0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40-0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4. CONCLUSIONS: Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.

Additional file 1: Supplementary results. Fig. S1. Participant flow chart. Table S1. ICD codes for incident fractures. Table S2. Baseline characteristics by diet group and sex. Table S3. Food and nutrient intake by diet group and sex. Table S4. Risks of subtypes of main site fractures. Table S5-Sensitivity analyses. Table S6. Risks of hip fractures by age, sex, menopausal status, physical activity and BMI.

Additional file 1 Methods 1. Assessment of dietary intake for meat. Methods 2. Assessment of health outcomes. Methods 3. Covariates. Table 1. Disease outcome definition and exclusion criteria. Table 2. Baseline characteristics of participants by total meat intake in UK Biobank (n = 467,384). Table 3. Baseline characteristics of participants by unprocessed red meat intake in UK Biobank (n = 468,328). Table 4. Baseline characteristics of participants by processed meat intake in UK Biobank (n = 472,844). Table 5. Baseline characteristics of participants by poultry meat intake in UK Biobank (n = 473,011). Table 6. Risk of 25 common conditions by total meat intake in UK Biobank. Table 7. Risk of 25 common conditions by unprocessed red and processed meat intake in UK Biobank. Table 8. Risk of 25 common conditions by unprocessed red meat intake in UK Biobank. Table 9. Risk of 25 common conditions by processed meat intake in UK Biobank. Table 10. Risk of 25 common conditions by poultry meat intake in UK Biobank. Figure 1. Participant flow chart of the study. Figure 2. Risk of 25 common conditions by higher daily intake of total meat excluding the first 4 years of follow-up, in never smokers. Figure 3. Risk of 25 common conditions by higher daily intake of unprocessed red and processed meat excluding the first 4 years of follow-up, in never smokers. Figure 4. Risk of 25 common conditions by higher daily intake of unprocessed red meat excluding the first 4 years of follow-up, in never smokers. Figure 5. Risk of 25 common conditions by higher daily intake of processed meat excluding the first 4 years of follow-up, in never smokers. Figure 6. Risk of 25 common conditions by higher daily intake of poultry meat excluding the first 4 years of follow-up, in never smokers. Figure 7. Risk of 25 common conditions per 100 g/day higher daily intake of total meat.

Additional file 1: Supplementary results. Fig. S1. Participant flow chart. Table S1. ICD codes for incident fractures. Table S2. Baseline characteristics by diet group and sex. Table S3. Food and nutrient intake by diet group and sex. Table S4. Risks of subtypes of main site fractures. Table S5-Sensitivity analyses. Table S6. Risks of hip fractures by age, sex, menopausal status, physical activity and BMI.