We reviewed patients diagnosed with CAD who underwent successful PCI between May 1, 2016 and May 31, 2019, and followed up coronary angiography within 6–24 months. Data from 1,025 participants were initially gathered. Study exclusion criteria included severe renal insufficiency with estimated glomerular filtration rate ≤30 ml/min/1.73 m² (n = 16); in-stent restenosis (n = 48); previous coronary artery bypass (n = 13); familial hypertriglyceridemia (n = 2); and lack of data (n = 177). A total of 769 patients meeting the inclusion and exclusion criteria were included in the analysis for this study (Figure 1). The study protocol complied with the Declaration of Helsinki and was approved by the Local Research Ethics Committee (Decision Date: 2022-12-22; Decision Numbers: IEC-C-008-A07-V1.0 and 2022-02-053-K01); all the enrolled patients gave written informed consent.

The optimal cut-off value of RLP-C was determined through receiver operating characteristic (ROC) curve analysis. Subsequently, patients were categorized into two groups: a low RLP-C group (n = 124) and a high RLP-C group (n = 166). Propensity score matching analysis was conducted between the two groups at a ratio of 1:2, incorporating baseline data such as diabetes mellitus, smoking history, presence of multiple vessel disease, High density lipoprotein cholesterol (HDL-C) levels, TG levels, and systolic pressure. Eventually, 83 cases in the high RLP-C group and 106 cases in the low RLP-C group were matched well. Then, Log-rank tests were employed to compare Kaplan–Meier curves for overall follow-up, evaluating the progression of NTLs between the two groups.

Gather clinical data on patients, encompassing gender, age, smoking, alcohol, past medical history. Record vital signs upon admission, including blood pressure, heart rate, height, weight, etc. Body mass index (BMI) was calculated by dividing weight in kilograms by the square of the height in meters. Coronary angiography analysis and QCA were performed by two experienced researchers.

Standardized procedures are followed for routine blood and biochemical tests, which are conducted in the fasting state. TG were assessed using the enzymatic colorimetric GPO-PAP method, TC was determined using the enzymatic-cholesterol oxidase peroxidase method, and both LDL-C and HDL-C were measured through direct homogeneous assay. RLP-C levels were derived by subtracting the sum of HDL-C and LDL-C from TC, following a standard lipid curve (8).

The degree of coronary artery stenosis is determined through QCA analysis, which is conducted by skilled technicians blinded to patient identity and clinical profile. The QCA software was utilized to assess all three major untreated coronary vessels, covering all side branches with a reference vessel diameter greater than 1.5 mm. This evaluation was conducted using angiographic views that were comparable for the NTL segments, between the baseline and follow-up periods. Measured variables included reference vessel diameter, minimal luminal diameter, and diameter stenosis.

NTLs: This coronary vascular lesion was not associated with ischemic symptoms or functional ischemia test results at baseline CAG. Coronary plaque progression: (1) Lesions with baseline diameter stenosis exceeding 50% progressed by more than 10%; (2) Lesions with initial diameter stenosis below 50%, including normal segments, progressed to over 30%; (3) Any non-target lesions progressed to occlusion. Blood lipid goals: LDL-C levels below 1.8 mmol/L with a reduction of more than 50% from baseline. Multivessel disease: two or more vessels with ≥50% diameter stenosis (9).

Continuous variables were analyzed using two-sided t-tests or Mann-Whitney U tests, and described by mean ± standard deviation (SD) or medians (interquartile ranges, IQR). Categorical variables were analyzed using chi-square or Fisher's exact test and presented as counts (percentages). Covariates with a univariate analysis P value <0.1 or clinically relevant risk factors were incorporated into the Cox multivariate regression model to determine the correlation between RLP-C and non-target lesion progression. In order to better reflect the impact of the change of independent variables on the outcome, continuous independent variables were evenly stratified into four quartiles, with the initial quartile (Q1) as the reference.

ROC curve analysis was used to evaluate the ability of RLP-C to predict NTLs progression. Patients were then categorized into high and low RLP-C groups based on the cutoff value. Propensity score matching was used to minimize between-group discrepancies. A standardized difference of less than 0.1 indicated satisfactory covariate balance. Following several matching attempts, the 1:2 nearest neighbor matching method with a caliper value of 0.2 achieved optimal balance between the groups. Log-rank tests were employed to assess Kaplan-Meier curves over the entire follow-up period, examining the progression of NTLs between the two groups.

To assess the association between RLP-C and other lipid parameters, either Pearson's correlation analysis or Spearman's correlation analysis was employed.

Statistical analyses were conducted using SPSS software for Windows (version 22.0, SPSS Inc., Chicago, Illinois, USA). A two-sided probability value of less than 0.05 was deemed statistically significant in all analyses.

