Pitavastatin, Procollagen Pathways, and Plaque Stabilization in Patients With HIV: A Secondary Analysis of the REPRIEVE Randomized Clinical Trial

Márton Kolossváry; Samuel R Schnittman; Markella V Zanni; Kathleen V Fitch; Carl J Fichtenbaum; Judith A Aberg; Gerald S Bloomfield; Carlos D Malvestutto; Judith Currier; Marissa R Diggs; Christopher deFilippi; Allison Ross Eckard; Adrian Curran; Murat Centinbas; Ruslan Sadreyev; Borek Foldyna; Thomas Mayrhofer; Julia Karady; Jana Taron; Sara McCallum; Michael T Lu; Heather J Ribaudo; Pamela S Douglas; Steven K Grinspoon

doi:10.1001/jamacardio.2024.4115

. 2024 Dec 11;10(3):254–264. doi: 10.1001/jamacardio.2024.4115

Pitavastatin, Procollagen Pathways, and Plaque Stabilization in Patients With HIV

A Secondary Analysis of the REPRIEVE Randomized Clinical Trial

Márton Kolossváry ¹, Samuel R Schnittman ^1,², Markella V Zanni ¹, Kathleen V Fitch ¹, Carl J Fichtenbaum ³, Judith A Aberg ⁴, Gerald S Bloomfield ⁵, Carlos D Malvestutto ⁶, Judith Currier ⁷, Marissa R Diggs ¹, Christopher deFilippi ⁸, Allison Ross Eckard ⁹, Adrian Curran ¹⁰, Murat Centinbas ^11,¹², Ruslan Sadreyev ^11,¹³, Borek Foldyna ¹⁴, Thomas Mayrhofer ^14,¹⁵, Julia Karady ^14,¹⁶, Jana Taron ^11,¹⁷, Sara McCallum ¹, Michael T Lu ¹⁴, Heather J Ribaudo ¹⁸, Pamela S Douglas ¹⁹, Steven K Grinspoon ^1,^✉

¹Metabolism Unit, Massachusetts General Hospital, Harvard Medical School, Boston

²Division of Infectious Diseases, Massachusetts General Hospital, Boston

³Division of Infectious Diseases, Department of Medicine, University of Cincinnati, Cincinnati, Ohio

⁴Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York

⁵Department of Medicine, Duke Global Health Institute, Duke Clinical Research Institute, Duke University, Durham, North Carolina

⁶Division of Infectious Diseases, Ohio State University Medical Center, Columbus

⁷Division of Infectious Diseases, University of California, Los Angeles, Los Angeles, California

⁸Inova Schar Heart and Vascular, Falls Church, Virginia

⁹Departments of Pediatrics and Medicine, Divisions of Infectious Diseases, Medical University of South Carolina, Charleston

¹⁰Infectious Diseases Department, Vall d’Hebron Research Institute, Hospital Universitari Vall d’Hebron, Barcelona, Spain

¹¹Department of Molecular Biology, Massachusetts General Hospital, Boston

¹²Department of Genetics, Harvard Medical School, Boston, Massachusetts

¹³Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston

¹⁴Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston

¹⁵School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany

¹⁶Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary

¹⁷Department of Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Germany

¹⁸Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

¹⁹Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina

Accepted for Publication: September 29, 2024.

Published Online: December 11, 2024. doi:10.1001/jamacardio.2024.4115

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Corresponding Author: Steven K. Grinspoon, MD, Metabolism Unit, Massachusetts General Hospital, Harvard Medical School, 50 Staniford St, 7th Floor, Office 768, Boston, MA 02114 (sgrinspoon@mgh.harvard.edu).

Author Contributions: Dr Grinspoon had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Kolossváry and Schnittman contributed equally to this work.

Concept and design: Kolossváry, Schnittman, Fitch, Aberg, Foldyna, Lu, Ribaudo, Douglas, Grinspoon.

Acquisition, analysis, or interpretation of data: Kolossváry, Schnittman, Zanni, Fichtenbaum, Aberg, Bloomfield, Malvestutto, Currier, Diggs, deFilippi, Eckard, Curran, Cetinbas, Sadreyev, Foldyna, Mayrhofer, Karády, Taron, McCallum, Lu, Ribaudo, Douglas, Grinspoon.

Drafting of the manuscript: Kolossváry, Schnittman, Foldyna, Grinspoon.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Kolossváry, Schnittman, Cetinbas, Sadreyev, Mayrhofer, Ribaudo.

