Abstract / Summary
Gout has become a rapidly expanding metabolic-inflammatory disease in China, but the long-term trajectory, changing attributable risks, and future burden among middle-aged adults remain incompletely defined. We assessed gout burden among Chinese adults aged 40 to 59 years from 1990 to 2023 and projected trends to 2035. Using Global Burden of Disease Study 2023 (GBD 2023) data, we analyzed gout prevalence, incidence, and disability-adjusted life years among Chinese adults aged 40 to 59 years. Joinpoint regression identified temporal inflection points; Das Gupta decomposition quantified contributions from population growth, aging, and epidemiological change; Bayesian age-period-cohort modeling projected burden to 2035; and population attributable fractions (PAFs) were estimated for high body mass index (BMI) and kidney dysfunction, with sex- and age-stratified analyses. Prevalent cases increased from 2.56 million in 1990 to 6.75 million in 2023, while the age-standardized prevalence rate rose by 21.08% (1226.32–1484.81 per 1,00,000). The steepest acceleration occurred during 2009 to 2018. Population growth accounted for approximately 70% to 73% of the absolute increase, epidemiological change for 19% to 24%, and aging for 6% to 8%. High BMI remained the dominant attributable risk factor, accounting for 32.2% of gout-related disability-adjusted life years in 2023, whereas the PAF attributable to kidney dysfunction declined from 6.5% to 4.7%. Men had a higher BMI-attributable PAF at ages 40 to 44 years, but women had a greater proportional burden from age 45 onward. Projections indicated that gout burden would remain at a high level through 2035. Gout burden among middle-aged adults in China has risen substantially and is increasingly shaped by metabolic risk. Population-level weight and dietary interventions, sex-sensitive prevention and screening, and guideline-concordant urate-lowering management are needed to curb the projected burden. As an ecological analysis of modeled estimates, these findings identify population-level associations rather than establish causation.