Abstract
Background: Recently, the function of high-density lipoprotein (HDL), rather than the HDL cholesterol (HDL-C) level, has been attracting more attention in risk prediction for coronary artery disease (CAD).
Methods and Results: Patients with clinically diagnosed familial hypercholesterolemia (FH; n=108; male/female, 51/57) were assessed cross-sectionally. Serum cholesterol uptake capacity (CUC) levels were determined using our original cell-free assay. Linear regression was used to determine associations between CUC and clinical variables, including low-density lipoprotein cholesterol and the carotid plaque score. Multivariable logistic regression analysis was used to test factors associated with the presence of CAD. Among the 108 FH patients, 30 had CAD. CUC levels were significantly lower among patients with than without CAD (median [interquartile range] 119 [92–139] vs. 142 [121–165] arbitrary units [AU]; P=0.0004). In addition, CUC was significantly lower in patients with Achilles tendon thickness ≥9.0 mm than in those without Achilles tendon thickening (133 [110–157] vs. 142 [123–174] AU; P=0.047). Serum CUC levels were negatively correlated with the carotid plaque score (Spearman’s r=0.37; P=0.00018). Serum CUC levels were significantly associated with CAD, after adjusting for other clinical variables (odds ratio=0.86, 95% CI=0.76–0.96, P=0.033), whereas HDL-C was not.
Conclusions: HDL function, assessed by serum CUC level, rather than HDL-C level, adds risk stratification information among FH patients.
Familial hypercholesterolemia (FH) is among the most common causes of coronary artery disease (CAD) worldwide, with an estimated frequency of 1 in 300 among the general population, and 1 in 31 among patients with CAD.1,2 Patients with FH have an extremely elevated risk for CAD due to a very high level of low-density lipoprotein cholesterol (LDL-C).3,4 Although there are many approaches to reduce LDL-C levels in patients with FH, including the use of statins, ezetimibe, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, their residual risks for CAD remain very high.5–7 We and others have clarified that other classical coronary risk factors, including hypertension, diabetes, and smoking, appear to be the residual risk factors for CAD in patients with FH.8–11 In contrast, recent studies have shown that cholesterol efflux capacity (CEC), which reflects the initial step of the reverse cholesterol transport mediated by high-density lipoprotein (HDL), is associated with probability of CAD independently of HDL cholesterol (HDL-C) levels.12 However, there are few data regarding this issue among patients with FH whose risk for CAD is extremely high,13,14 because CEC measurements are not instantly applicable in clinical settings as they require cell culture and radioisotopes.15 In order to overcome this limitation, we developed an assay that can measure the serum cholesterol uptake capacity (CUC) of HDL under cell-free and isotope-free conditions,15,16 and have shown its clinical usefulness among the general population.17,18 Currently, we have established a fully automated measurement system for CUC19 and have demonstrated its feasibility for coronary risk stratification.20 The aim of the present study was to investigate the association between CUC, which represents the function of HDL, and CAD among patients with FH.
Methods
Study Population
We retrospectively investigated 108 patients diagnosed with FH using the 2017 Japan Atherosclerosis Society (JAS) criteria21 who were admitted to Kanazawa University Hospital between 2019 and 2021. All individuals included in this study met at least 2 of the following 3 essential clinical criteria for FH specified by the JAS:21 (1) LDL-C ≥180 mg/dL; (2) the presence of tendon xanthoma (tendon xanthoma on the backs of the hands, elbows, knees [or other areas]), Achilles tendon hypertrophy or X-ray-assessed Achilles tendon thickness ≥9 mm, or xanthoma tuberosum; and (3) a family history of FH or premature CAD diagnosed among the patient’s first- or second-degree relatives. Differences between JAS criteria and the Dutch Lipid Clinical Network (DLCN) criteria are described elsewhere.22
Collection of Clinical Data
The presence of hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or receiving antihypertensive treatment. Diabetes was diagnosed according to previously described Japan Diabetes Society criteria.23 Smoking status was defined according to current smoking habit. Serum LDL-C concentrations were determined using the Friedewald formula if serum triglyceride concentrations were <400 mg/dL; otherwise, LDL-C concentrations were measured directly enzymatically. CAD was defined as having significant stenosis ≥75% in any coronary arteries determined by coronary computed tomography and/or coronary angiography.
