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Study on the Relationship Between Diet, Physical Health and Gut Microflora of Chinese College Students

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Abstract

Many elements of a modern lifestyle influence the gut microbiota but few studies have explored the effect of physical health level. This study was aimed to explore the relationship between diet, physical health and gut microbiota in Chinese college students. A total of 69 college students were recruited, including 27 college athletes (AS group) and 42 healthy controls (HC group). Fecal samples were collected for 16S rRNA sequencing. According to National Standards for Students’ Physical Health (2014 revision), physical fitness measurements, dietary intake and health-related data were collected via questionnaires. ①According to the physical fitness scores, the physical fitness level of AS group was significantly higher than that of HC group (P < 0.05), there were no significant differences between the two groups in the frequency of intake of food. The frequency and duration of physical activity in the AS group were higher than those in the HC group (P < 0.05); ②The proportion and relative abundances of microorganism composition is varying at two groups: on the phylum level, AS group had mainly increased Firmicutes, Actinobacteria and reduced Bacteroidetes, Proteobacteria; on the genus level, AS group had mainly increased Faecalibacterium, Bifidobacterium and reduced Bacteroides; ③The associations with the 10 most abundant bacterial genera and physical fitness, dietary factors were investigated. Changes in the gut microbiota abundance can be sometimes reflective of a physical health status. Loss of the balance of gut microbial populations will lead to flora disorders and diseases. Therefore, further studies are needed to reveal the mechanisms behind the gut microbiota in its potential role.

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Data Availability

(The 16S rRNA amplicon sequences have been submitted to the Sequence Read Archive (SRA) database under the accession number PRJNA785678).

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Acknowledgements

We thank the staff of the College of physical education belonged to, Wuhan University of Science and Technology, for their kind cooperation. At the same time, thanks to others who contributed to the experiment but not been listed as authors.

Funding

The research was supported by Key research project of philosophy and social sciences of Hubei Provincial Department of Education in 2020 (Project No. 20D026); the National Undergraduate Innovation and Entrepreneurship Training Project (Project Nos. 202010488014 and JCX201976); 2020 General Planning Fund Project for Humanities and Social Sciences of the Ministry of Education, China (Project No. 20YJA880053); WUST National Defence Pre-research Foundation, China (Project No. GF202003), Hubei Province Key Laboratory Open Fund (Project No.OHIC2018Y05); Hubei Province College students Innovation and Entrepreneurship Training program (Project No. 201810488062) and Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology (Project No. OHIC2020G05).

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Author notes

  1. Xiafen Hu, Jiaqi Guo and Jiadun Wang have contributed equally to this study.

Authors and Affiliations

  1. Institute of Infection, Immunology and Tumor Microenvironent, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xiafen Hu, Jiaqi Guo, Jiadun Wang, Wanxin Liu, Xiaochen Xiang, Siyang Chen, Xinquan Li, Wei Zhang, Hui Chen, Qingming Wu & Qiang Wang

  2. College of Physical Education, Wuhan University of Science and Technology, Wuhan, 430065, China

    Jinhan Tang

  3. The Third People’s Hospital of Hubei Province, Zhongshan Hospital of Hubei Province, Wuhan, 430030, Hubei, People’s Republic of China

    Rong Shu

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  1. Xiafen Hu
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  2. Jiaqi Guo
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Contributions

Conceptualization: JQG and WXL. Funding: XFH, JQG, QW and CH. Investigation: SYC and JDW. Resources: JHT. Data curation: XCX and XQL. Writing–original draft: XFH. Writing–review & editing: QW, RS and QMW. Supervision: WZ and HC. All authors reviewed and accepted the manuscript.

Corresponding authors

Correspondence to Rong Shu, Qingming Wu or Qiang Wang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

The study was approved by the Medical Ethics Committee of Wuhan University of Science and Technology School of Medicine (Approval No. 202189). All participants provided written informed consent.

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Supplementary Information

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284_2022_3055_MOESM1_ESM.tif

Supplementary file1 (TIF 29151 kb) Fig. S1 The physical fitness scores of AS group (n = 27) were significantly higher than those of HC group (n = 42).

284_2022_3055_MOESM2_ESM.tif

Supplementary file2 (TIF 18800 kb) Fig. S2 Composition of gut microbiota in AS group and HC group at the phylum level (a), and genus level (b).

284_2022_3055_MOESM3_ESM.tif

Supplementary file3 (TIF 20314 kb) Fig. S3 Correlation analysis between the AS and HC group. (a) Correlation network graph Nodes in the network represented the predominant genera, and the connection between nodes indicates the correlation between the two genera. The thickness of the line indicates the strength of the correlation, and the thicker the line, the stronger the correlation. Solid lines indicate a positive correlation and dashed lines indicate a negative correlation. (b) Correlation heat map. The gradient from blue to red reflects the change in correlation from positive to negative.

284_2022_3055_MOESM4_ESM.tif

Supplementary file4 (TIF 6840 kb) Fig. S4 Structure of the gut microbiomes in three subgroups. (a) Alpha diversity indices, such as Chao1 richness(P = 0.55), Shannon diversity index(P = 0.54), and Simpson index(P = 0.67) of each group. (b) Beta diversity analysis among three subgroups of AS group. principal component analysis(P = 0.08), unweighted principal coordinate analysis(P = 0.632) and weighted principal coordinate analysis(P = 0.381).

284_2022_3055_MOESM5_ESM.tif

Supplementary file5 (TIF 18486 kb) Fig. S5 LEfSe analysis of intestinal microflora enrichment in three subgroups. Red represents excellent group (EX), blue represents good group (GD), and green represents pass group (PA). (a) The histogram shows the LDA score computed for genera differentially abundant between three subgroups of AS group and identified using LEfSe. (b) Cladogram showing the most differentially abundant taxa identified by LEfSe.

Supplementary file6 (DOCX 15 kb)

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Hu, X., Guo, J., Wang, J. et al. Study on the Relationship Between Diet, Physical Health and Gut Microflora of Chinese College Students. Curr Microbiol 79, 370 (2022). https://doi.org/10.1007/s00284-022-03055-5

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