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Physiology and Biochemistry

A predictive regression model of the obesity-related inflammatory status based on gut microbiota composition

International Journal of Obesity volume 45, pages 2261–2268 (2021)Cite this article

Subjects

Abstract

Background and aim

Fecal microbiome disturbances are linked to different human diseases. In the case of obesity, gut microbiota seems to play a role in the development of low-grade inflammation. The purpose of the present study was to identify specific bacterial families and genera associated with an increased obesity-related inflammatory status, which would allow to build a regression model for the prediction of the inflammatory status of obese and overweight subjects based on fecal microorganisms.

Methods

A total of 361 volunteers from the Obekit trial (65 normal-weight, 110 overweight, and 186 obese) were classified according to four variables: waist/hip ratio (≥0.86 for women and ≥1.00 for men), leptin/adiponectin ratio (LAR, ≥3.0 for women and ≥1.4 for men), and plasma C-reactive protein (≥2 mg/L) and TNF levels (≥0.85 pg/mL). An inflammation score was designed to classify individuals in low (those subjects who did exceed the threshold value in 0 or 1 variable) or high inflammatory index (those subjects who did exceed the threshold value in 2 or more variables). Fecal 16 S rRNA sequencing was performed for all participants, and differential abundance analyses for family and genera were performed using the MicrobiomeAnalyst web-based platform.

Results

Methanobacteriaceae, Christensenellaceae, Coriobacteriaceae, Bifidobacteriaceae, Catabacteriaceae, and Dehalobacteriaceae families, and Methanobrevibacter, Eggerthella, Gemmiger, Anaerostipes, and Collinsella genera were significantly overrepresented in subjects with low inflammatory index. Conversely, Carnobacteriaceae, Veillonellaceae, Pasteurellaceae, Prevotellaceae and Enterobacteriaceae families, and Granulicatella, Veillonella, Haemophilus, Dialister Parabacteroides, Prevotella, Shigella, and Allisonella genera were more abundant in subjects with a high inflammatory index. A regression model adjusted by BMI, sex, and age and including the families Coriobacteriaceae and Prevotellaceae and the genus Veillonella was developed.

Conclusion

A microbiota-based regression model was able to predict the obesity-related inflammatory status (area under the ROC curve = 0.8570 ± 0.0092 Harrell’s optimism-correction) and could be useful in the precision management of inflammobesity.

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Fig. 1: Venn diagram showing the families and genera that were statistically associated and shared between the four inflammation-related variables.
Fig. 2: Bacterial families and genera diferential between individuals with high and low inflammatory score.
Fig. 3: Venn diagram of the families and genera identified by the inflammation score in comparison with those common taxa found in the four variables.
Fig. 4: Graphical representation of the multivariate model for inflammation prediction.

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Acknowledgements

The authors acknowledge the OBEKIT team (Marta Cuervo, Leticia Goñi, Ana Lorente, Miren Iosune Zubieta, and Laura Olazarán); Anna Barceló from the Servei de Genòmica Bioinformàtica of the Universitat Autònoma de Barcelona (Spain) for the technical analysis of gut microbiota; Elizabeth Guruceaga from the Center for Applied Medical Research (Pamplona, Spain) for the bioinformatic analysis of the sequences; Marta García Granero from the Department of Biochemistry & Genetics of the University of Navarra (Spain) for statistical advice. This research was supported by CIBER (CB12/03/30002) and Gobierno de Navarra: Obekit (PT024), Microbiota (PI035), and Nutribiota (0011-1411-2018-000040) projects. The PRODEP-Mexico program to ORL (UABC-PTC-796) is also gratefully acknowledged. The support from CINFA is also appreciated.

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Author notes

  1. These authors contributed equally: Paula Aranaz, Omar Ramos-Lopez.

Authors and Affiliations

  1. Center for Nutrition Research, University of Navarra, Pamplona, Spain

    Paula Aranaz, Amanda Cuevas-Sierra, J. Alfredo Martinez, Fermin I. Milagro & Jose I. Riezu-Boj

  2. Navarra Institute for Health Research (IdiSNA), Pamplona, Spain

    Paula Aranaz, J. Alfredo Martinez, Fermin I. Milagro & Jose I. Riezu-Boj

  3. Medicine and Psychology School, Autonomous University of Baja California, Tijuana, Baja California, Mexico

    Omar Ramos-Lopez

  4. Department of Nutrition, Food Science, and Physiology, University of Navarra, Pamplona, Spain

    Amanda Cuevas-Sierra, J. Alfredo Martinez & Fermin I. Milagro

  5. Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain

    J. Alfredo Martinez & Fermin I. Milagro

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Aranaz, P., Ramos-Lopez, O., Cuevas-Sierra, A. et al. A predictive regression model of the obesity-related inflammatory status based on gut microbiota composition. Int J Obes 45, 2261–2268 (2021). https://doi.org/10.1038/s41366-021-00904-4

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