Shi L, Li NJ. Comprehensive analysis of risk factors associated with carotid plaque in patients with type 2 diabetes mellitus. World J Diabetes 2025; 16(8): 104180 [DOI: 10.4239/wjd.v16.i8.104180]
Corresponding Author of This Article
Neng-Juan Li, MD, Department of Endocrinology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No. 318 Chaowang Road, Gongshu District, Hangzhou 310053, Zhejiang Province, China. nengjuanli1010@yeah.net
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Lei Shi, Department of Endocrinology, Zhejiang Hospital, Hangzhou 310030, Zhejiang Province, China
Neng-Juan Li, Department of Endocrinology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
Author contributions: Shi L spearheaded the conceptualization, data curation, formal analysis, methodology, resource gathering, software management, and drafting of the original manuscript; Li NJ contributed to the methodology and played a significant role in reviewing and editing the manuscript; and this collaborative effort ensured a comprehensive approach to the study's execution and documentation.
Supported by the Zhejiang Traditional Chinese Medicine Science and Technology Plan Project, No. 2021ZB133 and No. 2017ZB049.
Institutional review board statement: This study was approved by the Ethic committee of The Second Affiliated Hospital of Zhejiang Chinese Medical University on August 14, 2024.
Informed consent statement: Written informed consent for publication was obtained from all patients and/or their families included in this retrospective analysis.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data sets generated and analyzed during this study are not public, but under reasonable requirements at nengjuanli1010@yeah.net, the correspondence author can provide.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Neng-Juan Li, MD, Department of Endocrinology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, No. 318 Chaowang Road, Gongshu District, Hangzhou 310053, Zhejiang Province, China. nengjuanli1010@yeah.net
Received: December 13, 2024 Revised: April 7, 2025 Accepted: July 2, 2025 Published online: August 15, 2025 Processing time: 244 Days and 19 Hours
Abstract
BACKGROUND
Carotid atherosclerosis is a common complication in patients with type 2 diabetes mellitus (T2DM) and is closely associated with an increased risk of cardiovascular events.
AIM
To identify the key demographic, clinical, and biochemical factors associated with carotid plaque formation in T2DM patients and evaluate their predictive value.
METHODS
This retrospective study included 266 T2DM patients (control group, n = 158; observation group, n = 108) recruited between January 2021 and July 2024. Participants underwent carotid ultrasonography to measure intima-media thickness (IMT) and detect carotid plaques. Comprehensive demographic and biochemical data, including age, body mass index (BMI), fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), serum creatinine (Scr), urinary albumin-to-creatinine ratio (UACR), and serum uric acid (SUA), were collected. Statistical analyses, including Pearson correlation, logistic regression, and receiver operating characteristic (ROC) curve analysis, were performed to identify and evaluate factors associated with carotid plaque formation.
RESULTS
Significant differences in age, BMI, HbA1c, FPG, Scr, UACR, and SUA were observed between groups (all P < 0.05). Pearson correlation analysis showed IMT was positively associated with age, FPG, HbA1c, Scr, UACR, and SUA, and negatively with HDL-C. Multivariate logistic regression identified age (OR = 1.050, 95%CI: 1.015-1.087), FPG (OR = 1.096, 95%CI: 1.006-1.192), HbA1c (OR = 1.234, 95%CI: 1.057-1.445), SBP (OR = 1.018, 95%CI: 1.002-1.034), Scr (OR = 1.029, 95%CI: 1.011-1.046), UACR (OR = 1.024, 95%CI: 1.010-1.037), SUA (OR = 1.006, 95%CI: 1.003-1.009), and HDL-C (OR = 0.329, 95%CI: 0.119-0.917) as independent predictors of IMT (all P < 0.05). ROC analysis showed UACR (AUC = 0.718) and SUA (AUC = 0.651) had predictive value for carotid plaque.
