Abstract
Purpose
This research aims to investigate correlations between the neutrophil-to-lymphocyte*platelet (NLPR) ratio combined with lipid parameters and coronary artery disease (CAD), and to assess the value of these indicators in predicting the severity of coronary atherosclerosis in the Chinese population.
Patients and methods
This study included 474 Chinese patients aged 28 to 88 years who underwent coronary angiography (CAG). Patients were grouped by NLPR, residual cholesterol (RC), and low-density lipoprotein/high-density lipoprotein (LDL/HDL) levels. CAD was determined via CAG and defined as stenosis of ≥50% in at least one major coronary artery. The Gensini score was calculated to assess the severity of coronary atherosclerosis.
Results
Patients with different NLPR levels showed differences in age (P<0.001), gender (P=0.002), hypertension (P<0.001), hyperlipidemia (P=0.003), Gensini scores (P=0.002), and severe stenosis (P<0.001). Multivariate logistic regression analysis revealed that NLPR was independently correlated with severe stenosis (OR, 1.275; 95% CI, 1.066 to 1.525; P=0.008). After adjusting for variables, individuals with elevated LDL/HDL alone, elevated NLPR alone, and both elevated LDL/HDL and NLPR demonstrated progressively increased risks of severe stenosis relative to those with low levels of LDL/HDL and NLPR. Furthermore, only individuals with both elevated RC and NLPR had a significantly higher risk of severe stenosis compared to those with lower RC and NLPR. NLPR-RC and NLPR-LDL/HDL, as complex indexes combining NLPR and lipid parameters, were independently associated with severe stenosis. The areas under the receiver operating characteristic (ROC) curve for NLPR, NLPR-RC, and NLPR-LDL/HDL were 0.630, 0.643, and 0.658, respectively.
Conclusion
NLPR, NLPR-RC, and NLPR-LDL/HDL are independently associated with an increased risk of severe coronary artery stenosis. Compared with NLPR, the composite indicators, due to their higher sensitivity, might be applicable for screening severe coronary artery stenosis. However, these composite indicators still require prospective and multi-center validation before they can be widely applied.
Keywords: coronary artery disease, coronary atherosclerosis, neutrophil-to-lymphocyte*platelet, low-density lipoprotein / high-density lipoprotein, residual cholesterol
Introduction
Coronary artery disease (CAD) is one of the major diseases causing human death and disability worldwide. Its underlying pathological mechanism, atherosclerosis (AS), is a persistent, systemic inflammatory process characterized by gradual plaque buildup and vascular remodeling. Despite significant progress that has been made in the prevention, diagnosis, and treatment of CAD in recent years, its incidence remains high, imposing a heavy burden on patients and the healthcare system.1 Currently, CAG remains the “gold standard” for assessing the severity of coronary artery stenosis. However, it is an invasive procedure and cannot comprehensively reflect the inflammatory state of atherosclerosis throughout the body. Therefore, exploring convenient, noninvasive, and efficient biological markers to assess the severity of coronary artery stenosis is clinically significant for patient risk stratification, treatment strategy selection, and prognosis assessment.
Numerous studies in recent years have illustrated a strong correlation between chronic inflammation and the formation, progression, and terminal rupture of atherosclerotic plaques.2 NLPR is a novel, readily accessible systemic inflammatory marker with comprehensive diagnostic implications that reflects the body’s immune response and inflammatory status.3–5 Furthermore, NLPR demonstrates significant value in predicting the prognosis of certain diseases, such as acute kidney injury, chronic gallbladder stones, and COVID-19.3
Dyslipidemia, especially cholesterol deposition, is closely related to atherosclerosis. Reducing low-density lipoprotein cholesterol (LDL-C) is regarded as the primary goal in lipid management, and the level of high-density lipoprotein cholesterol (HDL-C) is negatively correlated with the risk of CAD.6,7 The LDL/HDL ratio integrates both indicators and is closely associated with coronary atherosclerosis. However, in clinical practice, some patients still have substantial residual cardiovascular risk even when their LDL-C levels are well controlled.8 Recent studies have shown that RC also plays a crucial role in the pathogenesis of atherosclerosis.9,10
Dyslipidemia promotes macrophage and vascular smooth muscle cell lipid accumulation and activates inflammatory signaling, whereas cytokines impair cholesterol efflux and amplify lipid accumulation. The interaction between chronic inflammation and dyslipidemia may jointly contribute to atherosclerosis.11 Thus, assessing chronic inflammation and dyslipidemia together might offer a more thorough evaluation of the severity of coronary artery stenosis compared to using a single biomarker. The effects of NLPR combined with either LDL/HDL or RC on the severity of coronary atherosclerosis remain unclear. Therefore, this study aims to reveal the potential roles of NLPR-RC and NLPR-LDL/HDL in the assessment of coronary atherosclerosis, provide new clinical evidence for understanding the lipid-inflammation interaction in CAD, and offer new insights for the future development of targeted risk management and therapeutic strategies.