Obtained funding: Zanni, Taron, Lu, Ribaudo, Douglas, Grinspoon.

Administrative, technical, or material support: Fitch, Fichtenbaum, Currier, Diggs, Foldyna, Taron, McCallum, Ribaudo, Douglas, Grinspoon.

Supervision: Eckard, Sadreyev, Foldyna, Lu, Douglas, Grinspoon.

Conflict of Interest Disclosures: Dr Schnittman reported being employed by Amgen as of June 24, 2024, unrelated to the submitted work; he did not participate in revisions of the submitted manuscript after this change in employment. Dr Zanni reported receiving grants from Gilead to her institution (MGH) outside the submitted work. Dr Fichtenbaum reported receiving grants from ViiV, Gilead, and Merck to his institution outside the submitted work. Dr Aberg reported receiving grants from the National Institutes of Health (NIH) subcontract Mass General Hospital, Emergent Biosolutions (COVID-19 trial), Gilead Sciences (multicenter trials), GlaxoSmithKline/ViiV (multicenter trials), Merck (multicenter trials), Pfizer (multicenter trials), Janssen (multicenter trials), Regeneron (multicenter trials), and ViiV Healthcare (investigator-initiated study) and data safety monitoring board fees/advisory board fees from Kintor Pharmaceuticals, GSK, Merck, and ViiV Healthcare outside the submitted work. Dr Malvestutto reported receiving advisory board fees from ViiV Healthcare and Gilead Sciences outside the submitted work. Dr Currier reported receiving advisory board fees from Merck outside the submitted work. Dr deFilippi reported receiving consulting fees for Abbott Diagnostics, QuidelOrtho, Roche Diagnostics, and Siemens Healthineers; speaking fees from Polymedco (makes an NT-proBNP assay); and institutional funding where serving as the principal investigator from the NIH, Abbott Diagnostics, FujiRebio, QuidelOrtho, Randox, Roche Diagnostics, and Siemens Healthineers. Dr Eckard reported receiving grants from the NIH during the conduct of the study and advisory board fees from Theratechnologies and Gilead Sciences outside the submitted work. Dr Curran reported receiving grants and personal fees from Gilead Sciences and ViiV Healthcare and personal fees from MSD and Janssen Cilag outside the submitted work. Dr Foldyna reported receiving grants from the NIH/National Heart, Lung, and Blood Institute (NHLBI), AstraZeneca, and Medtrace during the conduct of the study. Dr Taron reported receiving grants from German Research Foundation during the conduct of the study and consulting fees from Onc.AI outside the submitted work. Dr Lu reported receiving grants from the NHLBI, Kowa Pharmaceuticals America, the American Heart Association, AstraZeneca, Ionis, Johnson & Johnson Innovation, MedImmune, National Academy of Medicine, and Risk Management Foundation of the Harvard Medical Institutions outside the submitted work. Dr Ribaudo reported receiving grants from the NIH/NHLBI, Kowa Pharmaceuticals, ViiV Healthcare, and Gilead Sciences outside the submitted work. Dr Grinspoon reported receiving grants from the NIH, Kowa Pharmaceuticals America, Gilead Sciences, and ViiV Healthcare and advisory board fees from Theratechnologies, ViiV Healthcare, and Marathon Asset Management outside the submitted work. No other disclosures were reported.

Funding/Support: This study is supported through National Institutes of Health (NIH) grants U01HL123336 and 1UG3HL164285, to the Clinical Coordinating Center, and U01HL123339 and 1U24HL164284, to the Data Coordinating Center, as well as funding from Kowa Pharmaceuticals America, Gilead Sciences, and ViiV Healthcare. The National Institute of Allergy and Infectious Diseases (NIAID) supported this study through grants UM1 AI068636, which supports the AIDS Clinical Trials Group (ACTG) Leadership and Operations Center; and UM1 AI106701, which supports the ACTG Laboratory Center. Dr Grinspoon reported receiving support from the Nutrition Obesity Research Center at Harvard (P30DK040561). Dr Schnittman reported receiving grant support through his institution from NIH/NIAID (T32 AI007387).

Role of the Funder/Sponsor: The funders had no role in the collection, management, analysis, and interpretation of the data in this analysis or the decision to submit the manuscript for publication. The funders were given the opportunity to review and provide feedback on the manuscript.

Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute or the National Institute of Allergy and Infectious Diseases; the National Institutes of Health; or the US Department of Health and Human Services.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank the study participants, site staff, and study-associated personnel for their participation in the trial. In addition, we thank the following: Advancing Clinical Therapeutics Globally for HIV/AIDS and Other Infections (ACTG) for clinical site support; ACTG Clinical Trials Specialists (Laura Moran, MPH, and Jhoanna Roa, MD) for protocol development and implementation support; the data management center, Frontier Science Foundation, for data support; the Center for Biostatistics in AIDS Research for statistical support; and the Community Advisory Board for input from the community. Site staff and study-associated personnel performing the trial received research support for trial conduct unrelated to this manuscript.

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Corresponding author.

PMCID: PMC11771813 PMID: 39661372

This secondary analysis of the Randomized Trial to Prevent Vascular Events in HIV (REPREIVE) randomized clinical trial investigates the associations of statin therapy with protein and gene pathways in relation to plaque changes among people with HIV.

Key Points

Question

What are the associations of statin therapy with protein and gene pathways in relation to plaque changes among people with HIV (PWH)?

Findings

In this post hoc analysis of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial including 558 individuals, using targeted proteomic analyses, pitavastatin increased procollagen C-endopeptidase enhancer 1 (PCOLCE), a rate-limiting enzyme of collagen deposition. Changes in PCOLCE level were associated with a reduction in noncalcified plaque and changes in plaque composition, suggesting plaque stabilization independent of changes in low-density lipoprotein cholesterol; in transcriptomic analyses, pitavastatin increased collagen genes and pathways.

Meaning

This post hoc analysis of the REPRIEVE randomized clinical trial suggests a novel mechanism for plaque stabilization with statins through activation of collagen in PWH.

Abstract

Importance

In a mechanistic substudy of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial, pitavastatin reduced noncalcified plaque (NCP) volume, but specific protein and gene pathways contributing to changes in coronary plaque remain unknown.

Objective

To use targeted discovery proteomics and transcriptomics approaches to interrogate biological pathways beyond low-density lipoprotein cholesterol (LDL-C), relating statin outcomes to reduce NCP volume and promote plaque stabilization among people with HIV (PWH).

Design, Setting, and Participants

This was a post hoc analysis of the double-blind, placebo-controlled, REPRIEVE randomized clinical trial. Participants underwent coronary computed tomography angiography (CTA), plasma protein analysis, and transcriptomic analysis at baseline and 2-year follow-up. The trial enrolled PWH from April 2015 to February 2018 at 31 US research sites. PWH without known cardiovascular diseases taking antiretroviral therapy and with low to moderate 10-year cardiovascular risk were eligible. Data analyses were conducted from October 2023 to February 2024.

Intervention

Oral pitavastatin calcium, 4 mg per day.

Main Outcomes and Measures

Relative change in plasma proteomics, transcriptomics, and noncalcified plaque volume among those receiving treatment vs placebo.

Results

Among 558 individuals (mean [SD] age, 51 [6] years; 455 male [82%]) included in the proteomics assessment, 272 (48.7%) received pitavastatin and 286 (51.3%) received placebo. After adjusting for false discovery rates, pitavastatin increased abundance of procollagen C-endopeptidase enhancer 1 (PCOLCE), neuropilin 1 (NRP-1), major histocompatibility complex class I polypeptide-related sequence A (MIC-A) and B (MIC-B), and decreased abundance of tissue factor pathway inhibitor (TFPI), tumor necrosis factor ligand superfamily member 10 (TRAIL), angiopoietin-related protein 3 (ANGPTL3), and mannose-binding protein C (MBL2). Among these proteins, the association of pitavastatin with PCOLCE (a rate-limiting enzyme of collagen deposition) was greatest, with an effect size of 24.3% (95% CI, 18.0%-30.8%; P < .001). In a transcriptomic analysis, individual collagen genes and collagen gene sets showed increased expression. Among the 195 individuals with plaque at baseline (88 [45.1%] taking pitavastatin, 107 [54.9%] taking placebo), changes in NCP volume were most strongly associated with changes in PCOLCE (%change NCP volume/log₂-fold change = −31.9%; 95% CI, −42.9% to −18.7%; P < .001), independent of changes in LDL-C level. Increases in PCOLCE related most strongly to change in the fibro-fatty (<130 Hounsfield units) component of NCP (%change fibro-fatty volume/log₂-fold change = −38.5%; 95% CI, −58.1% to −9.7%; P = .01) with a directionally opposite, although nonsignificant, increase in calcified plaque (%change calcified volume/log₂-fold change = 34.4%; 95% CI, −7.9% to 96.2%; P = .12).

