Joint data analysis in nutritional epidemiology

Mariona Pinart, Katharina Nimptsch, Jildau Bouwman, Lars O. Dragsted, Chen Yang, Nathalie De Cock, Carl Lachat, Giuditta Perozzi, Raffaella Canali, Rosario Lombardo, Massimo D'Archivio, Michèle Guillaume, Anne Françoise Donneau, Stephanie Jeran, Jakob Linseisen, Christina Kleiser, Ute Nöthlings, Janett Barbaresko, Heiner Boeing, Marta Stelmach-MardasThorsten Heuer, Eamon Laird, Janette Walton, Paolo Gasparini, Antonietta Robino, Luis Castaño, Gemma Rojo-Martínez, Jordi Merino, Luis Masana, Marie Standl, Holger Schulz, Elena Biagi, Eha Nurk, Christophe Matthys, Marco Gobbetti, Maria de Angelis, Eberhard Windler, Birgit Christiane Zyriax, Jean Tafforeau, Tobias Pischon

Research output: Contribution to journalArticle

Abstract

Background: Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective: The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well asminimal requirements for joint data analysis. Methods: A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of "diet-related chronic diseases." Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information.Results: Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions: Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.
Original languageEnglish
Pages (from-to)285-297
Number of pages13
JournalJournal of Nutrition
Volume148
Issue number2
DOIs
Publication statusPublished - Jan 1 2018

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Information Dissemination
Nutrition Assessment
Epidemiology
Joints
Biomarkers
Health
Diet
Phenotype
Aptitude
Metabolomics
Tobacco Use
Genomics
Nutritional Status
Research
Ethics
Alcohol Drinking
Proteomics
Lipoproteins
Health Status
Observational Studies

Keywords

  • Data integration
  • Data sharing
  • Metadata
  • Nutritional phenotype
  • Observational studies

Cite this

Pinart, M., Nimptsch, K., Bouwman, J., Dragsted, L. O., Yang, C., De Cock, N., ... Pischon, T. (2018). Joint data analysis in nutritional epidemiology. Journal of Nutrition, 148(2), 285-297. https://doi.org/10.1093/jn/nxx037

Joint data analysis in nutritional epidemiology. / Pinart, Mariona; Nimptsch, Katharina; Bouwman, Jildau; Dragsted, Lars O.; Yang, Chen; De Cock, Nathalie; Lachat, Carl; Perozzi, Giuditta; Canali, Raffaella; Lombardo, Rosario; D'Archivio, Massimo; Guillaume, Michèle; Donneau, Anne Françoise; Jeran, Stephanie; Linseisen, Jakob; Kleiser, Christina; Nöthlings, Ute; Barbaresko, Janett; Boeing, Heiner; Stelmach-Mardas, Marta; Heuer, Thorsten; Laird, Eamon; Walton, Janette; Gasparini, Paolo; Robino, Antonietta; Castaño, Luis; Rojo-Martínez, Gemma; Merino, Jordi; Masana, Luis; Standl, Marie; Schulz, Holger; Biagi, Elena; Nurk, Eha; Matthys, Christophe; Gobbetti, Marco; de Angelis, Maria; Windler, Eberhard; Zyriax, Birgit Christiane; Tafforeau, Jean; Pischon, Tobias.

In: Journal of Nutrition, Vol. 148, No. 2, 01.01.2018, p. 285-297.

Research output: Contribution to journalArticle

Pinart, M, Nimptsch, K, Bouwman, J, Dragsted, LO, Yang, C, De Cock, N, Lachat, C, Perozzi, G, Canali, R, Lombardo, R, D'Archivio, M, Guillaume, M, Donneau, AF, Jeran, S, Linseisen, J, Kleiser, C, Nöthlings, U, Barbaresko, J, Boeing, H, Stelmach-Mardas, M, Heuer, T, Laird, E, Walton, J, Gasparini, P, Robino, A, Castaño, L, Rojo-Martínez, G, Merino, J, Masana, L, Standl, M, Schulz, H, Biagi, E, Nurk, E, Matthys, C, Gobbetti, M, de Angelis, M, Windler, E, Zyriax, BC, Tafforeau, J & Pischon, T 2018, 'Joint data analysis in nutritional epidemiology', Journal of Nutrition, vol. 148, no. 2, pp. 285-297. https://doi.org/10.1093/jn/nxx037
Pinart M, Nimptsch K, Bouwman J, Dragsted LO, Yang C, De Cock N et al. Joint data analysis in nutritional epidemiology. Journal of Nutrition. 2018 Jan 1;148(2):285-297. https://doi.org/10.1093/jn/nxx037
Pinart, Mariona ; Nimptsch, Katharina ; Bouwman, Jildau ; Dragsted, Lars O. ; Yang, Chen ; De Cock, Nathalie ; Lachat, Carl ; Perozzi, Giuditta ; Canali, Raffaella ; Lombardo, Rosario ; D'Archivio, Massimo ; Guillaume, Michèle ; Donneau, Anne Françoise ; Jeran, Stephanie ; Linseisen, Jakob ; Kleiser, Christina ; Nöthlings, Ute ; Barbaresko, Janett ; Boeing, Heiner ; Stelmach-Mardas, Marta ; Heuer, Thorsten ; Laird, Eamon ; Walton, Janette ; Gasparini, Paolo ; Robino, Antonietta ; Castaño, Luis ; Rojo-Martínez, Gemma ; Merino, Jordi ; Masana, Luis ; Standl, Marie ; Schulz, Holger ; Biagi, Elena ; Nurk, Eha ; Matthys, Christophe ; Gobbetti, Marco ; de Angelis, Maria ; Windler, Eberhard ; Zyriax, Birgit Christiane ; Tafforeau, Jean ; Pischon, Tobias. / Joint data analysis in nutritional epidemiology. In: Journal of Nutrition. 2018 ; Vol. 148, No. 2. pp. 285-297.
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abstract = "Background: Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective: The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well asminimal requirements for joint data analysis. Methods: A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of {"}diet-related chronic diseases.{"} Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information.Results: Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions: Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.",
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author = "Mariona Pinart and Katharina Nimptsch and Jildau Bouwman and Dragsted, {Lars O.} and Chen Yang and {De Cock}, Nathalie and Carl Lachat and Giuditta Perozzi and Raffaella Canali and Rosario Lombardo and Massimo D'Archivio and Mich{\`e}le Guillaume and Donneau, {Anne Fran{\cc}oise} and Stephanie Jeran and Jakob Linseisen and Christina Kleiser and Ute N{\"o}thlings and Janett Barbaresko and Heiner Boeing and Marta Stelmach-Mardas and Thorsten Heuer and Eamon Laird and Janette Walton and Paolo Gasparini and Antonietta Robino and Luis Casta{\~n}o and Gemma Rojo-Mart{\'i}nez and Jordi Merino and Luis Masana and Marie Standl and Holger Schulz and Elena Biagi and Eha Nurk and Christophe Matthys and Marco Gobbetti and {de Angelis}, Maria and Eberhard Windler and Zyriax, {Birgit Christiane} and Jean Tafforeau and Tobias Pischon",
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TY - JOUR

