Abstract
Objective: To investigate the correlation between serum Beclin 1 levels and multi-vessel disease (MVD). Methods: A total of 169 participants were enrolled in this retrospective study. Based on coronary angiographic findings, 106 patients diagnosed with coronary heart disease (CHD) were categorized into single-vessel disease (SVD) and MVD groups, and their Gensini scores were calculated respectively. General demographic data and serum biochemical indicators were collected. Serum Beclin 1 concentration was detected by enzyme-linked immunosorbent assay. The differential expression of Beclin 1 among the three groups was compared and analyzed. Logistic regression models and receiver operating characteristic (ROC) curves were used to explore the association between Beclin 1 level and MVD. Results: Compared to the control group, Beclin 1 levels were significantly decreased in both the groups, with the lowest level observed in the MVD group [2.45 (1.69, 4.17), 2.64 (2.24, 2.89), 1.09 (0.66, 1.78), P<0.001]. Multivariate analysis results demonstrated that Gensini score was an independent risk factor for MVD (OR = 1.045, 95% CI: 1.018-1.090, P<0.001), while Beclin 1 (OR = 0.372, 95% CI: 0.174-0.695, P = 0.001) and low-density lipoprotein cholesterol (LDL-C) (OR = 0.295, 95% CI: 0.128-0.619, P<0.001) served as independent protective factors against MVD The combined model (Gensini score + Beclin 1 + LDL-C yielded an AUC of 0.936, with an optimal predictive probability cutoff of ≥0.664, a sensitivity of 0.911, and a specificity of 0.963. Compared to the basic model (Gensini score + LDL-C), the combined model exhibited a higher AUC, indicating that the addition of Beclin 1 effectively improved the predictive efficacy for MVD. Conclusion: The combination of Beclin 1 level, Gensini score, and LDL-C had good predictive value for MVD.
Keywords: Multi-vessel disease, Beclin 1, Gensini score
Introduction
Coronary heart disease (CHD) is one of the most prevalent cardiovascular diseases and the leading cause of chronic disease-related mortality worldwide. Multi-vessel disease (MVD) is defined as the presence of >50% stenosis in two or more coronary arteries, or left main coronary artery lesions confirmed by coronary angiography, which is frequently accompanied by chronic total occlusion or diffuse severe stenosis of coronary vessels [1]. Approximately 50%-60% of patients undergoing coronary revascularization are diagnosed with MVD. Compared to patients with single-vessel disease (SVD), MVD patients have a higher incidence of postoperative complications such as shock, pulmonary edema and atrioventricular block, along with significantly higher mortality [2]. Therefore, early identification and diagnosis of MVD are of great clinical significance.
The occurrence and progression of CHD are mainly attributed to the formation and development of coronary atherosclerotic plaques [3], involving multiple pathologic mechanisms including endothelial injury, inflammation, oxidative stress, glucose and lipid metabolism disorders, and thrombosis [4-6]. Studies have confirmed that autophagy participates in the formation and remodeling of coronary atherosclerotic plaques, and its regulatory effect is independent of traditional risk factors such as age, smoking and hypertension [7,8]. Autophagy dysfunction is closely associated with the progression of atherosclerosis (AS) and the stability of atherosclerotic plaques [9]. Beclin 1, a core regulatory protein of autophagy mainly localized to the endoplasmic reticulum, plays a pivotal role in the autophagy signaling pathway, regulating the initiation, extension and maturation of autophagosomes [10,11]. Existing studies have shown that Beclin 1 is closely related to AS progression and exerts anti-atherosclerotic effects, thereby delaying the onset and progression of CHD [12-14]. Phosphorylation modification of Beclin 1 can inhibit autophagosome formation in macrophages, further promoting the progression of AS [15]. Feng et al. [16] found that apelin-13 could promote cholesterol efflux from macrophage foam cells by activating the Class III PI3K/Beclin 1-mediated autophagic pathway, thereby alleviating the pathologic progression of atherosclerosis. The above evidence indicates that Beclin 1 possesses anti-atherosclerotic properties. However, the correlation between Beclin 1 and MVD remains unclear. Clarifying this association will provide new insight for the early diagnosis of MVD and the reduction of adverse cardiovascular mortality. Accordingly, this study aimed to explore the correlation between serum Beclin 1 level and MVD.
Materials and methods
Study population
This retrospective cohort study was conducted at Beijing University of Chinese Medicine Third Affiliated Hospital. Patients admitted with chest pain from August 2022 to February 2024 were consecutively enrolled. The inclusion criteria were as follows: (1) aged 18-80 years; (2) underwent coronary angiography; (3) complete clinical and imaging data available. The exclusion criteria were as follows: (1) complicated severe heart failure, rheumatic heart disease or other organic heart diseases; (2) history of malignant tumors; (3) severe hepatic and renal dysfunction; (4) recent severe cerebrovascular accidents; (5) mental disorders; (6) active infectious diseases; (7) incomplete key clinical data. This study was approved by the Ethics Committee of Beijing University of Chinese Medicine Third Affiliated Hospital.
