Association between the atherogenic index of plasma and coronary collateral circulation in patients with chronic total occlusion

doi:10.21203/rs.3.rs-3605924/v1

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Research Article

Association between the atherogenic index of plasma and coronary collateral circulation in patients with chronic total occlusion

https://doi.org/10.21203/rs.3.rs-3605924/v1

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published 16 Jul, 2024

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Background: The atherogenicindex of plasma (AIP) isconsidered an independent risk factor for coronary artery disease (CAD). The present study investigated whether AIP correlates with the establishment of coronary collateral circulation (CCC) in CAD patients with chronic total occlusion (CTO).

Methods: This retrospective study included 1093 CAD patients with CTO confirmed by coronary angiography from January 2020 to December 2020 at Beijing Anzhen Hospital. Based on the Rentrop scoring system, the patients were divided into the good CCC group and the poor CCC group. AIP was calculated by log (triglyceride/high-density lipoprotein cholesterol). Meanwhile, the study population was further divided into four groups according to the quartiles of AIP.

Results: Patients in the poor CCC group had significantly higher AIP than those in the good CCC group (0.31 ± 0.27 vs. 0.14 ± 0.24, p < 0.001). Multivariate logistic regression analysis showed that there was an independent association between AIP and poor CCC, regardless of whether AIP was a continuous variable or a categorical variable(p < 0.001), after adjusting for confounding factors. Besides, this association remained stable in most subgroups. By incorporating AIP into the baseline model, the accuracy of identifying poor CCC was improved [AUC: baseline model, 0.661 vs. baseline model + AIP, 0.721, p for comparison < 0.001].

Conclusions: Elevated AIP is independently associated with an increased risk of poor CCC in CAD patients with CTO, and AIP may improve the ability to identify poor CCC in clinical practice.

Atherogenic index of plasma

Coronary artery disease

Chronic total occlusion

Coronary collateral circulation

Coronary artery disease (CAD) is a major contributor to the global burden of disease and is one of the most common causes of death. According to statistics, approximately 9.44 million people worldwide died from CAD in 2021, posing a serious public health and socioeconomic challenge [1]. With improvements in treatment technology, percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) have significantly reduced mortality and improved long-term outcomes in patients with CAD. However, there are still many patients with incomplete revascularization and "no-option" (patients ineligible for PCI or CABG with persistent angina symptoms despite optimal medical therapy) whose quality of life is severely impaired [2]. The difficulty in achieving complete revascularization in these patients with refractory angina is mainly due to the presence of advanced age, comorbidities, chronic total occlusion (CTO), multivessel disease, diffuse lesions or low ejection fraction [3]. CTO is considered the ultimate obstacle to current interventional therapy. Previous studies have shown that CTO-PCI had a lower success rate and higher complication rate than non-CTO PCI, and that CTO-PCI did not provide a significant benefit in reducing major adverse cardiovascular events (MACE) compared with optimal medical therapy [4, 5]. The guidelines for coronary artery revascularization have also downgraded CTO-PCI from a class IIa to a class IIb recommendation because of the lack of strong clinical research evidence [6]. Therefore, new therapeutic approaches in such population need to be explored.

The coronary collateral circulation (CCC), which acts as an anastomotic branch connecting the epicardial coronary arteries, provides an alternative source of blood supply to the ischaemic myocardium through collateral vessel remodelling and dilatation in case of epicardial coronary artery stenosis [7]. A large body of evidence suggests that well developed CCC plays a protective role and contributes to a reduction in the size of myocardial infarcts, a decrease in the formation of ventricular aneurysm and an increase in survival compared with poorly developed CCC [8, 9]. Hence, the establishment of well developed CCC may become a novel promising therapeutic strategy in patients with CTO.

However, there are various factors that can affect the establishment of CCC, and there is no reliable clinical indicator to assess whether CCC is well developed. Previous studies have shown that poorly developed CCC is closely associated with disorders of glucose and lipid metabolism [10, 11]. The atherogenic index of plasma (AIP), calculated from triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), is an effective surrogate marker for the assessment of lipid-related risk and atherosclerosis [12, 13]. Besides, AIP is not only an independent risk factor for hypertension, type 2 diabetes mellitus (T2DM), metabolic syndrome (MetS), CAD, and cardiovascular (CV) events [14–16], but has also been shown to be closely related to the progression of CTO [17, 18]. No study has investigated the correlation between AIP and CCC in CAD patients with CTO, and the present study aimed to fill this knowledge gap and explore its potential mechanism, providing a convenient indicator for the clinical assessment of CCC, which is of great practical importance.

Study population

This is a single-center, observational, and retrospective study. A total of 1513 patients who were diagnosed with CAD and had CTO in more than one major branch of the coronary artery confirmed by coronary angiography from January 2020 to December 2020 at Beijing Anzhen Hospital were included in this study. CTO refers to 100% stenosis with thrombolysis in myocardial infarction (TIMI) grade 0 flow for more than three months [19]. The exclusion criteria were as follows: (1) missing baseline data; (2) previous history of CABG; (3) suspicious familial hypertriglyceridemia; (4) type 1 diabetes mellitus; (5) acute infectious diseases; (6) tumor or immune system disease; (7) New York Heart Association (NYHA) class III-IV, or left ventricular ejection fraction (LVEF) < 30%; and (8) severe hepatic and renal insufficiency. Finally, 1093 CAD patients with CTO were enrolled in the present analysis, and the specific flow chart is shown in Fig. 1. The study was approved by the Ethics Committee of Beijing Anzhen Hospital and strictly followed the Declaration of Helsinki. The Ethics Committee of Beijing Anzhen Hospital, Capital Medical University waived the informed consent as the study is retrospective in nature. All patients’ identity information has been removed.

