Risks of misinterpretation of biomarker measurements in spot urine adjusted for creatinine - A problem especially for studies comparing plant based with omnivorous diets.

Biomarker measurements in spot urine are often adjusted for creatinine to control for dilution resulting from individual hydration. We here report on results of a study involving age- and sex-matched vegans and omnivores (n = 36 each). The daily urinary excretion of 2,3-dihydroxypropylmercapturic acid (DHPMA, a diet-independent endogenous C3-metabolite used as an example compound) was found not to be different in vegans and omnivores (median 433 µg/24 h each), however, creatinine-adjusted levels were 26% lower in omnivores (median 285 µg/g creatinine) than in vegans (median 383 µg/g creatinine, p = 0.003). This difference results from the higher urinary excretion of creatinine in the omnivores compared to vegans (median 1.51 vs. 1.21 g/24 h, p = 0.009). Linear regression showed - besides the fat-free mass - a significant impact of the factor diet (vegans vs. omnivores). This may be due to the consumption of meat and fish as exogenous sources of creatinine. A literature search revealed broad evidence for this interpretation, as creatinine is formed from creatine during heating of meat and fish. Accordingly, consumption leads to temporary increase of serum/plasma creatinine and urinary creatinine excretion, resulting in higher levels in omnivores compared to vegans/vegetarians. An adjustment of the urinary DHPMA concentrations using specific gravity revealed 13% lower values in omnivores (median 225 µg/L) than in vegans (median 260 µg/L, p = 0.07). Compared to creatinine-adjustment, adjustment for specific gravity introduces a smaller but still obvious difference between omnivores and vegans. Especially with respect to future studies comparing vegans, vegetarians and omnivores, researchers should be aware of the risks of severe misinterpretations if biomarker measurements in spot urine are adjusted for creatinine.

Quantitative allergenicity risk assessment of food products containing yellow mealworm (Tenebrio molitor).

Insect-based foods are starting to enter the EU market, raising concerns about their safety. Allergic consumers might be exposed to even a greater risk, since insects have proven to trigger allergic symptoms, particularly in patients sensitised to crustaceans. Current legislation does not enforce producers to include insects in the list of allergenic ingredients. Food allergenicity risk assessment (FARA) is still at its infancy, and the debate on the need to define allergen thresholds is open. In this paper, we aimed at applying the concepts of stochastic quantitative FARA to describe present and future scenarios of exposure to foods containing Tenebrio molitor, the yellow mealworm. According to our risk characterisation, mealworm-based food products represent a major risk for individuals allergic to crustaceans to develop symptoms after the consumption of a dose lower than a serving size. Moreover, other allergic consumers might be at risk. A correct labelling of insect containing foods would help safeguarding the health of EU allergic consumers. Quantitatively assessing the risk of allergenicity provides a clear description of the problem, facilitating the decisional process of the risk manager, supporting the implementation of effective allergen management procedures and limiting the phenomenon of uninformative precautionary labelling.

Usual Dietary Intake Estimation Based on a Combination of Repeated 24-H Food Lists and a Food Frequency Questionnaire in the KORA FF4 Cross-Sectional Study.

Background: Estimation of usual dietary intake poses a challenge in epidemiological studies. We applied a blended approach that combines the strengths provided by repeated 24-h food lists (24HFLs) and a food frequency questionnaire (FFQ). Methods: At least two web-based 24HFLs and one FFQ were completed by 821 participants in the KORA FF4 study. Consumption probabilities were estimated using logistic mixed models, adjusting for covariates and the FFQ data on consumption frequency. Intake amount of a consumed food item was predicted for each participant based on the results of the second Bavarian Food Consumption Survey (BVS II). By combining consumption probability and estimated consumption amount, the usual food intake for each participant was estimated. These results were compared to results obtained without considering FFQ information for consumption probability estimation, as well as to conventional FFQ data. Results: The results of the blended approach for food group intake were often higher than the FFQ-based results. Intraclass correlation coefficients between both methods ranged between 0.21 and 0.86. Comparison of both methods resulted in weighted kappa values based on quintiles ranging from fair (0.34) to excellent agreement (0.84). Omission of FFQ information in the consumption probability models distinctly affected the results at the group level, though individual intake data were slightly affected, for the most part. Conclusions: Usual dietary intake data based on the blended approach differs from the FFQ-based results both in absolute terms and in classification according to quintiles. The application of the blended approach has been demonstrated as a possible tool in nutritional epidemiology, as a comparison with published studies showed that the blended approach yields reasonable estimates. The inclusion of the FFQ information is valuable especially with regard to irregularly consumed foods. A validation study including biomarkers of dietary intake is warranted.

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.

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.

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.

Malnutrition predicts long-term survival in hospitalized patients with gastroenterological and hepatological diseases.

