1
|
D’Addezio L, Sette S, Piccinelli R, Le Donne C, Turrini A. FoodEx2 Harmonization of the Food Consumption Database from the Italian IV SCAI Children's Survey. Nutrients 2024; 16:1065. [PMID: 38613101 PMCID: PMC11013267 DOI: 10.3390/nu16071065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Estimating the habitual food and nutrient intakes of a population is based on dietary assessment methods that collect detailed information on food consumption. Establishing the list of foods to be used for collecting data in dietary surveys is central to standardizing data collection. Comparing foods across different data sources is always challenging. Nomenclatures, detail, and classification into broad food groups and sub-groups can vary considerably. The use of a common system for classifying and describing foods is an important prerequisite for analyzing data from different sources. At the European level, EFSA has addressed this need through the development and maintenance of the FoodEx2 classification system. The aim of this work is to present the FoodEx2 harmonization of foods, beverages, and food supplements consumed in the IV SCAI children's survey carried out in Italy. Classifying foods into representative food categories predefined at European level for intake and exposure assessment may lead to a loss of information. On the other hand, a major advantage is the comparability of data from different national databases. The FoodEx2 classification of the national food consumption database represented a step forward in the standardization of the data collection and registration. The large use of FoodEx2 categories at a high level of detail (core and extended terms) combined with the use of descriptors (facets) has minimized information loss and made the reference food categories at country level comparable with different food databases at national and international level.
Collapse
Affiliation(s)
- Laura D’Addezio
- Council for Agricultural Research and Economics, Research Centre for Food and Nutrition, 00178 Rome, Italy; (S.S.); (R.P.); (C.L.D.)
| | - Stefania Sette
- Council for Agricultural Research and Economics, Research Centre for Food and Nutrition, 00178 Rome, Italy; (S.S.); (R.P.); (C.L.D.)
| | - Raffaela Piccinelli
- Council for Agricultural Research and Economics, Research Centre for Food and Nutrition, 00178 Rome, Italy; (S.S.); (R.P.); (C.L.D.)
| | - Cinzia Le Donne
- Council for Agricultural Research and Economics, Research Centre for Food and Nutrition, 00178 Rome, Italy; (S.S.); (R.P.); (C.L.D.)
| | | |
Collapse
|
2
|
Mariotti F, Havard S, Morise A, Nadaud P, Sirot V, Wetzler S, Margaritis I. Perspective: Modeling Healthy Eating Patterns for Food-Based Dietary Guidelines-Scientific Concepts, Methodological Processes, Limitations, and Lessons. Adv Nutr 2021; 12:590-599. [PMID: 33508130 PMCID: PMC8166537 DOI: 10.1093/advances/nmaa176] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/06/2020] [Accepted: 12/16/2020] [Indexed: 01/05/2023] Open
Abstract
The relations between dietary features and human health are varied and complex. Health-related variables are many and they have intricate relations at different and interrelated nutritional levels: nutrients, food groups, and the complex overall pattern. Food-based dietary guidelines (FBDGs) are principally designed to synthesize this information to make it available to the public. Here, we describe the method used to establish healthy eating patterns (HEPs) for the latest French FBDGs, which consists of in-depth food pattern modeling using an enhanced optimization method that gathered all aspects of HEPs. We present the novelty of this food modeling approach for FBDGs, which aims to gather information related to nutrients, food contaminants, and epidemiological relations with long-term health, and to be combined with the objective of realistic dietary patterns that deviate minimally from the prevailing diet. We draw lessons from stepwise implementation of the method and discuss its strengths, limitations, and perspectives. In light of the modeled HEPs, we discuss the importance of food grouping; of accounting for dietary habits while not precluding modeled diets that can be realistic/acceptable; and of taking into account the exposure to food contaminants. We discuss the tolerance and flexibility to be applied to certain dietary reference values for nutrients and health-based guidance values for contaminants so that HEPs can ultimately be identified, and how account can be taken of varied health-related outcomes applied to food groups. Although the approach involves all the peculiar uncertainties of numerous optimization model parameters and input data, its merit is that it offers a rationalized approach to establishing HEPs with multiple constraints and competing objectives. It is also versatile because it is possible to operationalize further dimensions of dietary patterns to favor human and planetary health.