Methods
Study Participants
This cross-sectional study was based on the Chinese population. From February 2024 to December 2025, 474 patients aged 28 to 88 years who were suspected or diagnosed with CAD were hospitalized at the Second Affiliated Hospital of Nanjing University of Chinese Medicine and underwent CAG. Inclusion criteria: adult patients aged 18 years or above, first-time undergoing coronary angiography. Exclusion criteria included: history of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG), severe heart valve disease, cardiomyopathy, decompensated heart failure, endocarditis, severe renal dysfunction, severe hepatic insufficiency, malignant tumors, and other serious infection histories. This study was conducted after the approval of the Ethics Committee of the Second Affiliated Hospital of Nanjing University of Chinese Medicine (2025SEZKY-098-02). Our research was carried out in accordance with the Declaration of Helsinki. All participants subscribed informed consent.
Coronary Angiography
After Seldinger puncture of the patient’s radial artery or femoral artery, two experienced coronary intervention physicians used the Judkins technique to perform left and right coronary angiography (CAG), respectively. The results of coronary angiography were independently and blindly interpreted by two physicians.12 The intraclass correlation coefficient (ICC) was used to evaluate the interobserver consistency; when there were differences in interpretation, the angiographic images were jointly reviewed and discussed to determine the final degree of stenosis. CAD was defined as at least one major coronary artery with stenosis ≥ 50%.13 The severity of coronary atherosclerosis was evaluated using the Gensini scoring system. The calculation method for the Gensini score is based on the number of coronary artery stenosis segments, the importance of the lesion’s geographical distribution, and the degree of lumen stenosis.14 The third quartile of Gensini score was used as the cut-off point, and Gensini score ≥24 was defined as ‘severe stenosis’.
Demographic Data
With informed consent obtained from all participants, data were collected by trained clinical staff via structured face-to-face interviews. Demographic information, including age, smoking history, medication use, and past medical history, was systematically recorded. The smoking history of the research subjects was classified as never smoking and smoking (including past and current smoking). Height and weight measurements were performed by a single qualified physician using standardized protocols; body mass index (BMI) was subsequently calculated as weight (kg) divided by the square of height (m2) to objectively assess obesity status.
Blood Pressure Measurement
The resting blood pressure of the patients was measured three times using an electronic blood pressure monitor, and the average of the results was taken as the final data. Subjects with systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, or those taking antihypertensive drugs, were recorded as having hypertension.
Laboratory Measurements
After fasting for 12 hours, patients had their blood samples collected immediately between 7:00 and 8:00 am. Total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), low-density lipoprotein cholesterol (LDL-c, mmol/L), high-density lipoprotein cholesterol (HDL-c, mmol/L), lipoprotein a (Lp(a), mg/L), and fasting blood glucose (FBG, mmol/L) were determined by enzymatic method using an automatic biochemical analyzer (AU 2700 Olympus, 1st Chemical Ltd, Japan). Glycosylated hemoglobin (HbA1c) was measured by chromatography using a glycosylated hemoglobin analyzer (Variant II TURBO glycosylated hemoglobin meter, BIO-RAD, the USA). Hyperlipidemia diagnosis criteria: TG ≥ 2.3 mmol/L (200 mg/dL), TC ≥ 6.2 mmol/L (240 mg/dL), LDL-c ≥ 4.1 mmol/L (160 mg/dL), or being on lipid-lowering medication.15 Diabetes diagnosis criteria: FBG > 7.0 mmol/L (126 mg/dL), HbA1c ≥ 6.5%, or receiving hypoglycemic treatment. The hematologic markers were calculated as follows:
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Statistical Analysis
In this study, the sample size was estimated using Peduzzi’s EPV criterion (with EPV ≥ 10), and 15% of the data were set aside. This ensured that the requirements for multivariate Logistic regression, ROC, and related analyses were met. Missing data were generated using MICE for 20 datasets, and the results were combined using the Rubin rule. Sensitivity analysis was conducted using complete case analysis. We analyzed the data using a social science statistical software package (V.26.0; SPSS Corporation, Chicago, Illinois, USA). The K-S normality test was used to determine whether the continuous variables followed a normal distribution. Parameters with normal distributions are expressed as mean ± standard deviation; inter-group comparisons were conducted using independent sample t-tests or one-way analysis of variance (ANOVA). For the parameters with skewed distributions, the median and quartiles were used for description, and the differences between groups were analyzed using the Mann–Whitney U-test or the Kruskal–Wallis H-test. A χ2 analysis was performed to compare categorical variables between the groups.
