Abstract
Aim: Cholesterol uptake capacity (CUC) is a functional assessment of high-density lipoprotein (HDL) and has drawn attention for the risk stratification of atherosclerotic cardiovascular disease (ASCVD). This study evaluated the usefulness of HDL-CUC as a predictive marker for long-term ASCVD events in patients with coronary artery disease (CAD).
Methods: This retrospective observational study included 503 patients with CAD who underwent coronary revascularization. Blood was sampled from the participants within three months before or after index revascularization. The CUC was assayed using a previously reported automated system. The study population was divided into three groups according to the tertiles of CUC levels. The primary outcome was ASCVD events, which were defined as a composite of all-cause death, acute myocardial infarction, stroke, and peripheral artery disease.
Results: A total of 29 events were observed during the follow-up (median 2.8 years). The risk of the primary outcome in the low-CUC group was significantly higher than that in the high-CUC group (3-year incidence: low CUC 8.8% vs. high CUC 4.0%; log-rank p = 0.046). After adjusting for age and sex, the risk in the low-CUC group relative to that in the high-CUC group remained significantly high (hazard ratio 3.17, 95% confidence interval 1.05–9.54, p = 0.040).
Conclusion: Low CUC in patients with CAD were associated with a higher risk of ASCVD events after coronary revascularization than high CUC levels. The assessment of HDL functionality measured by CUC would be useful for the risk prediction of ASCVD after coronary revascularization.
Introduction
High-density lipoprotein (HDL) cholesterol levels are associated with the risk of atherosclerotic cardiovascular disease (ASCVD)1, 2). The association, however, does not allow for the conclusion of causality because high levels of HDL cholesterol do not necessarily lead to the protection of ASCVD3), and clinical studies have failed to prove the benefit of increasing HDL cholesterol4, 5). While targeting HDL cholesterol is insufficient for risk reduction, functional assessment of HDL has been drawing attention for the risk stratification of ASCVD.
An important function of HDL is the promotion of cholesterol efflux from the macrophages6). Decreased cholesterol efflux capacity (CEC) was reportedly associated with a higher risk of ASCVD, and the association remained independent of HDL cholesterol after adjusting for HDL cholesterol levels7, 8). However, a tackling issue of CEC lies in the difficulty of its measurement; the CEC assay requires complex procedures involving cultured cells and is hence time-consuming9). Recently, a novel cell-free assay system called cholesterol uptake capacity (CUC) has been developed for the assessment of the capacity of HDL to accept additional cholesterol. It correlates well with CEC and exhibits reproducibility in a relatively short time10, 11). Thus, CUC has the potential to overcome the technical issues of CEC, and functional assessment of HDL using CUC may be useful for the stratification of future ASCVD risk.
A recent study demonstrated that impaired HDL functionality assessed using CUC was associated with subsequent revascularization in patients with coronary artery disease (CAD)12). However, the case-control design of the study highlights the need for the further investigation of the association between HDL-CUC and future ASCVD events13). In addition, previous findings warrant a large-scale study focusing on hard ASCVD outcomes. Therefore, this study assessed the usefulness of HDL-CUC as a predictive marker of ASCVD among patients with CAD in a clinical practice setting.
Methods
Study Design and Population
The present study was a retrospective observational study that enrolled patients with CAD registered at the National Cerebral and Cardiovascular Center (NCVC) Biobank. The study data were collected around Q2 2023. To maintain the follow-up durations of the enrolled patients for at least one year, we defined the end of the follow-up as December 2022. Patients with coronary artery disease who underwent their first angiographic procedures at the NCVC between August 2016 and December 2021 were screened to ensure that the serum samples were obtained within approximately seven years. A total of 1602 patients were screened, and the following patients were excluded after reviewing hospital records: (1) patients who died on the day of admission for angiography (n = 16); (2) patients without revascularization procedures (angiography only, etc.; n = 1080); (3) patients without follow-up after the index hospitalization (n = 2); and (4) patients without a sufficient amount of serum (n = 1). A total of 503 patients were included in the study (Fig.1).
Baseline and follow-up information and clinical events were obtained from hospital records. Baseline patient characteristics, laboratory data, echocardiographic findings, and prescriptions were collected from admission, discharge, and angiography summaries. Follow-up started on the day of index revascularization. The primary outcome of this study was ASCVD events, defined as a composite of all-cause death, acute myocardial infarction (AMI), stroke, and peripheral artery disease. The secondary outcome was a composite of each component of the primary outcome and coronary revascularization events.
