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Abstract

Background

This study aims to investigate the role of the total cholesterol to high-density lipoprotein cholesterol ratio (THR) in predicting carotid stenosis in patients with acute ischemic stroke (AIS), aiming to establish its clinical utility in risk assessment and therapeutic decision-making.

Methods

180 patients with AIS and concomitant carotid stenosis were classified into three groups (classified as mild [<50%], moderate [50-70%], or severe [≥70%]) based on stenosis severity. These groups were compared to 180 healthy controls. Carotid stenosis was diagnosed in all participants using digital subtraction angiography, with the severity graded accordingly. THR and lipid profiles were analyzed within 24 h of admission. Statistical analyses included ANOVA, ROC curve evaluation, and multivariate regression to determine THR's predictive performance for stenosis severity.

Results

THR levels were significantly elevated in AIS patients versus controls (3.80 ± 1.07 vs 3.02 ± 0.67; p < 0.05) and correlated with stenosis severity (mild: 3.22 ± 0.85; moderate:3.86 ± 0.91; severe: 4.33 ± 1.16; p < 0.05). ROC analysis demonstrated robust diagnostic accuracy (AUC: 0.807; 95% CI: 0.744-0.870), with a cutoff of 4.15 yielding 81.1% sensitivity and 95.6% specificity for severe stenosis.

Conclusions

THR is a cost-effective, independent biomarker for carotid stenosis severity in AIS; its clinical use improves risk stratification and guides personalized care.

Keywords

THR stroke cerebral infarction DSA carotid artery stenosis

Introduction

Ischemic stroke, accounting for 70%–80% of all strokes, remains a leading global cause of mortality and disability, with carotid atherosclerosis contributing to 15%–30% of cases.^1–3 Carotid stenosis severity, traditionally assessed via imaging modalities such as digital subtraction angiography (DSA), is a well-established predictor of stroke recurrence and therapeutic decision-making.⁴ However, current risk stratification heavily relies on anatomical measurements (eg, stenosis ≥70%), which inadequately capture the dynamic interplay of lipid metabolism, inflammation, and endothelial dysfunction driving atherosclerosis progression. While lipid profiles—including low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C)—are routinely evaluated, their isolated predictive value for cerebrovascular events remains suboptimal.⁵ This underscores the need for biomarkers that reflect the multifaceted pathophysiology of carotid atherosclerosis.

The total cholesterol to high-density lipoprotein cholesterol ratio (THR) is a well-established lipid index and has been proposed as an alternative marker for assessing lipid abnormalities linked to atherosclerosis.⁶ It reflects the balance between cholesterol carried by atherogenic lipoproteins and cholesterol transported by antiatherogenic high-density lipoprotein particles.⁶ A higher THR is associated with an increased risk of atherosclerotic cardiovascular diseases.⁷ Growing evidence supports THR as a predictor for diabetes, prediabetes, chronic kidney disease, and cardiovascular events.⁸ Furthermore, existing evidence reports a link between elevated THR and an increased risk of stroke.⁹ The question of whether THR constitutes an independent risk factor for stroke has yet to be definitively resolved.

This study hypothesizes that THR, reflecting both atherogenic burden (TC) and vascular protection (HDL-C), may serve as a novel biomarker for carotid stenosis severity in acute ischemic stroke (AIS). To test this, we conducted a prospective observational study comparing THR levels across 180 AIS patients stratified by stenosis severity (mild [<50%], moderate [50-70%], severe [≥70%]) and 180 age-matched controls. By integrating DSA-confirmed stenosis grading with biochemical profiling, we aimed to:Establish the association between THR and stenosis severity;Determine THR's diagnostic accuracy using ROC analysis;Evaluate its independence from confounding factors (eg, hypertension, diabetes).

Materials and Methods

Patient Enrollment

Between January 2023 and June 2024, 180 patients with AIS (62.7% male, 37.3% female) were enrolled at the Department of Cerebrovascular Disease, Hangzhou Third People's Hospital. The patients had a mean age of 67.58 ± 9.34 years. A control group of 180 healthy volunteers (62.2% male, 37.8% female) was also included, with an average age of 67.86 ± 10.03 years. This study was approved by the Ethics Committee of Hangzhou Third People's Hospital and adhered to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants and/or their legal guardians.

