Abstract
Objective
Lipoprotein(a) (Lp[a]) and the atherogenic index of plasma (AIP) have been reported as predictive markers of coronary artery calcium (CAC). However, previous studies demonstrated that the cardiovascular risk associations with Lp(a) are attenuated in patients with low-density lipoprotein cholesterol (LDL-C) levels ≤135 mg/dL. However, few articles have identified the risk factors of CAC in patients without high LDL-C. Therefore, we performed this study to investigate the association of Lp(a) and AIP with CAC in patients with LDL-C levels ≤135 mg/dL.
Methods
This study included 625 lipid-lowering agent naive patients with LDL-C levels ≤135 mg/dL who underwent coronary computed tomographic angiography. We performed multivariate logistic regression analysis to evaluate the risk factors for a coronary artery calcium score (CACS) >0, CACS ≥400, and CAC ≥90th percentile.
Results
The mean age of the patients was 55.0±7.9 years and their mean LDL-C level was 94.7 ±23.3 mg/dL. Multivariate regression analysis showed that age, male sex, diabetes, hypertension, Lp(a), and AIP were independent predictors of CAS>0. Age, male sex, and diabetes were independent predictors of CACS≥400. Diabetes, hypertension, and AIP were independent predictors of CAC ≥90th percentile (all p<0.05). Unlike Lp(a), higher AIP tertiles were associated with significantly higher CAC percentiles and greater proportions of patients with CACS ≥400 and CAC ≥90th percentile.
Conclusion
In patients without high LDL-C, AIP could be a more reliable predictor of CAC than Lp(a).
Keywords: Vascular calcification, Lipoprotein (a), Low density lipoprotein cholesterol
INTRODUCTION
High low-density lipoprotein cholesterol (LDL-C) levels are a major risk factor for coronary artery disease (CAD). However, CAD also occurs in patients without high LDL-C. Sachdeva et al. reported that approximately 75% of patients admitted to a hospital with a CAD event demonstrated a relatively normal LDL-C level of less than 130 mg/dL, and 23% had an LDL-C level of less than 70 mg/dL.1 To prevent CAD in patients without high LDL-C, it is necessary to identify CAD-causing lipoproteins other than LDL-C.
Coronary artery calcium (CAC) has been reported as a marker of coronary artery atherosclerosis and a predictor of future atherosclerotic cardiovascular disease (ASCVD).2,3 Many studies have reported that small dense LDL-C (sdLDL) is the most powerful atherosclerotic lipoprotein parameter for predicting CAD, even more powerful than LDL-C.4,5 Previous articles have shown that the atherogenic index of plasma (AIP), defined as the logarithm of the ratio of plasma concentration of triglycerides (TGs) to high-density lipoprotein cholesterol (HDL-C) had a significant correlation with LDL particle size. Therefore, AIP has been described as a surrogate marker for sdLDL.6 Lipoprotein(a) (Lp[a]) is a low-density lipoprotein-like particle and apolipoprotein(a) is attached to apolipoprotein B via a disulfide bridge. Among several atherogenic lipoproteins, Lp(a) and AIP have demonstrated strong associations with CAC.7,8,9,10,11,12 In the ARIC study, an abundance of sdLDL predicted CAD events, even in the group with LDL-C levels <100 mg/dL.13 In addition, the JUPITER study confirmed the CAD risk associated with sdLDL, even in patients treated with rosuvastatin and with an average LDL-C levels of 54 mg/dL.14 However previous studies demonstrated that the ASCVD risk associations with Lp(a) were attenuated in patients with LDL-C levels below 135 mg/dL.15,16 To date, few articles have compared the associations of Lp(a) and AIP with CAC in patients without high LDL-C. Therefore, we performed this study to compare the associations of Lp(a) and AIP with CAC in patients with LDL-C levels ≤135 mg/dL.
MATERIALS AND METHODS
1. Study population and data collection
The study population was selected from the coronary computed tomographic angiography (CCTA) registry of our center. Between January 2013 and September 2020, 3,696 Korean patients who visited our hospital for chest discomfort underwent CCTA and lipid profile evaluation (total cholesterol [TC], LDL-C, HDL-C, TG, apolipoprotein [apo] A1, apo B, and Lp[a]). Total lipids and lipid subclass levels were measured with the patients in a fasting state (>8 hours after the last meal). Among the 3,696 patients, 1,207 patients had LDL-C levels ≤135 mg/dL. Of the 1,207 patients, 312 patients who underwent percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG), 235 patients who were maintained on lipid-lowering drugs (including statins), 30 patients with end-stage renal disease, and 5 patients with motion artifacts on CCTA were excluded. Finally, 625 patients were included in the analysis. The CCTA analysis was performed on 625 patients. The inclusion and exclusion criteria are shown in a flow diagram (Fig. 1). Among 625 patients, revascularization was performed after CCTA in patients with typical angina pectoris who met the following criteria: 1) Left main disease with stenosis >50%, 2) Proximal left anterior descending artery stenosis >50%, 3) 2- or 3-vessel disease with stenosis >50% with impaired left ventricular function (ejection fraction ≤35%), 4) Large area of ischemia detected by functional testing (>10%) or abnormal invasive fractional flow reserve, 5) A single remaining patent coronary artery with stenosis >50%, 6) Hemodynamically significant coronary stenosis in the presence of limiting angina or angina equivalent, with insufficient response to optimized medical therapy. The choice between CABG and PCI was made after an assessment of surgical risk and CAD complexity.17 The coronary revascularization rate was investigated. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. The Institutional Review Board of Daegu Catholic University Medical Center approved the study and waived the requirement for patients to provide informed consent because of the study’s retrospective nature (CR-22-031-L).