The study enrolled a total of 769 patients, with 291 (37.84%) meeting the lipid goals, including 43 (14.78%) in the NTLs progression group. Additionally 478 (62.16%) failed to meet the lipid goals, including 165 (34.52%) in the NTLs progression group (Figure 1).

The average follow-up duration was 13.08 ± 5.2 months. Among patients meeting the lipid goals, the progression group had a shorter follow-up duration (13.34 ± 5.27 vs. 11.54 ± 4.57; P = 0.035), a higher rate of smoking (42.3% vs. 60.5%; P = 0.027) and history of diabetes mellitus (23.0% vs. 44.19%, P = 0.003) and MVD (34.7% vs. 58.1%, P = 0.003) (Table 1).

Compared to the non-progression group, patients with well-controlled LDL-C who still experienced NTLs progression exhibited significantly higher baseline RLP-C levels (0.70 ± 0.45 vs. 0.86 ± 0.41; P = 0.035) and follow-up RLP-C levels (0.54 ± 0.27 vs. 0.73 ± 0.29; P < 0.001), as well as higher TG levels (1.36 ± 0.63 vs. 1.71 ± 0.62; P = 0.001). Additionally, the progression group had significantly lower follow-up HDL-C levels (1.29 ± 0.32 vs. 1.15 ± 0.27; P = 0.007). No significant differences were observed in TC and LDL-C levels (P > 0.05) (Table 2).

To ascertain the contribution of baseline RLP-C to NTLs progression under well-controlled LDL-C conditions, we incorporated risk factors with P < 0.1 or factors potentially associated with NTLs progression in clinical settings into our regression analysis, including systolic blood pressure (SBP), DM, MVD, HDL-C, and TG. After accounting for other confounding factors, the analysis showed that RLP-C, diabetes, smoking, and multi-vessel disease independently increased the risk of NTLs progression. Elevated RLP-C, as a residual lipid risk factor, significantly contributed to non-target lesion progression (HR = 3.515, 95% CI: 1.133–10.905, P = 0.03) (Figure 2).

ROC curve analysis revealed an AUC of 0.721 (95% CI = 0.635–0.807, P < 0.001), indicating the strong predictive capability of RLP-C for NTLs progression risk in LDL well-controlled patients post-PCI. The baseline RLP-C level of 0.555 mmol/L was determined as the optimal cutoff point for predicting progression risk, with a sensitivity of 81.4% and specificity of 63.7% (Figure 3).

To further adjust for potential risk factors that could cause imbalance between the high (n = 124) and low (n = 167) RLP-C groups, Propensity Score Matching (PSM) was applied. After the PSM analysis, unmatched cases were excluded, and 83 participants were reassigned to the high RLP-C group, while 106 participants were assigned to the low RLP-C group. Notably, the balance effect of the covariates after applying PSM was deemed satisfactory (Figure 4).

No notable variances were detected between the low and high RLP-C groups regarding DM history, smoking, multi-vessel disease, HDL-C, TG, or SBP after propensity score matching. Kaplan–Meier curves illustrated a significantly higher cumulative rate of NTLs progression in patients with RLP-C levels ≥0.555 mmol/L compared to the others (Log-rank P = 0.002) (Figure 5).

All participants with better LDL-C control were divided into 2 groups based on RLP-C cutoff values, regardless of whether NTL progression occurred. In patients with high RLP-C, there was a significantly higher proportion of diabetes (32.8% vs. 21.1%, P = 0.024), a slightly higher proportion of multi-vessel disease (44.0% vs. 33.7%, P = 0.074) and a shorter average follow-up duration (13.34 ± 5.27 vs. 11.54 ± 4.57, P = 0.035) compared to the low RLP-C group. No statistically significant differences were observed in terms of gender, age, BMI, hypertension, smoking.

At baseline, no notable differences were evident in vessel lesion diameter, stenosis rate, or location between the two groups. However, during follow-up, individuals with elevated RLP-C levels exhibited markedly reduced lesion diameters (1.46 ± 0.78 vs. 1.70 ± 0.80, P = 0.01) and an elevated risk of revascularization (16.9% vs. 8.4%, P = 0.028). The incidence of myocardial infarction remained low in both groups (Table 3). Correlation analysis indicated a positive correlation between RLP-C and TG as well as TC, with the strongest correlation observed with TG (r = 0.583, P < 0.01 at follow-up; r = 0.551, P < 0.01 at baseline). RLP-C exhibited an inverse relationship with HDL-C and demonstrated no significant correlation with LDL-C (Table 4). Upon categorizing patients into four groups based on quartiles of TG levels, an increase in both RLP-C levels and its proportion in TC was observed with higher TG levels (Figure 6).