An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology

Gut Microbiome Composition in Obese and Non-Obese Persons: A Systematic Review and Meta-Analysis

Whether the gut microbiome in obesity is characterized by lower diversity and altered composition at the phylum or genus level may be more accurately investigated using high-throughput sequencing technologies. We conducted a systematic review in PubMed and Embase including 32 cross-sectional studies assessing the gut microbiome composition by high-throughput sequencing in obese and non-obese adults. A significantly lower alpha diversity (Shannon index) in obese versus non-obese adults was observed in nine out of 22 studies, and meta-analysis of seven studies revealed a non-significant mean difference (-0.06, 95% CI -0.24, 0.12, I2 = 81%). At the phylum level, significantly more Firmicutes and fewer Bacteroidetes in obese versus non-obese adults were observed in six out of seventeen, and in four out of eighteen studies, respectively. Meta-analyses of six studies revealed significantly higher Firmicutes (5.50, 95% 0.27, 10.73, I2 = 81%) and non-significantly lower Bacteroidetes (-4.79, 95% CI -10.77, 1.20, I2 = 86%). At the genus level, lower relative proportions of Bifidobacterium and Eggerthella and higher Acidaminococcus, Anaerococcus, Catenibacterium, Dialister, Dorea, Escherichia-Shigella, Eubacterium, Fusobacterium, Megasphera, Prevotella, Roseburia, Streptococcus, and Sutterella were found in obese versus non-obese adults. Although a proportion of studies found lower diversity and differences in gut microbiome composition in obese versus non-obese adults, the observed heterogeneity across studies precludes clear answers

Greenhouse gas emissions from the grassy outdoor run of organic broilers

Nitrous oxide (N<sub>2</sub>O), methane (CH<sub>4</sub>) and carbon dioxide (CO<sub>2</sub>) fluxes over the grassy outdoor run of organically grown broilers were monitored using static chambers over two production batches in contrasted seasons. Measured N<sub>2</sub>O and CH<sub>4</sub> fluxes were extremely variable in time and space for both batches, with fluxes ranging from a small uptake by soil to large emissions peaks, the latter of which always occurred in the chambers located closest to the broiler house. In general, fluxes decreased with increasing distance to the broiler house, demonstrating that the foraging of broilers and the amount of excreted nutrients (carbon, nitrogen) largely control the spatial variability of emissions. Spatial integration by kriging methods was carried out to provide representative fluxes on the outdoor run for each measurement day. Mechanistic relationships between plot-scale estimates and environmental conditions (soil temperature and water content) were calibrated in order to fill gaps between measurement days. Flux integration over the year 2010 showed that around 3 ± 1 kg N<sub>2</sub>O-N ha<sup>−1</sup> were emitted on the outdoor run, equivalent to 0.9% of outdoor N excretion and substantially lower than the IPCC default emission factor of 2%. By contrast, the outdoor run was found to be a net CH<sub>4</sub> sink of about −0.56 kg CH<sub>4</sub>-C ha<sup>−1</sup>, though this sink compensated less than 1.5% (in CO<sub>2</sub> equivalents) of N<sub>2</sub>O emissions. The net greenhouse gas (GHG) budget of the outdoor run is explored, based on measured GHG fluxes and short-term (1.5 yr) variations in soil organic carbon

EURRECA-Evidence-Based Methodology for Deriving Micronutrient Recommendations

The EURopean micronutrient RECommendations Aligned (EURRECA) Network of Excellence explored the process of setting micronutrient recommendations to address the variance in recommendations across Europe. Work centered upon the transparent assessment of nutritional requirements via a series of systematic literature reviews and meta-analyses. In addition, the necessity of assessing nutritional requirements and the policy context of setting micronutrient recommendations was investigated. Findings have been presented in a framework that covers nine activities clustered into four stages: stage one Defining the problem describes Activities 1 and 2: Identifying the nutrition-related health problem and Defining the process; stage two Monitoring and evaluating describes Activities 3 and 7: Establishing appropriate methods, and Nutrient intake and status of population groups; stage three Deriving dietary reference values describes Activities 4, 5, and 6: Collating sources of evidence, Appraisal of the evidence, and Integrating the evidence; stage four Using dietary reference values in policy making describes Activities 8 and 9: Identifying policy options, and Evaluating policy implementation. These activities provide guidance on how to resolve various issues when deriving micronutrient requirements and address the methodological and policy decisions, which may explain the current variation in recommendations across Europe. [Supplementary materials are available for this article. Go to the publisher's online edition of Critical Reviews in Food Science and Nutrition for the following free supplemental files: Additional text, tables, and figures.]This is the peer-reviewed version of the article: Dhonukshe-Rutten Rosalie, Bouwman Jildau, Brown Kerry A., Cavelaars Adrienne E., Collings Rachel, Grammatikaki Evangelia, de Groot Lisette, Gurinović Mirjana A., Harvey Linda, Hermoso Maria, Hurst Rachel, Kremer Bas, Ngo Joy, Novaković Romana, Raats Monique M., Rollin Fanny, Serra-Majem Lluis, Souverein Olga W., Timotijević Lada, van't Veer Pieter, "EURRECA-Evidence-Based Methodology for Deriving Micronutrient Recommendations" 53, no. 10 (2013):999-1040, [https://doi.org/10.1080/10408398.2012.749209

