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Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries, Genes and Nutrition

Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0—control group—receiving conventional, non-PN advice; Level 1—receiving PN advice based on dietary intake data alone; Level 2—receiving PN advice based on dietary intake and phenotypic data; and Level 3—receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8 years (ranging from 18 to 79 years). Of these participants, 60.9 % were women and 96.7 % were from white-European background. The mean BMI for all randomised participants was 25.5 kg m−2, and 44.8 % of the participants had a BMI ≥ 25.0 kg m−2. Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.

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O’Mahony Cian, Vilone Giulia
EFSA Supporting Publications – Volume10, Issue4 April 2013 415E
19/04/2013

Compiled European Food Consumption Database

Food consumption data is a key element of EFSA’s risk assessment activities, forming the basis of dietary exposure assessment at the European level. In 2011, EFSA released the Comprehensive European Food Consumption Database, gathering detailed consumption data from 34 national food consumption surveys representing 66,492 individuals from 22 EU Member States. Due to different survey methodologies used, national survey data cannot be combined to generate average European estimates of dietary exposure. Although the EU menu project, which aims to collect harmonised food consumption data at EU level, will address this limitation of the Comprehensive database, data from this project will not be available until 2018. The present methodological study was executed to assess how the compatibility or existing consumption data as well as the representativeness of food dietary exposure and risk estimates at the European level could be improved through the development of a “Compiled European Food Consumption Database To create Such a dat abase, the usual intake distributions of 589 food items representing the total diet were estimated for 36 clusters, each one composed of subjects belonging to the same age class (children, adolescents or adults). gender and having a similar diet. An adapted form of the NCI (National Cancer Institute) method was used for this, with a number of important modifications. Season, body weight and whether or not the food was consumed at the weekend were used to predict the probability of consumption. Additionally, the gamma distribution was found to be more suitable for modelling the distribution of food amounts n the different food groups instead f the normal distribution. These distributions were combined with food correlation matrices according to the Iman and Conover method in order to simulate 28 days of consumption for 40,000 simulated individuals. The simulated data were validated by comparing the consumption statistics (e.g. mean, median and certain percentiles) of the simulated individuals to the same statistics estimated from the observed individuals of the Comprehensive Database. The same comparison was done at food group level for each cluster. The opportunities and limitations of using the simulated database for exposure assessments are described.

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O’Mahony Cian, Dennis L Seman
In book: The Stability and Shelf Life of Food, Edition: Second, Chapter: 9, Publisher: Elsevier, Editors: Persis Subramaniam, pp.253-284
01/12/2016

Modeling the Microbiological Shelf Life of Foods and Beverages

From about 1985 to 2015, the subject of predictive microbiology has become a mature area of study in and of itself. The ability to predict the growth of a bacterial species within a food matrix for a given set of intrinsic and environmental conditions offers many advantages and benefits to the food industry professional, and chief among these is the ability to determine shelf life using mathematical models.

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Oldring PK, O’Mahony C, Dixon J, Vints M, Mehegan J, Dequatre C, Castle L.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2014;31(3):444-65. doi: 10.1080/19440049.2013.862348. Epub 2014 Jan 15.
15/01/2014

Development of a new modelling tool (FACET) to assess exposure to chemical migrants from food packaging.

The approach used to obtain European Union-wide data on the usage and concentration of substances in different food packaging materials is described. Statistics were collected on pack sizes and market shares for the different materials used to package different food groups. The packaging materials covered were plastics (both flexible and rigid), metal containers, light metal packaging, paper and board, as well as the adhesives and inks used on them. An explanation as to how these data are linked in various ways in the FACET exposure modelling tool is given as well as an overview of the software along with examples of the intermediate tables of data. The example of bisphenol A (BPA), used in resins that may be incorporated into some coatings for canned foodstuffs, is used to illustrate how the data in FACET are combined to produce concentration distributions. Such concentration distributions are then linked probabilistically to the amounts of each food item consumed, as recorded in national food consumption survey diaries, in order to estimate exposure to packaging migrants. Estimates of exposure are at the level of the individual consumer and thus can be expressed for various percentiles of different populations and subpopulations covered by the national dietary surveys.

