Venugopal Giriprasad, Khan Zaiba Hasan, Dash Rishikesh, Tulsian Vinay, Agrawal Siwani, Rout Sudeshna, Mahajan Preetam, Ramadass Balamurugan
Center of Excellence for Clinical Microbiome Research (CCMR), All India Institute of Medical Sciences (AIIMS), Bhubaneswar, Odisha, India.
Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India.
Front Nutr. 2023 Jul 13;10:1200688. doi: 10.3389/fnut.2023.1200688. eCollection 2023.
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.
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.
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 and were predominating. Using ROC analysis, had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, and 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.
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.
铁在地球上储量丰富，但由于其在人体中的低溶解度，不易被定殖细菌利用。宿主和微生物群为铁展开激烈竞争。补充的铁在小肠中的吸收率<15%，其余的铁是导致生态失调的一个来源。在低收入和中等收入人群中，肠道微生物群特征与贫血预测水平之间的关系尚不清楚。本研究旨在确定与贫血中中性粒细胞与淋巴细胞比值（NLR）和平均红细胞体积（MCV）相关的具有预测潜力的肠道微生物群特征。
从奥里萨邦低收入农村人群中招募了104名年龄在10至70岁之间的参与者。测量了血红蛋白（HGB）、NLR和MCV等血液学参数，并使用百分位数对NLR进行分类。使用16s rRNA测序分析了61名贫血参与者和43名非贫血参与者的微生物群特征，随后进行生物信息学分析以确定多样性、相关性和指示物种。应用多层感知器神经网络（MLPNN）模型用于预测贫血。
在低、中、高四分位数NLR组的贫血参与者中观察到显著的微生物群多样性。对于NLR处于低四分位数的贫血参与者，α指数表明细菌过度生长，并且一致地，我们确定 占主导地位。使用ROC分析， 在预测低NLR的贫血方面具有更好的区分度（AUC = 0.803）。相比之下， 和 分别是中、高四分位数NLR的指标。而在MCV较低的非贫血参与者中，细菌改变与性别呈负相关。此外，我们的多层感知器神经网络（MLPNN）模型从20个最丰富的操作分类单元、HGB水平、NLR、MCV和指示物种中预测贫血或非贫血的准确率也达到了89%。
这些发现有力地将贫血血液学参数与微生物群联系起来。肠道微生物群与NLR之间的这种预测性关联可以进一步评估，并用于设计精准营养模型，以及预测社区和临床环境中的铁补充和饮食干预反应。
