Correlation of blood lipids, glucose, and inflammatory indices with the occurrence and prognosis of lesion complexity in unstable angina, a retrospective cohort study

Introduction

Unstable angina (UA) is a clinical type of non-ST segment elevation acute coronary syndrome (NSTE-ACS) that is characterized by a rapid onset and easily develops into myocardial infarction (MI) or sudden death (1). Owing to the negative serum cardiac markers of UA, these patients are often overlooked in clinical practice, which may lead to catastrophic clinical outcomes, especially for those patients with complex lesions. There are numerous evaluation indicators related to the grading and risk stratification of acute coronary syndrome (ACS), such as the Global Registry of Acute Coronary Events (GRACE) score, for predicting the risk level of patients with ACS. However, no multitarget predictive evaluation models exist that can identify and predict complex lesions in UA in the early stages before coronary angiography (CAG) or that can evaluate the prognosis of patients with UA and complex lesions (2). The Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score can assess the complexity of coronary artery lesions, but it needs to be calculated after CAG, and the effectiveness of SYNTAX score and its derived score (SYNTAX II, etc.) on the long-term impact of clinical events in patients with UA and complex lesions is limited (3). Therefore, early attention to UA with complex lesions and the establishment of pre-CAG clinical prediction models can prevent further deterioration in patients and improve prognosis.

The predictive factors for these complex lesions have typically been traditional indicators, such as low-density lipoprotein cholesterol (LDL-C), age, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP), which lack specificity (4,5). While a recent study indicated that indicators, such as cystatin C levels, can influence the occurrence of major adverse cardiocerebrovascular events (MACCEs) in ACS patients after percutaneous coronary intervention (PCI), this study focused on single target and did not encompass the impact of multiple metabolic markers on outcomes (6). In recent years, with the development of metabolomics and genomics for cardiovascular diseases, novel cardiometabolic biomarkers and related pathogenic genes and their heritability have been examined (7,8). Although some novel biomarkers, such as microRNAs are closely linked to UA, their routine use in patients is limited due to the high cost and logistical challenges associated with their detection (9). In addition to novel cardiometabolic biomarkers, psychological and mental factors also influence the prognosis of ACS patients (10). Although a previous study on predictive models for MACCEs in AMI-PCI patients (11), further research is needed to determine the correlation between blood lipids, glucose, inflammatory indices, and the occurrence and prognosis of UA with complex lesions, as well as the management of patients with UA after PCI. In this context, we conducted this study to develop a multivariable prediction model which could predict the occurrence and prognosis of UA with complex lesions. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2122/rc).

Methods

Study design and population

Patients who underwent PCI and were diagnosed with UA at the Heart Center of Beijing Chaoyang Hospital, Capital Medical University, from March 2019 to December 2020, were retrospectively included in this study. Complex lesions were those that met any of the following conditions: (I) ≥3 stents implanted; (II) ≥3 lesions treated; (III) 2 stent implantations for bifurcate lesions; (IV) total stent length ≥60 mm; and (V) chronic total-occlusion (CTO) lesions (12). Patients with UA who underwent PCI were divided into a complex lesion group and noncomplex lesion group according to the CAG results.

The inclusion criteria for patients were as follows: (I) new-onset or aggravated chest pain symptoms on exertion or angina without elevated serum cardiac markers diagnosed as UA (1); (II) UA and coronary artery stenosis confirmed via CAG and completion of PCI treatment; and (III) regular intake of medication according to the clinician’s advice and completion of follow-up. Meanwhile, the exclusion criteria were as follows: (I) no PCI or medication being the sole treatment; (II) coronary artery bypass graft (CABG) treatment; (III) ACS with ST-segment elevation myocardial infarction (STEMI) or non-ST segment elevation myocardial infarction (NSTEMI); (IV) presence of terminal malignant tumors, uncontrolled infections, or severe autoimmune diseases that could seriously affect prognosis; (V) incomplete clinical data for blood lipid, glucose level, or inflammatory indices during admission; (VI) intolerance to anticoagulants; (VII) incomplete follow-up data; and (VIII) lack of consent to participate in the study.

