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采用逻辑回归结合决策树模型评估中国西部2型糖尿病患者颈动脉内膜中层厚度的危险因素分析

Analysis of risk factors for carotid intima-media thickness in patients with type 2 diabetes mellitus in Western China assessed by logistic regression combined with a decision tree model.

作者信息

Zhou Yuan-Yuan, Qiu Hong-Mei, Yang Ying, Han Yuan-Yuan

机构信息

1Department of Endocrinology and Metabolism, Fourth Affiliated Hospital of Kunming Medical University, The Second People's Hospital of Yunnan Province, Kunming, 650021 China.

2Department of Endocrinology and Metabolism, Sixth Affiliated Hospital of Kunming Medical University, The People's Hospital of Yuxi City, Yuxi, 653100 China.

出版信息

Diabetol Metab Syndr. 2020 Jan 28;12:8. doi: 10.1186/s13098-020-0517-8. eCollection 2020.

DOI:10.1186/s13098-020-0517-8
PMID:32015760
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6988356/
Abstract

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with type 2 diabetes (T2DM). Carotid intima-media thickness (CIMT) is considered a preclinical stage of atherosclerosis. Therefore, it is necessary to identify the related risk factors for CIMT to facilitate the early prevention of CVD. Previous studies have shown that visceral fat area (VFA) is a risk factor for T2DM and CVD. However, few studies have focused on the effects of VFA on CIMT associated with T2DM. Moreover, considering that the body fat distribution shows regional and racial heterogeneity, the purpose of this study was to investigate the predictive value of VFA and other risk factors for CIMT associated with T2DM in Western China.

METHODS

In a cross-sectional study, a total of 1372 patients with T2DM were divided into the CIMT (-) group (n = 965) and the CIMT (+) group (n = 407) based on CIMT values. In addition to the univariate analyses, logistic regression analysis and a decision tree model were simultaneously performed to establish a correlation factor model for CIMT.

RESULTS

Univariate analyses showed that sex, smoking status, age, heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, weight, body mass index (BMI), waist circumference, hip circumference, waist-hip ratio, VFA, subcutaneous fat area, and the levels of 2-h C-peptide, serum creatinine, urea nitrogen and uric acid were significantly different between the two groups (all < 0.05). Smoking, increased VFA, female sex and increased BMI were risk factors in the logistic regression analyses (OR = 5.759, OR = 1.364, OR = 2.239, OR = 1.186, respectively). In the decision tree model, smoking was the root node, followed by sex, waist circumference, VFA and chronic kidney disease (CKD) in order of importance.

CONCLUSIONS

In addition to smoking, sex and BMI, VFA has a significant effect on CIMT associated with T2DM in the Chinese Han population in Western China. In addition, the decision tree model could help clinicians make more effective decisions, with its simplicity and intuitiveness, making it worth promoting in future medical research. ChiCTR, ChiCTR1900027739. Registered 24 November 2019-Retrospectively registered, http://www.chictr.org.cn/index.aspx.

摘要

背景

心血管疾病(CVD)是2型糖尿病(T2DM)患者发病和死亡的主要原因。颈动脉内膜中层厚度(CIMT)被认为是动脉粥样硬化的临床前期阶段。因此,有必要确定CIMT的相关危险因素,以促进CVD的早期预防。既往研究表明,内脏脂肪面积(VFA)是T2DM和CVD的危险因素。然而,很少有研究关注VFA对与T2DM相关的CIMT的影响。此外,考虑到身体脂肪分布存在区域和种族异质性,本研究的目的是探讨VFA和其他危险因素对中国西部T2DM相关CIMT的预测价值。

方法

在一项横断面研究中,根据CIMT值将1372例T2DM患者分为CIMT(-)组(n = 965)和CIMT(+)组(n = 407)。除单因素分析外,同时进行逻辑回归分析和决策树模型,以建立CIMT的相关因素模型。

结果

单因素分析显示,两组患者的性别、吸烟状况、年龄、心率、收缩压(SBP)、舒张压(DBP)、身高、体重、体重指数(BMI)、腰围、臀围、腰臀比、VFA、皮下脂肪面积以及2小时C肽、血清肌酐、尿素氮和尿酸水平存在显著差异(均<0.05)。在逻辑回归分析中,吸烟、VFA增加、女性和BMI增加是危险因素(OR分别为5.759、1.364、2.239、1.186)。在决策树模型中,吸烟是根节点,其次按重要性顺序为性别、腰围、VFA和慢性肾脏病(CKD)。

结论

在中国西部汉族人群中,除吸烟、性别和BMI外,VFA对与T2DM相关的CIMT有显著影响。此外,决策树模型简单直观,有助于临床医生做出更有效的决策,值得在未来医学研究中推广。中国临床试验注册中心,ChiCTR1900027739。2019年11月24日注册——回顾性注册,http://www.chictr.org.cn/index.aspx。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b90a/6988356/64c3a4d81374/13098_2020_517_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b90a/6988356/6f2f7bc49b35/13098_2020_517_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b90a/6988356/64c3a4d81374/13098_2020_517_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b90a/6988356/6f2f7bc49b35/13098_2020_517_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b90a/6988356/64c3a4d81374/13098_2020_517_Fig2_HTML.jpg

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