Collapse
Affiliation(s)
- François Mariotti
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, Paris, France
| | - Sabrina Havard
- Risk Assessment Department, ANSES, Maisons-Alfort, France
| | - Anne Morise
- Risk Assessment Department, ANSES, Maisons-Alfort, France
| | - Perrine Nadaud
- Risk Assessment Department, ANSES, Maisons-Alfort, France
| | | | | | | |
Collapse
|
3
|
Joslowski G, Yang J, Aronsson CA, Ahonen S, Butterworth M, Rautanen J, Norris JM, Virtanen SM, Uusitalo U. Development of a harmonized food grouping system for between-country comparisons in the TEDDY Study. J Food Compost Anal 2017; 63:79-88. [PMID: 29151672 PMCID: PMC5690566 DOI: 10.1016/j.jfca.2017.07.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The Environmental Determinants of Diabetes in the Young (TEDDY) is an international study aiming to investigate associations between dietary and other environmental factors and the risk of developing islet autoimmunity and type 1 diabetes. Dietary intake was assessed using a 24-hour recall and repeated 3-day food records and analyzed using country-specific food composition databases (FCDBs) in Finland, Germany, Sweden, and the U.S. with respective in-house calculation programs. A food grouping harmonization process between four country-specific FCDBs was conducted to evaluate and achieve comparability on food group definitions and quantification of food consumption across the countries. Systematic review revealed that the majority of existing food groups of the TEDDY FCDBs were not comparable. Therefore, a completely new classification system of 15 mutually exclusive main food groups (e.g. vegetables) and 89 subgroups (e.g. root vegetables, leafy vegetables) was developed. Foods and beverages were categorized into basic foods (single ingredient) and composite dishes (multiple ingredients). Composite dishes were broken down to ingredients using food composition data available in the FCDBs or generic recipes created for the harmonization effort. The daily consumption of every food group across FCDBs was quantified consistently as either raw or prepared weight depending on the food group to achieve maximal comparability.
Collapse
Affiliation(s)
- Gesa Joslowski
- Institute of Diabetes Research, Helmholtz Zentrum München and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München and Forschergruppe Diabetes e.V., Koelner Platz 1, Munich, 80804, Germany
| | - Jimin Yang
- Health Informatics Institute, Department of Pediatrics, Morsani College of Medicine, University of South Florida, 3650 Spectrum Blvd, Tampa, FL, 33612, USA
| | - Carin Andrén Aronsson
- Department of Clinical Sciences, Lund University, CRC 60:11, Jan Waldenstroms gata 35, Malmö, SE-20502, Sweden
| | - Suvi Ahonen
- National Institute for Health and Welfare, Department of Public Health Solutions, Nutrition Unit. University of Tampere, Faculty of Social Sciences. Science Centre, Tampere University Hospital. Mannerheimintie 166, Helsinki, 00300, Finland
| | - Martha Butterworth
- Health Informatics Institute, Department of Pediatrics, Morsani College of Medicine, University of South Florida, 3650 Spectrum Blvd, Tampa, FL, 33612, USA
| | - Jenna Rautanen
- National Institute for Health and Welfare, Department of Public Health Solutions, Nutrition Unit. Mannerheimintie 166, Helsinki, 00300, Finland
| | - Jill M. Norris
- Department of Epidemiology, University of Colorado Denver, Colorado School of Public Health, 13001 East 17th Place, Aurora, CO, 80045, USA
| | - Suvi M. Virtanen
- National Institute for Health and Welfare, Department of Public Health Solutions, Nutrition Unit. University of Tampere, Faculty of Social Sciences. Science Centre, Tampere University Hospital. Center for Child Health Research, University of Tampere and Tampere University Hospital. Mannerheimintie 166, Helsinki, 00300, Finland
| | - Ulla Uusitalo
- Health Informatics Institute, Department of Pediatrics, Morsani College of Medicine, University of South Florida, 3650 Spectrum Blvd, Tampa, FL, 33612, USA
| | | |
Collapse
|