Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis
2021
Mariona Pinart | Katharina Nimptsch | Sofia K. Forslund | Kristina Schlicht | Miguel Gueimonde | Patrizia Brigidi | Silvia Turroni | Wolfgang Ahrens | Antje Hebestreit | Maike Wolters | Andreas Dötsch | Ute Nöthlings | Kolade Oluwagbemigun | Rafael R. C. Cuadrat | Matthias B. Schulze | Marie Standl | Michael Schloter | Maria De Angelis | Patricia Iozzo | Maria Angela Guzzardi | Geertrui Vlaemynck | John Penders | Daisy M. A. E. Jonkers | Maya Stemmer | Giulia Chiesa | Duccio Cavalieri | Carlotta De Filippo | Danilo Ercolini | Francesca De Filippis | David Ribet | Najate Achamrah | Marie-Pierre Tavolacci | Pierre Déchelotte | Jildau Bouwman | Matthias Laudes | Tobias Pischon
In any research field, data access and data integration are major challenges that even large, well-established consortia face. Although data sharing initiatives are increasing, joint data analyses on nutrition and microbiomics in health and disease are still scarce. We aimed to identify observational studies with data on nutrition and gut microbiome composition from the Intestinal Microbiomics (INTIMIC) Knowledge Platform following the findable, accessible, interoperable, and reusable (FAIR) principles. An adapted template from the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) consortium was used to collect microbiome-specific information and other related factors. In total, 23 studies (17 longitudinal and 6 cross-sectional) were identified from Italy (7), Germany (6), Netherlands (3), Spain (2), Belgium (1), and France (1) or multiple countries (3). Of these, 21 studies collected information on both dietary intake (24 h dietary recall, food frequency questionnaire (FFQ), or Food Records) and gut microbiome. All studies collected stool samples. The most often used sequencing platform was Illumina MiSeq, and the preferred hypervariable regions of the 16S rRNA gene were V3–V4 or V4. The combination of datasets will allow for sufficiently powered investigations to increase the knowledge and understanding of the relationship between food and gut microbiome in health and disease.
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