Initial studies have indicated diabetes and obesity to be risk factors for hepatocellular carcinoma; but the association between other metabolic risk factors and primary liver cancer (PLC) has not been investigated. The metabolic syndrome and cancer project (Me‐Can) includes cohorts from Norway, Austria and Sweden with data on 578,700 subjects. We used Cox proportional hazard models to calculate relative risks (RRs) of PLC by body mass index (BMI), blood pressure and plasma levels of glucose, cholesterol and triglycerides as continuous standardized variables (z‐score with mean = 0 and standard deviation (SD) = 1) and their standardized sum of metabolic syndrome (MetS) z‐score. RRs were corrected for random error in measurements. During an average follow‐up of 12.0 years (SD = 7.8), 266 PLCs were diagnosed among cohort members. RR of liver cancer per unit increment of z‐score adjusted for age, smoking status and BMI and stratified by birth year, sex and sub‐cohorts, was for BMI 1.39 (95% confidence interval (CI) 1.24–1.58), mid blood pressure 2.08 (0.95–4.73), blood glucose 2.13 (1.55–2.94) cholesterol 0.62 (0.51–0.76) and serum triglycerides 0.85 (0.65–1.10). The RR per one unit increment of the MetS z‐score was 1.35 (1.12–1.61). BMI, glucose and a composite MetS score were positively and cholesterol negatively associated with risk of liver cancer.

To investigate the association between total serum cholesterol (TSC) and cancer incidence in the Metabolic syndrome and Cancer project (Me-Can). Me-Can consists of seven cohorts from Norway, Austria, and Sweden including 289,273 male and 288,057 female participants prospectively followed up for cancer incidence (n = 38,978) with a mean follow-up of 11.7 years. Cox regression models with age as the underlying time metric were used to estimate hazard ratios (HR) and their 95% confidence intervals (CI) for quintiles of cholesterol levels and per 1 mmol/l, adjusting for age at first measurement, body mass index (BMI), and smoking status. Estimates were corrected for regression dilution bias. Furthermore, we performed lag time analyses, excluding different times of follow-up, in order to check for reverse causation. In men, compared with the 1st quintile, TSC concentrations in the 5th quintile were borderline significantly associated with decreasing risk of total cancer (HR = 0.94; 95%CI: 0.88, 1.00). Significant inverse associations were observed for cancers of the liver/intrahepatic bile duct (HR = 0.14; 95%CI: 0.07, 0.29), pancreas cancer (HR = 0.52, 95% CI: 0.33, 0.81), non-melanoma of skin (HR = 0.67; 95%CI: 0.46, 0.95), and cancers of the lymph−/hematopoietic tissue (HR = 0.68, 95%CI: 0.54, 0.87). In women, hazard ratios for the 5th quintile were associated with decreasing risk of total cancer (HR = 0.86, 95%CI: 0.79, 0.93) and for cancers of the gallbladder (HR = 0.23, 95%CI: 0.08, 0.62), breast (HR = 0.70, 95%CI: 0.61, 0.81), melanoma of skin (HR = 0.61, 95%CI: 0.42, 0.88), and cancers of the lymph−/hematopoietic tissue (HR = 0.61, 95%CI: 0.44, 0.83). TSC was negatively associated with risk of cancer overall in females and risk of cancer at several sites in both males and females. In lag time analyses some associations persisted, suggesting that for these cancer sites reverse causation did not apply.

Pre-treatment with clopidogrel results in a reduction of ischaemic events in non-ST-elevation acute coronary syndromes. Data on upstream clopidogrel in the setting of primary percutaneous coronary intervention (PCI) are limited. The aim of this study was to investigate whether clopidogrel loading before arrival at the PCI centre may result in an improved outcome of primary PCI for ST-elevation myocardial infarction (STEMI). In a multicentre registry of acute PCI, 5955 patients undergoing primary PCI in Austria between January 2005 and December 2009 were prospectively enrolled. The patients consisted of two groups, a clopidogrel pre-treatment group (n = 1635 patients) receiving clopidogrel before arrival at the PCI centre and a peri-interventional clopidogrel group (n = 4320 patients) receiving clopidogrel at a later stage. Multiple logistic regression analysis including major confounding factors stratified by the participating centres was applied to investigate the effect of pre-treatment with clopidogrel on the in-hospital mortality. Additionally, two subgroups, with or without the use of GP IIb/IIIa antagonist therapy in the catheterization laboratory, were analysed. On univariate analysis, clopidogrel pre-treatment was associated with a reduced in-hospital mortality (3.4 vs. 6.1%, P< 0.01) after primary PCI. On multivariate analysis, clopidogrel pre-treatment remained an independent predictor of in-hospital mortality [odds ratio (OR) = 0.60, 95% confidence interval (CI) 0.35–0.99; P =0.048], especially in patients receiving additional GP IIb/IIIa antagonist therapy in the catheterization laboratory (OR = 0.40, 95% CI 0.19–0.83; P =0.01). Clopidogrel pre-treatment before arrival at the PCI centre is associated with reduced mortality in a real world setting of primary PCI. These results strongly support the recommendation of clopidogrel treatment ‘as soon as possible’ in patients with STEMI undergoing pimary PCI.

