Abstract
Background:
The role of lipoprotein (a), or Lp(a), in the development of obstructive coronary artery disease (CAD) and high-risk plaque (HRP) among primary prevention patients with stable chest pain is unknown. We sought to evaluate the relationship of Lp(a), independent of low-density lipoprotein cholesterol (LDL-C), with the presence of obstructive CAD and HRP in an attempt to improve understanding of the residual risk imparted by Lp(a) on CAD.
Methods:
We performed a secondary analysis among PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) Trial participants who had coronary computed tomographic angiography (CTA) performed and Lp(a) data available. Lp(a) concentration was analyzed as a binary variable with elevated Lp(a) defined as ≥50 mg/dL. “Stenosis ≥ 50%” was defined as ≥50% coronary artery stenosis in any epicardial vessel, and “Stenosis ≥ 70%” was defined as ≥70% coronary artery stenosis in any epicardial vessel and/or ≥50% left main coronary artery stenosis. HRP was defined as presence of plaque on CTA imaging with evidence of positive remodeling, low CT attenuation, or napkin ring sign. Multivariate logistic regression models were constructed to evaluate the association between Lp(a) and the outcomes of obstructive CAD and HRP stratified by LDL-C ≥100 mg/dL vs. <100 mg/dL.
Results:
Of the 1,815 patients who underwent CTA and had Lp(a) data available, those with elevated Lp(a) were more commonly female and Black than those with lower Lp(a). Elevated Lp(a) was associated with both Stenosis ≥ 50% (OR 1.57, 95% CI 1.14–2.15, p=0.005) and Stenosis ≥ 70% (OR 2.05, 95% CI 1.34–3.11, p=0.0008) in multivariate models, and this relationship was not modified by LDL-C ≥100 mg/dL vs. <100 mg/dL (interaction p>0.4). Elevated Lp(a) was not associated with HRP when adjusted for obstructive CAD.
Conclusions:
This study of patients without known CAD found that elevated Lp(a) ≥50 mg/dL was independently associated with the presence of obstructive CAD regardless of controlled vs. uncontrolled LDL-C, but was not independently associated with HRP when Stenosis ≥ 50% or ≥ 70% was accounted for. Further research is warranted to delineate the role of Lp(a) in the residual risk for ASCVD that patients may have despite optimal LDL-C lowering.
Keywords: lipoprotein (a), lipids, atherosclerotic cardiovascular disease, high-risk coronary plaque
Introduction
The traditional lipid management strategy to reduce atherosclerotic cardiovascular disease (ASCVD) risk places greatest emphasis on reduction of serum low-density lipoprotein cholesterol (LDL-C), reflected in the most recent American College of Cardiology/American Heart Association (ACC/AHA) guideline on the management of blood cholesterol.1–3 Although management of LDL-C and other traditional risk factors is effective, many patients remain at increased ASCVD risk despite optimal medical therapy.3 Evidence is mounting that this residual risk may be at least partially related to elevations in lipoprotein (a), or Lp(a).
Lp(a) is a lipoprotein that contains apolipoprotein (a), which is derived from the plasminogen gene and likely contributes to the proatherogenic, proinflammatory, and prothrombotic properties of Lp(a).4 Indeed, Lp(a) is strongly associated with premature and accelerated atherosclerosis, ischemic heart disease, and calcific aortic stenosis, independent of LDL-C.5–9 As such, elevated Lp(a) is recognized in the 2018 ACC/AHA cholesterol guidelines as an ASCVD risk enhancer.10,11
Although there are currently no approved therapies targeting elevated Lp(a), multiple novel therapies are under active clinical investigation,12–14 which raises the possibility that Lp(a) may soon become a modifiable risk factor. However, current understanding of the degree to which Lp(a) imparts residual risk among individuals without known coronary artery disease (CAD) is suboptimal, especially since most studies have not systematically assessed high-risk plaque (HRP), which may be a mediator of residual risk. The Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) was a randomized trial of participants that compared coronary computed tomographic angiography (CTA) to noninvasive stress testing in symptomatic patients without a diagnosis of CAD.15,16 This population of patients at risk for CAD provides an optimal cohort to evaluate the role of Lp(a), independent of LDL-C, in the development of obstructive CAD and HRP. Herein, we evaluate the relationship of Lp(a) with the presence of obstructive CAD and features of HRP in an attempt to improve understanding of the residual risk imparted by Lp(a) on ASCVD and to better understand the population who could benefit the most from targeted reduction of Lp(a).