Data collection
Relevant clinical data of enrolled patients were extracted from the hospital electronic medical record system, including general baseline information and laboratory biochemical test results.
Baseline clinical data included gender, age, height, weight, blood pressure, history of diabetes mellitus (DM) and smoking history. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). All participants underwent fasting venous blood collection (3 mL) in the early morning. Serum levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were detected routinely.
Detection of serum Beclin 1 level
During coronary angiography, 15 mL arterial blood was collected after successful right radial artery catheterization, and centrifuged at 3000 r/min for 10 minutes. The supernatant (100 μL) was extracted and stored at -80°C for subsequent detection. Serum Beclin 1 level was measured using a commercial Human BECN1 ELISA Kit (E-EL-H0564, Elabscience, China) in strict accordance with the manufacturer’s instructions. Briefly, corresponding antibody and blocking solution were added sequentially for room-temperature incubation, followed by addition of enzyme substrate solution for chromogenic reaction. After termination of the reaction, the absorbance of each well was measured at 450 nm using a microplate reader. The standard curve was plotted to calculate the serum Beclin 1 concentration of each sample.
Gensini score evaluation
The severity of coronary artery stenosis was quantitatively evaluated using the Gensini scoring system based on coronary angiographic results [17]. The basic score was determined by the degree of coronary stenosis: 0 points for no stenosis, 1 point for stenosis ≤25%, 2 points for 25%-50% stenosis, 4 points for 50%-75% stenosis, 8 points for 75%-90% stenosis, 16 points for 90%-99% stenosis, and 32 points for total occlusion (100% stenosis). The basic score was further weighted according to the lesion location: ×5 for left main coronary artery lesions; ×2.5 for proximal anterior descending artery and proximal circumflex artery lesions; ×1.5 for middle anterior descending artery lesions; ×1.0 for right coronary artery, distal anterior descending artery, diagonal branch, left ventricular posterior branch and obtuse marginal branch lesions; ×0.5 for other minor vascular lesions. The total Gensini score of each patient was the sum of weighted scores of all stenotic vessels.
Statistical analysis
SPSS 22.0 and GraphPad Prism 9.0 software were used for data statistical analysis. Normally distributed quantitative data were expressed as mean ± standard deviation (χ ± sd), and one-way ANOVA was used for inter-group comparison. Non-normally distributed quantitative data were expressed as median (interquartile range) [M (Q1, Q3)], and the Mann-Whitney U nonparametric test was adopted for group comparison. Qualitative data were presented as case numbers and percentages (%), and compared using the χ2 test or Fisher’s exact test as appropriate.
MVD was set as the dependent variable (SVD = 0, MVD = 1). Candidate independent variables included age, BMI, sex, smoking history, drinking history, hypertension, diabetes mellitus, hyperlipidemia, TC, TG, LDL-C, HDL-C, Beclin 1 and Gensini score. First, univariate Firth’s penalized likelihood binary logistic regression was performed for all candidate variables to avoid statistical bias caused by data separation. Subsequently, variables with P<0.05 in univariate analysis were included in the multivariate binary logistic regression model, and the backward stepwise method was used for variable screening (elimination criterion: P>0.05). The results were presented as odds ratio (OR) and 95% confidence interval (95% CI), and P<0.05 was defined as statistically significant.
A predictive model for MVD was constructed based on multivariate Firth’s binary logistic regression results. ROC curve analysis was performed to evaluate the discriminatory efficacy of single indicators and the combined model for MVD. The area under the curve (AUC), optimal cutoff value, sensitivity and specificity were calculated. The optimal cutoff value was determined based on the maximum Youden index. For indicators negatively correlated with MVD, reverse ROC analysis was conducted, and the cutoff values and judgment criteria were reported based on the original scale of the indicators.
Results
Baseline clinical and biochemical characteristics of participants
A total of 169 participants were enrolled in this study, including 106 CHD patients and 63 participants with normal coronary angiography results (control group). The 106 CHD patients were further divided into an SVD group (n = 27) and a MVD group (n = 79) according to the number of stenotic coronary arteries (the patient screening flowchart is shown in Figure 1). Among all participants, 104 were male and 65 were female. The median age was 61 (56, 68) years in the control group, 64 (55, 71) years in the SVD group, and 65 (51, 69) years in the MVD group. The baseline clinical and biochemical characteristics of the three groups are summarized in Table 1. There were no significant differences in age, hypertension prevalence, diabetes prevalence, hyperlipidemia prevalence, TG level, or BMI among the three groups. Compared to the control group, the SVD and MVD groups had significantly higher proportions of male patients, smokers and drinkers (all P<0.05). No significant inter-group differences were observed in the prevalence of hypertension, diabetes or hyperlipidemia (all P>0.05). In addition, CHD patients (both SVD and MVD groups) had lower serum TC and LDL-C levels than healthy controls (both P<0.05).
Figure 1.
Participant screening flowchart. Abbreviations: MVD, multi-vessel disease; SVD, single-vessel disease.
Table 1.
