Food groups and risk of coronary heart disease, stroke and heart failure: A systematic review and dose-response meta-analysis of prospective studies.

BACKGROUND: Despite growing evidence for food-based dietary patterns' potential to reduce cardiovascular disease risk, knowledge about the amounts of food associated with the greatest change in risk of specific cardiovascular outcomes and about the quality of meta-evidence is limited. Therefore, the aim of this meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups (whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages [SSB]) and the risk of coronary heart disease (CHD), stroke and heart failure (HF). METHODS: We conducted a systematic search in PubMed and Embase up to March 2017 for prospective studies. Summary risk ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random effects model for highest versus lowest intake categories, as well as for linear and non-linear relationships. RESULTS: Overall, 123 reports were included in the meta-analyses. An inverse association was present for whole grains (RRCHD: 0.95 (95% CI: 0.92-0.98), RRHF: 0.96 (0.95-0.97)), vegetables and fruits (RRCHD: 0.97 (0.96-0.99), and 0.94 (0.90-0.97); RRstroke: 0.92 (0.86-0.98), and 0.90 (0.84-0.97)), nuts (RRCHD: 0.67 (0.43-1.05)), and fish consumption (RRCHD: 0.88 (0.79-0.99), RRstroke: 0.86 (0.75-0.99), and RRHF: 0.80 (0.67-0.95)), while a positive association was present for egg (RRHF: 1.16 (1.03-1.31)), red meat (RRCHD: 1.15 (1.08-1.23), RRstroke: 1.12 (1.06-1.17), RRHF: 1.08 (1.02-1.14)), processed meat (RRCHD: 1.27 (1.09-1.49), RRstroke: 1.17 (1.02-1.34), RRHF: 1.12 (1.05-1.19)), and SSB consumption (RRCHD: 1.17 (1.11-1.23), RRstroke: 1.07 (1.02-1.12), RRHF: 1.08 (1.05-1.12)) in the linear dose-response meta-analysis. There were clear indications for non-linear dose-response relationships between whole grains, fruits, nuts, dairy, and red meat and CHD. CONCLUSION: An optimal intake of whole grains, vegetables, fruits, nuts, legumes, dairy, fish, red and processed meat, eggs and SSB showed an important lower risk of CHD, stroke, and HF.

Associations of food groups and cardiometabolic and inflammatory biomarkers: does the meal matter?

Increased attention has been paid to circadian patterns and how predisposition to metabolic disorders can be affected by meal timing. Currently, it is not clear which role can be attributed to the foods selected at meals. On a cross-sectional sub-cohort study (815 adults) within the European Prospective Investigation into Cancer and Nutrition-Potsdam study, we investigated whether the same foods (vegetables, fruits, refined grains, whole grains, red and processed meats) eaten at different meals (breakfast, lunch or dinner) show different associations with biomarkers of cardiometabolic risk. Meal-specific usual intakes were calculated from multiple 24-h dietary recalls. Multivariable-adjusted linear regression models showed that intake of vegetables at breakfast was associated with lower LDL-cholesterol (-0·37 mmol/l per 50 g; 95 % CI -0·61, -0·12) and vegetables at dinner was associated with higher HDL-cholesterol (0·05 mmol/l per 50 g; 95 % CI 0, 0·10). Fruit intake at breakfast was associated with lower glycated Hb (HbA1c) (-0·06 % per 50 g; 95 % CI -0·10, -0·01) and fruits at dinner with lower C-reactive protein (CRP) (-0·21 mg/l per 50 g; 95 % CI -0·42, -0·01). Red and processed meat intake at breakfast was associated with higher HbA1c (0·25 % per 50 g; 95 % CI 0·05, 0·46) and CRP (0·76 mg/l per 50 g; 95 % CI 0·15, 1·36). Our results suggest that by preferring fruits and vegetables and avoiding red and processed meats at specific meals (i.e. breakfast and dinner), cardiometabolic profiles and ultimately chronic disease risk could be improved. Lunch seemed to be a less important meal in terms of food-biomarker associations.