Data collection and definitions

Basic clinical information, including age, sex, body mass index (BMI), smoking history, medical history, laboratory measurements, and angiographic results, was collected from the medical record management system of Beijing Anzhen Hospital. LVEF was determined using Doppler echocardiography by experienced echocardiographers at admission. The following laboratory measurements were determined by standard techniques in the central laboratory of Beijing Anzhen Hospital and using blood samples extracted after fasting for more than 8 hours: high-sensitivity C-reactive protein (hs-CRP), creatinine, urea, uric acid, fasting blood glucose (FBG), glycosylated albumin (GA), glycosylated hemoglobin A1c (HbA1c), TG, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and HDL-C. AIP was calculated as follows: AIP = log (TG [mmol/L]/HDL-C [mmol/L]) [12]. Non-HDL-C was calculated as follows: non-HDL-C = TC (mmol/L) – HDL-C (mmol/L). Castelli’s risk index (CRI) was calculated as follows: CRI-I = TC (mmol/L)/HDL-C (mmol/L). CRI-II = LDL-C (mmol/L)/HDL-C (mmol/L) [20].

Hypertension was defined as a previous diagnosis of hypertension or use of antihypertensive medication, or a new diagnosis of hypertension after admission based on the following guidelines: repeated office systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg [21]. T2DM was defined as a previous diagnosis of T2DM or use of glucose-lowering medication, or a new diagnosis of T2DM after admission based on the following guidelines: (1) FBG ≥ 7.0 mmol/L (126 mg/dL); (2) 2-h blood glucose ≥ 11.1 mmol/L (200 mg/dL) during 75 g oral glucose tolerance test (OGTT); (3) HbA1c ≥ 6.5% (48 mmol/mol); and (4) classic symptoms of hyperglycemia or hyperglycemic crisis, a random blood glucose ≥ 11.1 mmol/L (200 mg/dL) [22]. Dyslipidaemia was defined as a previous diagnosis of dyslipidaemia or use of lipid-lowering medication, or a new diagnosis of dyslipidaemia after admission based on the following guidelines: (1) TC ≥ 5.2 mmol/L, (2) HDL-C < 1.0 mmol/L, (3) LDL-C ≥ 3.4 mmol/L, and (4) TG ≥ 1.7 mmol/L [23].

Procedure and evaluation of coronary collateral circulation

CCC in CTO was assessed by coronary angiography performed in the selected population via the radial or femoral artery with 6F or 7F angiography catheters. The procedure results were interpreted and recorded by at least two professional and independent interventional cardiologists who were unaware of the purpose of this study. CCC was graded according to the Rentrop scoring system as follows: Rentrop grade 0, no visible collateral circulation filling; Rentrop grade 1, collateral circulation filling of branches of the vessel to be dilated but the epicardial segment was not visible; Rentrop grade 2, partial collateral circulation filling of the epicardial segment of the vessel to be dilated; Rentrop grade 3, complete collateral circulation filling of the epicardial segment of the vessel to be dilated [24]. The collateral vessel with the highest Rentrop grade was used for the final analysis when patients had more than one collateral vessel. Moreover, patients were further divided into the good CCC group (Rentrop grade 2–3) and the poor CCC group (Rentrop grade 0–1) based on the Rentrop scoring system.

Statistical analysis

Continuous variables with a normal distribution were presented as the mean ± standard deviation (SD), and the independent sample t test was used for comparisons between the groups. Medians with interquartile ranges (IQR) were used for continuous variables that did not conform to a normal distribution, and the Mann-Whitney U test was used for comparisons between the groups. Categorical variables were expressed as frequencies (percentages), and comparisons between the groups were performed by Pearson’s chi-squared test. The evaluation of the correlation between the AIP and cardiovascular risk factors was conducted using either the Pearson correlation test or Spearman’s rank sum test. Univariate logistic regression analysis was applied to identify risk factors that affected CCC in CAD patients with CTO. Variables with significant differences in the univariate analysis or clinical significance were included in the multivariate logistic regression analysis to further assess whether AIP was an independent risk factor for poor CCC. The results were displayed as the odds ratio (OR) and 95% confidence interval (CI). To control for the influence of confounding factors on the results of this study, three models were constructed to evaluate the correlation between AIP and CCC. Model 1 was adjusted for age, sex, BMI, current smoking, hypertension, T2DM, and dyslipidaemia; Model 2 was adjusted for variables of model 1 and hs-CRP, creatinine, uric acid, FBG, HbA1c, TC, LDL-C, and LVEF; Model 3 was adjusted for variables of model 2 and number of lesions and vessels with CTO. Besides, AIP was presented as a continuous or a categorical variable to validate the independent association between AIP and poor CCC in each model. Restricted cubic spline (RCS) curve was constructed to illustrate the linear or non-linear relationship between AIP and poor CCC with the adjustment of Model 3. Receiver operating characteristic (ROC) curves with DeLong’s test were used to calculate the area under the curve (AUC) and the optimal cut-off value to further evaluate the incremental effect of AIP on discrimination capacity beyond the baseline model, including age, sex, BMI, current smoking, hypertension, T2DM, dyslipidaemia, hs-CRP, creatinine, uric acid, FBG, HbA1c, LVEF, number of lesions, and vessel with CTO. Meanwhile, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were tested to explore the discrimination capacity of AIP for identifying poor CCC. Furthermore, subgroup analysis was performed to investigate whether the evaluation value of the AIP for poor CCC was consistent across subgroups. All statistical analyses were conducted using SPSS 26.0 (IBM Corporation, IL, USA) and R Programming Language 4.2.1 (Vienna, Austria). All p values were two-tailed, and a p value < 0.05 was considered statistically significant.

