Comparative safety assessment of genetically modified crops: focus on equivalence with reference varieties could contribute to more efficient and effective field trials.

The initial compositional analysis of plants plays an important role within the internationally harmonized comparative safety assessment approach for genetically modified plants. Current EFSA guidance prescribes two types of comparison, namely difference tests with regard to a conventional comparator or control, and equivalence tests with regard to a collection of commercial reference varieties. The experience gained so far shows that most of the statistically significant differences between the test and control can be discounted based on the fact that they are still within equivalence limits of reference varieties with a presumed history of safe use. Inclusion of a test variety and reference varieties into field trial design, and of the statistical equivalence test would already suffice for the purpose of finding relevant parameters that warrant further assessment, hence both the inclusion of a conventional counterpart and the performance of difference testing can be omitted. This would also allow for the inclusion of safety testing regimes into plant variety testing VCU (value for cultivation and use) or other, independent variety trials.

Correction to: Lack of adverse effects in subchronic and chronic toxicity/carcinogenicity studies on the glyphosate-resistant genetically modified maize NK603 in Wistar Han RCC rats.

In the original publication, the starting point in time for the three feeding trials.

Lack of adverse effects in subchronic and chronic toxicity/carcinogenicity studies on the glyphosate-resistant genetically modified maize NK603 in Wistar Han RCC rats.

In 2012, a controversial study on the long-term toxicity of a Roundup herbicide and the glyphosate-tolerant genetically modified (GM) maize NK603 was published. The EC-funded G-TwYST research consortium tested the potential subchronic and chronic toxicity as well as the carcinogenicity of the glyphosate-resistant genetically modified maize NK603 by performing two 90-day feeding trials, one with GM maize inclusion rates of 11 and 33% and one with inclusion rates of up to 50%, as well as a 2-year feeding trial with inclusion rates of 11 and 33% in male and female Wistar Han RCC rats by taking into account OECD Guidelines for the testing of chemicals and EFSA recommendations on the safety testing of whole-food/feed in laboratory animals. In all three trials, the NK603 maize, untreated and treated once with Roundup during its cultivation, and the conventional counterpart were tested. Differences between each test group and the control group were evaluated. Equivalence was assessed by comparing the observed difference to differences between non-GM reference groups in previous studies. In case of significant differences, whether the effects were dose-related and/or accompanied by changes in related parameters including histopathological findings was evaluated. It is concluded that no adverse effects related to the feeding of the NK603 maize cultivated with or without Roundup for up to 2 years were observed. Based on the outcome of the subchronic and combined chronic toxicity/carcinogenicity studies, recommendations on the scientific justification and added value of long-term feeding trials in the GM plant risk assessment process are presented.

Variability of control data and relevance of observed group differences in five oral toxicity studies with genetically modified maize MON810 in rats.

The data of four 90-day feeding trials and a 1-year feeding trial with the genetically modified (GM) maize MON810 in Wistar Han RCC rats performed in the frame of EU-funded project GRACE were analysed. Firstly, the data obtained from the groups having been fed the non-GM maize diets were combined to establish a historical control data set for Wistar Han RCC rats at the animal housing facility (Slovak Medical University, Bratislava, Slovakia). The variability of all parameters is described, and the reference values and ranges have been derived. Secondly, the consistency of statistically significant differences found in the five studies was analysed. In order to do so, the body weight development, organ weight, haematology and clinical biochemistry data were compared between the studies. Based on the historical control data, equivalence ranges for these parameters were defined, and the values measured in the GM maize-fed groups were compared with these equivalence ranges. Thirdly, the (statistical) power of these feeding studies with whole food/feed was assessed and detectable toxicologically relevant group differences were derived. Linear mixed models (LMM) were applied, and standardized effect sizes (SES) were calculated in order to compare different parameters as well as to provide an overall picture of group and study differences at a glance. The comparison of the five feeding trials showed a clear study effect in the control data. It also showed inconsistency both in the frequency of statistically significant differences and in the difference values between control and test groups.