Magnetic resonance imaging (MRI), brain computed tomography, and physical examination were used to diagnose all patients and healthy participants. Age, sex, history of alcohol and/or tobacco use, and the presence of diabetes and/or hypertension were among the clinical and demographic information gathered from both groups. Participants with a history of transient ischemic attack or hemorrhagic stroke, among other cerebrovascular disorders, were not allowed to participate. Additionally excluded were patients who had received endovascular intervention or thrombolysis as treatment for AIS.

Inclusion Criteria

Subjects must meet the following criteria to be enrolled:

First-ever diagnosis of AIS confirmed by head CT or MRI imaging.

Presence of concomitant carotid artery stenosis confirmed by DSA.

Age range of 18 to ≤85 years.

Exclusion Criteria

Subjects will be excluded if they:

Have a history of prior stroke (ischemic/hemorrhagic) or transient ischemic attack (TIA).

Have other cerebrovascular abnormalities (eg, aneurysms, arteriovenous malformations).

Have undergone thrombolytic therapy or endovascular interventions for acute cerebral infarction.

Suffer from severe comorbidities (eg, malignancies, end-stage renal disease, or decompensated hepatic dysfunction) that may influence lipid metabolism or blood markers.

Are pregnant or lactating.

Have used medications affecting cholesterol metabolism (eg, statins, cholesterol absorption inhibitors) within six months prior to the study.

Refuse to provide informed consent or have concerns about the confidentiality terms.

Digital Subtraction Angiography and Grouping

DSA was used to evaluate the degree of carotid artery stenosis in accordance with accepted standards. DSA is an invasive imaging method that creates detailed images of the cerebral vasculature by injecting a contrast agent into the circulation and then using radiography techniques. Qualified interventional neurologists carried out the procedure in a catheterization lab, usually using the right femoral or radial artery to reach the vasculature. Two skilled interventional neurology specialists assessed the angiographic data to guarantee precision and consistency. “DSA is widely utilized to assess the location and severity of cerebral artery stenosis in patients with AIS, and it is considered the gold standard for diagnosing this condition. Based on the degree of stenosis in the carotid artery, patients were classified into three categories: severe (≥70%), moderate (≥50% and <70%), and mild (<50%) stenosis (Figures 1 and 2).¹⁰

Figure 1.

DSA Showing Moderate Stenosis (∼60%) of the Left Internal Carotid Artery at the Origin.

Figure 2.

DSA Showing Severe Stenosis (∼95%) of the Left Internal Carotid Artery at the Origin.

Data Collection and Biochemical Parameter Testing

Blood samples were collected from all participants within 24 h of admission after an overnight fast (at least 8 h), ensuring the collection of fasting venous blood samples. The collected blood samples were promptly transported to the hospital's clinical laboratory for processing to ensure sample quality and the accuracy of the test results. Biochemical parameters were analyzed using the hospital's fully automated biochemical analyzer (Architect Plus C8000, Abbott, USA) in this study.

Statistical Analysis

Statistical analyses were performed using SPSS software (International Business Machines Corp., Armonk, NY, USA). The Kolmogorov–Smirnov test was employed to assess the normality of the variables. For normally and non-normally distributed variables, an independent samples t-test and the Wilcoxon rank-sum test were used, respectively. Continuous variables were expressed as mean ± standard deviation and categorical variables were presented as frequency and percentage. Between-group comparisons were conducted using the t-test or Mann–Whitney U test for continuous variables and the chi-square test or Fisher's exact test for categorical variables. The predictive efficiency of THR for carotid artery stenosis based on different criteria was evaluated using receiver operating characteristic (ROC) curve analysis. A p-value < 0.05 was considered statistically significant.

Results

Characteristics of Patients with AIS

Table 1 details the baseline and clinical characteristics of 180 patients with AIS and concomitant carotid artery stenosis, as well as 180 healthy controls. No statistically significant differences were observed between the two groups regarding age, sex, hypertension, diabetes, BMI, (TC), (TG), (HDL-C), (LDL-C), (CRP), (apoA) and (apoB) (all p > 0.05).

Table 1.

