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Correlation Between Serum Uric Acid Level and Central Body Fat Distribution in Patients with Type 2 Diabetes

Authors Zong J, Sun Y, Zhang Y, Yuan J, Wang X, Zhang R, Zhao X, Zhao X, Gu Y

Received 1 May 2020

Accepted for publication 25 June 2020

Published 16 July 2020 Volume 2020:13 Pages 2521—2531


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Professor Ming-Hui Zou

Jing Zong,1,2,* Yi Sun,1,* Yiqian Zhang,1,3 Jin Yuan,1 Xinlei Wang,1 Rongping Zhang,1 Xiaoqin Zhao,1 Xuying Zhao,1 Yunjuan Gu1

1Department of Endocrinology and Metabolism, The Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, People’s Republic of China; 2Geriatric Department, Nantong First Geriatric Hospital, Nantong, Jiangsu Province, People’s Republic of China; 3Department of Endocrinology and Metabolism, Changshu No.1 People’s Hospital, Nantong, Jiangsu Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yunjuan Gu
Department of Endocrinology and Metabolism, The Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, Jiangsu Province, People’s Republic of China
Tel +86 15851207860
Fax +86-513-8116-1906

Background: The aim of this study was to investigate the correlation between serum uric acid level and central body fat distribution in patients with type 2 diabetes (T2DM).
Methods: A total of 867 patients with T2DM were enrolled. Measurements of central fat distribution were obtained by dual energy X-ray absorptiometry. Patients were stratified into three groups according to their levels of serum uric acid (SUA). Multiple linear regression analysis was used to determine the association between SUA and central body fat distribution. Logistic regression analysis was used to estimate the risk factors for hyperuricemia (HUA). Mediation analysis was applied to assess the overall, direct, and indirect mediators of SUA levels.
Results: Multiple linear regression analysis showed that SUA levels were significantly positively correlated with waist circumference (WC), body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), Android fat mass, Gynoid fat mass, fasting c-peptide (F-CP), and area under the curve of C-peptide (P < 0.05 for all). VAT [odds ratio (OR), 2.367; 95% confidence interval (CI), 1.078– 5.197; P < 0.001)], WC (OR, 1.041; 95% CI, 1.011– 1.072; P < 0.001), high-density lipoprotein (OR, 0.274; 95% CI, 0.104– 0.727; P < 0.001), and estimated glomerular filtration rate (OR, 0.966; 95% CI, 0.959– 0.973; P < 0.001) were found to be independent risk factors for T2DM patients with HUA. After mediation analysis, BMI and central obesity were found to have different partial effects on the association between SUA and F-CP (P < 0.001).
Conclusion: In patients with T2DM, HUA was positively correlated with F-CP and central body fat distribution, especially VAT. These results suggest that central obesity may play a role in the positive correlation between HUA and insulin resistance (IR).

Keywords: serum uric acid, central obesity, type 2 diabetes, insulin resistance

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