The baseline data, laboratory results, imaging results, and follow-up period of the two groups were collected, and the blood lipid and glucose levels, inflammatory indices, SYNTAX scores, and clinical outcome events after 3 years of follow-up from both groups were calculated. This study was approved by the Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (No. 2024-345) and conformed to principles of the Helsinki Declaration (as revised in 2013). Written informed consent was obtained from all the patients.

Biochemical analysis and definitions

All patients underwent fasting serum sample collection and laboratory tests (hemogram and lipid profiles) after fasting overnight (>12 h). The concentrations of high-density lipoprotein cholesterol (HDL-C), LDL-C, total cholesterol (TC), and triglycerides (TGs) were measured via standard laboratory procedures with a fully automatic biochemical analyzer (ADVIA 2400 Clinical Chemistry System, Siemens Healthineers, Erlangen, Germany). Lipoprotein (a) [Lp(a)] was assayed via immunoturbidimetry with a fully automatic biochemical analyzer (ADVIA 2400 Clinical Chemistry System). Glycosylated hemoglobin A1c (HbA1c) was measured via high-performance liquid chromatography with an automated glycohemoglobin analyzer (Tosoh Corporation, Tokyo, Japan). hs-CRP levels were measured via Ultrasensitive CRP Kit (Aidian Oy, Jyväskylä, Finland). Small dense low-density lipoprotein cholesterol (sLDL-C) levels were measured using a commercial kit (McBioengineering, Beijing, China). RLP-C was calculated as follows: RLP-C (mmol/L) = TC (mmol/L) − HDL (mmol/L) − LDL (mmol/L) (13). The TG-glucose (TyG) index was calculated as follows: Napierian logarithm (ln) [TG (mg/dL) × fasting plasma glucose (FPG) (mg/dL)/2] (14). The neutrophil:lymphocyte ratio (NLR) was calculated based on the ratio of absolute neutrophil (NE) and lymphocyte (LYN) counts (15). The hs-CRP:albumin ratio (CAR) was defined as the ratio of hs-CRP to albumin (ALB) (16).

PCI procedure and SYNTAX score

All patients who underwent PCI were given 300 mg of aspirin (Bayer AG, Leverkusen, Germany) and 300 mg of clopidogrel bisulfate (Sanofi Winthrop Industrie, Paris, France) or 180 mg of ticagrelor (AstraZeneca, Cambridge, UK). All patients were administered 3,000 IU of heparin (Shanghai No. 1 Biochemical Pharmaceutical Co., Ltd., Shanghai, China) intrathecally at the beginning of angiography, and additional heparin was provided during PCI if required (100 IU/kg of body weight). The surgeon determined the use of tirofiban during PCI. The SYNTAX score was calculated using the SYNTAX score online calculator (https://m.medsci.cn/scale/show.do?id=f1f22922dc) with reference to the algorithm of a previous study (3). The results were assessed by two interventional cardiologists with at least 5 years of interventional experience based on the angiography results. Cardiologists were blinded to the patients’ blood lipid, glucose, and inflammatory indices and to the patients’ grouping. If there were any discrepancies in the results of patients’ SYNTAX score calculations, the final decision was made through discussion among all cardiologists at the heart center. All patients routinely received 100 mg of aspirin (Bayer AG) once daily and 75 mg of clopidogrel bisulfate (Sanofi Winthrop Industrie) once daily or 90 mg of ticagrelor (AstraZeneca) twice daily after PCI unless contraindications (such as bleeding) were present.

