Prevalence and risk factors of hyperuricemia in health examination population in Yangzhou, 2020

doi:10.3969/j.issn.1004-583X.2023.05.007

Abstract

Abstract:

Objective To analyze the prevalence and risk factors of hyperuricemia (HUA) in health examination population in Yangzhou, Jiangsu Province in 2020. Methods A retrospective analysis was conducted on 12188 healthy adults over 20 years who had received health examinations in Yangzhou, Jiangsu Province. The prevalence of HUA in different age and gender groups was measured, and the correlation between biochemical parameters and the occurrence of HUA was analyzed. Results The prevalence of HUA among the health examination population in Yangzhou, Jiangsu Province in 2020 was 18.7%, which was 5.6% for females and 24.8% for males, respectively. Among males, the prevalence of HUA was the highest among young men aged 20-29 years and 30-39 years, reaching 31.9% and 29.9% respectively. Spearmen correlation analysis and logistic regression analysis indicated that body mass index (BMI) was positively correlated with HUA. The receiver-operating characteristic curve (ROC) analysis showed BMI>23.84 kg/m² could be used to predict the occurrence of HUA. Conclusion In 2020, the prevalence of HUA is 18.7% in health examination population in Yangzhou. Young men aged 20-39 years have the highest prevalence among males, indicating that the high prevalence of HUA among young males can not be ignored. BMI is positively correlated with HUA occurrence, which may be a potential risk factor for HUA.

Key words: hyperuricemia (HUA), uric acid, age, body mass index (BMI), prevalence

CLC Number:

R589.7

Liu Jing, Luo Na, Feng Shangyong, Wang Yan, Zhang Zhenwen, She Dunmin. Prevalence and risk factors of hyperuricemia in health examination population in Yangzhou, 2020[J]. Clinical Focus, 2023, 38(5): 428-432.

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URL: https://huicui.hebmu.edu.cn/EN/10.3969/j.issn.1004-583X.2023.05.007