Methods
The design, rationale, and primary results of the PROMISE trial have been published.15,16 In brief, PROMISE was a multicenter, randomized trial completing enrollment of 10,003 participants between July 2010 and September 2013. The study used a pragmatic comparative effectiveness design to compare clinical outcomes after anatomical testing using CTA vs. functional noninvasive stress testing for nonurgent, noninvasive evaluation of patients with symptoms suggestive of CAD, but without known CAD. Local or central institutional review boards approved the trial, and all patients provided written informed consent.
Study Population
The PROMISE study included men older than age 54 and women older than age 64, as well as men aged 45–54 and women aged 50–64 with at least one additional ASCVD risk factor.15 Exclusion criteria included hemodynamic instability, suspected acute coronary syndrome, known CAD or evaluation of CAD in the last 12 months, history of cardiomyopathy, history of severe congenital or valvular disease, life expectancy less than 2 years, and those with contraindications to CTA. This post hoc analysis includes PROMISE participants in the CTA arm who also had biospecimens available and for whom Lp(a) measurements were obtained (N=1,815; Figure 1). Participants in the CTA arm without available biospecimens were not included in this study, nor were those in the functional testing arm.
Figure 1:
CONSORT diagram.
Biomarker Evaluation
Lp(a) was measured in mg/dL using the Quantia Lp(a) assay from Abbott Laboratories, and samples were processed in two batches. LDL-C measurements were obtained using LipoScience (Labcorp) nuclear magnetic resonance assays.
Outcomes
Computed tomography (CT) measurements were obtained via contrast-enhanced coronary CTA using ≥64-slice multidetector CT. The technical quality of all studies was assessed, and quality control was ensured via overread of 10% of all studies.15,16 All CT plaque evaluation was done by the Massachusetts General Hospital CT core lab.15,16
For this analysis, obstructive CAD was defined at 2 levels: Stenosis ≥ 50% was defined as coronary artery stenosis in any epicardial vessel of ≥50%, and Stenosis ≥ 70% was defined as coronary artery stenosis ≥70% in any epicardial vessel and/or left main coronary artery stenosis ≥50%. HRP was defined as presence of any plaque on CTA imaging with evidence of any of the HRP features: positive remodeling, low CT attenuation, or napkin ring sign.
Statistical Analysis
Baseline characteristics of PROMISE participants are presented as mean ± standard deviation for continuous variables or count (percentage) of patients for categorical variables. Lp(a) concentration was analyzed as a binary variable, with elevated Lp(a) defined as ≥50 mg/dL and nonelevated Lp(a) defined as <50 mg/dL based on current ACC/AHA guidelines.11 Univariable and multivariable logistic regression models were constructed to evaluate the association between Lp(a) and the outcomes of Stenosis ≥ 50% and Stenosis ≥ 70%. Univariable models were adjusted for Lp(a) sample batch. Multivariable models were further adjusted for sex, age, race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, and statin use. Missing data for these adjustment factors were median- and mode-imputed. Univariable and multivariable logistic regression models using the same adjustment factors were developed for the outcome of HRP, only among the subset of the population with either nonobstructive or obstructive CAD. To evaluate the potential residual risk related to Lp(a) beyond that estimated by LDL-C alone, we further stratified the models by LDL-C ≥100 mg/dL vs. <100 mg/dL. This threshold was chosen because this was a primary prevention population. Interaction analyses were performed to evaluate for the presence of interaction between LDL-C and Lp(a). A two-sided p-value <0.05 was considered statistically significant.
Results
A total of 1,815 participants were included in this PROMISE substudy, of whom 405 (22.3%) had Lp(a) ≥50 mg/dL and 1,410 (77.7%) had Lp(a) <50 mg/dL. Supplemental Table 1 outlines baseline characteristics in the population for this analysis vs. the overall PROMISE trial population. Baseline characteristics stratified by elevated Lp(a) and nonelevated Lp(a) groups are presented in Table 1. The mean Lp(a) in the elevated Lp(a) group was 90.2±31.5 mg/dL and 14.1±12.1 mg/dL in the nonelevated group. The mean age was similar for both groups. There were higher proportions of females (57.3% vs. 51.6%) and Black participants (17.8% vs. 6.0%) in the elevated Lp(a) group compared with the nonelevated group. LDL-C was significantly higher in the elevated Lp(a) group, and a higher proportion of participants in the elevated group were on statin therapy (52.9% vs. 42.4%). Aspirin use was similar for both groups.