Baseline characteristics

The final analysis enrolled 1093 participants with a mean age of 58.8 ± 10.2 years, and 936 (85.6%) cases were male. Based on the Rentrop scoring system, patients were divided into the good CCC group (Rentrop grade 2–3, n = 775) and the poor CCC group (Rentrop grade 0–1, n = 318). The baseline characteristics of the demographics, medical history, laboratory measurements, and angiographic results are shown in Table 1. Compared with the good CCC group, the poor CCC group had a higher level of AIP (0.31 ± 0.27 vs. 0.14 ± 0.24, p < 0.001). In addition, the poor CCC group had a higher proportion of individuals with high BMI and dyslipidaemia than the good CCC group. Although there were no significant differences, the poor CCC group had more current smokers and more patients with T2DM and three-vessel disease compared with the good CCC group. In terms of laboratory measurements, patients in the poor CCC group had higher levels of hs-CRP, uric acid, FBG, HbA1c, TG, TC, LDL-C, and non-HDL-C, but lower levels of HDL-C than those in the good CCC group. Besides, left anterior descending artery (LAD) with CTO tended to form poor CCC. There was no significant difference in other characteristics between the two groups.

Table 1

Baseline characteristics of the total population stratified by CCC group
Characteristics	Total population (n = 1093)	Good CCC group (Rentrop grade 2–3) (n = 775)	Poor CCC group (Rentrop grade 0–1) (n = 318)	p value
Demographics
Age, years	58.8 ± 10.2	59.1 ± 10.2	58.0 ± 10.1	0.100
Male	936 (85.6%)	666 (85.9%)	270 (84.9%)	0.729
BMI, kg/m²	26.36 ± 3.30	26.23 ± 3.32	26.70 ± 3.21	0.032
Current Smoking	597 (54.6%)	409 (52.8%)	188 (59.1%)	0.065
Medical history
Hypertension	734 (67.2%)	528 (68.1%)	206 (64.8%)	0.317
T2DM	463 (42.4%)	316 (40.8%)	147 (46.2%)	0.112
Dyslipidaemia	900 (82.3%)	620 (80.0%)	280 (88.1%)	0.002
Laboratory measurements
hs-CRP, mg/L	1.02 (0.52, 2.63)	0.97 (0.48, 2.37)	1.12 (0.68, 3.14)	0.002
Creatinine, mmol/L	78.34 ± 19.61	77.98 ± 18.89	79.22 ± 21.28	0.342
Urea, mmol/L	5.59 ± 1.83	5.52 ± 1.80	5.75 ± 1.90	0.057
Uric acid, mmol/L	355.54 ± 94.99	349.57 ± 93.32	370.11 ± 97.56	0.001
FBG, mmol/L	5.88 (5.11, 7.24)	5.75 (5.06, 7.00)	6.22 (5.22, 8.10)	༜0.001
GA, %	14.39 (12.96, 17.16)	14.37 (13.00, 16.80)	14.47 (12.91, 17.93)	0.455
HbA1c, %	6.10 (5.70, 6.90)	6.10 (5.70, 6.75)	6.20 (5.80, 7.38)	0.001
TG, mmol/L	1.5 (1.1, 2.1)	1.4 (1.1, 1.8)	2.1 (1.3, 2.8)	༜0.001
TC, mmol/L	3.92 ± 0.99	3.83 ± 0.94	4.14 ± 1.07	༜0.001
LDL-C, mmol/L	2.27 ± 0.88	2.22 ± 0.85	2.39 ± 0.95	0.004
HDL-C, mmol/L	1.04 ± 0.25	1.06 ± 0.25	0.99 ± 0.24	༜0.001
Non-HDL-C, mmol/L	2.88 ± 0.97	2.77 ± 0.91	3.14 ± 1.06	༜0.001
AIP	0.19 ± 0.26	0.14 ± 0.24	0.31 ± 0.27	< 0.001
LVEF, %	59 ± 8	59 ± 8	59 ± 8	0.389
Angiographic results
Number of lesions				0.912
One-vessel disease	154 (14.1%)	111 (14.3%)	43 (13.5%)	0.803
Two-vessel disease	313 (28.6%)	223 (28.8%)	90 (28.3%)	0.934
Three-vessel disease	626 (57.3%)	441 (56.9%)	185 (58.2%)	0.750
Vessels with CTO				0.078
LAD	373 (34.1%)	249 (32.1%)	124 (39.0%)	0.035
LCX	192 (17.6%)	137 (17.7%)	55 (17.3%)	0.950
RCA	528 (48.3%)	389 (50.2%)	139 (43.7%)	0.060
CCC, coronary collateral circulation; BMI, body mass index; T2DM, type 2 diabetes mellitus; hs-CRP, high-sensitivity C-reactive protein; FBG, fasting blood glucose; GA, glycated albumin; HbA1c, glycosylated hemoglobin A1c; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIP, atherogenic index of plasma; LVEF, left ventricular ejection fraction; CTO, chronic total occlusion; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery.

