Evidence map›Paper›PMID 37985845›Full record

ArticleScientific reports2023

Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder.

Qina Ye, Shaodan Sun, Jian Deng, Xiaogang Chen, Jing Zhang, Suihua Lin, Hongxuan Du, Jinxiong Gao, Xiaoyin Zou, Xiaoling Lin and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Qina YeDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Shaodan SunGuangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Jian DengDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Xiaogang ChenDepartment of Pediatrics, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Jing ZhangDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Suihua LinDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Hongxuan DuDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Jinxiong GaoDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Xiaoyin ZouDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China.
Xiaoling LinDepartment of Pediatrics, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Yawen CaiDepartment of Pediatrics, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Zhuoming LuDepartment of Traditional Chinese Medicine, Guangzhou Women and Children Medical Center, No. 9 Jinsui Road, Guangzhou, 510623, China. 739835365@qq.com.ORCID 0000-0001-5739-9364

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate the gut microbiota distribution and its functions in children with avoidant/restrictive food intake disorder (ARFID). A total of 135 children were enrolled in the study, including 102 children with ARFID and 33 healthy children. Fecal samples were analyzed to explore differences in gut microbiota composition and diversity and functional differences between the ARFID and healthy control (HC) groups via 16S rDNA and metagenomic sequencing. The gut microbiota composition and diversity in children with ARFID were different from those in heathy children, but there is no difference in the composition and diversity of gut microbiota between children at the age of 3-6 and 7-12 with ARFID. At the phylum level, the most abundant microbes in the two groups identified by 16S rDNA and metagenomic sequencing were the same. At the genus level, the abundance of Bacteroides was higher in the ARFID group (P > 0.05); however, different from the result of 16SrDNA sequencing, metagenomic sequencing showed that the abundance of Bacteroides in the ARFID group was significantly higher than that in the HC group (P = 0.041). At the species level, Escherichia coli, Streptococcus thermophilus and Lachnospira eligens were the most abundant taxa in the ARFID group, and Prevotella copri, Bifidobacterium pseudocatenulatum, and Ruminococcus gnavus were the top three microbial taxa in the HC group; there were no statistically significant differences between the abundance of these microbial taxa in the two groups. LefSe analysis indicated a greater abundance of the order Enterobacterales and its corresponding family Enterobacteriaceae, the family Bacteroidaceae and corresponding genus Bacteroides, the species Bacteroides vulgatus in ARFID group, while the abundance of the phylum Actinobacteriota and its corresponding class Actinobacteria , the order Bifidobacteriales and corresponding family Bifidobacteriaceae, the genus Bifidobacterium were enriched in the HC group. There were no statistically significant differences in the Chao1, Shannon and Simpson indices between the Y1 and Y2 groups (P = 0.1, P = 0.06, P = 0.06). At the phylum level, Bacillota, Bacteroidota, Proteobacteria and Actinobacteriota were the most abundant taxa in both groups, but there were no statistically significant differences among the abundance of these bacteria (P = 0.958, P = 0.456, P = 0.473, P = 0.065). At the genus level, Faecalibacterium was more abundant in the Y2 group than in the Y1 group, and the difference was statistically significant (P = 0.037). The KEGG annotation results showed no significant difference in gut microbiota function between children with ARFID and healthy children; however, GT26 was significantly enriched in children with ARFID based on the CAZy database. The most abundant antibiotic resistance genes in the ARFID group were the vanT, tetQ, adeF, ermF genes, and the abundance of macrolide resistance genes in the ARFID group was significantly higher than that in the HC group (P = 0.041). Compared with healthy children, children with ARFID have a different distribution of the gut microbiota and functional genes. This indicates that the gut microbiome might play an important role in the pathogenesis of ARFID.Clinical trial registration: ChiCTR2300074759.

Indexed as

ActinobacteriaAvoidant Restrictive Food Intake DisorderMicrobiotaAnti-Bacterial AgentsBacteriaChildDrug Resistance, BacterialEatingHumansMacrolidesRNA, Ribosomal, 16SAnti-Bacterial AgentsMacrolidesRNA, Ribosomal, 16S

Identifiers

PMID37985845
PMCPMC10661725

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.