[Relationship between blood uric acid levels and body composition in patients with polycystic ovary syndrome].

X Li, J F Zhang, Y R Feng, Q T Tang, D Kuai, W Y Tian, H Y Zhang
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Abstract

Objective: To analyze the difference in blood uric acid levels between patients with polycystic ovary syndrome (PCOS) and healthy women of childbearing age, and to investigate the correlation between body composition and blood uric acid levels. Methods: A total of 153 eligible childbearing age patients with PCOS treated at Tianjin Medical University General Hospital from January 2018 to March 2022 were selected, and 153 healthy women with normal menstruation were selected as the control group. Fasting blood uric acid levels were measured by venous blood test, and body composition was measured by a body composition analyzer. Group comparisons were made to analyze the correlation between body composition and blood uric acid levels. Results: The incidence of hyperuricemia was higher in patients with PCOS than that in the control group [30.1% (46/153) vs 2.0% (3/153)], with a statistically significant difference (χ2=44.429, P<0.001). Blood uric acid level was also significantly higher in patients with PCOS than that in the control group [(371±98) vs (265±67) μmol/L; t=11.170, P<0.001]. Among PCOS patients, there were statistically significant differences in weight, body mass index (BMI), body fat mass, skeletal muscle mass, percent body fat, lean body weight, fat mass/lean body weight, percent skeletal muscle, and visceral fat level between the hyperuricemia group and the normal blood uric acid group (all P<0.001), but no significant difference was observed in waist-hip ratio (P=0.348). The following body composition indicators: weight, BMI, waist-hip ratio, body fat mass, skeletal muscle mass, percent body fat, visceral fat level, lean body weight, and fat mass/lean body weight in all subjects, the PCOS patients and the control group, were positively correlated with blood uric acid levels (all P<0.01). The blood uric acid level in PCOS obese patients was higher than that in non-obese PCOS patients, and the difference was statistically significant [(425±83) vs (336±91) μmol/L; t=6.133, P<0.001]. The blood uric acid level in central obesity PCOS patients was also higher than that in non-central obesity PCOS patients [(385±95) vs (299±79) μmol/L], the difference was statistically significant (t=4.261, P<0.001). The blood uric acid level in normal-weight obese PCOS patients was higher than that in normal-weight non-obese PCOS patients [(333±73) vs (277±54) μmol/L], and the difference was statistically significant (t=2.848, P=0.006). Blood uric acid levels in normal-weight [(315±74) vs (255±67) μmol/L], overweight [(362±102) vs (276±57) μmol/L], and obese PCOS patients [(425±83) vs (303±74) μmol/L] were all higher than those in the corresponding control groups, with statistically significant differences (all P<0.001). Conclusions: PCOS patients have a higher incidence of hyperuricemia than healthy women of childbearing age. Blood uric acid levels are closely correlated with body composition indicators, such as weight, BMI, waist-hip ratio, body fat mass, skeletal muscle mass, percent body fat, and visceral fat level. Body composition analysis of women with PCOS could help identify potentially obese people more accurately and carry out individualized treatment, thereby reducing the risk of metabolic abnormalities.

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多囊卵巢综合征患者血尿酸水平与机体成分的关系
目的:分析多囊卵巢综合征(PCOS)患者与健康育龄妇女血尿酸水平的差异,探讨机体成分与血尿酸水平的相关性。方法:选择2018年1月至2022年3月在天津医科大学总医院就诊的符合条件的育龄PCOS患者153例,同时选择月经正常的健康女性153例作为对照组。静脉血试验测定空腹血尿酸水平,体成分分析仪测定体成分。进行组间比较,分析身体成分与血尿酸水平之间的相关性。结果:PCOS患者高尿酸血症发生率高于对照组[30.1% (46/153)vs 2.0%(3/153)],差异有统计学意义(χ2=44.429, Pt=11.170, PPP=0.348)。所有被试、PCOS患者和对照组的体重、BMI、腰臀比、体脂量、骨骼肌量、体脂百分比、内脏脂肪水平、瘦体重、脂肪质量/瘦体重与血尿酸水平均呈正相关(均Pt=6.133, Pt=4.261, Pt=2.848, P=0.006)。正常体重[(315±74)vs(255±67)μmol/L]、超重[(362±102)vs(276±57)μmol/L]、肥胖PCOS患者[(425±83)vs(303±74)μmol/L]血尿酸水平均高于相应对照组,差异均有统计学意义(p均)。结论:PCOS患者高尿酸血症发生率高于健康育龄妇女。血尿酸水平与身体组成指标密切相关,如体重、BMI、腰臀比、体脂量、骨骼肌量、体脂百分比和内脏脂肪水平。对多囊卵巢综合征(PCOS)女性进行身体成分分析,有助于更准确地识别潜在的肥胖人群,进行个体化治疗,从而降低代谢异常的风险。
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2.10
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12682
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