T1 - Joint data analysis in nutritional epidemiology

AU - Pinart, Mariona

AU - Nimptsch, Katharina

AU - Bouwman, Jildau

AU - Dragsted, Lars O.

AU - Yang, Chen

AU - De Cock, Nathalie

AU - Lachat, Carl

AU - Perozzi, Giuditta

AU - Canali, Raffaella

AU - Lombardo, Rosario

AU - D'Archivio, Massimo

AU - Guillaume, Michèle

AU - Donneau, Anne Françoise

AU - Jeran, Stephanie

AU - Linseisen, Jakob

AU - Kleiser, Christina

AU - Nöthlings, Ute

AU - Barbaresko, Janett

AU - Boeing, Heiner

AU - Stelmach-Mardas, Marta

AU - Heuer, Thorsten

AU - Laird, Eamon

AU - Walton, Janette

AU - Gasparini, Paolo

AU - Robino, Antonietta

AU - Castaño, Luis

AU - Rojo-Martínez, Gemma

AU - Merino, Jordi

AU - Masana, Luis

AU - Standl, Marie

AU - Schulz, Holger

AU - Biagi, Elena

AU - Nurk, Eha

AU - Matthys, Christophe

AU - Gobbetti, Marco

AU - de Angelis, Maria

AU - Windler, Eberhard

AU - Zyriax, Birgit Christiane

AU - Tafforeau, Jean

AU - Pischon, Tobias

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Background: Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective: The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well asminimal requirements for joint data analysis. Methods: A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of "diet-related chronic diseases." Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information.Results: Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions: Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.

AB - Background: Joint data analysis from multiple nutrition studies may improve the ability to answer complex questions regarding the role of nutritional status and diet in health and disease. Objective: The objective was to identify nutritional observational studies from partners participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) Consortium, as well asminimal requirements for joint data analysis. Methods: A predefined template containing information on study design, exposure measurements (dietary intake, alcohol and tobacco consumption, physical activity, sedentary behavior, anthropometric measures, and sociodemographic and health status), main health-related outcomes, and laboratory measurements (traditional and omics biomarkers) was developed and circulated to those European research groups participating in the ENPADASI under the strategic research area of "diet-related chronic diseases." Information about raw data disposition and metadata sharing was requested. A set of minimal requirements was abstracted from the gathered information.Results: Studies (12 cohort, 12 cross-sectional, and 2 case-control) were identified. Two studies recruited children only and the rest recruited adults. All studies included dietary intake data. Twenty studies collected blood samples. Data on traditional biomarkers were available for 20 studies, of which 17 measured lipoproteins, glucose, and insulin and 13 measured inflammatory biomarkers. Metabolomics, proteomics, and genomics or transcriptomics data were available in 5, 3, and 12 studies, respectively. Although the study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data. Forty-one descriptors of minimal requirements for the study data were identified to facilitate data integration. Conclusions: Combining study data sets will enable sufficiently powered, refined investigations to increase the knowledge and understanding of the relation between food, nutrition, and human health. Furthermore, the minimal requirements for study data may encourage more efficient secondary usage of existing data and provide sufficient information for researchers to draft future multicenter research proposals in nutrition.

KW - Data integration

KW - Data sharing

KW - Metadata

KW - Nutritional phenotype

KW - Observational studies

U2 - 10.1093/jn/nxx037

DO - 10.1093/jn/nxx037

M3 - Article

VL - 148

SP - 285

EP - 297

JO - Journal of Nutrition

JF - Journal of Nutrition

SN - 0022-3166

IS - 2

ER -