中国全科医学 ›› 2023, Vol. 26 ›› Issue (29): 3636-3639.DOI: 10.12114/j.issn.1007-9572.2023.0253

• 论著 • 上一篇    下一篇

血清尿酸纵向轨迹对新发高三酰甘油血症的影响:前瞻性队列研究

何应梅1, 贾雪1, 朱国军2, 刘冰1,*()   

  1. 1.442000 湖北省十堰市,湖北医药学院卫生管理与卫生事业发展研究中心
    2.442000 湖北省十堰市铁建医院
  • 收稿日期:2023-03-06 修回日期:2023-06-09 出版日期:2023-10-15 发布日期:2023-06-16
  • 通讯作者: 刘冰

  • 作者贡献:何应梅负责数据整理与统计学分析、图与表的绘制以及论文撰写;贾雪与朱国军负责数据收集与审查;刘冰负责文章的质量控制与审查,对文章整体负责,监督管理。
  • 基金资助:
    国家自然科学基金面上项目(71774049)

Effect of Serum Uric Acid Longitudinal Trajectory on New-onset Hypertriglyceridemia: a Prospective Cohort Study

HE Yingmei1, JIA Xue1, ZHU Guojun2, LIU Bing1,*()   

  1. 1. Center of Health Adminstration and Development Studies, Hubei University of Medicine, Shiyan 442000, China
    2. Shiyan Railway Hospital, Shiyan 442000, China
  • Received:2023-03-06 Revised:2023-06-09 Published:2023-10-15 Online:2023-06-16
  • Contact: LIU Bing

摘要: 背景 高血清尿酸(SUA)水平可能与高三酰甘油血症相关,但仍缺乏SUA纵向轨迹变化对新发高三酰甘油血症影响的前瞻性队列研究。 目的 探讨SUA纵向变化轨迹与新发高三酰甘油血症的相关性。 方法 纳入2015—2020年在十堰市铁建医院进行健康体检的建筑职工3 871人为研究对象,收集研究对象一般资料、体格检查资料与实验室检测结果。构建群组轨迹模型(GBTM)对研究对象尿酸轨迹进行分组,采用线性趋势χ2检验检测高三酰甘油血症发病密度随SUA分层的线性趋势。采用广义估计方程模型(GEE)分析各指标与高三酰甘油血症的关系。 结果 GBTM分组第1组轨迹呈现低位SUA波动水平(250~350 μmol/L),第2组轨迹呈现中位SUA波动水平(>350~450 μmol/L),第3组轨迹呈现高位SUA波动水平(>450 μmol/L),根据轨迹特征将3组依次命名为低位波动组(n=1 103)、中位波动组(n=2 141)、高位波动组(n=627)。线性趋势χ2检验结果发现,高三酰甘油血症发病密度随尿酸波动水平的上升而升高(χ2趋势=146.728,P<0.001)。三组受试者年龄、总胆固醇(TC)、三酰甘油(TG)、SUA、肌酐(Cr)、收缩压(SBP)、舒张压(DBP)、BMI比较,差异有统计学意义(P<0.05)。GEE分析结果显示,TC、Cr、SBP、DBP、空腹血糖、BMI为高三酰甘油血症发生的影响因素(P<0.05),以低位波动组为参照,中位波动组〔RR=2.294,95%CI(1.834,2.868)〕、高位波动组〔RR=3.012,95%CI(2.295,3.953)〕高三酰甘油血症的发生风险升高(P<0.05)。 结论 高三酰甘油血症发病密度随着SUA轨迹的升高而增加,SUA轨迹升高是高三酰甘油血症发生的危险因素,控制SUA在参考范围波动将可能有助于降低高三酰甘油血症的患病风险。

关键词: 高三酰甘油血症, 高尿酸血症, 血脂异常, 群组轨迹模型, 影响因素分析, GEE分析, 队列研究

Abstract:

Background

High serum uric acid (SUA) levels may be associated with hypertriglyceridemia. However, prospective cohort studies on the effect of longitudinal trajectory changes in SUA on new-onset hypertriglyceridemia are still lacking.

Objective

To investigate the correlation between the longitudinal trajectory of SUA and the new-onset hypertriglyceridemia.

Methods

A total of 3 871 architecture employees who underwent physical examinations in Shiyan Railway Hospital from 2015 to 2020 were selected as study subjects. The general data, physical examination data and laboratory test results of the study subjects were collected. Group-based trajectory model (GBTM) was used to group the uric acid trajectories of the study subjects, linear trend χ2 test was used to detect linear trends in the density of hypertriglyceridemia development with SUA stratification. Generalized estimation equation (GEE) was used to analyze the relationship between each index and hypertriglyceridemia.

Results

The trajectories of GBTM group 1 showed low SUA fluctuation levels (250-350 μmol/L), group 2 showed medium SUA fluctuation levels (>350-450 μmol/L), and group 3 showed high SUA fluctuation levels (>450 μmol/L), the three groups were named as low fluctuation group (n=1 103), medium fluctuation group (n=2 141), and high fluctuation group (n=627) sequentially according to the trajectory characteristics. The results of linear trend χ2 test revealed that the density of hypertriglyceridemia development increased with the rise of uric acid fluctuating levels (χ2trend=146.728, P<0.001). There were significant differences in age, total cholesterol (TC), triacylglycerol (TG), SUA, creatinine (Cr), systolic blood pressure (SBP), diastolic blood pressure (DBP), and BMI among the three groups (P<0.05). GEE analysis showed that TC, Cr, SBP, DBP, fasting blood glucose and BMI were influencing factors in the development of hypertriglyceridemia (P<0.05), and the risk of hypertriglyceridemia was increased in the medium fluctuation group〔RR=2.294, 95%CI (1.834, 2.868) 〕and high fluctuation group〔RR=3.012, 95%CI (2.295, 3.953) 〕using the low fluctuation group as a reference (P<0.05) .

Conclusion

The incidence density of hyperhyperacylglyceremia increases with the increase of uric acid locus, which is a risk factor for hyperacylglyceremia. Controlling SUA fluctuation in the normal range may help to reduce the risk of hyperacylglyceremia.

Key words: Hypertriglyceridemia, Hyperuricemia, Dyslipidemias, Group-based trajectory model, Root cause analysis, GEE analysis, Cohort study