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Comiskey, Api, Barrett, Ellis, McNamara, O’Mahony, Robison, Rose, Safford, Smith, Tozer
Regul Toxicol Pharmacol. 2017 Aug;88:144-156. doi: 10.1016/j.yrtph.2017.05.017. Epub 2017 May 27.
27/05/2017

Integrating habits and practices data for soaps, cosmetics and air care products into an existing aggregate exposure model.

In order to accurately assess aggregate exposure to a fragrance material in consumers, data are needed on consumer habits and practices, as well as the concentration of the fragrance material in those products. The present study describes the development of Phase 2 Creme RIFM model by expanding the previously developed Phase 1 model to include an additional six product types. Using subject-matching algorithms, the subjects in the Phase 1 Creme RIFM database were paired with subjects in the SUPERB and BodyCare surveys based on age and gender. Consumption of the additional products was simulated to create a seven day diary allowing full data integration in a consistent format. The inhalation route was also included for air care and other products where a fraction of product used is inhaled, derived from the RIFM 2-box model. The expansion of the Phase 1 Creme RIFM model has resulted in a more extensive and refined model, which covers a broader range of product categories and now, includes all relevant routes of exposure. An evaluation of the performance of the model has been carried out in an accompanying publication to this one.

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Comiskey D., Api AM2, Barratt C3, Daly EJ1, Ellis G4, McNamara C1, O’Mahony C1, Robison SH5, Safford B6, Smith B7, Tozer S8.
Regul Toxicol Pharmacol. 2015 Aug;72(3):660-72. doi: 10.1016/j.yrtph.2015.05.012. Epub 2015 May 19
19/05/2015

Novel database for exposure to fragrance ingredients in cosmetics and personal care products.

Exposure of fragrance ingredients in cosmetics and personal care products to the population can be determined by way of a detailed and robust survey. The frequency and combinations of products used at specific times during the day will allow the estimation of aggregate exposure for an individual consumer, and to the sample population. In the present study, habits and practices of personal care and cosmetic products have been obtained from market research data for 36,446 subjects across European countries and the United States in order to determine the exposure to fragrance ingredients. Each subject logged their product uses, time of day and body application sites in an online diary for seven consecutive days. The survey data did not contain information on the amount of product used per occasion or body measurements, such as weight and skin surface area. Nevertheless, this was found from the literature where the likely amount of product used per occasion or body measurement could be probabilistically chosen from distributions of data based on subject demographics. The daily aggregate applied consumer product exposure was estimated based on each subject’s frequency of product use, and Monte Carlo simulations of their likely product amount per use and body measurements. Statistical analyses of the habits and practices and consumer product exposure are presented, which show the robustness of the data and the ability to estimate aggregate consumer product exposure. Consequently, the data and modelling methods presented show potential as a means of performing ingredient safety assessments for personal care and cosmetics products.

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Safford B., A.M.Api, C.Barratt, D.Comiskey, G.Ellis, E.J. Daly, C.McNamara, C.O’Mahony, S.Robison, B.Smith, S.Tozeri
Regulatory Toxicology and Pharmacology Volume 72, Issue 3, August 2015, Pages 673-682
01/08/2015

Use of an aggregate exposure model to estimate consumer exposure to fragrance ingredients in personal care and cosmetic products.

Background:
Ensuring the toxicological safety of fragrance ingredients used in personal care and cosmetic products is essential in product development and design, as well as in the regulatory compliance of the products. This requires an accurate estimation of consumer exposure which, in turn, requires an understanding of consumer habits and use of products. Where ingredients are used in multiple product types, it is important to take account of aggregate exposure in consumers using these products. This publication investigates the use of a newly developed probabilistic model, the Creme RIFM model, to estimate aggregate exposure to fragrance ingredients using the example of 2-phenylethanol (PEA). The output shown demonstrates the utility of the model in determining systemic and dermal exposure to fragrances from individual products, and aggregate exposure. The model provides valuable information not only for risk assessment, but also for risk management. It should be noted that data on the concentrations of PEA in products used in this article were obtained from limited sources and not the standard, industry wide surveys typically employed by the fragrance industry and are thus presented here to illustrate the output and utility of the newly developed model. They should not be considered an accurate representation of actual exposure to PEA.
Methods:

Determination of aggregate exposure to a number of fragrance ingredients was conducted using a model developed by Creme Global in conjunction with RIFM (described here as the Creme RIFM model). Full details of the model are given in a concurrent publication (Comiskey et al., 2015).