BACKGROUND & AIMS: Malnutrition is a common problem in hospitalized patients, influencing treatment outcomes, length of hospital stay, quality of life and overall survival. However, the association of nutritional status parameters with long-term mortality has not yet been studied systematically in gastroenterological-hepatological patients. The present study aimed to assess the association between nutritional status parameters as characterized by Nutritional Risk Screening (NRS), anthropometry, serum transferrin, bioelectrical impedance analysis (BIA) and long-term overall survival in hospitalized gastroenterological-hepatological patients. METHODS: Nutritional status was assessed in 644 gastroenterological-hepatological patients by NRS score. In addition, body mass index (BMI) and serum transferrin were determined and BIA was performed. Mid-upper arm circumference (MUAC) and triceps skinfold thickness (TST) were measured. Patients were followed for a mean period of 67 months (mean 54.8, range 0-107 months). RESULTS: During malnutrition screening, 475 (73.8%) patients were diagnosed as sufficiently nourished by NRS (NRS 0-2), while an increased risk of malnutrition was found in 169 (26.2%) patients (NRS≤3). Malnutrition was significantly associated with less favourable results for BMI (p < 0.001), serum transferrin (p < 0.001), BIA (p < 0.001), MUAC (p < 0.001) and TST (p < 0.05). Overall 5-year survival rates (YSR) were much shorter in malnourished patients whether with (5-YSR: 43.9%) or without (73.6%) malignancy. Overall 5-year survival rates (YSR) were much shorter in malnourished patients whether with (5-YSR: 43.9%) or without (73.6%) malignancy. By the multivariable analysis the NRS ≥3 and, phase angle (PhA) over the 5th percentile or over the mean of the cohort were found to be associated with long-term survival. CONCLUSIONS: Malnutrition is highly prevalent in hospitalized gastroenterological-hepatological patients and is associated with distinct clinical diagnoses. In the present study we demonstrated that malnutrition characterized by the NRS, anthropometry, serum transferrin and BIA, not only predicts short-term but also significantly poor long-term outcome in these patients.

Traditional risk factors for essential hypertension: analysis of their specific combinations in the EPIC-Potsdam cohort.

Appropriate interventions might improve the prevention of essential hypertension. This requires a comprehensive view of modifiable lifestyle factors (MLFs) distribution and effect. To determine how six MLFs (general adiposity, abdominal adiposity, alcohol consumption, smoking, diet, physical inactivity) for risk of hypertension are distributed and how their combinations affect the risk, a prospective study cohort of 11,923 healthy participants from the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study was used. Of these, 1,635 developed hypertension during a mean follow-up of 10.3 years. Mutually exclusive combinations, clustering and interactions of MLFs were then investigated stratifying by sex, Hazard Ratios (HRs) and Population Attributable Risks (PARs%) were calculated. General adiposity alone was sufficient to increase the risk of hypertension (HR = 1.86, PAR% 3.36), and in this cohort it played a major role in enhancing the risk of hypertension, together with smoking and physical inactivity. MLFs had a different impact and a different modulation of risk in women and men, and they showed a remarkable tendency to occur in specific patterns with higher prevalence than expected. This indication can help to promote a holistic approach through multifactorial preventive strategies addressing more than a factor at a time. For prevention of hypertension addressing adiposity together with smoking, promoting at the same time physical activity should be the first choice.

Design and characterization of dietary assessment in the German National Cohort.

BACKGROUND/OBJECTIVES: The aim of the study was to describe a novel dietary assessment strategy based on two instruments complemented by information from an external population applied to estimate usual food intake in the large-scale multicenter German National Cohort (GNC). As proof of concept, we applied the assessment strategy to data from a pretest study (2012-2013) to assess the feasibility of the novel assessment strategy. SUBJECTS/METHODS: First, the consumption probability for each individual was modeled using three 24 h food lists (24h-FLs) and frequencies from one food frequency questionnaire (FFQ). Second, daily consumed food amounts were estimated from the representative German National Nutrition Survey II (NVS II) taking the characteristics of the participants into account. Usual food intake was estimated using the product of consumption probability and amounts. RESULTS: We estimated usual intake of 41 food groups in 318 men and 377 women. The participation proportion was 100, 84.4, and 68.5% for the first, second, and third 24h-FL, respectively. We observed no associations between the probability of participating and lifestyle factors. The estimated distributions of usual food intakes were plausible and total energy was estimated to be 2707 kcal/day for men and 2103 kcal/day for women. The estimated consumption frequencies did not differ substantially between men and women with only few exceptions. The differences in energy intake between men and women were mostly due to differences in estimated daily amounts. CONCLUSIONS: The combination of repeated 24h-FLs, a FFQ, and consumption-day amounts from a reference population represents a user-friendly dietary assessment approach having generated plausible, but not yet validated, food intake values in the pretest study.

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.

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.

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.

Meal and habitual dietary networks identified through Semiparametric Gaussian Copula Graphical Models in a German adult population.