CONCLUSION
This study highlights the multifactorial nature of carotid atherosclerosis in T2DM, with age, BMI, poor glycemic control, renal dysfunction, and metabolic disturbances identified as key risk factors. The findings underscore the importance of comprehensive risk assessment and targeted interventions to prevent vascular complications in this high-risk population.
Core Tip: This study aims to shed light on the key risk factors contributing to carotid plaque formation in patients with type 2 diabetes mellitus (T2DM), a condition that significantly increases the risk of cardiovascular events. Our findings indicate that a range of demographic, clinical, and biochemical factors, including age, glycemic control, renal dysfunction, and metabolic disturbances, play a crucial role in the development of carotid atherosclerosis. Through a comprehensive analysis involving carotid ultrasonography, logistic regression, and receiver operating characteristic curve analysis, we identify important predictors of carotid plaque formation and provide valuable insights for improving cardiovascular risk assessment in T2DM patients.
Citation: Shi L, Li NJ. Comprehensive analysis of risk factors associated with carotid plaque in patients with type 2 diabetes mellitus. World J Diabetes 2025; 16(8): 104180
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by sustained hyperglycemia resulting from insulin resistance, impaired insulin secretion, or both. Its global prevalence continues to rise, driven by increasing rates of obesity, physical inactivity, and population aging. In addition to its core metabolic abnormalities, T2DM is associated with a spectrum of complications, including both microvascular and macrovascular diseases, which significantly contribute to the overall morbidity and mortality in affected individuals. Among these, atherosclerotic cardiovascular diseases, including coronary artery disease and carotid artery disease, are of paramount concern[1-3].
Carotid plaque, a hallmark of subclinical atherosclerosis, marks a pivotal stage in the progression of cardiovascular disease and is linked to increased risks of stroke and myocardial infarction[4,5]. In patients with T2DM, chronic hyperglycemia, dyslipidemia, oxidative stress, and systemic inflammation accelerate plaque formation. Early identification and risk stratification are crucial for timely intervention[6,7]. The pathogenesis of carotid plaque is multifactorial, involving traditional risk factors such as hypertension, dyslipidemia, smoking, and obesity, as well as T2DM-specific mechanisms, including glycemic variability, insulin resistance, advanced end-glycation products (AGEs), endothelial dysfunction, and chronic inflammation. However, the relative contribution and interaction of these factors in pathogenesis remain inadequately defined. Moreover, population-specific differences in risk profiles underscore the need for regionally tailored investigations to guide preventive strategies[8,9].
Recent studies have highlighted the critical role of biomarkers in predicting cardiovascular risk in T2DM patients, including those with carotid atherosclerosis[10-12]. However, few studies have integrated these biomarkers with conventional risk markers, such as glycemic control and renal function, to provide a comprehensive risk assessment for carotid plaque formation. This gap underscores the need for a holistic approach to identifying early predictors of atherosclerosis in T2DM patients. We hypothesize that the integration of metabolic, renal, and vascular biomarkers can improve the prediction of carotid plaque formation in T2DM patients beyond traditional risk factors alone. This study aims to systematically identify and evaluate demographic, metabolic, and renal risk factors independently associated with carotid plaque formation in patients with T2DM. By applying comprehensive statistical analyses, including correlation, regression, and receiver operating characteristic (ROC) curve evaluation, the study seeks to clarify the predictive value of specific biomarkers and support more accurate vascular risk stratification in this high-risk population.
