The human gut microbiome has been studied extensively, but its variation across different populations remains poorly understood. This study identified three distinct clusters, or enterotypes, in the gut microbiome that are not specific to any country or continent. These enterotypes were confirmed in larger datasets, suggesting that gut microbiota variation is generally stratified rather than continuous. The enterotypes are primarily driven by species composition, but some functional roles are not necessarily provided by abundant species, highlighting the importance of functional analysis. Individual host characteristics such as age, gender, and body mass index (BMI) do not explain the enterotypes, but data-driven marker genes or functional modules can be identified for these traits. For example, twelve genes significantly correlate with age and three functional modules with BMI, suggesting potential diagnostic applications of microbial markers. The study analyzed 39 human gut metagenomes from four countries, revealing that the majority of sequences belong to bacteria, with a small proportion from eukaryotes, archaea, and viruses. Phylogenetic and functional analysis showed that the Firmicutes and Bacteroidetes phyla dominate the gut microbiome. The enterotypes were identified based on the abundance of specific genera, such as Bacteroides, Prevotella, and Ruminococcus. Functional analysis revealed that these enterotypes have distinct metabolic pathways and microbial interactions, which may influence host health. The study also identified functional biomarkers for host properties, such as age, gender, and BMI, showing that certain genes and modules are significantly correlated with these traits. These findings suggest that the gut microbiome has a complex structure, with enterotypes representing distinct, well-balanced microbial communities that may respond differently to diet and drug intake. The study highlights the importance of functional analysis in understanding the gut microbiome and its potential applications in diagnosing and predicting health conditions.The human gut microbiome has been studied extensively, but its variation across different populations remains poorly understood. This study identified three distinct clusters, or enterotypes, in the gut microbiome that are not specific to any country or continent. These enterotypes were confirmed in larger datasets, suggesting that gut microbiota variation is generally stratified rather than continuous. The enterotypes are primarily driven by species composition, but some functional roles are not necessarily provided by abundant species, highlighting the importance of functional analysis. Individual host characteristics such as age, gender, and body mass index (BMI) do not explain the enterotypes, but data-driven marker genes or functional modules can be identified for these traits. For example, twelve genes significantly correlate with age and three functional modules with BMI, suggesting potential diagnostic applications of microbial markers. The study analyzed 39 human gut metagenomes from four countries, revealing that the majority of sequences belong to bacteria, with a small proportion from eukaryotes, archaea, and viruses. Phylogenetic and functional analysis showed that the Firmicutes and Bacteroidetes phyla dominate the gut microbiome. The enterotypes were identified based on the abundance of specific genera, such as Bacteroides, Prevotella, and Ruminococcus. Functional analysis revealed that these enterotypes have distinct metabolic pathways and microbial interactions, which may influence host health. The study also identified functional biomarkers for host properties, such as age, gender, and BMI, showing that certain genes and modules are significantly correlated with these traits. These findings suggest that the gut microbiome has a complex structure, with enterotypes representing distinct, well-balanced microbial communities that may respond differently to diet and drug intake. The study highlights the importance of functional analysis in understanding the gut microbiome and its potential applications in diagnosing and predicting health conditions.