Clinical and biochemical characteristics of participants in the control group, SVD group, and MVD group
| Control (n = 63) | CHD (n =106) | t/x2/z/F | P | ||
|---|---|---|---|---|---|
|
| |||||
| SVD (n = 27) | MVD (n = 79) | ||||
| Gender (male, %) | 25 (39.7%) | 16 (59.3%) | 63 (79.7%) | 23.840 | <0.001 |
| Age (years) | 61 (56, 68) | 64 (55, 71) | 65 (51, 69) | 0.656 | 0.720 |
| BMI (kg/m2) | 24.78 (22.34, 28.70) | 25.35 (23.44, 28.37) | 24.21 (22.49, 26.30) | 3.447 | 0.178 |
| Smoking (n%) | 15 (23.8%) | 10 (37.0%) | 40 (50.6%) | 10.682 | 0.005 |
| Alcohol consumption (n%) | 12 (19.0%) | 6 (22.2%) | 30 (38.0%) | 6.778 | 0.034 |
| Medical history | |||||
| Hypertension (n%) | 41 (65.1%) | 17 (63.0%) | 53 (67.1%) | 0.168 | 0.919 |
| DM (n%) | 19 (30.2%) | 6 (22.2%) | 30 (38.0%) | 2.535 | 0.282 |
| Hyperlipidemia (n%) | 50 (79.4%) | 20 (74.1%) | 66 (83.5%) | 1.227 | 0.541 |
| biochemical characteristics | |||||
| TC (mmol/L) | 4.75 (4.01, 5.53) | 4.30 (3.80, 4.94) | 3.55 (2.84, 4.35) | 31.731 | <0.001 |
| TG (mmol/L) | 1.56 (1.05, 2.20) | 1.40 (1.06, 1.88) | 1.58 (1.05, 2.31) | 0.440 | 0.803 |
| LDL-C (mmol/L) | 2.69 (2.10, 3.33) | 2.25 (1.99, 3.22) | 1.84 (1.27, 2.17) | 29.089 | <0.001 |
| HDL-C (mmol/L) | 1.32 (1.06, 1.53) | 1.17 (1.03, 1.34) | 1.08 (0.91, 1.30) | 12.387 | 0.002 |
Abbreviations: BMI, body mass index; DM, diabetes mellitus; TC, total cholesterol; TG, serum levels of triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; CHD, Coronary Heart Disease; SVD, single-vessel disease; MVD, multi-vessel disease.
Differential expression of Beclin 1 among the three groups
As shown in Table 2 and Figure 2A, serum Beclin 1 levels were significantly lower in CHD patients than in healthy controls (P<0.05). ELISA results showed that the median Beclin 1 level was 2.64 (2.24, 2.89) in the SVD group and 1.09 (0.66, 1.78) in the MVD group, with the MVD group exhibiting the lowest Beclin 1 level.
Table 2.
Beclin 1 level and Gensini scores of participants in the control group, SVD group, and MVD group
| Control (n = 63) | CHD (n = 106) | t/x2/z/F | P | ||
|---|---|---|---|---|---|
|
| |||||
| SVD (n = 27) | MVD (n = 79) | ||||
| Beclin 1 (ng/mL) | 2.45 (1.69, 4.17) | 2.64 (2.24, 2.89) | 1.09 (0.66, 1.78) | 47.278 | <0.001 |
| Gensini scores | 12 (4, 20) | 52 (30, 104) | -6.176 | <0.001 | |
Abbreviations: CHD, Coronary Heart Disease; SVD, single-vessel disease; MVD, multi-vessel disease.
Figure 2.
Comparison of Beclin 1 levels and Gensini scores among the three groups. A. Boxplot with error bars showing the distribution of serum Beclin 1 levels in the control, single-vessel disease (SVD), and multi-vessel disease (MVD) groups. The overall difference was assessed using the Kruskal-Wallis test, followed by pairwise Mann-Whitney U tests. B. Boxplot with error bars comparing the Gensini scores between the SVD and MVD groups, assessed by the Mann-Whitney U test. The bold horizontal line in each box represents the median, the lower and upper hinges correspond to the first and third quartiles, and the whiskers extend to the furthest data points within 1.5 times the interquartile range. data points are overlaid to show the underlying distribution.
Gensini score differences among the three groups
Coronary angiography-based Gensini score analysis showed that the median Gensini score was 12 (4, 20) in the SVD group and 52 (30, 104) in the MVD group (Table 2; Figure 2B), with significantly lower scores in the SVD group.
Correlation between Beclin 1 level and Gensini score
All 106 CHD patients were stratified into three subgroups according to Gensini score: low-score group (Gensini score <11, n = 15), medium-score group (11 ≤ Gensini score ≤38, n = 39) and high-score group (Gensini score >38, n = 52). As shown in Table 3 and Figure 3A, the median Beclin 1 level was 2.64 (2.24, 3.85) in the low-score group, 2.05 (1.38, 2.66) in the medium-score group, and 0.74 (0.60, 1.56) in the high-score group, with significant inter-group differences. Correlation analysis confirmed a significant negative correlation between Gensini score and serum Beclin 1 level (Table 3; Figure 3B).