Table 1.
Baseline characteristics of PROMISE substudy population stratified by Lp(a) concentration
| Characteristic | Overall | Lp(a) < 50 mg/dL | Lp(a) ≥ 50 mg/dL | p |
|---|---|---|---|---|
| n | 1815 | 1410 | 405 | |
| Age, mean (SD) | 60.2 (8.0) | 60.1 (8.0) | 60.5 (7.8) | 0.3 |
| Female, n (%) | 959 (52.8) | 727 (51.6) | 232 (57.3) | 0.05 |
| Self-reported race, n (%) | <0.001 | |||
| White | 1582 (87.5) | 1263 (90.0) | 319 (79.0) | |
| Black or African American | 156 ( 8.6) | 84 ( 6.0) | 72 (17.8) | |
| Asian | 30 ( 1.7) | 24 ( 1.7) | 6 ( 1.5) | |
| American Indian or Alaska Native | 13 ( 0.7) | 10 ( 0.7) | 3 ( 0.7) | |
| Native Hawaiian or other Pacific Islander | 4 ( 0.2) | 4 ( 0.3) | 0 ( 0.0) | |
| Multi-racial | 22 ( 1.2) | 18 ( 1.3) | 4 ( 1.0) | |
| Body mass index, kg/m2, mean (SD) | 30.6 (5.9) | 30.7 (5.9) | 30.0 (5.8) | 0.04 |
| LDL-C, mg/dL, mean (SD) | 121.0 (34.8) | 119.6 (33.6) | 126.0 (38.3) | 0.001 |
| HDL-C, mg/dL, mean (SD) | 53.5 (13.5) | 52.8 (13.4) | 55.9 (13.5) | <0.001 |
| Total cholesterol, mg/dL, mean (SD) | 205.6 (42.6) | 203.7 (40.9) | 212.3 (47.6) | <0.001 |
| Triglycerides, mg/dL, mean (SD) | 157.5 (113.1) | 161.9 (117.9) | 142.4 (92.9) | 0.002 |
| Lp(a), mg/dL, mean (SD) | 31.1 (36.6) | 14.1 (12.1) | 90.2 (31.5) | <0.001 |
| Statin use at baseline, n (%) | 785 (44.8) | 578 (42.4) | 207 (52.9) | <0.001 |
| Aspirin use at baseline, n (%) | 821 (46.8) | 627 (46.0) | 194 (49.6) | 0.233 |
| Presence of obstructive CAD, n (%) | ||||
| ≥50% coronary artery stenosis | 248 (13.7) | 177 (12.6) | 71 (17.5) | 0.013 |
| ≥70% coronary artery stenosis or ≥50% left main stenosis | 115 ( 6.3) | 76 ( 5.4) | 39 ( 9.6) | 0.003 |
PROMISE = Prospective Multicenter Imaging Study for Evaluation of Chest Pain; Lp(a) = lipoprotein (a); LDL-C = low-density lipoprotein cholesterol; HDL-C = high-density lipoprotein cholesterol; CAD = coronary artery disease.
Lp(a) Levels and Obstructive CAD
In both univariate and multivariate models, elevated Lp(a) ≥50 mg/dL was associated with higher odds of obstructive CAD (Table 2, Figure 2). Specifically, elevated Lp(a) was associated with stenosis ≥ 50% in univariate (OR 1.48, 95% CI 1.09–1.99, p=0.01) and multivariate models (OR 1.57, 95% CI 1.14–2.15, p=0.005). Similarly, elevated Lp(a) was associated with Stenosis ≥ 70% in both univariate (OR 1.86, 95% CI 1.24–2.77, p=0.002) and multivariate models (OR 2.05, 95% CI 1.34–3.11, p=0.0008).
Table 2.