An AMI event was adjudicated by acute myocardial injury with clinical evidence of acute myocardial ischemia requiring hospitalization, detection of elevated cardiac troponin levels, and at least one of the following: new ischemic electrocardiogram changes, development of pathological Q waves, imaging evidence of new loss of viable myocardium or new regional wall motion abnormality in a pattern consistent with ischemic etiology, and identification of a coronary thrombus by angiography. Stroke during follow-up was defined as ischemic or hemorrhagic stroke detected by computed tomography and/or magnetic resonance imaging, requiring hospitalization. Peripheral artery disease was adjudicated as an event when the patient required hospitalization for peripheral revascularization. A coronary revascularization event was adjudicated when hospital charts revealed that coronary revascularization was unplanned at the time of the index procedure. All clinical events were adjudicated by a board-certified cardiologist blinded to the CUC.
Written informed consent was obtained from all participants in the NCVC Biobank for clinical data collection, research use of the samples, and publication of the study results. The study protocol followed the principles of the Declaration of Helsinki and was approved by the Institutional Review Board (R22022).
Blood Sampling
Blood was sampled from the participants within three months before or after index revascularization. Serum was separated from the blood samples, and the resulting serum samples were aliquoted and frozen at −80℃ until assayed.
Automated CUC Assay
The development of an automated CUC assay has been discussed previously11). In this study, the assay principle was applied to the HISCL-5000 system (Sysmex), a fully automated immunoassay system used for in vitro diagnostics. In brief, the serum sample was diluted in phosphate-buffered saline (PBS) and the diluted sample was incubated with 0.5 mM Bio–PEG3-labeled cholesterol in reaction buffer (PBS containing 0.3% casein Na) at 42℃ for 3 min. HDL was captured using an anti-apolipoprotein A1 (apoA1) antibody (mAb 8E10) coated on magnetic particles at 42℃ for 2 min. After washing the particles with wash buffer, 100 mL of alkaline phosphatase-conjugated streptavidin (Promega, Madison, WI, USA) in dilution buffer (0.1 M triethanolamine [pH 7.5] containing 10 mg/mL BSA, 5 mg/mL casein Na, 1 mM MgCl2, and 0.1 mM ZnCl2) was added, and the mixture was incubated at 42℃ for 3 min. After washing the particles, CDP-Star chemiluminescent substrate was added and the mixture was incubated at 42℃ for 5 min. Chemiluminescence was measured by counting the cells. All procedures were performed using an HISCL-5000 instrument. The CUC assay was standardized using pooled serum samples.
Statistical Analyses
The study population was divided into 3 groups by the tertile points of CUC (low-CUC group, CUC＜74.23 [A.U.], n = 165; intermediate group, 74.23 ≤ CUC＜94.40, n = 169; high-CUC group, 94.40 ≤ CUC, n = 169). Categorical variables were presented as numbers and percentages and were compared using the χ2 test when appropriate, and with Fisher’s exact test otherwise. Continuous variables were presented as the mean±standard deviation or median (25th to 75th percentile) according to their distributions and were compared with the use of analysis of variance when appropriate and with the Kruskal-Wallis rank sum test otherwise. The cumulative incidence of the primary outcome was estimated using the Kaplan-Meier method. The risks of a group relative to another group for clinical events were estimated using the Cox proportional hazard model and expressed as hazard ratios (HRs) and their 95% confidence intervals (CIs).
Multivariable Cox proportional hazards models were used to adjust for clinical baseline characteristics. The proportional hazard assumption was examined using Schoenfeld residuals, and the assumption was acceptable when CUC was included as a continuous variable; however, the assumption was violated when the CUC groups were included as a categorical variable. The assumption for the categorical variable was regarded as acceptable when comparisons were made between the low- and high-CUC groups and between the intermediate- and high-CUC groups. To determine the relationship between CUC as a continuous variable and the primary outcome, CUC was included in a Cox proportional hazard model with the use of restricted cubic splines using the rms package in R. Three knots were used for the analysis because the further addition of a knot did not improve the Akaike information criterion. For the exploratory analysis, the study population was divided into subgroups stratified by HDL cholesterol (≥ 40 vs. ＜40 mg/dL), apoA1 (≥ 110 vs. ＜110 mg/dL), and the prescription status of statins at the time of admission. Subsequently, the risks of clinical outcomes were compared in each subgroup category. For the survival analysis, the high-CUC group was regarded as the reference for comparison because the second tertile point of 94.40 (A.U.) was close to the median CUC for the non-revascularization group in a previous study12). The principal comparison was made between low and high CUC; additional comparisons were made between intermediate and high CUC, and between low/intermediate and high CUC. The analyses for the secondary outcomes were performed similarly to those for the primary outcome.
