Abstract
The disruption of the human gut microbiota has been linked
to host health conditions, including various diseases. However,
no reliable index for measuring and predicting a healthy microbiome
is currently available. Here, the sequencing data of
1,663 Koreans were obtained from three independent studies.
Furthermore, we pooled 3,490 samples from public databases
and analyzed a total of 5,153 fecal samples. First, we analyzed
Korean gut microbiome covariates to determine the influence
of lifestyle on variation in the gut microbiota. Next, patterns
of microbiota variations across geographical locations and
disease statuses were confirmed using a global cohort and disease
data. Based on comprehensive comparative analysis, we
were able to define three enterotypes among Korean cohorts,
namely, Prevotella type, Bacteroides type, and outlier type.
By a thorough categorization of dysbiosis and the evaluation
of microbial characteristics using multiple datasets, we identified
a wide spectrum of accuracy levels in classifying health
and disease states. Using the observed microbiome patterns,
we devised an index named the gut microbiome index (GMI)
that could consistently predict health conditions from human
gut microbiome data. Compared to ecological metrics, the
microbial marker index, and machine learning approaches,
GMI distinguished between healthy and non-healthy individuals
with a higher accuracy across various datasets. Thus,
this study proposes a potential index to measure health status
of gut microbiome that is verified from multiethnic data
of various diseases, and we expect this model to facilitate further
clinical application of gut microbiota data in future.
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