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中国全科医学

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1990—2021年中国与全球帕金森病的疾病负担分析与未来趋势预测

吴一璇1，肖亮满1，刘鑫1，严梓萁1，王毓婷2，林舒敏1，庄礼兴3，4*   

  1. 1.广州中医药大学第一临床医学院 2.广州中医药大学深圳医院（福田） 3.广州中医药大学第一附属医院岭南针灸康复研究所 4.广东省中医临床研究院
  • 收稿日期:2025-07-25 修回日期:2025-09-24 接受日期:2025-10-09
  • 通讯作者: 庄礼兴
  • 基金资助:
    基于SCFAs/HDACs/FoxP3轴激活Treg细胞抑制肠IL-17+γδT细胞的靳三针治疗帕金森病的作用机制研究(82474622); 靳三针疗法传承创新及治疗帕金森病效应机制研究(2022KCXTD006); 基于γδT细胞 IL-17信号通路介导的“肠脑对话”探讨“靳三针调神针法”改善帕金森病焦虑的疗效及机制研究(2022ZD04)

Analysis of the Disease Burden of Parkinson's Disease in China and Globally from 1990 to 2021 and Prediction of Future Trends

WU Yixuan1，XIAO Liangman1，LIU Xin1，YAN Ziqi1，WANG Yuting2，LIN Shumin1，ZHUANG Lixing3，4*   

  • Received:2025-07-25 Revised:2025-09-24 Accepted:2025-10-09
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摘要： 背景　帕金森病（Parkinson’s disease, PD）是一种常见的神经退行性疾病，全球疾病负担持续加重，中国由于人口基数大、老龄化进程快，面临更严峻的挑战。目前，现有研究缺乏对全球与中国PD疾病负担差异的深入分析及PD与其他老年常见疾病的共病分析。目的　基于全球疾病负担（Global Burden of Disease, GBD）数据库，分析1990—2021年中国与全球PD疾病负担差异及趋势，预测未来疾病负担变化，为优化PD防治策略提供科学依据。方法　利用GBD2021数据，提取全球及中国PD发病率、患病率、死亡率和伤残调整寿命年（Disability-Adjusted Life Years, DALYs）等指标；采用年龄-时期-队列（Age-Period-Cohort, APC）模型分析流行病学趋势，结合贝叶斯年龄-时期-队列（Bayesian Age-Period-Cohort, BAPC）模型预测2022—2035年疾病负担。结果　1990—2021年中国PD发病例数增长455.7%（全球220.1%），年龄标准化发病率（age-standardized incidence rate, ASIR）年均增长2.16%（全球1.09%）；患病人数增长678.9%（全球273.8%）。中国年龄标准化死亡率（age-standardized rates of mortality，ASMR）显著下降，其估计年度百分比变化（Estimated Annual Percent Change，EAPC）为−0.76%，而全球呈上升趋势（EAPC=3.28%）。APC模型显示，中国PD负担增速高于全球，男性及80岁以上人群尤为突出。BAPC预测至2035年，中国PD的ASIR（32.3/10万）和年龄标准化患病率（age-standardized prevalence rate，ASPR）（333/10万）将远超全球水平。结论　PD疾病负担增长迅猛，主要受人口老龄化驱动，但中国死亡率控制趋势优于全球。PD在高龄和男性患者中更好发，也与多种老年常见疾病存在关联。未来需加强管理、优化政策，并关注共病管理以降低整体负担。本研究为制定针对性的PD防控策略提供了数据支持。

关键词: 帕金森病, 发病率, 疾病负担, 预测分析, 年龄－时期－队列模型, 贝叶斯年龄-时期-队列

Abstract: Background　Parkinson’s disease (PD) is a common neurodegenerative disorder, and its global disease burden continues to increase. Due to China’s large population base and rapid aging process, the country faces even more severe challenges. Currently, existing studies still lack in-depth analysis of the disparities in the global and Chinese burden of PD, as well as comorbidity analyses between PD and other common geriatric diseases. Objective　This study aims to analyze the differences and trends in PD disease burden between China and the global population from 1990 to 2021 using the Global Burden of Disease (GBD) database, predict future disease burden changes, and provide scientific evidence for optimizing PD prevention and control strategies. Methods　Data on PD incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were extracted from the GBD 2021 database for both China and the global population. The Age-Period-Cohort (APC) model was employed to analyze epidemiological trends, while the Bayesian Age-Period-Cohort (BAPC) model was used to predict disease burden from 2022 to 2035. Results　From 1990 to 2021, the number of PD cases in China increased by 455.7% (global increase: 220.1%), with an average annual growth rate of 2.16% in age-standardized incidence rate (ASIR) (global: 1.09%). The number of prevalent cases rose by 678.9% (global: 273.8%). China experienced a significant decline in age-standardized rates of mortality (ASMR) , with an estimated annual percentage change (EAPC) of −0.76%, whereas the global trend showed an increase (EAPC = 3.28%). The APC model revealed that China’s PD burden grew faster than the global average, particularly among males and individuals aged 80 years and older. The BAPC model predicted that by 2035, China’s ASIR（32.3/100 000) and age-standardized prevalence rate (ASPR = 333/100 000) will far exceed global levels. Conclusion　The disease burden of PD has increased substantially worldwide, primarily driven by population aging. However, the trend in mortality control in China is more favorable than the global average. PD is more prevalent among older adults and males and exhibits stable comorbidity patterns with various age-related diseases. Future efforts should focus on enhanced management, optimized policies, and improved comorbidity care to reduce the overall burden. This study provides data-driven evidence to support the development of targeted prevention and control strategies for PD.

Key words: Parkinson's disease, Incidence rate, Disease burden, Predictive analysis, Age-Period-Cohort model, Bayesian Age-Period-Cohort model

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