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

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 p < 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.

Effect of a 6-Month Controlled Lifestyle Intervention on Common Carotid Intima-Media Thickness

“…To evaluate the difference in ccIMT change (mean and max, respectively) between the two groups a one-way analysis of covariance (ANCOVA) was used, using the baseline ccIMT values (mean and max, respectively) as covariates (33,34). The ANCOVA analyses were then repeated adjusting for several confounders gained from the literature [35][36][37][38] and from associations observed in this study population. Apart from baseline mean or max ccIMT, the covariates adjusted for were sex, age, smoker status, body mass index (BMI), total cholesterol, LDL cholesterol, HDL cholesterol, systolic and diastolic blood pressure, and HbA1c.…”

Tenofovir Alafenamide Rescues Renal Tubules in Patients with Chronic Hepatitis B

“…A decision-tree algorithm is a data-mining technique that reveals a series of classification rules by identifying priorities. It has been used to identify the profiles associated with the progression of chronic kidney disease [ 55 ]. The decision-tree analysis showed that age was the initial classifier for the ADV/TDF-related renal tubular dysfunction.…”

Association of AGT, ACE, NOS3, TNF, MMP9, CYBA polymorphism with subclinical arterial wall changes

“…Представлены только факторы, продемонстрировавшие статистическую значимость. [46][47][48]. ТКИМ является одним из маркеров раннего сосудистого старения и часто используется в крупных исследованиях, включая генетические, как маркер раннего атеросклероза в связи с относительной простотой, доступностью и неинвазивностью методики измерения [49].…”