The Detailed Clinical Information of Healthy Volunteers and Acute Ischemic Stroke Patients. (Mean ± SD).

Characters	Mild Stenosis(60)	Moderate Stenosis(60)	Severe Stenosis(60)	Healthy Volunteers(180)	P
Gender
Male[n(%)]	36 (60%)	37 (61.6%)	40 (66.7%)	112 (62.2%)	0.212
Female[n(%)]	24 (40%)	23 (38.4%)	20 (33.3%)	68 (37.8%)	0.306
Age(years)	66.03 ± 8.47	66.76 ± 10.16	68.97 ± 9.37	67.86 ± 10.03	0.236
BMI	20.12 ± 2.31	21.03 ± 1.92	20.67 ± 2.52	20.55 ± 2.24	0.312
Hypertension[n(%)]	30 (50%)	32 (53.3%)	29 (48.3%)	89 (49.4%)	0.439
Diabetes[n(%)]	19 (31.7%)	22 (36.7%)	23 (38.3%)	62 (34.4%)	0.324
Smoking[n(%)]	18 (30%)	21 (35%)	20 (33.3%)	56 (31.1%)	0.078
Drinking[n(%)]	18 (30%)	16 (26.7)	15 (25%)	46 (25.5%)	0.455
Vascular diseases[n(%)]	5 (8.3%)	5 (8.3%)	6 (10%)	8 (4.4%)	0.672
Neutrophil	3.88 ± 1.93	4.62 ± 1.57	4.58 ± 2.05	3.89 ± 1.61	0.126
Monocyte	0.46 ± 0.17	0.46 ± 0.16	0.50 ± 0.15	0.41 ± 0.18	0.352
CRP(mg/L)	3.67 ± 1.07	2.98 ± 1.62	3.32 ± 1.45	3.43 ± 1.39	0.492
TG(mmol/L)	1.21 ± 0.64	1.39 ± 0.89	2.25 ± 2.23	1.10 ± 0.61	0.202
TC(mmol/L)	3.94 ± 1.04	4.28 ± 1.03	4.58 ± 1.42	4.21 ± 1.19	0.263
LDL(mmol/L)	2.43 ± 0.95	2.80 ± 0.93	3.04 ± 1.13	2.58 ± 1.00	0.119
HDL(mmol/L)	1.24 ± 0.27	1.13 ± 0.24	1.07 ± 0.28	1.41 ± 0.37	0.267
apoA(mmol/L)	1.22 ± 0.22	1.16 ± 0.26	1.17 ± 0.29	1.38 ± 0.28	0.592
apoB(mmol/L)	0.75 ± 0.21	0.87 ± 0.23	0.90 ± 0.24	0.77 ± 0.21	0.278
apoA/apoB	1.74 ± 0.58	1.42 ± 0.55	1.36 ± 0.43	1.88 ± 0.61	0.532
THR	3.22 ± 0.85	3.86 ± 0.91	4.33 ± 1.16	3.02 ± 0.67	0.032

THR Levels

Figure 3 shows that the THR levels were significantly higher in patients with AIS and carotid artery stenosis than in healthy controls (3.80 ± 1.07 vs 3.02 ± 0.67 ; p < 0.05, 95% CI 0.51-1.04). Additionally, Figure 4 illustrates that THR levels were significantly higher in patients with severe stenosis than in those with mild or moderate stenosis (3.22 ± 0.85 vs 3.86 ± 0.91 vs 4.33 ± 1.16; p < 0.05, 95% CI 0.68-1.54).

Figure 3.

The THR Level in Patients with Acute Ischemic Stroke and Carotid Artery Stenosis is Significantly Higher Than That in Healthy Volunteers.

Figure 4.

The THR Level in Patients with Severe Stenosis is Higher than That in Patients With Mild Stenosis, and the THR Level in Patients With Moderate Stenosis is Higher than That in Patients With Mild Stenosis.