Fig. 1. Enrollment flow chart for analysis.
AIP, atherogenic index of plasma; CAC, coronary artery calcium; CACS, coronary artery calcium score; CCTA, coronary computed tomographic angiography; ESRD, end-stage renal disease; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein(a); PCI, percutaneous coronary intervention.
2. Acquisition and analysis of CCTA images
Computed tomography (CT) scans were performed with a 256-slice CT device (Definition Flash; Siemens Healthineers AG, Erlangen, Germany) or a 512-slice CT (Revolution CT; GE Healthcare, Chicago, IL, USA). All patients with an initial heart rate ≥60 beats/min were given an oral beta-blocker (propranolol 20 mg) to achieve a target heart rate of 50 to 60 beats/min. Sublingual nitroglycerin was administered immediately before scanning. An iodine contrast agent (60–70 mL) was administered into the antecubital vein within 10 seconds followed by 25 mL of saline solution injected at 5.0 mL/second. The CT-reconstructed imaging data were transferred to a GE Centricity system (GE Healthcare Bio-Sciences Corp., Piscataway, NJ, USA) for postprocessing and subsequent image analysis. A radiologist read each scan independently at a central reading center. Plaque incidence and severity were investigated. Plaques were defined as structures ≥1 mm2 within and/or adjacent to the vessel lumen and were clearly distinguishable from the lumen and the surrounding pericardial tissue.18 Stenosis of 50% or more in 1 vessel was defined as 1 vessel disease, and stenosis of 50% or more in 2 or more vessels was defined as multivessel disease. The coronary artery calcium score (CACS) was calculated with the Agatston method using a commercially available reconstruction program for 3-dimensional reconstruction and measurement (Aquarius iNtuition TM Ver.4.4.12; TeraRecon, Foster City, CA, USA).19,20 CACS >0 was defined as detectable coronary artery calcium.10 The CAC percentiles reported followed the results of Hoff et al.21
3. Statistical analysis
Data were expressed as number (%) and mean ± standard deviation. Categorical data were compared using the χ2 test or the Fisher exact test. Continuous variables were compared using the Student’s t-test and Kruskal–Wallis H test when they were normally and non-normally distributed, respectively. We divided patients into 3 groups according to CACS (CACS =0, 0< CACS <400, and CACS ≥400) and CAC percentile (CACS =0, CACS >0 and CAC <90th percentile, and CAC ≥90th percentile) to compare clinical characteristics and lipid data in each group (Table 1). Hypertension (HTN) was defined as a systolic blood pressure ≥140 mm Hg or a diastolic blood pressure ≥90 mm Hg or current antihypertensive treatment. Diabetes mellitus (DM) was defined as follows: a fasting plasma glucose concentration ≥126 mg/dL, a 2-hour plasma glucose concentration ≥200 mg/dL on a standard 75-g oral glucose tolerance test, hemoglobin a1c ≥6.5%, or current treatment of diabetes.22 We investigated the smoking history of patients and classified both current and past smokers as having a smoking history. The estimated glomerular filtration rate (eGFR) was investigated as an indicator of renal function. Univariate analysis using logistic regression was performed to identify potential independent predictors of CACS >0, CACS ≥400 and CAC ≥90th percentile. Variables with a p-value <0.05 in the univariate analysis were included in the multivariate analysis to identify independent predictors of CACS >0, CACS ≥400 and CAC ≥90th percentile. The p-value <0.05 was considered to indicate statistical significance. Statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA).
Table 1. Characteristics of individuals according to CACS and CAC percentiles.
| Variables | Total patients (n=625) | CACS =0 (n=313) | 0< CACS <400 (n=229) | CACS ≥400 (n=83) | p-value | CACS =0 (n=313) | CACS >0 and CAC <90th percentile (n=235) | CAC ≥90th percentile (n=77) | p-value |
|---|---|---|---|---|---|---|---|---|---|
| Age (yr) | 55.0±7.9 | 52.3±8.6 | 57.1±6.0 | 59.4±5.4 | <0.001 | 52.3±8.6 | 58.2±5.4 | 56.3±7.2 | <0.001 |
| Male | 390 (62.4) | 148 (47.3) | 177 (77.3) | 65 (78.3) | <0.001 | 148 (47.3) | 196 (83.4) | 46 (59.7) | <0.001 |
| Diabetes | 143 (22.9) | 42 (13.4) | 61 (26.6) | 40 (48.2) | <0.001 | 42 (13.4) | 64 (27.2) | 37 (48.1) | <0.001 |
