Evidence map›Paper›PMID 40158160›Full record

ArticleNutrition journal2025

Multiomics approach reveals the comprehensive interactions between nutrition and children's gut microbiota, and microbial and host metabolomes.

Mingyu Zhu, Qi Wang, Yan Yang, Xiaobing Liu, Jiawen Zhang, Guanghao Li, Wenqing Liu, Xuesong Xiang, Juanjuan Chen

Abstract read
In one paragraph

Article in Nutrition journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
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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

9 authors.

Mingyu Zhu *Nation Health Commission Key Laboratory of Public Nutrition and Health, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
Qi Wang *Cuiying Biomedical Research Center, The Second Hospital & Clinical Medical School, Lanzhou, 730030, China.
Yan YangDepartment of Endocrinology and Metabolism, The Second Hospital & Clinical Medical School, Lanzhou, 730030, China.
Xiaobing LiuNation Health Commission Key Laboratory of Public Nutrition and Health, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
Jiawen ZhangNation Health Commission Key Laboratory of Public Nutrition and Health, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China.
Guanghao LiSchool of Global Public Health, New York University, New York, NY, 10003, USA.
Wenqing LiuDepartment of General Surgery, The Second Hospital & Clinical Medical School, Lanzhou, 730000, China.
Xuesong XiangNation Health Commission Key Laboratory of Public Nutrition and Health, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China. xiangxs@ninh.chinacdc.cn.
Juanjuan ChenCuiying Biomedical Research Center, The Second Hospital & Clinical Medical School, Lanzhou, 730030, China. ery_juanjuanchen@lzu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gut microbiome can modulate nutrient metabolism to produce many metabolites interacting with the host. However, the intricate interactions among dietary intake, the gut microbiome and metabolites, and host metabolites need to be further explored although some studies have been devoted to it. Here, in a cross-sectional studies, 88 children aged 2-12 years were enrolled from northwestern China. The dietary intake data were collected via a designed food frequency questionnaire to calculate plant-based diet indices (PDIs). Stool and plasma samples were collected for metagenomic and broad-targeted metabolomic analysis. Spearman's rank correlation was used to describe the associations between nutrients/PDIs and the gut microbiota and metabolites. PDI was significantly positively associated with Bilophila wadsworthia, Bacteroides thetaiotaomicron, and Alistipes indistinctus, etc., but was obviously negatively correlated with Roseburia intestinalis, Faecalibacterium prausnitzii, etc. However, these species showed no significant associations with either healthy PDI (hPDI) or unhealthy PDI (uPDI). Interestingly, hPDI was significantly positively related to species, including Ruminococcus bicirculans, and was significantly negatively associated with uPDI, and vice versa. The above correlation trends were also observed between PDIs and predicted gut microbial functional pathways, microbial metabolites and the host metabolome. Notably, the significantly related pathways were focused mainly on substances and energy metabolism. PDI was significantly positively associated with the fecal contents of P-aminobenzoate, chenodeoxycholic acid, 4,6-dihydroxyquinoline, quinoline-4,8-diol, etc., but was significantly negatively associated with those of TMAO, FFA, creatine phosphate, etc. In plasma, PDI was significantly positively associated with sarcosine, ornithine, L-histidine, etc., but was distinctly negatively correlated with FFAs, carnitine C2:0, etc. Strikingly, the healthy plant-based diet index (hPDI) is correlated with increased levels of metabolites related to tryptophan metabolism, whereas the unhealthy PDI (uPDI) is linked to increased levels of metabolites associated with tyrosine and sphingolipid metabolism, which are pathways commonly associated with Western diets. Our studies provide reliable data support and a comprehensive understanding of the effects of dietary intake on the gut microbiome and microbial and host metabolites and lay a foundation for further studies of the diet-gut microbiota-microbial metabolites and host metabolism.

Indexed as

DietGastrointestinal MicrobiomeMetabolomeMetabolomicsNutritional StatusChildChild, PreschoolChinaCross-Sectional StudiesFecesFemaleHumansMaleMetagenomicsMultiomicsChildrenCorrelation analysisDietary intakePlant-based indicesShotgun metagenomic sequencingUntargeted metabolomics

Identifiers

PMID40158160
PMCPMC11954295

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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.