Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study

Giriprasad Venugopal; Zaiba Hasan Khan; Rishikesh Dash; Vinay Tulsian; Siwani Agrawal; Sudeshna Rout; Preetam Mahajan; Balamurugan Ramadass

doi:10.3389/fnut.2023.1200688

Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study

Front Nutr. 2023 Jul 13:10:1200688. doi: 10.3389/fnut.2023.1200688. eCollection 2023.

Authors

Giriprasad Venugopal¹, Zaiba Hasan Khan¹, Rishikesh Dash¹, Vinay Tulsian¹, Siwani Agrawal², Sudeshna Rout², Preetam Mahajan³, Balamurugan Ramadass^{1

2

4}

Affiliations

¹ Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India.
² Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India.
³ Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India.
⁴ Adelaide Medical School Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia.

Abstract

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.

Keywords: MCV; MLPNN; NLR; anemia; gut microbiome; indicator species; iron supplementation.