Ankylosing spondylitis status and risk of secondary systemic amyloidosis: A two-sample mendelian randomization study

There is still controversy regarding the causal relationship between ankylosing spondylitis (AS) and secondary systemic amyloidosis (SSA). This study utilized aggregated data from genome-wide association studies (GWAS) on population cohorts to investigate whether a causal relationship exists between AS and SSA.

The genetic causal relationship between AS status and SSA was analyzed utilizing a two-sample Mendelian randomization (TSMR). The analyses were conducted using the weighted mode method (WM²), inverse variance weighted method (IVW), simple mode (SM), weighted median method (WM¹), and Mendelian randomization Egger regression (MR-Egger). Among these methods, the primary results were based on the IVW approach. The association was evaluated using the odds ratio (OR) along with a 95% confidence interval (95% CI).

The IVW analysis revealed a positive causal relationship between AS status and SSA (OR = 1.411, 95 % CI = 1.069, 1.862, P = 0.015). Meanwhile, the WM¹ (OR = 1.394, 95 % CI = 1.115, 1.742, P = 0.004) and WM² (OR = 1.393, 95 % CI = 1.112, 1.743, P = 0.045) methods also identified a positive causal relationship between AS status and SSA. The MR-Egger method did not identify a causal relationship between AS and SSA (OR = 1.175, 95 % CI = 0.888, 1.555, P = 0.342). The SM results demonstrated that the observed genotypes did not exhibit statistically significant differences between AS and SSA (OR = 1.184, 95 % CI = 0.416, 3.366, P = 0.767). The results of the MR-Egger regression suggested that the results were unaffected by bias caused by genetic pleiotropy (Intercept = 0.283, SE = 0.134, P = 0.126). Cochran's Q test did not reveal any significant heterogeneity (Q = 1.759, P = 0.624). The "leave-one-out" analysis further confirmed that the absence of any single SNP did not impact the robustness of our results.

This study revealed a positive causal relationship between AS status and the occurrence of SSA, providing new insights into the genetic analysis of SSA.