Multivariable Regression and ROC Curve Analysis

To evaluate the independent predictive value of THR after adjusting for potential confounders, we performed a multivariable logistic regression analysis. The model was adjusted for age, sex, hypertension, diabetes mellitus, smoking history, alcohol consumption, and BMI. After controlling for these variables, THR remained a significant predictor of the severity of carotid artery stenosis. For severe stenosis (≥70%), the adjusted odds ratio (OR) for THR was 4.15 (95% CI: 2.34-7.38, p < 0.001). ROC curve analysis demonstrated that the optimal cutoff value of THR for predicting severe carotid stenosis was 4.15, with a sensitivity of 81.1%, specificity of 95.6%, and an area under the curve (AUC) of 0.807 (95% CI: 0.744-0.870, p < 0.001) (Figure 5).

Figure 5.

The ROC Curve of THR Levels in Patients with AIS and Carotid Artery Stenosis. The Area Under the Curve (AUC) is 0.807 (95% CI 0.744-0.870, p < 0.001).

ROC Curve Comparison of Lipid Parameters

To evaluate the relative diagnostic value of THR versus established lipid markers, ROC analyses were performed for LDL-C alongside THR (Figure 6). For severe stenosis (≥70%), THR demonstrated superior discriminative power (AUC: 0.807, 95% CI: 0.744-0.870) compared to LDL-C(AUC: 0.658, 95% CI: 0.577-0.739). The optimal cutoff for THR remained 4.15 (sensitivity: 81.1%, specificity: 95.6%), while LDL-C(cutoff:3.65 mmol/L) showed lower sensitivity (68.9%) and specificity (82.1%).

Figure 6.

Receiver Operating Characteristic (ROC) Curve Comparison of THR and LDL-C for Predicting Severe Carotid Stenosis (≥70%) in Acute Ischemic Stroke Patients.

Discussion

Timely diagnosis and intervention in ischemic stroke are critical for improving patient outcomes and ensuring a favorable prognosis. Carotid artery stenosis is a major contributor to the risk of ischemic stroke, with its pathogenesis closely related to blood lipid levels.¹¹ While routine assessments of individual lipid markers—such as TG, TC, LDL, VLDL, and HDL—are commonly used to evaluate blood lipid status. This has led to the exploration of more robust lipid indices, such as the TC/HDL-C ratio (THR), which reflects overall lipid metabolic status and is recognized as a cardiovascular risk factor. THR represents the balance between serum TC and HDL-C, with HDL-C exhibiting antiatherosclerotic properties.^12,13 Elevated THR levels may indicate lipid metabolic dysfunction and an increased risk of atherosclerosis, making it a potential biomarker for ischemic stroke associated with carotid artery stenosis.

We hypothesize that the relationship between THR and ischemic stroke with carotid artery stenosis involves multiple mechanisms. First, elevated TC or reduced HDL-C levels are well-established risk factors for atherosclerosis, characterized by the accumulation of lipid plaques on vessel walls, resulting in the narrowing and hardening of the arteries.^14,15 Second, elevated THR is often linked to increased inflammation and oxidative stress, both of which are critical in atherosclerosis development.^16–18 Inflammation damages endothelial cells and accelerates lipid plaque deposition, while oxidative stress aggravates vascular wall damage, further advancing arterial stiffness. Finally, elevated THR may also contribute to endothelial dysfunction, impairing the regulation of vascular tone and increasing the risk of atherosclerosis and thrombosis.^19,20

Our results demonstrate that patients with AIS and carotid artery stenosis exhibit higher levels of THR, indicating its potential predictive value for this condition. Previous studies have shown that abnormal increases in THR are associated with an elevated risk of cardiovascular diseases.²¹ Notably, our study is the first to reveal that patients with ischemic stroke caused by carotid artery stenosis have significantly higher THR levels compared to healthy controls (p < 0.05), with particular emphasis on acute infarction cases. To the best of our knowledge, this is the first study to demonstrate a positive correlation between THR levels and the degree of carotid artery stenosis in patients with AIS. Furthermore, our findings indicate that patients with carotid artery stenosis have significantly higher THR levels than healthy controls, and those with elevated THR levels exhibit more severe carotid artery stenosis. These findings support the potential of THR as a predictive biomarker for assessing the severity of carotid artery stenosis in patients with ischemic stroke. This insight enhances our understanding of the progression of ischemic stroke and provides a basis for early diagnosis and personalized treatment strategies. Patients with elevated THR levels may benefit from early intervention and ongoing monitoring, which could slow disease progression and improve quality of life.

