HDL Cholesterol Efflux Does Not Predict Cardiovascular Risk in Hemodialysis Patients : Journal of the American Society of Nephrology

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HDL Cholesterol Efflux Does Not Predict Cardiovascular Risk in Hemodialysis Patients

Kopecky, Chantal*; Ebtehaj, Sanam; Genser, Bernd‡,§,‖; Drechsler, Christiane¶,**; Krane, Vera¶,**; Antlanger, Marlies*; Kovarik, Johannes J.*; Kaltenecker, Christopher C.*; Parvizi, Mojtaba††; Wanner, Christoph¶,**; Weichhart, Thomas‡‡; Säemann, Marcus D.*; Tietge, Uwe J.F.

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Journal of the American Society of Nephrology 28(3):p 769-775, March 2017. | DOI: 10.1681/ASN.2016030262
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Plasma levels of HDL cholesterol (HDL-C) are inversely correlated with the risk of atherosclerotic cardiovascular disease (CVD) in large population studies,1 but recent findings called the usefulness of static HDL-C measurements as a predictive biomarker into question.2–5 Rather, determining functional properties of the HDL particle represents an emerging concept in cardiovascular research.6,7 Accordingly, promoting HDL-C efflux from macrophage foam cells plays a central role and is regarded crucial in reverse cholesterol transport (RCT).8,9 In populations with normal kidney function, a lower cholesterol efflux capacity was associated with increased cardiovascular morbidity and mortality, even independent of HDL-C concentrations.10,11

Patients with ESRD represent a population with an excessively increased cardiovascular risk,12 which is even potentiated by diabetes as a frequent comorbidity.13 Importantly, measurements of HDL-C levels have a rather limited predictive value in these patients,14–16 making it conceivable that alterations in HDL function represent an underlying causative contributor to atherosclerotic risk. However, to date, the potential importance of this novel concept for patients with ESRD has not been assessed. Therefore, we investigated in this work if cholesterol efflux as a key metric of HDL functionality is predictive for cardiovascular risk and overall mortality in patients with ESRD participating in the German Diabetes Dialysis Study (4D Study), a prospective trial exploring the efficacy of atorvastatin treatment in subjects with type 2 diabetes mellitus on hemodialysis.

In this post hoc analysis of the 4D Study, we measured HDL-C efflux in 1147 patients who were divided into tertiles on the basis of cholesterol efflux capacity: first tertile, median =0.73 (interquartile range [IQR], 0.67–0.77); second tertile, median =0.89 (IQR, 0.86–0.94); and third tertile, median =1.08 (IQR, 1.02–1.20). Baseline characteristics of the study participants according to tertiles of cholesterol efflux capacity are shown in Table 1. The proportion of men decreased significantly with increasing HDL efflux capacity along with the levels of the inflammatory proteins C-reactive protein (CRP) and HDL–bound serum amyloid A (SAA[HDL]). Higher cholesterol efflux was paralleled by increased plasma total cholesterol, LDL cholesterol (LDL-C), HDL-C, apoA-I, albumin, apoC-III, symmetric dimethylarginine, and carbamylated albumin. Of note, patients in the first tertile had a shorter duration of hemodialysis treatment and lower phosphate levels. There was no difference in age, body mass index, systolic BP, hypertension, or previous history of CVD throughout the tertiles.