All analyses were performed using R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria). The reported p values were 2-sided, and p values ＜0.05 were considered significant in this study.
Results
Patient Characteristics and Medications
Table 1 summarizes the characteristics of the study population. The mean age and standard deviation of the study population were 70±12 years old, and 23% of them were female. Patients with ST-elevation myocardial infarction and acute coronary syndrome comprised 39% (n = 196) and 70% (n = 353) of the study population, respectively. Most patients (n = 452, 90%) underwent percutaneous coronary intervention (PCI) as index revascularization, whereas 10% (n = 51) underwent coronary artery bypass grafting (CABG). Drug-eluting stents were implanted in most patients who underwent PCI (n = 417, 93%). Statins were prescribed to 33% (n = 167) of patients at the time of admission. The median CUC in the study population was 83.5 (68.6, 100.1 [A.U.]).
Table 1.Patient characteristics
|
Total
(n = 503)
|
Low CUC
(CUC ＜74.3)
(n = 165)
|
Intermediate CUC
(74.3 ≤ CUC＜94.4)
(n = 169)
|
High CUC
(94.4 ≤ CUC)
(n = 169)
|
p
|
| Age (years) |
70±12 |
68±12 |
70±12 |
71±11 |
0.015 |
| Female |
117 (23%) |
18 (11%) |
41 (24%) |
58 (34%) |
＜0.001 |
| Hypertension |
424 (84%) |
144 (87%) |
143 (85%) |
137 (81%) |
0.294 |
| Diabetes |
261 (52%) |
90 (55%) |
88 (52%) |
83 (49%) |
0.609 |
| Chronic kidney disease |
111 (22%) |
38 (23%) |
36 (21%) |
37 (22%) |
0.928 |
| Smoking |
342 (68%) |
127 (77%) |
114 (67%) |
101 (60%) |
0.003 |
| History of revascularization |
48 (9.7%) |
18 (11%) |
14 (8.5%) |
16 (9.6%) |
0.746 |
| LVEF (%) |
52±11 |
51±11 |
52±12 |
53±11 |
0.397 |
| STEMI |
196 (39%) |
65 (39%) |
65 (38%) |
66 (39%) |
0.984 |
| ACS |
353 (70%) |
111 (67%) |
120 (71%) |
122 (72%) |
0.592 |
| Index revascularization |
|
|
|
|
0.38 |
| CABG |
51 (10%) |
21 (13%) |
16 (9.5%) |
14 (8.3%) |
|
| PCI |
452 (90%) |
144 (87%) |
153 (91%) |
155 (92%) |
|
| Target of RCA |
209 (42%) |
76 (46%) |
66 (39%) |
67 (40%) |
0.338 |
| Target of LAD |
293 (58%) |
91 (55%) |
102 (60%) |
100 (60%) |
0.63 |
| Target of LCX |
140 (28%) |
49 (30%) |
50 (30%) |
41 (24%) |
0.455 |
| Target of LMT |
65 (13%) |
24 (15%) |
19 (11%) |
22 (13%) |
0.653 |
| Multivessel disease |
264 (53%) |
93 (57%) |
93 (55%) |
78 (46%) |
0.119 |
| DES |
417 (93%) |
127 (90%) |
144 (94%) |
146 (95%) |
0.229 |
| EES |
301 (67%) |
94 (67%) |
106 (69%) |
101 (66%) |
0.778 |
| SES |
106 (24%) |
30 (21%) |
35 (23%) |
41 (27%) |
0.537 |
| ZES |
20 (4.5%) |
6 (4.3%) |
7 (4.6%) |
7 (4.5%) |
0.989 |
| DCB |
25 (5.6%) |
10 (7.1%) |
10 (6.5%) |
5 (3.2%) |
0.291 |
| Statins at admission |
167 (33%) |
61 (37%) |
51 (30%) |
55 (33%) |
0.409 |
| Creatinine (mg/dL) |
0.89 (0.75, 1.06) |
0.94 (0.80, 1.07) |
0.89 (0.74, 1.06) |
0.83 (0.71, 1.00) |
0.004 |
| HbA1c (%) |
6.0 (5.6, 6.9) |
6.2 (5.8, 7.1) |
5.9 (5.6, 6.8) |
5.9 (5.6, 6.4) |
＜0.001 |
| BNP (pg/mL) |
55 (25, 150) |
56 (26, 152) |
49 (23, 125) |
66 (30, 180) |
0.304 |
