Abstract
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. 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 as minimal requirements for joint data analysis. 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. 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. 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.
Highlights
The joint analysis of individual-level data from multiple nutrition studies may improve the ability to answer complex questions in studying the role of diet and metabolism in health and disease that individual studies are underpowered to examine [1]
The study authors were willing to share metadata, most refused, were hesitant, or had legal or ethical issues related to sharing raw data
Consortium assembly This study was conducted within the framework of ENPADASI, a knowledge hub comprising 51 partners from 9 countries that aim to provide the open-access Data Sharing Initiative for Nutrition (DASH-IN) infrastructure with easy-to-follow instructions for data- and metadata-sharing processes and tools to address political, legal, and ethical barriers to enable joint data analyses [15, 16]
Summary
The joint analysis of individual-level data from multiple nutrition studies may improve the ability to answer complex questions in studying the role of diet and metabolism in health and disease that individual studies are underpowered to examine [1]. A joint individual-level data analysis, unlike in study-level meta-analysis, offers the possibility to reuse data in new ways by combining individual data from different studies, thereby increasing the diversity of samples and the robustness of statistical subgroup analyses (i.e., increasing statistical efficiency and flexibility) This is relevant for nutrition studies on biomarkers, because their laboratory analysis is usually expensive and joint data analysis may provide an efficient way of using existing biomarker data. 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
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