Conclusions and Relevance

Results of this secondary analysis of the REPRIEVE randomized clinical trial suggest that PCOLCE may be associated with the atherosclerotic plaque stabilization effects of statins by promoting collagen deposition in the extracellular matrix transforming vulnerable plaque phenotypes to more stable coronary lesions.

Trial Registration

ClinicalTrials.gov Identifier: NCT02344290

Introduction

People with HIV (PWH) are at increased risk of major adverse cardiovascular events (MACE) and demonstrate increased noncalcified coronary plaque (NCP) and vulnerable plaque, more prone to rupture, which has been shown to be predictive of events in the general population.^1,2,3,4 The Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial demonstrated that among PWH with low-moderate–predicted atherosclerotic cardiovascular disease (ASCVD) risk, pitavastatin calcium reduced MACE by 36% over a median of 5.6 years compared with placebo.^5,6 The reduction in MACE was more significant than predicted by low-density lipoprotein cholesterol (LDL-C) lowering alone and was independent of baseline LDL-C level. Moreover, results from the mechanistic substudy of the REPRIEVE trial showed that pitavastatin reduced NCP volume.⁷

Here, we leveraged data from the REPRIEVE mechanistic substudy, using a targeted proteomics approach to investigate potential mechanisms of statin therapy on NCP, relating changes in proteins and plaque components among PWH. We further validated our proteomic results using a transcriptomics approach to assess changes in gene expression.

Methods

The designs of the overall REPRIEVE randomized clinical trial and the US-based mechanistic substudy have been previously published.^8,9 In the REPRIEVE trial, individuals with HIV aged 40 to 75 years taking antiretroviral therapy (ART) for 6 months or longer and at low to moderate 10-year ASCVD risk (using the 2013 American College of Cardiology/American Heart Association pooled cohort equation [PCE] risk calculator) were enrolled.⁸ Enrollment LDL-C thresholds decreased for increasing PCE risk categories.⁸ Participants with prior statin use within 90 days of study entry or history of ASCVD were excluded. At 31 US sites, individuals were given the opportunity to co-enroll in the mechanistic substudy.⁹ Detailed inclusion and exclusion criteria of the REPRIEVE trial and the mechanistic substudy are available in the published study protocols.^5,7 Race and ethnicity were assessed as previously reported to understand the generalizability of our results.⁷ Participants self-identified with the following races and ethnicities: Asian, Black or African American, Hispanic, White, or other, which included Alaska Native, American Indian, Asian Pacific Islander, unknown, or multiracial. The REPRIEVE trial protocol was approved by the institutional review board of Massachusetts General Brigham and by the ethics committee at each participating site. All the participants were provided with study information, including discussion of risks and benefits, and they signed written informed consent. This study followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines.

Analysis Populations

Among the 804 individuals randomized in the mechanistic substudy, participants were included in the proteomic and transcriptomic analyses on a per-protocol basis: those who completed treatment until 2-year follow-up and had 2 available plasma samples for measurements. Individuals whose proteomic or transcriptomic samples did not pass routine quality control were excluded from the corresponding analyses.

For analyses evaluating coronary plaque indices within the proteomic analysis population, participants were required to have 2 coronary computed tomography angiography (CTA) examinations with coronary artery atherosclerosis defined as visible and quantifiable plaque on the baseline images. Plasma proteomic and transcriptomic measurements were required to be within 90 days of the CTA examinations.

Proteomic Analyses

Fasting plasma samples were drawn at baseline and 2-year follow-up and stored at −80 °C. Three commercially available multiplex immunoassays were used (Olink Target 96 Cardiovascular III [Olink], Immuno-oncology [Olink], and Cardiometabolic [Olink]) to quantify 275 unique proteins.¹⁰ Similar to previous publications, we excluded a protein from analysis if 50% or more of the study samples had values below the lower limit of detection.^11,12,13 Overall, 20 proteins were excluded (eFigure 1 in Supplement 1). Proteins are expressed in Normalized Protein Expression (NPX) values, which are in log₂ scale. Temporal changes in proteomic markers were defined as the relative change in protein abundance in NPX units (NPX [follow-up/baseline]). Details are provided in the eMethods in Supplement 1.

