Dietary intake of acrylamide in the Norwegian EuroMix biomonitoring study: Comparing probabilistic dietary estimates with haemoglobin adduct measurements

Acrylamide is a probable human carcinogen with widespread exposure via food. The present study compared acrylamide intake measurements obtained from haemoglobin adduct levels and self-registered dietary consumption data in a group of 144 Norwegian healthy adults. Acrylamide adducts to N-terminal valine in haemoglobin were measured and used to estimate the intake via the internal dose approach which showed a median (interquartile range) of 0.24 (0.19-0.30) μg/kg bw/day. Data from weighed food records and food frequency questionnaires from the same individuals were used for probabilistic modelling of the intake of acrylamide. The median acrylamide intake was calculated to be 0.26 (0.16-0.39) and 0.30 (0.23-0.39) μg/kg bw/day, respectively from the two sources of self-registered dietary consumption data. Overall, a relatively good agreement was observed between the methods in pairwise comparison in Bland-Altman plots, with the methods disagreeing with 7% or less of the values. The intake estimates obtained with the two dietary consumption methods and one biomarker method are in line with earlier dietary estimates in the Norwegian population. The Margin of Exposure indicate a possible health risk concern from dietary acrylamide. This is the first study with a comparison in the same individuals of acrylamide intake estimates obtained with these methods.

Getting the food list 'right': an approach for the development of nutrition-relevant food lists for household consumption and expenditure surveys

Objective

The present paper aimed to demonstrate how 24 h dietary recall data can be used to generate a nutrition-relevant food list for household consumption and expenditure surveys (HCES) using contribution analysis and stepwise regression.

Design

The analysis used data from the 2011/12 Bangladesh Integrated Household Survey (BIHS), which is nationally representative of rural Bangladesh. A total of 325 primary sampling units (PSU=village) were surveyed through a two-stage stratified sampling approach. The household food consumption module used for the analysis consisted of a 24 h open dietary recall in which the female member in charge of preparing and serving food was asked about foods and quantities consumed by the whole household.

Setting

Rural Bangladesh.

Participants

A total of 6500 households.

Results

The original 24 h open dietary recall data in the BIHS were comprised of 288 individual foods that were grouped into ninety-four similar food groups. Contribution analysis and stepwise regression were based on nutrients of public health interest in Bangladesh (energy, protein, fat, Fe, Zn, vitamin A). These steps revealed that a list of fifty-nine food items captures approximately 90 % of the total intake and up to 90 % of the between-person variation for the key nutrients based on the diets of the population.

Conclusions

The study illustrates how 24 h open dietary recall data can be used to generate a country-specific nutrition-relevant food list that could be integrated into an HCES consumption module to enable more accurate and comprehensive household-level food and nutrient analyses.

User-documented food consumption data from publicly available apps: an analysis of opportunities and challenges for nutrition research

BACKGROUND

The need for a better understanding of food consumption behaviour within its behavioural context has sparked the interest of nutrition researchers for user-documented food consumption data collected outside the research context using publicly available nutrition apps. The study aims to characterize the scientific, technical, legal and ethical features of this data in order to identify the opportunities and challenges associated with using this data for nutrition research.

METHOD

A search for apps collecting food consumption data was conducted in October 2016 against UK Google Play and iTunes storefronts. 176 apps were selected based on user ratings and English language support. Publicly available information from the app stores and app-related websites was investigated and relevant data extracted and summarized. Our focus was on characteristics related to scientific relevance, data management and legal and ethical governance of user-documented food consumption data.

RESULTS

Food diaries are the most common form of data collection, allowing for multiple inputs including generic food items, packaged products, or images. Standards and procedures for compiling food databases used for estimating energy and nutrient intakes remain largely undisclosed. Food consumption data is interlinked with various types of contextual data related to behavioural motivation, physical activity, health, and fitness. While exchange of data between apps is common practise, the majority of apps lack technical documentation regarding data export. There is a similar lack of documentation regarding the implemented terms of use and privacy policies. While users are usually the owners of their data, vendors are granted irrevocable and royalty free licenses to commercially exploit the data.

CONCLUSION

Due to its magnitude, diversity, and interconnectedness, user-documented food consumption data offers promising opportunities for a better understanding of habitual food consumption behaviour and its determinants. Non-standardized or non-documented food data compilation procedures, data exchange protocols and formats, terms of use and privacy statements, however, limit possibilities to integrate, process and share user-documented food consumption data. An ongoing research effort is required, to keep pace with the technical advancements of food consumption apps, their evolving data networks and the legal and ethical regulations related to protecting app users and their personal data.

FoodCASE: A system to manage food composition, consumption and TDS data

Food and nutrition scientists, nowadays, need to manage an increasing amount of data regarding food composition, food consumption and Total Diet Studies (TDS). The corresponding datasets can contain information about several thousand different foods, in different versions from different studies. FoodCASE is a system that has been developed to manage these different datasets. It also support flexible means of linking between datasets and generally provide support for the different processes involved in the acquisition, management and processing of data. In this paper, the most important concepts to implement existing guidelines and standards for proper food data management are presented, as well as different use cases of data import and proofs of concepts demonstrating the ability to manage data in FoodCASE.