| LDL cholesterol (mg/dL) |
115 (90, 138) |
115 (92, 138) |
120 (93, 149) |
108 (89, 133) |
0.048 |
| HDL cholesterol (mg/dL) |
46 (38, 55) |
38 (33, 42) |
46 (40, 50) |
59 (51, 66) |
＜0.001 |
| Total cholesterol (mg/dL) |
189 (162, 217) |
183 (154, 209) |
193 (165, 222) |
193 (172, 215) |
0.009 |
| Triglycerides (mg/dL) |
124 (82, 178) |
158 (100, 223) |
137 (100, 175) |
93 (65, 124) |
＜0.001 |
| Lipoprotein (a) (mg/dL) |
11 (5, 20) |
9 (4, 19) |
11 (5, 23) |
12 (5, 21) |
0.209 |
| Apolipoprotein A1 (mg/dL) |
125 (113, 140) |
108 (99, 116) |
125 (118, 133) |
146 (137, 161) |
＜0.001 |
| Apolipoprotein B (mg/dL) |
78 (64, 94) |
83 (67, 103) |
79 (65, 98) |
73 (61, 86) |
＜0.001 |
| sd-LDL cholesterol (mg/dL) |
32 (24, 45) |
36 (26, 53) |
34 (25, 47) |
29 (21, 37) |
＜0.001 |
| CUC (A.U.) |
84 (69, 100) |
64 (58, 68) |
83 (79, 88) |
111 (100, 126) |
＜0.001 |
The study population was divided into three groups based on the tertile points of the CUC levels.
ACS, acute coronary syndrome; BNP, brain natriuretic peptide; CABG, coronary artery bypass grafting; CUC, cholesterol uptake capacity; DCB, drug-coated balloon; DES, drug-eluting stent; EES, everolimus-eluting stent; HDL, high-density lipoprotein; LAD, left anterior descending artery; LCX, left circumflex coronary artery; LDL, low-density lipoprotein; LMT, left main trunk; LVEF, left ventricular ejection fraction; PCI, percutaneous coronary intervention; RCA, right coronary artery; sd-LDL, small dense low-density lipoprotein; SES, sirolimus-eluting stent; STEMI, ST-segment elevation myocardial infarction; ZES, zotarolimus-eluting stent.
The patient characteristics associated with CUC were the age, sex, and smoking status. Patients with a lower CUC were significantly younger than those with a higher CUC (68±12 years for the low-CUC group, 70±12 years for the intermediate-CUC group, and 71±11 years for the high-CUC group; p = 0.02). More female patients were included in the high-CUC group than in the low-CUC group (n = 18, 11% for the low n = 41, 24% for the intermediate, and n = 58, 34% for the high-CUC group; p＜0.001). Patients who had a history of smoking or were current smokers were more frequently observed in the lower CUC group than in the higher CUC group (n = 127; 77% for the low n = 114, 67% for the intermediate, and n = 101, 60% for the high-CUC group; p = 0.003). Patient backgrounds were not significantly different among the three groups. Laboratory data showed that the CUC groups were correlated with HDL cholesterol and apoA1 levels, while they were inversely correlated with triglyceride, apolipoprotein B (apoB), and small dense low-density lipoprotein (sd-LDL) cholesterol levels. The distribution of CUC is summarized in a histogram (Supplementary Fig.1).
The median follow-up duration was 2.8 (interquartile range 1.4, 3.7) years, and a total of 29 events were observed as the primary outcome during the entire follow-up. The cumulative 3-year incidence rate of the entire study population was 7.3% (Supplementary Fig.2).
