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.

METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.

METHODS: Among 477 312 participants, intakes of 23 nutrients were estimated from validated dietary questionnaires. Using results from a previous principal component (PC) analysis, four major nutrient patterns were identified. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed for the association of each of the four patterns and CRC incidence using multivariate Cox proportional hazards models with adjustment for established CRC risk factors.

RESULTS: During an average of 11 years of follow-up, 4517 incident cases of CRC were documented. A nutrient pattern characterised by high intakes of vitamins and minerals was inversely associated with CRC (HR per 1 s.d.=0.94, 95% CI: 0.92-0.98) as was a pattern characterised by total protein, riboflavin, phosphorus and calcium (HR (1 s.d.)=0.96, 95% CI: 0.93-0.99). The remaining two patterns were not significantly associated with CRC risk.