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ABSTRACTCitizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens’ awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.