The cumulative 3-year incidence of the primary outcome in the low-CUC group was 8.8%, and the risk was significantly higher than that in the high-CUC group (4.0%; log-rank p = 0.046 for low vs. high). The result was similar between intermediate and high CUC (intermediate 9.5% vs. high 4.0%; log-rank p = 0.024) (Fig.2A and Table 2). After adjusting for age and sex, the risks for the primary outcome in the low- and intermediate-CUC groups relative to the high-CUC group remained significantly high (for low versus high CUC, adjusted HR 3.17, 95% CI 1.05−9.54, p = 0.040; for intermediate versus high CUC, adjusted HR 3.12, 95% CI 1.11−8.78, p = 0.03) (Table 3). The differences among the low-, intermediate-, and high-CUC groups for the secondary outcome showed similar results (Fig.2B, Table 2, and Table 3).
Table 2.Cumulative 3-year incidences of clinical events
| Event |
CUC group |
N of patients |
N of patients
with event
within 3 years
|
Cumulative
3-year
incidence
|
Log-rank p
for Low vs.
High and Int. vs.
High
|
Log-rank p
for Low/Int. vs.
High
|
|
Primary outcome
(All-cause death + AMI + Stroke + PAD)
|
Low |
165 |
13 |
8.8% |
0.046 |
0.021 |
| Intermediate |
169 |
11 |
9.5% |
0.024 |
|
| High |
169 |
5 |
4.0% |
- |
|
| All-cause death |
Low |
165 |
7 |
4.9% |
0.343 |
0.267 |
| Intermediate |
169 |
5 |
3.5% |
0.317 |
|
| High |
169 |
4 |
3.2% |
- |
|
| AMI |
Low |
165 |
1 |
0.7% |
0.314 |
0.143 |
| Intermediate |
169 |
2 |
1.6% |
0.069 |
|
| High |
169 |
0 |
0.0% |
- |
|
| Stroke |
Low |
165 |
3 |
1.9% |
0.297 |
0.258 |
| Intermediate |
169 |
2 |
1.9% |
0.296 |
|
| High |
169 |
1 |
0.6% |
- |
|
| PAD |
Low |
165 |
2 |
1.3% |
0.149 |
0.155 |
| Intermediate |
169 |
2 |
2.2% |
0.155 |
|
| High |
169 |
0 |
0.0% |
- |
|
|
Secondary outcome
(Primary outcome + Revascularization)
|
Low |
165 |
35 |
25.2% |
0.01 |
0.003 |
| Intermediate |
169 |
34 |
25.4% |
0.005 |
|
| High |
169 |
17 |
12.1% |
|
|
| Revascularization |
Low |
165 |
24 |
18.5% |
0.085 |
0.039 |
| Intermediate |
169 |
25 |
18.1% |
0.047 |
|
| High |
169 |
13 |
8.8% |
- |
|
Estimates for the cumulative 3-year incidence are presented using the Kaplan-Meier estimates of the three CUC groups. AMI, acute myocardial infarction; CUC, cholesterol uptake capacity; PAD, peripheral artery disease.
Table 3.Adjusted risks for the primary outcome relative to high CUC
| (A) Primary outcome |
Adjusted HR†
|
95% CI |
p
|
| Low relative to High CUC |
3.17 |
1.05, 9.54 |
0.040 |
| Intermediate relative to High CUC |
3.12 |
1.11, 8.78 |
0.03 |
| (B) Secondary outcome |
Adjusted HR‡
|
95% CI |
p
|
| Low relative to High CUC |
2.07 |
1.13, 3.79 |
0.02 |
| Intermediate relative to High CUC |
2.30 |
1.31, 4.05 |
0.004 |
†Adjusting factors for the primary outcome: age and sex.
‡Adjusting factors for the secondary outcome: age, sex, smoking, hypertension, diabetes, chronic kidney disease, acute coronary syndrome, and index revascularization (coronary artery bypass grafting or percutaneous coronary intervention).
CI, confidence interval; CUC, cholesterol uptake capacity; HR, hazard ratio.
The restricted cubic spline analysis for CUC as a continuous variable showed that the risk for the primary outcome relative to the second tertile point (CUC 94.4 [A.U.]) decreased with increasing CUC until around the second tertile point and remained unchanged thereafter (Fig.2C). The relationship between the secondary outcome and CUC level as a continuous variable showed a similar result (Fig.2D).
For the individual components of the primary outcome, the associations between CUC and clinical outcomes were similar, although the differences were not significant, as the number of events was not large. The cumulative 3-year incidences for the secondary outcome of coronary revascularization were 18.5%, 18.1%, and 8.8% in the low-, intermediate-, and high-CUC groups, respectively (log-rank p = 0.085 for low vs. high and log-rank p = 0.047 for intermediate vs. high). The results are summarized in Table 2.
