The Impact of Triglycerides on Rheumatoid Arthritis: Risk Factor and Mendelian Randomization Study

Abstract

Objective: This study investigates the association between triglycerides and Rheumatoid arthritis (RA) risk through risk factor analysis and Mendelian randomization (MR).

Methods: Data from the Dryad database were used for a case-control study with 455 participants (224 with RA and 231 controls), with a median age of 54 years (IQR: 45-62) and 34% male participants. Logistic regression analyses identified risk factors, and correlation coefficient analysis assessed associations between triglycerides and RA. A two-sample MR analysis was conducted using genetic variants associated with triglyceride levels as instrumental variables.

Results: Logistic regression identified higher triglyceride levels, a history of non-smoking, lower levels of C-reactive protein, and apolipoprotein A as significant risk factors for RA (all P < 0.05). MR analysis showed no significant causal relationship, with odds ratios (IVW OR = 0.944, P = 0.154) close to 1. Heterogeneity tests showed no significant variation in causal estimates, supporting the absence of a causal link between triglycerides and RA.

Conclusion: While elevated triglyceride levels are associated with an increased risk of RA, MR suggests that triglycerides do not play a direct causal role in its development. These findings indicate that triglyceride management may not be a primary focus in RA treatment, but further research into the mechanisms underlying RA progression is needed.

Keywords: Mendelian randomization; causal relationship; rheumatoid arthritis; risk factor; triglycerides.

Figure 1

Heatmap showing the correlation coefficients between risk factors and RA in the multivariate model. The strongest correlation is observed between triglycerides and RA, with a correlation coefficient of 0.20 (P < 0.001).

Figure 2

(A) Forest plot of SNPs associated with triglycerides and RA. Black dots represent the effect of individual SNPs on RA, red dots indicate the combined causal estimate using all SNPs as a single instrument, and horizontal lines represent the 95% confidence intervals. (B) Forest plot illustrating the MR analysis results.

Figure 3

Scatter plot depicting the association between triglycerides and RA. The plot shows the effect sizes of SNP-triglycerides and SNP-RA with 95% confidence intervals. The regression slope of the line corresponds to the causal estimates from five MR methods (IVW, weighted median estimator, MR-Egger, simple mode, and weighted mode).

Figure 4

Leave-one-out analysis plot for the relationship between triglycerides and RA. No single SNP significantly influences the overall results.

Figure 5

MR-Egger regression funnel plot. The distribution of all SNPs is essentially symmetrical, indicating no significant bias in the analysis.

Figure 6

Forest plot of the reverse MR analysis evaluating the causal effect of RA on triglyceride levels. Each black dot represents the causal estimate of an individual SNP on triglycerides levels. The red diamond represents the combined causal estimate using all SNPs as a single instrument. Horizontal lines indicate 95% CIs.

Figure 7

Scatter plot illustrating the associations between SNPs related to RA and their effects on TG levels. Each dot represents a single SNP, with error bars indicating 95% CIs for SNP-RA (x-axis) and SNP-TG (y-axis) effects. The slope of each line corresponds to the causal estimate derived from five MR methods: inverse variance weighted (IVW), weighted median estimator (WME), MR-Egger, simple mode, and weighted mode.

Figure 8

Leave-one-out sensitivity analysis for the reverse MR analysis. Each point shows the overall IVW estimate after removing one SNP at a time. The dashed red line represents the pooled estimate using all SNPs. The analysis demonstrates that no single SNP had a disproportionate influence on the overall result, supporting the robustness of the findings.

Figure 9

Funnel plot assessing the symmetry of SNP effects in the reverse MR analysis. The vertical line indicates the pooled IVW estimate. Symmetrical distribution of SNPs around this line suggests minimal bias due to directional pleiotropy or unbalanced heterogeneity.