MATERIALS AND METHODS
Study design
This retrospective cross-sectional study was conducted at our hospital to analyze the risk factors associated with carotid plaque formation in patients with T2DM. The study spanned from January 2021 to July 2024. Eligible participants were adults aged 18 years or older with a confirmed diagnosis of T2DM based on the American Diabetes Association criteria. Inclusion required the availability of carotid ultrasonography data, including clear measurements of intima-media thickness (IMT) and carotid plaque characteristics, as well as comprehensive and reliable medical records containing demographic information, glycemic control parameters [glycated hemoglobin (HbA1c), fasting glucose], and other relevant laboratory results. Written informed consent was obtained from all participants or their legal representatives. Exclusion criteria included a history of malignancies, chronic kidney disease requiring dialysis, or severe liver dysfunction (Child-Pugh C). Patients with type 1 diabetes mellitus, gestational diabetes, or other secondary forms of diabetes, as well as those with autoimmune diseases or systemic inflammatory disorders, such as rheumatoid arthritis or lupus, were excluded (to minimize the confounding effects of systemic inflammation and focus specifically on the role of metabolic and renal factors in carotid plaque formation in patients with T2DM). Additionally, patients with a recent history of cardiovascular events, including myocardial infarction, stroke, or carotid revascularization within the past three months, were not eligible for the study.
A total of 266 participants were enrolled and divided into two groups: The control group (n = 158) comprised patients with T2DM alone, while the observation group (n = 108) included patients with T2DM and confirmed carotid plaque (Figure 1). The research design and protocols adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and received approval from the Ethics Committee of the Second Affiliated Hospital of Zhejiang Chinese Medical University, ensuring compliance with ethical standards and the protection of participant rights[13].
Figure 1 Strengthening the Reporting of Observational Studies in Epidemiology flow diagram of participant selection and group classification.
T2DM: Type 2 diabetes mellitus.
Measurement of carotid artery indicators
Carotid IMT was measured to assess arterial wall changes. Measurements were taken from the leading edge of the first echogenic line (representing the lumen-intima interface) to the leading edge of the second echogenic line (representing the collagen-rich upper layer of the adventitia). IMT values were recorded at the carotid sinus, 10 mm proximal to the bifurcation in the common carotid artery, and 10 mm distal to the bifurcation in the internal carotid artery. The mean of these measurements was calculated to determine the final IMT value for each participant. An IMT ≥ 1.5 mm was defined as indicative of carotid plaque formation[14]. All carotid ultrasonography examinations in this study were performed by two experienced sonographers, each with more than three years of clinical practice in vascular ultrasound. The sonographers were blinded to all participants’ clinical and biochemical data throughout the procedure. In addition, all sonographers involved in the study received regular training and calibration to ensure standardized imaging protocols and maintain high measurement reliability.
Collection and analysis of clinical and biochemical data
Comprehensive demographic, anthropometric, and biochemical data were collected for all participants. Information on age, sex, body mass index (BMI), and blood pressure was documented. After a 12-hour overnight fast, 15 mL of venous blood was drawn from the antecubital vein, and 1.5 mL of midstream morning urine was collected. Biochemical analyses included the measurement of total cholesterol, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), serum uric acid (SUA), urea, and serum creatinine (Scr). Fasting plasma glucose (FPG) was determined using the glucose oxidase method, and HbA1c was measured via immunoinhibition assay. Fasting insulin (FIns) and fasting C-peptide levels were also evaluated. Urine protein (UAlb) and urinary creatinine (Ucr) levels were assessed to calculate the urinary albumin-to-creatinine ratio (UACR), defined as UAlb/Ucr. The homeostasis model assessment for insulin resistance (HOMA-IR) was calculated as HOMA-IR = (FPG × FIns)/22.5. These data provided a comprehensive profile of metabolic, renal, and cardiovascular risk factors, essential for further analysis.
Statistical analysis
Data was analyzed using SPSS software (Version 27.0; IBM Corp., Armonk, NY, United States). Continuous variables with a normal distribution were compared between groups using independent sample t-tests, with results reported as mean ± SD. Categorical variables were summarized as frequencies and percentages, and comparisons or associations were assessed using χ² tests. When χ² test assumptions were not met, Fisher’s exact test was applied for precise probability analysis. Pearson correlation analysis was conducted to examine the relationships between carotid plaque and other parameters in patients with T2DM. Logistic regression analysis was employed to identify factors influencing carotid plaque formation in T2DM patients. ROC curve analysis was performed to evaluate the predictive value of SUA and the UACR for carotid plaque development in this population. All statistical tests were two-tailed, and a P value of less than 0.05 was considered to indicate statistical significance.
