The Causal Relationship Between Genetically Determined Plasma Metabolites and Rheumatoid Arthritis
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Presently, research examining the impact of plasma metabolites on rheumatoid arthritis (RA) is scarce. We utilized a bidirectional two-sample Mendelian randomization (MR) analysis to explore the potential causal link between 1400 plasma metabolites and RA. METHODS: We performed a two-sample MR analysis to assess the causal association between 1400 plasma metabolites and RA. The primary method of two-sample MR Analysis was the Inverse Variance Weighted (IVW) model, and the secondary methods were the Weighted Median (WM) and MR Egger methods. We conducted sensitivity analyses using Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. Steiger test was used for validation of the metabolites. The main results were validated in the UK Biobank. RESULTS: In the discovery dataset, 60 metabolites were identified as significantly associated with the onset of RA. A notable finding was the strong correlation between Valve levels and RA risk, showing the highest positive correlation (OR [95% CI]: 1.361 (1.112, 1.667), p = 0.0028). Subsequent analysis of the validation dataset revealed 46 metabolites linked to RA, with X-22771 levels displaying the strongest positive association (OR [95% CI]: 1.002 (1.00, 1.004), p = 0.037). Notably, Glycohydrocolate levels exhibited a protective effect on RA in both datasets. Specifically, the effect size in the initial dataset was (OR [95% CI]:0.867 (0.753, 1.000), p = 0.050), whereas in the validation dataset, the effect was weaker (OR [95% CI]: 0.999 (0.997, 1.000), p = 0.048). These findings were further validated through a series of sensitivity analyses, affirming their robustness and reliability. CONCLUSIONS: This study highlights a strong correlation between elevated Valine levels and an increased risk of RA, as well as potential protective effects of Glycohydrohorate in independent datasets.
Authors: Kunpeng Song, Julei Ma, Bing Wang
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,520
OpenAlex ID: W4405386334