TY - JOUR
T1 - Gut Microbiota in Nutrition and Health with a Special Focus on Specific Bacterial Clusters
AU - Bresser, Lucas R. F.
AU - de Goffau, Marcus C.
AU - Levin, Evgeni
AU - Nieuwdorp, Max
N1 - Funding Information: M.N. is supported by a Novo Nordisk Foundation CAMIT grant 2018 number 25704 (on which M.C.d.G. is appointed) and a personal ZONMW VICI grant 2020 (09150182010020). Publisher Copyright: © 2022 by the authors.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Health is influenced by how the gut microbiome develops as a result of external and internal factors, such as nutrition, the environment, medication use, age, sex, and genetics. Alpha and beta diversity metrics and (enterotype) clustering methods are commonly employed to perform population studies and to analyse the effects of various treatments, yet, with the continuous development of (new) sequencing technologies, and as various omics fields as a result become more accessible for investigation, increasingly sophisticated methodologies are needed and indeed being developed in order to disentangle the complex ways in which the gut microbiome and health are intertwined. Diseases of affluence, such as type 2 diabetes (T2D) and cardiovascular diseases (CVD), are commonly linked to species associated with the Bacteroides enterotype(s) and a decline of various (beneficial) complex microbial trophic networks, which are in turn linked to the aforementioned factors. In this review, we (1) explore the effects that some of the most common internal and external factors have on the gut microbiome composition and how these in turn relate to T2D and CVD, and (2) discuss research opportunities enabled by and the limitations of some of the latest technical developments in the microbiome sector, including the use of artificial intelligence (AI), strain tracking, and peak to trough ratios.
AB - Health is influenced by how the gut microbiome develops as a result of external and internal factors, such as nutrition, the environment, medication use, age, sex, and genetics. Alpha and beta diversity metrics and (enterotype) clustering methods are commonly employed to perform population studies and to analyse the effects of various treatments, yet, with the continuous development of (new) sequencing technologies, and as various omics fields as a result become more accessible for investigation, increasingly sophisticated methodologies are needed and indeed being developed in order to disentangle the complex ways in which the gut microbiome and health are intertwined. Diseases of affluence, such as type 2 diabetes (T2D) and cardiovascular diseases (CVD), are commonly linked to species associated with the Bacteroides enterotype(s) and a decline of various (beneficial) complex microbial trophic networks, which are in turn linked to the aforementioned factors. In this review, we (1) explore the effects that some of the most common internal and external factors have on the gut microbiome composition and how these in turn relate to T2D and CVD, and (2) discuss research opportunities enabled by and the limitations of some of the latest technical developments in the microbiome sector, including the use of artificial intelligence (AI), strain tracking, and peak to trough ratios.
KW - diet pattern
KW - human health
KW - machine learning
KW - microbiome
KW - omics
UR - http://www.scopus.com/inward/record.url?scp=85139762248&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/cells11193091
DO - https://doi.org/10.3390/cells11193091
M3 - Review article
C2 - 36231053
SN - 2073-4409
VL - 11
JO - Cells
JF - Cells
IS - 19
M1 - 3091
ER -