https://huicui.hebmu.edu.cn/EN/Y2023/V38/I5/428

Figures/Tables 6

References 23

[1]	Ichida K, Matsuo H, Takada T, et al. Decreased extra-renal urate excretion is a common cause of hyperuricemia[J]. Nat Commun, 2012, 3: 764. doi: 10.1038/ncomms1756 pmid: 22473008
[2]	Su J, Wei Y, Liu M, et al. Anti-hyperuricemic and nephroprotective effects of Rhizoma Dioscoreae septemlobae extracts and its main component dioscin via regulation of mOAT1, mURAT1 and mOCT2 in hypertensive mice[J]. Arch Pharm Res, 2014, 37(10): 1336-1344. doi: 10.1007/s12272-014-0413-6 pmid: 24866061
[3]	Dong X, Zhang H, Wang F, et al. Epidemiology and prevalence of hyperuricemia among men and women in Chinese rural population: The Henan Rural Cohort Study[J]. Mod Rheumatol, 2019; 30(5): 910-920. doi: 10.1080/14397595.2019.1660048 URL
[4]	Yu W, Cheng JD. Uric acid and cardiovascular disease: an update from molecular mechanism to clinical perspective[J]. Front Pharmacol, 2020, 11:582680.
[5]	Yanai H, Adachi H, Hakoshima M, et al. Molecular biological and clinical understanding of the pathophysiology and treatments of hyperuricemia and its association with metabolic syndrome, cardiovascular diseases and chronic kidney disease[J]. Int J Mol Sci, 2021, 22(17):9221. doi: 10.3390/ijms22179221 URL
[6]	Qi D, Liu J, Wang C, et al. Sex-specific differences in the prevalence of and risk factors for hyperuricemia among a low-income population in China: A cross-sectional study[J]. Postgrad Med, 2020, 132 (6): 559-567. doi: 10.1080/00325481.2020.1761133 URL
[7]	Liu Z, Su X, Xiao M, et al. Association between eating away from home and hyperuricemia: A population-based nationwide cross-sectional study in China[J]. Biomed Res Int, 2019: 2792681.
[8]	Liu H, Zhang XM, Wang YL, et al. Prevalence of hyperuricemia among Chinese adults: A national cross-sectional survey using multistage, stratified sampling[J]. J Nephrol, 2014, 27 (6): 653-658. doi: 10.1007/s40620-014-0082-z pmid: 24687401
[9]	Piao W, Zhao L, Yang Y, et al. The prevalence of hyperuricemia and its correlates among adults in China: Results from CNHS 2015-2017[J]. Nutrients, 2022, 14(19):4095. doi: 10.3390/nu14194095 URL
[10]	Wang R, Tang Z, Sun F, et al. Prevalence of hyperuricemia in the elderly in 7 areas of China[J]. Zhonghua Liu Xing Bing Xue Za Zhi, 2018, 39 (3): 286-288.
[11]	Dong X, Zhang H, Wang F, et al. Epidemiology and prevalence of hyperuricemia among men and women in Chinese rural population: The Henan Rural Cohort Study[J]. Mod Rheumatol, 2020, 30(5):910-920. doi: 10.1080/14397595.2019.1660048 pmid: 31442098
[12]	Zhang Q, Gong H, Lin C, et al. The prevalence of gout and hyperuricemia in middle-aged and elderly people in Tibet Autonomous Region, China: A preliminary study[J]. Medicine (Baltimore), 2020, 99 (2): e18542.
[13]	佘敦敏, 朱妍, 冯尚勇, 等. 扬州市高尿酸血症发病率及危险因素调查[J]. 临床荟萃, 2012, 27(5): 417-419.
[14]	陆昀, 祁华金, 李凤, 等. 江苏苏州、无锡和常州地区中青年健康体检人群理想心血管健康行为和因素的调查分析[J]. 中华高血压杂志, 2015, 23(10):964-973.
[15]	孙洁, 周卫红, 顾天伟, 等. 南京鼓楼医院体检人群超重率和肥胖率及其与心血管疾病风险因素的关联研究[J]. 中华内分泌代谢杂志, 2021, 37(1):39-44.
[16]	Song P, Wang H, Xia W, et al. Prevalence and correlates of hyperuricemia in the middle-aged and older adults in China[J]. Sci Rep, 2018, 8(1):4314. doi: 10.1038/s41598-018-22570-9 pmid: 29531237
[17]	李亚茹, 赵海鹰, 冀笑怡, 等. 大学生血尿酸与儿茶酚胺的相关性[J]. 临床荟萃, 2021, 36(4):357-360.
[18]	Ali N, Perveen R, Rahman S, et al. Prevalence of hyperuricemia and the relationship between serum uric acid and obesity: A study on Bangladeshi adults[J]. PLoS One, 2018, 13(11): e0206850.
[19]	Shirasawa T, Ochiai H, Yoshimoto T, et al. Cross-sectional study of associations between normal body weight with central obesity and hyperuricemia in Japan[J]. BMC Endocr Disord, 2020, 20(1): 2. doi: 10.1186/s12902-019-0481-1 pmid: 31906920
[20]	Chang X, DeFries RS, Liu L, et al. Understanding dietary and staple food transitions in China from multiple scales[J]. PLos One, 2018, 13(4): e0195775.
[21]	Han A, Sun T, Ming J, et al. Are the Chinese moving toward a healthy diet evidence from macro data from 1961 to 2017[J]. Int J Environ Res Public Health, 2020, 17(15):5294. doi: 10.3390/ijerph17155294 URL
[22]	Caliceti C, Calabria D, Roda A, et al. Fructose intake, serum uric acid, and cardiometabolic disorders: A critical review[J]. Nutrients 2017, 9(4): 395. doi: 10.3390/nu9040395 URL
[23]	Teng GG, Tan CS, Santosa A, et al. Serum urate levels and consumption of common beverages and alcohol among Chinese in Singapore[J]. Arthritis Care Res (Hoboken), 2013, 65(9):1432-1440. doi: 10.1002/acr.21999 pmid: 23463601