| HTN | 242 (38.7) | 94 (30.0) | 102 (44.5) | 46 (55.4) | <0.001 | 94 (30.0) | 106 (45.1) | 42 (54.5) | <0.001 |
| Smoking | 205 (32.8) | 90 (28.8) | 89 (38.9) | 26 (31.3) | 0.044 | 90 (28.8) | 90 (38.8) | 25 (32.5) | 0.062 |
| SBP (mmHg) | 126.1±17.5 | 126.3±17.3 | 125.2±18.0 | 128.3±16.8 | 0.362 | 126.3±17.3 | 125.2±17.5 | 128.6±18.4 | 0.326 |
| DBP (mmHg) | 75.7±11.9 | 75.6±12.1 | 75.5±11.8 | 76.4±11.4 | 0.827 | 75.6±12.1 | 75.5±11.9 | 76.4±11.1 | 0.845 |
| BMI (kg/m2) | 24.4±3.60 | 24.7±3.94 | 24.1±3.28 | 24.0±2.91 | 0.060 | 24.7±3.94 | 24.1±3.22 | 24.0±3.08 | 0.061 |
| eGFR (mL/min/1.73 m2) | 93.7±18.1 | 97.4±16.7 | 91.2±17.5 | 86.6±21.3 | <0.001 | 97.4±16.7 | 90.7±16.3 | 87.8±24.3 | <0.001 |
| FBG (mg/dL) | 114.0±44.2 | 108.3±41.4 | 114.2±37.2 | 134.9±62.7 | <0.001 | 108.3±41.4 | 116.3±39.0 | 130.0±62.5 | <0.001 |
| TC (mg/dL) | 154.5±27.8 | 156.5±27.5 | 153.9±28.5 | 148.6±26.2 | 0.065 | 156.5±27.5 | 153.3±28.0 | 149.8±27.8 | 0.126 |
| TG (mg/dL) | 121.6±90.4 | 116.9±88.1 | 124.4±96.8 | 131.8±79.9 | 0.344 | 116.9±88.1 | 118.3±82.2 | 151.1±115.8 | 0.009 |
| HDL-C (mg/dL) | 46.6±15.1 | 48.7±16.1 | 45.2±13.1 | 42.8±15.2 | 0.001 | 48.7±16.1 | 45.5±13.2 | 41.7±15.0 | <0.001 |
| LDL-C (mg/dL) | 94.7±23.3 | 95.4±22.8 | 94.9±24.6 | 91.3±21.2 | 0.348 | 95.4±22.8 | 94.9±24.2 | 90.9±22.3 | 0.303 |
| Apo B (mg/dL) | 85.9±18.6 | 85.0±18.1 | 87.2±19.5 | 85.3±18.2 | 0.374 | 85.0±18.1 | 86.8±19.8 | 86.5±17.3 | 0.506 |
| Apo A1 (mg/dL) | 128.2±30.1 | 131.8±29.1 | 125.9±28.9 | 121.6±34.9 | 0.008 | 131.8±29.1 | 125.7±29.1 | 121.6±34.8 | 0.008 |
| Lp(a) (mg/dL) | 20.3±24.0 | 16.2±17.2 | 24.9±29.0 | 23.4±28.1 | <0.001 | 16.2±17.2 | 25.3±29.2 | 22.0±27.5 | <0.001 |
| TG/HDL-C | 3.15±3.42 | 3.02±3.81 | 3.19±3.12 | 3.53±2.58 | 0.474 | 3.02±3.81 | 3.00±2.67 | 4.15±3.68 | 0.023 |
| AIP | 0.36±0.32 | 0.33±0.34 | 0.38±0.31 | 0.44±0.30 | 0.007 | 0.33±0.34 | 0.36±0.30 | 0.50±0.31 | <0.001 |
Data are given as mean ± standard deviation, or as number (%).
CACS, coronary artery calcium score; CAC, coronary artery calcium; HTN, hypertension; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Apo B, apolipoprotein B; Apo A1, apolipoprotein A1; Lp(a), lipoprotein(a); AIP, atherogenic index of plasma.
RESULTS
The mean age and LDL-C level of the 625 patients were 55.0±7.9 years and 94.7±23.3 mg/dL, respectively. Of the total patients, 62.4% were male and 22.9% had diabetes. After CCTA, 79 cases of revascularization were performed, of which 77 were PCI and 2 were CABG. The mean CACS and CAC percentile of all patients were 194.3±573.0 and 32.5%±38.1%, respectively. CACS >0 was present in 49.9% of the total patients, CACS ≥400 in 13.3% of all patients and CAC ≥90th percentile in 12.3% of the patients.
1. Characteristics of individuals according to CACS and CAC percentiles
The characteristics of individuals according to CACS and CAC percentiles are presented in Table 1. In a comparison among CACS =0, 0< CACS <400, and CACS ≥400, the CACS ≥400 group showed significantly higher age, fasting blood glucose (FBG), and AIP than the other 2 groups. The CACS ≥400 group showed significantly lower eGFR, HDL-C, and apo A1 than the other 2 groups. The proportions of men, patients with DM, and patients with HTN were significantly higher in the CACS ≥400 group than in the other 2 groups. The lowest AIP was found in the CACS =0: it was higher in the 0< CACS <400 group and highest in the CACS ≥400 group. However, there was no significant difference in Lp(a) between the 0< CACS <400 and CACS ≥400 groups. In a comparison among CACS =0, CAC <90th percentile (CACS >0), and CAC ≥90th percentile, the CAC ≥90th percentile group showed significantly higher FBG, TG, TG/HDL-C ratio and AIP than the other 2 groups. Furthermore, the CAC ≥90th percentile group showed significantly lower eGFR, HDL-C, and apo A1 than the other 2 groups. The proportions of patients with DM, and HTN were significantly higher in the CAC ≥90th percentile group than in the other 2 groups. The lowest AIP was observed in the CACS =0 group, while higher levels were observed in the CAC <90th percentile (CACS >0) group, and the highest values were found in the CAC ≥90th percentile group. However, there was no significant difference in Lp(a) between the CAC <90th percentile (CACS >0), and CAC ≥90th percentile groups.