Association between elevated Lp(a) ≥50 mg/dL and presence and degree of obstructive CAD stratified by LDL-C.
| Univariate | Multivariate1 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||
| Presence of Obstructive CAD | Population | # with obstructive CAD (cases) | # without obstructive CAD (controls) | OR | 95% CI | P-value | OR | 95% CI | P-value | P-interaction |
|
| ||||||||||
| Stenosis ≥50% | Overall | 248 | 1567 | 1.48 | 1.09–1.99 | 0.01 | 1.5–7 | 1.14–2.15 | 0.005 | - |
| LDL-C ≥100 mg/dL | 187 | 1136 | 1.56 | 1.11–2.19 | 0.01 | 1.72 | 1.19–2.45 | 0.003 | 0.42 | |
| LDL-C <100 mg/dL | 61 | 431 | 1.18 | 0.60–2.20 | 0.60 | 1.11 | 0.55–2.13 | 0.8 | ||
|
| ||||||||||
| Stenosis ≥70%2 | Overall | 115 | 1700 | 1.86 | 1.24–2.77 | 0.002 | 2.05 | 1.34–3.11 | 0.0008 | - |
| LDL-C ≥100 mg/dL | 88 | 1235 | 2.00 | 1.25–3.13 | 0.003 | 2.28 | 1.41–3.65 | 0.0007 | 0.49 | |
| LDL-C <100 mg/dL | 27 | 465 | 1.40 | 0.54–3.28 | 0.50 | 1.33 | 0.49–3.25 | 0.60 | ||
Lp(a) = lipoprotein (a); LDL-C = low-density lipoprotein cholesterol; CAD = coronary artery disease; OR = odds ratio; CI = confidence interval.
Adjustment variables for overall models include sex, age, self-reported race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, and statin use; stratified models are not adjusted for LDL-C.
Includes ≥70% coronary artery stenosis or ≥50% left main coronary artery stenosis.
Figure 2:
Association between elevated Lp(a) ≥50 mg/dL and obstructive CAD, stratified by LDL-C level. Estimates are derived from models adjusted for sex, age, self-reported race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, and statin use; stratified models are not adjusted for LDL-C.
Given our focus in this analysis on the role of Lp(a) in assessing residual risk, we then evaluated associations between Lp(a) with obstructive CAD stratified by LDL-C (≥100 mg/dL vs. <100 mg/dL). As outlined in Table 2, the effect estimate of the association between Lp(a) and both Stenosis ≥ 50% and ≥ 70% was greater among those with LDL-C ≥100 mg/dL. However, this difference was not statistically significant in interaction analyses of LDL-C and Lp(a), suggesting that the relationship between Lp(a) and obstructive CAD was not modified by controlled vs. uncontrolled LDL-C.
Lp(a) and HRP
The association between Lp(a) and HRP was evaluated among 1,187 individuals with any CAD (i.e., excluding those with no CAD). Elevated Lp(a) ≥50 mg/dL was associated with the presence of HRP in both univariate (OR 1.43, 95% CI 1.04–1.94, p=0.02) and multivariate models (OR 1.44, 95% CI 1.04–1.98, p=0.02) (Table 3, Figure 3). However, this association was attenuated when further adjusted for the presence of obstructive CAD (p>0.1 in models adjusted for either Stenosis ≥ 50% or Stenosis ≥ 70%).
Table 3.
Association between elevated Lp(a) ≥50 mg/dL and presence of HRP stratified by LDL-C.
| Univariate | Multivariate1 | Multivariate+ Stenosis ≥50%2 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
||||||||||||
| Population | # with HRP (cases) | # without HRP (controls) | OR | 95% CI | P-value | OR | 95% CI | P-value | OR | 95% CI | P-value | P-interaction |
|
| ||||||||||||
| Overall | 279 | 908 | 1.43 | 1.04–1.94 | 0.02 | 1.44 | 1.04–1.98 | 002 | 1.30 | 0.93–1.81 | 0.12 | |
| LDL-C ≥100 mg/dL | 212 | 646 | 1.49 | 1.04–2.10 | 0.03 | 1.52 | 1.06–2.17 | 0.02 | 1.36 | 0.93–1.98 | 0.10 | 0.56 |
| LDL-C <100 mg/dL | 67 | 262 | 1.14 | 0.56–2.21 | 0.71 | 1.17 | 0.56–2.32 | 0.66 | 1.09 | 0.51–2.19 | 0.81 | |
Lp(a) = lipoprotein (a); HRP = high-risk plaque; LDL-C = low-density lipoprotein cholesterol; OR = odds ratio; CI = confidence interval.
Adjustment variables include sex, age, self-reported race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, and statin use.
Adjustment variables include sex, age, self-reported race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, statin use, and Stenosis ≥50%.