Lipid and Additional Biomarker Measurements

We used measurements from fasting blood samples for cholesterol (LDL-C, non–high-density lipoprotein [HDL] cholesterol) and inflammatory (high-sensitivity C-reactive protein [hs-CRP], lipoprotein-associated phospholipase A2 [Lp-PLA2], oxidized low-density lipoprotein [ox-LDL]) markers in the analysis as these were shown to change significantly in the primary analysis.⁷ Changes in these biomarkers were evaluated on a log₂ scale (log₂[follow-up/baseline]), whereas absolute changes in lipids were calculated on the original scale (follow-up − baseline) (eMethods in Supplement 1).

CTA Image Acquisition and Plaque Quantification

Detailed image acquisition and plaque quantification methodology of the mechanistic substudy has been previously published.⁷ Voxels between the outer coronary artery wall and the lumen were considered plaque (total plaque volume) and based on the voxel attenuation in Hounsfield units (HU) categorized as NCP (<350 HU) or calcified plaque (≥350 HU) volume.^14,15 NCP was further divided into fibro-fatty (<130 HU) and fibrous (130-350 HU) components.¹⁶ Further details are presented in the eMethods in Supplement 1. Temporal changes in coronary plaque volumes were calculated on a log₁₀ scale (log₁₀[follow-up/baseline]). The study was powered to observe temporal changes in NCP volumes and therefore NCP volume changes were our primary outcome.⁷ All additional plaque components are considered secondary outcomes.

Transcriptomic Validation Analyses

RNA was extracted from blood samples using Preserved Blood RNA Purification Kit II (Norgen), followed by RNA Clean & Concentrator-5 workflow (Zymo) and global and rRNA depletion using NEBNext Globin & rRNA Depletion Kit (New England Biolabs). Sequencing reads were mapped to reference genome mm10 with ENSEMBL annotation using STAR.¹⁷ Read counts for individual genes were produced using HTSeq (eMethods in Supplement 1).¹⁸

Statistical Analysis

Continuous variables are presented as means and SDs, whereas categorical parameters are shown as counts and percentages. In the proteomic analyses, we used linear regression to estimate and volcano plots to present the association of statin treatment with protein abundance changes. Multiple comparisons were adjusted for using the false discovery rate (FDR) method by Benjamini and Hochberg.¹⁹ Protein-protein interaction analysis was done using the STRING database.

We used univariable linear regression to evaluate the association between LDL-C level, proteins significantly changing in response to statin therapy, and changes in NCP volume. Proteins showing a significant univariate association with NCP volume change at a P < .05 level were entered into a multivariable model that included LDL-C level. Analogous univariable analyses were completed for secondary plaque components.

In transcriptomic analyses, we assessed statistically significant changes using generalized linear models in individual gene expression between the groups over time. Differentially expressed genes were defined based on the criteria of an FDR less than 0.05. Gene set enrichment analysis was performed on hallmark reactome gene sets related to extracellular matrix collagen processes (eMethods in Supplement 1). All statistical tests were 2-tailed, and an α level of .05 was used to guide statistical inference. All statistical analyses were done in the R environment, version 4.1.3 (R Project for Statistical Computing). Data analyses were conducted from October 2023 to February 2024.

Results

Study Population

Overall, 804 participants were randomized in the mechanistic substudy between April 2015 and February 2018. Of these, 558 individuals (mean [SD] age, 51 [6] years; 103 female [18%]; 455 male [82%]) met all inclusion and exclusion criteria for the current proteomic analysis; 272 (48.7%) received pitavastatin, and 286 (51.3%) received placebo (Figure 1). Baseline demographics, cardiovascular risk factors, and HIV-related health history were similar between individuals randomized to pitavastatin or placebo (Table 1). Participants self-identified with the following races: 5 Asian (1%), 206 Black or African American (37%), 293 White (53%), and 54 other (10%). Participants self-identified with the following ethnicities (of a total of 549 participants): 141 Hispanic or Latino (26%) and 408 not Hispanic or Latino (74%). The mean (SD) 10-year ASCVD risk score was 5.0% (3.1%), and the mean (SD) LDL-C level at baseline was 108 (30) mg/dL (to convert to millimoles per liter, multiply by 0.0259). Individuals were taking ART for a mean (SD) of 12 (7) years. Mean (SD) CD4 count was 641 (294) cells/mm³, and 284 individuals of 545 (52%) had a nadir CD4 count less than 200 cells/mm³. The vast majority of participants had good viral control, with 482 of 550 (88%) demonstrating a viral load lower than the lower limit of quantification and 536 of 550 (97%) with HIV-1 RNA less than 400 copies/mL, respectively.