Meanwhile, patients were divided into four groups according to the quartiles of AIP: Q1 group (AIP < 0.01, n = 278), Q2 group (0.01 ≤ AIP < 0.18, n = 267), Q3 group (0.18 ≤ AIP < 0.36, n = 276), and Q4 group (AIP ≥ 0.36, n = 272). The baseline characteristics of the total population stratified by the quartiles of AIP are shown in Table 2. Patients with higher levels of AIP were more likely to be younger, have a higher BMI and have a higher proportion of dyslipidaemia. Moreover, the levels of hs-CRP, creatinine, urea, uric acid, FBG, HbA1c, TG, TC, LDL-C, and non-HDL-C in the higher AIP group were significantly increased, while the level of HDL-C was significantly decreased compared with those in the lower AIP group. Other characteristics between the two groups were not significantly different.

Table 2

**Baseline characteristics of the total population stratified by the quartiles of AIP**
Characteristics	Total population (n = 1093)	Q1 group (n = 278)	Q2 group (n = 267)	Q3 group (n = 276)	Q4 group (n = 272)	p value
Demographics
Age, years	58.8 ± 10.2	61.3 ± 9.3	59.4 ± 9.7	57.8 ± 10.3	56.5 ± 10.8	< 0.001
Male	936 (85.6%)	228 (82.0%)	231 (86.5%)	234 (84.8%)	243 (89.3%)	0.097
BMI, kg/m²	26.36 ± 3.30	25.29 ± 3.47	26.12 ± 2.74	26.78 ± 3.44	27.29 ± 3.14	< 0.001
Current Smoking	597 (54.6%)	139 (50.0%)	139 (52.1%)	154 (55.8%)	165 (60.7%)	0.064
Medical history
Hypertension	734 (67.2%)	176 (63.3%)	188 (70.4%)	182 (65.9%)	188 (69.1%)	0.283
T2DM	463 (42.4%)	100 (36.0%)	115 (43.1%)	126 (45.7%)	122 (44.9%)	0.085
Dyslipidaemia	900 (82.3%)	167 (60.1%)	194 (72.7%)	268 (97.1%)	271 (99.6%)	< 0.001
Laboratory measurements
hs-CRP, mg/L	1.02 (0.52, 2.63)	0.77 (0.42, 1.86)	0.89 (0.51, 2.08)	1.12 (0.60, 3.02)	1.29 (0.75, 3.31)	< 0.001
Creatinine, mmol/L	78.34 ± 19.61	75.20 ± 16.76	77.41 ± 18.50	78.64 ± 19.18±	82.16 ± 22.97	< 0.001
Urea, mmol/L	5.59 ± 1.83	5.56 ± 1.71	5.45 ± 1.69	5.41 ± 1.49	5.94 ± 2.29	0.003
Uric acid, mmol/L	355.54 ± 94.99	320.24 ± 81.43	348.32 ± 88.61	355.67 ± 91.17	398.58 ± 101.24	< 0.001
FBG, mmol/L	5.88 (5.11, 7.24)	5.57 (5.00, 6.58)	5.75 (5.11, 6.94)	6.30 (5.12, 7.88)	6.06 (5.21, 7.70)	< 0.001
GA, %	14.39 (12.96, 17.16)	14.50 (13.23, 16.51)	14.40 (12.96, 16.99)	14.28 (12.90, 17.78)	14.30 (12.75, 17.44)	0.834
HbA1c, %	6.10 (5.70, 6.90)	5.90 (5.60, 6.60)	6.10 (5.70, 7.00)	6.20 (5.70, 6.90)	6.20 (5.80, 7.20)	< 0.001
TG, mmol/L	1.76 ± 0.91	0.94 ± 0.23	1.36 ± 0.26	1.79 ± 0.35	2.96 ± 0.89	< 0.001
TC, mmol/L	3.92 ± 0.99	3.73 ± 0.92	3.82 ± 0.96	3.92 ± 0.96	4.20 ± 1.07	< 0.001
LDL-C, mmol/L	2.27 ± 0.88	2.11 ± 0.83	2.25 ± 0.88	2.32 ± 0.85	2.38 ± 0.95	0.002
HDL-C, mmol/L	1.04 ± 0.25	1.26 ± 0.25	1.08 ± 0.18	0.97 ± 0.18	0.85 ± 0.15	< 0.001
Non-HDL-C, mmol/L	2.88 ± 0.97	2.47 ± 0.86	2.74 ± 0.88	2.95 ± 0.89	3.36 ± 1.02	< 0.001
AIP	0.19 ± 0.26	-0.13 ± 0.12	0.10 ± 0.05	0.27 ± 0.05	0.53 ± 0.13	< 0.001
LVEF, %	59 ± 8	60 ± 7	59 ± 9	59 ± 8	59 ± 8	0.584
Angiographic results
Number of lesions						0.376
One-vessel disease	154 (14.1%)	39 (14.0%)	45 (16.9%)	36 (13.0%)	34 (12.5%)	0.475
Two-vessel disease	313 (28.6%)	83 (29.9%)	82 (30.7%)	80 (29.0%)	68 (25.0%)	0.468
Three-vessel disease	626 (57.3%)	156 (56.1%)	140 (52.4%)	160 (58.0%)	170 (62.5%)	0.122
Vessels with CTO						0.291
LAD	373 (34.1%)	99 (35.6%)	99 (37.1%)	88 (31.9%)	87 (32.0%)	0.479
LCX	192 (17.6%)	43 (15.5%)	54 (20.2%)	53 (19.2%)	42 (15.4%)	0.320
RCA	528 (48.3%)	136 (48.9%)	114 (42.7%)	135 (48.9%)	143 (52.6%)	0.143
All abbreviations are as in Table 1.