RESULTS
Comparison of demographic, clinical, and biochemical characteristics between control and observation groups
The comparison between the control group (n = 158) and the observation group (n = 108) revealed significant differences in several key parameters. Patients in the observation group were older (P = 0.002) and had a higher BMI (P = 0.004), suggesting a potential association between age, adiposity, and carotid plaque formation. Biochemical markers showed notable differences. Glycemic control was poorer in the observation group, with higher levels of HbA1c (P = 0.006) and FPG (P = 0.034). Markers of renal function, including Scr and SUA, were significantly elevated in the observation group (both P < 0.001), alongside a higher UACR (P < 0.001), indicating metabolic and renal dysfunction. Carotid IMT, a key marker of atherosclerosis, was markedly higher in the observation group (P < 0.001). In contrast, parameters such as TG, LDL-C, and blood pressure showed no significant differences between the groups (Table 1). These findings suggest that carotid plaque formation in patients is driven by a combination of metabolic and vascular factors, emphasizing the need for integrated clinical assessments.
Table 1 Comparison of demographic, clinical, and biochemical characteristics between the control and observation groups.
Parameter
Control group (n = 158)
Observation group (n = 108)
Statistics (t/χ2)
P value
Sex (M/F)
70/88
50/58
0.103
0.748
Age (years)
63.85 ± 8.12
67.10 ± 8.05
3.217
0.002
FC-P (ng/mL)
3.10 ± 1.70
3.18 ± 2.25
0.330
0.742
DBP (mmHg)
82.30 ± 10.50
84.10 ± 9.10
1.448
0.149
TG (mmol/L)
2.05 ± 1.10
2.20 ± 1.30
1.014
0.312
HOMA-IR
4.35 ± 6.20
5.00 ± 6.15
0.842
0.400
FIns (mU/mL)
12.10 ± 12.30
14.15 ± 16.25
1.170
0.243
LDL-C (mmol/L)
3.02 ± 0.90
2.85 ± 1.00
1.446
0.149
BMI (kg/m²)
26.10 ± 3.40
27.45 ± 4.20
2.887
0.004
TC (mmol/L)
4.90 ± 1.15
4.68 ± 1.20
1.505
0.133
SBP (mmHg)
131.20 ± 16.50
135.02 ± 17.80
1.796
0.074
HDL-C (mmol/L)
1.12 ± 0.32
1.04 ± 0.24
2.208
0.028
HbA1c (%)
7.30 ± 1.70
7.90 ± 1.80
2.760
0.006
UR (mmol/L)
5.42 ± 1.34
5.95 ± 1.60
2.926
0.004
FPG (mmol/L)
7.82 ± 3.22
8.70 ± 3.42
2.134
0.034
Scr (μmol/L)
62.50 ± 13.40
70.20 ± 22.50
3.492
< 0.001
SUA (μmol/L)
320.28 ± 76.06
355.16 ± 92.56
3.360
< 0.001
UACR (mg/g)
1.05 ± 0.40
1.36 ± 0.50
5.602
< 0.001
IMT (mm)
1.06 ± 0.12
2.25 ± 1.00
14.82
< 0.001
Correlation between IMT and clinical indicators
The Pearson correlation analysis revealed significant associations between IMT and several clinical indicators, emphasizing the multifactorial nature of carotid atherosclerosis. Age showed the strongest positive correlation with IMT (r = 0.235, P < 0.001), suggesting that vascular aging plays a critical role in arterial wall thickening. Similarly, Scr (r = 0.232, P < 0.001) and UACR (r = 0.283, P < 0.001) were strongly correlated with IMT, highlighting the impact of renal dysfunction on vascular health. Metabolic parameters also demonstrated significant associations. FPG (r = 0.201, P = 0.002), HbA1c (r = 0.164, P = 0.014), and SUA (r = 0.188, P = 0.005) were positively correlated with IMT, indicating that poor glycemic control and metabolic disturbances contribute to atherosclerosis. Among lipid parameters, HDL-C was inversely correlated with IMT (r = -0.152, P = 0.023), reinforcing its protective role against vascular changes. In contrast, factors such as TG, LDL-C, and BMI showed no statistically significant correlation with IMT (P > 0.05). Blood pressure parameters exhibited mixed results, with systolic blood pressure (SBP) showing a weak but significant positive correlation (r = 0.144, P = 0.035), while diastolic blood pressure (DBP) had no meaningful association (P = 0.943; Table 2). Overall, these findings underscore the interplay between aging, metabolic dysregulation, renal function, and vascular health in the progression of carotid atherosclerosis, suggesting that addressing these factors may mitigate the risk of advanced vascular pathology.