The model uses probabilistic (Monte Carlo) simulation to allow sampling from distributions of data sets providing a more realistic estimate of aggregate exposure to individuals across a population. The Creme RIFM

Results:

The results for both applied product exposure and fragrance ingredient exposure are reported below. It should be noted that the applied product amount refers to the amount of product that is retained on the skin after application, taking into account the product retention factors. This product retention factor also helps define exposure to the individual fragrance ingredients.

The applied product and fragrance ingredient exposures are presented in the form of box-and-whisker plots which shows

Conclusions:
The dietary feedback system was used to deliver personalized dietary advice within a multi-country study. Overall, there was good agreement between the manual and automated feedback systems, giving promise to the use of automated systems for personalizing dietary advice.

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Hall B, Steiling W, Safford B, Coroama M, Tozer S, Firmani C, McNamara C, Gibney M.
Food Chem Toxicol. 2011 Feb;49(2):408-22. doi: 10.1016/j.fct.2010.11.016. Epub 2010 Nov 18.
18/11/2010

European consumer exposure to cosmetic products, a framework for conducting population exposure assessments Part 2.

Access to reliable exposure data is essential for the evaluation of the toxicological safety of ingredients in cosmetic products. This study complements the data set obtained previously (Part 1) and published in 2007 by the European cosmetic industry acting within COLIPA. It provides, in distribution form, exposure data on daily quantities of five cosmetic product types: hair styling, hand cream, liquid foundation, mouthwash and shower gel. In total 80,000 households and 14,413 individual consumers in five European countries provided information using their own products. The raw data were analysed using Monte Carlo simulation and a European Statistical Population Model of exposure was constructed. A significant finding was an inverse correlation between the frequency of product use and the quantity used per application recorded for mouthwash and shower gel. The combined results of Part 1 (7 product types) and Part 2 (5 products) reported here, bring up to date and largely confirm the current exposure parameters concerning some 95% of the estimated daily exposure to cosmetics use in the EU. The design of this study, with its relation to demographic and individual diversity, could serve as a model for studies of populations’ exposure to other consumer products.

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McNamara C, Rohan D, Golden D, Gibney M, Hall B, Tozer S, Safford B, Coroama M, Leneveu-Duchemin MC, Steiling W.
Food Chem Toxicol. 2007 Nov;45(11):2086-96. Epub 2007 Jul 7.
07/07/2007

Probabilistic modelling of European consumer exposure to cosmetic products.

In this study, we describe the statistical analysis of the usage profile of the European population to seven cosmetic products. The aim of the study was to construct a reliable model of exposure of the European population from use of the selected products: body lotion, shampoo, deodorant spray, deodorant non-spray, facial moisturiser, lipstick and toothpaste. The first step in this process was to gather reliable data on consumer usage patterns of the products. These data were sourced from a combination of market information databases and a controlled product use study by the trade association Colipa. The market information study contained a large number of subjects, in total 44,100 households and 18,057 habitual users (males and females) of the studied products, in five European countries. The data sets were then combined to generate a realistic distribution of frequency of use of each product, combined with distribution of the amount of product used at each occasion using the CREMe software. A Monte Carlo method was used to combine the data sets. This resulted in a new model of European exposure to cosmetic products being constructed.

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Hall B, Tozer S, Safford B, Coroama M, Steiling W, Leneveu-Duchemin MC, McNamara C, Gibney M.
Food Chem Toxicol. 2007 Nov;45(11):2097-108. Epub 2007 Jun 16.
16/06/2007

European consumer exposure to cosmetic products, a framework for conducting population exposure assessments.

Access to reliable exposure data is essential to evaluate the toxicological safety of ingredients in cosmetic products. This study was carried out by European cosmetic manufacturers acting within the trade association Colipa, with the aim to construct a probabilistic European population model of exposure. The study updates, in distribution form, the current exposure data on daily quantities of six cosmetic products. Data were collected using a combination of market information databases and a controlled product use study. In total 44,100 households and 18,057 individual consumers in five European countries provided data using their own products. All product use occasions were recorded, including those outside of home. The raw data were analysed using Monte Carlo simulation and a European Statistical Population Model of exposure was constructed. A significant finding was an inverse correlation between frequency of product use and quantity used per application for body lotion, facial moisturiser, toothpaste and shampoo. Thus it is not appropriate to calculate daily exposure to these products by multiplying the maximum frequency value by the maximum quantity per event value. The results largely confirm the exposure parameters currently used by the cosmetic industry. Design of this study could serve as a model for future assessments of population exposure to chemicals in products other than cosmetics.

