Data from: Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals

Background: Interactions between the diet and intestinal microbiota play a
role in health and disease, including obesity and related metabolic
complications. There is great interest to use dietary means to manipulate
the microbiota to promote health. Currently, the impact of dietary change
on the microbiota and the host metabolism is poorly predictable and highly
individual. We propose that the responsiveness of the gut microbiota may
depend on its composition, and associate with metabolic changes in the
host. Methodology: Our study involved three independent cohorts of obese
adults (n = 78) from Belgium, Finland, and Britain, participating in
different dietary interventions aiming to improve metabolic health. We
used a phylogenetic microarray for comprehensive fecal microbiota analysis
at baseline and after the intervention. Blood cholesterol, insulin and
inflammation markers were analyzed as indicators of host response. The
data were divided into four training set – test set pairs; each
intervention acted both as a part of a training set and as an independent
test set. We used linear models to predict the responsiveness of the
microbiota and the host, and logistic regression to predict responder vs.
non-responder status, or increase vs. decrease of the health parameters.
Principal Findings: Our models, based on the abundance of several, mainly
Firmicute species at baseline, predicted the responsiveness of the
microbiota (AUC = 0.77–1; predicted vs. observed correlation = 0.67–0.88).
Many of the predictive taxa showed a non-linear relationship with the
responsiveness. The microbiota response associated with the change in
serum cholesterol levels with an AUC of 0.96, highlighting the involvement
of the intestinal microbiota in metabolic health. Conclusion: This
proof-of-principle study introduces the first potential microbial
biomarkers for dietary responsiveness in obese individuals with impaired
metabolic health, and reveals the potential of microbiota signatures for
personalized nutrition.