Table 1. - Baseline characteristics of the study participants according to tertiles of cholesterol efflux capacity
Parameter Tertile 1, n=383 Tertile 2, n=382 Tertile 3, n=382 P Value
Cholesterol efflux capacity 0.73 [0.67–0.77] 0.89 [0.86–0.94] 1.08 [1.02–1.20]
Age, yr 66.7 (8.2) 66.2 (8.4) 66.0 (8.2) 0.49
Body mass index, kg/m2 27.5 (4.7) 27.7 (5.0) 27.4 (4.8) 0.72
Duration of diabetes, yr 17.8 (8.2) 18.4 (8.9) 18.1 (8.5) 0.62
Duration of dialysis, mo 5.5 [3.2–10.4] 5.6 [2.6–11.8] 6.7 [3.4–12.2] 0.03
Men, n (%) 233 (61.0) 207 (54.2) 188 (49.1) 0.004
Nonsmoker, n (%) 213 (55.8) 234 (61.3) 235 (61.4) 0.41
History, n (%)
 Arrhythmia 81 (21.2) 69 (18.1) 62 (16.2) 0.20
 Congestive heart failure 166 (43.5) 130 (34.0) 115 (30.0) <0.001
 Stroke/TIA 68 (17.8) 66 (17.3) 68 (17.8) 0.98
 Peripheral vascular disease 174 (45.6) 166 (43.5) 174 (45.4) 0.81
 MI/CABG/PTCA/CAD 128 (33.5) 106 (27.8) 109 (28.5) 0.17
 Hypertension 336 (88.0) 341 (89.3) 341 (89.0) 0.83
Systolic BP, mmHg 144.4 (23.2) 146.5 (20.9) 146.4 (21.8) 0.32
Diastolic BP, mmHg 74.6 (10.7) 76.3 (10.5) 76.6 (11.4) 0.02
Total cholesterol, mg/dl 209.8 (42.0) 221.5 (41.8) 226.9 (40.8) <0.001
Triglycerides, mg/dl 219.5 [149.0–345.0] 227.5 [158.0–332.0] 210.0 [139.0–298.0] <0.01
LDL-C, mg/dl 119.1 (27.9) 126.9 (28.9) 131.9 (30.2) <0.001
HDL-C, mg/dl 31.2 (9.9) 35.6 (12.5) 42.3 (15.6) <0.001
C-reactive protein, mg/L 6.2 [3.1–13.4] 5.4 [2.2–11.1] 7.1 [2.6–11.1] 0.01
Albumin, g/dl 3.8 (0.3) 3.8 (0.3) 3.9 (0.3) <0.001
Hemoglobin, g/dl 10.9 (1.4) 10.9 (1.3) 10.9 (1.3) 0.90
Hemoglobin A1c, % 6.7 (1.3) 6.7 (1.3) 6.8 (1.2) 0.69
Phosphate, mg/dl 5.8 (1.7) 6.1 (1.6) 6.2 (1.6) <0.01
apoA-I, mg/dl 115.8 (19.9) 125.5 (20.0) 137.6 (25.5) <0.001
SAA(HDL) 6.7 [3.0–13.9] 5.6 [2.7–12.3] 5.3 [2.9–10.2] 0.01
SP-B(HDL) 7.3 [4.0–13.0] 6.6 [3.5–13.7] 5.6 [3.1–11.7] 0.17
apoC-III, mg/dl 19.3 (9.5) 20.8 (10.0) 20.9 (9.0) 0.03
apoC-II, mg/dl 6.0 (3.1) 6.5 (3.3) 6.4 (2.7) 0.07
ADMA 0.9 (0.2) 0.9 (0.2) 0.9 (0.2) 0.37
SDMA 2.5 (0.8) 2.6 (0.8) 2.6 (0.8) 0.03
Carbamylated albumin 0.6 (0.3) 0.6 (0.3) 0.7 (0.3) <0.001
Data shown are means (SDs) or medians [IQRs] if not indicated otherwise. P values for comparisons of groups were calculated from ANOVA models (for continuous variables) or logistic regression models (for categorical variables). TIA, transitory ischemic attack; MI, myocardial infarction; CABG, coronary artery bypass grafting surgery; PTCA, percutaneous transluminal coronary angioplasty; CAD, coronary artery disease; SP-B(HDL), HDL–bound surfactant protein B; ADMA, asymmetric dimethylarginine; SDMA, symmetric dimethylarginine.

We next determined clinical parameters correlated with cholesterol efflux capacity in patients on dialysis (Supplemental Table 1) and found strong positive correlations with plasma HDL-C (r=0.25; P<0.001), apoA-I (r=0.26; P<0.001), and albumin (r=0.16; P<0.001). There were weaker correlations with LDL-C (r=0.10; P=0.001) and dialysis duration (r=0.10; P=0.001). Of note, there was no significant relation between cholesterol efflux and the inflammation markers CRP and SAA(HDL).

