Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice

In order to assess nutritional adequacy, valid estimates of nutrient intake are required. One of the main errors in dietary assessment is misreporting. The objective was to review the extent, nature and determinants of misreporting in dietary assessment, how this affects reported intakes of micronutrients and how this is identified and measured, and to identify the best ways of dealing with misreporting when interpreting results. A systematic literature search was conducted for studies of misreporting of dietary intake in adults by 24 hour recalls or by estimated or weighed food records, published up to March 2008. Thirty-seven relevant studies were identified. Possible causes of misreporting were identified. Methods most used to identify misreporting were the Goldberg cut-off (46 % studies) and the doubly labelled water technique (24 % studies). The magnitude of misreporting of energy intake was similar in all three dietary assessment methods. The percentage of under-reporters was about 30 % and energy intake was underestimated by approximately 15 %. Seven papers presented usable data for micronutrient intake. Absolute intakes of Fe, Ca and vitamin C (the three micronutrients addressed in all papers) were on average 30 % lower in low-energy reporters (LER) than that in non-LER and, although results were not consistent, there was a tendency for micronutrient density to be higher in LER. Excluding underreporters or using energy adjustment methods for micronutrient intakes is discussed. Residual method of energy adjustment seems to be a good tool for practice to decrease an influence of misreporting when interpreting results of studies based on food records and 24 hour recall

Monitoring of genetically modified food on the Czech food market and a cross-country comparison

The presence of genetically modified organisms (GMOs) was analysed in food samples from the Czech food markets. Four different types of food samples (soya beans, soya bean products, maize flour, and rice) were collected at twelve places in four terms in the years 2008–2013. It represents a total 1152 food samples. Soya and maize were chosen, because these are the major transgenic crops grown worldwide. Increased cultivation of GM rice in China, India, Indonesia, and the Philippines has been observed. Polymerase chain reaction-based methods were applied to detect GMOs. GMOs were detected in 107 samples (9.3%). The results show that in food from the Czech food market GMOs mainly in maize flour and rice were found. GM maize was detected in 63 (21.9%) maize flour samples. Maize lines MON810, NK603, and Bt176 were detected in 14 (22%), 9 (14%), and 1 (1.6%) maize flour samples, respectively. Unauthorised GM rice was detected in 39 (13.5%) rice samples. Unauthorised rice Bt63 was detected in one sample of rice. Roundup Ready soya was detected in 4 (1.4%) soya bean samples and in 1 (0.35%) soya product sample. These results were compared with results of the world´s studies

Predicting urinary creatinine excretion and its usefulness to identify incomplete 24h urine collections

Studies using 24 h urine collections need to incorporate ways to validate the completeness of the urine samples. Models to predict urinary creatinine excretion (UCE) have been developed for this purpose; however, information on their usefulness to identify incomplete urine collections is limited. We aimed to develop a model for predicting UCE and to assess the performance of a creatinine index using para-aminobenzoic acid (PABA) as a reference. Data were taken from the European Food Consumption Validation study comprising two non-consecutive 24 h urine collections from 600 subjects in five European countries. Data from one collection were used to build a multiple linear regression model to predict UCE, and data from the other collection were used for performance testing of a creatinine index-based strategy to identify incomplete collections. Multiple linear regression (n 458) of UCE showed a significant positive association for body weight (ß = 0·07), the interaction term sex × weight (ß = 0·09, reference women) and protein intake (ß = 0·02). A significant negative association was found for age (ß = - 0·09) and sex (ß = - 3·14, reference women). An index of observed-to-predicted creatinine resulted in a sensitivity to identify incomplete collections of 0·06 (95 % CI 0·01, 0·20) and 0·11 (95 % CI 0·03, 0·22) in men and women, respectively. Specificity was 0·97 (95 % CI 0·97, 0·98) in men and 0·98 (95 % CI 0·98, 0·99) in women. The present study shows that UCE can be predicted from weight, age and sex. However, the results revealed that a creatinine index based on these predictions is not sufficiently sensitive to exclude incomplete 24 h urine collections

Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center

A case study of neurodevelopmental risks from combined exposures to lead, methyl-mercury, inorganic arsenic, polychlorinated biphenyls, polybrominated diphenyl ethers and fluoride

We performed a mixture risk assessment (MRA) case study of dietary exposure to the food contaminants lead, methylmercury, inorganic arsenic (iAs), fluoride, non-dioxin-like polychlorinated biphenyls (NDL-PCBs) and polybrominated diphenyl ethers (PBDEs), all substances associated with declines in cognitive abilities measured as IQ loss. Most of these chemicals are frequently measured in human biomonitoring studies. A component-based, personalised modified reference point index (mRPI) approach, in which we expressed the exposures and potencies of our chosen substances as lead equivalent values, was applied to perform a MRA for dietary exposures. We conducted the assessment for four different age groups (toddlers, children, adolescents, and women aged 18–45 years) in nine European countries. Populations in all countries considered exceeded combined tolerable levels at median exposure levels. NDL-PCBs in fish, other seafood and dairy, lead in grains and fruits, methylmercury in fish and other seafoods, and fluoride in water contributed most to the combined exposure. We identified uncertainties for the likelihood of co-exposure, assessment group membership, endpoint-specific reference values (ESRVs) based on epidemiological (lead, methylmercury, iAs, fluoride and NDL-PCBs) and animal data (PBDE), and exposure data. Those uncertainties lead to a complex pattern of under- and overestimations, which would require probabilistic modelling based on expert knowledge elicitation for integration of the identified uncertainties into an overall uncertainty estimate. In addition, the identified uncertainties could be used to refine future MRA for cognitive decline.European Union’s Horizon 2020 research and innovation programme under grant agreement number 874583—the Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project. The author Mousumi Chatterjee is grateful for a Daphne Jackson Trust (UK) fellowship. The authors would like to thank EFSA providing the food consumption databases, data owners for giving permission to use the data, and Gerda van Donkersgoed and Matthijs Sam (RIVM) for organising the food consumption data and chemical concentration data

Coupled climate response to Atlantic Multidecadal Variability in a multi-model multi-resolution ensemble

North Atlantic sea surface temperatures (SSTs) underwent pronounced multidecadal variability during the twentieth and early twenty-first century. We examine the impacts of this Atlantic Multidecadal Variability (AMV), also referred to as the Atlantic Multidecadal Oscillation (AMO), on climate in an ensemble of five coupled climate models at both low and high spatial resolution. We use a SST nudging scheme specified by the Coupled Model Intercomparision Project’s Decadal Climate Prediction Project Component C (CMIP6 DCPP-C) to impose a persistent positive/negative phase of the AMV in the North Atlantic in coupled model simulations; SSTs are free to evolve outside this region. The large-scale seasonal mean response to the positive AMV involves widespread warming over Eurasia and the Americas, with a pattern of cooling over the Pacific Ocean similar to the Pacific Decadal Oscillation (PDO), together with a northward displacement of the inter-tropical convergence zone (ITCZ). The accompanying changes in global atmospheric circulation lead to widespread changes in precipitation. We use Analysis of Variance (ANOVA) to demonstrate that this large-scale climate response is accompanied by significant differences between models in how they respond to the common AMV forcing, particularly in the tropics. These differences may arise from variations in North Atlantic air-sea heat fluxes between models despite a common North Atlantic SST forcing pattern. We cannot detect a widespread effect of increased model horizontal resolution in this climate response, with the exception of the ITCZ, which shifts further northwards in the positive phase of the AMV in the higher resolution configuratio

Contrasting responses of Atlantic and Pacific tropical cyclone activity to Atlantic Multidecadal Variability