Table 2 Pearson correlation analysis of intima-media thickness with other indicators.
Item
IMT (r)
P value
DBP
0.006
0.943
FC-P
0.01
0.901
TG
0.028
0.671
HOMA-IR
0.035
0.612
Fins
0.049
0.489
LDL-C
0.088
0.198
TC
-0.091
0.174
BMI
0.098
0.162
SBP
0.144
0.035
HDL-C
-0.152
0.023
HbA1c
0.164
0.014
UR
0.171
0.011
SUA
0.188
0.005
FPG
0.201
0.002
Age
0.235
< 0.001
Scr
0.232
< 0.001
UACR
0.283
< 0.001
Independent risk factors for increased IMT
Multivariate logistic regression analysis identified several independent factors significantly associated with increased IMT, emphasizing the multifactorial nature of carotid atherosclerosis. Metabolic indicators, including FPG [β = 0.091, odds ratio (OR) = 1.096, 95%CI: 1.006-1.192, P = 0.039] and HbA1c (β = 0.210, OR = 1.234, 95%CI: 1.057-1.445, P = 0.009), were strongly associated with IMT, highlighting the impact of poor glycemic control on vascular changes. Higher mean age was another risk factor (β = 0.049, OR = 1.050, 95%CI: 1.015-1.087, P = 0.005). Renal function markers were also significant predictors. Scr (β = 0.028, OR = 1.029, 95%CI: 1.011-1.046, P = 0.001) and UACR (β = 0.024, OR = 1.024, 95%CI: 1.010-1.037, P < 0.001) demonstrated strong associations, underscoring the role of renal dysfunction in promoting vascular pathology. SUA (β = 0.006, OR = 1.006, 95%CI: 1.003-1.009, P = 0.003) was also a significant factor, further linking metabolic disturbances to carotid atherosclerosis. Lipid metabolism played a dual role, with HDL-C showing a protective effect (β = -1.112, OR = 0.329, 95%CI: 0.119-0.917, P = 0.026), while SBP exhibited a modest but significant association with increased IMT (β = 0.014, OR = 1.018, 95%CI: 1.002-1.034, P = 0.038). Higher serum urea level also emerged as a significant predictor (β = 0.240, OR = 1.271, 95%CI: 1.062-1.529, P = 0.012), suggesting a link between nitrogen metabolism and vascular changes (Table 3).
Table 3 Multivariate logistic regression analysis of variables associated with intima-media thickness.