2. Independent predictors of CACS >0, CACS ≥400, and CAC ≥90th percentile
According to the multivariate regression analysis, age (odds ratio [OR], 1.108; 95% confidence interval [CI], 1.069–1.148; p<0.001), male (OR, 4.687; 95% CI, 2.755–7.973; p<0.001), DM (OR, 2.481; 95% CI, 1.436–4.285; p=0.001), HTN (OR, 1.883; 95% CI, 1.198–2.960; p=0.006), Lp(a) (OR, 1.020; 95% CI, 1.010–1.029; p<0.001) and AIP (OR, 2.064; 95% CI, 1.041–4.094; p=0.038) were independently associated with CACS >0 (Table 2). Age (OR, 1.113; 95% CI, 1.057–1.171; p<0.001), male (OR, 2.115; 95% CI, 1.003–4.460; p=0.049), and DM (OR, 2.779; 95% CI, 1.522–5.076; p=0.001) were independent predictors for CACS ≥400 (Table 3). DM (OR, 2.872; 95% CI, 1.607–5.134; p<0.001), HTN (OR, 1.765; 95% CI, 1.024–3.043; p=0.041) and AIP (OR, 3.233; 95% CI, 1.415–7.389; p=0.005) were independent predictors for CAC ≥90th percentile (Table 4).
Table 2. Independent predictors for CACS >0.
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | |
| Age | 1.106 | 1.079–1.134 | <0.001 | 1.108 | 1.069–1.148 | <0.001 |
| Male | 3.854 | 2.726–5.450 | <0.001 | 4.687 | 2.755–7.973 | <0.001 |
| Diabetes | 3.089 | 2.065–4.618 | <0.001 | 2.481 | 1.436–4.285 | 0.001 |
| HTN | 2.102 | 1.514–2.920 | <0.001 | 1.883 | 1.198–2.960 | 0.006 |
| Smoking history | 1.446 | 1.034–2.024 | 0.031 | 0.920 | 0.527–1.605 | 0.768 |
| BMI | 0.948 | 0.906–0.991 | 0.019 | 0.953 | 0.896–1.013 | 0.119 |
| SBP | 0.999 | 0.990–1.008 | 0.848 | |||
| DBP | 1.001 | 0.988–1.014 | 0.911 | |||
| eGFR | 0.975 | 0.965–0.985 | <0.001 | 0.994 | 0.982–1.006 | 0.290 |
| LDL-C | 0.997 | 0.991–1.004 | 0.423 | |||
| Apo B | 1.005 | 0.997–1.014 | 0.245 | |||
| Apo A1 | 0.992 | 0.987–0.997 | 0.004 | 0.996 | 0.989–1.003 | 0.235 |
| Lp(a) | 1.016 | 1.008–1.023 | <0.001 | 1.020 | 1.010–1.029 | <0.001 |
| AIP | 2.007 | 1.225–3.288 | 0.006 | 2.064 | 1.041–4.094 | 0.038 |
CACS, coronary artery calcium score; OR, odds ratio; CI, confidence interval; HTN, hypertension; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; Apo B, apolipoprotein B; Apo A1, apolipoprotein A1; Lp(a), lipoprotein(a); AIP, atherogenic index of plasma.
Table 3. Independent predictors for CACS ≥400.
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | |
| Age | 1.126 | 1.077–1.177 | <0.001 | 1.113 | 1.057–1.171 | <0.001 |
| Male | 2.411 | 1.392–4.177 | 0.002 | 2.115 | 1.003–4.460 | 0.049 |
| Diabetes | 3.965 | 2.451–6.414 | <0.001 | 2.779 | 1.522–5.076 | 0.001 |
| HTN | 2.195 | 1.376–3.501 | 0.001 | 1.581 | 0.911–2.743 | 0.104 |
| Smoking history | 0.925 | 0.563–1.521 | 0.759 | |||
| BMI | 0.961 | 0.899–1.028 | 0.244 | |||
| SBP | 1.008 | 0.995–1.021 | 0.221 | |||
| DBP | 1.006 | 0.987–1.026 | 0.548 | |||
| eGFR | 0.978 | 0.967–0.990 | <0.001 | 0.997 | 0.983–1.011 | 0.633 |
| LDL-C | 0.993 | 0.983–1.003 | 0.153 | |||
| Apo B | 0.988 | 0.986–1.011 | 0.787 | |||
| Apo A1 | 0.991 | 0.983–0.999 | 0.031 | 0.996 | 0.986–1.005 | 0.392 |
| Lp(a) | 1.006 | 0.997–1.014 | 0.211 | |||
| AIP | 2.391 | 1.194–4.789 | 0.014 | 1.503 | 0.601–3.761 | 0.384 |
CACS, coronary artery calcium score; OR, odds ratio; CI, confidence interval; HTN, hypertension; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; Apo B, apolipoprotein B; Apo A1, apolipoprotein A1; Lp(a), lipoprotein(a); AIP, atherogenic index of plasma.
Table 4. Independent predictors for CAC percentile ≥90th percentile.
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p-value | OR | 95% CI | p-value | |
| Diabetes | 3.857 | 2.352–6.326 | <0.001 | 2.872 | 1.607–5.134 | <0.001 |
| HTN | 2.088 | 1.291–3.378 | 0.003 | 1.765 | 1.024–3.043 | 0.041 |
| Smoking history | 0.983 | 0.591–1.636 | 0.947 | |||
| BMI | 0.965 | 0.900–1.034 | 0.313 | |||
| SBP | 1.009 | 0.996–1.023 | 0.192 | |||
| DBP | 1.006 | 0.986–1.026 | 0.569 | |||
| eGFR | 0.982 | 0.970–0.994 | 0.003 | 0.991 | 0.978–1.003 | 0.155 |
| LDL-C | 0.992 | 0.982–1.002 | 0.127 | |||
| Apo B | 1.002 | 0.989–1.015 | 0.746 | |||
| Apo A1 | 0.991 | 0.983–1.000 | 0.039 | 0.997 | 0.988–1.007 | 0.589 |
| Lp(a) | 1.003 | 0.994–1.013 | 0.536 | |||
| AIP | 4.320 | 2.103–8.871 | <0.001 | 3.233 | 1.415–7.389 | 0.005 |
CACS, coronary artery calcium score; OR, odds ratio; CI, confidence interval; HTN, hypertension; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; Apo B, apolipoprotein B; Apo A1, apolipoprotein A1; Lp(a), lipoprotein(a); AIP, atherogenic index of plasma.
