Department of Microbiology and Immunology, Faculty of Pharmacy, Misr International University, Cairo, Egypt.
Department of Pediatrics, Endocrinology and Diabetes Division, Faculty of Medicine, Suez Canal University, Ismailia, Egypt.
Curr Microbiol. 2024 Jul 8;81(8):259. doi: 10.1007/s00284-024-03771-0.
Deciphering the gut microbiome's link to obesity is crucial. Our study characterized the gut microbial community in Egyptian children and investigated the effect of covariates on the gut microbiome, body mass index (BMI), geographical location, gender, and age. We used 16S rRNA sequencing to characterize the gut microbial communities of 49 children. We then evaluated these communities for diversity, potential biomarkers, and functional capacity. Alpha diversity of the non-obese group was higher than that of the obese group (Chao1, P = 0.006 and observed species, P = 0.003). Beta diversity analysis revealed significant variations in the gut microbiome between the two geographical locations, Cairo and Ismailia (unweighted UniFrac, P = 0.03) and between obesity statuses, obese and non-obese (weighted UniFrac, P = 0.034; unweighted UniFrac, P = 0.015). We observed a significantly higher Firmicutes/Bacteroidetes ratio in obese males than in non-obese males (P = 0.004). Interestingly, this difference was not seen in females (P = 0.77). Multivariable association with linear models (MaAsLin2) identified 8 microbial features associated with obesity, 12 associated with non-obesity, and found 29 and 13 features specific to Cairo and Ismailia patients, respectively. It has also shown one microbial feature associated with patients under five years old. MaAsLin2, however, failed to recognize any association between gender and the gut microbiome. Moreover, it could find the most predominant features in groups 2-9 but not in group 1. Another method used in the analysis is the Linear discriminant analysis Effect Size (LEfSe) approach, which effectively identified 19 biomarkers linked to obesity, 9 linked non-obesity, 20 linked to patients residing in Cairo, 14 linked to patients in Ismailia, one linked to males, and 12 linked to females. LEfSe could not, however, detect any prevalent bacteria among children younger or older than five. Future studies should take advantage of such correlations, specifically BMI, to determine the interventions needed for obesity management.
解析肠道微生物组与肥胖的关系至关重要。我们的研究描述了埃及儿童的肠道微生物群落，并调查了协变量对肠道微生物组、体重指数 (BMI)、地理位置、性别和年龄的影响。我们使用 16S rRNA 测序来描述 49 名儿童的肠道微生物群落。然后，我们评估了这些群落的多样性、潜在生物标志物和功能能力。非肥胖组的α多样性高于肥胖组（Chao1，P=0.006 和观察物种，P=0.003）。β多样性分析表明，两个地理位置（开罗和伊斯梅利亚）和肥胖状况（肥胖和非肥胖）之间的肠道微生物组存在显著差异（非加权 UniFrac，P=0.03）和（加权 UniFrac，P=0.034；非加权 UniFrac，P=0.015）。我们观察到肥胖男性的厚壁菌门/拟杆菌门比值明显高于非肥胖男性（P=0.004）。有趣的是，这种差异在女性中并不明显（P=0.77）。多变量与线性模型（MaAsLin2）的关联确定了 8 个与肥胖相关的微生物特征，12 个与非肥胖相关的微生物特征，发现 29 个和 13 个特征分别与开罗和伊斯梅利亚的患者相关。它还显示了一个与五岁以下患者相关的微生物特征。然而，MaAsLin2 未能识别出性别与肠道微生物组之间的任何关联。此外，它可以在组 2-9 中找到最主要的特征，但不能在组 1 中找到。分析中使用的另一种方法是线性判别分析效应大小 (LEfSe) 方法，该方法有效地确定了 19 个与肥胖相关的生物标志物，9 个与非肥胖相关的生物标志物，20 个与居住在开罗的患者相关的生物标志物，14 个与居住在伊斯梅利亚的患者相关的生物标志物，一个与男性相关的生物标志物和 12 个与女性相关的生物标志物。然而，LEfSe 无法检测到五岁以下或五岁以上儿童中普遍存在的细菌。未来的研究应该利用这种相关性，特别是 BMI，来确定肥胖管理所需的干预措施。