Factors
β value
SE value
Wald value
OR value
95%CI for OR
P value
FPG
0.091
0.044
4.278
1.096
1.006-1.192
0.039
SBP
0.014
0.009
2.325
1.018
1.002-1.034
0.038
HDL-C
-1.112
0.528
4.978
0.329
0.119-0.917
0.026
UR
0.240
0.108
1.788
1.271
1.062-1.529
0.012
HbA1c
0.210
0.081
6.986
1.234
1.057-1.445
0.009
Age
0.049
0.020
7.823
1.050
1.015-1.087
0.005
SUA
0.006
0.002
8.567
1.006
1.003-1.009
0.003
Scr
0.028
0.012
1.872
1.029
1.011-1.046
0.001
UACR
0.024
0.007
11.210
1.024
1.010-1.037
< 0.001
Predictive value of UACR and SUA for carotid plaque formation
ROC curve analysis was performed to evaluate the predictive value of UACR and SUA for carotid plaque formation in patients with T2DM. The AUC for UACR was 0.718 (95%CI: 0.656-0.811), indicating a relatively low predictive capability. The optimal cutoff value for UACR was determined to be 15.118 mg/g, with a sensitivity of 68.1% and a specificity of 72.5%, suggesting its potential as a possible marker for carotid atherosclerosis. However, the AUC for SUA was 0.651 (95%CI: 0.578-0.736), reflecting a low predictive ability when a cutoff value was 351.2 mmol/L, yielding a sensitivity of 58.6% and a specificity of 68.3% only. The area under the ROC curves for UACR (0.718) and SUA (0.651) indicates only moderate discriminatory ability for carotid plaque detection; given their limited sensitivity and specificity, these biomarkers are more appropriately used as adjunctive risk-stratification tools rather than definitive diagnostic tests (Figure 2).
Figure 2 Receiver operating characteristic curves for urinary albumin-to-creatinine ratio and serum uric acid in predicting carotid plaque formation in patients with type 2 diabetes mellitus.
UACR: Urinary albumin-to-creatinine ratio; SUA: Serum uric acid.
DISCUSSION
This study provides an analysis of the risk factors associated with carotid plaque formation in patients with T2DM, a population at high risk for cardiovascular events. The key metabolic and renal biomarkers, such as FPG, HbA1c, SUA, and UACR, which independently correlate with carotid plaque formation were analyzed in this research. The study highlights the importance of integrated clinical assessments that combine metabolic control, renal function, and vascular health to predict the progression of atherosclerosis in T2DM patients[15,16]. By focusing on these specific biomarkers, this research advances the understanding of the multifactorial nature of carotid atherosclerosis, particularly the interaction between glycemic control, renal dysfunction, and lipid metabolism. Elevated UACR, emerged as probable predictor of carotid plaque formation, with a low predictive power. This can complement other clinical indicators to provide a more comprehensive risk profile for T2DM patients. Given that carotid atherosclerosis is often asymptomatic until advanced stages, early identification of at-risk individuals through various biomarkers could possibly help in the timely implementation of preventive strategies[17,18]. These strategies may include tighter glycemic control, monitoring of renal function, and tailored interventions to mitigate vascular complications, ultimately reducing the burden of cardiovascular diseases in T2DM patients.
Higher age was associated with an increased risk of excess carotid IMT, reflecting the well-established influence of vascular aging-endothelial dysfunction, arterial stiffening, and reduced reparative capacity-all of which drive atherosclerosis. The higher BMI observed in the observation group further highlights the role of adiposity, which promotes systemic inflammation and metabolic dysregulation, creating a pro-atherogenic state conducive to carotid plaque formation. Strong associations between glycemic markers, particularly FPG and HbA1c, and IMT underscore the pivotal role of poor glycemic control[19,20]. Chronic hyperglycemia exacerbates endothelial dysfunction via oxidative stress, advanced glycation end-products, and inflammatory pathways, thereby accelerating atherogenesis. Renal function markers, including Scr and UACR, also demonstrated some correlations with higher IMT and were identified as potential predictors of plaque formation. Renal dysfunction in T2DM is frequently associated with endothelial damage, altered hemodynamics, and systemic inflammation, all of which contribute to vascular remodeling. High UACR, showed some predictive value, supporting its utility as a potential non-invasive biomarker for early vascular damage in diabetes. These findings collectively highlight the need for integrated metabolic, renal, and vascular assessments to better stratify cardiovascular risk in T2DM patients.
