ObjectiveTo systematically analyze the temporal trends of pancreatic cancer burden in globally and China from 1990 to 2021 using the Global Burden of Disease Study 2021 (GBD 2021) database and predict disease burden changes over the next 15 years. MethodsThe data of the incidence, death, disability-adjusted life years (DALYs) and age-standardized rate data of pancreatic cancer in GBD 2021 were extracted to analyze the epidemic status. Joinpoint regression models were employed to calculate average annual percentage changes (AAPC) and identify trend transitions. An auto-regressive integrated moving average (ARIMA) model was utilized to predict disease burden from 2022 to 2036. ResultsIn 2021, the global age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR) for pancreatic cancer were 5.96 (per 100 000), 5.95 (per 100 000), and 130.33 (per 100 000). Corresponding rates in China were 5.64 (per 100 000), 5.72 (per 100 000), and 137.23 (per 100 000). From 1990 to 2021, the average annual growth rate of ASIR (AAPC=0.72%), ASMR (AAPC=0.56%) and ASDR (AAPC=0.36%) were significantly higher than the global rate (ASIR: AAPC=0.27%; ASMR: AAPC=0.16%; ASDR: AAPC=0.02%). Age-specific analysis showed that the crude incidence, mortality, and DALYs rates for the population aged ≥60 years old in China (AAPC: 0.37%–1.55%) were all increasing at a higher rate than the same age group globally (AAPC: –0.02%–0.77%). Sex differences were significant, with greater disease burden in men than in women. ARIMA model predicted that Chinese and global ASIR and ASMR will continue to rise by 2036, with persistently steeper increases in males than females. ConclusionThe disease burden of pancreatic cancer in China is growing faster than that of the world, so early screening and prevention of pancreatic cancer should be strengthened.
Objective To investigate the associations of circulating inflammatory proteins and risk of psoriasis by using a two-sample Mendelian randomization (MR) approach. Methods Based on the genome-wide association study (GWAS) of inflammatory proteins and psoriasis, genetic variants associated with circulating inflammatory proteins were selected as instrumental variables (IVs), and genetic association data of psoriasis were extracted. The inverse-variance weighted method was used as the primary MR method, with statistical power analysis conducted to evaluate the test power. MR-Egger regression, weighted median, maximum likelihood, and MR Pleiotropy RESidual Sum and Outlier (PRESSO) tests were used to evaluate the influence of horizontal pleiotropy on the MR association estimates. Additionally, as sensitivity analysis, a GWAS of psoriasis from the FinnGen database was used as a replication dataset to evaluate the robustness of the results. Results A total of 558 single nucleotide polymorphisms associated with 74 inflammatory proteins were included. After False Discovery Rate (FDR) corrections, genetically predicted circulating levels of C-X-C motif chemokine ligand 9 (CXCL9) [odds ratio (OR)=1.76, 95% confidence interval (CI) (1.46, 2.11), P=2.31×10?9], C-C motif chemokine ligand 19 (CCL19) [OR=1.41, 95%CI (1.22, 1.62), P=2.97×10?6], and tumor necrosis factor-beta (TNFB) [OR=1.31, 95%CI (1.13, 1.52), P=3.56×10?4] were found to be significantly associated with an increased risk of psoriasis. Sensitivity analyses yielded similar results. Statistical power analysis indicated that the power to detect these associations was greater than 99%. Conclusion Genetically predicted circulating CXCL9, CCL19, and TNFB levels are positively associated with risk of psoriasis.
