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Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study
BACKGROUND: Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut micr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390256/ https://www.ncbi.nlm.nih.gov/pubmed/37528994 http://dx.doi.org/10.3389/fnut.2023.1200688 |
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author | Venugopal, Giriprasad Khan, Zaiba Hasan Dash, Rishikesh Tulsian, Vinay Agrawal, Siwani Rout, Sudeshna Mahajan, Preetam Ramadass, Balamurugan |
author_facet | Venugopal, Giriprasad Khan, Zaiba Hasan Dash, Rishikesh Tulsian, Vinay Agrawal, Siwani Rout, Sudeshna Mahajan, Preetam Ramadass, Balamurugan |
author_sort | Venugopal, Giriprasad |
collection | PubMed |
description | BACKGROUND: Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia. METHODS: One hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia. RESULTS: Significant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species. CONCLUSION: These findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings. |
format | Online Article Text |
id | pubmed-10390256 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103902562023-08-01 Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study Venugopal, Giriprasad Khan, Zaiba Hasan Dash, Rishikesh Tulsian, Vinay Agrawal, Siwani Rout, Sudeshna Mahajan, Preetam Ramadass, Balamurugan Front Nutr Nutrition BACKGROUND: Iron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia. METHODS: One hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia. RESULTS: Significant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species. CONCLUSION: These findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings. Frontiers Media S.A. 2023-07-13 /pmc/articles/PMC10390256/ /pubmed/37528994 http://dx.doi.org/10.3389/fnut.2023.1200688 Text en Copyright © 2023 Venugopal, Khan, Dash, Tulsian, Agrawal, Rout, Mahajan and Ramadass. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Nutrition Venugopal, Giriprasad Khan, Zaiba Hasan Dash, Rishikesh Tulsian, Vinay Agrawal, Siwani Rout, Sudeshna Mahajan, Preetam Ramadass, Balamurugan Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title | Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title_full | Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title_fullStr | Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title_full_unstemmed | Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title_short | Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study |
title_sort | predictive association of gut microbiome and nlr in anemic low middle-income population of odisha- a cross-sectional study |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10390256/ https://www.ncbi.nlm.nih.gov/pubmed/37528994 http://dx.doi.org/10.3389/fnut.2023.1200688 |
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