INDICATORS OF THE QUALITATIVE AND QUANTITATIVE COMPOSITION OF THE GUT COMMENSAL MICROBIOTA AS BIOMARKERS OF HOMEOSTASIS (Part 1)

In recent years, practical medicine has faced a surge of information highlighting the emergence of new microbes in the human body's microbiota, leading to challenges in interpreting laboratory test results. This study aims to equip medical professionals, including doctors of various specialties, medical students, and interns, with a comprehensive understanding of the current knowledge on the human gastrointestinal microbial profile. Our analysis included 53 articles from international literature sources indexed in PubMed, Scopus, and Google Scholar databases. These articles were identified through an electronic search. The composition of the human microbiome is shaped by alterations in environmental conditions, living environments, diet, climate, genetics, and various other factors. Following birth, the pivotal role in microbiome formation involves the vertical, horizontal, and mixed transfer of microorganisms. Navigating the diversity of resident microbiota can be challenging, leading scientists to suggest biomarkers such as diversity indices, enterotypes, and established ratios at typical taxonomic levels (genus, species) for convenience. In clinical practice, these indicators serve as indirect markers of microflora properties that model health or disease. To understand the distribution of species within types, ecologists introduced concepts like α-, β-, and γ-diversity, grounded in different mathematical models. These indicators are also employed to assess the human body microbiota. The division into enterotypes was proposed by scientists on the basis that the phylogenetic (species) composition of each category determines its own functional feature, which is likely to be related to long-term eating habits. Conclusion. Scientists endeavor to assess the intricate microbiome system and its substantial impact on the human body, as well as the adverse effects of dysbiosis, employing mathematical models applied in microbiology.