During a median follow-up of 4.1 years, a total of 423 study participants reached the combined primary end point (composite of cardiac death, nonfatal myocardial infarction, and stroke), 410 experienced cardiac events (cardiac death and nonfatal myocardial infarction), and 561 died (all-cause mortality). In an univariate Cox regression analysis examining the prognostic effect of baseline cholesterol efflux capacity for selected end points (Figure 1), we found no association with cholesterol efflux and the combined primary end point (hazard ratio [HR] per 1-SD increase, 0.96; 95% confidence interval [95% CI], 0.88 to 1.06; P=0.42), all cardiac events combined (HR per 1-SD increase, 0.92; 95% CI, 0.83 to 1.02; P=0.11), or all-cause mortality (HR per 1-SD increase, 0.96; 95% CI, 0.88 to 1.05; P=0.39). Multivariate Cox regression analyses with additional adjustment for a number of relevant clinical parameters (Table 2) further strengthened the conclusion that cholesterol efflux capacity was not associated with the risk for cardiovascular events or mortality in patients on hemodialysis. The respective Cox models according to tertiles of cholesterol efflux are shown in Supplemental Table 2.

Figure 1.:
HDL cholesterol efflux capacity is not associated with end points. Kaplan–Meier curves according to tertiles of cholesterol efflux capacity. Prognostic effect of cholesterol efflux capacity on (A) combined primary end point (composite of cardiac death, nonfatal myocardial infarction, and stroke), (B) cardiac events combined, and (C) all-cause mortality.
Table 2. - HRs for combined primary end point, all cardiac events combined, and all-cause mortality by cholesterol efflux capacity
Combined Primary End Point, 498 Events All Cardiac Events Combined, 534 Events All-Cause Mortality, 561 Events
HR (95% CI) per 1-SD Increase P Value HR (95% CI) per 1-SD Increase P Value HR (95% CI) per 1-SD Increase P Value
Model 1 0.96 (0.88 to 1.06) 0.42 0.92 (0.83 to 1.02) 0.11 0.96 (0.88 to 1.05) 0.39
Model 2 0.96 (0.88 to 1.05) 0.38 0.92 (0.82 to 1.03) 0.15 0.98 (0.90 to 1.06) 0.54
Model 3 0.89 (0.73 to 1.09) 0.27 0.77 (0.62 to 0.95) 0.02 0.91 (0.76 to 1.08) 0.26
Model 4 0.98 (0.91 to 1.06) 0.64 0.94 (0.85 to 1.04) 0.23 1.00 (0.95 to 1.06) >0.99
Model 5 0.98 (0.91 to 1.05) 0.55 0.94 (0.85 to 1.04) 0.22 0.99 (0.94 to 1.05) 0.85
Model 6 0.98 (0.91 to 1.05) 0.56 0.94 (0.85 to 1.04) 0.22 0.99 (0.94 to 1.05) 0.83
Continuous Cox regression model to assess the prognostic effect of cholesterol efflux on selected end points. Combined primary end point (composite of cardiac death, nonfatal myocardial infarction, and stroke). Model 1: univariate; model 2: adjusted for age and sex; model 3: adjusted for age, sex, and CRP; model 4: adjusted for traditional risk factors (age, sex, coronary artery disease, arrhythmia, transitory ischemic attack, congestive heart failure, peripheral vascular disease, smoking, systolic/diastolic BP, body mass index, albumin, phosphate, hemoglobin, hemoglobin A1c, and duration of dialysis); model 5: adjusted for traditional risk factors, LDL-C, HDL-C, and apoA-I; and model 6: adjusted for traditional risk factors, LDL-C, HDL-C, apoA-I, and CRP.

Next, participants were also stratified according to event occurrence (Supplemental Table 3). Baseline HDL-C efflux, total cholesterol, and HDL-C levels were not different between patients who reached the defined end points and those who did not. For all three analyzed end points, patients with an event during follow-up had a higher prevalence of preexisting CVD and longer diabetes duration as well as lower hemoglobin and phosphate levels. Notably, patients who died displayed pronounced signs of wasting and inflammation indicated by higher age, lower body mass index, higher prevalence of cardiovascular complications, higher plasma LDL-C, and higher levels of CRP and SAA(HDL).

