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

K Korpela, H J Flint, A M Johnstone, J Lappi, Kaisa Poutanen, E Dewulf, N Delzenne, W M de Vos, A Salonen (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

80 Citations (Scopus)

Abstract

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
Original languageEnglish
Article numbere90702
JournalPLoS ONE
Volume9
Issue number3
DOIs
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

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Microbiota
intestinal microorganisms
Health
Nutrition
Area Under Curve
Cholesterol
cholesterol
Biomarkers
Microarrays
Metabolism
Belgium
Finland
blood serum
Gastrointestinal Microbiome
microbiome
Logistics
Blood
United Kingdom
Linear Models
biomarkers

Cite this

Korpela, K ; Flint, H J ; Johnstone, A M ; Lappi, J ; Poutanen, Kaisa ; Dewulf, E ; Delzenne, N ; de Vos, W M ; Salonen, A. / Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. In: PLoS ONE. 2014 ; Vol. 9, No. 3.
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abstract = "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",
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Korpela, K, Flint, HJ, Johnstone, AM, Lappi, J, Poutanen, K, Dewulf, E, Delzenne, N, de Vos, WM & Salonen, A 2014, 'Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals', PLoS ONE, vol. 9, no. 3, e90702. https://doi.org/10.1371/journal.pone.0090702

Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. / Korpela, K; Flint, H J; Johnstone, A M; Lappi, J; Poutanen, Kaisa; Dewulf, E; Delzenne, N; de Vos, W M; Salonen, A (Corresponding Author).

In: PLoS ONE, Vol. 9, No. 3, e90702, 2014.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals

AU - Korpela, K

AU - Flint, H J

AU - Johnstone, A M

AU - Lappi, J

AU - Poutanen, Kaisa

AU - Dewulf, E

AU - Delzenne, N

AU - de Vos, W M

AU - Salonen, A

PY - 2014

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N2 - 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

AB - 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

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DO - 10.1371/journal.pone.0090702

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JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

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