Intake of 12 food groups and disability-adjusted life years from coronary heart disease, stroke, type 2 diabetes, and colorectal cancer in 16 European countries.

Our aim was to estimate and rank 12 food groups according to disability-adjusted life years (DALYs) from coronary heart disease (CHD), stroke, type 2 diabetes (T2D), and colorectal cancer (CRC) in 16 European countries. De novo published non-linear dose-response meta-analyses of prospective studies (based on 297 primary reports), and food consumption data from the European Food Safety Authority Comprehensive European Food Consumption Database in Exposure Assessment, and DALY estimates from the Institute for Health Metrics and Evaluation were used. By implementing disease-specific counterfactual scenarios of theoretical minimum risk exposure level (TMRELs), the proportion of DALYs attributed to 12 food groups was estimated. In addition, a novel modelling approach was developed to obtain a single (optimized) TMREL across diseases. Four scenarios were analysed (A: disease-specific TMRELs/all food-disease associations; B: disease-specific TMRELs/only significant food-disease associations; C: single TMREL/all food-disease associations; D: single TMREL/only significant food-disease associations). Suboptimal food intake was associated with the following proportions of DALYs; Scenario A (highest-estimate) and D (lowest-estimate): CHD (A: 67%, D: 52%), stroke (A: 49%, D: 30%), T2D (A: 57%, D: 51%), and CRC (A: 54%, D: 40%). Whole grains (10%) had the highest impact on DALYs, followed by nuts (7.1%), processed meat (6.4%), fruit (4.4%) and fish and legumes (4.2%) when combining all scenarios. The contribution to total DALYs of all food groups combined in the different scenarios ranged from 41-52% in Austria to 51-69% in the Czech-Republic. These findings could have important implications for planning future food-based dietary guidelines as a public health nutrition strategy.

Gaussian graphical models identified food intake networks and risk of type 2 diabetes, CVD, and cancer in the EPIC-Potsdam study.

PURPOSE: The aim of the study was to investigate the association between the previously identified Gaussian graphical models' (GGM) food intake networks and risk of major chronic diseases as well as intermediate biomarkers in the European Prospective Investigation into Cancer and nutrition (EPIC)-Potsdam cohort. METHODS: In this cohort analysis of 10,880 men and 13,340 women, adherence to the previously identified sex-specific GGM networks as well as principal component analysis identified patterns was investigated in relation to risk of major chronic diseases, using Cox-proportional hazard models. Associations of the patterns with intermediate biomarkers were cross-sectionally analyzed using multiple linear regressions. RESULTS: Results showed that higher adherence to the GGM Western-type pattern was associated with increased risk (Hazard Ratio: 1.55; 95% CI 1.13-2.15; P trend = 0.004) of type 2 diabetes (T2D) in women, whereas adherence to a high-fat dairy (HFD) pattern was associated with lower risk of T2D both in men (0.69; 95% CI 0.54-0.89; P trend < 0.001) and women (0.71; 95% CI: 0.53, 0.96; P trend = 0.09). Among PCA patterns, HFD pattern was associated with lower risk of T2D (0.74; 95% CI 0.58-0.95; P trend < 0.001) in men and bread and sausage pattern was associated with higher risk of T2D (1.79; 95% CI 1.29-2.48; P trend < 0.001) in women. Moreover, The GGM-HFD pattern was positively associated with HDL-C in men and inversely associated with C-reactive protein in women. CONCLUSION: Overall, these results show that GGM-identified networks reflect dietary patterns, which could also be related to risk of chronic diseases.

Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake.