Assessment of the Carotid Plaque Score
The parameters for carotid ultrasound were obtained with Aplio carotid ultrasonography (Toshiba Medical Systems, Tokyo, Japan) using a standard 7.5-MHz transducer. Images were obtained by trained sonographers fully blinded to the clinical data. The carotid plaque score was determined as the sum of the maximum thickness of plaques (mm), which was >1.1 mm in each segment (S1–S4) on both sides (S1, region of the internal carotid artery [ICA] <15 mm distal to its bifurcation from the common carotid artery [CCA]; S2, region of the ICA and CCA <15 mm proximal to the bifurcation; S3, region of the CCA >15 and <30 mm proximal to the bifurcation; S4, region of the CCA >30 mm proximal to the bifurcation below the flow divider). Details are provided elsewhere.24
Assessment of CUC
Serum samples were diluted in phosphate-buffered saline (PBS) and incubated with 0.5 μM biotin-polyethylene glycol (PEG) 3-labeled cholesterol in reaction buffer (PBS containing 0.3% casein Na) at 42℃ for 3 min. HDL was captured by an anti-apolipoprotein A1 antibody-coated magnetic particle at 42℃ for 2 min. After washing the particles, 100 μL alkaline phosphatase-conjugated streptavidin (Promega) in dilution buffer (0.1 M tetraethyl ammonium [pH 7.5] containing 10 mg/mL bovine serum albumin, 5 mg/mL casein Na, 1 mM MgCl2, and 0.1 mM ZnCl2) was added to the samples and incubated at 42℃ for 3 min. After washing the particles, CDP-Star chemiluminescent substrate was added and samples were incubated at 42℃ for 5 min; then, chemiluminescence was measured as counts. All procedures were performed using an automated immunoassay system (HI-1000; Sysmex). The CUC assay was standardized using pooled serum from healthy individuals to correct interassay variation.
Genetic Analysis
The coding regions of the LDL receptor (LDLR), PCSK9, apolipoprotein B (APOB), and LDL receptor adaptor protein 1 (LDLRAP1) genes were sequenced using a next-generation sequencer as described previously.25 We also evaluated variations in the copy number of LDLR using eXome Hidden Markov Model software.26 The pathogenicity of the variants was determined using allele frequency information obtained from ExAC Asian population data, in silico annotation tools, and the ClinVar database. Allele frequency <5% was defined as a rare mutation among the Asian population. Finally, variants were classified as pathogenic according to the standard American College of Medical Genetics and Genomics criteria.27
Ethical Considerations
This study was approved by the ethics committees of Kanazawa University (2019-252) and Kobe University (B210217). All procedures were conducted in accordance with the ethical standards of the human research (institutional and national) committee and Declaration of Helsinki (1975, revised in 2008). Informed consent for genetic analyses was obtained from all study participants.
Statistical Analysis
Categorical variables are reported as percentages and were compared using Fisher’s exact or the Chi-squared test, whichever was appropriate. Normally distributed continuous variables are reported as the mean±SD. Continuous variables that were not normally distributed are reported as the median with interquartile range (IQR). Mean values of continuous variables were compared using Student’s t-test, whereas median values were compared using the non-parametric Wilcoxon Mann-Whitney rank-sum test. Spearman’s correlation coefficients between CUC and LDL-C, as well as between CUC and the carotid plaque score were evaluated. Multivariable logistic regression analysis was performed to assess associations between CAD and clinical factors. A multivariable linear regression model was used to assess correlations between CUC and clinical factors. Statistical analyses were conducted using R statistics (https://www.r-project.org). P<0.05 was considered statistically significant.
Results
Clinical Characteristics
Patients’ clinical characteristics are summarized in Table 1. The mean age of patients was 46 years. Almost half the patients (51/108; 47.2%) were male. A pathogenic mutation was identified as FH in 66 (61.1%) patients (Supplementary Figure). Sixty-six (61.1%) patients were receiving lipid-lowering therapy during assessment. Pathogenic variants as FH were identified among 92 (85.2%) patients. Under these circumstances, the median LDL-C level was 154 mg/dL, and the median carotid plaque score was 4.0. The median serum CUC level was 133 arbitrary units (AU). After patients were divided into 2 groups based on the presence of CAD, significant differences were observed in all parameters evaluated, except for triglyceride and FH pathogenic variants.
