Affiliation:
1. Department of Food and Nutrition, Institute of Basic Science, Obesity/Diabetes Research Center, Hoseo University, Asan, South Korea.
Abstract
Objectives:
The association between fecal microbiota and height in children has yielded conflicting findings, warranting further investigation into potential differences in fecal bacterial composition between children with short stature and those of standard height based on enterotypes (ETs).
Methods:
According to the height z score for age and gender, the children were categorized into normal-stature (NS; n = 335) and short-stature (SS; n = 152) groups using a z score of −1.15 as a separator value. The human fecal bacterial FASTA/Q files (n = 487) were pooled and analyzed with the QIIME 2 platform with the National Center for Biotechnology Information alignment search tool. According to ETs, the prediction models by the machine learning algorithms were used for explaining SS, and their quality was validated.
Results:
The proportion of SS was 16.4% in ET Enterobacteriaceae (ET-E) and 68.1% in Prevotellaceae (ET-P). The Chao1 and Shannon indexes were significantly lower in the SS than in the NS groups only in ET-P. The fecal bacteria related to SS from the prediction models were similar regardless of ETs. However, in network analysis, the negative correlations between fecal bacteria in the NS and SS groups were much higher in the ET-P than in the ET-E. In the metagenome function, fecal bacteria showed an inverse association of biotin and secondary bile acid synthesis and downregulation of insulin/insulin-like growth factor-1-driven phosphoinositide 3-kinase Akt signaling and AMP-kinase signaling in the SS group compared with the NS group in both ETs.
Conclusion:
The gut microbial compositions in children were associated with height. Strategies to modify and optimize the gut microbiota composition should be investigated for any potential in promoting height in children.
Subject
Gastroenterology,Pediatrics, Perinatology and Child Health
Cited by
1 articles.
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1. The use of machine learning in paediatric nutrition;Current Opinion in Clinical Nutrition & Metabolic Care;2024-01-31