Background: This study aims to analyze the historical trends of inflammatory bowel disease (IBD) burden in the elderly from 1990 to 2021 and forecast future trends up to 2051.Methods: Data from the Global Burden of Disease Study 2021 were utilized. Age-standardized rates (ASR) for incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were calculated. Estimated annual percentage changes (EAPCs) were computed to quantify temporal trends. A Bayesian Age-Period-Cohort model was employed to project future trends.Results: From 1990 to 2021, the global number of elderly IBD increased from 573,500 to 1,278,190. The age-standardized incidence rate (ASIR) rose from 8.01 to 8.77 per 100,000, while the age-standardized prevalence rate (ASPR) slightly decreased from 118.14 to 117.29 per 100,000. Death number increased from 14,400 to 33,490, but the age-standardized mortality rate decreased from 3.21 to 2.84 per 100,000. DALYs increased from 324,100 to 683,750, with the age-standardized DALYs rate declining from 68.78 to 60.88 per 100,000. Significant geographical variations were observed, with high Socio-demographic Index regions showing the highest burden. Projections suggest that by 2051, elderly IBD prevalence number may reach 2,316,000, with ASIR and ASPR potentially rising after 2035 and 2042, respectively. Deaths and DALYs are projected to increase to 75,000 and 1,401,000 respectively, despite continued declines in ASRs.Conclusion: The absolute burden of IBD in the elderly population is projected to increase substantially by 2051, despite decreasing ASRs. These findings underscore the need for tailored healthcare strategies and resource allocation to address the growing challenge of elderly IBD globally.

Background: Rapidly progressive interstitial lung disease (RP-ILD) is a significant complication that determines the prognosis of dermatomyositis (DM). Early RP-ILD diagnosis can improve screening and diagnostic efficiency and provide meaningful guidance to carry out early and aggressive treatment. Methods: A retrospective screening of 284 patients with DM was performed. Clinical and laboratory characteristics of the patients were recorded. The risk factors of RP-ILD in DM patients were screened by logistic regression (LR) and machine learning methods, and the prediction models of RP-ILD were developed by machine learning methods, namely least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost). Results: According to the result of univariate LR, disease duration is a protective factor for RP-ILD, and ESR, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are risk factors for RP-ILD. The top 10 important variables of the 3 machine learning models were intersected to obtain common important variables, and there were 5 common important variables, namely disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody. The AUC of LASSO, RF and XGBoost test set were 0.661, 0.667 and 0.867, respectively. We further validated the performance of these three models on validation set, and the results showed that, the AUC of LASSO, RF and XGBoost were 0.764, 0.727 and 0.909, respectively. Based on the results of the models, XGBoost is the optimal model in this study. Conclusion: Disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are vital risk factors for RP-ILD in DM. The prediction model constructed using XGBoost can be used for risk identification and early intervention in DM patients with RP-ILD and practical application.