| Title: | Associations of diet with infectious diseases in UK Biobank |
| Journal: | Journal of Biomedical Research |
| Published: | 29 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38807412/ |
| DOI: | https://doi.org/10.7555/jbr.37.20230319 |
Publication 11982
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
The current study used multivariable logistic regression analysis to investigate associations between the intake frequencies of 13 food groups (or four diet groups) and infectious diseases. The analysis included 487849 participants from the UK Biobank, with 75209 participants diagnosed with infectious diseases. Participants reporting the highest intake frequency of processed meat (odds ratio [OR] = 1.0964, 95% confidence interval [CI]: 1.0622-1.1318) and red meat (OR = 1.0895, 95% CI: 1.0563-1.1239) had a higher risk of infectious diseases, compared with those with the lowest intake frequency. Consuming fish 2.0-2.9 times (OR = 0.8221, 95% CI: 0.7955-0.8496), cheese ≥ 5.0 times (OR = 0.882 2, 95% CI: 0.855 9-0.9092), fruit 3.0-3.9 servings (OR = 0.8867, 95% CI: 0.8661-0.9078), and vegetables 2.0-2.9 servings (OR = 0.9372, 95% CI: 0.9189-0.9559) per week were associated with a lower risk of infection. Low meat-eaters (OR = 0.9404, 95% CI: 0.9243-0.9567), fish-eaters (OR = 0.8391, 95% CI: 0.7887-0.8919), and vegetarians (OR = 0.9154, 95% CI: 0.8561-0.9778) had a lower risk of infectious diseases, compared with regular meat-eaters. The mediation analysis revealed that glycosylated hemoglobin, white blood cell count, and body mass index served as the mediators in the associations between diet and infectious diseases. The current study indicates that the intake frequency of food groups is a risk factor for infectious diseases, and fish-eaters have a lower risk of infection.</p>
8 Authors
- Junlan Tu
- Xuehong Cai
- Yifan Wang
- Xiangyu Ye
- Meijie Yu
- Sheng Yang
- Rongbin Yu
- Peng Huang
1 Application
| Application ID | Title |
|---|---|
| 67665 | Developing scalable computational tools for understanding polygenic trait architectures in large-scale genome-wide association studies |
Enabling scientific discoveries that improve human health