3. Characteristics of CCTA according to tertiles of the AIP and Lp(a)
The characteristics of CCTA stratified by tertiles of based on the AIP and Lp(a) are shown in Table 5. The cut-off points of AIP between tertiles were 0.209 (between the first and second tertiles) and 0.485 (between the second and third tertiles). The cut-off points of Lp(a) between tertiles were 6.6 (between the first and second tertiles) and 18.9 (between the second and third tertiles). In a comparison of CACS and CAC percentiles according to the tertiles of AIP, the CACS and CAC percentiles increased as the tertiles of AIP increased. However, no positive correlation was observed between the CACS or CAC percentiles and the tertiles of Lp(a) (Fig. 2). When comparing the tertiles of AIP, significant increase in CAC percentile, and the proportion of patients with CACS ≥400, CAC ≥90th percentile, plaque, multivessel disease, revascularization were observed in ascending order from tertiles 1 to tertiles 2, and 3. In a comparison of the tertiles of Lp(a), no significant relationships were found with the CAC percentile or the proportions of patients with CACS ≥400, CAC ≥90th percentile, plaque, multivessel disease, and revascularization (Table 5).
Table 5. Characteristics of coronary computed tomographic angiography stratified by tertiles of the Lp(a) and AIP.
| Variables | Total patients (n=625) | Tertile 1 of the AIP <0.209 (n=208) | Tertile 2 of the AIP 0.209–0.485 (n=209) | Tertile 3 of the AIP >0.485 (n=208) | p-value | Total patients (n=625) | Tertile 1 of the Lp(a) <6.6 (n=207) | Tertile 2 of the Lp(a) 6.6–18.9 (n=206) | Tertile 3 of the Lp(a) >18.9 (n=210) | p-value |
|---|---|---|---|---|---|---|---|---|---|---|
| CACS | 194.3±573.0 | 134.7±415.7 | 189.4±586.7 | 258.9±680.7 | 0.086 | 194.3±573.0 | 188.3±540.4 | 169.7±473.9 | 218.9±682.5 | 0.677 |
| CACS >0 | 312 (49.9) | 90 (43.3) | 113 (54.1) | 109 (52.4) | 0.060 | 312 (49.9) | 99 (47.8) | 91 (44.2) | 120 (57.1) | 0.024 |
| CACS ≥400 | 83 (13.3) | 21 (10.1) | 22 (10.5) | 40 (19.2) | 0.008 | 83 (13.3) | 28 (13.5) | 26 (12.6) | 28 (13.3) | 0.960 |
| CAC percentile | 32.5±38.1 | 27.4±36.0 | 33.0±36.6 | 37.0±41.0 | 0.037 | 32.5±38.1 | 32.9±38.5 | 29.9±38.3 | 34.0±37.3 | 0.528 |
| ≥90th percentile | 77 (12.3) | 15 (7.2) | 21 (10.0) | 41 (19.7) | <0.001 | 77 (12.3) | 28 (13.5) | 26 (12.6) | 22 (10.5) | 0.620 |
| CAC volume (mm3) | 155.1±452.0 | 110.4±334.5 | 147.8±459.6 | 207.0±535.6 | 0.089 | 155.1±452.0 | 151.2±421.3 | 136.6±378.4 | 172.6±539.0 | 0.716 |
| CAC mass (mg) | 39.1±123.8 | 29.0±101.0 | 34.8±117.6 | 53.4±147.6 | 0.110 | 39.1±123.8 | 38.1±114.7 | 36.2±112.1 | 41.6±142.2 | 0.901 |
| Presence of plaque | 393 (62.9) | 114 (54.8) | 136 (65.1) | 143 (68.8) | 0.010 | 393 (62.9) | 131 (63.3) | 122 (59.2) | 138 (65.7) | 0.385 |
| Multivessel disease | 59 (9.4) | 9 (4.3) | 22 (10.5) | 28 (13.5) | 0.005 | 59 (9.4) | 13 (6.3) | 19 (9.2) | 26 (12.4) | 0.100 |
| Revascularization | 79 (12.7) | 15 (7.2) | 25 (12.0) | 39 (18.8) | 0.002 | 79 (12.7) | 21 (10.1) | 27 (13.1) | 31 (14.8) | 0.357 |
Data are given as mean±standard deviations, or as number (%).
Lp(a), lipoprotein(a); AIP, atherogenic index of plasma; CACS, coronary artery calcium score; CAC, coronary artery calcium.
Fig. 2. Comparison of mean CACS between the tertiles of the Lp(a) (A). Comparison of mean CACS between the tertiles of the AIP (B). Comparison of mean CAC percentile between the tertiles of the Lp(a) (C). Comparison of mean CAC percentile between the tertiles of the AIP (D). The column bar graph demonstrated the mean CACS or mean CAC percentile with a 95% confidence interval.
CACS, coronary artery calcium score; Lp(a), lipoprotein(a); AIP, atherogenic index of plasma; CAC, coronary artery calcium.