Our study also shows that THR is an independent predictor of carotid artery stenosis severity. As a simple and accessible inflammatory marker, THR may aid in predicting the severity of carotid artery stenosis in patients with ischemic stroke. The sensitivity and specificity of THR for diagnosing ischemic stroke with carotid artery stenosis are 81.1% and 95.6%, respectively. ROC curve analysis further supports THR as a promising predictive biomarker for carotid artery stenosis severity in patients with ischemic stroke patients. In addition to demonstrating the association between THR and carotid stenosis severity, our study further highlights the superior diagnostic performance of THR compared to traditional lipid parameters such as LDL-C. As shown in Figure 6, THR exhibited a significantly higher AUC (0.807, 95% CI: 0.744-0.870) than LDL-C (0.658, 95% CI: 0.577-0.739), indicating that THR has greater accuracy in identifying patients with severe stenosis (≥70%). THR integrates both atherogenic burden (TC) and vascular protection (HDL-C), offering a holistic view of lipid metabolism dysfunction. In contrast, LDL-C solely reflects atherogenic lipoprotein activity, explaining its inferior diagnostic performance. Our ROC analysis (Figure 6) confirms THR's superiority, aligning with its pathophysiological role in endothelial dysfunction and inflammation driving carotid atherosclerosis. These findings provide important insights into the potential clinical utility of THR as a diagnostic marker for ischemic stroke in the context of carotid artery stenosis.

Limitations

First, it is a single-center observational study with a relatively small sample size, which may introduce bias. Future studies should involve larger, multi-center cohorts to validate the clinical utility of THR in ischemic stroke with carotid artery stenosis. Additionally, although we confirmed the predictive potential of THR through ROC curve analysis, further long-term follow-up studies are required to investigate its role in stroke recurrence and long-term prognosis. Moreover, while our findings indicate a significant correlation between THR and carotid artery stenosis severity, future research should explore the combined use of THR with other clinical indicators (such as hemodynamic changes and imaging findings) to improve the accuracy of stroke diagnosis.

Conclusions

Our study reveals that THR is closely associated with carotid artery stenosis and serves as an independent predictor of severe stenosis. Unlike many other biomarkers, THR is inexpensive and easily accessible, making it a valuable tool for predicting severe carotid artery stenosis. The clinical use of THR as a predictive biomarker for the degree of carotid artery stenosis in ischemic stroke patients is highlighted by this study.

Footnotes

Acknowledgements

The authors thank the study participants.

ORCID iDs

Cong Wu

Yi Lv

Ethics Approval and Consent to Participate

Patient permission/consent declarations All patients signed an informed consent approved by the institutional Review Board.

Authors’ Contributions

CYZ is resposible for the guarantor of integrity of the entire study, study concepts, data acquisition, manuscript review; CW is resposible for the study design, experimental studies, manuscript editing; YL is resposible for the literature research, manuscript preparation; CHW is resposible for the statistical analysis and the clinical studies. All authors read and approved the final manuscript.

Funding

This project was supported by the Zhejiang Traditional Chinese Medicine Science and Technology Project, Hangzhou, Zhejiang, China [Grant number: 2022ZA136].

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of Data and Materials

All data and figures were obtained from clinical trials in our center and are absolutely true and valid, the data that support the findings of this study are available from the corresponding author upon reasonable request.

References

Feigin

Brainin

Norrving

, et al. World Stroke Organization (WSO): Global stroke fact sheet 2022. Int J Stroke. 2022;17(1):18-29.

Petty

Brown

Jr , et al. Ischemic stroke subtypes: a population-based study of incidence and risk factors. Stroke. 1999;30(12):2513-2516.

Rabinstein

. Update on treatment of acute ischemic stroke. Continuum: Lifel Learn Neurol. 2020;26(2):268-286.

Scalzo

Liebeskind

Medicine

MMI

. Perfusion angiography in acute ischemic stroke. Comput Math Methods Med. 2016;2016(1):2478324.

Millán

, Pintó X, Muñoz A, et al. Lipoprotein ratios: physiological significance and clinical usefulness in cardiovascular prevention. Vasc Health Risk Manag. 2009;5:757-765.