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Daly Edmond James, David Rohan, Cronan McNamara
Publication of US20110138055A1 2011-06-09
09/06/2011

Patent: Resource allocation system : US 20110138055 A1

The present application provides a scalable system for managing requests for compute resources using a cloud computing architecture. The system estimates the total processing time of each computation in advance and monitors the progress of each computation to provide a more accurate estimate of remaining processing time. In this way, a determination may be made as each new computation request is received as to whether an additional resource is required or whether an existing resource would be suitable.

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McNamara Cronan, O’Mullane, Brian, Creme Software Ltd.
Publication of US20170076113A1
16/03/2017

Patent: System and method for secure analysis of datasets: 20170076113

The present application provides a computer system which allows a user to make available a dataset for analysis by others whilst hiding the contents of the dataset.

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Ross John; Driver, Jeffrey; Lunchick, Curt; O’Mahony, Cian
Outlooks on Pest Management, Volume 26, Number 1, February 2015, pp. 33-37(5)
01/02/2015

Models for estimating human exposure to pesticides, Outlooks on Pest Management

Any quantitative understanding of human risk from exposure to pesticides requires knowledge of both hazard (the intrinsic ability of a pesticide to cause harm) and exposure (absorbed dose), i.e., risk is directly proportional to the product of hazard and exposure. Thus, regardless of potential high hazard, risk may be insignificant if exposure is very low, and exposure-driven risk assessment is increasingly being recognized as being the best path forward for the protection of human health. In fact, regulatory agencies did not start doing quantitative risk assessments for pesticides using endpoints other than lethality until the 1970s in part because the analytical tools to sensitively measure exposure were lacking. Quantifying exposure to pesticides required analytical methods such as gas chromatography and liquid chromatography that weren?t commercially available until the mid-1960s to early 1970s, respectively. With the advent of quadrapole mass spectroscopy in the early 1970s the ability to quantify sub milligram per kilogram bodyweight exposures to a wide variety of pesticides with confidence became commonplace. Analytical capability has continued to improve, and it is now possible to measure exposures in the nanogram and sometimes pictogram per kilogram range. As our quantitative knowledge of human exposure matured, it was desirable to extrapolate the knowledge from one chemical that had been measured to others that had not. Indeed, by the early 1980s it became evident that handler exposure to conventional pesticides was generic and not chemical specific. Part of the driving factor to do this modeling was that definitive exposure measurements for one chemical under one set of conditions was costly (>?100,000) and time consuming (months), and the combinations and permutations of exposure scenarios and pesticides are staggering. Models allow us to estimate the exposure to a new active substance or rank exposure of one pesticide to others used in similar conditions. The objective of this paper is to present a brief overview of the range of human exposure models that are available, and the route or pathway of exposure for which they estimate dose with the hope that it provides an appreciation of the basic approaches, chronology and effort expended in developing them.

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Pigat, S., O’Mahony, C.
The FASEB Journal 29 (1_supplement), 384.8
18/01/2019

A Framework for the Predictive Modelling of Public Health Nutrition Strategies

Research Questions

Within public health nutrition, it is of crucial importance to monitor adequate as well as safe nutritional intakes within a population. Food policy initiatives around dietary intakes include voluntary industry reformulation, portion size reductions, food fortification and consumer behavioral changes. Predictive intake models can be used to assess the likely impact of such policies before their implementation.

Methods

Creme Nutrition®, a web based dietary intake software which combines national food consumption and food composition data, includes various models to assess the impact of different strategies, including probabilistic food substitution, portion size modification, and food reformulation. A case study was used to demonstrate the model for sodium reduction using the National Health and Nutrition Examination Survey (NHANES) 2008-2010. In this model, sodium content in bread was reduced by 20%, soups were replaced by low sodium soups containing no more than 120mg/100g and pretzel consumption was substituted by one apple at a replacement probability of 70% to model partial consumer adherence probabilistically.

Results

After modelling sodium intakes in the US population, mean total daily sodium intakes in adults decrease from 3671.9±34.1mg/day to 3512.9±33mg/day. For the high sodium consumers (97.5%ile) total daily sodium intakes are reduced from 7337.85±185.6mg/day to 7090.7±170.8mg/day.