DISCUSSION
The primary findings of our study were as follows: In Korean patients with LDL-C levels below 135 mg/dL, 1) both Lp(a) and AIP were independent predictors of CACS >0, 2) AIP was an independent predictor of CAC ≥90th percentile and 3) in contrast to Lp(a), higher AIP tertiles were significantly associated with a higher CAC percentile and greater proportions of patients with CACS ≥400 and CAC ≥90th percentile.
The current methods for assessing the severity of CAC include absolute CACS and age-, sex-specific CAC percentiles. Both CACS and CAC percentiles have been identified as strong predictors of cardiovascular events.23,24 The absence of CAC (CACS =0) has been associated with a very low risk of future cardiovascular events25,26, while CACS ≥400 and CAC ≥90th percentile have been identified as the highest cardiovascular risk groups.23,27,28 We investigated the predictors of CACS >0, CACS ≥400, and CAC ≥90th percentile to identify risk factors for CAC. Previous studies have demonstrated strong associations of Lp(a) and AIP with CAC.7,8,9,10,11,12 However, several studies have shown a weakened association between Lp(a) and ASCVD risk in patients without high LDL-C.15,16,29,30 Therefore, whether a consistent association exists between Lp(a) and CAC is questionable in patients without high LDL-C. In the present study, Lp(a) did not predict CACS ≥400 and CAC ≥90th percentile whereas, AIP independently predicted both CAC >0 and CAC ≥90th percentile. In addition, in contrast to Lp(a), higher AIP tertiles showed the significant associations with higher CAC percentiles and greater proportions of patients with CACS ≥400 and CAC ≥90th percentile (Table 5). The fact that atherosclerosis is a time-dependent pathological change may explain why, according to the multivariate analysis, AIP predicted CAC ≥ 90th percentile, but not CACS ≥400. That is, it is difficult to predict the current CACS based on the current AIP. However, AIP can better predict CAC ranking in specific groups defined according to age and sex. Considering the above results, AIP could be a more reliable marker than Lp(a) for predicting CAC in patients without high LDL-C.
Our findings support the view that the correlation between Lp(a) and CAD risk is attenuated in patients with low LDL-C levels. The biological evidence underlying to this association is not yet fully understood. However, Zhu et al.30 presented the following suggestions. Patients with very low LDL-C levels tend to have high levels of activity of LDL receptors and a strong metabolic capacity for Lp(a). Even high Lp(a) levels can be metabolized in a timely manner, and its biological effects are attenuated. Conversely, patients with high LDL-C levels have low levels or activity of LDL receptors and Lp(a) is not efficiently metabolized, resulting in amplified biological effects.30 Several papers have demonstrated a strong association between sdLDL and arterial calcification.31,32 Previous articles have shown that AIP had a significant correlation with LDL particle size and that it could be a marker of sdLDL.6 High AIP has been associated with a high incidence of ASCVD, as well as high CACS.33,34,35 In this study, higher AIP tertiles were significantly associated with higher proportions of patients with plaque, multivessel disease, and revascularization. However, tertiles of Lp(a) showed no significant relationships with the proportions of patients with plaque, multivessel disease, and revascularization The present study demonstrated that AIP plays an important role in both coronary calcification and atherosclerosis in patients without high LDL-C. Efforts to reduce AIP in patients without high LDL-C may further reduce ASCVD risk. Obesity and smoking are well known to be associated with high AIP.36,37,38,39 We expect that future studies will demonstrate that regulating these 2 factors prevents the generation of CAC or inhibits its progression.
This study has limitations. First, this was a single-center study. Second, the study population was composed of Koreans. Studies with large numbers of patients or participants of different races are needed to confirm and generalize our findings. Third, because our study targeted patients who underwent CCTA for chest discomfort, the plaque incidence, and CACS were high. Therefore, in order to apply the results of this study to the general population, a large-scale study including asymptomatic patients should be conducted. However, unlike previous papers that compared the relationship between Lp(a) or AIP with CAC separately, this study is the first to compared the associations of Lp(a) and AIP with CAC. Moreover, our study was the first to demonstrate that AIP could be a more reliable marker than Lp(a) for CAC prediction in patients without high LDL-C. Although LDL-C is the main target for preventing coronary atherosclerosis, ASCVD still occurs in patients without high LDL-C.1 Thus, our study suggests that different atherogenic lipoprotein parameters according to the patient's LDL-C level should be used to predict residual ASCVD risk.
Footnotes
Funding: This work was supported by a research grant from Daegu Catholic University Medical Center.
Conflict of Interest: The authors have no conflicts of interest to declare.
Data Availability Statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
- Conceptualization: Hong SP, Kim CY, Jung HW.
- Data curation: Hong SP, Kim CY, Jung HW.
- Formal analysis: Hong SP, Kim CY, Jung HW.
- Funding acquisition: Hong SP, Jung HW.
- Investigation: Hong SP, Kim CY, Jung HW.
- Methodology: Hong SP, Kim CY, Jung HW.
- Project administration: Hong SP, Kim CY, Jung HW.
- Resources: Hong SP, Kim CY, Jung HW.
- Software: Hong SP, Kim CY, Jung HW.
- Supervision: Hong SP, Kim CY, Jung HW.
- Validation: Hong SP, Jung HW.
- Visualization: Hong SP, Jung HW.
- Writing - original draft: Hong SP, Jung HW.
- Writing - review & editing: Hong SP, Jung HW.