The ORs for the occurrence of severe stenosis were calculated by univariate and multivariate Logistic regression analysis, and the results were presented with 95% confidence intervals (95% CI).
The sensitivity, specificity, predictive value, and the areas under the ROC curves of NLPR, NLPR-RC and NLPR-LDL/HDL in identifying severe stenosis were calculated.19
Results
The Baseline Characteristics of the Participants
The baseline characteristics of the subjects were presented in Tables 1 and 2. The NLPR, RC, and LDL/HDL were divided into tertiary levels according to their tertiles, respectively. Specifically: NLPR was categorized as Level 1 (0 < NLPR ≤ 0.96), Level 2 (0.96 < NLPR ≤ 1.57), and Level 3 (NLPR > 1.57); RC was categorized as Level 1 (0 ≤ RC ≤ 0.54 mmol/L), Level 2 (0.54 < RC ≤ 0.92 mmol/L), and Level 3 (RC > 0.92 mmol/L); LDL/HDL was categorized as Level 1 (0 < LDL/HDL < 2.15), Level 2 (2.15 ≤ LDL/HDL < 2.84), and Level 3 (LDL/HDL ≥ 2.84). There were differences in age (P < 0.001), gender (P = 0.002), hypertension (P < 0.001), hyperlipidemia (P = 0.003), TG (P = 0.026), TC (P < 0.001), LDL (P < 0.001), LDL/HDL (P = 0.003), and Gensini scores (P = 0.002) among patients with different NLPR levels. The prevalence of “severe stenosis” was higher among patients with NLPR level 3. Between patients with severe stenosis and those with non-severe stenosis, there are significant differences in age (P = 0.003), gender (P = 0.001), hypertension (P = 0.012), diabetes (P < 0.001), smoking (P < 0.001), HDL (P = 0.026), FBG (P < 0.001), and NLPR (P < 0.001).
Table 1.
Characteristics of Patients with Different NLPR Levels
| Characteristic | Levels of NLPR | H/X2 | P Value | ||
|---|---|---|---|---|---|
| Level 1 (n=158) | Level 2 (n=158) | Level 3 (n=158) | |||
| Age (years) | 61(53,69) | 66(58,72) | 68(59,75) | 20.712 | <0.001 |
| Male, n (%) | 61(38.6%) | 81(51.3%) | 92(58.2%) | 12.508 | 0.002 |
| Hypertension, n (%) | 81(51.3%) | 107(67.7%) | 113(71.5%) | 15.802 | <0.001 |
| Diabetes, n (%) | 41(25.9%) | 52(32.9%) | 58(36.7%) | 4.334 | 0.114 |
| Hyperlipidemia, n (%) | 50(31.6%) | 37(23.4%) | 24(15.2%) | 11.929 | 0.003 |
| Smoking, n (%) | 32(20.3%) | 42(26.6%) | 50(31.6%) | 5.330 | 0.070 |
| TG (mmol/L) | 1.52(1.05,2.51) | 1.43(1.00,2.18) | 1.39(0.92,1.88) | 7.326 | 0.026 |
| TC (mmol/L) | 5.08(4.26,6.15) | 5.01(4.01,5.71) | 4.38(3.83,5.24) | 17.733 | <0.001 |
| LDL (mmol/L) | 3.07(2.57,3.76) | 2.99(2.38,3.51) | 2.59(2.13,3.17) | 21.891 | <0.001 |
| HDL (mmol/L) | 1.20(0.98,1.41) | 1.17(1.01,1.43) | 1.14(0.95,1.29) | 3.423 | 0.181 |
| FBG (mmol/L) | 5.23(4.73,6.17) | 5.48(4.84,6.66) | 5.31(4.74,6.15) | 3.137 | 0.208 |
| RC (mmol/L) | 0.78(0.48,1.08) | 0.69(0.39,1.07) | 0.74(0.43,0.94) | 2.745 | 0.254 |
| LDL/HDL | 2.66(2.04,3.36) | 2.55(1.94,3.06) | 2.34(1.86,2.76) | 11.753 | 0.003 |
| Gensini scores | 8.0(3.0,20.0) | 8.0(3.0,20.0) | 16.0(4.0,32.0) | 12.520 | 0.002 |
| Severe stenosis, n (%) | 28(17.7%) | 31(19.6%) | 60(38.0%) | 21.027 | <0.001 |
Abbreviations: TG, triglycerides; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; RC, residual cholesterol; LDL/HDL, low-density lipoprotein/high-density lipoprotein.
Table 2.
