Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
Science Office, Luxembourg Institute of Health, Strassen, Luxembourg.
BMJ Open. 2024 Mar 8;14(3):e083558. doi: 10.1136/bmjopen-2023-083558.
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.
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.
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.
尽管国际社会做出了努力，但肥胖人群的数量仍在不断增加。肥胖预防的一个重要方面涉及到早期识别高危人群，以便及时进行风险分层并采取相应措施。然而，肥胖是复杂的多因素的，单一的（生物）标志物不太可能对个体的风险分层和预后进行最佳评估；相反，需要一组综合标志物。这种多成分解释将整合来自不同领域的生物标志物，例如经典标志物（如人体测量学、血脂）、多组学（如遗传学、蛋白质组学、代谢组学）、生活方式和行为特征（如饮食、身体活动、睡眠模式）、心理特征（心理健康状况，如抑郁）以及其他宿主因素（如肠道微生物多样性），并通过机器学习等先进的解释工具进行整合。在本文中，我们将介绍一个方案，该方案将用于进行范围综述，旨在总结和绘制与肥胖相关的多成分（生物）标志物领域的最新技术状态，重点关注这些生物标志物的可用性和有效性。
将使用预定义的关键词在 PubMed、Scopus、CINAHL 和 Embase 数据库中进行搜索，以确定截至 2024 年 1 月发表的英文同行评议文章。一旦下载到 EndNote 进行去重处理，将使用 CADIMA 在两步程序中由两名独立审查员审查和选择摘要和全文文章。然后将由多名独立审查员进行数据提取。将遵循系统评价和荟萃分析扩展的首选报告项目和电子搜索策略同行评审指南。将对来自不同领域的至少两种标志物组合进行映射和讨论。
不需要伦理批准；数据将依赖于已发表的文章。研究结果将在国际同行评议期刊上以开放获取的方式发表。这项综述将为肥胖预防的未来研究和公共卫生策略指明方向，为多成分干预铺平道路。