年龄(岁)	性别	UA (μmol/L)	HUA患病率 [%(95%CI)]
20~29	女性	270±49.7	5.2(3.5~6.9)
	男性	389±79.6^△	31.9(28.9~34.9)^△
30~39	女性	263±52.5	5.5(4.1~6.9)
	男性	387±73.6^△	29.9(27.9~32.0)^△
40~49	女性	253±50.5	2.8(1.8~3.9)
	男性	373±73.1^△	23.4(21.6~25.3)^△
50~59	女性	274±52.6	6.5(4.8~8.3)
	男性	362±75.0^△	20.7(18.9~22.5)^△
60~69	女性	284±57.0	9.4(6.3~12.5)
	男性	359±75.5^△	19.5(17.0~22.0)^△
70~79	女性	291±58.5	12.5(6.7~18.3)
	男性	363±84.0^△	24.7 (20.6~28.7)^△
全部	女性	267±53.2	5.6(4.8~6.3)
	男性	373±76.2^△	24.8(23.9~25.7)^△

年龄(岁)	性别	UA (μmol/L)	HUA患病率 [%(95%CI)]
20~29	女性	270±49.7	5.2(3.5~6.9)
	男性	389±79.6^△	31.9(28.9~34.9)^△
30~39	女性	263±52.5	5.5(4.1~6.9)
	男性	387±73.6^△	29.9(27.9~32.0)^△
40~49	女性	253±50.5	2.8(1.8~3.9)
	男性	373±73.1^△	23.4(21.6~25.3)^△
50~59	女性	274±52.6	6.5(4.8~8.3)
	男性	362±75.0^△	20.7(18.9~22.5)^△
60~69	女性	284±57.0	9.4(6.3~12.5)
	男性	359±75.5^△	19.5(17.0~22.0)^△
70~79	女性	291±58.5	12.5(6.7~18.3)
	男性	363±84.0^△	24.7 (20.6~28.7)^△
全部	女性	267±53.2	5.6(4.8~6.3)
	男性	373±76.2^△	24.8(23.9~25.7)^△

BMI分组		例数	患病率[%(95% CI)]
正常体重组	女性	2914	5.2 (4.4~6.0)
	男性	3253	15.8 (14.6~17.1)^*
超重组	女性	785	6.8(5.0~8.5)
	男性	3883	27.4 (26.0~28.8)^*△
肥胖组	女性	153	7.2(3.1~11.3)
	男性	1200	40.7(37.9~43.4)^*△&

BMI分组		例数	患病率[%(95% CI)]
正常体重组	女性	2914	5.2 (4.4~6.0)
	男性	3253	15.8 (14.6~17.1)^*
超重组	女性	785	6.8(5.0~8.5)
	男性	3883	27.4 (26.0~28.8)^*△
肥胖组	女性	153	7.2(3.1~11.3)
	男性	1200	40.7(37.9~43.4)^*△&

变量	年龄	BMI	身高	体重	性别	FBG	TG	TC	HDL	LDL	Cr	BUN
UA	0.003 (P=0.756)	0.396 (P<0.01)	0.474 (P<0.01)	0.562 (P<0.01)	-0.610 (P<0.01)	0.099 (P<0.01)	0.416 (P<0.01)	0.131 (P<0.01)	-0.426 (P<0.01)	0.177 (P<0.01)	0.585 (P<0.01)	0.197 (P<0.01)
HUA	-0.034 (P<0.01)	0.242 (P<0.01)	0.193 (P<0.01)	0.292 (P<0.01)	-0.229 (P<0.01)	0.066 (P<0.01)	0.269 (P<0.01)	0.104 (P<0.01)	-0.237 (P<0.01)	0.101 (P<0.01)	0.285 (P<0.01)	0.092 (P<0.01)