Trajectory and influencing factors of fasting blood glucose fluctuations in patients with type 2 diabetes mellitus in community

Yueyao XU; Huiyan JIAO; Meng NIE; Zhiquan YUAN; Na WU; Chengying LI; Long WU; Yonghong WU; Li ZHONG; Yafei LI; Jingyuan YANG

doi:10.16016/j.2097-0927.202209169

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Trajectory and influencing factors of fasting blood glucose fluctuations in patients with type 2 diabetes mellitus in community

Public Health and Preventive Medicine | 更新时间：2026-01-09

- Trajectory and influencing factors of fasting blood glucose fluctuations in patients with type 2 diabetes mellitus in community
- Journal of Army Medical University Vol. 45, Issue 8, Pages: 786-793(2023)
- 作者机构：
  
  1.550025 贵阳，贵州医科大学公共卫生与健康学院流行病与卫生统计学系环境污染与疾病监控教育部重点实验室
  2.400038 重庆，陆军军医大学(第三军医大学)军事预防医学系军队流行病学教研室
  3.400032 重庆，沙坪坝区双碑社区卫生服务中心
  4.401120 重庆，重庆医科大学附属第三医院心血管疾病中心
- 作者简介：
- 基金信息：
- DOI：10.16016/j.2097-0927.202209169
  CLC： R195.4;R446.122;R587.1
- Received：19 September 2022，
  
  Revised：2022-11-16，
  
  Published：30 April 2023
- 稿件说明：
移动端阅览
Yueyao XU, Huiyan JIAO, Meng NIE, et al. Trajectory and influencing factors of fasting blood glucose fluctuations in patients with type 2 diabetes mellitus in community[J]. Journal of Army Medical University, 2023, 45(8): 786-793.
DOI：

Yueyao XU, Huiyan JIAO, Meng NIE, et al. Trajectory and influencing factors of fasting blood glucose fluctuations in patients with type 2 diabetes mellitus in community[J]. Journal of Army Medical University, 2023, 45(8): 786-793. DOI： 10.16016/j.2097-0927.202209169.

摘要

目的

构建社区2型糖尿病患者的空腹血糖波动的轨迹模型，并分析不同类别血糖波动轨迹的影响因素。

方法

本研究采用回顾性队列研究，纳入769例于2012年1月至2019年12月在重庆市沙坪坝区双碑社区卫生服务中心建立居民健康档案并加入社区随访管理的2型糖尿病患者，基于群体的轨迹模型(group-based trajectory modelling，GBTM)来识别、构建糖尿病患者的空腹血糖波动轨迹，利用贝叶斯信息准则(Bayesian information criterion，BIC)、平均验后分组概率(average posterior probability，AvePP)等评价指标选择最佳亚组数的轨迹，比较不同类别轨迹在人口学特征、个人健康状况、疾病史、生活方式、服药依从性等方面的差异，运用无序多分类Logistic回归模型分析不同波动轨迹的影响因素。

结果

运用GBTM分析，将社区769名2型糖尿病患者分为3个不同波动轨迹亚组，分别为“低水平平稳组”、“高水平下降组”和“中水平上升组”。以低水平平稳组为对照，无序多分类Logistic回归分析结果显示：有高血压病史、未服药患者被归为“高水平下降组”的可能性较低，即这些患者的血糖从高水平逐渐下降的可能性较低(

=0.50，95%

：0.28~0.90，

=0.021；

=0.13，95%

：0.03~0.56，

=0.006)。但是偶尔或不锻炼、饮酒或已戒酒的患者更倾向于被归为“高水平下降组”(

=2.02，95%

：1.15~3.55，

=0.014；

=2.32，95%

：1.16~4.63，

=0.017)。与规律服药的糖尿病患者相比，间断服药的糖尿病患者更倾向于被归为“中水平上升组” (

=6.02，95%

：2.86~12.66，

0.001)。

结论

社区2型糖尿病患者空腹血糖轨迹呈现不同的动态变化特征；高血压史、服药依从性、锻炼频率和饮酒可能影响患者出现不同血糖波动轨迹。

Abstract

Objective

To construct trajectory models of fasting blood glucose fluctuations in type 2 diabetes mellitus (T2DM) patients in community

and analyze the influencing factors of the trajectory of glucose fluctuation in different categories.

Methods

A retrospective cohort study was carried out on 769 T2DM patients who had health records and been involved in the diabetic management in Shuangbei Community Health Service Center of Shapingba District

Chongqing from 2012 to 2019. Group-based trajectory modelling (GBTM) was employed to identify and construct the fluctuation trajectory of fasting blood glucose in the patients. BIC

AvePP and other evaluation indicators were used to select the optimum subgroup number model. Then the differences in demographic characteristics

health status

medical history

lifestyle

medication compliance

etc

were compared among different categories. Multinomial logistic regression model was constructed to explore the influencing fac

tors of different fluctuation trajectories.

Results

The 769 T2DM subjects were divided into 3 groups of different fluctuation trajectories by GBTM

that is

low-level steady

high-level falling

and medium-level rising groups. Multinomial logistic regression analysis indicated that the patients with a history of hypertension and not taking medication were less likely to be assigned into the high-level falling group than into the low-level steady group

indicating that their high glucose level was not predisposed to a progressive decline (

=0.50

95%

: 0.28~0.90

=0.021;

=0.13

95%

: 0.03~0.56

=0.006). However

the subjects who occasionally or not exercised

consumed alcohol or abstained from alcohol were more likely to be assigned into the high-level falling group (

=2.02

95%

: 1.15~3.55

=0.014;

=2.32

95%

: 1.16~4.63

=0.017). When compared with the patients with regular medication

those with intermittent medication were more likely to be assigned into the medium-level rising group (

=6.02

95%

: 2.86~12.66

0.001).

Conclusion

There are different trajectories of dynamic changes in fasting glucose for T2DM patients. History of hypertension

medication adherence

frequency of exercise

and alcohol consumption may be associated with different trajectories of blood glucose fluctuations.

关键词

Keywords

references

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