Characteristics of Patients with Severe Stenosis and Non-Severe Stenosis
| Characteristic | Non-Severe Stenosis (n=355) | Severe Stenosis (n=119) | Z/X2 | P Value |
|---|---|---|---|---|
| Age (years) | 65(55,71) | 68(60,74) | −2.981 | 0.003 |
| Male, n (%) | 160(45.1%) | 74(62.2%) | 10.444 | 0.001 |
| Hypertension, n (%) | 214(60.3%) | 87(73.1%) | 6.327 | 0.012 |
| Diabetes, n (%) | 91(25.6%) | 60(50.4%) | 25.223 | <0.001 |
| Hyperlipidemia, n (%) | 89(25.1%) | 22(18.5%) | 2.154 | 0.142 |
| Smoking, n (%) | 77(21.7%) | 47(39.5%) | 14.628 | <0.001 |
| TG (mmol/L) | 1.41(0.99,2.08) | 1.49(1.04,2.09) | −1.114 | 0.265 |
| TC (mmol/L) | 4.85(3.97,5.79) | 4.77(4.09,5.58) | −0.358 | 0.721 |
| LDL (mmol/L) | 2.93(2.28,3.61) | 2.91(2.32,3.41) | −0.401 | 0.688 |
| HDL (mmol/L) | 1.18(0.99,1.41) | 1.14(0.93,1.30) | −2.223 | 0.026 |
| FBG (mmol/L) | 5.20(4.73,5.89) | 6.04(4.94,7.60) | −5.155 | <0.001 |
| NLPR | 1.11(0.79,1.70) | 1.57(0.98,2.57) | −4.242 | <0.001 |
| NLPR levels | 21.027 | <0.001 | ||
| Level 1, n (%) | 130(36.6%) | 28(23.5%) | ||
| Level 2, n (%) | 127(35.8%) | 31(26.1%) | ||
| Level 3, n (%) | 98(27.6%) | 60(50.4%) | ||
| RC (mmol/L) | 0.72(0.39,1.03) | 0.75(0.53,1.04) | −1.437 | 0.151 |
| RC levels | 4.735 | 0.094 | ||
| Level 1, n (%) | 128(36.1%) | 30(25.2%) | ||
| Level 2, n (%) | 113(31.8%) | 45(37.8%) | ||
| Level 3, n (%) | 114(32.1%) | 44(37.0%) | ||
| LDL/HDL | 2.45(1.92,3.06) | 2.60(2.04,3.24) | −1.445 | 0.149 |
| LDL/HDL levels | 3.860 | 0.145 | ||
| Level 1, n (%) | 127(35.8%) | 31(26.1%) | ||
| Level 2, n (%) | 113(31.8%) | 45(37.8%) | ||
| Level 3, n (%) | 115(32.4%) | 43(36.1%) |
Abbreviations: TG, triglycerides; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; RC, residual cholesterol; LDL/HDL, low-density lipoprotein/high-density lipoprotein; NLPR, neutrophil-to-lymphocyte*platelet.
Univariate Logistic Regression Analysis with Respect to the Presence of Severe Stenosis
A univariate logistic regression analysis was conducted to identify the risk factors for severe stenosis. The results are presented in Table 3. For every 1-year increase in age, the risk of severe stenosis increased by 3% (OR, 1.030; 95% CI 1.010 to 1.051; P = 0.004). The risk of severe stenosis in male patients was approximately twice that of female patients (OR, 2.004; 95% CI 1.310 to 3.067; P = 0.001). Additionally, patients with hypertension were approximately 1.8-fold as likely to develop severe stenosis (OR, 1.791; 95% CI 1.134 to 2.831; P = 0.013) as those without hypertension. Moreover, the risk of severe stenosis in patients with diabetes is approximately three times that of non-diabetic patients (OR, 2.950; 95% CI 1.917 to 4.541; P < 0.001) than those without diabetes. Compared to patients without smoking history, patients with smoking history had approximately 2.4 times the risk of severe stenosis (OR, 2.357; 95% CI 1.509 to 3.681; P < 0.001).
Table 3.