References
- 1.Sachdeva A, Cannon CP, Deedwania PC, Labresh KA, Smith SC, Jr, Dai D, et al. Lipid levels in patients hospitalized with coronary artery disease: an analysis of 136,905 hospitalizations in get with the guidelines. Am Heart J. 2009;157:111–117.e2. doi: 10.1016/j.ahj.2008.08.010. [DOI] [PubMed] [Google Scholar]
- 2.Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336–1345. doi: 10.1056/NEJMoa072100. [DOI] [PubMed] [Google Scholar]
- 3.Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291:210–215. doi: 10.1001/jama.291.2.210. [DOI] [PubMed] [Google Scholar]
- 4.Ikezaki H, Lim E, Cupples LA, Liu CT, Asztalos BF, Schaefer EJ. Small dense low-density lipoprotein cholesterol is the most atherogenic lipoprotein parameter in the prospective Framingham Offspring Study. J Am Heart Assoc. 2021;10:e019140. doi: 10.1161/JAHA.120.019140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Superko H, Garrett B. Small dense LDL: scientific background, clinical relevance, and recent evidence still a risk even with 'Normal' LDL-C levels. Biomedicines. 2022;10:829. doi: 10.3390/biomedicines10040829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)) Clin Biochem. 2001;34:583–588. doi: 10.1016/s0009-9120(01)00263-6. [DOI] [PubMed] [Google Scholar]
- 7.Won KB, Heo R, Park HB, Lee BK, Lin FY, Hadamitzky M, et al. Atherogenic index of plasma and the risk of rapid progression of coronary atherosclerosis beyond traditional risk factors. Atherosclerosis. 2021;324:46–51. doi: 10.1016/j.atherosclerosis.2021.03.009. [DOI] [PubMed] [Google Scholar]
- 8.Won KB, Han D, Lee JH, Choi SY, Chun EJ, Park SH, et al. Atherogenic index of plasma and coronary artery calcification progression beyond traditional risk factors according to baseline coronary artery calcium score. Sci Rep. 2020;10:21324. doi: 10.1038/s41598-020-78350-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Nam JS, Kim MK, Nam JY, Park K, Kang S, Ahn CW, et al. Association between atherogenic index of plasma and coronary artery calcification progression in Korean adults. Lipids Health Dis. 2020;19:157. doi: 10.1186/s12944-020-01317-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Chung YH, Lee BK, Kwon HM, Min PK, Choi EY, Yoon YW, et al. Coronary calcification is associated with elevated serum lipoprotein (a) levels in asymptomatic men over the age of 45 years: a cross-sectional study of the Korean national health checkup data. Medicine (Baltimore) 2021;100:e24962. doi: 10.1097/MD.0000000000024962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Verweij SL, de Ronde MW, Verbeek R, Boekholdt SM, Planken RN, Stroes ES, et al. Elevated lipoprotein(a) levels are associated with coronary artery calcium scores in asymptomatic individuals with a family history of premature atherosclerotic cardiovascular disease. J Clin Lipidol. 2018;12:597–603.e1. doi: 10.1016/j.jacl.2018.02.007. [DOI] [PubMed] [Google Scholar]
- 12.Cho JH, Lee DY, Lee ES, Kim J, Park SE, Park CY, et al. Increased risk of coronary artery calcification progression in subjects with high baseline Lp(a) levels: the Kangbuk Samsung Health Study. Int J Cardiol. 2016;222:233–237. doi: 10.1016/j.ijcard.2016.07.219. [DOI] [PubMed] [Google Scholar]
- 13.Hoogeveen RC, Gaubatz JW, Sun W, Dodge RC, Crosby JR, Jiang J, et al. Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: the Atherosclerosis Risk In Communities (ARIC) study. Arterioscler Thromb Vasc Biol. 2014;34:1069–1077. doi: 10.1161/ATVBAHA.114.303284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Mora S, Caulfield MP, Wohlgemuth J, Chen Z, Superko HR, Rowland CM, et al. Atherogenic lipoprotein subfractions determined by ion mobility and first cardiovascular events after random allocation to high-intensity statin or placebo: the Justification for the Use of Statins in Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Circulation. 2015;132:2220–2229. doi: 10.1161/CIRCULATIONAHA.115.016857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Afshar M, Rong J, Zhan Y, Chen HY, Engert JC, Sniderman AD, et al. Risks of incident cardiovascular disease associated with concomitant elevations in lipoprotein(a) and low-density lipoprotein cholesterol-the Framingham Heart Study. J Am Heart Assoc. 2020;9:e014711. doi: 10.1161/JAHA.119.014711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Afshar M, Pilote L, Dufresne L, Engert JC, Thanassoulis G. Lipoprotein(a) interactions with low-density lipoprotein cholesterol and other cardiovascular risk factors in premature acute coronary syndrome (ACS) J Am Heart Assoc. 2016;5:e003012. doi: 10.1161/JAHA.115.003012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Neumann FJ, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40:87–165. [Google Scholar]
- 18.Won KB, Park GM, Yang YJ, Ann SH, Kim YG, Yang DH, et al. Independent role of low-density lipoprotein cholesterol in subclinical coronary atherosclerosis in the absence of traditional cardiovascular risk factors. Eur Heart J Cardiovasc Imaging. 2019;20:866–872. doi: 10.1093/ehjci/jez091. [DOI] [PubMed] [Google Scholar]
- 19.Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M, Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15:827–832. doi: 10.1016/0735-1097(90)90282-t. [DOI] [PubMed] [Google Scholar]