The results of subgroup analyses are summarized in Supplementary Table 1. In patients with maintained HDL cholesterol (≥ 40 mg/dL), the cumulative 3-year incidences were 5.9%, 11.1%, and 4.2% in the low-, intermediate-, and high-CUC groups, respectively (log-rank p = 0.42 for low vs. high; log-rank p = 0.02 for intermediate vs. high). A similar tendency was observed in patients with maintained apoA1 levels (≥ 110 mg/dL) (4.6%, 9.4%, and 3.1%, respectively; log-rank p = 0.54 for low vs. high; and log-rank p = 0.01 for intermediate vs. high). In addition, a higher risk in the low- and intermediate-CUC groups was observed in patients not taking statins (9.8%, 10.8%, and 2.9%, respectively; log-rank p = 0.02 for low vs. high, and log-rank p = 0.02 for intermediate vs. high), while there was no marked difference among the CUC groups in patients taking statins (6.7%, 6.7%, and 6.0%, respectively; log-rank p = 0.76 for low vs. high, and log-rank p = 0.56 for intermediate vs. high).
Supplementary Table 1.Subgroup analysis
| (A) Primary outcome |
CUC group |
N of patients |
N of patients
with event
within 3 years
|
Cumulative
3-year
incidence
|
Log-rank p for
Low vs. High
and Int. vs.
High
|
Log-rank p
for Low/Int. vs.
High
|
| HDL cholesterol |
| ≥ 40 mg/dL |
Low |
52 |
3 |
5.9% |
0.42 |
0.041 |
| Int. |
118 |
9 |
11.1% |
0.02 |
|
| High |
155 |
5 |
4.2% |
|
|
| ＜40 mg/dL |
Low |
97 |
10 |
11.9% |
0.51 |
0.53 |
| Int. |
32 |
2 |
8.0% |
0.63 |
|
| High |
4 |
0 |
0% |
|
|
| Apolipoprotein A1 |
| ≥ 110 mg/dL |
Low |
77 |
3 |
4.6% |
0.54 |
0.040 |
| Int. |
157 |
10 |
9.4% |
0.01 |
|
| High |
166 |
4 |
3.1% |
|
|
| ＜110 mg/dL |
Low |
88 |
10 |
12.7% |
0.37 |
0.36 |
| Int. |
12 |
1 |
11.1% |
0.46 |
|
| High |
3 |
1 |
50.0% |
|
|
| Statins at admission |
| Yes |
Low |
61 |
4 |
6.7% |
0.76 |
0.61 |
| Int. |
51 |
2 |
6.7% |
0.56 |
|
| High |
55 |
3 |
6.0% |
|
|
| No |
Low |
104 |
9 |
9.8% |
0.02 |
0.01 |
| Int. |
118 |
9 |
10.8% |
0.02 |
|
| High |
114 |
2 |
2.9% |
|
|
| (B) Secondary outcome |
CUC group |
N of patients |
N of patients with event within 3 years |
Cumulative 3-year incidence |
Log-rank p for Low vs. High and Int. vs.
High
|
Log-rank p for Low/Int. vs.
High
|
| HDL cholesterol |
| ≥ 40 mg/dL |
Low |
52 |
7 |
15.5% |
0.74 |
0.07 |
| Int. |
118 |
22 |
24.2% |
0.03 |
|
| High |
155 |
16 |
12.3% |
|
|
| ＜40 mg/dL |
Low |
97 |
27 |
31.9% |
0.27 |
0.28 |
| Int. |
32 |
7 |
26.8% |
0.33 |
|
| High |
4 |
0 |
0% |
|
|
| Apolipoprotein A1 |
| ≥ 110 mg/dL |
Low |
77 |
16 |
25.1% |
0.044 |
0.003 |
| Int. |
157 |
33 |
26.1% |
0.003 |
|
| High |
166 |
16 |
11.3% |
|
|
| ＜110 mg/dL |
Low |
88 |
19 |
25.6% |
0.89 |
0.85 |
| Int. |
12 |
1 |
11.1% |
0.46 |
|
| High |
3 |
1 |
50.0% |
|
|
| Statins at admission |
| Yes |
Low |
61 |
11 |
23.3% |
0.83 |
0.83 |
| Int. |
51 |
8 |
19.6% |
0.87 |
|
| High |
55 |
8 |
15.7% |
|
|
| No |
Low |
104 |
24 |
26.5% |
0.003 |
＜0.001 |
| Int. |
118 |
26 |
28.1% |
＜0.001 |
|
| High |
114 |
9 |
10.2% |
|
|
CUC, cholesterol uptake capacity; HDL, high-density lipoprotein.