Figure 1. — Overall, 558 individuals’ data were used to evaluate temporal changes in proteomic markers in relation to pitavastatin therapy. In total, 589 individuals’ data were used for transcriptomic analyses. Overall, 195 individuals’ data were used for analyses involving changes in coronary plaque volumes. CT indicates computed tomography; CTA, computed tomography angiography; QA, quality assurance; QC, quality control.

Table 1. Characteristics of the Protein Analysis Population at Baseline^a.

Variable	Overall (N = 558)	Placebo (n = 286)	Pitavastatin (n = 272)
Demographics
Age, mean (SD), y	51 (6)	51 (6)	51 (6)
Sex, No. (%)
Female	103 (18)	53 (19)	50 (18)
Male	455 (82)	233 (81)	222 (82)
Race, No. (%)
Asian	5 (1)	4 (1)	1 (0.3)
Black or African American	206 (37)	106 (37)	100 (37)
White	293 (53)	154 (54)	139 (51)
Other^b	54 (10)	22 (8)	32 (11.7)
Ethnicity, No./total No. (%)
Hispanic or Latino	141/549 (26)	68/286 (25)	73/272 (27)
Not Hispanic or Latino	408/549 (74)	213/286 (75)	195/272 (73)
Cardiovascular risk factors
BMI, mean (SD)^c	27.3 (4.4)	27.3 (4.3)	27.3 (4.5)
ASCVD risk score, mean (SD), %	5.0 (3.1)	4.9 (2.9)	5.0 (3.2)
Use of antihypertensive medication, No. (%)	113 (20)	57 (20)	56 (21)
Use of antidiabetic medication, No. (%)	1 (0.2)	0	1 (0.4)
Family history of premature CVD, No./total No. (%)
No	415/542 (77)	222/286 (80)	193/272 (72)
Yes	125/542 (23)	55/286 (20)	70/272 (28)
Smoking status, No./total No. (%)
Current	140/556 (25)	70/286 (24)	70/272 (26)
Former	182/556 (33)	87/286 (31)	95/272 (35)
Never	234/556 (42)	128/286 (45)	106/272 (39)
Lipids, mean (SD)
Total cholesterol, mg/dL	186 (36)	186 (37)	185 (35)
LDL-C, mg/dL	108 (30)	108 (31)	108 (29)
Non-HDL-C, mg/dL	134 (35)	134 (36)	134 (34)
Triglycerides, mg/dL	133 (74)	133 (77)	132 (72)
Biomarkers, mean (SD)
High-sensitivity C-reactive protein, mg/L	4.5 (10.4)	4.4 (9.5)	4.5 (11.2)
Oxidized low-density lipoprotein, U/L	57.3 (21.2)	57.4 (21.1)	57.3 (21.3)
Lipoprotein-associated phospholipase A2, ng/mL	132.4 (55.4)	130.5 (56.7)	134.5 (54.1)
HIV-related health history, mean (SD)
Total ART use, y	12 (7)	12 (6)	12 (7)
Nadir CD4, No./total No. (%), cells/mm³
<50	116/545 (21)	58/286 (21)	58/272 (22)
50-199	168/545 (31)	92/286 (32)	76/272 (28)
200-349	155/545 (28)	78/286 (28)	77/272 (29)
350+	106/545 (20)	52/286 (19)	54/272 (21)
CD4 count, mean (SD), cells/mm³	641 (294)	645 (301)	636 (286)
HIV-1 RNA, No./total No. (%), copies/mL
<LLQ	482/550 (88)	245/286 (88)	237/272 (88)
LLQ -<400	54/550 (10)	29/286 (10)	25/272 (9)
400+	14/550 (2)	6/286 (2)	8/272 (3)
Entry ART regimen class
NRTI + INSTI	234 (42)	123 (43)	111 (41)
NRTI + NNRTI	150 (27)	78 (27)	72 (26)
NRTI + PI	100 (18)	42 (15)	58 (21)
NRTI-sparing	21 (4)	13 (5)	8 (3)
Other NRTI-containing	53 (9)	30 (10)	23 (9)

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Abbreviations: ART, antiretroviral therapy; ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index; CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; HIV, human immunodeficiency virus; INSTI, integrase strand transfer inhibitor; LDL-C, low-density lipoprotein cholesterol; LLQ, lower limit of quantification; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, nonnucleoside reverse transcriptase inhibitory; PI, protease inhibitor.

