Patients and intervals
Of 1,280 patients with AS treated at our centre, 677 had a history of treatment with TNFi (Fig. 2). Among them, we excluded those who had BASDAI >4 at least once during treatment, those whose mSASSS could not be interpolated because they only had one radiograph examination, and patients with less than 6 months of TNFi prescription. Finally, 333 patients were included in this study. The baseline characteristics of the patients are shown in Table 1.
In the 333 patients, the number of baseline (start point of TNFi therapy) data were 611 because there were patients who have been prescribed TNFi for more than two periods. From the study patients, 2,956 intervals were obtained by dividing the treatment periods by 6-month intervals. The characteristics of the 2,956 intervals are summarized in Supplementary Table 2, and the changes in mSASSS according to 6-month intervals are shown in Supplementary Fig. 1.
Initial models for mSASSS over time
An initial linear mixed model was constructed for mSASSS using time and baseline mSASSS (Supplementary Table 3). The baseline mSASSS and the time (6-month intervals) were positively correlated with mSASSS (β=1.025, 95% CI: 1.012–1.039, p<0.001 and β=0.432, 95% CI: 0.409–0.454, p<0.001, respectively).
Relationship between inflammation and mSASSS over time
The baseline and lagged values of CRP, ESR, and ASDAS were added to the initial models as explanatory variables. The beta coefficients for the relationships between lagged inflammatory markers and mSASSS are shown in Fig. 3 and Supplementary Tables 4 and 5. CRP showed a significant positive correlation with the mSASSS at the lagged times of 12, 18, 24, 30, and 36 months (Fig. 3A), whereas ESR and ASDAS did not show significant correlations at any lagged time (Fig. 3B and 3C, respectively). In the cumulative sums of inflammatory markers, the cumulative sum of CRP in the previous 18, 24, 30, and 36 months, the cumulative sum of ESR in the previous 24, 30, 36 months, and the cumulative sum of ASDAS in the previous 6 months showed significantly positive correlations with mSASSS (Fig. 3D, 3E, and 3F, respectively). Among the inflammatory markers and the time points that showed significant correlations with the mSASSS, we selected CRP lagged by 18 months and the cumulative sum of CRP in the previous 24 months, which had the highest significant beta coefficients, as the main independent variables to investigate the relationship between inflammation and mSASSS.
Models for the relationship between CRP lagged by 18 months and mSASSS
Table 2 shows the linear mixed models with log CRP lagged by 18 months as the main independent variables. Model 0 showed the correlations between each variable and mSASSS. In models 1 and 2, log CRP lagged by 18 months showed significant positive correlations with mSASSS (β=0.619, 95% CI: 0.181–1.057, p=0.006, and β=0.584, 95% CI: 0.145–1.023, p=0.009, respectively). When clinical characteristics were added in model 1 and 2, log CRP lagged by 18 months also showed significant positive correlations with mSASSS (β=0.633, 95% CI: 0.178–1.089, p=0.006, and β=0.594, 95% CI: 0.138–1.051, p=0.011, respectively). In addition, peripheral arthritis in models 1 and 2 with clinical characteristics showed significant negative correlations with mSASSS (β=-0.669, 95% CI: -2.364–-0.003, p=0.049, and β=-1.201, 95% CI: -2.382–-0.020, p=0.046, respectively). Among the models, model 1 with clinical characteristics showed the best fit, with an AIC value of 7172.285.
Models for the relationship between the cumulative sum of CRP in the previous 24 months and mSASSS
Table 3 shows the linear mixed models with the cumulative sum of log CRP in the previous 24 months as the main independent variable. Model 0 showed the correlations between each variable and mSASSS. In models 1 and 2, the cumulative sum of log CRP in the previous 24 months showed positive correlations with mSASSS (β=0.042, 95% CI: 0.0014–0.069, p=0.003, and β=0.039, 95% CI: 0.011–0.067, p=0.006, respectively). When clinical characteristics were added in model 1, the cumulative sum of log CRP in the previous 24 months also showed a significant positive correlation with mSASSS (β=0.043, 95% CI: 0.014–0.071, p=0.004). In addition, peripheral arthritis had a significant negative correlation with mSASSS (β=-1.234, 95% CI: -2.436–-0.032, p=0.044). Among the models, model 1 with baseline characteristics showed the best fit, with an AIC value of 7,061.801.