Zhou

Y-G

Tian

Xie

W-N

. Total cholesterol to high-density lipoprotein ratio and nonalcoholic fatty liver disease in a population with chronic hepatitis B. World J Hepatol. 2022;14(4):791.

Chu

S-Y

Jung

J-H

Park

M-J

, et al. Risk assessment of metabolic syndrome in adolescents using the triglyceride/high-density lipoprotein cholesterol ratio and the total cholesterol/high-density lipoprotein cholesterol ratio. Ann Pediatr Endocrinol Metab. 2019;24(1):41-48.

Xiao

Cao

Han

, et al. A non-linear connection between the total cholesterol to high-density lipoprotein cholesterol ratio and stroke risk: a retrospective cohort study from the China Health and Retirement Longitudinal Study. Eur J Med Res. 2024;29(1):175.

Hobson II

Mackey

Ascher

, et al. Management of atherosclerotic carotid artery disease: clinical practice guidelines of the Society for Vascular Surgery. J Vasc Surg. 2008;48(2):480-486.

10.

Hindy

Engström

Larsson

, et al. Role of blood lipids in the development of ischemic stroke and its subtypes: a Mendelian randomization study. Stroke. 2018;49(4):820-827.

11.

Che

Zhong

Zhang

, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22(1):34.

12.

Chen

Qin

, et al. Higher triglyceride to high-density lipoprotein cholesterol ratio increases cardiovascular risk: 10-year prospective study in a cohort of Chinese adults. J Diabetes Investig. 2020;11(2):475-481.

13.

Koba

Hirano

. Dyslipidemia and atherosclerosis. Nippon Rinsho. 2011;69(1):138-143.

14.

Zemel

Sowers

. Relation between lipids and atherosclerosis: Epidemiologie evidence and clinical implications. Am J Cardiol. 1990;66(21):7-12.

15.

Alemzadeh

Kichler

Disorders

. Comparison of apolipoprotein (ApoB/ApoA-1) and lipoprotein (total cholesterol/HDL) ratios in obese adolescents. Metab Syndr Relat Disord. 2018;16(1):40-45.

16.

Badimon

Storey

Vilahur

, et al. Update on lipids, inflammation and atherothrombosis. Thromb Haemostasis. 2011;105(S6):S34-S42.

17.

Koutsaliaris

Moschonas

Pechlivani

, et al. Inflammation, oxidative stress, vascular aging and atherosclerotic ischemic stroke. Curr Med Chem. 2022;29(34):5496-5509.

18.

Gimbrone

Jr García-cardeña

. Endothelial cell dysfunction and the pathobiology of atherosclerosis. Circ Res. 2016;118(4):620-636.

19.

Thorand

Baumert

Döring

, et al. Association of cardiovascular risk factors with markers of endothelial dysfunction in middle-aged men and women. Thromb Haemostasis. 2006;95(01):134-141.

20.

Guo

Chen

Hong

, et al. Comparison of the predicting value of neutrophil to high-density lipoprotein cholesterol ratio and monocyte to high-density lipoprotein cholesterol ratio for in-hospital prognosis and severe coronary artery stenosis in patients with ST segment elevation acute myocardial infarction following percutaneous coronary intervention: a retrospective study. J Inflamm Res. 2023;16:4541-4557.

21.

Yaghi

Elkind

. Lipids and cerebrovascular disease: research and practice. Stroke. 2015;46(11):3322-3328.

THR as a Novel Biomarker for Carotid Stenosis Severity in Acute Ischemic Stroke: Implications for Risk Stratification and Personalized Management

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Materials and Methods

Patient Enrollment

Inclusion Criteria

Exclusion Criteria

Digital Subtraction Angiography and Grouping

Data Collection and Biochemical Parameter Testing

Statistical Analysis

Results

Characteristics of Patients with AIS

THR Levels

Multivariable Regression and ROC Curve Analysis

ROC Curve Comparison of Lipid Parameters

Discussion

Limitations

Conclusions

Footnotes

Acknowledgements

ORCID iDs

Ethics Approval and Consent to Participate

Authors’ Contributions

Funding

Declaration of Conflicting Interests

Availability of Data and Materials

References