Correlation between AIP and cardiovascular risk factors

Correlation analysis demonstrated that AIP was related to various cardiovascular risk factors. As shown in Table 3, AIP had a positive association with male, BMI, current smoking, T2DM, dyslipidaemia, hs-CRP, creatinine, uric acid, FBG, HbA1c, TG, TC, LDL-C, and non-HDL-C, while a negative association with age and HDL-C (all p < 0.05).

Table 3

Correlation between AIP and cardiovascular risk factors
Variable	Correlation coefficient	p value
Age	-0.177	< 0.001
Male	0.065	0.033
BMI	0.233	< 0.001
Current smoking	0.088	0.004
Hypertension	0.037	0.218
T2DM	0.076	0.012
Dyslipidaemia	0.441	< 0.001
hs-CRP	0.197	< 0.001
Creatinine	0.143	< 0.001
Uric acid	0.312	< 0.001
FBG	0.149	< 0.001
HbA1c	0.156	< 0.001
TG	0.891	< 0.001
TC	0.178	< 0.001
LDL-C	0.107	< 0.001
HDL-C	-0.663	< 0.001
Non-HDL-C	0.352	< 0.001
LVEF	0.006	0.836
All abbreviations are as in Table 1.

Association between AIP and coronary collateral circulation

Figure 2a was performed to compare the difference in AIP between the poor CCC group and the good CCC group. It showed that AIP was significantly higher in the poor CCC group than those in the good CCC group (p < 0.001). Furthermore, we found that the prevalence of poor CCC was significantly higher in the higher AIP group. The percentages of poor CCC according to the quartiles of AIP were 16.5%, 18.7%, 28.3%, and 52.9%, respectively (p < 0.001), which increased stepwise from the lowest AIP quartile to the highest quartile (Fig. 2b).

Multivariate logistic regression analysis showed that AIP was an independent risk factor for poor CCC, regardless of whether AIP was a continuous variable or a categorical variable (Table 4). When AIP was considered a continuous variable, each 1-unit higher AIP was independently associated with an elevated risk of poor CCC after adjustment with different models (all p < 0.001). When AIP was considered a categorical variable, an increasing risk for poor CCC was observed with increasing AIP after adjusting for different models (all p for trend < 0.001). Moreover, the risk of poor CCC was significantly increased in the Q3 group and Q4 group compared with the Q1 group (all p < 0.05), but not in the Q2 group (Table 4).

Table 4

Predictive value of AIP for poor CCC in different models
	OR	95% CI	p value	p for trend
Crude model
AIP as a categorical variable^*				< 0.001
Q1	ref	ref	ref
Q2	1.16	0.75–1.81	0.504
Q3	1.99	1.32–3.01	0.001
Q4	5.67	3.85–8.50	< 0.001
AIP as a continuous variable^**	15.07	8.66–26.69	< 0.001
Model 1
AIP as a categorical variable^*				< 0.001
Q1	ref	ref	ref
Q2	1.20	0.77–1.89	0.421
Q3	2.07	1.33–3.29	0.002
Q4	6.09	3.92–9.64	< 0.001
AIP as a continuous variable^**	17.61	9.38–33.80	< 0.001
Model 2
AIP as a categorical variable^*				< 0.001
Q1	ref	ref	ref
Q2	1.11	0.70–1.76	0.662
Q3	1.71	1.07–2.75	0.025
Q4	4.41	2.75–7.15	< 0.001
AIP as a continuous variable^**	10.11	5.09–20.51	< 0.001
Model 3
AIP as a categorical variable^*				< 0.001
Q1	ref	ref	ref
Q2	1.08	0.68–1.72	0.742
Q3	1.71	1.07–2.75	0.026
Q4	4.47	2.79–7.28	< 0.001
AIP as a continuous variable^**	10.42	5.21–21.31	< 0.001
Model 1: adjusted for age, male, BMI, current smoking, hypertension, T2DM, and dyslipidaemia.
Model 2: adjusted for variables in Model 1 and hs-CRP, creatinine, uric acid, FBG, HbA1c, TC, LDL-C, and LVEF.
Model 3: adjusted for variables in Model 2 and number of lesions and vessels with CTO.
* The HR was examined regarding the Q1 group as a reference.
** The HR was examined by per 1-unit increase of AIP.
OR, odds ratio; CI, confidence interval; ref, reference; other abbreviations are as in Table 1.

