ArticlePeerJ2023
Absence of enterotypes in the human gut microbiomes reanalyzed with non-linear dimensionality reduction methods.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessment of nutrient intake by gut microbiota enterotype in Japanese subjects.Gut microbes reports · 2026Article
- Functional archetypes in the human gut microbiome reveal metabolic diversity, stability, and influence disease-associated signatures.Microbiome · 2025Article
- Plasticity of the gut microbiome of golden snub-nosed monkeys (Rhinopithecus roxellana) in response to seasonal variation in diet.NPJ biofilms and microbiomes · 2025Article
- Identification of an intestinal microbiota enterotypes in ageing man diagnosed with benign prostatic hyperplasia (BPH).Scientific reports · 2025Article
- The gut microbiota of healthy individuals remains resilient in response to the consumption of various dietary fibers.Scientific reports · 2024Article
- Chronic kidney disease and gut microbiota.Heliyon · 2023Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enterotypes of the human gut microbiome have been proposed to be a powerful prognostic tool to evaluate the correlation between lifestyle, nutrition, and disease. However, the number of enterotypes suggested in the literature ranged from two to four. The growth of available metagenome data and the use of exact, non-linear methods of data analysis challenges the very concept of clusters in the multidimensional space of bacterial microbiomes. Using several published human gut microbiome datasets of variable 16S rRNA regions, we demonstrate the presence of a lower-dimensional structure in the microbiome space, with high-dimensional data concentrated near a low-dimensional non-linear submanifold, but the absence of distinct and stable clusters that could represent enterotypes. This observation is robust with regard to diverse combinations of dimensionality reduction techniques and clustering algorithms.
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