Figure 3:
Association between elevated Lp(a) ≥50 mg/dL and HRP among individuals with nonobstructive or obstructive CAD. Multivariate models are adjusted for sex, age, self-reported race, body mass index, LDL-C ≥100 mg/dL vs. <100 mg/dL, and statin use.
Similar to the above analysis with obstructive CAD, we evaluated the association between Lp(a) with HRP stratified by LDL-C (≥100 mg/dL vs. <100 mg/dL). As outlined in Table 3, the interaction term was nonsignificant, indicating that the relationship between Lp(a) and HRP was not modified by controlled vs. uncontrolled LDL-C.
Discussion
Improved management of traditional ASCVD risk factors has highlighted the importance of addressing residual ASCVD risk. This study, which leveraged high-quality, carefully phenotyped, core-lab-adjudicated clinical trial data, adds to the growing body of evidence implicating Lp(a) as an important risk factor for CAD. Our findings, which demonstrate an independent association of elevated Lp(a) with obstructive CAD in patients without known CAD, provides evidence in an important demographic of those at-risk for CAD, suggesting this population may benefit from novel therapies directed at Lp(a) as a primary prevention strategy. Though it was not statistically significant, there was a trend towards a stronger relationship between Lp(a) and obstructive CAD among those with uncontrolled LDL-C vs. those with controlled LDL-C, which runs contrary to the concept of Lp(a) imparting residual risk. Finally, elevated Lp(a) was associated with HRP, though this relationship was attenuated when the presence of obstructive CAD was accounted for.
In both univariate and multivariate analyses, we found that elevated Lp(a) ≥50 mg/dL was associated with obstructive CAD as determined by CTA in patients without previously diagnosed CAD but with stable chest pain. Previous studies, particularly those utilizing CTA, that have investigated the relationship between Lp(a) and CAD have primarily focused on the analysis of plaque volume rather than the presence of obstructive CAD or qualitative high-risk plaque characteristics. One such example includes a recent post hoc analysis of six trials that employed coronary intravascular ultrasound imaging to assess atheroma volume in which investigators found elevated Lp(a) to be independently associated with high percent atheroma volume in a population of 3,943 participants.17 Another study of 814 participants who had undergone two sequential coronary CT angiograms at least 6 months apart found that when evaluating Lp(a) as a continuous variable, Lp(a) concentration was positively associated with the odds of CAD progression in multivariate models.18 The same study found that in models evaluating Lp(a) as a binary variable with a cutoff of 300 mg/dL, elevated Lp(a) >300 mg/dL was associated with an even higher odds of CAD progression.18 In addition to its association with CAD, in previous imaging studies focusing on the role of Lp(a) in carotid atherosclerotic disease, elevated Lp(a) was shown to be an independent predictor of carotid plaque burden both by carotid magnetic resonance imaging and multidetector row CT.19,20
We had an interest in assessing whether Lp(a) may serve as a marker of residual risk in individuals with controlled LDL-C. To our surprise, when stratified by LDL-C, the association of Lp(a) with obstructive CAD was significant only in those with LDL-C ≥100 mg/dL, although the interaction term for LDL-C level was nonsignificant. Nonetheless, this finding could suggest that in this population of symptomatic but undiagnosed patients, Lp(a) is not a marker of residual risk. It is possible that there are synergistic effects of LDL-C and Lp(a), both apolipoprotein B–containing particles, on plaque formation that increases the risk of obstructive CAD.17,21,22
We further evaluated whether Lp(a) was associated with HRP features. While we found that Lp(a) was associated with HRP, these results were attenuated when obstructive CAD was included in the model, suggesting that the observed association was primarily related to the previously seen association between Lp(a) and CAD. Other studies have shown an association of Lp(a) with HRP features. One such recent study found that Lp(a) was associated with CTA findings of high-risk plaque features defined as low attenuation plaque, positive remodeling, napkin-ring sign, spotty calcification, a minimum lumen area <4 mm2, or luminal plaque burden ≥70%.23 Another study using optical coherence tomography across 255 patients found that patients with Lp(a) > 25 mg/dL had an increased prevalence of thin-cap fibroatheroma as compared with those with Lp(a) < 25 mg/dL.24 However, neither of these studies adjusted for the presence of obstructive CAD, which may explain the discrepancy between these results and ours.