Conclusions

The proposed approach demonstrates the viability of assessing and combining different scenarios to predict the impact of a change on a population’s or a sub-population’s diet via public health initiatives.

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Comiskey Damien & Cian O’Mahony, E.J. Daly, Cronan McNamara
Toxicology Letters, Volume 229, Supplement, 10 September 2014, Page S111
10/09/2014

Combining databases to estimate population exposure to cosmetics and personal care products

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Celis-Morales C, …, E. J. Daly, Victor Akujobi, Rick O’Riordan, et al on behalf of the Food4Me Study
December 2014Genes & Nutrition 10(450):1-13
10/12/2014

Baseline characteristics of the Food4Me Proof of Principle Study: a web-based randomised controlled trial of personalised nutrition in seven European countries

Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0—control group—receiving conventional, non-PN advice; Level 1—receiving PN advice based on dietary intake data alone; Level 2—receiving PN advice based on dietary intake and phenotypic data; and Level 3—receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8 years (ranging from 18 to 79 years). Of these participants, 60.9 % were women and 96.7 % were from white-European background. The mean BMI for all randomised participants was 25.5 kg m−2, and 44.8 % of the participants had a BMI ≥ 25.0 kg m−2. Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.

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Tennant David & Diána Bánáti, Marc Kennedy, Jürgen König, Cian O’Mahony, Susanne Kettler
Food and Chemical Toxicology, Volume 109, Part 1, November 2017, Pages 68-80
02/08/2017

Assessing and reporting uncertainties in dietary exposure analysis – Part II: Application of the uncertainty template to a practical example of exposure assessment

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future.

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Hayaishi Ikuo, Cronan McNamara, David Rohan, Edmond James Daly
Japanese Application No. 2009-100734 filed on Apr. 17, 2009
17/04/2009

Patent: Printing apparatus, image processing apparatus, image processing method, and computer program

In image processing that performs an image search, user convenience is improved. An image processing apparatus includes a permitted time setting unit that sets a permitted necessary time for image search, a search condition setting unit that sets the number of search stages in series relations with each other and search conditions in the respective search stages on the basis of the permitted necessary time, and an image search unit that sequentially performs image search for the search stages by using the set search conditions.

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O’Sullivan Aaron J., Sandrine Pigat, Cian O’Mahony, Michael J. Gibney & Aideen I. McKevitt
Pages 1660-1671 | Received 11 May 2016, Accepted 08 Aug 2016, Accepted author version posted online: 09 Sep 2016, Published online: 30 Sep 2016
09/09/2016

Probabilistic modelling to assess exposure to three artificial sweeteners of young Irish patients aged 1–3 years with PKU and CMPA

The choice of suitable normal foods is limited for individuals with particular medical conditions, e.g., inborn errors of metabolism (phenylketonuria – PKU) or severe cow’s milk protein allergy (CMPA). Patients may have dietary restrictions and exclusive or partial replacement of specific food groups with specially formulated products to meet particular nutrition requirements. Artificial sweeteners are used to improve the appearance and palatability of such food products to avoid food refusal and ensure dietary adherence. Young children have a higher risk of exceeding acceptable daily intakes for additives than adults due to higher food intakes kg–1 body weight. The Budget Method and EFSA’s Food Additives Intake Model (FAIM) are not equipped to assess partial dietary replacement with special formulations as they are built on data from dietary surveys of consumers without special medical requirements impacting the diet. The aim of this study was to explore dietary exposure modelling as a means of estimating the intake of artificial sweeteners by young PKU and CMPA patients aged 1–3 years. An adapted validated probabilistic model (FACET) was used to assess patients’ exposure to artificial sweeteners. Food consumption data were derived from the food consumption survey data of healthy young children in Ireland from the National Preschool and Nutrition Survey (NPNS, 2010–11). Specially formulated foods for special medical purposes were included in the exposure model to replace restricted foods. Inclusion was based on recommendations for adequate protein intake and dietary adherence data. Exposure assessment results indicated that young children with PKU and CMPA have higher relative average intakes of artificial sweeteners than healthy young children. The reliability and robustness of the model in the estimation of patient additive exposures was further investigated and provides the first exposure estimates for these special populations.