Table 3.
Association between Beclin 1 and Gensini score
| Gensini score | t/x2/z/F | P | |||
|---|---|---|---|---|---|
|
| |||||
| low score (n = 15) | medium score (n = 39) | high score (n = 52) | |||
| Beclin 1 (ng/mL) | 2.64 (2.24, 3.85) | 2.05 (1.38, 2.66) | 0.74 (0.60, 1.56) | 47.818 | <0.001 |
Figure 3.
Association between Beclin 1 levels and Gensini scores. A. Boxplot with error bars illustrating Beclin 1 levels across three groups categorized by Gensini score: Low (<11), Medium (11-38), and High (>38). Statistical significance was determined using the Kruskal-Wallis test and subsequent pairwise Mann-Whitney U tests. Boxplot elements are defined as median (horizontal line), interquartile range (box), and 1.5 times the interquartile range (whiskers). B. Scatter plot demonstrating the inverse correlation between continuous Gensini scores and Beclin 1 levels. The solid black line represents a running median smoother (window size = 15) to visualize the non-linear trend. The correlation was evaluated using Spearman’s rank correlation coefficient (p).
Logistic regression analysis of MVD risk factors
Univariate Firth’s binary logistic regression analysis showed that Gensini score, Beclin 1, LDL-C, TC and gender were significantly correlated with MVD (all P<0.05). The detailed results were: Gensini score (OR = 1.076, 95% CI: 1.041-1.126, P<0.001); Beclin 1 (OR = 0.265, 95% CI: 0.140-0.449, P<0.001); LDL-C (OR = 0.518, 95% CI: 0.319-0.787, P = 0.002); TC (OR = 0.659, 95% CI: 0.456-0.909, P = 0.011); and sex (male = 1, female = 0) (OR = 2.682, 95% CI: 1.052-6.814, P = 0.039) (Tables 4 and 5; Figure 4).
Table 4.
Variable assignment for Firth logistic regression analysis of MVD
| Variable | Assignment method |
|---|---|
| Dependent variable | |
| MVD | 1 = Yes (MVD), 0 = No (SVD) |
| Independent variables | |
| Gender | 1 = Male, 0 = Female |
| Age (years) | Continuous, as original value |
| BMI (kg/m2) | Continuous, as original value |
| Smoking | 1 = Yes, 0 = No |
| Alcohol consumption | 1 = Yes, 0 = No |
| Hypertension | 1 = Yes, 0 = No |
| DM | 1 = Yes, 0 = No |
| Hyperlipidemia | 1 = Yes, 0 = No |
| TC (mmol/L) | Continuous, as original value |
| TG (mmol/L) | Continuous, as original value |
| LDL-C (mmol/L) | Continuous, as original value |
| HDL-C (mmol/L) | Continuous, as original value |
| Gensini score | Continuous, as original value |
| Beclin 1 (ng/mL) | Continuous, as original value |
Abbreviations: BMI, body mass index; DM, diabetes mellitus; TC, total cholesterol; TG, serum levels of triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; SVD, single-vessel disease; MVD, multi-vessel disease.
Table 5.
Univariate firth logistic regression analysis of factors associated with MVD
| Variable | β | OR (95% CI) | P |
|---|---|---|---|
| Gender | 0.9867 | 2.682 (1.052-6.814) | 0.039 |
| TC (mmol/L) | -0.4164 | 0.659 (0.456-0.909) | 0.011 |
| LDL-C (mmol/L) | -0.6585 | 0.518 (0.319-0.787) | 0.002 |
| Gensini score | 0.0734 | 1.076 (1.041-1.126) | <0.001 |
| Beclin 1 (ng/mL) | -1.329 | 0.265 (0.140-0.449) | <0.001 |
| DM | 0.712 | 2.038 (0.792-5.847) | 0.143 |
| Alcohol consumption | 0.712 | 2.038 (0.792-5.847) | 0.143 |
| BMI | -0.0795 | 0.924 (0.821-1.030) | 0.156 |
| Smoking | 0.5358 | 1.709 (0.716-4.234) | 0.229 |
| Hyperlipidemia | 0.589 | 1.802 (0.626-4.936) | 0.266 |
| HDL-C | -0.5063 | 0.603 (0.129-2.930) | 0.523 |
| TG | 0.0656 | 1.068 (0.848-1.517) | 0.617 |
| Hypertension | 0.1917 | 1.211 (0.484-2.942) | 0.676 |
| Age | -0.0064 | 0.994 (0.957-1.030) | 0.728 |
Abbreviations: BMI, body mass index; DM, diabetes mellitus; TC, total cholesterol; TG, serum levels of triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; β, regression coefficient; OR, odds ratio; CI, confidence interval; MVD, multi-vessel disease.
Figure 4.