SI conversion factor: To convert C-reactive protein to milligrams per deciliter, divide by 10; total cholesterol, LDL-C, and HDL-C to millimoles per liter, multiply by 0.0259; triglycerides to millimoles per liter, multiply by 0.0113.

^{^a}

Percentages are presented considering the proportion of available data presented in parenthesis for parameters with missing values.

^{^b}

Other race includes Alaska Native, American Indian, Asian Pacific Islander, unknown, or multiracial.

^{^c}

Calculated as weight in kilograms divided by height in meters squared.

Proteomic and LDL-C Changes Associated With Pitavastatin Therapy

In the proteomics cohort of 558 individuals, among the 255 plasma proteins assessed, 7 were altered in response to statin therapy after 2 years at a FDR-corrected P < .05: circulating levels of mannose-binding protein C (MBL2), angiopoietin-related protein 3 (ANGPTL3), tissue factor pathway inhibitor (TFPI), and tumor necrosis factor ligand superfamily member 10 (TRAIL) decreased, whereas neuropilin 1 (NRP-1), major histocompatibility complex class I polypeptide-related sequence A (MIC-A) and B (MIC-B), and procollagen C-endopeptidase enhancer 1 (PCOLCE) increased in response to pitavastatin treatment allocation (Figure 2A). Protein names and identification numbers are presented in eTable 1 in Supplement 1. Change in relevant clinical variables for the protein analysis population during follow-up is presented in eTable 2 in Supplement 1. A similar pattern in protein responses was seen in the coronary plaque analysis cohort, but only the association with PCOLCE was significant (eFigure 2 in Supplement 1).

The treatment effect of pitavastatin was largest on PCOLCE (effect size, 24.3%; 95% CI, 18.0%-30.8%; P < .001) (Figure 2B) and was similar among individuals with plaque (n = 195; effect size, 27.2%; 95% CI, 16.7%-38.7%; P < .001) and without plaque (n = 363; effect size, 22.0%; 95% CI, 14.5%-30.0%; P < .001). LDL-C levels decreased by an average of −29.6 mg/dL (95% CI, −38.3 to −20.9 mg/dL; P < .001,) which was equivalent to a 32.7% (95% CI, −38.3% to −27.1%; P < .001) reduction in response to statin therapy.

Proteins showing significant abundance changes among statin users have been linked to important inflammatory and immunological pathways, such as natural killer (NK) cell-mediated cytotoxicity and complement and coagulation cascades (Figure 2C). Among 4 distinct clusters (Figure 2D), proteins in the red cluster were mostly associated with semaphorin signal for neuronal axon guidance and other processes, elements of the yellow cluster were associated with apoptosis modulation and signaling, proteins in the green cluster were involved with complement and coagulation cascades and the blue cluster was mostly associated with cholesterol metabolism (eTable 3 in Supplement 1).

Transcriptomic Validation

Among 12 929 protein-encoding genes, 13 showed significant difference in temporal changes between the pitavastatin group vs placebo (Figure 3A and eTable 4 in Supplement 1). Collagen alpha-1(XII) chain (COL12A1) and collagen alpha-3(VI) chain (COL6A3) and fibronectin (FN1) showed increased temporal expression among statin users compared with placebo. Assembly of collagen fibrils, collagen formation, biosynthesis, degradation, collagen chain trimerization, and crosslinking reactome pathways all showed significant gene set enrichment (Figure 3B and eTable 5 in Supplement 1). The following leading-edge genes demonstrated core enrichment in at least 1 of the gene sets: collagen type VI alpha-2 chain (COL6A2), collagen type VI alpha-3 chain (COL6A3), collagen type VII alpha-1 chain (COL7A1), collagen type IX alpha-2 chain (COL9A2), collagen type IX alpha-3 chain (COL9A3), collagen type XII alpha-1 chain (COL12A1), matrix metallopeptidase 9 (MMP9), matrix metallopeptidase 15 (MMP15), PCOLCE, peroxidasin (PXDN), prolyl 4-hydroxylase subunit alpha 2 (P4HA2), serpin family H member 1 (SERPINH1).