Furthermore, we attempted to determine a potential role for cholesterol efflux capacity in the treatment efficacy of atorvastatin in the 4D Study population. Interestingly, stratified subgroup analysis by tertiles indicated a potential effect modification of atorvastatin on cardiac events (Supplemental Figure 1), although the overall effect was not significant (P=0.19). In patients with the lowest cholesterol efflux capacity, atorvastatin reduced the risk for all cardiac events combined (HR per 1-SD increase, 0.66; 95% CI, 0.47 to 0.92; P=0.02), which remained significant after additional adjustment with a number of relevant clinical parameters (Table 3). By contrast, in the higher tertiles of cholesterol efflux, no differential effect of statin treatment was observed.

Table 3. - Cox regression models assessing treatment efficacy of atorvastatin on all cardiac events combined stratified by tertiles of cholesterol efflux
Model 1 Model 2 Model 3 Model 4 Model 5
HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value
Tertile 1 0.66 (0.47 to 0.92) 0.02 0.65 (0.47 to 0.92) 0.01 0.68 (0.49 to 0.94) 0.02 0.70 (0.50 to 0.98) 0.04 0.70 (0.50 to 0.97) 0.03
Tertile 2 0.85 (0.61 to 1.19) 0.34 0.85 (0.61 to 1.18) 0.33 0.85 (0.61 to 1.18) 0.33 0.85 (0.61 to 1.18) 0.32 0.85 (0.61 to 1.18) 0.33
Tertile 3 0.85 (0.56 to 1.27) 0.42 0.84 (0.56 to 1.26) 0.40 0.70 (0.48 to 1.02) 0.07 0.69 (0.47 to 1.01) 0.06 0.69 (0.47 to 1.01) 0.06
Model 1: univariate; model 2: adjusted for age and sex; model 3: adjusted for traditional risk factors (age, sex, coronary artery disease, arrhythmia, transient ischemic attack, congestive heart failure, peripheral vascular disease, smoking, systolic/diastolic BP, body mass index, albumin, phosphate, hemoglobin, hemoglobin A1c, and duration of dialysis); model 4: adjusted for traditional risk factors, LDL-C, HDL-C, and apoA-I; and model 5: adjusted for traditional risk factors, LDL-C, HDL-C, apoA-I, and CRP.

This post hoc analysis of the 4D Study shows that HDL-C efflux capacity is not associated with cardiovascular events or mortality in a large and sufficiently powered cohort of patients with diabetes and ESRD on hemodialysis. These data are consistent with our previous observation that HDL-C efflux did not predict all–cause or specific CVD mortality in kidney transplant recipients. Intriguingly, however, higher efflux was independently associated with graft survival; thus, cholesterol efflux might represent a meaningful predictor of graft outcome.17

Hence, our results extend previous observations that plasma HDL-C levels have little association with the risk of cardiovascular morbidity or mortality in ESRD18 to the emerging area of HDL function studies. However, our findings are in apparent contrast to the prevailing view in the cardiovascular field that, at least in cohorts with normal or only mildly impaired kidney function, the cholesterol efflux capacity of HDL might be an even stronger predictor for CVD risk than the mere measurement of circulating levels of HDL-C or apoA-I.10,11 Although it should be noted that not all data on risk prediction by measuring cholesterol efflux are consistent with such a concept,19 we do believe that specific characteristics of patients with ESRD affect the outcome of our study. Smaller cross–sectional studies carried out in the setting of renal failure indicated several functional impairments of HDL, which are not restricted to decreased cholesterol efflux but affect its anti–inflammatory,19 antioxidative,20 or endothelial health–promoting activities21 to a similar degree. Potential underlying reasons for the dysfunctional HDL in kidney disease could be the high inflammatory and oxidative stress burden of ESRD that contributes to the excessive cardiovascular risk and is accelerated by the presence of diabetes.22,23 In fact, certain modifications in the protein composition of uremic HDL linked to chronic systemic inflammation conceivably contribute to rendering the particle ineffective in providing sufficient antiatherogenic protection.19,20 In this respect, we have previously found that HDL of patients with ESRD has lost its anti-inflammatory property and even acts proinflammatory as a direct consequence of enrichment with the acute–phase protein serum amyloid A.19 Subsequently, we could show that high SAA(HDL) levels were predictive of cardiac events in the 4D Study population.24 Such data suggest that the protein cargo on HDL might provide useful biomarkers to predict clinical events.