Univariate Logistic Regression Analysis with Respect to the Presence of Severe Stenosis
| Characteristic | B | OR | 95% CI | P |
|---|---|---|---|---|
| Age (years) | 0.030 | 1.030 | 1.010 to 1.051 | 0.004 |
| Gender (female=0/male=1) | 0.695 | 2.004 | 1.310 to 3.067 | 0.001 |
| Hypertension, (N=0/Y=1) | 0.583 | 1.791 | 1.134 to 2.831 | 0.013 |
| Diabetes, (N=0/Y=1) | 1.082 | 2.950 | 1.917 to 4.541 | <0.001 |
| Hyperlipidemia, (N=0/Y=1) | −0.389 | 0.678 | 0.402 to 1.142 | 0.144 |
| Smoking, (N=0/Y=1) | 0.857 | 2.357 | 1.509 to 3.681 | <0.001 |
| NLPR | 0.346 | 1.414 | 1.192 to 1.677 | <0.001 |
| NLPR levels | ||||
| Level 1 | NS | 1 | NS | NS |
| Level 2 | 0.125 | 1.133 | 0.643 to 1.997 | 0.665 |
| Level 3 | 1.045 | 2.843 | 1.691 to 4.779 | <0.001 |
| RC (mmol/L) | 0.108 | 1.114 | 0.829 to 1.496 | 0.476 |
| RC levels | ||||
| Level 1 | NS | 1 | NS | NS |
| Level 2 | 0.530 | 1.699 | 1.003 to 2.877 | 0.049 |
| Level 3 | 0.499 | 1.647 | 0.971 to 2.793 | 0.064 |
| LDL/HDL | 0.192 | 1.212 | 0.951 to 1.544 | 0.120 |
| LDL/HDL levels | ||||
| Level 1 | NS | 1 | NS | NS |
| Level 2 | 0.489 | 1.631 | 0.967 to 2.752 | 0.067 |
| Level 3 | 0.426 | 1.532 | 0.905 to 2.593 | 0.112 |
Abbreviations: NLPR, neutrophil-to-lymphocyte*platelet; RC, residual cholesterol; LDL/HDL, low-density lipoprotein/high-density lipoprotein.
The risk of severe stenosis increased by 41.4% for every 1 unit increase in NLPR (OR, 1.414; 95% CI 1.192 to 1.677; P < 0.001). Compared to patients with NLPR Level 1, patients with NLPR Level 3 had approximately 2.8 times the risk of severe stenosis (OR, 2.843; 95% CI 1.691 to 4.779; P < 0.001). The risk of severe stenosis in patients with RC Level 2 is approximately 1.7 times that of patients with RC Level 1 (OR, 1.699; 95% CI 1.003 to 2.877; P = 0.049).
Multivariate Logistic Regression Analysis with Respect to the Presence of Severe Stenosis
To determine the risk factors for severe stenosis in all the patients, a multivariate logistic regression analysis was conducted in this study. After adjusting for age, gender, hypertension, diabetes, and smoking history, the results of the multivariate regression model were presented in Table 4. NLPR remained independently associated with severe stenosis (OR, 1.275; 95% CI 1.066 to 1.525; P = 0.008). Compared with patients with NLPR Level 1, those with NLPR Level 3 have approximately twice the risk of severe stenosis (OR, 2.036; 95% CI 1.159 to 3.575; P = 0.013).
Table 4.
Multivariate Logistic Regression Analysis with Respect to the Presence of Severe Stenosis
| Characteristic | B | OR | 95% CI | P |
|---|---|---|---|---|
| NLPR | 0.243 | 1.275 | 1.066 to 1.525 | 0.008 |
| NLPR levels | ||||
| Level 1 | NS | 1 | NS | NS |
| Level 2 | −0.107 | 0.898 | 0.493 to 1.638 | 0.727 |
| Level 3 | 0.711 | 2.036 | 1.159 to 3.575 | 0.013 |
Notes: Adjusted variables: age, gender, hypertension, diabetes, and smoking history.
Abbreviation: NLPR, neutrophil-to-lymphocyte*platelet.
Combined Effects of NLPR with RC and NLPR with LDL/HDL on Severe Stenosis
To investigate the combined effect of NLPR and RC on severe stenosis, this study divided the patients into 4 groups based on the tertiles of NLPR and RC. Group 1 consisted of those with low levels of both NLPR and RC, defined as NLPR ≤ 1.57 and RC ≤ 0.54 mmol/L; Group 2 included those with low NLPR and elevated RC, defined as NLPR ≤ 1.57 and RC > 0.54 mmol/L; Group 3 consisted of those with elevated NLPR and low RC, defined as NLPR > 1.57 and RC ≤ 0.54 mmol/L; Group 4 included those with both elevated NLPR and RC, and the inclusion criteria were NLPR > 1.57 and RC > 0.54 mmol/L. The univariate Logistic regression analysis indicated that the risk of severe stenosis in Group 3 and Group 4 is approximately 2.7 times (OR, 2.703; 95% CI 1.018 to 7.181; P = 0.046) and 4.2 times (OR, 4.169; 95% CI 1.706 to 10.193; P = 0.002) that of Group 1, respectively. After adjusting for variables, the risk of severe stenosis in Group 4 patients was approximately 2.7 times that of Group 1 patients (OR, 2.695; 95% CI 1.050 to 6.914; P = 0.039).