Food Additives and Contaminants journal software for modelling dietary exposure to food chemicals and nutrients

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State of the art review of Big Data and web-based Decision Support Systems (DSS) for food safety risk assessment with respect to climate change

Technology is being developed to handle vast amounts of complex data from diverse sources. The terms “Big Data” and “Decision Support Systems” (DSS) refer to computerised multidimensional data management systems that support stakeholders in making use of modern data-driven approaches to identify and solve problems and to enable enhanced decision making. Big Data has become ubiquitous in food safety. Information in the food supply chain is scattered and involves heterogenicity in format, scale, geographical origin. Also, interactions among environmental factors, food contamination, and foodborne diseases are complex, dynamic, and challenging to predict. Therefore, this state-of-the-art review article focuses on the underlying architecture of Big Data and web-based technologies for food safety, focusing on climate change influences. Challenges in adopting Big Data in food safety are presented, and future research directions regarding technologies/methods in the food supply chain are summarised and analysed. The analysis and discussion provided aim to assist agri-food researchers and stakeholders in taking initiatives and gathering insights on the application of Big Data and web-based DSS for food safety, which would alleviate challenges and facilitate the implementation of Big Data in food safety risk assessment while considering the possible implications of climate change.

web-based Decision Support Systems (DSS) for food safety risk assessment

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Design, development and validation of software for modelling dietary exposure to food chemicals and nutrients.

The Monte Carlo computational system for stochastic modelling of dietary exposure to food chemicals and nutrients is presented. This system was developed through a European Commission-funded research project. It is accessible as a Web-based application service. The system allows and supports very significant complexity in the data sets used as the model input, but provides a simple, general purpose, linear kernel for model evaluation. Specific features of the system include the ability to enter (arbitrarily) complex mathematical or probabilistic expressions at each and every input data field, automatic bootstrapping on subjects and on subject food intake diaries, and custom kernels to apply brand information such as market share and loyalty to the calculation of food and chemical intake.

Food Additives and Contaminants journal software for modelling dietary exposure to food chemicals and nutrients

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Plumb Jenny, Sandrine Pigat, Foteini Bompola, Maeve Cushen, Hannah Pinchen, Eric Nørby, Siân Astley, Jacqueline Lyons, Mairead Kiely and Paul Finglas
Nutrients – Volume 9, Issue 4
23/03/2017

eBASIS (Bioactive Substances in Food Information Systems) and Bioactive Intakes: Major Updates of the Bioactive Compound Composition and Beneficial Bioeffects Database and the Development of a Probabilistic Model to Assess Intakes in Europe

eBASIS (Bioactive Substances in Food Information Systems), a web-based database that contains compositional and biological effects data for bioactive compounds of plant origin, has been updated with new data on fruits and vegetables, wheat and, due to some evidence of potential beneficial effects, extended to include meat bioactives. eBASIS remains one of only a handful of comprehensive and searchable databases, with up-to-date coherent and validated scientific information on the composition of food bioactives and their putative health benefits. The database has a user-friendly, efficient, and flexible interface facilitating use by both the scientific community and food industry. Overall, eBASIS contains data for 267 foods, covering the composition of 794 bioactive compounds, from 1147 quality-evaluated peer-reviewed publications, together with information from 567 publications describing beneficial bioeffect studies carried out in humans. This paper highlights recent updates and expansion of eBASIS and the newly-developed link to a probabilistic intake model, allowing exposure assessment of dietary bioactive compounds to be estimated and modelled in human populations when used in conjunction with national food consumption data. This new tool could assist small- and medium-sized enterprises (SMEs) in the development of food product health claim dossiers for submission to the European Food Safety Authority (EFSA).

Nutrients MDPI eBASIS Bioactive Substances in Food Information Systems

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Vilone Giulia, Comiskey D, Heraud F, O’Mahony C.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2014;31(10):1639-51. doi: 10.1080/19440049.2014.955886. Epub 2014 Sep 10.
10/09/2014

Statistical method to assess usual dietary intakes in the European population.

