Antiaging Checkup: Gut Age

Omics analysis combining metagenomic information on the gut microbiota and various background information is providing important information on what factors influence the gut microbiota and how it is formed. The results of a study conducted in the Netherlands are significant [1]. They performed shotgun metagenomic sequence analysis on the gut microbiota of 8208 Dutch people from a three-generation cohort including 2756 families, collecting information on classification, functional pathways, antibiotic resistance, pathogenic factors, disease status, geographical and social environment, drug use, blood collection, urinalysis findings, etc., and analyzed the relationship between each item and the bacterial flora. As a result, when clustering was performed using principal coordinate analysis (PCoA), it was observed that Prevotella copri (P. copri) had a significant influence, and it was also shown that there was a positive correlation with general health and a lower risk of irritable bowel syndrome when P. copri was abundant. The impact on the composition of the gut microbiota was explained by 48.6% due to cohabitation, while genetic factors accounted for only 6.6%, and the contribution of stool characteristics, past medical history, used drugs, physical characteristics, etc. was high. Summarizing the factors related to the composition of the gut microbiota (Table 137.1), it is clear that food and nutrition including alcohol, environmental factors in childhood and currently, and socio-economic factors are evident [1]. In particular, as pointed out in previous studies, it is clear that carbohydrates such as sugars, fats, and proteins affect the gut microbiota, and in addition, birth conditions, breastfeeding, and childhood living environment are important environmental factors in the formation of resident bacterial flora. Such basic information provides important insights to consider background factors when performing gut microbiota analysis in relation to disease association, drug susceptibility, health status evaluation, etc. It shows that it is difficult to interpret without excluding the contribution of confounding factors in the analysis where each information is not available, starting from the condition of the stool at the time of collection, the situation in childhood, and the history of medication.