中国全科医学 ›› 2026, Vol. 29 ›› Issue (01): 84-90.DOI: 10.12114/j.issn.1007-9572.2025.0144

• 论著 • 上一篇    下一篇

肌肉脂肪比例与非肥胖高尿酸血症的相关性及预测价值研究

李纪新, 邱林杰, 任燕, 王文茹, 杨振宇, 刘峰兆, 李美洁, 栗文婕, 张晋*()   

  1. 100091 北京市,中国中医科学院西苑医院
  • 收稿日期:2025-03-09 修回日期:2025-07-13 出版日期:2026-01-05 发布日期:2025-12-18
  • 通讯作者: 张晋

  • 作者贡献:

    李纪新负责数据整理分析、论文撰写及修改;邱林杰负责数据整理及分析;任燕负责数据分析和论文修改;王文茹、杨振宇、刘峰兆负责数据收集;李美洁、栗文婕负责研究思路指导;张晋负责研究设计构思、研究思路指导、数据整体分析、论文修改指导。

  • 基金资助:
    国家重点研发计划子课题(2018YFC2000600); 北京薪火传承3+3项目(2023-SZ-A51); 中国中医科学院科技创新工程(CI2021A03005); 中国中医科学院西苑医院具有知识产权的医疗机构制剂和中药新药的研发与转化专项任务(XYZY0301-16)

Correlation of Muscle-fat Ratio with Non-obese Hyperuricaemia and the Predictive Value

LI Jixin, QIU Linjie, REN Yan, WANG Wenru, YANG Zhenyu, LIU Fengzhao, LI Meijie, LI Wenjie, ZHANG Jin*()   

  1. Xiyuan Hospital of China Academy of Chinese Medicine Sciences, Beijing 100091, China
  • Received:2025-03-09 Revised:2025-07-13 Published:2026-01-05 Online:2025-12-18
  • Contact: ZHANG Jin

摘要: 背景 非肥胖高尿酸血症发病隐匿,常规BMI等人体测量学指标对该疾病发生风险的评价存在一定的局限性,因此寻找简便效优的非肥胖高尿酸血症识别方法具有重要意义。 目的 探究肌肉脂肪比例(MFR)与非肥胖高尿酸血症和血尿酸水平的相关性及其预测价值。 方法 本研究纳入2021—2024年在中国中医科学院西苑医院参加健康体检的非肥胖成年人,以是否患有高尿酸血症,将研究对象分为患高尿酸血症和未患高尿酸血症,比较两者基线水平。采用多因素Logistic回归和多元线性回归分析探讨MFR与高尿酸血症患病和血尿酸水平的相关性;依据性别、年龄、吸烟史、饮酒史和既往疾病史进行交互作用检验;运用受试者工作特征(ROC)曲线评估MFR对于非肥胖高尿酸血症的诊断预测效能。 结果 本研究共纳入非肥胖样本1 869例,患高尿酸血症428例,未患高尿酸血症1 441例。多因素Logistic回归分析结果显示,调整混杂因素后,MFR水平升高是高尿酸血症发生的保护因素(OR=0.02,95%CI=0.01~0.04,P<0.05);与MFR Q1(MFR:0.562~0.995)相比,MFR Q2(MFR:1.000~1.257)(OR=0.14,95%CI=0.09~0.22,P<0.05)、MFR Q3(MFR:1.258~1.638)(OR=0.14,95%CI=0.08~0.24,P<0.05)、MFR Q4(MFR:1.640~6.383)(OR=0.04,95%CI=0.02~0.09,P<0.05)发生高尿酸血症的风险降低(P<0.05)。多元线性回归分析结果显示,调整混杂因素后,与MFR Q1相比,MFR Q2(β=-31.32,95%CI=-40.30~-22.33,P<0.05)、MFR Q3(β=-28.08,95%CI=-38.73~-17.43,P<0.05)、MFR Q4(β=-34.94,95%CI=-48.15~-21.73,P<0.05)的血尿酸水平降低。亚组分析显示,相较于男性,在女性人群中MFR的升高与更低的高尿酸血症和更低水平的血尿酸相关(P<0.05)。ROC曲线显示,MFR预测非肥胖高尿酸血症发生风险的ROC曲线下面积为0.759(95%CI=0.732~0.786),最佳临界值为0.992,灵敏度为58.4%,特异度为85.3%。 结论 在非肥胖人群中MFR水平升高,发生高尿酸血症的风险和血尿酸水平降低,且MFR预测非肥胖高尿酸血症具有良好的价值,可用于非肥胖高尿酸血症的早期识别。