Table 1. Characteristics of the Study Participants
| |
All
(n=108) |
CAD
(n=30) |
No CAD
(n=78) |
P value |
| Age (years) |
46±19 |
62±13 |
40±18 |
<2.2×10−16 |
| Male sex (%) |
51 (47.2) |
25 (83.3) |
26 (33.3) |
8.7×10−6 |
| BMI (kg/m2) |
23.1±0.7 |
23.3±0.8 |
23.0±0.7 |
0.18 |
| Hypertension (%) |
26 (24.1) |
22 (73.3) |
4 (5.1) |
7.3×10−13 |
| Diabetes (%) |
13 (12.0) |
12 (40.0) |
1 (1.3) |
1.9×10−7 |
| Smoking (%) |
29 (26.9) |
24 (80.0) |
5 (6.4) |
7.1×10−14 |
| Total cholesterol (mg/dL) |
230 [180–321] |
164 [135–197] |
287 [208–334] |
6.5×10−6 |
| Triglyceride (mg/dL) |
81 [61–118] |
90 [64–120] |
77 [60–116] |
0.57 |
| HDL-C (mg/dL) |
56 [47–66] |
50 [39–57] |
59 [49–68] |
0.0019 |
| LDL-C (mg/dL) |
154 [102–237] |
94 [71–121] |
203 [129–243] |
3.1×10−6 |
| FH pathogenic variants (%) |
92 (85.2) |
26 (86.6) |
66 (84.6) |
1.0 |
| Lipid-lowering therapy (%) |
66 (61.1) |
26 (86.6) |
40 (51.3) |
0.002 |
| Achilles tendon thickeningA (%) |
74 (68.5) |
30 (100) |
44 (56.4) |
1.4×10−4 |
| Carotid plaque score |
4.0 [0.0–9.7] |
13.9 [7.0–18.8] |
2.3 [0.0–5.1] |
2.2×10−10 |
| CUC (AU) |
133 [115–157] |
119 [92–139] |
142 [121–165] |
0.0004 |
Unless indicated otherwise, data are given as the mean±SD, median [interquartile range], or n (%). AAchilles tendon thickening was defined as ≥9.0 mm based on X-ray assessments. BMI, body mass index; CAD, coronary artery disease; CUC, cholesterol uptake capacity; FH, familial hypercholesterolemia; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.
The serum CUC level was positively correlated with HDL-C level, regardless of CAD status (Spearman’s r=0.95, P=2.4×10−15
among patients with CAD; Spearman’s r=0.95, P<2.2×10−16
among patients without CAD; Figure 1A). Conversely, serum CUC levels were negatively correlated with serum LDL-C levels among patients with CAD (Spearman’s r=−0.25, P=0.04), but not among patients without CAD (Figure 1B).
Serum CUC levels were normally distributed in patients with and without CAD (Figure 2A). Serum CUC was significantly lower among patients with than without CAD (median [IQR] 119 [92–139] vs. 142 [121–165] AU, respectively; P=0.0004; Figure 2B). In addition, CUC levels were significantly lower among patients with Achilles tendon thickness ≥9.0 mm than in those without Achilles tendon thickening (median [IQR] 133 [110–157] vs. 142 [123–174] AU, respectively; P=0.047; Figure 3A). There was a negative correlation between serum CUC and carotid plaque score assessed by carotid ultrasonography (Spearman’s r=0.37, P=0.00018; Figure 3B).
Factors Associated With CAD
In the multivariable logistic regression model, several factors were independently associated with the presence of CAD, including age (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.02–1.29, P=0.017), hypertension (OR 7.85, 95% CI 2.10–13.60, P=0.033), and diabetes (OR 11.45, 95% CI 1.46–21.44, P=0.0021). However, there was no significant association between the presence of CAD and HDL-C (OR 1.21, 95% CI 0.96–1.46, P=0.22), LDL-C (OR 0.77, 95% CI 0.45–1.09, P=0.18), and lipid-lowering therapy (OR 0.96, 95% CI 0.08–1.84, P=0.48; Table 2). There was a significant association between serum CUC and CAD, independent of other variables, including HDL-C (per 10 AU: OR 0.86, 95% CI 0.76–0.96, P=0.033).