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.

Enhancing the interpretation of statistical P values in toxicology studies: implementation of linear mixed models (LMMs) and standardized effect sizes (SESs).

In this paper, we compare the traditional ANOVA approach to analysing data from 90-day toxicity studies with a more modern LMM approach, and we investigate the use of standardized effect sizes. The LMM approach is used to analyse weight or feed consumption data. When compared to the week-by-week ANOVA with multiple test results per week, this approach results in only one statement on differences in weight development between groups. Standardized effect sizes are calculated for the endpoints: weight, relative organ weights, haematology and clinical biochemistry. The endpoints are standardized, allowing different endpoints of the same study to be compared and providing an overall picture of group differences at a glance. Furthermore, in terms of standardized effect sizes, statistical significance and biological relevance are displayed simultaneously in a graph.

Evaluation of a two-part regression calibration to adjust for dietary exposure measurement error in the Cox proportional hazards model: A simulation study.

Dietary questionnaires are prone to measurement error, which bias the perceived association between dietary intake and risk of disease. Short-term measurements are required to adjust for the bias in the association. For foods that are not consumed daily, the short-term measurements are often characterized by excess zeroes. Via a simulation study, the performance of a two-part calibration model that was developed for a single-replicate study design was assessed by mimicking leafy vegetable intake reports from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) study. In part I of the fitted two-part calibration model, a logistic distribution was assumed; in part II, a gamma distribution was assumed. The model was assessed with respect to the magnitude of the correlation between the consumption probability and the consumed amount (hereafter, cross-part correlation), the number and form of covariates in the calibration model, the percentage of zero response values, and the magnitude of the measurement error in the dietary intake. From the simulation study results, transforming the dietary variable in the regression calibration to an appropriate scale was found to be the most important factor for the model performance. Reducing the number of covariates in the model could be beneficial, but was not critical in large-sample studies. The performance was remarkably robust when fitting a one-part rather than a two-part model. The model performance was minimally affected by the cross-part correlation.

Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies.

Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose-response curve between fruit and vegetable consumption and carotenoid (α-carotene, ß-carotene, ß-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258.0 g, the correlation between observed and predicted intake was 0.78 and the mean difference between observed and predicted intake was - 1.7 g (limits of agreement: - 466.3, 462.8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201.1 g, the correlation was 0.65 and the mean bias was 2.4 g (limits of agreement: -368.2, 373.0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.

Safety assessment of plant varieties using transcriptomics profiling and a one-class classifier.

An important part of the current hazard identification of novel plant varieties is comparative targeted analysis of the novel and reference varieties. Comparative analysis will become much more informative with unbiased analytical approaches, e.g. omics profiling. Data analysis estimating the similarity of new varieties to a reference baseline class of known safe varieties would subsequently greatly facilitate hazard identification. Further biological and eventually toxicological analysis would then only be necessary for varieties that fall outside this reference class. For this purpose, a one-class classifier tool was explored to assess and classify transcriptome profiles of potato (Solanum tuberosum) varieties in a model study. Profiles of six different varieties, two locations of growth, two year of harvest and including biological and technical replication were used to build the model. Two scenarios were applied representing evaluation of a 'different' variety and a 'similar' variety. Within the model higher class distances resulted for the 'different' test set compared with the 'similar' test set. The present study may contribute to a more global hazard identification of novel plant varieties.

Replacement of meat and dairy by plant-derived foods: estimated effects on land use, iron and SFA intakes in young Dutch adult females.

