1 out of 4 Patients with Gout Exhibit Depressive Symptoms

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The prevalence of depression among patients with gout is high, according to data published in Frontiers in Public Health.¹ Additionally, depression is affected by the current disease stage, the number of tophi, gender, number of flares within the past year, last attack days, and knowledge about gout. The nomogram model used in this study proved to be both a scientific and practical method to predict depression among this patient population.

Gout is currently considered the second most prevalent metabolic disease after diabetes, with a global increase in gout prevalence due in part to changes in dietary habits, evolving lifestyles, and economic growth. Gout rates vary from .1% to 6.8% internationally, with a 1.1% prevalence in China.² Gout flares and pain reduce quality of life among these patients and often lead to anxiety, depression, and other psychological issues that can impact the compliance of gout treatment.³

“Early identification and targeted interventions can alleviate the psychological stress on gout patients, thereby enhancing their adherence to treatment and improving their overall quality of life,” wrote a team of investigators from The First Hospital of China Medical University and the Peking Union Medical College Hospital, in China. “However, current research on depression among gout patients remains limited to status surveys, and there is a lack of a reliable tool to identify depressive symptoms specifically tailored for this patient population.”

To determine the risk factors for depression in patients with gout, investigators recruited 469 adult patients from a Class III Grade A hospital in Northeast China between November 2022 and July 2023. Subjects completed the General Information Questionnaire, Self-Rating Depression Scale, Self-Efficacy Scale for Managing Chronic Disease (SEMCD), the Gout Knowledge Questionnaire, and the Social Support Rating Scale. The depression risk prediction model and nomogram were created using univariate and multivariate logistic regression analyses. The performance of the model was verified using the bootstrap method.

According to survey data, the detection rate of depressive symptoms among patients with gout was 25.16% (n = 118). Independent risk factors for post-gout depression included male gender, a lack of knowledge about gout, the number of attacks in the past year, the duration of the last attack, the number of tophi, and the acute attack period. Prolonged gout, repeated flares, and severe pain impact the psychological status of patients. Marital status, the number of joints involved, self-efficacy, and the number of chronic diseases also affected depression rates. Female gender, chronic arthritis period, a knowledge of gout, social support, and the interictal period were protective factors (P <.05).

No significant differences were observed regarding age, education levels, family monthly income, working status, insurance status, family history, family support, or body mass index.

Both the calibration (χ2 = 11.348, P = .183, P >.05) and discrimination (area under the curve [AUC] = .858, 95% confidence interval [CI]: .818 – .897) of the nomogram model for depressive symptoms in patients with gout were considered good.

Investigators noted limitations including the larger AUC for the validation cohort compared with the development cohort. However, this is most likely due to the small sample size and high variability. Additionally, certain influencing factors were not included in the analysis, including medication, diet, and the amount of vitamin D and uric acid in the blood. Finally, generalizability may be limited as investigators only recruited outpatients with gout from a singular tertiary hospital in northeast China.

“This study has established a risk prediction model for depression in this population, which is conducive to early identification of depression in this population, and the model has good test efficiency,” investigators concluded.

References

1. ^{Hao X, Wang A. Development and validation of a prediction nomogram for depressive symptoms in gout patients. Front Public Health. 2024;12:1356814. Published 2024 Jul 19. doi:10.3389/fpubh.2024.1356814}
2. ^{Dehlin, M, Jacobsson, L, and Roddy, E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat Rev Rheumatol. (2020) 16:380–90. doi: 10.1038/s41584-020-0441-1}
3. ^{Kuo, CF, Grainge, MJ, Mallen, C, Zhang, W, and Doherty, M. Comorbidities in patients with gout prior to and following diagnosis: case-control study. Ann Rheum Dis. (2016) 75:210–7. doi: 10.1136/annrheumdis-2014-206410}