Subsequently, the RCS curve revealed a non-linear relationship between AIP and poor CCC with the adjustment of Model 3. The risk of poor CCC was relatively flat until approximately 0.18 of AIP and then started to increase rapidly afterwards (p for non-linear = 0.003, p for overall < 0.001) (Fig. 3).

Incremental effect of AIP for coronary collateral circulation

The ROC curve showed that the optimal cut-off value of AIP for identifying poor CCC was 0.26, with a sensitivity of 61.6% and a specificity of 70.7%. The AUC of AIP for identifying poor CCC was 0.689 (95% CI 0.653–0.725, p < 0.001) (Fig. 4a). As shown in Fig. 4b and Table 5, incorporating AIP into the baseline model had the most significant incremental effect for assessing the status of CCC compared with other indicators (AUC: baseline model, 0.661 vs. baseline model + AIP, 0.721, p for comparison < 0.001). In addition, IDI and NRI also demonstrated that the inclusion of AIP compared with other indicators had the maximum enhancement to identify poor CCC on the basis of the baseline model, with an NRI of 0.455 and an IDI of 0.063 (all p < 0.001) (Table 5).

Table 5

Incremental effect of AIP and other indicators for identifying poor CCC
	AUC	95% CI	p value	NRI	95% CI	p value	IDI	95% CI	p value
Baseline model	0.661	0.626–0.697	Ref	Ref	Ref	Ref	Ref	Ref	Ref
+ AIP	0.721	0.687–0.755	< 0.001	0.455	0.328 ~ 0.582	< 0.001	0.063	0.047 ~ 0.079	< 0.001
+ TC	0.678	0.644–0.713	0.039	0.159	0.029 ~ 0.289	0.016	0.013	0.005 ~ 0.020	< 0.001
+ HDL-C	0.670	0.635–0.706	0.142	0.076	-0.054 ~ 0.205	0.254	0.005	0.001 ~ 0.010	0.021
+ LDL-C	0.667	0.632–0.702	0.271	0.127	-0.003 ~ 0.256	0.056	0.004	-0.001 ~ 0.007	0.061
+ Non-HDL-C	0.685	0.651–0.720	0.013	0.252	0.123 ~ 0.382	< 0.001	0.018	0.009 ~ 0.026	< 0.001
+ CRI-I	0.697	0.662–0.731	0.001	0.281	0.152 ~ 0.410	< 0.001	0.029	0.017 ~ 0.040	< 0.001
+ CRI-II	0.676	0.641–0.711	0.046	0.194	0.065 ~ 0.324	0.003	0.011	0.004 ~ 0.018	0.003
The baseline model included age, male, BMI, current smoking, hypertension, T2DM, dyslipidaemia, hs-CRP, creatinine, uric acid, FBG, HbA1c, LVEF, number of lesions, and vessels with CTO.
AUC, area under the curve; CI, confidence interval; NRI, net reclassification improvement; IDI, integrated discrimination improvement; Ref, reference; CRI, Castelli’s risk index; other abbreviations are as in Table 1.

Subgroup analysis

Subgroup analysis of the total population according to age (< 65 or ≥ 65 years), sex (female or male), BMI (< 24 or ≥ 24 kg/m²), current smoking (yes or no), hypertension (with or without), dyslipidaemia (with or without), T2DM (with or without), LDL-C (< 1.8 or ≥ 1.8 mmol/L), and HbA1c (< 6.5% or ≥ 6.5%) was performed to further verify the evaluation value of AIP for poor CCC in different subgroups. In subgroups other than those without dyslipidaemia, an increased AIP (per 1-unit) was consistently associated with poor CCC after adjusting for confounders with Model 3 (all p < 0.05) (Fig. 5). Furthermore, age and dyslipidaemia were found to have interactions with AIP (all p for interaction < 0.05) (Fig. 5).

To the best of our knowledge, the present study is the first to investigate the association between AIP and CCC in CAD patients with CTO. Based on our analyses, we have the following main findings: (1) Patients with a higher AIP had a significantly higher incidence of poor CCC than those with a lower AIP. (2) AIP was independently associated with poor CCC, regardless of whether AIP was a continuous variable or a categorical variable. (3) The inclusion of AIP in the baseline model improved the ability to identify the risk of poor CCC. (4) AIP was associated with various cardiovascular risk factors, and there was a non-linear relationship between AIP and poor CCC. In conclusion, AIP may be a potentially reliable alternative indicator for the early identification of poor CCC in CAD patients with CTO.