Similarly, the patients were divided into 4 groups based on the tertiles of NLPR and LDL/HDL to investigate the combined effect of NLPR and LDL/HDL on severe stenosis. The specific grouping criteria are as follows: Group 5, NLPR ≤ 1.57, LDL/HDL < 2.15; Group 6, NLPR ≤ 1.57, LDL/HDL ≥ 2.15; Group 7, NLPR > 1.57, LDL/HDL < 2.15; Group 8, NLPR > 1.57, LDL/HDL ≥ 2.15. The patients in Group 6 (OR, 2.272; 95% CI 1.124–4.594; P =0.022), Group 7 (OR, 4.103; 95% CI 1.795–9.378; P =0.001), and Group 8 (OR, 5.424; 95% CI 2.577–11.417; P < 0.001) demonstrated progressively increased risks of severe stenosis relative to those in Group 5. Even after adjusting for variables, this trend persisted, and the risk of severe stenosis for patients in Group 6, Group 7, and Group 8 was approximately 2.1 (OR, 2.121; 95% CI 1.019–4.413; P =0.044), 2.8 (OR, 2.802; 95% CI 1.180–6.654; P =0.020), and 4.5 times (OR, 4.477; 95% CI 2.057,9.746; P < 0.001) that of patients in Group 5, respectively.
The above results are presented in Table 5.
Table 5.
Combined Effects of NLPR with RC and NLPR with LDL/HDL on Severe Stenosis
| n (%) | Model I | Model II | |||
|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | ||
| The combination of NLPR and RC | |||||
| Group 1 | 48(10.1%) | NS | NS | ||
| Group 2 | 268(56.5%) | 1.410(0.599–3.322) | 0.432 | 1.074(0.439–2.629) | 0.875 |
| Group 3 | 57(12.0%) | 2.703(1.018–7.181) | 0.046 | 1.721(0.617–4.799) | 0.299 |
| Group 4 | 101(21.3%) | 4.169(1.706–10.193) | 0.002 | 2.695(1.050–6.914) | 0.039 |
| P for trend | <0.001 | 0.006 | |||
| The combination of NLPR and LDL/HDL | |||||
| Group 5 | 99(20.9%) | NS | NS | ||
| Group 6 | 217(45.8%) | 2.272(1.124–4.594) | 0.022 | 2.121(1.019–4.413) | 0.044 |
| Group 7 | 59(12.4%) | 4.103(1.795–9.378) | 0.001 | 2.802(1.180–6.654) | 0.020 |
| Group 8 | 99(20.9%) | 5.424(2.577–11.417) | <0.001 | 4.477(2.057,9.746) | <0.001 |
| P for trend | <0.001 | 0.001 | |||
Notes: Model I was unadjusted model. Model II adjusted age, gender, hypertension, diabetes, and smoking history.
Abbreviations: NLPR, neutrophil-to-lymphocyte*platelet; RC, residual cholesterol; LDL/HDL, low-density lipoprotein/high-density lipoprotein.
Association of NLPR-RC and NLPR-LDL/HDL with Severe Stenosis
To deeply explore the effects of NLPR and RC, as well as NLPR and LDL/HDL, on severe stenosis, this study combined NLPR and RC, and NLPR and LDL/HDL, and further analyzed the associations of NLPR-RC and NLPR-LDL/HDL with severe stenosis. Before constructing complex indicators, the variance inflation factor (VIF) was used to assess the multicollinearity between NLPR and RC, as well as between NLPR and LDL/HDL. The VIF values were all less than 5, indicating that the multicollinearity was acceptable The results of the logistic regression analysis are presented in Table 6. In the univariate logistic regression analysis, NLPR-RC (OR, 1.278; 95% CI 1.070 to 1.526; P = 0.007) and NLPR-LDL/HDL (OR, 1.192; 95% CI 1.104 to 1.288; P < 0.001) were both associated with severe stenosis; after adjusting for variables, NLPR-RC (OR, 1.211; 95% CI 1.031 to 1.422; P = 0.020) and NLPR-LDL/HDL (OR, 1.159; 95% CI 1.070 to 1.257; P < 0.001) remained independently associated with severe stenosis.
Table 6.
Association of NLPR-RC and NLPR-LDL/HDL with Severe Stenosis
| Model I | Model II | |||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| NLPR-RC | 1.278(1.070–1.526) | 0.007 | 1.211(1.031–1.422) | 0.020 |
| NLPR-LDL/HDL | 1.192(1.104–1.288) | <0.001 | 1.159(1.070–1.257) | <0.001 |
Notes: Model I was unadjusted model. Model II adjusted age, gender, hypertension, diabetes, and smoking history.
Abbreviations: NLPR, neutrophil-to-lymphocyte*platelet; RC, residual cholesterol; LDL/HDL, low-density lipoprotein/high-density lipoprotein.
