Unraveling Microbiota-Induced Confounders in Health Studies, and in Regulatory Decisions

Microbiota research is an expanding field, illuminating the extensive influence microorganisms exert on human health. The complexity arises from the understanding that non-communicable diseases, historically viewed as unrelated to microbial influence, may indeed have microbial components potentially linked to microbiota transmission. The dynamic nature of microbial identity, influenced by factors such as diet and antibiotics, alongside social interactions involving physical contact with diverse microbiomes, raises questions surrounding their genetic impact. Despite advances, uncertainty remains regarding how social interactions with different microbiomes influence individual and community health. Research has shown that transmission of microbiota between individuals can alter physiological and psychological conditions, indicating the potential for microbial confounders in statistical comparisons. However, there is a significant gap in evaluating how microbiota transfer among individuals affects scientific and medical analyses.This Research Topic aims to identify and elucidate biases linked to microbiota phenotypes in medical research, particularly those previously misunderstood or unrecognized. By highlighting these biases, the goal is to refine clinical interpretations and enhance the robustness of methodologies. Central to this aim is examining the confounding effects of microbiota variations across individuals in close contact, integrating these factors into statistical analyses. A key objective is evaluating how these biases impact regulatory decisions and the development of policies for pharmaceutical and medical device approval processes. By emphasizing regulatory implications, the Research Topic seeks contributions that provide insights into the operationalization of microbiome data within the regulatory framework, ultimately facilitating the development of efficacious and safe health products.To gather further insights into microbiota-induced biases, we welcome articles addressing, but not limited to, the following themes:&#8226 Questionnaire studies linking disease prevalence and severity to microbiota phenotypes&#8226 Research on physiological and pathological conditions related to microbiota&#8226 Using artificial intelligence and deep learning to detect microbiota-induced biases&#8226 Identifying statistical biases related to microbiota variations&#8226 Exploring prognostic, diagnostic, therapeutic, and prophylactic biases from microbiota&#8226 Developing protocols to correct for biases&#8226 Comprehensive review papersWe invite a broad range of article types, including methodological studies, registered clinical trials, cohort studies, and comparative analyses. Topic editors: Reza Rastmanesh, Balachandar Vellingiri Source: https://www.frontiersin.org/research-topics/71189/unraveling-microbiota-induced-confounders-in-health-studies-and-in-regulatory-decisions