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Clustering of gout-related comorbidities and their relationship with gout flares: a data-driven cluster analysis of eight comorbidities

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Abstract

Objectives

To study the aggregation of multiple comorbidities in people with gout and explore differences in prognosis of gout flares among different subgroups.

Methods

Hierarchical clustering was performed to identify homogeneous subgroups among 2639 people with gout using eight comorbidities. A one-year follow-up of acute gout flares in 463 of these people was conducted; the incidence and the timing of gout flares in each cluster were assessed to explore prognosis of gout flares. Binary logistic regression was applied to assess factors associated with gout flares.

Results

In baseline study, we identified five subgroups (C1–C5). C1 (n = 671, 25%) was characterized by isolated gout with few comorbidities. C2 (n = 258, 10%) were all obese. Almost all people in C3 (n = 335, 13%) had diabetes (99.7%). All people in C4 (n = 938, 36%) had dyslipidemia. C5 (n = 437, 17%) had the highest proportion of cardiovascular disease (CVD, 53%), chronic kidney disease (CKD, 56%), and cancer (7%). In follow-up study, C5 had the highest incidence (71.9%) and earliest onset (median 3 months) of gout flares. C2 had the lowest incidence (52.1%) and the latest onset (median 10 months) of gout flares. The highest relative risk for gout recurrent was seen for C5 (OR = 2.09). Other factors associated with the risk of gout flares were age at diagnosis of gout, duration of gout, presence of tophi, and smoking ≥ 20 cigarettes/day.

Conclusions

We clustered people with gout into five groups with varying comorbidities. People with CVD, CKD, and cancer had the highest risk of gout flares and should receive comprehensive care.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank the trial staff as well as all people with gout who agreed to participate in this study.

Funding

This study was supported by the National Key R&D Programme of China (2019YFA0904500, 2022YFC2503300), the National Natural Science Foundation of China (81870616, 82170904), the Clinical Trial Programme of Shanghai Municipal Health Commission (202240130), the Clinical Trial Programme of Shanghai No.10 Hospital (YNCR2A009), and Medical enterprise integration Innovation achievement transformation Programme of Shanghai Shen-kang Hospital Development Centre (SHDC2022CRD042) to HC.

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Authors and Affiliations

  1. Department of Endocrinology and Metabolism, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, China

    S. Liu, H. Sun, S. Yang, S. Qu & H. Chen

  2. Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of Medicine, 227 Chongqing South Road, Shanghai, 200025, China

    N. Liang & Y. Gao

Authors
  1. S. Liu
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  2. H. Sun
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  3. S. Yang
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  6. S. Qu
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  7. H. Chen
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Contributions

SL: designed the study, analyzed and interpreted the data, and wrote the paper. HS: participated in the conception of the study. SY, NL and YG: participated in the collection of trial data. SQ and HC made critical revisions to the original manuscript.

Corresponding authors

Correspondence to S. Qu or H. Chen.

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The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the Ethics Committee of Shanghai Tenth People’s Hospital. It was conducted in accordance with the Declaration of Helsinki and the Guidelines for Good Clinical Practice.

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All participants gave written informed consent before taking part in the study.

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Liu, S., Sun, H., Yang, S. et al. Clustering of gout-related comorbidities and their relationship with gout flares: a data-driven cluster analysis of eight comorbidities. J Endocrinol Invest 47, 1119–1128 (2024). https://doi.org/10.1007/s40618-023-02224-y

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  • DOI: https://doi.org/10.1007/s40618-023-02224-y

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