This study has several limitations, most notably its observational nature as a post hoc analysis of a larger clinical trial. However, adjustments to models were made to limit confounding as much as possible based on available data. The limited number of trial participants with Lp(a) measurements and even fewer with Lp(a) levels ≥50 mg/dL could have also masked any possible associations due to a lack of power. Additionally, while the absence of known coronary disease at baseline in trial participants was useful in extending current evidence to this population, the overall ASCVD risk of the study population was lower than a typical secondary prevention population where a higher burden of disease could be expected.
Overall, this study of patients without known CAD found that elevated Lp(a) ≥50 mg/dL was independently associated with the presence of obstructive CAD regardless of controlled vs. uncontrolled LDL-C but was not independently associated with HRP when obstructive CAD was accounted for. Further research is warranted to delineate the role of Lp(a) in the residual risk for ASCVD that patients may have despite optimal LDL-C lowering.
Supplementary Material
Highlights:
Relationship between lipoprotein(a) and coronary atherosclerosis is unclear
In PROMISE, Lp(a) ≥50 mg/dL was associated with obstructive coronary disease
This relationship was independent of LDL-C control
However, Lp(a) was not associated with high-risk coronary plaque
Further research is needed to understand the role of Lp(a) in CAD
Funding:
This work was supported by the National Heart, Lung, and Blood Institute (grant numbers 1R01HL098237, 1R01HL098236, and 1R01HL098305). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Declaration of Competing Interest
Dr. Shah reports research grants from Amgen, Janssen, Eli Lily, Novartis, and National Institutes of Health, and service as a consultant/advisor for Merck, Amgen, Norvartis, and New Amsterdam Pharma.
Dr. Ferencik reports consulting fees from Cleerly, HeartFlow, Elucid, Siemens Healthineers, and BioMarin, and stock options from Elucid, and is a member of an advisory board for Cleerly.
Dr. Lu reports research funding to his institution from the American Heart Association, AstraZeneca, Ionis, Johnson & Johnson Innovation, Kowa Pharmaceuticals America, MedImmune, National Academy of Medicine, National Heart, Lung, and Blood Institute, and Risk Management Foundation of the Harvard Medical Institutions outside the submitted work.
Dr. Foldyna reports research grants from National Institutes of Health/National Heart, Lung, and Blood Institute (R01HL170877, 5P30DK040561), AstraZeneca, Cleerly Health, MedImmune, and MedTrace.
Dr. Pagidipati reports research support from Alnylam, Amgen, Bayer, Boehringer Ingelheim, Eggland’s Best, Eli Lilly, Novartis, Novo Nordisk, Merck; service on consultation/advisory panels for Amgen, Bayer, Boehringer Ingelheim, CRISPR Therapeutics, Eli Lilly, Esperion, AstraZeneca, Merck, Novartis, and Novo Nordisk; service as an Executive Committee member for trials sponsored by Novo Nordisk and by Amgen and by AstraZeneca; service on a Data and Safety Monitoring Board for trials sponsored by Johnson & Johnson and Novartis; and service on a medical advisory board for Miga Health.
Footnotes
The remaining authors have no competing interests to declare.
CRediT authorship contribution statement
Thomas O’Toole: investigation, writing original draft
Nishant P. Shah: conceptualization, investigation, writing – review/edit
Stephanie Nicole Giamberardino: formal analysis, methodology, writing review
Lydia Coulter Kwee: formal analysis, methodology, writing review, supervision
Deepak Voora: investigation, methodology, writing review
Robert W. McGarrah: investigation, methodology, writing review
Maros Ferencik: investigation, methodology, writing review
Michael T. Lu: investigation, methodology, writing review
William E. Kraus: investigation, methodology, writing review
Borek Foldyna: investigation, methodology, writing review
Pamela S. Douglas: conceptualization, investigation, methodology, writing review, funding acquisition, supervision
Svati H. Shah: conceptualization, investigation, methodology, writing review, funding acquisition, supervision
Neha J. Pagidipati: conceptualization, investigation, methodology, writing review, supervision
PROMISE ClinicalTrials.gov identifier = NCT01174550
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Data sharing:
PROMISE data sets are available at https://biolincc.nhlbi.nih.gov/studies/promise. There are no commercial use data restrictions, and no data restrictions based on area of research.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
PROMISE data sets are available at https://biolincc.nhlbi.nih.gov/studies/promise. There are no commercial use data restrictions, and no data restrictions based on area of research.