Food consumption data are a key element of EFSA’s risk assessment activities, forming the basis of dietary exposure assessment at the European level. In 2011, EFSA released the Comprehensive European Food Consumption Database, gathering consumption data from 34 national surveys representing 66,492 individuals from 22 European Union member states. Due to the different methodologies used, national survey data cannot be combined to generate European estimates of dietary exposure. This study was executed to assess how existing consumption data and the representativeness of dietary exposure and risk estimates at the European Union level can be improved by developing a ‘Compiled European Food Consumption Database’. To create the database, the usual intake distributions of 589 food items representing the total diet were estimated for 36 clusters composed of subjects belonging to the same age class, gender and having a similar diet. An adapted form of the National Cancer Institute (NCI) method was used for this, with a number of important modifications. Season, body weight and whether or not the food was consumed at the weekend were used to predict the probability of consumption. A gamma distribution was found to be more suitable for modelling the distribution of food amounts in the different food groups instead of a normal distribution. These distributions were combined with food correlation matrices according to the Iman-Conover method in order to simulate 28 days of consumption for 40,000 simulated individuals. The simulated data were validated by comparing the consumption statistics of the simulated individuals and food groups with the same statistics estimated from the Comprehensive Database. The opportunities and limitations of using the simulated database for exposure assessments are described.

Food Additives and Contaminants journal software for modelling dietary exposure to food chemicals and nutrients

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McNamara C, Mehegan J, O’Mahony C, Safford B, Smith B, Tennant D, Buck N, Ehrlich V, Sardi M, Haldemann Y, Nordmann H, Jasti PR.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2011 Dec;28(12):1636-44. doi: 10.1080/19440049.2011.606232. Epub 2011 Oct 13.
04/10/2013

Uncertainty analysis of the use of a retailer fidelity card scheme in the assessment of food additive intake.

Background:

The feasibility of using a retailer fidelity card scheme to estimate food additive intake was investigated in an earlier study. Fidelity card survey information was combined with information provided by the retailer on levels of the food colour Sunset Yellow (E110) in the foods to estimate a daily exposure to the additive in the Swiss population. As with any dietary exposure method the fidelity card scheme is subject to uncertainties and in this paper the impact of uncertainties associated with input variables including the amounts of food purchased, the levels of E110 in food, the proportion of food purchased at the retailer, the rate of fidelity card usage, the proportion of foods consumed outside of the home and bodyweights and with systematic uncertainties was assessed using a qualitative, deterministic and probabilistic approach. An analysis of the sensitivity of the results to each of the probabilistic inputs was also undertaken. The analysis identified the key factors responsible for uncertainty within the model and demonstrated how the application of some simple probabilistic approaches can be used quantitatively to assess uncertainty.

 

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O’Sullivan Aaron J., Cian O’Mahony, Leo Meunier, Nik Loveridge & Aideen I. McKevitt
Pages 1453-1463 | Received 21 Mar 2018, Accepted 06 Jun 2018, Accepted author version posted online: 18 Jun 2018, Published online: 11 Jul 2018
11/07/2018

Investigation of the potential for a simplified exposure tool in medical nutrition (SETIM) to minimise exposures to sweeteners in young patients aged 1-3 years with PKU and CMPA

Children with phenylketonuria (PKU) and severe cow’s milk protein allergy (CMPA) consume prescribed, specially formulated, foods for special medical purposes (FSMPs) in addition to having restricted intake of normal foods. These vulnerable patients are exposed to artificial sweeteners from the consumption of a combination of both free and prescribed foods. Young patients with PKU and CMPA aged from 1 to 3 years have a higher risk of exceeding the acceptable daily intake (ADI) for sweeteners than age-matched healthy children. A probabilistic modelling approach has been adapted successfully to assess the exposure of young patients with PKU and CMPA to low-calorie sweeteners. To assist professionals in the screening and formulation of foods containing food additives for such patients, a simplified exposure method/tool has been developed. The tool is intended to ensure that total dietary exposure can be considered. The simplified tool is not intended to replace the probabilistic model but may be used as a screening tool to determine if further investigation on exposure is warranted. The aim of this study was to develop and validate this simplified exposure tool to support those currently used by healthcare professionals (HCPs) using data available from the probabilistic modelling of exposure in young children with PKU and CMPA. The probabilistic model does not allow for swift screening of exposure scenarios nor is the present EFSA Food Additive Intake Assessment Model (FAIM) fully suitable for application to medical foods. The simplified exposure tool in medical nutrition (SETIM) reported here is both reliable and consistent and provides additive usage levels which minimise regular exposure above the ADI in patients. In addition to the usefulness of SETIM for the medical nutrition industry, the tool has the potential to enhance the practice of evidence-based medical nutrition by official risk assessment bodies, registration authorities and healthcare professionals.

Food Additives and Contaminants journal software for modelling dietary exposure to food chemicals and nutrients

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