Forest plot of univariate analysis. Abbreviations: BMI, body mass index; TC, total cholesterol; TG, serum levels of triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
All significant variables in univariate analysis were included in the multivariate Firth’s binary logistic regression model. The final independent influencing factors for MVD were Gensini score, Beclin 1 and LDL-C. Multivariate analysis results indicated that Gensini score was an independent risk factor for MVD (OR = 1.045, 95% CI: 1.018-1.090, P<0.001), while Beclin 1 (OR = 0.372, 95% CI: 0.174-0.695, P = 0.001) and LDL-C (OR = 0.295, 95% CI: 0.128-0.619, P<0.001) were independent protective factors for MVD (Table 6; Figure 5).
Table 6.
Multivariate firth logistic regression analysis of factors associated with MVD
| Variable | β | OR (95% CI) | P |
|---|---|---|---|
| LDL-C (mmol/L) | -1.2195 | 0.295 (0.128-0.619) | <0.001 |
| Gensini score | 0.0441 | 1.045 (1.018-1.090) | <0.001 |
| Beclin 1 (ng/mL) | -0.9882 | 0.372 (0.174-0.695) | 0.001 |
Abbreviations: LDL-C, low-density lipoprotein cholesterol; β, regression coefficient; OR, odds ratio; CI, confidence interval; MVD, multi-vessel disease.
Figure 5.
Forest plot of multivariate analysis. Abbreviation: LDL-C, low-density lipoprotein cholesterol.
ROC curve analysis of the predictive value of Beclin 1 for MVD
Based on multivariate regression results, Gensini score, Beclin 1 and LDL-C were combined to construct a predictive model for MVD. Single-indicator ROC analysis showed that the AUC of Gensini score was 0.899 (optimal cutoff value ≥21.000, sensitivity = 0.861, specificity = 0.852); the AUC of Beclin 1 was 0.875 (optimal cutoff value ≤2.052, sensitivity = 0.861, specificity = 0.926); the AUC of LDL-C was 0.748 (optimal cutoff value ≤1.880, sensitivity = 0.570, specificity = 0.889). The combined model (Gensini score + Beclin 1 + LDL-C) achieved an AUC of 0.936, with an optimal predictive probability cutoff of ≥0.664, a sensitivity of 0.911 and a specificity of 0.963 (Figure 6). The basic model (Gensini score + LDL-C) had an AUC of 0.912, with an optimal cutoff of ≥0.635, a sensitivity of 0.848 and a specificity of 0.889. The higher AUC of the combined model verified that the addition of Beclin 1 significantly improved the predictive efficacy of the model for MVD (Figure 7).
Figure 6.
ROC curves. Abbreviation: LDL-C, low-density lipoprotein cholesterol.
Figure 7.
Combined predictive model.
Discussion
Coronary heart disease (CHD) is characterized by coronary atherosclerotic lesions that cause luminal stenosis or vascular occlusion. MVD is defined as >50% stenosis in two or more coronary arteries or left main coronary artery lesions on angiography, often accompanied by chronic total occlusion or diffuse severe coronary stenosis. The pathogenesis of MVD is multifactorial, involving systemic atherosclerosis, impaired cardiac function in non-infarcted myocardial regions, and reduced blood flow in severely stenotic non-infarct-related arteries [18]. Compared to SVD, MVD is associated with poorer long-term prognosis. A previous study by Lekston found that the 12-month cumulative incidence of major adverse cardiovascular events reached 32.5% in MVD patients [19], which is consistent with the severe disease phenotype of MVD observed in our study. In this study, we adopted the clinically validated Gensini scoring system to evaluate coronary lesion severity, and confirmed that MVD patients had significantly higher Gensini scores than SVD patients [20]. Univariate regression analysis further verified that Gensini score was independently associated with MVD.
The correlation between Gensini score and MVD has solid biological and clinical rationales. Although the Gensini score is an angiography-based evaluation index rather than a tomographic measurement, it comprehensively reflects the severity and distribution of coronary stenosis, and serves as a reliable surrogate indicator of total coronary atherosclerotic burden. Current clinical consensus emphasizes that total plaque burden and diffuse lesion distribution, rather than focal luminal stenosis alone, are core determinants of adverse cardiovascular prognosis. A review in the European Heart Journal pointed out that coronary atherosclerotic burden is a powerful predictor of major adverse cardiovascular events, and non-obstructive coronary disease involving more than four vascular segments carries a cardiovascular risk comparable to obstructive CHD [21]. A prospective Danish cohort study by Fuchs also confirmed that both obstructive and extensive diffuse coronary atherosclerosis significantly increased the risk of myocardial infarction, with the highest risk observed in patients with combined obstructive and extensive lesions [22]. Collectively, these findings validate our result that elevated Gensini score was positively correlated with MVD, indicating that angiographic total lesion burden can effectively reflect the complexity and severity of coronary artery disease.
Atherosclerosis is the core pathological basis of CHD progression. Emerging studies have confirmed that Beclin 1, a key autophagy regulatory protein, is closely involved in AS progression [23]. Our study found that serum Beclin 1 levels were gradually decreased in the control group, SVD group, and MVD group in turn, with significant inter-group differences. In addition, stratified analysis based on Gensini score showed that patients with more severe coronary lesions (higher Gensini scores) had lower Beclin 1 levels. Further logistic regression analysis identified Beclin 1 as an independent protective factor against MVD, which may be attributed to its regulatory effects on autophagy activation and apoptotic inhibition.