The Predictive Ability of NLPR, NLPR-RC and NLPR-LDL/HDL for Severe Stenosis
Figure 1 shows the ROC analysis of NLPR, NLPR-RC and NLPR-LDL/HDL in the diagnosis of coronary artery “severe stenosis”. The optimal diagnostic cutoff point for NLPR in diagnosing severe stenosis is 1.47, the sensitivity is 55.5%, the specificity is 69.6%, and the Youden index is 0.251 (area under the curve is 0.630, 95% CI 0.570–0.689, P < 0.001); the optimal diagnostic cutoff point for NLPR-RC is 0.70, the sensitivity is 76.5%, the specificity is 47.6%, and the Youden index is 0.241 (area under the curve is 0.643, 95% CI 0.585–0.700, P < 0.001); the optimal diagnostic cutoff point for NLPR-LDL/HDL is 2.58, the sensitivity is 80.7%, the specificity is 46.2%, and the Youden index is 0.269 (area under the curve is 0.658, 95% CI 0.601–0.714, P < 0.001). NLPR-LDL/HDL has relatively high sensitivity for predicting severe stenosis, and its area under the curve (AUC) is higher than those of NLPR and NLPR-RC. Although the AUC differences did not reach statistical significance, the composite indexes, due to their higher sensitivity, might be applicable for screening severe stenosis.
Figure 1.
The receiver operating characteristic (ROC) curve for the “severe stenosis” status of coronary arteries.
Discussion
In this study, NLPR was positively correlated with the risk of severe coronary artery stenosis. The combined effects of NLPR and RC, as well as NLPR and LDL/HDL, increased the risk of severe stenosis. NLPR-RC and NLPR-LDL/HDL were also independent risk factors for severe coronary artery stenosis. Compared with NLPR, the composite indicators, due to their higher sensitivity, might be applicable for screening severe coronary artery stenosis. However, these composite indicators have an exploratory nature and require prospective and multi-center validation before they can be widely applied.
Current studies indicate that sustained activation of the immune system and inflammatory response occurs throughout the entire process of plaque formation, progression, instability, and even rupture.20–22 NLPR is a new type of inflammatory biomarker that can reflect the intensity of the systemic immune response.23 It is of great value in predicting the prognosis of certain diseases, such as acute kidney injury, chronic gallbladder stones, and COVID-19.3–5 NLPR is calculated from neutrophil, lymphocyte, and platelet counts. Neutrophils are central to innate immunity. Upon activation, they release various proteases and reactive oxygen species, which can directly damage the vascular endothelium, exacerbate plaque instability, and facilitate thrombosis.22,24 Lymphocytes mediate adaptive immunity and immune regulation in humans, exerting dual modulatory effects on atherosclerosis, either inhibitory or proatherogenic.21,25 Platelets are not only central to thrombosis, but the inflammatory mediators released following platelet activation also act as important amplifiers of the inflammatory cascade.26,27 A retrospective case-control study showed that the NLPR levels of patients with carotid atherosclerosis were significantly higher than those of the non-carotid atherosclerosis group. NLPR can be used as an inflammatory marker for predicting the risk of carotid atherosclerosis, which reflects the role of NLPR in the development of atherosclerosis. Furthermore, the value of NLPR in cardiovascular diseases is also quite significant.28
Abnormal lipid metabolism, especially the deposition of cholesterol, is the initiating and core aspect of atherosclerosis.2 LDL-c has been regarded as the primary target of lipid management.29–31 Meanwhile, HDL-c, as an important component of serum cholesterol, has the function of transporting cholesterol from the outside of the liver to the inside of the liver.6,32 Its level is negatively correlated with the risk of CAD. Previous studies have used the ratio of LDL to HDL to assess the formation of atherosclerotic plaques. The results showed that an elevated LDL/HDL ratio increases the risk of coronary heart disease, which is closely related to the rupture of coronary plaques.33,34 RC, defined as the sum of triglyceride-rich lipoproteins, also plays a crucial role in the pathogenesis of atherosclerosis.35 Numerous prospective studies and Mendelian randomization studies have confirmed that even after adjusting for LDL-C and other traditional risk factors, elevated RC levels remain independently associated with the risk of atherosclerotic diseases such as CAD and ischemic stroke.9,36 The pathogenic mechanism may be that RC particles are more likely to penetrate the vascular endothelium, be engulfed by macrophages to form foam cells, and directly participate in and exacerbate the inflammatory response of the vascular wall. In patients with CAD, especially acute coronary syndrome, RC levels are significantly correlated with the prognosis of the patients.37