Another interesting outcome of our study was a potential effect modification of statin treatment. We found that atorvastatin reduced the risk for all cardiac events in patients of the first cholesterol efflux tertile. Given the general controversy of beneficial treatment effects of statins in patients on dialysis,25–27 the observed effect modification needs to be viewed with caution, and it will be critical to validate our results in independent patient collectives. However, even after adjustment for a number of relevant confounders, this effect remained significant, indicating a potential clinical relevance of our observation. Thus, our data suggest that cholesterol efflux capacity might be useful as a potential tool to identify subgroups of patients with ESRD that could benefit from statin treatment.

A potential limitation of our study is that the patients already had a long history of diabetes and likely, also developed CVD over a prolonged period of time. Therefore, baseline cholesterol efflux may not fully reflect HDL efflux capacity during follow-up. With respect to the efflux assay, our assay system mainly differs in the choice of cell line from comparable studies in non-CKD cohorts.10,11 Such studies used cholesterol–equilibrated murine J774 cells with limited baseline expression of ABCA1, which on induction with cAMP, becomes the dominant efflux pathway. In contrast, we used a human cell line that was loaded with cholesterol to reflect foam cells and in which all efflux pathways are active.7 These differences might affect the results to a certain extent; however, it has to be noted that, at present, no gold standard or comparative studies are available for efflux assays. In addition, albumin was shown to facilitate cholesterol efflux7; because in our cohort, plasma albumin levels were within the normal range throughout the tertiles, we do not expect this parameter to have a major effect on the results but can also not formally exclude it. Furthermore, cholesterol efflux, as evaluated in this study, does not take ESRD-related changes in cells and tissues into account, which also play a role in RCT. Here, specifically increased ACAT-1 activity in, for example, macrophages is worth mentioning.28,29 In addition, modifications of SR-BI by reactive oxygen species, myeloperoxidase, and glycation occur that negatively affect its cholesterol uptake function.30 Such alterations possibly lead to an even more substantial reduction in overall RCT in ESRD.

In conclusion, our study is the first to investigate the predictive potential of cholesterol efflux capacity in patients on dialysis. Remarkably, there was no association of this key metric of HDL function and cardiovascular outcomes in this specific patient population. Nonetheless, we believe that our study significantly contributes to clarify the role of HDL in ESRD and adds important information to the understanding of the underlying pathophysiology of CVD in ESRD. The ultimate clinical goal remains the identification of novel and reliable risk predictors for CVD in patients with ESRD that also offer the possibility to be influenced by targeted therapies.

Concise Methods

Study Participants

The design of the 4D Study has been described previously.25 The 4D Study was a double–blind, randomized, multicenter trial including 1255 patients with type 2 diabetes mellitus who were 18–80 years of age and had a previous duration of hemodialysis of <2 years. Patients were recruited between March of 1998 and October of 2002 and randomly assigned to receive daily treatment with 20 mg atorvastatin (n=619) or placebo (n=636). Participants were followed up at 4 weeks and every 6 months after randomization. At each follow-up visit, information about any suspected end point or serious adverse event was recorded. The study was approved by the local ethics committees and performed according to the Declaration of Helsinki, and informed consent was obtained from all participants.