Numerous studies have proven that Beclin 1-mediated autophagy plays a critical role in delaying atherosclerosis and alleviating coronary artery damage [7]. A study by Chen demonstrated that Tongxinluo, a traditional Chinese medicine, could inhibit atherosclerotic plaque progression by upregulating Beclin 1-dependent autophagy to reduce lipid accumulation in macrophages [24]. Ye et al. found that Beclin 1 knockdown inhibited vascular re-endothelialization and aggravated neointimal hyperplasia through mediating the crosstalk between autophagy and apoptosis [25]. Appropriate Beclin 1 expression also regulates vascular endothelial cell inflammation, oxidative stress and permeability, maintaining vascular endothelial homeostasis [26,27]. A recent case-control study by Grazide reported that decreased circulating levels of autophagy-related proteins ATG5 and Beclin 1 were closely associated with acute myocardial infarction, suggesting that reduced peripheral Beclin 1 levels may reflect impaired autophagic flux in advanced coronary artery disease [28]. Our finding of reduced Beclin 1 levels in MVD patients is consistent with this conclusion, indicating that downregulated Beclin 1 may represent a maladaptive autophagic response to severe and diffuse coronary atherosclerotic lesions. Notably, the biological significance of circulating Beclin 1 in stable and unstable CHD remains uncertain. Further mechanistic studies combining plaque imaging, inflammatory phenotyping and long-term clinical outcomes are required to confirm whether Beclin 1 serves as a biomarker of atherosclerotic burden, plaque vulnerability or compensatory autophagic response.
The negative correlation between LDL-C and MVD observed in this study requires cautious and differentiated interpretation. Mechanistically, sustained elevated LDL-C is a well-established causal risk factor for arterial lipid deposition, plaque proliferation, and atherosclerotic cardiovascular events [29], and long-term LDL-C reduction can effectively decrease atherosclerotic burden. Therefore, the inverse association between LDL-C and MVD in our multivariate model does not indicate a protective effect of high LDL-C on diffuse coronary disease. This statistical phenomenon is attributed mainly to the limitations of cross-sectional study design. Single-point LDL-C detection at admission cannot reflect long-term cumulative lipid exposure. Patients with severe MVD usually receive standardized intensive lipid-lowering therapy and lifestyle intervention after symptom onset, resulting in reduced intraoperative LDL-C levels. In addition, traditional lipid risk factors cannot fully explain the heterogeneity of coronary atherosclerotic phenotypes, and residual confounding factors such as unrecorded medication use may affect the statistical results [30].
Several limitations should be considered when interpreting these findings. First, the modest sample size and class imbalance between groups may affect coefficient stability and precision. Second, all biomarkers were measured at a single time point; therefore, dynamic exposure history, particularly for LDL-C, could not be captured. Third, the regression model was developed in an observational setting and may still be influenced by residual confounding, unmeasured medication use, and disease-management history. Nevertheless, the use of Firth logistic regression was appropriate in this small-sample context and helps reduce bias caused by sparse data and separation. Overall, our findings support the view that MVD is best understood as a composite phenotype reflecting both anatomic atherosclerotic burden and underlying biological dysregulation, rather than as a purely angiographic descriptor.
In conclusion, the combined detection of Beclin 1, Gensini score, and LDL-C provides a reliable predictive tool for MVD.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (8177020516).
Disclosure of conflict of interest
None.
References
- 1.Lopes NH, Paulitsch Fda S, Gois AF, Pereira AC, Stolf NA, Dallan LO, Ramires JA, Hueb WA. Impact of number of vessels disease on outcome of patients with stable coronary artery disease: 5-year follow-up of the medical, angioplasty, and bypass surgery study (MASS) Eur J Cardiothorac Surg. 2008;33:349–354. doi: 10.1016/j.ejcts.2007.11.025. [DOI] [PubMed] [Google Scholar]
- 2.Karamfiloff KK, Stoykova ZD, Georeva PG, Trendafilova DK, Jorgova JB. Multivessel disease as a prognostic factor for mortality in STEMI patients. J Biomed Clin Res. 2015;8:30–34. [Google Scholar]
