| Title: | Serum uric acid, gout, cardiometabolic diseases, and multimorbidity: a prospective cohort study using multi-state model |
| Journal: | npj Aging |
| Published: | 25 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42185276/ |
| DOI: | https://doi.org/10.1038/s41514-026-00413-6 |
| URL: | https://www.nature.com/articles/s41514-026-00413-6_reference.pdf |
Publication 19921
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Abstract
Multimorbidity, particularly cardiometabolic multimorbidity (CMM), is a growing global health challenge, defined by the co-occurrence of cardiometabolic diseases (CMDs) like type 2 diabetes, ischemic heart disease, and stroke. While serum uric acid (SUA) and gout have been linked to various chronic conditions, their roles in multimorbidity and the CMM trajectory remain unexplored in large populations. Using data from over 400,000 UK Biobank participants, we explored the associations between SUA, gout, 36 chronic conditions, and multimorbidity. A multi-state model was applied to investigate SUA and gout's roles in the CMM trajectory, including transitions from CMD-free status to first CMD (FCMD), CMM, and death. Analyses were conducted for the overall population and stratified by sex. We observed that higher SUA levels and gout were associated with a higher likelihood of multimorbidity and multiple chronic conditions, particularly CMDs. Multi-state analysis revealed that both SUA and gout increased the risk of most transitions. Classifying FCMDs by specific CMDs further revealed distinct roles of SUA/gout in disease-specific transitions, even at the same stage. Sex-specific analyses showed a stronger impact in females compared to males. These findings highlight the importance of managing SUA levels and gout to prevent multimorbidity and slow CMM progression, particularly in females.</p>
12 Authors
- Chuanghai Wu
- Guowei Zhong
- Ying Yang
- Baizhao Peng
- Dexian Li
- Zihao Jiang
- Wen Fang
- Shuai Ji
- Xinghong Zhou
- Hiu Yee Kwan
- Yanyan Liu
- Xiaoshan Zhao
1 Application
| Application ID | Title |
|---|---|
| 90369 | Analysis of the Dynamic Characteristics of Aging-related Serum Uric Acid in Nervous and Digestive System Disorders with Machine Learning Methods |
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