OBJECTIVE: Reduction in the current high levels of meat and dairy consumption may contribute to environmental as well as human health. Since meat is a major source of Fe, effects on Fe intake need to be evaluated, especially in groups vulnerable to negative Fe status. In the present study we evaluated the effects of replacing meat and dairy foods with plant-based products on environmental sustainability (land requirement) and health (SFA and Fe intakes) in women. DESIGN: Data on land requirements were derived from existing calculation methods. Food composition data were derived from the Dutch Food Composition Table 2006. Data were linked to the food consumption of young Dutch women. Land requirements and nutrient intakes were evaluated at baseline and in two scenarios in which 30% (Scenario_30%) or 100% (Scenario_100%) of the dairy and meat consumption was randomly replaced by the same amount of plant-based dairy- and meat-replacing foods. SETTING: The Netherlands. SUBJECTS: Three hundred and ninety-eight young Dutch females. RESULTS: Replacement of meat and dairy by plant-based foods benefited the environment by decreasing land use. The intake of SFA decreased considerably compared with the baseline situation. On average, total Fe intake increased by 2.5 mg/d, although most of the Fe intake was from a less bioavailable source. CONCLUSIONS: Replacement of meat and dairy foods by plant-based foods reduced land use for consumption and SFA intake of young Dutch females and did not compromise total Fe intake.

Improved family-wise error rate control in multiple equivalence testing.

Equivalence testing is an important component of safety assessments, used for example by the European Food Safety Authority, to allow new food or feed products on the market. The aim of such tests is to demonstrate equivalence of characteristics of test and reference crops. Equivalence tests are typically univariate and applied to each measured analyte (characteristic) separately without multiplicity correction. This increases the probability of making false claims of equivalence (type I errors) when evaluating multiple analytes simultaneously. To solve this problem, familywise error rate (FWER) control using Hochberg's method has been proposed. This paper demonstrates that, in the context of equivalence testing, other FWER-controlling methods are more powerful than Hochberg's. Particularly, it is shown that Hommel's method is guaranteed to perform at least as well as Hochberg's and that an "adaptive" version of Bonferroni's method, which uses an estimator of the proportion of non-equivalent characteristics, often substantially outperforms Hommel's method. Adaptive Bonferroni takes better advantage of the particular context of food safety where a large proportion of true equivalences is expected, a situation where other methods are particularly conservative. The different methods are illustrated by their application to two compositional datasets and further assessed and compared using simulated data.

FAIR environmental and health registry (FAIREHR)- supporting the science to policy interface and life science research, development and innovation.

The environmental impact on health is an inevitable by-product of human activity. Environmental health sciences is a multidisciplinary field addressing complex issues on how people are exposed to hazardous chemicals that can potentially affect adversely the health of present and future generations. Exposure sciences and environmental epidemiology are becoming increasingly data-driven and their efficiency and effectiveness can significantly improve by implementing the FAIR (findable, accessible, interoperable, reusable) principles for scientific data management and stewardship. This will enable data integration, interoperability and (re)use while also facilitating the use of new and powerful analytical tools such as artificial intelligence and machine learning in the benefit of public health policy, and research, development and innovation (RDI). Early research planning is critical to ensuring data is FAIR at the outset. This entails a well-informed and planned strategy concerning the identification of appropriate data and metadata to be gathered, along with established procedures for their collection, documentation, and management. Furthermore, suitable approaches must be implemented to evaluate and ensure the quality of the data. Therefore, the 'Europe Regional Chapter of the International Society of Exposure Science' (ISES Europe) human biomonitoring working group (ISES Europe HBM WG) proposes the development of a FAIR Environment and health registry (FAIREHR) (hereafter FAIREHR). FAIR Environment and health registry offers preregistration of studies on exposure sciences and environmental epidemiology using HBM (as a starting point) across all areas of environmental and occupational health globally. The registry is proposed to receive a dedicated web-based interface, to be electronically searchable and to be available to all relevant data providers, users and stakeholders. Planned Human biomonitoring studies would ideally be registered before formal recruitment of study participants. The resulting FAIREHR would contain public records of metadata such as study design, data management, an audit trail of major changes to planned methods, details of when the study will be completed, and links to resulting publications and data repositories when provided by the authors. The FAIREHR would function as an integrated platform designed to cater to the needs of scientists, companies, publishers, and policymakers by providing user-friendly features. The implementation of FAIREHR is expected to yield significant benefits in terms of enabling more effective utilization of human biomonitoring (HBM) data.