BACKGROUND: Meals differ in their nutritional content. This variation has not been fully addressed despite its potential contribution in understanding eating behavior. The aim of this study was to investigate the between-meal and between-individual variance in energy and macronutrient intake as a measure of variation in intake and the meal type-specific relative importance of predictors of these intake variations. METHODS: Energy and macronutrient intake were derived from three 24 h dietary recalls in an EPIC-Potsdam sub-cohort of 814 German adults. Intra-class correlation was calculated for participants and meal type. Predictors of intake were assessed using meal type-specific multilevel regression models in a structural equation modeling framework at intake and participant levels using the Pratt Index. The importance of the predictor energy misreporting was assessed in sensitivity analyses on 682 participants. 95% confidence intervals were calculated based on 1000 bootstrap samples. RESULTS: Differences between meal types explain a large proportion of the variation in intake (intra-class correlation: 39% for energy, 25% for carbohydrates, 47% for protein, and 33% for fat). Between-participant variation in intake was much lower, with a maximum of 3% for carbohydrate and fat. Place of meal was the most important intake-level predictor of energy and macronutrient intake (Pratt Index of up to 65%). Week/weekend day was important in the breakfast meal, and prior interval (hours passed since last meal) was important for the afternoon snack and dinner. On the participant level, sex was the most important predictor, with Pratt Index of up to 95 and 59% in the main and in the sensitivity analysis, respectively. Energy misreporting was especially important at the afternoon snack, accounting for up to 69% of the explained variance. CONCLUSIONS: The meal type explains the highest variation in energy and macronutrient intakes. We identified key predictors of variation in the intake and in the participant levels. These findings suggest that successful dietary modification efforts should focus on improving specific meals.

Correction to: Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake.

Following publication of the original article [1], the authors reported an error in Table 3. The correct Table 3 is provided below.

Effects of oils and solid fats on blood lipids: a systematic review and network meta-analysis.

The aim of this network meta-analysis (NMA) is to compare the effects of different oils/solid fats on blood lipids. Literature searches were performed until March 2018. Inclusion criteria were as follows: i) randomized trial (3 weeks study length) comparing at least two of the following oils/solid fats: safflower, sunflower, rapeseed, hempseed, flaxseed, corn, olive, soybean, palm, and coconut oil, and lard, beef-fat, and butter; ii) outcomes LDL-cholesterol (LDL-C), total cholesterol (TC), HDL-cholesterol (HDL-C), and triacylglycerols (TGs). A random dose-response (per 10% isocaloric exchange) NMA was performed and surface under the cumulative ranking curve (SUCRA) was estimated. Fifty-four trials were included in the NMA. Safflower oil had the highest SUCRA value for LDL-C (82%) and TC (90%), followed by rapeseed oil (76% for LDL-C, 85% for TC); whereas, palm oil (74%) had the highest SUCRA value for TG, and coconut oil (88%) for HDL-C. Safflower, sunflower, rapeseed, flaxseed, corn, olive, soybean, palm, and coconut oil as well beef fat were more effective in reducing LDL-C (-0.42 to -0.23 mmol/l) as compared with butter. Despite limitations in these data, our NMA findings are in line with existing evidence on the metabolic effects of fat and support current recommendations to replace high saturated-fat food with unsaturated oils.

Food groups and risk of colorectal cancer.

The aim of this systematic review and meta-analysis was to summarize the evidence on the relationship between intake of 12 major food groups, including whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, processed meat and sugar-sweetened beverages with risk of colorectal cancer (CRC). We conducted a systematic search in PubMed and Embase for prospective studies investigating the association between these 12 food groups and risk of CRC until April 2017. Summary risk ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random effects model for high vs. low intake categories, as well as for linear and nonlinear relationships. An inverse association was observed for whole grains (RR30g/d : 0.95, 95% CI 0.93, 0.97; n = 9 studies), vegetables (RR100g/d : 0.97, 95% CI 0.96, 0.98; n = 15), fruit (RR100g/d : 0.97, 95% CI 0.95, 0.99; n = 16) and dairy (RR200g/d : 0.93, 95% CI 0.91, 0.94; n = 15), while a positive association for red meat (RR100g/d : 1.12, 95% CI 1.06, 1.19; n = 21) and processed meat (RR50g/d : 1.17, 95% CI 1.10, 1.23; n = 16), was seen in the linear dose-response meta-analysis. Some evidence for nonlinear relationships was observed between vegetables, fruit and dairy and risk of colorectal cancer. Findings of this meta-analysis showed that a diet characterized by high intake of whole grains, vegetables, fruit and dairy products and low amounts of red meat and processed meat was associated with lower risk of CRC.

