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This research is being conducted as part of the HealthyW8 project, which received funding from the European Union's Horizon Europe Research and Innovation Programme under the grant agreement no 101080645. JAT, CB, AS and MM- M are also funded by CIBEROBN (CB12/03/30038) of the Instituto de Salud Carlos III, Spain. YD has received funding from the EU Horizon 2020 project COVIRNA (grant agreement # 101016072), the National Research Fund (grants # C14/BM/8225223, C17/BM/11613033 and COVID- 19/2020- 1/14719577/miRCOVID), the COST Association (Actions # CA17129 and CA21153), the Ministry of Higher Education and Research (no specific grant no.) and the Heart Foundation- Daniel Wagner of Luxembourg (no specific grant no).

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Multicomponent (bio)markers for obesity risk prediction: a scoping review protocol

Publicado en:Bmj Open. 14 (3): e083558- - 2024-03-01 14(3), DOI: 10.1136/bmjopen-2023-083558

Autores: Vahid, Farhad; Dessenne, Coralie; Tur, Josep A; Bouzas, Cristina; Devaux, Yvan; Malisoux, Laurent; Monserrat-Mesquida, Margalida; Sureda, Antoni; Desai, Mahesh S; Turner, Jonathan D; Lamy, Elsa; Perez-Jimenez, Maria; Ravn-Haren, Gitte; Andersen, Rikke; Forberger, Sarah; Nagrani, Rajini; Ouzzahra, Yacine; Fontefrancesco, Michele Filippo; Onorati, Maria Giovanna; Bonetti, Gino Gabriel; de-Magistris, Tiziana; Bohn, Torsten

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Introduction Despite international efforts, the number of individuals struggling with obesity is still increasing. An important aspect of obesity prevention relates to identifying individuals at risk at early stage, allowing for timely risk stratification and initiation of countermeasures. However, obesity is complex and multifactorial by nature, and one isolated (bio)marker is unlikely to enable an optimal risk stratification and prognosis for the individual; rather, a combined set is required. Such a multicomponent interpretation would integrate biomarkers from various domains, such as classical markers (eg, anthropometrics, blood lipids), multiomics (eg, genetics, proteomics, metabolomics), lifestyle and behavioural attributes (eg, diet, physical activity, sleep patterns), psychological traits (mental health status such as depression) and additional host factors (eg, gut microbiota diversity), also by means of advanced interpretation tools such as machine learning. In this paper, we will present a protocol that will be employed for a scoping review that attempts to summarise and map the state-of-the-art in the area of multicomponent (bio)markers related to obesity, focusing on the usability and effectiveness of such biomarkers.Methods and analysis PubMed, Scopus, CINAHL and Embase databases will be searched using predefined key terms to identify peer-reviewed articles published in English until January 2024. Once downloaded into EndNote for deduplication, CADIMA will be employed to review and select abstracts and full-text articles in a two-step procedure, by two independent reviewers. Data extraction will then be carried out by several independent reviewers. Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and Peer Review of Electronic Search Strategies guidelines will be followed. Combinations employing at least two biomarkers from different domains will be mapped and discussed.Ethics and dissemination Ethical approval is not required; data will rely on published articles. Findings will be published open access in an international peer-reviewed journal. This review will allow guiding future directions for research and public health strategies on obesity prevention, paving the way towards multicomponent interventions.

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