Chronic inflammation and abnormal lipid metabolism do not exist in isolation. RC and LDL/HDL interact synergistically with the inflammatory pathway during the development of atherosclerosis. Lipid components such as LDL and VLDL can activate vascular endothelial cells and monocytes/ neutrophils in the circulation, promoting the expression of adhesion molecules and pro-inflammatory cytokines.38 Furthermore, the inflammatory state, such as elevated interleukin-6, can also stimulate the liver to synthesize VLDL, leading to a further increase in cholesterol levels and forming a “lipid-inflammation vicious cycle”.39 However, in this study, there is a seemingly contradictory relationship between NLPR and traditional lipid indicators. The highest NLPR group had significantly lower TC, LDL, and LDL/HDL than the lower NLPR groups, despite having higher Gensini scores and a substantially greater prevalence of severe stenosis. This might be related to the use of lipid-lowering drugs. Some participants received unrecorded lipid-lowering treatment and these medications may reduce peripheral lipid concentrations without synchronously resolving local vascular inflammation, thereby decoupling conventional lipid markers from this inflammatory index. Information on lipid-lowering medication was unavailable in the current study, so quantitative assessment of this confounding effect could not be performed. Future studies should collect data on lipid-lowering therapy to further clarify its influence on NLPR. Emerging evidence indicates that combining inflammatory and lipid biomarkers enhances the predictive efficacy of metabolic disorders and cardiovascular risk.40,41 This study investigated the effect of NLPR combined with lipid metabolism indicators on severe coronary artery stenosis. Meanwhile, complex indicators, NLPR-LDL/HDL and NLPR-RC, were constructed to quantify the synergistic interaction of lipid metabolism and inflammatory pathways on coronary atherosclerosis. The results showed that elevated LDL/HDL alone, elevated NLPR alone, and both elevated LDL/HDL and NLPR were independently associated with an increased risk of severe coronary artery stenosis. However, only both elevated RC and NLPR were independently associated with an increased risk of severe coronary artery stenosis. NLPR-LDL/HDL and NLPR-RC are positively correlated with the risk of severe coronary artery stenosis. In the ROC curve analysis, the AUC of NLPR-LDL/HDL was the largest, although the differences in AUCs were not statistically significant. NLPR interacts with RC and LDL/HDL synergistically to promote the initiation and progression of coronary atherosclerosis. This finding reinforces the importance of comprehensive evaluation of RC, LDL/HDL and NLPR levels in clinical practice. Therefore, patients with abnormal values of the aforementioned indicators may require more frequent follow-ups to reduce the risk of major adverse cardiovascular events (MACEs).
Previous studies often combined inflammatory markers such as NLR with traditional lipid indicators to assess the severity of coronary artery disease. In this study, we innovatively replaced NLR with NLPR to more comprehensively reflect the immune and inflammatory status. We constructed two composite indicators: NLPR-LDL/HDL and NLPR-RC. Currently, few studies have simultaneously constructed these lipid indicators based on NLPR as composite indicators and analyzed their correlation with coronary atherosclerosis. This study also has some limitations. Firstly, it is a single-center cross-sectional study with a small sample size. Secondly, the levels of RC, LDL/HDL and NLPR were only assessed at one time point, ignoring the cumulative effects of these indicators over time. Thirdly, all the included research subjects were patients who underwent CAG due to confirmed or suspected CAD. Finally, the composite indicators were constructed exploratorily, and only internal validation has been completed. No independent external validation has been conducted yet, and the generalization ability of the model needs to be tested; the cutoff value of the model is derived from the current research cohort, and it may deviate in other populations. Subsequent independent external cohort validation needs to be carried out to evaluate the model’s extrapolation performance; on this basis, a prospective multicenter cohort study should be further conducted to fix the model parameters and risk thresholds, and to confirm the discrimination ability, calibration status, and net benefit of clinical decision-making of this composite index in multiple centers in real populations, and to assess whether it can be used for clinical risk stratification.
Conclusion
NLPR, NLPR-RC, and NLPR-LDL/HDL are independently associated with an increased risk of severe coronary artery stenosis. Compared with NLPR, the composite indicators, due to their higher sensitivity, might be applicable for screening severe coronary artery stenosis. However, these composite indicators have an exploratory nature and require prospective and multi-center validation before they can be widely applied.
Acknowledgments
The authors thank all patients who participated in this study for their cooperation and support.
Funding Statement
The study did not receive any specific funding from any funding agency in the public, commercial or non-profit sectors.
Data Sharing Statement
The data used to support the findings of this study are available from the corresponding author upon request.
Ethics Approval
Ethics Committee of the Second Affiliated Hospital of Nanjing University of Chinese Medicine (2025SEZKY-098-02).
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that there is no conflict of interest regarding the publication of this paper.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.