Multimodal (Bio)Markers and Risk of Obesity - A Comprehensive Scoping Review

Obesity has been associated with several chronic diseases, especially non-communicable ones and related comorbidities. Despite international efforts to decrease the prevalence of obesity, the number of persons struggling with this ailment is not decreasing. An important aspect is obesity prevention, including the early detection of the risk, i.e., whether an individual is likely to develop obesity, in order to allow for early risk stratification and countermeasure initiation. However, obesity is a complex and multi-factorial complication, and many factors appear to play a role, including age, sex, diet, physical activity (PA), psychological and emotional status, genetic make-up, epigenetics, and gut microbiota. One isolated biomarker, therefore, could not enable optimal risk stratification and prognosis for the individual; rather, a combined set or multimodal approach to tackle risk prediction is demanded. Such a multimodal interpretation would integrate biomarkers from various domains, such as more classical markers (insulin, leptin), multi-omics (e.g., genetics, epigenomics, transcriptomics, proteomics, metabolomics), behavioral attributes (dietary, PA, and sleep patterns, smoking status), psychological traits (mental health status, depression, eating disorders), and gut-microbiota (composition, diversity) into a combined interpretation, also employing more advanced interpretation tools, such as machine learning and artificial intelligence. In this scoping review, we aim to summarize the current state of the art in this area, highlighting the progress and novel approaches in combating obesity, and focusing on the feasibility and effectiveness of such biomarkers and their application within clinical trials. In addition, we outline potential future steps and recommendations for future approaches.