Table 2. Factors Associated With CAD
| Variable |
OR |
95% CI |
P value |
| Age (per year) |
1.12 |
1.02–1.29 |
0.017 |
| Male sex (yes vs. no) |
2.30 |
0.75–3.85 |
0.27 |
| BMI (per 1 kg/m2) |
1.24 |
0.36–2.12 |
0.19 |
| Hypertension (yes vs. no) |
7.85 |
2.10–13.60 |
0.033 |
| Diabetes (yes vs. no) |
11.45 |
1.46–21.44 |
0.0021 |
| Smoking (yes vs. no) |
14.2 |
0.21–27.9 |
0.26 |
| HDL-C (per 1 mg/dL) |
1.21 |
0.96–1.46 |
0.22 |
| LDL-C (per 10 mg/dL) |
0.77 |
0.45–1.09 |
0.18 |
| Lipid-lowering therapy |
0.96 |
0.08–1.84 |
0.48 |
| CUC (per 10 AU) |
0.86 |
0.76–0.96 |
0.033 |
CI, confidence interval; OR, odds ratio. Other abbreviations as in Table 1.
In the multivariable linear regression model, we found a positive correlation between age and CUC. However, there was no correlation between CUC and other clinical variables, including sex, hypertension, diabetes, smoking, FH pathogenic variants, and lipid-lowering therapy (Table 3).
Table 3. Factors Correlated With Cholesterol Uptake Capacity
| Variable |
β coefficient (95% CI) |
P value |
| Age |
0.061 (0.012, 0.113) |
0.023 |
| Male sex |
−0.024 (−0.058, 0.010) |
0.521 |
| BMI (per 1 kg/m2) |
−0.013 (−0.060, 0.034) |
0.268 |
| Hypertension |
−0.024 (−0.084, 0.044) |
0.246 |
| Diabetes |
0.041 (−0.108, 0.026) |
0.301 |
| Smoking |
−0.045 (−0.124, 0.045) |
0.741 |
| LDL-C |
0.011 (−0.108, 0.926) |
0.293 |
| FH pathogenic variants |
0.031 (−0.058, 0.110) |
0.178 |
| Lipid-lowering therapy |
−0.036 (−0.076, 0.004) |
0.219 |
Abbreviations as in Tables 1,2.
Discussion
In this study we investigated the associations between CUC, which represents HDL function, and CAD among patients with FH who have an extremely high risk for CAD, and found that: (1) CUC was negatively correlated with the carotid plaque score; (2) CUC was negatively correlated with LDL-C among patients with CAD; and (3) CUC was significantly associated with CAD independent of HDL-C. Our study data support the notion that the function of HDL, rather than the HDL-C level, is one of the key factors for CAD among patients with FH.
Patients with FH have an extremely elevated risk for CAD due to their life-long exposure to elevated LDL-C levels. However, the severity of this disease varies considerably in terms of susceptibility to CAD. Several factors explain this variability, such as sex, hypertension, diabetes, smoking, HDL-C, and lipoprotein (a). Several clinical trials over the past decade have failed to show the clinical usefulness of increasing HDL-C levels in terms of these factors.28,29 Conversely, CEC, which reflects the initial step of the reverse cholesterol transport mediated by HDL, has emerged as potentially important factor for CAD among the general population. Thus far, few studies have specifically investigated this issue among patients with FH, because CEC measurements are not instantly applicable to high-throughput assessment as they require cell culture and radioisotopes, and procedures are not standardized, making it difficult to compare different studies.15 Recently, we developed a useful assay that does not require the use of radioisotopes or cells.15 The current fully automated assay system does not require sample preparation to isolate HDL from the serum, and the assay showed high reproducibility (coefficient of variation <5%) and short processing time (<20 min). Because of the cell-free system, CUC differs from CEC in that it does not reflect ATP-binding cassette transporter A1-mediated efflux.15 However, we previously revealed that CUC was significantly correlated with CEC using apoB-depleted serum (Spearman’s r2=0.4749).16 In this context, the CUC assay is complementary to the CEC in understanding the role of HDL functionality in cardiovascular disease. In the present study, we found that serum CUC was highly correlated with HDL-C concentrations, indicating that the amount of HDL is a major determinant of CUC. Conversely, although CUC showed a significant inverse association with CAD, no such relationship could be demonstrated for HDL-C. We previously reported similar findings,16–18,20 suggesting that functional assessment of HDL by CUC has more important clinical implications than quantitative HDL measurement.