- 20.Lee SY, Kim TH, Han K, Shin JM, Kim JY, Kim D, et al. Feasibility of coronary artery calcium scoring on dual-energy chest computed tomography: a prospective comparison with electrocardiogram-gated calcium score computed tomography. J Clin Med. 2021;10:653. doi: 10.3390/jcm10040653. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hoff JA, Chomka EV, Krainik AJ, Daviglus M, Rich S, Kondos GT. Age and gender distributions of coronary artery calcium detected by electron beam tomography in 35,246 adults. Am J Cardiol. 2001;87:1335–1339. doi: 10.1016/s0002-9149(01)01548-x. [DOI] [PubMed] [Google Scholar]
- 22.Tsimihodimos V, Gonzalez-Villalpando C, Meigs JB, Ferrannini E. Hypertension and diabetes mellitus: coprediction and time trajectories. Hypertension. 2018;71:422–428. doi: 10.1161/HYPERTENSIONAHA.117.10546. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Budoff MJ, Nasir K, McClelland RL, Detrano R, Wong N, Blumenthal RS, et al. Coronary calcium predicts events better with absolute calcium scores than age-sex-race/ethnicity percentiles: MESA (Multi-Ethnic Study of Atherosclerosis) J Am Coll Cardiol. 2009;53:345–352. doi: 10.1016/j.jacc.2008.07.072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wang FM, Rozanski A, Arnson Y, Budoff MJ, Miedema MD, Nasir K, et al. Cardiovascular and all-cause mortality risk by coronary artery calcium scores and percentiles among older adult males and females. Am J Med. 2021;134:341–350.e1. doi: 10.1016/j.amjmed.2020.07.024. [DOI] [PubMed] [Google Scholar]
- 25.Sarwar A, Shaw LJ, Shapiro MD, Blankstein R, Hoffmann U, Cury RC, et al. Diagnostic and prognostic value of absence of coronary artery calcification. JACC Cardiovasc Imaging. 2009;2:675–688. doi: 10.1016/j.jcmg.2008.12.031. [DOI] [PubMed] [Google Scholar]
- 26.Taylor AJ, Bindeman J, Feuerstein I, Cao F, Brazaitis M, O’Malley PG. Coronary calcium independently predicts incident premature coronary heart disease over measured cardiovascular risk factors: mean three-year outcomes in the Prospective Army Coronary Calcium (PACC) project. J Am Coll Cardiol. 2005;46:807–814. doi: 10.1016/j.jacc.2005.05.049. [DOI] [PubMed] [Google Scholar]
- 27.Neves PO, Andrade J, Monção H. Coronary artery calcium score: current status. Radiol Bras. 2017;50:182–189. doi: 10.1590/0100-3984.2015.0235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Erbel R, Möhlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol. 2010;56:1397–1406. doi: 10.1016/j.jacc.2010.06.030. [DOI] [PubMed] [Google Scholar]
- 29.Verbeek R, Hoogeveen RM, Langsted A, Stiekema LC, Verweij SL, Hovingh GK, et al. Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting. Eur Heart J. 2018;39:2589–2596. doi: 10.1093/eurheartj/ehy334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhu L, Zheng J, Gao B, Jin X, He Y, Zhou L, et al. The correlation between lipoprotein(a) elevations and the risk of recurrent cardiovascular events in CAD patients with different LDL-C levels. BMC Cardiovasc Disord. 2022;22:171. doi: 10.1186/s12872-022-02618-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yao T, Long Q, Li J, Li G, Ding Y, Cui Q, et al. Small dense low-density lipoprotein cholesterol is strongly associated with NIHSS score and intracranial arterial calcification in acute ischemic stroke subjects. Sci Rep. 2020;10:7645. doi: 10.1038/s41598-020-64715-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Prado KB, Shugg S, Backstrand JR. Low-density lipoprotein particle number predicts coronary artery calcification in asymptomatic adults at intermediate risk of cardiovascular disease. J Clin Lipidol. 2011;5:408–413. doi: 10.1016/j.jacl.2011.07.001. [DOI] [PubMed] [Google Scholar]
- 33.Kim JJ, Yoon J, Lee YJ, Park B, Jung DH. Predictive value of the atherogenic index of plasma (AIP) for the risk of incident ischemic heart disease among non-diabetic Koreans. Nutrients. 2021;13:3231. doi: 10.3390/nu13093231. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Guo Q, Zhou S, Feng X, Yang J, Qiao J, Zhao Y, et al. The sensibility of the new blood lipid indicator--atherogenic index of plasma (AIP) in menopausal women with coronary artery disease. Lipids Health Dis. 2020;19:27. doi: 10.1186/s12944-020-01208-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Cai G, Shi G, Xue S, Lu W. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine (Baltimore) 2017;96:e8058. doi: 10.1097/MD.0000000000008058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Zhu X, Yu L, Zhou H, Ma Q, Zhou X, Lei T, et al. Atherogenic index of plasma is a novel and better biomarker associated with obesity: a population-based cross-sectional study in China. Lipids Health Dis. 2018;17:37. doi: 10.1186/s12944-018-0686-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Shen SW, Lu Y, Li F, Yang CJ, Feng YB, Li HW, et al. Atherogenic index of plasma is an effective index for estimating abdominal obesity. Lipids Health Dis. 2018;17:11. doi: 10.1186/s12944-018-0656-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Wakabayashi I. Associations of smoking and drinking with new lipid-related indices in women with hyperglycemia. Womens Health Rep (New Rochelle) 2021;2:23–31. doi: 10.1089/whr.2020.0100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Kim SK, Kim HC, Shim JS, Kim DJ. Effects of cigarette smoking on blood lipids in Korean men: Cardiovascular and Metabolic Diseases Etiology Research Center cohort. Korean J Intern Med. 2020;35:369–382. doi: 10.3904/kjim.2019.133. [DOI] [PMC free article] [PubMed] [Google Scholar]