Functional relevance of radiographic spinal progression in axial spondyloarthritis: results from the GErman SPondyloarthritis Inception Cohort.

Objectives: The aim of the study was to investigate the functional relevance of the development of structural damage in the spine in patients with early axial spondyloarthritis (axSpA). Methods: Altogether, 210 patients with early axSpA (symptom duration ⩽10 years) who completed a 2-year clinical and radiographic follow-up in the GErman SPondyloarthritis Inception Cohort were included. An association between structural damage in the spine [modified Stoke AS Spine Score (mSASSS)] and functional status (the BASFI) or spinal mobility (the BASMI) was assessed in a longitudinal linear mixed model analysis; both unstandardized (ß) and standardized (ßstand) regression coefficients were calculated. Results: There was an association between mSASSS and BASFI: ß = 0.05 (95% CI: 0.03, 0.08) and ßstand = 0.20 (95% CI: 0.11, 0.59) adjusted for disease activity parameters (the BASDAI and CRP), the presence of definite radiographic sacroiliitis and sex. An association between mSASSS and BASMI was stronger: ß = 0.08 (95% CI: 0.05, 0.11) and ßstand = 0.41 (95% CI: 0.25, 0.57) adjusted for the same parameters. These data indicate that, over time, an increase of 20 or 12 mSASSS points would be responsible for an increase of one BASFI or one BASMI point, respectively. Disease activity (BASDAI) also showed a significant association with BASFI [ß = 0.79 (95% CI: 0.71, 0.86) and ßstand = 0.71 (95% CI: 0.63, 0.77)] and BASMI [ß = 0.22 (95% CI: 0.15, 0.30) and ßstand = 0.28 (95% CI: 0.18, 0.37)]. Conclusion: Structural damage in the spine and disease activity are both determinants of the functional status and spinal mobility in early axSpA.

The NutriAct Family Study: a web-based prospective study on the epidemiological, psychological and sociological basis of food choice.

BACKGROUND: Most studies on food choice have been focussing on the individual level but familial aspects may also play an important role. This paper reports of a novel study that will focus on the familial aspects of the formation of food choice among men and women aged 50-70 years by recruiting spouses and siblings (NutriAct Family Study; NFS). METHODS: Data is collected prospectively via repeatedly applied web-based questionnaires over the next years. The recruitment for the NFS started in October 2016. Participants are recruited based on an index person who is actively participating in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. This index person was asked to invite the spouse, a sibling or an in-law. If a set of family members agreed to participate, access to individualized web-based questionnaires assessing dietary intake, other health related lifestyle habits, eating behaviour, food responsiveness, personality, self-regulation, socio-economic status and socio-cultural values was provided. In the first phase of the NSF, recruitment rates were monitored in detail and participants' comments were analysed in order to improve the feasibility of procedures and instruments. DISCUSSION: Until August 4th 2017, 4783 EPIC-Participants were contacted by mail of which 446 persons recruited 2 to 5 family members (including themselves) resulting in 1032 participants, of whom 82% had started answering or already completed the questionnaires. Of the 4337 remaining EPIC-participants who had been contacted, 1040 (24%) did not respond at all, and 3297 (76%) responded but declined, in 51% of the cases because of the request to recruit at least 2 family members in the respective age range. The developed recruitment procedures and web-based methods of data collection are capable to generate the required study population including the data on individual and inter-personal determinants which will be linkable to food choice. The information on familial links among the study participants will show the role of familial traits in midlife for the adoption of food choices supporting healthy aging.

Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies.

The aim of this systematic review and meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups and risk of type 2 diabetes (T2D). We conducted a systematic search in PubMed, Embase, Medline (Ovid), Cochrane Central, and Google Scholar for prospective studies investigating the association between whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSB) on risk of T2D. Summary relative risks were estimated using a random effects model by contrasting categories, and for linear and non-linear dose-response relationships. Six out of the 12 food-groups showed a significant relation with risk of T2D, three of them a decrease of risk with increasing consumption (whole grains, fruits, and dairy), and three an increase of risk with increasing consumption (red meat, processed meat, and SSB) in the linear dose-response meta-analysis. There was evidence of a non-linear relationship between fruits, vegetables, processed meat, whole grains, and SSB and T2D risk. Optimal consumption of risk-decreasing foods resulted in a 42% reduction, and consumption of risk-increasing foods was associated with a threefold T2D risk, compared to non-consumption. The meta-evidence was graded "low" for legumes and nuts; "moderate" for refined grains, vegetables, fruit, eggs, dairy, and fish; and "high" for processed meat, red meat, whole grains, and SSB. Among the investigated food groups, selecting specific optimal intakes can lead to a considerable change in risk of T2D.

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data.

BACKGROUND: Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data. METHODS: We proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study. RESULTS: Using the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations. CONCLUSIONS: The proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the 'predictors', including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the 'responses'). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.

Eating out is different from eating at home among individuals who occasionally eat out. A cross-sectional study among middle-aged adults from eleven European countries.

Eating out has been linked to the current obesity epidemic, but the evaluation of the extent to which out of home (OH) dietary intakes are different from those at home (AH) is limited. Data collected among 8849 men and 14,277 women aged 35-64 years from the general population of eleven European countries through 24-h dietary recalls or food diaries were analysed to: (1) compare food consumption OH to those AH; (2) describe the characteristics of substantial OH eaters, defined as those who consumed 25 % or more of their total daily energy intake at OH locations. Logistic regression models were fit to identify personal characteristics associated with eating out. In both sexes, beverages, sugar, desserts, sweet and savoury bakery products were consumed more OH than AH. In some countries, men reported higher intakes of fish OH than AH. Overall, substantial OH eating was more common among men, the younger and the more educated participants, but was weakly associated with total energy intake. The substantial OH eaters reported similar dietary intakes OH and AH. Individuals who were not identified as substantial OH eaters reported consuming proportionally higher quantities of sweet and savoury bakery products, soft drinks, juices and other non-alcoholic beverages OH than AH. The OH intakes were different from the AH ones, only among individuals who reported a relatively small contribution of OH eating to their daily intakes and this may partly explain the inconsistent findings relating eating out to the current obesity epidemic.

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.

Serum metabolites related to cardiorespiratory fitness, physical activity energy expenditure, sedentary time and vigorous activity.

The aim of our study was to investigate the relationship between objectively measured physical activity (PA) and cardiorespiratory fitness (CRF) and serum metabolites measured by targeted metabolomics in a population- based study. A total of 100 subjects provided 2 fasting blood samples and engaged in a CRF and PA measurement at 2 visits 4 months apart. CRF was estimated from a step test, whereas physical activity energy expenditure (PAEE), time spent sedentary and time spend in vigorous activity were measured by a combined heart rate and movement sensor for a total of 8 days. Serum metabolite concentrations were determined by flow injection analysis tandem mass spectrometry (FIA-MS/MS). Linear mixed models were applied with multivariable adjustment and p-values were corrected for multiple testing. Furthermore, we explored the associations between CRF, PA and two metabolite factors that have previously been linked to risk of Type 2 diabetes. CRF was associated with two phosphatidylcholine clusters independently of all other exposures. Lysophosphatidylcholine C14:0 and methionine were significantly negatively associated with PAEE and sedentary time. CRF was positively associated with the Type 2 diabetes protective factor. Vigorous activity was positively associated with the Type 2 diabetes risk factor in the mutually adjusted model. Our results suggest that CRF and PA are associated with serum metabolites, especially CRF with phosphatidylcholines and with the Type 2 diabetes protective factor. PAEE and sedentary time were associated with methionine. The identified metabolites could be potential mediators of the protective effects of CRF and PA on chronic disease risk.