Background: We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in.Methods: We illustrate the approach using data for the diagnosis of ovarian cancer (n= 5914, 33% event fraction) and obstructive coronary artery disease (CAD;n= 4888, 44% event fraction). We used logistic regression to develop a prediction model consisting only of a priori selected predictors and assumed linear relations for continuous predictors. We mimicked prospective patient recruitment by developing the model on 100 randomly selected patients, and we used bootstrapping to internally validate the model. We sequentially added 50 random new patients until we reached a sample size of 3000 and re-estimated model performance at each step. We examined the required sample size for satisfying the following stopping rule: obtaining a calibration slope ≥ 0.9 and optimism in the c-statistic (or AUC) < = 0.02 at two consecutive sample sizes. This procedure was repeated 500 times. We also investigated the impact of alternative modeling strategies: modeling nonlinear relations for continuous predictors and correcting for bias on the model estimates (Firth’s correction).Results: Better discrimination was achieved in the ovarian cancer data (c-statistic 0.9 with 7 predictors) than in the CAD data (c-statistic 0.7 with 11 predictors). Adequate calibration and limited optimism in discrimination was achieved after a median of 450 patients (interquartile range 450–500) for the ovarian cancer data (22 events per parameter (EPP), 20–24) and 850 patients (750–900) for the CAD data (33 EPP, 30–35). A stricter criterion, requiring AUC optimism < = 0.01, was met with a median of 500 (23 EPP) and 1500 (59 EPP) patients, respectively. These sample sizes were much higher than the well-known 10 EPP rule of thumb and slightly higher than a recently published fixed sample size calculation method by Riley et al. Higher sample sizes were required when nonlinear relationships were modeled, and lower sample sizes when Firth’s correction was used.Conclusions: Adaptive sample size determination can be a useful supplement to fixed a priori sample size calculations, because it allows to tailor the sample size to the specific prediction modeling context in a dynamic fashion.

Objectives: Brain tumour has few established determinants. We assessed to which extent risk of brain tumour was related to metabolic syndrome factors in adults. Methods: In the Me-Can project, 580 000 individuals from Sweden, Austria, and Norway were followed for a median of 10 years after baseline measurement. Data on brain tumours were obtained from national cancer registries. The factors of metabolic syndrome (BMI, SBP and DBP, and blood levels of glucose, cholesterol, and triglycerides), separately and combined, were analysed in quintiles and for transformed z-scores (mean transformed to 0 and standard deviation to 1). Cox proportional hazards multivariate regression models were used, with corrections for measurement error. Results: During follow-up, 1312 primary brain tumours were diagnosed, predominantly meningioma (n = 348) and high-grade glioma (n = 436). For meningioma, the hazard ratio was increased for z-scores of SBP [hazard ratio = 1.27 per unit standard deviation, 95% confidence interval (CI) 1.03–1.57], of DBP (hazard ratio = 1.29, 95% CI 1.04–1.58), and of the combined metabolic syndrome score (hazard ratio = 1.31, 95% CI 1.11–1.54). An increased risk of high-grade glioma was found for DBP (hazard ratio = 1.23, 95% CI 1.01–1.50) and triglycerides (hazard ratio = 1.35, 95% CI 1.05–1.72). For both meningioma and high-grade glioma, the risk was more than double in the fifth quintiles of DBP compared to the lowest quintile. For meningioma this risk was even larger for SBP. Conclusion: Increased blood pressure was associated with risk of brain tumours, especially of meningiomas.