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

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.

Esta investigación se está llevando a cabo como parte del proyecto HealthyW8, que recibió financiación del Programa de Investigación e Innovación Horizonte Europa de la Unión Europea en virtud del acuerdo de subvención nº 101080645. JAT, CB, AS y MM-M también están financiados por el CIBEROBN (CB12/03/30038) del Instituto de Salud Carlos III, España. YD ha recibido financiación del proyecto COVIRNA de Horizonte 2020 de la UE (acuerdo de subvención n.º 101016072), del Fondo Nacional de Investigación (subvenciones n.º C14/BM/8225223, C17/BM/11613033 y COVID-19/2020-1/14719577/miRCOVID), de la Asociación COST (acciones n.º CA17129 y CA21153), del Ministerio de Educación Superior e Investigación (sin número específico de subvención) y de la Fundación del Corazón-Daniel Wagner de Luxemburgo (sin número específico de subvención).

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