High SUA also showed a probable association with carotid plaque formation, reflecting its role in metabolic disturbances. Elevated SUA levels contribute to vascular inflammation, oxidative stress, and endothelial dysfunction, promoting atherosclerosis. While SUA only a lower predictive ability compared to UACR, it might be considered a probable indicator in diabetic populations. HDL-C was inversely correlated with IMT, supporting its protective role in vascular health through reverse cholesterol transport and anti-inflammatory effects, which reduce atherosclerosis risk[21]. SBP showed a weak but significant association with IMT, indicating hemodynamic stress contributing to arterial thickening, while the lack of correlation with DBP suggests more complex pressure dynamics in plaque formation. ROC analysis indicated that UACR demonstrated limited discriminative capacity (AUC = 0.718) and SUA exhibited low discriminative capacity (AUC = 0.651). Although these values preclude their use as standalone diagnostic tests, UACR remains a valuable indicator of renal microvascular injury, while SUA provides additional prognostic insight when incorporated into a multifactorial risk model[22,23]. Combined with other metabolic and vascular biomarkers, they reflect the synergistic contributions of insulin resistance, endothelial dysfunction, and oxidative stress to carotid plaque development[24,25]. The inverse relationship with HDL-C underscores the importance of lipid management in reducing cardiovascular risk.
Our study underscores the modest discriminatory performance of UACR (AUC = 0.718) and the low performance of SUA (AUC = 0.651) for carotid plaque detection in T2DM. These values indicate that, while neither marker suffices alone, UACR reflects renal microvascular injury and SUA captures metabolic imbalance-both contributing incremental information when combined with other clinical indicators. Liu et al[26] demonstrated heightened cardiovascular risk in T2DM patients with ischemic heart failure, consistent with our emphasis on vascular vulnerability. Wågen Hauge et al[27] reported that bariatric surgery ameliorates metabolic and cardiovascular outcomes, whereas our noninvasive panel offers a pragmatic approach to early risk stratification without procedural burden. Yin et al[28] identified the uric acid-to-albumin ratio as a predictor of carotid atherosclerosis; our work extends this by integrating UACR, albeit with only moderate predictive ability, to provide a more comprehensive risk profile. Collectively, these findings support a multimarker strategy - acknowledging individual limitations - for enhanced early detection of carotid atherosclerosis in T2DM.
This retrospective study has several limitations. The reliance on previously recorded data may omit relevant variables and unmeasured confounders, and the relatively homogeneous study population limits generalizability. Reliable information on diabetes duration was not consistently available in the medical records, precluding its evaluation as a risk factor for carotid atherosclerosis. Although UACR and SUA demonstrated some predictive value for carotid plaque formation, their sensitivity and specificity were suboptimal/poor; future research should integrate these biomarkers into multivariable risk models or combine them with emerging markers to improve diagnostic performance. Furthermore, lifestyle factors (e.g., diet, physical activity, smoking) and hemodynamic parameters (e.g., pulse pressure, antihypertensive treatment) should be systematically assessed in future studies to enhance the comprehensiveness of cardiovascular risk evaluation. Prospective, longitudinal studies with larger, more diverse populations are warranted to elucidate the progression of carotid atherosclerosis and refine risk stratification in T2DM patients.
CONCLUSION
This retrospective study suggests that age, BMI, poor glycemic control (HbA1c, FPG), renal dysfunction (Scr, UACR), and metabolic disturbances (HDL-C) could be associated with carotid plaque formation in patients with T2DM. These findings might support the use of comprehensive risk assessments and targeted strategies to potentially reduce vascular complications in this high-risk population.
ACKNOWLEDGEMENTS
We sincerely appreciate the support of all clinical research personnel in our laboratory.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade A, Grade A, Grade B, Grade B, Grade C
Novelty: Grade B, Grade B, Grade B
Creativity or Innovation: Grade B, Grade B, Grade B
Scientific Significance: Grade B, Grade B, Grade B
P-Reviewer: Pappachan JM; Wu H; Zhang J; Zhou LL S-Editor: Li L L-Editor: A P-Editor: Xu ZH
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