关键词: 肌肉脂肪比例, 高尿酸血症, 非肥胖, 体脂率, 相关性研究, 预测价值

Abstract:

Background

Non-obese hyperuricaemia has an insidious onset, and anthropometric indicators such as the conventional BMI have limitations in evaluating the risk of developing this disease. Therefore, it is important to find a simple and inexpensive method to identify non-obese hyperuricaemia.

Objective

To investigate the correlation of muscle-fat ratio (MFR) with non-obese hyperuricaemia and blood uric acid levels and its predictive value.

Methods

Non-obese adults who participated in physical examinations in the Xiyuan Hospital of China Academy of Chinese Medicine Sciences from 2021 to 2024 were included in this study. The research subjects were divided into those with hyperuricemia and those without hyperuricemia based on whether they had hyperuricemia or not, and the baseline levels of the two groups were compared. The correlation of MFR with the prevalence of hyperuricemia and blood uric acid levels in non-obese people was analyzed by multivariate Logistic regression and multiple linear regression. Interaction tests were conducted according to gender, age, smoking history, alcohol consumption history and previous disease history. The diagnostic and predictive efficacy of MFR in diagnosing non-obese hyperuricaemia was assessed by using receiver operating characteristic (ROC) curves.

Results

A total of 1 869 non-obese participants were included in this study, including 428 cases of hyperuricaemia, and 1 441 cases of non-hyperuricaemia. After adjusting for confounders, multivariate Logistic regression showed that an elevated MFR was a protective factor for hyperuricaemia (OR=0.02, 95%CI=0.01-0.04, P<0.05). Compared with MFR Q1 (MFR: 0.562-0.995), the risk of hyperuricaemia in MFR Q2 (MFR: 1.000-1.257) (OR=0.14, 95%CI=0.09-0.22, P<0.05), MFR Q3 (MFR: 1.258-1.638) (OR=0.14, 95%CI=0.08-0.24, P<0.05), and MFR Q4 (MFR: 1.640-6.383) (OR=0.04, 95%CI=0.02-0.09, P<0.05) was significantly reduced (P<0.05). The results of multiple linear regression analysis showed that after adjusting for confounders, the blood uric acid levels of MFR Q2 (β=-31.32, 95%CI=-40.30 to -22.33, P<0.05), MFR Q3 (β=-28.08, 95%CI=-38.73 to -17.43, P<0.05), and MFR Q4 (β=-34.94, 95%CI=-48.15 to -21.73, P<0.05) were significantly reduced compared with MFR Q1. Subgroup analysis showed that elevated MFR in female populations was correlated with significantly lower risk of hyperuricaemia and lower levels of hematologic uric acid compared with men (P<0.05). The ROC curve shows that the area under the curve (AUC) of MFR in predicting the risk of non-obese hyperuricaemia was 0.759 (95%CI=0.732-0.786), with an optimal critical value of 0.992, a sensitivity of 58.4%, and a specificity of 85.3%.

Conclusion

The MFR level is elevated in non-obese populations, and the risk of hyperuricaemia and blood uric acid levels are reduced. MFR predicts non-obese hyperuricaemia and can be used for the early identification of hyperuricaemia in non-obese populations.

Key words: Muscle-fat ratio, Hyperuricaemia, Non-obesity, Body fat percentage, Correlation study, Predictive value