In this study we also checked whether CUC was associated with other important clinical surrogate biomarkers, including LDL-C, carotid plaque score, and Achilles tendon thickening. We found that serum CUC was significantly lower in patients with a higher carotid plaque score or Achilles tendon thickening, suggesting that serum CUC may be also a good biomarker for assessing these conditions. In fact, it has been shown that Achilles tendon thickening is associated with CAD as well as the severity of the lesion.30,31 Accordingly, Achilles tendon thickening can be regarded as a surrogate marker of atherosclerosis in patients with FH. Ogura et al also demonstrated an inverse relationship between CEC and the presence of cardiovascular disease, as well as Achilles tendon thickening and carotid intima-media thickness in heterozygous FH.14 We found that serum CUC was negatively correlated with LDL-C concentrations only among patients with CAD. In fact, CUC is a kind of protective property independent of LDL-C in general. However, based on the results obtained in the present study, we assume that in patients with FH, elevated LDL-C with reduced CUC had synergistic effects on the development of atherosclerosis in these patients. The findings of the present study confirmed the impact of impaired HDL function on cardiovascular risk stratification in heterozygous FH patients. We believe that patients with FH whose CUC is reduced should be treated more intensively, although the LDL-C of patients with FH should first be lowered.
In the present study we found that CUC was positively correlated with age. The precise mechanism underlying the differences in CUC according age is uncertain. However, one possible reason is that age differences in dietary patterns affect CUC. We previously reported that the administration of eicosatetraenoic acid (EPA) improved the cholesterol efflux capacity of HDL in patients with dyslipidemia.32 Recently, the Hisayama Study demonstrated an increasing trend in serum EPA concentrations with age in a Japanese community.33 We cannot make any definitive conclusions regarding this issue in the present study, but age differences in dietary patterns may affect CUC. Further investigations are required to clarify this issue.
The present study has several limitations. First, we retrospectively analyzed data from a single center. Second, approximately 60% of patients were receiving lipid-lowering therapy, mainly statins, at the time of the assessment, which could have affected the results. Among the study participants, 1 patient was treated with a peroxisome proliferator-activated receptor agonist, whereas no patients were treated with probucol, which could have affected HDL-C and HDL function. Third, this study was performed exclusively in Japanese patients; thus, the results cannot be extrapolated to other ethnicities. Fourth, the number of samples was small, which could attenuate the results, especially those of the multivariable analysis. Fifth, we could not match the characteristics of patients with and without CAD in this study. In fact, we acknowledge that the group without CAD consists of very young patients (premenopausal women predominate) and few diabetic patients. These factors could have affected the results. Sixth, we did not include HDL-C in the multivariable linear regression analysis because there was a significant correlation between CUC and HDL-C in this study (Spearman’s r=0.95). The degree of correlation between CUC and HDL-C was so strong that we could not assess clinical parameters correlated with CUC independent of HDL-C in this study.
In conclusion, HDL function assessed by serum CUC, rather than HDL-C concentration, adds risk stratification information among patients with FH.
Acknowledgments
The authors thank Kazuko Honda and Sachio Yamamoto for their outstanding technical assistance.
Sources of Funding
This work was supported by grants from MEXT/JSPS KAKENHI (19K08575, 20H03927, 21K08066), a grant from the Ministry of Health, Labor and Welfare of Japan (Sciences Research Grant for Research on Rare and Intractable Diseases) and the Japanese Circulation Society (Project for Genome Analysis in Cardiovascular Diseases), a grant from the Japan Agency for Medical Research and Development (19188592 to H.T.), and institutional funding from Sysmex Corporation to Kobe University Graduate School of Medicine.
Disclosures
The authors declare the following financial interests/personal relationships that may be considered as potential competing interests: R.T., M.N., and K.H. 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; A.H., K.M., and T. Iino are employees of Sysmex Corporation, but Sysmex Corporation had no role in study design or conduct, or in manuscript preparation. The other authors have no known competing financial interests or personal relationships. K.H. is a member of Circulation Journal’s Editorial Board.
Author Contributions
Study conception: H.T., R.T., and M.K. Data collection: H.T., H.O., A.N., R.T., A.H., K.M., T. Ishida, K.H., M.T., and M.K. Analysis and interpretation of results: H.T., H.O., A.N., R.T., K.H., M.T., and M.K. Draft manuscript preparation: H.T., H.O., A.N., R.T., K.H., M.T., and M.K. All authors reviewed the results and approved the final version of the manuscript.
IRB Information
This study was approved by the ethics committees of Kanazawa University (2019-252) and Kobe University (B210217).
Data Availability
Raw data were generated at Kanazawa University. Derived data supporting the findings of this study are available from the corresponding author (H.T.) on request.
Supplementary Files
Please find supplementary file(s);
https://doi.org/10.1253/circj.CJ-22-0560
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