The Global Open Science Cloud: Vision and Initial Successes

The Global Open Science Cloud has the potential to advance the way scientific data and resources are shared and accessed, and how global collaboration happens. However, addressing the challenges associated with its creation and ensuring inclusivity, interoperability, data privacy, and sustainability are crucial for its success. The collaborative efforts of stakeholders from different disciplines, regions, and sectors will be essential in realising the vision of a truly global and open science platform. The achievements of GOSC so far, including successful collaborations, funded projects, and the development of a common reference framework, demonstrate its potential and progress towards its goals

Association between diet-quality scores, adiposity, total cholesterol and markers of nutritional status in European adults: findings from the Food4Me study

Diet-quality scores (DQS), which are developed across the globe, are used to define adherence to specific eating patterns and have been associated with risk of coronary heart disease and type-II diabetes. We explored the association between five diet-quality scores (Healthy Eating Index,HEI; Alternate Healthy Eating Index, AHEI; MedDietScore, MDS; PREDIMED Mediterranean DietScore, P-MDS; Dutch Healthy Diet-Index, DHDI) and markers of metabolic health (anthropometry,objective physical activity levels (PAL), and dried blood spot total cholesterol (TC), total carotenoids,and omega-3 index) in the Food4Me cohort, using regression analysis. Dietary intake was assessed using a validated Food Frequency Questionnaire. Participants (n= 1480) were adults recruited from seven European Union (EU) countries. Overall, women had higher HEI and AHEI than men (p< 0.05), and scores varied significantly between countries. For all DQS, higher scores were associated with lower body mass index, lower waist-to-height ratio and waist circumference, and higher total carotenoids and omega-3-index (p trends < 0.05). Higher HEI, AHEI, DHDI, and P-MDS scores were associated with increased daily PAL, moderate and vigorous activity, and reduced sedentary behaviour (p trend < 0.05). We observed no association between DQS and TC. To conclude,higher DQS, which reflect better dietary patterns, were associated with markers of better nutritional status and metabolic health

Mediterranean diet adherence and genetic background roles within a web-based nutritional intervention: the Food4Me study

Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers

Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance

FFQ, food diaries and 24 h recall methods represent the most commonly used dietary assessment tools in human studies on nutrition and health, but food intake biomarkers are assumed to provide a more objective reflection of intake. Unfortunately, very few of these biomarkers are sufficiently validated. This review provides an overview of food intake biomarker research and highlights present research efforts of the Joint Programming Initiative 'A Healthy Diet for a Healthy Life' (JPI-HDHL) Food Biomarkers Alliance (FoodBAll). In order to identify novel food intake biomarkers, the focus is on new food metabolomics techniques that allow the quantification of up to thousands of metabolites simultaneously, which may be applied in intervention and observational studies. As biomarkers are often influenced by various other factors than the food under investigation, FoodBAll developed a food intake biomarker quality and validity score aiming to assist the systematic evaluation of novel biomarkers. Moreover, to evaluate the applicability of nutritional biomarkers, studies are presently also focusing on associations between food intake biomarkers and diet-related disease risk. In order to be successful in these metabolomics studies, knowledge about available electronic metabolomics resources is necessary and further developments of these resources are essential. Ultimately, present efforts in this research area aim to advance quality control of traditional dietary assessment methods, advance compliance evaluation in nutritional intervention studies, and increase the significance of observational studies by investigating associations between nutrition and health. Keywords: Dietary assessment; Food intake biomarkers; Food metabolome; Metabolomics

The impact of MTHFR 677C → T risk knowledge on changes in folate intake: findings from the Food4Me study

Background It is hypothesised that individuals with knowledge of their genetic risk are more likely to make health-promoting dietary and lifestyle changes. The present study aims to test this hypothesis using data from the Food4Me study. This was a 6-month Internet-based randomised controlled trial conducted across seven centres in Europe where individuals received either general healthy eating advice or varying levels of personalised nutrition advice. Participants who received genotype-based personalised advice were informed whether they had the risk (CT/TT) (n = 178) or non-risk (CC) (n = 141) alleles of the methylenetetrahydrofolate reductase (MTHFR) gene in relation to cardiovascular health and the importance of a sufficient intake of folate. General linear model analysis was used to assess changes in folate intake between the MTHFR risk, MTHFR non-risk and control groups from baseline to month 6 of the intervention. Results There were no differences between the groups for age, gender or BMI. However, there was a significant difference in country distribution between the groups (p = 0.010). Baseline folate intakes were 412 ± 172, 391 ± 190 and 410 ± 186 μg per 10 MJ for the risk, non-risk and control groups, respectively. There were no significant differences between the three groups in terms of changes in folate intakes from baseline to month 6. Similarly, there were no changes in reported intake of food groups high in folate. Conclusions These results suggest that knowledge of MTHFR 677C → T genotype did not improve folate intake in participants with the risk variant compared with those with the non-risk variant