CCC is present in both CAD and non-CAD patients. The establishment of CCC is a dynamic process involving both angiogenesis and arteriogenesis [25]. Fulton et al. found that the calibre of CCC in non-CAD patients ranged from 10–200 µm, whereas the calibre of CCC in CAD patients could be enlarged to 100–800 µm [26]. Moreover, the number of CCC changes with alterations in vascular pressure and resistance, and the incidence of functional anastomotic branches may increase from 9% in normal hearts to 95% in CTO lesions [27]. CCC, as a rich arterio-arterial network, is able to prevent or reduce myocardial ischaemia in CAD patients. A meta-analysis including 12 independent studies evaluated the impact of CCC on all-cause death in patients with stable or acute CAD. They found that the risk of mortality in the good CCC group was significantly lower than that in the poor CCC group, suggesting that good CCC is closely associated with improved prognosis [8]. Therefore, research into the risk factors for CCC is urgently required.

AIP, a comprehensive lipid index, refers to the logarithm of the molar ratio of TG to HDL-C, which is a simple indicator of atherosclerosis [12]. A cross-sectional study in Iran found that AIP was correlated with waist circumference, BMI, blood pressure, HDL-C, LDL-C, TG, TC, FBG, and physical activity, which is similar to our findings, suggesting that AIP could be used as a regular monitoring indicator of cardiovascular disease (CVD) risk [28]. In addition, the significant association of AIP with the occurrence, development and prognosis of CAD has been confirmed by numerous studies. Wu et al. conducted a meta-analysis showing that a higher AIP may be independently correlated with a higher odds of CAD [15]. According to Kim and his research team, AIP has been shown to be a useful tool for identifying patients at a high risk of CV events. They showed that patients with higher AIP were at greater risk of future CV events and that AIP applied was superior to TG or HDL-C, which applied alone [16]. Zheng et al. demonstrated that AIP could be used as an effective predictor of poor prognosis in non-diabetic patients with CAD after PCI [29]. A retrospective study by Qin et al., which included 2356 patients with T2DM who underwent PCI, revealed that patients with a higher AIP had a significantly higher probability of adverse CV events during 4 years of follow-up. Even after adjusting for confounding variables, the such independent association of AIP with adverse CV events remained [30]. Beyond the effect of diabetes status, Wang et al. further investigated the prognostic value of AIP in the population with LDL < 1.8 mmol/L. They exhibited that despite well-controlled LDL-C levels, elevated AIP was still significantly associated with an increased risk of major adverse cardiovascular and cerebrovascular events (MACCE) in acute coronary syndrome (ACS) patients undergoing PCI [31]. Furthermore, Zhu and his colleagues confirmed that AIP was independently related to the risk of in-stent restenosis (ISR) in patients with ACS, especially in the LDL-C < 1.8 mmol/L subgroup [32]. However, fewer studies have examined the evaluation value of AIP in patients with CTO. Guelker et al. reported that AIP was related to the J-CTO score (representing the complexity of CTO), which may help to improve procedural planning and quality of intervention [17]. Another study suggested that AIP was significantly higher in the CTO groups compared with the non-CTO group. AIP was an independent risk factor for CTO and could predict the presence of CTO and disease severity [18].

Given the importance of CCC in improving the quality of life in patients with CTO [9] and the value of AIP in patients with CAD, we extended previous studies and explored the relationship between AIP and CCC in CAD patients with CTO for the first time. In the present study, we found that an elevated AIP was an independent risk factor for an increased risk of poor CCC in CAD patients with CTO. In other words, if an elevated AIP is detected clinically, it means that the CCC in patients with CTO tends to be poorly developed and the compensatory protection against cardiac ischaemia is weakened, which needs to further discuss whether to perform PCI and the procedure approach of PCI. Interestingly, the relationship between AIP and poor CCC in such population is non-linear. There appears to be no positive correlation between AIP and poor CCC when AIP is below 0.18. One potential explanation is that when AIP is very low, there may be a balance between the beneficial effects of lower TG and the harmful effects of higher HDL-C. This may be the possible reason why there was no significant association with poor CCC in the Q2 group, while there was a significant association in the Q3 and Q4 groups. Additionally, AIP was added to the established baseline model for analysis, and the results showed that the addition of AIP may improve the ability to identify poor CCC, which was superior to the addition of the single standard atherosclerotic lipid profile. To further confirm the reliability and stability of this study, we also performed subgroup analysis and found that the AIP did not exhibit good predictive value in patients without dyslipidaemia. This may be due to the fact that there were fewer patients in this subgroup, and another possible explanation is that AIP in this subgroup was very low, which did not reach the cut-off value of AIP for identifying poor CCC.