Medication and follow-up

All patients were followed-up through outpatient visits, telephone calls, or readmissions for CAG (patients with recurrent UA symptoms). Routine outpatient or telephone follow-ups were conducted at 3 months, 6 months, 1 year, 2 years, and 3 years. The endpoint events of follow-up were MACCEs, MI, target vessel revascularization (TVR), death, and stroke. MACCEs included death, MI, TVR, and stroke. Death was considered cardiogenic unless there was clear evidence to prove that it was not correlated with cardiogenic factors (17). MI was considered to be a cardiac troponin I (cTnI) level > the upper reference limit (URL), symptoms of myocardial ischemia, and a patient meeting any of the following requirements: (I) recent pathological Q-wave or newly occurring ischemic electrocardiogram change; (II) coronary artery occlusion or severe stenosis with thrombus confirmed by angiography; and (III) new-onset myocardial activity reduction or ventricular wall motion abnormalities consistent with the cause of ischemia confirmed by imaging (18). During the follow-up period, outpatient cardiovascular physicians determined the use of β-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs), and proprotein convertase subtilisin/kexin type 9 (PCSK-9) inhibitor.

Statistical analysis

To estimate sample size, we made assumptions based on two previous studies (19,20) and the average incidence of MACCEs reported therein was selected for the sample size calculation. The sample size was calculated based on an expected prevalence rate (π) of 9.5%, an allowable error (δ) of 0.03, and a two-tailed standard normal variate value with u_α/2 set at 1.96. The initial calculation yielded a sample size of 367. To account for a potential 20% loss to follow-up, the sample size was adjusted to 440.

For quantitative data, normally distributed data are expressed as the mean ± standard deviation (SD), and t-tests were used for two-group comparisons. Variables with skewed distribution data are shown as the median and interquartile range (IQR), and the Wilcoxon rank-sum test was used for intergroup comparisons. Categorical variables are expressed as percentages and were analyzed using the Chi-squared test or Fisher exact test. The cumulative incidence rate of endpoint events in the two groups of patients was determined using Kaplan-Meier survival analysis and evaluated using the log-rank test. Multivariate logistic regression analysis was used to determine the risk factors affecting the incidence of complex lesions in patients with UA, and the results are presented in a forest plot. Univariate and multivariate logistic regression analyses were used to determine the risk factors for MACCEs in the complex lesion group. Statistically significant factors in the univariate logistic regression analysis were included in the final multivariate logistic regression model. Based on the final results of the multivariate logistic regression model in the UA complex lesion group, a nomogram was established using the R (The R Foundation of Statistical Computing) to identify the risk factors for MACCEs in the complex lesion group during the follow-up period. The area under the curve (AUC) was then calculated to evaluate the predictive accuracy of the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the ability of the new predictive model based on the nomogram and SYNTAX score to predict the incidence of MACCEs in the complex lesion group. A two-tailed P value <0.05 was considered to be statistically significant. All statistical analyses were performed using SPSS 26 (IBM Corp., Armonk, NY, USA) and R version 3.6.1.

Results

Laboratory metabolic indices and demographic characteristics

A total of 523 patients were included, with 248 and 275 patients in the complex and noncomplex lesion groups, respectively (Figure 1). In the complex lesion group, 29 (11.7%) patients experienced MACCEs, while 16 (5.8%) experienced MACCEs in the noncomplex lesion group. There were no significant differences between the two groups in terms of sex, age, medical history, or demographic characteristics (Table 1).

Figure 1 Study flowchart. CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention; DAPT, dual antiplatelet therapy; NSTEMI, non-ST segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction; UA, unstable angina.