Gaussian graphical models (GGMs) are exploratory methods that can be applied to construct networks of food intake. Such networks were constructed for meal-structured data, elucidating how foods are consumed in relation to each other at meal level. Meal-specific networks were compared with habitual dietary networks using data from an EPIC-Potsdam sub-cohort study. Three 24-hour dietary recalls were collected cross-sectionally from 815 adults in 2010-2012. Food intake was averaged to obtain the habitual intake. GGMs were applied to four main meals and habitual intakes of 39 food groups to generate meal-specific and habitual dietary networks, respectively. Communities and centrality were detected in the dietary networks to facilitate interpretation. The breakfast network revealed five communities of food groups with other vegetables, sauces, bread, margarine, and sugar & confectionery as central food groups. The lunch and afternoon snacks networks showed higher variability in food consumption and six communities were detected in each of these meal networks. Among the central food groups detected in both of these meal networks were potatoes, red meat, other vegetables, and bread. Two dinner networks were identified with five communities and other vegetables as a central food group. Partial correlations at meals were stronger than on the habitual level. The meal-specific dietary networks were only partly reflected in the habitual dietary network with a decreasing percentage: 64.3% for dinner, 50.0% for breakfast, 36.2% for lunch, and 33.3% for afternoon snack. The method of GGM yielded dietary networks that describe combinations of foods at the respective meals. Analysing food consumption on the habitual level did not exactly reflect meal level intake. Therefore, interpretation of habitual networks should be done carefully. Meal networks can help understand dietary habits, however, GGMs warrant validation in other populations.

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.

Generating the evidence for risk reduction: a contribution to the future of food-based dietary guidelines.

A major advantage of analyses on the food group level is that the results are better interpretable compared with nutrients or complex dietary patterns. Such results are also easier to transfer into recommendations on primary prevention of non-communicable diseases. As a consequence, food-based dietary guidelines (FBDG) are now the preferred approach to guide the population regarding their dietary habits. However, such guidelines should be based on a high grade of evidence as requested in many other areas of public health practice. The most straightforward approach to generate evidence is meta-analysing published data based on a careful definition of the research question. Explicit definitions of study questions should include participants, interventions/exposure, comparisons, outcomes and study design. Such type of meta-analyses should not only focus on categorical comparisons, but also on linear and non-linear dose-response associations. Risk of bias of the individual studies of the meta-analysis should be assessed, rated and the overall credibility of the results scored (e.g. using NutriGrade). Tools such as a measurement tool to assess systematic reviews or ROBIS are available to evaluate the methodological quality/risk of bias of meta-analyses. To further evaluate the complete picture of evidence, we propose conducting network meta-analyses (NMA) of intervention trials, mostly on intermediate disease markers. To rank food groups according to their impact, disability-adjusted life years can be used for the various clinical outcomes and the overall results can be compared across the food groups. For future FBDG, we recommend to implement evidence from pairwise and NMA and to quantify the health impact of diet-disease relationships.

Contribution to the understanding of how principal component analysis-derived dietary patterns emerge from habitual data on food consumption.

Background: Principal component analysis (PCA) is a widely used exploratory method in epidemiology to derive dietary patterns from habitual diet. Such dietary patterns seem to originate from intakes on multiple days and eating occasions. Therefore, analyzing food intake of study populations with different levels of food consumption can provide additional insights as to how habitual dietary patterns are formed. Objective: We analyzed the food intake data of German adults in terms of the relations among food groups from three 24-h dietary recalls (24hDRs) on the habitual, single-day, and main-meal levels, and investigated the contribution of each level to the formation of PCA-derived habitual dietary patterns. Design: Three 24hDRs were collected in 2010-2012 from 816 adults for an European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam subcohort study. We identified PCA-derived habitual dietary patterns and compared cross-sectional food consumption data in terms of correlation (Spearman), consistency (intraclass correlation coefficient), and frequency of consumption across all days and main meals. Contribution to the formation of the dietary patterns was obtained through Spearman correlation of the dietary pattern scores. Results: Among the meals, breakfast appeared to be the most consistent eating occasion within individuals. Dinner showed the strongest correlations with "Prudent" (Spearman correlation = 0.60), "Western" (Spearman correlation = 0.59), and "Traditional" (Spearman correlation = 0.60) dietary patterns identified on the habitual level, and lunch showed the strongest correlations with the "Cereals and legumes" (Spearman correlation = 0.60) habitual dietary pattern. Conclusions: Higher meal consistency was related to lower contributions to the formation of PCA-derived habitual dietary patterns. Absolute amounts of food consumption did not strongly conform to the habitual dietary patterns by meals, suggesting that these patterns are formed by complex combinations of variable food consumption across meals. Dinner showed the highest contribution to the formation of habitual dietary patterns. This study provided information about how PCA-derived dietary patterns are formed and how they could be influenced.

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.

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.