Metabolic Quotient in Gout: Clinical Validation and Prognostic Value for a Frequently Relapsing Course

Khadzhimetova Oygul Ikramovna

Authors

Khadzhimetova Oygul Ikramovna Department of Internal Medicine, Nephrology, Hemodialysis and Rehabilitation, Urgench State Medical Institute

Keywords:

Gout, hyperuricemia, metabolic syndrome, insulin resistance, metabolic coefficient, SII, CRP, prognosis, ROC analysis, logistic regression

Abstract

Gout is considered a systemic metabolic-inflammatory disease in which the severity of clinical manifestations and the risk of complications are determined not only by uric acid levels but also by the overall metabolic burden. In outpatient practice, physicians require a simple and reproducible tool to quantify the "metabolic burden" and relate it to the risk of frequent attacks and a severe disease phenotype. To develop and clinically validate a metabolic quotient for gout and evaluate its prognostic value for disease activity and adverse clinical and laboratory outcomes. A single-center controlled study (2021–2025, Urgench outpatient clinic) was conducted. Patients with gout and a control group were examined. The metabolic coefficient (MC) was calculated as a summary assessment of metabolic burden components (obesity, metabolic syndrome, type 2 diabetes mellitus, hypertension, and dyslipidemia) with subsequent gradation by risk level. Clinical activity indicators (attack frequency, pain according to the VAS scale, and tophi), SUA, creatinine/eGFR, CRP, and systemic inflammatory indices (including SII) were assessed. Correlation analysis, multivariate logistic regression for adverse outcomes, and ROC analysis were performed to determine MC thresholds. MC values were statistically significantly higher in patients with gout than in the control group, reflecting the high prevalence of metabolic burden in the disease structure. An increase in MC was accompanied by increased attack frequency, a higher likelihood of tophi/frequently recurring disease, worsening renal function, and increased systemic inflammation as measured by CRP and SII. In multivariate models, the metabolic coefficient (MC) remained independently associated with the risk of an unfavorable clinical phenotype after adjusting for age, sex, SUA, and eGFR. ROC analysis confirmed the clinical utility of the MC for risk stratification and allowed us to identify thresholds at which the likelihood of frequent attacks and a severe phenotype increases sharply. The metabolic coefficient (MC) is a simple and reproducible tool reflecting the "metabolic burden" in gout and has clinically significant predictive value for attack frequency, a severe phenotype, and systemic inflammation. Using the MC at the baseline visit allows us to identify high-risk patients and justify a more intensive strategy for disease and comorbidity control.

References

[1] Akbari M, Tamtaji OR, Lankarani KB, et al. The effects of curcumin on glycemic control and insulin resistance: a systematic review and meta-analysis of randomized controlled trials. Phytother Res. 2019;33(1):3–14.

[2] Brubaker L, Wolfe AJ. The urinary microbiota: a paradigm shift for bladder disorders? Curr Opin Obstet Gynecol. 2017;29(5):407–412.

[3] Chen-Xu M, Yokose C, Rai SK, Pillinger MH, Choi HK. Contemporary prevalence of gout and hyperuricemia in the United States and decade trends: NHANES 2007–2016. Arthritis Rheumatol. 2019;71(6):991–999.

[4] Dai W, Wang H, Fang J, et al. Curcumin inhibits bacterial biofilm formation and quorum sensing: experimental and mechanistic evidence. Front Microbiol. 2020;11:—.

[5] Dalbeth N, Choi HK, Joosten LAB, et al. Gout. Nat Rev Dis Primers. 2019;5:69.

[6] Dalbeth N, Merriman TR, Stamp LK. Gout. Lancet. 2016;388(10055):2039–2052.

[7] Dehlin M, Jacobsson L, Roddy E. Global epidemiology of gout: prevalence, incidence, treatment patterns and risk factors. Nat Rev Rheumatol. 2020;16(7):380–390.

[8] FitzGerald JD, Dalbeth N, Mikuls T, et al. 2020 American College of Rheumatology guideline for the management of gout. Arthritis Care Res (Hoboken). 2020;72(6):744–760.

[9] Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13(5):269–284.

[10] GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222.

[11] Geerlings SE. Clinical presentations and epidemiology of urinary tract infections. Microbiol Spectr. 2016;4(5):—.

[12] Kanbay M, Jensen T, Solak Y, et al. Uric acid in metabolic syndrome and cardiometabolic disease. Nat Rev Nephrol. 2016;12(12):711–726.

[13] Kunnumakkara AB, Bordoloi D, Padmavathi G, et al. Curcumin, the golden nutraceutical: multitargeting for multiple chronic diseases. Br J Pharmacol. 2017;174(11):1325–1348.

[14] Kuo C-F, Grainge MJ, Zhang W, Doherty M. Rising burden of gout in the UK but continuing suboptimal management: a nationwide population study. Ann Rheum Dis. 2015;74(4):661–667.

[15] Nitzan O, Elias M, Chazan B, Saliba W. Urinary tract infections in patients with type 2 diabetes mellitus: review of prevalence, diagnosis, and management. Diabetes Metab Syndr Obes. 2015;8:129–136.

[16] Panahi Y, Hosseini MS, Khalili N, et al. Effects of curcumin on serum cytokines and inflammatory markers: a systematic review and meta-analysis. Phytother Res. 2016;30(10):1524–1533.

[17] Richette P, Doherty M, Pascual E, et al. 2016 updated EULAR evidence-based recommendations for the management of gout. Ann Rheum Dis. 2017;76(1):29–42.

[18] Safiri S, Kolahi A-A, Cross M, et al. Global, regional, and national burden of gout, 1990–2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis. 2020;79(1):31–38.