Nutrients, Vol. 17, Pages 2320: The Gut Microbiome Obesity Index: A New Analytical Tool in the Metagenomics Workflow for the Evaluation of Gut Dysbiosis in Obese Humans
Nutrients doi: 10.3390/nu17142320
Authors:
Maria Kulecka
Paweł Jaworski
Natalia Zeber-Lubecka
Aneta Bałabas
Magdalena Piątkowska
Paweł Czarnowski
Barbara Frączek
Wiesław Tarnowski
Michał Mikula
Jerzy Ostrowski
Background/Objectives: Our aim was to create a new method for analyzing metagenomics data, named the gut microbiome obesity index, using a set of taxa/biological functions that correlated with BMI. Methods: A total of 109 obese patients (73 women and 36 men, median BMI 43.0 kg/m2), 87 healthy control (HC) individuals (39 females and 48 males, median BMI 22.7 kg/m2), and 109 esports players (five females and 104 males, median BMI 23.0 kg/m2) were included in the study. To conduct metagenomic and metabolomic analyses, DNA and selected metabolites were isolated from fecal samples and used for whole-genome shotgun sequencing and gas chromatography/mass spectrometry, respectively. Results: Compared with HCs and esports players, obese patients with a BMI > 40 kg/m2 had a significantly higher alpha diversity, as analyzed by the Shannon index, and significant dissimilarities in beta diversity. Both richness and diversity measures were correlated with BMI. Compared with HCs and esports players, 12 differential bacteria were found in the overall obesity group and 42 were found in those with a BMI > 40 kg/m2. Most of the altered species belonged to the Lachnospiraceae family. When the logarithmic relationship of the sums of the bacteria correlated with BMI was calculated to establish a taxonomic health index, it better differentiated between the obesity groups than a standard analytical pipeline; however, it did not differentiate between the HC and the BMI < 35 kg/m2 obesity group. Therefore, we created a functional index based on BMI-associated biological pathways, which differentiated between all obesity groups. Conclusions: Of the obesity indices used to distinguish between healthy and obese microbiota analyzed in this study, a function-based index was more useful than a taxonomy-based index. We believe that gut microbiome indexes could be useful as part of routine metagenomics evaluations. However, an index developed in one geographical area might not be applicable to individuals in a different region and, therefore, further studies should develop separate indices for different populations or geographical regions rather than relying on a single index.
Background/Objectives: Our aim was to create a new method for analyzing metagenomics data, named the gut microbiome obesity index, using a set of taxa/biological functions that correlated with BMI. Methods: A total of 109 obese patients (73 women and 36 men, median BMI 43.0 kg/m2), 87 healthy control (HC) individuals (39 females and 48 males, median BMI 22.7 kg/m2), and 109 esports players (five females and 104 males, median BMI 23.0 kg/m2) were included in the study. To conduct metagenomic and metabolomic analyses, DNA and selected metabolites were isolated from fecal samples and used for whole-genome shotgun sequencing and gas chromatography/mass spectrometry, respectively. Results: Compared with HCs and esports players, obese patients with a BMI > 40 kg/m2 had a significantly higher alpha diversity, as analyzed by the Shannon index, and significant dissimilarities in beta diversity. Both richness and diversity measures were correlated with BMI. Compared with HCs and esports players, 12 differential bacteria were found in the overall obesity group and 42 were found in those with a BMI > 40 kg/m2. Most of the altered species belonged to the Lachnospiraceae family. When the logarithmic relationship of the sums of the bacteria correlated with BMI was calculated to establish a taxonomic health index, it better differentiated between the obesity groups than a standard analytical pipeline; however, it did not differentiate between the HC and the BMI < 35 kg/m2 obesity group. Therefore, we created a functional index based on BMI-associated biological pathways, which differentiated between all obesity groups. Conclusions: Of the obesity indices used to distinguish between healthy and obese microbiota analyzed in this study, a function-based index was more useful than a taxonomy-based index. We believe that gut microbiome indexes could be useful as part of routine metagenomics evaluations. However, an index developed in one geographical area might not be applicable to individuals in a different region and, therefore, further studies should develop separate indices for different populations or geographical regions rather than relying on a single index. Read More