Microsomal triglyceride transfer protein -164 T > C gene polymorphism and risk of cardiovascular disease: results from the EPIC-Potsdam case-cohort study.

BACKGROUND: The microsomal triglyceride transfer protein (MTTP) is encoded by the MTTP gene that is regulated by cholesterol in humans. Previous studies investigating the effect of MTTP on ischemic heart disease have produced inconsistent results. Therefore, we have tested the hypothesis that the rare allele of the -164T > C polymorphism in MTTP alters the risk of cardiovascular disease (CVD), depending on the cholesterol levels. METHODS: The -164T > C polymorphism was genotyped in a case-cohort study (193 incident myocardial infarction (MI) and 131 incident ischemic stroke (IS) cases and 1 978 non-cases) nested within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, comprising 27 548 middle-aged subjects. The Heinz Nixdorf Recall study (30 CVD cases and 1 188 controls) was used to replicate our findings. RESULTS: Genotype frequencies were not different between CVD and CVD free subjects (P = 0.79). We observed an interaction between the -164T > C polymorphism and total cholesterol levels in relation to future CVD. Corresponding stratified analyses showed a significant increased risk of CVD (HR(additve) = 1.38, 95% CI: 1.07 to 1.78) for individuals with cholesterol levels <200 mg/dL in the EPIC-Potsdam study. HR(additive) was 1.06, 95% CI: 0.33 to 3.40 for individuals in the Heinz Nixdorf Recall study. A borderline significant decrease in CVD risk was observed in subjects with cholesterol levels ≥ 200 mg/dL (HR(additve) = 0.77, 95% CI: 0.58 to 1.03) in the EPIC-Potsdam study. A similar trend was observed in the independent cohort (HR(additve) = 0.60, 95% CI: 0.29 to 1.25). CONCLUSIONS: Our study suggests an interaction between MTTP -164T > C functional polymorphism with total cholesterol levels. Thereby risk allele carriers with low cholesterol levels may be predisposed to an increased risk of developing CVD, which seems to be abolished among risk allele carriers with high cholesterol levels.

Evaluating the effect of measurement error when using one or two 24 h dietary recalls to assess eating out: a study in the context of the HECTOR project.

Eating out is often recorded through short-term measurements and the large within-person variability in intakes may not be adequately captured. The present study aimed to understand the effect of measurement error when using eating-out data from one or two 24 h dietary recalls (24hDR), in order to describe intakes and assess associations between eating out and personal characteristics. In a sample of 366 adults from Potsdam, Germany, two 24hDR and a FFQ were collected. Out-of-home intakes were estimated based on either one 24hDR or two 24hDR or the Multiple Source Method (MSM) combining the two 24hDR and the questionnaire. The distribution of out-of-home intakes of energy, macronutrients and selected foods was described. Multiple linear regression and partial correlation coefficients were estimated to assess associations between out-of-home energy intake and participants' characteristics. The mean daily out-of-home intakes estimated from the two 24hDR were similar to the usual intakes estimated through the MSM. The out-of-home energy intake, estimated through either one or two 24hDR, was positively associated with total energy intake, inversely with age and associations were stronger when using the two 24hDR. A marginally significant inverse association between out-of-home energy intake and physical activity at work was observed only on the basis of the two 24hDR. After applying the MSM, all significant associations remained and were more precise. Data on eating out collected through one or two 24hDR may not adequately describe intake distributions, but significant associations between eating out and participants' characteristics are highly unlikely to appear when in reality these do not exist.