Discussion
This retrospective observational study investigated the relationship between CUC and atherosclerotic events in patients with CAD. The main findings of the present study were as follows: first, patients with a low CUC were associated with a higher risk of atherosclerotic events in patients with CAD undergoing PCI or CABG than patients with a high CUC; second, the ability to stratify the risk of cardiovascular events by CUC might be retained in patients with maintained HDL and apoA1 levels.
The results of this study have shown the usefulness of HDL functionality assessment by CUC for the stratification of ASCVD risk, and are in line with previous studies on CEC7, 8). An important function of HDL is to promote reverse cholesterol transport from the periphery to the liver, where cholesterol efflux from macrophages to HDL is involved8). This mechanism is thought to protect against atherosclerosis6); thus, evaluating HDL functionality may provide risk stratification for future ASCVD. CEC is a traditional method of evaluating HDL functionality and measure HDL particle capacity. However, its measurement requires a complex cell-based assay using cultured macrophages labeled with 3H-labeled cholesterol and a cholesterol acceptor and is thus time-consuming. While CEC presents a technical challenge in clinical settings, CUC requires a simpler cell-free assay system using fluorescently labeled cholesterol and an apoA1-specific antibody. Despite technical and theoretical differences, CUC correlated well with CEC and was feasible with automated analysis systems10, 11). Thus, this study has added evidence supporting the usefulness of HDL functionality measured by CUC in clinical settings for future ASCVD in patients with CAD.
Low HDL cholesterol levels were associated with an increased risk of ASCVD2). At the same time, individuals with high HDL cholesterol levels had high all-cause mortality3, 14). This discrepancy is partly explained by the functionality of HDL, as previous reports have shown that CEC is a negative risk factor independent of HDL cholesterol7, 8). The subgroup analysis of this study might also imply the ability of CUC to stratify ASCVD risk, even in patients with high HDL and apoA1 levels. As there were no significant differences due to the limited number of patients and events in each stratum, it should be acknowledged that no definitive conclusion can be drawn from the subgroup analysis. Nevertheless, this finding is in line with results from previous studies7, 8) and would strengthen the evidence that the function of HDL is useful for the risk stratification of cardiovascular events independent of the quantity of HDL. However, the association between CUC and ASCVD may vary depending on the presence or absence of statins. This study found that CUC was associated with ASCVD events only in the group that was not taking statins. Because statins were found to improve the HDL function15-17), it is understandable that the relationship between CUC and cardiovascular events was less apparent in the group taking statins than in the group not taking statins.
The present study differs from previous studies that showed the utility of HDL functionality measured using CUC in clinical settings12, 18). First, this study was based on time-to-event analyses and thus confirmed the importance of HDL functionality for the risk prediction of future ASCVD. Second, the patients’ backgrounds substantially differed from those of previous studies, the most striking being the indication for revascularization in the study population. Our study consisted largely of patients with acute coronary syndrome (70%) who would have been at a high risk for cardiovascular events after revascularization. Indeed, CUC might reflect this difference; the median of 83.5 (A.U.) in the whole population of the present study was almost the same as that of patients experiencing the event (revascularization) in a previous study (84.3 [A.U.])12). Despite this difference, the results of this study are in line with those of a previous study12). In addition, the spline analysis in the present study suggested that the risk of cardiovascular events increased with lower CUC below the tertile point, 94.4 (A.U.), which is close to the median value of 92.0 (A.U.) for the non-revascularization group in a previous study12). These differences and similarities suggest that HDL functionality measured by CUC would be useful for predicting the risk of ASCVD in various clinical practice settings.