- 3.Yan L, Hu X, He J, Gan H, Shi Y, Li L, Li S, Wei K, Wei J, Cai J, Li B, Pang P. Folate-modified biomimetic nanovesicles loaded with a PU. 1 inhibitor alleviate atherosclerosis by suppressing inflammation. J Nanobiotechnology. 2025;24:29. doi: 10.1186/s12951-025-03825-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Du J, Wu W, Zhu B, Tao W, Liu L, Cheng X, Zhao M, Wu J, Li Y, Pei K. Recent advances in regulating lipid metabolism to prevent coronary heart disease. Chem Phys Lipids. 2023;255:105325. doi: 10.1016/j.chemphyslip.2023.105325. [DOI] [PubMed] [Google Scholar]
- 5.Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, Agewall S, Dickstein K, Edvardsen T, Escaned J, Gersh BJ, Svitil P, Gilard M, Hasdai D, Hatala R, Mahfoud F, Masip J, Muneretto C, Valgimigli M, Achenbach S, Bax JJ ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41:407–477. doi: 10.1093/eurheartj/ehz425. [DOI] [PubMed] [Google Scholar]
- 6.Zhao J, Fan H, Wang T, Yu B, Mao S, Wang X, Zhang W, Wang L, Zhang Y, Ren Z, Liang B. TyG index is positively associated with risk of CHD and coronary atherosclerosis severity among NAFLD patients. Cardiovasc Diabetol. 2022;21:123. doi: 10.1186/s12933-022-01548-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Qiao L, Ma J, Zhang Z, Sui W, Zhai C, Xu D, Wang Z, Lu HX, Zhang M, Zhang C, Chen W, Zhang Y. deficient chaperone-mediated autophagy promotes inflammation and atherosclerosis. Circ Res. 2021;129:1141–1157. doi: 10.1161/CIRCRESAHA.121.318908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zhu Y, Yang T, Duan J, Mu N, Zhang T. MALAT1/miR-15b-5p/MAPK1 mediates endothelial progenitor cells autophagy and affects coronary atherosclerotic heart disease via mTOR signaling pathway. Aging (Albany NY) 2019;11:1089–1109. doi: 10.18632/aging.101766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Torisu K, Singh KK, Torisu T, Lovren F, Liu J, Pan Y, Quan A, Ramadan A, Al-Omran M, Pankova N, Boyd SR, Verma S, Finkel T. Intact endothelial autophagy is required to maintain vascular lipid homeostasis. Aging Cell. 2016;15:187–191. doi: 10.1111/acel.12423. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Li X, Yang KB, Chen W, Mai J, Wu XQ, Sun T, Wu RY, Jiao L, Li DD, Ji J, Zhang HL, Yu Y, Chen YH, Feng GK, Deng R, Li JD, Zhu XF. CUL3 (cullin 3)-mediated ubiquitination and degradation of BECN1 (beclin 1) inhibit autophagy and promote tumor progression. Autophagy. 2021;17:4323–4340. doi: 10.1080/15548627.2021.1912270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Feng J, Chen R, Chen Y, Deng J, Wei J. Defective autophagy in vascular smooth muscle cells promote uremic accelerated atherosclerosis. Ren Fail. 2025;47:2538268. doi: 10.1080/0886022X.2025.2538268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hebah HA, Kamel HM, Bastawy IM, Ahmed FA. Association between serum beclin 1 level and cardiac valvular calcification in hemodialysis patients. Curr Probl Cardiol. 2024;49:102519. doi: 10.1016/j.cpcardiol.2024.102519. [DOI] [PubMed] [Google Scholar]
- 13.Xia B, Liang J, Lu Y, Ding J, Peng J, Li F, Dai J, Liu Y, Wang J, Wan C, Luo P. Lactoferrin influences atherosclerotic progression by modulating macrophagic AMPK/mTOR signaling-dependent autophagy. Sci Rep. 2025;15:10585. doi: 10.1038/s41598-025-95181-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Shi G, Liu D, Zhou B, Liu Y, Hao B, Yu S, Wu L, Wang M, Song Z, Wu C, Zhu J, Qian X. Ginsenoside Rb1 alleviates oxidative low-density lipoprotein-induced vascular endothelium senescence via the SIRT1/Beclin-1/autophagy axis. J Cardiovasc Pharmacol. 2020;75:155–167. doi: 10.1097/FJC.0000000000000775. [DOI] [PubMed] [Google Scholar]
- 15.Sano M, Maejima Y, Nakagama S, Shiheido-Watanabe Y, Tamura N, Hirao K, Isobe M, Sasano T. Neutrophil extracellular traps-mediated Beclin-1 suppression aggravates atherosclerosis by inhibiting macrophage autophagy. Front Cell Dev Biol. 2022;10:876147. doi: 10.3389/fcell.2022.876147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Yao F, Lv YC, Zhang M, Xie W, Tan YL, Gong D, Cheng HP, Liu D, Li L, Liu XY, Zheng XL, Tang CK. Apelin-13 impedes foam cell formation by activating Class III PI3K/Beclin-1-mediated autophagic pathway. Biochem Biophys Res Commun. 2015;466:637–643. doi: 10.1016/j.bbrc.2015.09.045. [DOI] [PubMed] [Google Scholar]
- 17.Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini score calculation. Atherosclerosis. 2019;287:181–183. doi: 10.1016/j.atherosclerosis.2019.05.012. [DOI] [PubMed] [Google Scholar]