This research assesses the influences of Atlantic Multidecadal Variability (AMV) on global tropical cyclones (TCs) using two large ensembles of idealized global climate model simulations with opposite signs of AMV forcings superimposed (i.e., AMV+ and AMV–). We first detect TCs and then compare TC activity by basin in the two AMV experiments. We find contrasting responses of Atlantic and Pacific TC frequency to the AMV anomalies. Compared to AMV–, AMV+ significantly increases TC frequency in the North Atlantic, including those making landfalls. The increase is explained by warmer sea surface temperature, higher relative humidity, increased relative vorticity, and weaker vertical wind shear under AMV+. By contrast, AMV+ decreases TC occurrence over the western North Pacific and South Pacific, which is tied to stronger vertical wind shear and lower relative humidity. The opposite responses of TC activity to AMV+ are attributed to strengthened Walker Circulation between the Atlantic and Pacific

Hyphal network whole field imaging allows for accurate estimation of anastomosis rates and branching dynamics of the filamentous fungus Podospora anserina

The success of filamentous fungi in colonizing most natural environments can be largely attributed to their ability to form an expanding interconnected network, the mycelium, or thallus, constituted by a collection of hyphal apexes in motion producing hyphae and subject to branching and fusion. In this work, we characterize the hyphal network expansion and the structure of the fungus Podospora anserina under controlled culture conditions. To this end, temporal series of pictures of the network dynamics are produced, starting from germinating ascospores and ending when the network reaches a few centimeters width, with a typical image resolution of several micrometers. The completely automated image reconstruction steps allow an easy post-processing and a quantitative analysis of the dynamics. The main features of the evolution of the hyphal network, such as the total length L of the mycelium, the number of "nodes" (or crossing points) N and the number of apexes A, can then be precisely quantified. Beyond these main features, the determination of the distribution of the intra-thallus surfaces (S; i; ) and the statistical analysis of some local measures of N, A and L give new insights on the dynamics of expanding fungal networks. Based on these results, we now aim at developing robust and versatile discrete/continuous mathematical models to further understand the key mechanisms driving the development of the fungus thallus

HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR - Description, model computational performance and basic validation

A new global high-resolution coupled climate model, EC-Earth3P-HR has been developed by the EC-Earth consortium, with a resolution of approximately 40 km for the atmosphere and 0.25° for the ocean, alongside with a standard-resolution version of the model, EC-Earth3P (80 km atmosphere, 1.0 ° ocean). The model forcing and simulations follow the High Resolution Model Intercomparison Project (HighResMIP) protocol. According to this protocol, all simulations are made with both high and standard resolutions. The model has been optimized with respect to scalability, performance, data storage and post-processing. In accordance with the HighResMIP protocol, no specific tuning for the high-resolution version has been applied. Increasing horizontal resolution does not result in a general reduction of biases and overall improvement of the variability, and deteriorating impacts can be detected for specific regions and phenomena such as some Euro-Atlantic weather regimes, whereas others such as the El Niño-Southern Oscillation show a clear improvement in their spatial structure. The omission of specific tuning might be responsible for this. The shortness of the spin-up, as prescribed by the HighResMIP protocol, prevented the model from reaching equilibrium. The trend in the control and historical simulations, however, appeared to be similar, resulting in a warming trend, obtained by subtracting the control from the historical simulation, close to the observational one

Tropical cyclone integrated kinetic energy in an ensemble of HighResMIP simulations

This study investigates tropical cyclone integrated kinetic energy, a measure which takes into account the intensity and the size of the storms and which is closely associated with their damage potential, in three different global climate models integrated following the HighResMIP protocol. In particular, the impact of horizontal resolution and of the ocean coupling are assessed. We find that, while the increase in resolution results in smaller and more intense storms, the integrated kinetic energy of individual cyclones remains relatively similar between the two configurations. On the other hand, atmosphere-ocean coupling tends to reduce the size and the intensity of the storms, resulting in lower integrated kinetic energy in that configuration. Comparing cyclone integrated kinetic energy between a present and a future scenario did not reveal significant differences between the two periods