Transformations of summary statistics as input in meta-analysis for linear dose-response models on a logarithmic scale: a methodology developed within EURRECA.

BACKGROUND: To derive micronutrient recommendations in a scientifically sound way, it is important to obtain and analyse all published information on the association between micronutrient intake and biochemical proxies for micronutrient status using a systematic approach. Therefore, it is important to incorporate information from randomized controlled trials as well as observational studies as both of these provide information on the association. However, original research papers present their data in various ways. METHODS: This paper presents a methodology to obtain an estimate of the dose-response curve, assuming a bivariate normal linear model on the logarithmic scale, incorporating a range of transformations of the original reported data. RESULTS: The simulation study, conducted to validate the methodology, shows that there is no bias in the transformations. Furthermore, it is shown that when the original studies report the mean and standard deviation or the geometric mean and confidence interval the results are less variable compared to when the median with IQR or range is reported in the original study. CONCLUSIONS: The presented methodology with transformations for various reported data provides a valid way to estimate the dose-response curve for micronutrient intake and status using both randomized controlled trials and observational studies.

Measurement uncertainty for detection of visual impurities in granular feed and food materials in relation to the investigated amount of material.

The presence is regulated of visually detectable seeds from a selection of toxic plants and fungi mycelium bodies (sclerotia) in feed (Directive 2002/32/EC) and in food (Regulation (EC) 1881/2006). Homogenisation as typical for chemical analyses is not applicable, and dedicated approaches are needed for visual examination methods. Visual methods require two parameters to characterise measurement uncertainties for both unit counts and unit weights. A new approach is to divide approximately 2 kg of sample material into four subsamples of approximately 500 g and to separately examine the four subsamples for numbers and particle weights of seeds or sclerotia. This study is the first to produce datasets on inhomogeneity among subsamples of a sample for visually detectable undesirable substances. Analytical thresholds were calculated from a simulation model and bootstrap procedures based on our data. The analytical thresholds assuring a controlled false-negative rate of 5% for decisions in compliance with legal limits depend on the diversity of the unit counts and weights, the level of the legal limit and the amount of material examined initially in the step-wise approach, either one or two subsamples. A procedure is proposed for examination in practice where only two subsamples, or alternatively even only one subsample, would be examined. If the resulting level of contamination exceeds the relevant threshold additional subsamples need to be examined as well. In most of the investigated cases, analytical thresholds could be established for the examination of just one subsample (500 g) taken from a sample of 2 kg. However, for ergot sclerotia in food with a legal limit of 200 mg kg-1, at least two subsamples (1000 g) need to be examined in the first step. Other groups of visually detectable undesirable substances exist which need further attention.

Multivariate equivalence testing for food safety assessment.

Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data.

A statistical assessment of differences and equivalences between genetically modified and reference plant varieties.

BACKGROUND: Safety assessment of genetically modified organisms is currently often performed by comparative evaluation. However, natural variation of plant characteristics between commercial varieties is usually not considered explicitly in the statistical computations underlying the assessment. RESULTS: Statistical methods are described for the assessment of the difference between a genetically modified (GM) plant variety and a conventional non-GM counterpart, and for the assessment of the equivalence between the GM variety and a group of reference plant varieties which have a history of safe use. It is proposed to present the results of both difference and equivalence testing for all relevant plant characteristics simultaneously in one or a few graphs, as an aid for further interpretation in safety assessment. A procedure is suggested to derive equivalence limits from the observed results for the reference plant varieties using a specific implementation of the linear mixed model. Three different equivalence tests are defined to classify any result in one of four equivalence classes. The performance of the proposed methods is investigated by a simulation study, and the methods are illustrated on compositional data from a field study on maize grain. CONCLUSIONS: A clear distinction of practical relevance is shown between difference and equivalence testing. The proposed tests are shown to have appropriate performance characteristics by simulation, and the proposed simultaneous graphical representation of results was found to be helpful for the interpretation of results from a practical field trial data set.

