Multivariate Analysis and Correlation Study Shows the Impact of Anthropometric and Demographic Variables on Gut Microbiota in Obese Egyptian Children

• Published in: Current microbiology 2024-08, Vol.81 (8), p.259, Article 259
• Main Authors: Ezz El Deen, Nada Mohamed, Karem, Mona, El Borhamy, Mervat Ismail, Hanora, Amro Mohamed Said, Fahmy, Nora, Zakeer, Samira
• Format: Article
• Language: English
• Subjects: Anthropometry; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Biomarkers; Biomedical and Life Sciences; Biotechnology; Body Mass Index; Body size; Child; Child, Preschool; Children; Demographic variables; Discriminant analysis; Egypt; Female; Females; Gastrointestinal Microbiome; Gender; Geographical distribution; Geographical locations; Humans; Intestinal microflora; Life Sciences; Male; Males; Microbial activity; Microbiology; Microbiomes; Microbiota; Microorganisms; Multivariate Analysis; Obesity; Obesity - microbiology; Pediatric Obesity - microbiology; RNA, Ribosomal, 16S - genetics; rRNA 16S; Weight control
• ISSN: 0343-8651; 1432-0991; 1432-0991
• 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.