The mean first blood glucose (FBG) levels in the complex lesion group were significantly higher than those in the noncomplex lesion group (6.51±2.76 vs. 5.67±2.16 mmol/L; P<0.001), but there was no statistically significant difference in LDL or TC levels between the two groups. The mean HDL-C level (0.99±0.24 mmol/L) in the noncomplex lesion group was higher than that in the complex lesion group (0.92±0.25 mmol/L) (P=0.001). The mean level of sLDL-C in the complex lesion group was higher than that in noncomplex lesion group (0.94±0.47 vs. 0.78±0.40 mmol/L; P<0.001), and the median level of Lp(a) in the complex lesion group (median: 31.25 mg/dL, IQR: 11.93–67.48 mg/dL) was also higher than that in noncomplex lesion group (median: 15.00 mg/dL, IQR: 8.50–28.70 mg/dL) (P<0.001). No significant difference was found between the two groups for other laboratory indicators such as left ventricular ejection fraction or BNP level. In terms of metabolic indicators, the TyG index in the complex lesion group was higher than that in the noncomplex lesion group (8.95±0.65 vs. 8.72±0.68) (P<0.001), and the RLP-C level in complex lesion group (0.87±0.62 mmol/L) was higher than that in noncomplex lesion group (0.67±0.39 mmol/L) (P<0.001). Among the inflammatory indicators, the NE count in complex lesion group [(4.96±2.20)×10⁹] was higher than that in noncomplex lesion group [(4.39±1.69)×10⁹] (P=0.001), the LYN count in the complex lesion group [(1.72±0.61)×10⁹] was lower than that in the noncomplex lesion group [(1.97±0.68)×10⁹] (P<0.001), and the NLR in complex lesion group was higher than that in noncomplex lesion group (3.36±2.27 vs. 2.58±1.86; P<0.001). The erythrocyte sedimentation rate (ESR) in the complex lesion group (median: 8.00 mm/h, IQR: 4.00–12.00 mm/h) was higher than that in noncomplex lesion group (median: 6.00 mm/h, IQR: 2.00–10.00 mm/h) (P<0.001), and the CAR in the complex lesion group (median: 0.06, IQR: 0.02–0.10) was higher than that in noncomplex lesion group (median: 0.03, IQR: 0.01–0.06) (P<0.001) (Table 2). Multivariate logistic regression analysis was conducted on indicators that showed significant differences, and it was found that high RLP-C level, high Lp(a) level, high sLDL-C level, high hs-CRP level, low LYN level, low ALB level, and low CAR were associated with a risk of complex lesions forming in patients with UA (Figure 2).

Figure 2 Multivariate logistic regression analysis of risk factors associated with lesion complexity in patients with UA. OR, odds ratio; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; FBG, first blood glucose; TyG, triglyceride-glucose; RLP-C, remnant lipoproteins cholesterol; sLDL-C, small dense low- density lipoprotein cholesterol; Lp(a), lipoprotein (a); NE, neutrophil; LYN, lymphocyte; NLR, neutrophil:lymphocyte ratio; HbA1c, glycosylated hemoglobin A1c; hs-CRP, high-sensitivity C-reactive protein; ALB, albumin; CAR, high-sensitivity C-reactive protein:albumin ratio; ESR, erythrocyte sedimentation rate; UA, unstable angina.

PCI characteristics and follow-up results

The SYNTAX score in the noncomplex lesion group (15.81±6.84) was lower than that in complex lesion group (30.10±8.14) (P<0.001), while the number and total length of stents implanted in the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) in patients in the complex lesion group were significantly higher than those in the noncomplex lesion group (P<0.001). The use of coronary rotational atherectomy/excimer laser coronary atherectomy (ELCA) and cutting balloon was more frequent in patients with complex lesions than in those with noncomplex lesions (Table 3). During the follow-up period, no significant differences were found in the use of β-blockers, ACEI/ARB, PCSK-9 inhibitors, or other drugs between the two groups. At the 1-year follow-up, there was no significant difference in the incidence of adverse events between the two groups. However, after 3 years of follow-up, the incidence of TVR and MACCEs in the complex lesion group was significantly higher than that in the noncomplex lesion group (TVR: 8.1% vs. 4.0%, P=0.049; MACCE: 11.7% vs. 5.8%; P=0.02) (Table 4 and Figure 3).