Several aspects of HDL functionality warrant consideration of the usefulness of HDL CUC. First, HDL particles are known to play different roles in the reverse cholesterol transport system, depending on their size. Small HDL particles, classified as HDL3, actively acquire cholesterol through interactions with ATP-binding cassette transporter A1 (ABCA1) on donor cells, whereas large HDL particles, classified as HDL2, passively take up cholesterol. Since CUC measurement does not involve the use of cells, it is presumed to primarily reflect the passive cholesterol uptake process mediated by large HDL particles rather than the ABCA1-dependent cholesterol efflux process mediated by small HDL particles9). In patients with hypertriglyceridemia, HDL particles become smaller, and we previously demonstrated that CUC decreases in proportion to the size of HDL particles19). Second, the antioxidant capacity of HDL may also be valuable for risk stratification of ASCVD. In the present study, HDL functionality was evaluated based solely on CUC. However, HDL is known to exhibit a wide range of anti-atherosclerotic functions beyond reverse cholesterol transport, including anti-inflammatory, antioxidant, anti-thrombotic, and endothelial-protective effects. Other HDL functionalities beyond CUC may also be useful for risk stratification of ASCVD events. Previously, we demonstrated that paraoxonase-1 activity, which plays a critical role in the antioxidant function of HDL, is reduced in patients with recurrent coronary artery disease, whereas the levels of myeloperoxidase, an enzyme that oxidizes HDL, are elevated in this population20). Combining CUC with other HDL functional markers may improve the accuracy of risk stratification for the prevention and management of ASCVD.
Strengths and Limitations
The strength of this study is that it is the first to show the usefulness and feasibility of CUC for risk stratification of future ASCVD events in a real-world clinical practice setting. Nevertheless, this study has several limitations. First, this study had a single-center observational study design with a limited number of participants and events, and there might have been patient factors related to the clinical events that could not be adjusted for. The small number of events limited our conclusion to the fact that, although a low CUC level was associated with a higher risk of ASCVD events, it was not significantly related to each individual component of the primary outcome, presumably due to the relatively small study population. Furthermore, there might have been some selection bias due to the single-center study design; indeed, the study population included a very high proportion of patients with acute coronary syndrome. However, as similarities and differences were considered in the discussion section, the predictive ability of CUC seemed to be retained regardless of the study population. Second, the time intervals between the index revascularization and blood sampling were not uniform. A prospective study that specifies a certain time for blood sampling would help overcome this issue. Third, clinical events might have been underreported because of the single-center retrospective study design. However, the NCVC, the institution where this study was conducted, is a tertiary hospital specializing in cardiovascular disease; thus, almost all patients with a history of cardiovascular disease at the NCVC would have been treated there when they experienced a new cardiovascular event.
Conclusion
A low serum CUC in patients with CAD was associated with a higher risk of ASCVD events after coronary revascularization than high serum CUC levels. The assessment of HDL functionality measured using CUC would be useful for predicting the risk of ASCVD.
Conflicts of Interest
The authors declare the following financial interests/personal relationships that may be considered potential competing interests. Ryuji Toh, Manabu Nagao, and Ken-ichi Hirata belong to the Division of Evidence-based Laboratory Medicine, Kobe University Graduate School of Medicine, which developed the CUC measurement assay and was established by an endowment fund from the Sysmex Corporation. However, there is no declaration of a consultant or advisory role, stock ownership, honoraria, expert testimony, or patent. Katsuhiro Murakami, Amane Harada, Jeeeun Kim, Yuto Kobayashi, and Keiko Miwa are employees of Sysmex Corporation. The other authors have no known competing financial interests or personal relationships to declare.
Funding
Funding for this study was provided by the Sysmex Corporation.
Author Contributions (CRediT Author Statement)
Yusuke Yoshikawa: Conceptualization, Data curation, formal analysis, Investigation, Methodology, Validation, Visualization, Writing the original draft.
Ryuji Toh: Conceptualization, Methodology, Writing – review & editing.
Katsuhiro Murakami: Investigation, Resources, Writing – review & editing.
Amane Harada: Investigation, Resources, Writing – review & editing.
Jeeeum Kim: Investigation.
Yuto Kobayashi: Investigation.
Keiko Miwa: Investigation.
Manabu Nagao: Conceptualization.
Tatsuro Ishida: Supervision.
Ken-Ichi Hirata: Supervision.
Misa Takegami: Conceptualization, Data curation, Writing – review & editing.
Kunihiro Nishimura: Conceptualization, Methodology, Writing – review and editing, Supervision, Project administration, and funding acquisition.
Acknowledgement
This study was performed using samples acquired from the National Center Biobank Network (NCBN)/ NCVC Biobank resource. For further details, refer to the following links: (http://ncbiobank.org/; https://www.ncvc.go.jp/english/oic/biobank/).
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