Uncertainty in intake due to portion size estimation in 24-hour recalls varies between food groups.

Portion size estimation is expected to be one of the largest sources of uncertainty in dietary assessment of the individual. Therefore, we demonstrated a method to quantify uncertainty due to portion size estimation in the usual intake distributions of vegetables, fruit, bread, protein, and potassium. Dutch participants of the European Food Consumption Validation study completed 2 nonconsecutive 24-h recall interviews. In short, the uncertainty analysis consists of Monte Carlo simulations drawing values for portion size from lognormal uncertainty distributions. The uncertainty of the usual intake distribution and accompanying parameters (IQR and the shrinkage factor) were estimated. For the food groups, portion size uncertainty had the greatest effect for vegetables and the least for fruit: the relative 95% uncertainty interval (UI) of the IQR of the usual intake distribution was 0.61-1.35 for vegetables, 0.77-1.24 for bread, and 0.99-1.10 for fruit. For protein and potassium, the resulting relative width of the UI of the IQR for portion size uncertainty are similar: 0.88-1.14 for protein and 0.86-1.14 for potassium. Furthermore, a sensitivity analysis illustrated the importance of the specified uncertainty distributions. The examples show that uncertainty in portion sizes may be more important for some foods such as vegetables. This may reflect differential quantification errors by food groups that deserve further consideration. In conclusion, the presented methodology allows the important quantification of portion size uncertainty and extensions to include other sources of uncertainty is straightforward.

Impact of foods with health logo on saturated fat, sodium and sugar intake of young Dutch adults.

OBJECTIVE: Health logos are introduced to distinguish foods with 'healthier' nutrient composition from regular foods. In the present study, we evaluated the effects of changed food compositions according to health logo criteria on the intake of saturated fat, sugar and sodium in a Dutch population of young adults. DESIGN: Foods in the Dutch food composition table were evaluated against nutrient criteria for logo eligibility. Three replacement scenarios were compared with the nutrient intake 'as measured' in the Dutch consumption survey. The foods not complying with health logo criteria were replaced either by 'virtual' foods exactly complying with the health logo criteria, with real 2007 market shares (scenario I) and 100% market shares (scenario II), or by existing similar foods with a composition that already complied with the health logo criteria (scenario III). RESULTS: The percentage reduction in nutrient intake with the current 2007 market shares of 'health logo foods' was -2.5% for SFA, 0% for sodium and -1% for sugar. With a 100% market share these reductions would be -10% for SFA, -4% for sodium and -6% for sugar. This may lead to a reduction of -40% for SFA, -23% for sodium and -36% for sugar in the most optimal replacement scenario. CONCLUSIONS: With 'health logo foods', available in 2007 and current consumption patterns, small reductions can be achieved for SFA and sugar. For additional reductions, lowering the fat/sodium content of meat (products) towards health logo criteria and drinks without sugar towards limits far below health logo criteria would be the most effective reformulation strategy.

Equivalence tests for safety assessment of genetically modified crops using plant composition data.

The evaluation of compositional characteristics of plants harvested from field trials is an important step in the safety assessment of a genetically modified crop and its derived products for food and feed. The European Food Safety Authority (EFSA) evaluates safety by testing for equivalence between the GM genotype and other genotypes, typically with a history of safe use. Here, a new equivalence test is proposed, which addresses issues with the EFSA test. The method is motivated by a recently proposed equivalence test for analysis of data from animal feeding trials. In order to be suitable for practical safety assessment, the new method has a statistical power set to a desired value, e.g. 95%, by construction. In addition, we assess distributions rather than average values. This way, equivalence limits can also be established when there is limited genotypic variation. The original EFSA equivalence test breaks down in this case. The method is illustrated by its application to data from a field study on maize grain. Simulation studies indicate that the proposed test has appropriate performance characteristics and is competitive with respect to recently proposed alternatives, including the EFSA/EU equivalence test.