Figure 3 Kaplan-Meier curves for the comparison of (A) 3-year MI incidence, (B) 3-year mortality, (C) 3-year target reconstruction incidence, and (D) 3-year incidence of MACCEs between the noncomplex lesion group and complex lesion group. HR, hazard ratio; CI, confidence interval; MI, myocardial infarction; MACCEs, major adverse cardiocerebrovascular events.

Subgroup analysis of complex lesions

Univariate logistic regression analysis showed that the high TyG index, high RLP-C level, high sLDL-C level, high Lp(a) level, high NLR level, high BNP level and high SYNTAX score were risk factors for MACCEs in the complex lesion group at 3 years of follow-up. Multivariate logistic regression analysis indicated that high RLP-C level, high sLDL-C level, high Lp(a) level, and high NLR level were independent risk factors for MACCE in the complex lesion group at 3 years of follow-up (Table 5). A nomogram was then generated to predict MACCEs in the complex lesion group at the 3-year follow-up (Table 6 and Figure 4). The AUC of this model for predicting MACCEs in the complex lesion group at the 3-year follow-up [AUC =0.935; 95% confidence interval (CI): 0.881–0.989] was higher than that of the SYNTAX score (AUC =0.671; 95% CI: 0.584–0.757) (P<0.001) (Figure 5).

Figure 4 A nomogram predicting MACCEs in the complex lesion group. In the nomogram, each risk factor is located on the corresponding variable axis, and the score for each risk factor corresponds to the “Points” axis. Calculating the sum of the respective risk factor scores corresponds to the “Points” axis, from which each patient’s total score can be determined. According to the “Total points” axis, the risk corresponding to each patient’s total score for the target event is 0.001–0.8 (risk axis). RLP-C, remnant lipoproteins cholesterol; sLDL-C, small dense low- density lipoprotein cholesterol; Lp(a), lipoprotein (a); NLR, neutrophil:lymphocyte ratio; MACCE, major adverse cardiocerebrovascular event.

Figure 5 Comparison of ROC curves between our proposed prediction model and the traditional SYNTAX score in predicting the prognosis of patients in complex lesion group. AUC, area under the curve; CI, confidence interval; SYNTAX, Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery; ROC, receiver operating characteristic.

Discussion

In this study, we found that high RLP-C level, high Lp(a) level, high sLDL-C level, high hs-CRP level, low LYN level, low ALB level, and low CAR were independent risk factors for complex lesions in patients with UA. Even if optimal drug treatment was administered to both groups, the incidence of MACCEs was higher in the complex lesion group during follow-up than in the noncomplex lesion group. Additionally, the nomogram plot showed that high RLP-C level, high sLDL-C level, high Lp(a) level, and high NLR level were independent risk factors for MACCEs in the complex lesion group at follow-up. Moreover, the new model constructed via nomogram prediction had a stronger predictive ability for MACCEs than did the SYNTAX score in the complex lesion group.

The prognosis of patients with complex lesions was found to be worse than that of those with noncomplex lesions, with a 3-year follow-up MACCE incidence rate of 11.7%, which is in line with Kawashima et al.’s study (21). UA lesions involve a complex coronary anatomical morphology and multivessel stenosis. Once an STEMI or NSTEMI develops, the condition becomes critical. Therefore, early identification and effective management of patients with complex UA lesions during follow-up is critical. CAG is the gold-standard examination method but is invasive, often requires hospitalization, lacks convenience, and does not reduce the risk of death in patients with coronary artery disease (22). Novel biomarkers, such as long noncoding RNA and some peptides, can predict the prognosis and genesis of coronary artery disease (23,24), but these specific biomarkers have certain weaknesses, such as inconvenience and the inability to be routinely performed in hospitalized patients due to cost. Although some studies have shown that traditional biomarkers, such as hs-CRP, BNP, and LDL-C, can predict the prognosis of patients with multivessel lesions, their predictive ability for the prognosis of other complex lesions remains unclear (25,26). Thus, establishing a novel multitarget, convenient biomarker prediction model that incorporates blood lipid, glucose, and inflammatory indices for the predicting the genesis and prognosis of complex lesions in UA is essential.