The underlying mechanism between high AIP and poor CCC has not been fully elucidated. A possible mechanism is that AIP is able to reflect disturbances in lipid metabolism, which have an impact on the formation of CCC. AIP is derived from TG and HDL-C. Previous studies have shown that TG and HDL-C are independent predictors of poor CCC [10, 33]. TG is involved in the pro-inflammatory, pro-coagulant and pro-apoptotic pathways that play a critical role in atherosclerosis [34]. HDL-C has the ability to promote cholesterol efflux, inhibit vascular inflammation or oxidative stress, reduce thrombosis, and improve endothelial cells function [35]. Hypertriglyceridemia can activate cholesterylester transfer protein (CETP). CETP transfers TG from apolipoprotein B lipoprotein particles to HDL-C through TG exchange of cholesterol esters, resulting in a decrease in HDL-C levels and an increase in LDL-C and very low-density lipoprotein cholesterol (VLDL-C) levels in plasma [36]. High cholesterol and LDL-C levels lead to subendothelial lipid deposition, macrophage foam cells formation, plaque progression, vascular endothelial cells dysfunction and impaired collateral vessel formation [37]. In addition, triglyceride-rich lipoprotein (TRL) and apolipoprotein B lipoprotein particles in the plasma of patients with high TG easily form small and dense low-density lipoprotein cholesterol (sdLDL-C) through hepatic metabolism mediated by apolipoprotein C (APO-C) III [38]. sdLDL-C, the most atherogenic lipid index [39], has been shown to penetrate the vascular endothelium more than LDL-C, thus stimulating vascular endothelial cells to secrete inflammatory mediators and adhesion molecules, resulting in dysfunction of vascular endothelial cells [40]. A recent study found that AIP could serve as a substitute indicator of sdLDL-C and was significantly negatively correlated with the molecular diameter of LDL-C [41]. All of the above mechanisms between AIP and the formation of CCC need to be further confirmed.

There are some limitations in this study. First, as a retrospective observational study, it could not prove the causal relationship between AIP and CCC in patients with CTO, and the potential confounding factors cannot be completely ruled out. Second, the patients included in this study were all from the same center, and selection bias could not be avoided. Third, AIP was greatly affected by the levels of TG and HDL-C, but it was not adjusted for the use, type or dosage of lipid-lowering drugs, which may have biased the results. Finally, the gold standard, the collateral flow index (CFI), was not used to assess the CCC, and only the Rentrop scoring system was used. Future prospective, multicenter, large-scale studies are needed to determine the association between AIP and CCC.

Elevated AIP is independently associated with an increased risk of poor CCC in CAD patients with CTO, and AIP may improve the ability to identify poor CCC in clinical practice. Therefore, AIP can be considered a reliable surrogate for the assessment of CCC. A high AIP may remind the clinician that the CCC in patients with CTO is likely to be poorly developed and insufficient to provide compensatory protection to the ischaemic myocardium, thus facilitating the choice of interventional strategies and therapeutic regimens.

AIP, atherogenic index of plasma; CAD, coronary artery disease; CCC, coronary collateral circulation; CTO, chronic total occlusion; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; RCS, restricted cubic spline; ROC, receiver operating characteristic; AUC, area under the curve; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; MACE, major adverse cardiovascular events; T2DM, type 2 diabetes mellitus; MetS, metabolic syndrome; CV, cardiovascular; TIMI, thrombolysis in myocardial infarction; NYHA, New York Heart Association; LVEF, left ventricular ejection fraction; BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; FBG, fasting blood glucose; GA, glycosylated albumin; HbA1c, glycosylated hemoglobin A1c; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; CRI, Castelli’s risk index; SBP, systolic blood pressure; DBP, diastolic blood pressure; OGTT, oral glucose tolerance test; SD, standard deviation; IQR, interquartile ranges; OR, odds ratio; CI, confidence interval; NRI, net reclassification improvement; IDI, integrated discrimination improvement; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; Ref, reference; CVD, cardiovascular disease; MACCE, adverse cardiovascular and cerebrovascular events; ACS, acute coronary syndrome; ISR, in-stent restenosis; CETP, cholesterylester transfer protein; VLDL-C, very low-density lipoprotein cholesterol; TRL, triglyceride-rich lipoprotein; sdLDL-C, small and dense low-density lipoprotein cholesterol; APO, apolipoprotein C.

Ethics approval and consent to participate

This study was reviewed and approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University and was performed in line with the Declaration of Helsinki. The Ethics Committee of Beijing Anzhen Hospital, Capital Medical University waived the informed consent as the study is retrospective in nature. All patients’ identity information has been removed.

Consent for publication

Not applicable.

Availability of data and materials

The data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the grant from National Key Research and Development Program of China (2022YFC3602500); Capital’s Funds for Health Improvement and Research (CFH 2020-2-2063).

Authors’ contributions

STD and JMQ made substantial contributions to data collection, data analysis and manuscript writing. STD, JMQ, AG and YJZ contributed to the study design. AG, ZHZ, XH, YK, MSM and CF made contributions to data collection and data analysis. ZQY, HYH and YJZ performed manuscript revision. All the authors have read and approved the final manuscript.

Acknowledgements

We would like to express our gratitude sincerely to all those who helped us during the writing of this manuscript and thank all the peer reviewers for their opinions and suggestions.

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Association between the atherogenic index of plasma and coronary collateral circulation in patients with chronic total occlusion

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Abstract

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Introduction

Methods

Study population

Data collection and definitions

Procedure and evaluation of coronary collateral circulation

Statistical analysis

Results

Baseline characteristics

Correlation between AIP and cardiovascular risk factors

Association between AIP and coronary collateral circulation

Incremental effect of AIP for coronary collateral circulation

Subgroup analysis

Discussion

Limitations

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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