Laboratory Procedures

HDL cholesterol efflux capacity was determined from THP-1 macrophages toward apoB-depleted plasma as acceptor following a previously reported protocol.17 Briefly, THP-1 human monocytes (American Type Culture Collection, Manassas, VA) were seeded in 48-well plates in RPMI 1640 Glutamax Medium (Gibco, Carlsbad, CA) containing 10% FBS and penicillin (100 U/ml)/streptomycin (100 μg/ml) and differentiated into macrophages with 100 nM phorbol myristate acetate for 24 hours. Then, macrophages were loaded for 24 hours with acetylated LDL (50 μg protein/ml) and 1 μCi/ml 3H cholesterol (PerkinElmer, Waltham, MA) followed by overnight equilibration with RPMI 1640 Glutamax Medium containing 2% BSA. Thereafter, cells were washed with PBS, and 2% of individual apoB–depleted plasma samples was added in RPMI 1640 Glutamax Medium containing penicillin/streptomycin. After 5 hours of efflux, the medium was collected and centrifuged in a tabletop centrifuge (Hettich, Tuttlingen, Germany) for 5 minutes at 10,000 rpm to pellet cellular debris, and radioactivity was determined in an aliquot by liquid scintillation counting (Packard 1600CA Tri-Carb; Packard, Meriden, CT). To the cells, 0.1 M NaOH was added for at least 30 minutes, and then, radioactivity remaining in the cells was determined. Cholesterol efflux was calculated as the percentage of radiolabel recovered in the medium related to the total added dose of radioactivity (counts from the medium added to counts from cells). Values obtained from negative control cells without added apoB–depleted patient plasma were subtracted to correct for unspecific efflux. Finally, values were normalized to a plasma pool from healthy controls present on every plate. Thus, values given for efflux do not have a specific unit. All determinations were carried out in duplicates at the same time using the same reagents to reduce variation caused by different assay conditions. The intra-assay CV of this method is 5.4%, and the interassay CV is 7.9%.

End Points

For our post hoc analysis, we evaluated the following predefined and centrally adjudicated outcome measures: (1) combined primary end point (composite of cardiac death, nonfatal myocardial infarction, and stroke), (2) all cardiac events combined (cardiac death and nonfatal myocardial infarction), and (3) all-cause mortality.

Statistical Analyses

We examined characteristics of subgroups defined by tertiles of cholesterol efflux by calculating descriptive statistics (means and SDs for continuous variables and frequency tables for categorical variables). We compared the distribution of important cardiovascular parameters across tertiles by using ANOVA (for continuous variables) or chi-squared tests (for categorical variables). Furthermore, we conducted a correlation analysis calculating Pearson correlation coefficients to investigate which clinical parameters were correlated with cholesterol efflux capacity in patients on dialysis. We used time to event analysis (extended Cox regression model allowing for multiple events) aimed to (1) evaluate the effect of cholesterol efflux on end point occurrence, and (2) investigate the effect of cholesterol efflux on the efficacy of atorvastatin. For 1, we conducted a pooled analysis in both randomization groups, including a dummy variable for randomization group and efflux capacity as continuous covariates, standardized by the sample SD. For 2, we conducted efficacy analysis calculating HRs for atorvastatin versus placebo stratified by efflux tertiles. For both analyses, we conducted sensitivity analysis, fitting different models that each included different covariates: model 1: no additional covariate adjustment; model 2: adjusted for age and sex; model 3: adjusted for age, sex, and CRP; model 4: adjusted for traditional risk factors (age, sex, coronary artery disease, arrhythmia, transitory ischemic attack, congestive heart failure, peripheral vascular disease, smoking, systolic/diastolic BP, body mass index, albumin, phosphate, hemoglobin, hemoglobin A1c, and duration of dialysis); model 5: adjusted for traditional risk factors, LDL-C, HDL-C, and apoA-I; and model 6: adjusted for traditional risk factors, LDL-C, HDL-C, apoA-I, and CRP.

All statistical analyses were performed using the statistical software package STATA (StataCorp. 2011, Stata Statistical Software: Release 13; StataCorp., College Station, TX).



Published online ahead of print. Publication date available at www.jasn.org.

This article contains supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2016030262/-/DCSupplemental.

This work was supported by a grant from The Netherlands Organization for Scientific Research (VIDI Grant 917-56-358 to U.J.F.T.).


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end-stage renal disease; cardiovascular; risk factors; HDL; cholesterol efflux

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