There were no differences in BNP levels between the two groups, which differs from the findings of Kotecha et al. (27). This is because the levels of myocardial injury markers, including BNP, are not elevated in patients with UA, and the cardiac function might have been similar between the two groups, regardless of lesion complexity. Similarly, there were no differences in lipid indicators including TC, LDL-C, and TGs between the two groups, and LDL-C was not a risk factor for complex lesions, which may be related to the high proportion of statin use (95.41%) during follow-up. LDL-C level has been widely recognized as a risk factor for the occurrence and prognosis of coronary artery disease. Even with optimal statin therapy, some patients may still experience MACCEs despite their LDL-C being controlled within the normal scope recommended by the relevant guidelines.

Lp(a) was found to be a risk factor for the occurrence of complex UA lesions and MACCEs, which is consistent with the findings of Xu et al. (28), who reported that Lp(a) was related to the complexity of lesions, but not to long-term prognosis, in patients with coronary artery disease undergoing PCI. Chieng et al. (29) also found that a simultaneous increase in Lp(a) and LDL-C level is an independent predictor of high SYNTAX scores and lesion complexity in patients with early-onset coronary heart disease. These findings may be attributable to Lp(a) being associated with the accelerated progression of low-density coronary atherosclerotic plaque (necrotic core), which in turn hastens the process of atherosclerosis (30). Nurmohamed et al. using coronary computed tomography angiography scans, reported that higher levels of Lp(a) were correlated with the progression of plaque burden and increased inflammation of pericoronary adipose tissue, which may also help explain our results (31). In our study, sLDL-C was identified as another risk factor for the occurrence of complex lesions and poor prognosis in UA, which is in line with previous research (32,33). As a subgroup of LDL-C, sLDL-C is considered the main pathogenic component of LDL-C because of its high content of apolipoprotein B (Apo B), which facilitates its penetration into the arterial wall and leads to atherosclerosis. sLDL-C can also promote endothelial cell dysfunction and induce platelet aggregation in endothelial cells (34). In short, an increase in sLDL-C level leads to the occurrence of complex UA lesions, resulting in severe damage to the coronary artery. RLP-C was also found to be a risk factor associated with the occurrence and prognosis of complex UA lesions and may increase the risk of MACCEs in patients with UA and complex lesions. This finding is consistent with previous reports (35,36), which indicated that high RLP-C levels can lead to the occurrence of coronary artery disease and increase the risk of coronary artery disease in patients with type 2 diabetes mellitus. RLP-C includes intermediate-density lipoprotein cholesterol and very LDL-C, which have been shown to be associated with increased residual cardiovascular risk as derived lipid metabolism indices (37). RLP-C is more likely to penetrate the artery wall and accumulate in the vascular endothelium, which is absorbed by macrophages, thus accelerating the development of atherosclerosis and increasing the residual risk of cardiovascular disease. Delialis et al. (38) also confirmed that RLP-C is associated with a residual risk of atherosclerosis, which supports the findings of our study.

In terms of glucose metabolism, the TyG index in the complex lesion group was higher than that in the noncomplex lesion group. Univariate logistic regression analysis suggested that the TyG index was a risk factor for MACCEs in the complex lesion group, but in the multivariate logistic regression analysis, the TyG index was not a risk factor, which differs slightly from the study by Wang et al. (39). In their study, a higher average TyG index and TyG index variability were closely associated with the incidence of MACCEs. Another study proposed that the TyG index may be an important indicator for evaluating the occurrence and prognosis of cardiovascular disease and may predict MACCEs in patients with premature coronary artery disease (40). Wang et al. (41) reported that a higher TyG index was associated with multivessel coronary artery disease and predictive coronary artery disease severity of coronary artery disease in patients with early diabetes mellitus. Wang et al. (42) found that the TyG index could replace the coronary artery calcification score to predict the severity and prognosis of multivessel lesions in patients with ACS. The conclusions of our study are similar to those of the studies mentioned above; however, there are slight discrepancies owing to the variability in the included risk factors and subgroups.