- 18.Batra MK, Rasool SI, Solangi BA, Khan N, Karim M, Hassan Rizvi SN. Multivessel disease as a prognostic marker in patients presenting for primary percutaneous coronary intervention. J Ayub Med Coll Abbottabad. 2018;30:534–538. [PubMed] [Google Scholar]
- 19.Lekston A, Tajstra M, Gąsior M, Gierlotka M, Pres D, Hudzik B, Trzeciak P, Kalarus Z, Poloński L, Zembala M. Impact of multivessel coronary disease on one-year clinical outcomes and five-year mortality in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention. Kardiol Pol. 2011;69:336–343. [PubMed] [Google Scholar]
- 20.Neeland IJ, Patel RS, Eshtehardi P, Dhawan S, McDaniel MC, Rab ST, Vaccarino V, Zafari AM, Samady H, Quyyumi AA. Coronary angiographic scoring systems: an evaluation of their equivalence and validity. Am Heart J. 2012;164:547–552. e1. doi: 10.1016/j.ahj.2012.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Nurmohamed NS, van Rosendael AR, Danad I, Ngo-Metzger Q, Taub PR, Ray KK, Figtree G, Bonaca MP, Hsia J, Rodriguez F, Sandhu AT, Nieman K, Earls JP, Hoffmann U, Bax JJ, Min JK, Maron DJ, Bhatt DL. Atherosclerosis evaluation and cardiovascular risk estimation using coronary computed tomography angiography. Eur Heart J. 2024;45:1783–1800. doi: 10.1093/eurheartj/ehae190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fuchs A, Kühl JT, Sigvardsen PE, Afzal S, Knudsen AD, Møller MB, de Knegt MC, Sørgaard MH, Nordestgaard BG, Køber LV, Kofoed KF. Subclinical coronary atherosclerosis and risk for myocardial infarction in a Danish cohort: a prospective observational cohort study. Ann Intern Med. 2023;176:433–442. doi: 10.7326/M22-3027. [DOI] [PubMed] [Google Scholar]
- 23.Umahara T, Uchihara T, Hirao K, Shimizu S, Hashimoto T, Kohno M, Hanyu H. Essential autophagic protein Beclin 1 localizes to atherosclerotic lesions of human carotid and major intracranial arteries. J Neurol Sci. 2020;15:116836. doi: 10.1016/j.jns.2020.116836. [DOI] [PubMed] [Google Scholar]
- 24.Chen Y, Yu F, Zhang Y, Li M, Di M, Chen W, Liu X, Zhang Y, Zhang M. Traditional Chinese medication tongxinluo attenuates lipidosis in Ox-LDL-stimulated macrophages by enhancing Beclin-1-induced autophagy. Front Pharmacol. 2021;12:673366. doi: 10.3389/fphar.2021.673366. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ye LX, Yu J, Liang YX, Zeng JS, Huang RX, Liao SJ. Beclin 1 knockdown retards re-endothelialization and exacerbates neointimal formation via a crosstalk between autophagy and apoptosis. Atherosclerosis. 2014;237:146–154. doi: 10.1016/j.atherosclerosis.2014.08.052. [DOI] [PubMed] [Google Scholar]
- 26.Zhao Y, Zhu M, Chen W, Chang Q, Shen L, Yan R, Yang L, Li J, Xu R, Fu Y, Liu Y. TFPIα alleviated vascular endothelial cell injury by inhibiting autophagy and the class III PI3K/Beclin-1 pathway. Thromb Res. 2020;195:151–157. doi: 10.1016/j.thromres.2020.07.017. [DOI] [PubMed] [Google Scholar]
- 27.Leonard A, Millar MW, Slavin SA, Bijli KM, Dionisio Santos DA, Dean DA, Fazal F, Rahman A. Critical role of autophagy regulator Beclin1 in endothelial cell inflammation and barrier disruption. Cell Signal. 2019;61:120–129. doi: 10.1016/j.cellsig.2019.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Grazide MH, Ruidavets JB, Martinet W, Elbaz M, Vindis C. Association of circulating autophagy proteins ATG5 and Beclin 1 with acute myocardial infarction in a case-control study. Cardiology. 2024;149:217–224. doi: 10.1159/000537816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ference BA, Braunwald E, Catapano AL. The LDL cumulative exposure hypothesis: evidence and practical applications. Nat Rev Cardiol. 2024;21:701–716. doi: 10.1038/s41569-024-01039-5. [DOI] [PubMed] [Google Scholar]
- 30.Figtree GA, Vernon ST, Harmer JA, Gray MP, Arnott C, Bachour E, Barsha G, Brieger D, Brown A, Celermajer DS, Channon KM, Chew NWS, Chong JJH, Chow CK, Cistulli PA, Ellinor PT, Grieve SM, Guzik TJ, Hagström E, Jenkins A, Jennings G, Keech AC, Kott KA, Kritharides L, Mamas MA, Mehran R, Meikle PJ, Natarajan P, Negishi K, O’Sullivan J, Patel S, Psaltis PJ, Redfern J, Steg PG, Sullivan DR, Sundström J, Vogel B, Wilson A, Wong D, Bhatt DL, Kovacic JC, Nicholls SJ CRE for CAD Collaborators. Clinical pathway for coronary atherosclerosis in patients without conventional modifiable risk factors: JACC state-of-the-art review. J Am Coll Cardiol. 2023;82:1343–1359. doi: 10.1016/j.jacc.2023.06.045. [DOI] [PMC free article] [PubMed] [Google Scholar]