Trends in self-reported past alcoholic beverage consumption and ethanol intake from 1950 to 1995 observed in eight European countries participating in the European Investigation into Cancer and Nutrition (EPIC).

OBJECTIVE To describe the trends of self-reported past consumption of alcoholic beverages and ethanol intake from 1950 to 1995 within the European Prospective Investigation into Cancer and Nutrition (EPIC). DESIGN Data on consumption of beer/cider, wine and liqueur/spirits were obtained retrospectively at age 20, 30 and 40 years to calculate average consumption and ethanol intake for the time periods 1950-1975 (at age 20), 1960-1985 (at age 30) and 1970-1995 (at age 40). Regression analysis was conducted with the time period data to assess trends in past alcoholic beverage consumption and ethanol intake with time. SETTING The EPIC project. SUBJECTS In total, 392 064 EPIC participants (275 249 women and 116 815 men) from 21 study centres in eight European countries. RESULTS Generally, increases in beer/cider consumption were observed for most EPIC centres for 1950-1975, 1960-1985 and 1970-1995. Trends in wine consumption differed according to geographical location: downward trends with time were observed for men in southern European EPIC centres, upward trends for those in middle/northern European study centres. For women, similar but less pronounced trends were observed. Because wine consumption was the major contributor to ethanol intake for both men and women in most study centres, time trends for ethanol intake showed a similar geographical pattern to that of wine consumption. CONCLUSION The different trends in alcoholic beverage consumption and ethanol intake suggest that information depicting lifetime history of ethanol intake should be included in analyses of the relationship between ethanol and chronic diseases, particularly in multi-centre studies such as EPIC.

Combined impact of healthy lifestyle factors on colorectal cancer: a large European cohort study.

BACKGROUND Excess body weight, physical activity, smoking, alcohol consumption and certain dietary factors are individually related to colorectal cancer (CRC) risk; however, little is known about their joint effects. The aim of this study was to develop a healthy lifestyle index (HLI) composed of five potentially modifiable lifestyle factors - healthy weight, physical activity, non-smoking, limited alcohol consumption and a healthy diet, and to explore the association of this index with CRC incidence using data collected within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. METHODS In the EPIC cohort, a total of 347,237 men and women, 25- to 70-years old, provided dietary and lifestyle information at study baseline (1992 to 2000). Over a median follow-up time of 12 years, 3,759 incident CRC cases were identified. The association between a HLI and CRC risk was evaluated using Cox proportional hazards regression models and population attributable risks (PARs) have been calculated. RESULTS After accounting for study centre, age, sex and education, compared with 0 or 1 healthy lifestyle factors, the hazard ratio (HR) for CRC was 0.87 (95% confidence interval (CI): 0.44 to 0.77) for two factors, 0.79 (95% CI: 0.70 to 0.89) for three factors, 0.66 (95% CI: 0.58 to 0.75) for four factors and 0.63 (95% CI: 0.54 to 0.74) for five factors; P-trend <0.0001. The associations were present for both colon and rectal cancers, HRs, 0.61 (95% CI: 0.50 to 0.74; P for trend <0.0001) for colon cancer and 0.68 (95% CI: 0.53 to 0.88; P-trend <0.0001) for rectal cancer, respectively (P-difference by cancer sub-site = 0.10). Overall, 16% of the new CRC cases (22% in men and 11% in women) were attributable to not adhering to a combination of all five healthy lifestyle behaviours included in the index. CONCLUSIONS Combined lifestyle factors are associated with a lower incidence of CRC in European populations characterized by western lifestyles. Prevention strategies considering complex targeting of multiple lifestyle factors may provide practical means for improved CRC prevention.

Population pharmacokinetics of teicoplanin in children.

Teicoplanin is frequently administered to treat Gram-positive infections in pediatric patients. However, not enough is known about the pharmacokinetics (PK) of teicoplanin in children to justify the optimal dosing regimen. The aim of this study was to determine the population PK of teicoplanin in children and evaluate the current dosage regimens. A PK hospital-based study was conducted. Current dosage recommendations were used for children up to 16 years of age. Thirty-nine children were recruited. Serum samples were collected at the first dose interval (1, 3, 6, and 24 h) and at steady state. A standard 2-compartment PK model was developed, followed by structural models that incorporated weight. Weight was allowed to affect clearance (CL) using linear and allometric scaling terms. The linear model best accounted for the observed data and was subsequently chosen for Monte Carlo simulations. The PK parameter medians/means (standard deviation [SD]) were as follows: CL, [0.019/0.023 (0.01)] × weight liters/h/kg of body weight; volume, 2.282/4.138 liters (4.14 liters); first-order rate constant from the central to peripheral compartment (Kcp), 0.474/3.876 h(-1) (8.16 h(-1)); and first-order rate constant from peripheral to central compartment (Kpc), 0.292/3.994 h(-1) (8.93 h(-1)). The percentage of patients with a minimum concentration of drug in serum (Cmin) of <10 mg/liter was 53.85%. The median/mean (SD) total population area under the concentration-time curve (AUC) was 619/527.05 mg · h/liter (166.03 mg · h/liter). Based on Monte Carlo simulations, only 30.04% (median AUC, 507.04 mg · h/liter), 44.88% (494.1 mg · h/liter), and 60.54% (452.03 mg · h/liter) of patients weighing 50, 25, and 10 kg, respectively, attained trough concentrations of >10 mg/liter by day 4 of treatment. The teicoplanin population PK is highly variable in children, with a wider AUC distribution spread than for adults. Therapeutic drug monitoring should be a routine requirement to minimize suboptimal concentrations. (This trial has been registered in the European Clinical Trials Database Registry [EudraCT] under registration number 2012-005738-12.).

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. 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. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.