In terms of the inflammatory index, our nomogram identified NLR was an independent risk factor for poor prognosis in patients with UA and complex lesions. Similarly, Soylu et al. (43) found a positive correlation between NLR and GRACE and SYNTAX scores in the prognosis and lesion complexity of patients with NSTE-ACS. The study by Yuan et al. also showed that the combination of NLR and lipid metabolism indicators demonstrated higher specificity in assessing the severity of coronary artery disease in patients with ACS (44). NLR is a relatively novel inflammatory index that can better reflect the inflammatory and oxidative stress status and is often elevated in patients with coronary artery disease. Long-term chronic inflammation and oxidative stress can lead to an increase in NE count, damage to LYN proliferation, and an increase in NLR, all of which are related to MACCEs in patients with UA and complex lesions.

The new prediction model established by the nomogram had a larger AUC than did the SYNTAX score, indicating a better predictive ability for the prognosis of complex lesions of UA. We then applied patients’ blood lipid, glucose, and inflammatory indices instead of repeat CAG to predict the outcomes and MACCE occurrence in patients with UA and complex lesions, and these may be early indicators for physicians to enact early prevention. It is possible that the occurrence and prognosis of coronary artery disease are influenced by multiple target factors, including coronary anatomical factors, blood glucose levels, lipid metabolism disorders, and long-term chronic inflammation. The SYNTAX score is an anatomical score that focuses more on evaluating the complexity of patient lesions to guide PCI treatment, whereas the predictive model established in our study focuses more on clinical management during postoperative follow-up. Thus, this novel predictive model has a better ability to predict MACCEs in patients with UA and complex lesions. The predictive ability of this new model is superior to the SYNTAX score, which corroborates the findings of Li et al. (45), who also reported the combination of indicators was superior in predicting the lesion complexity of coronary artery disease.

Certain limitations to this study should be acknowledged. First, we employed a retrospective single-center design, and further large-scale, multicenter, randomized clinical trials with longer follow-up periods are needed. Second, although we identified novel lipid indices, such as Lp(a) and sLDL-C, as risk factors, there is currently no widely used treatment for effectively suppressing these risk factors and their progression. Although the novel small interfering RNA lipid-lowering drug, inclisiran, can control LDL-C levels while reducing Lp(a), it has not yet been widely used in clinical practice. Third, the lack of an external validation sample for the nomogram. Fourth, the lack of precise information regarding the time interval between the onset of UA symptoms and the performance of diagnostic tests represents a limitation of the study. Finally, the multiple metabolic indicators and stent implantation data examined in this study were not included in the genome-wide association study and UK Biobank study data sources; therefore, Mendelian randomization analyses could not be conducted.

Conclusions

In this study, blood lipid and glucose levels and inflammatory indices were found to be associated with the occurrence of complex lesions in UA. Compared with the SYNTAX score, the new model constructed using high RLP-C level, high sLDL-C level, high Lp(a) level, and high NLR level had a stronger ability to predict MACCEs in patients with UA and complex lesions during follow-up. We developed a multitarget and multisystem metabolic-related risk factor model for predicting the occurrence and prognosis of complex lesions in UA. Our findings may offer novel insights into the early identification of patients with UA and complex lesions and improve the prevention and treatment targets for cardiologists in the long-term clinical management of patients with UA after PCI.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2122/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2122/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2122/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2122/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was approved by the Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (No. 2024-345) and conformed to the principles of the Helsinki Declaration (as revised in 2013). Written informed consent was obtained from all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Gray)