Coronary Plaque Characteristics

Overall, 195 participants (88 [45.1%] taking pitavastatin, 107 [54.9%] taking placebo) had plaque at baseline and a second scan for comparison of change over time (Figure 1). Participant characteristics stratified by treatment allocation were also similarly well balanced in this group (eTable 6 in Supplement 1). Change in relevant clinical variables for the population with coronary plaque analysis during follow-up is presented in eTable 7 in Supplement 1. The mean (SD) plaque volume at baseline in the population with coronary plaque analysis was 127 (238) mm³, of which the majority (106 [225] mm³) was NCP volume. NCP volume was reduced by a mean (SD) of 9.4% (14.9%) in the pitavastatin group, whereas it increased by 5.2% (19.0%) in the placebo group.

Association Between Proteomic Markers, LDL-C Level, and Temporal Plaque Volume Change

Among the 7 plasma proteins altered in association with pitavastatin treatment, the change in PCOLCE was strongest in association with the change in NCP volume (Table 2). Each doubling of PCOLCE was associated with a 31.9% decrease (95% CI, −42.9% to −18.7%; P < .001) in NCP volume after 2 years compared with baseline. Inverse associations were also found between changes in ANGPTL3, MBL2, and changes in NCP volume. Changes in MIC-A and MIC-B, NRP1, TFPI, and TRAIL were not associated with changes in NCP volume (Table 2). Changes in LDL-C were not associated with changes in NCP volume (% change NCP volume/10 mg/dL change = 1.5%; 95% CI, −1.2% to 4.3%; P = .26).

Table 2. Univariable Linear Regression for Association Between Change in Low-Density Lipoprotein Cholesterol (LDL-C) Level, Plasma Proteins, and Plaque Components^a.

Variable	Plaque volume, mm³
	Total		Calcified (>350 HU)		Noncalcified (<350 HU)		Fibro-fatty (<130 HU)		Fibrous (130-350 HU)
	Estimate, % change (95% CI)	P value	Estimate, % change (95% CI)	P value	Estimate, % change (95% CI)	P value	Estimate, % change (95% CI)	P value	Estimate, % change (95% CI)	P value
LDL-C	0.2 (−01.5 to 1.9)	.83	−3.7 (−9.5 to 2.3)	.22	1.5 (−1.2 to 4.3)	.26	6.1 (0.5 to 11.8)	.03	0.4 (−1.9 to 2.6)	.74
ANGPTL3	−10.2 (−20.5 to 1.3)	.08	−1.2 (−33.8 to 47.5)	.95	−19.8 (−34.0 to −2.6)	.03	−28.0 (−52.3 to 8.9)	.12	−20.7 (−32.3 to −7.0)	.004
MBL2	−5.0 (−14.7 to 5.8)	.35	7.3 (−25.1 to 53.6)	.70	−18.7 (−31.5 to −3.5)	.02	−25.9 (−48.5 to 6.8)	.11	−13.5 (−25.0 to −0.4)	.04
MIC-A/B	0.2 (−18.4 to 22.9)	.99	6.7 (−46.4 to 112.3)	.85	−11.1 (−36.2 to 23.7)	.48	−25.7 (−63.0 to 49.4)	.40	−3.3 (−26.4 to 27.1)	.81
NRP1	−1.0 (−22.7 to 26.9)	.94	13.8 (−50.1 to 159.5)	.76	−30.0 (−53.0 to 4.3)	.08	−46.6 (−77.0 to 24.1)	.14	−13.9 (−38.1 to 19.8)	.37
PCOLCE	−10.9 (−20.4 to −0.2)	.046	34.4 (−7.9 to 96.2)	.12	−31.9 (−42.9 to −18.7)	<.001	−38.5 (−58.1 to −9.7)	.01	−22.2 (−32.9 to −9.7)	.001
TFPI	2.5 (−13.3 to 21.1)	.77	77.3 (−0.4 to 215.4)	.05	1.5 (−22.6 to 33.0)	.91	−0.6 (−43.8 to 75.8)	.98	−8.1 (−26.4 to 14.9)	.46
TRAIL	−1.3 (−18.2 to 19.0)	.89	9.6 (−40.2 to 100.7)	.77	−8.9 (−32.7 to 23.3)	.54	18.2 (−37.7 to 124.1)	.61	−9.9 (−29.8 to 15.6)	.41

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Abbreviations: ANGPTL3, angiopoietin-related protein 3; HU, Hounsfield unit; MBL2, mannose-binding protein C; MHC, major histocompatibility complex; MIC-A/B, MHC class I polypeptide-related sequence A/B; NRP1, neuropilin-1; PCOLCE, procollagen C-endopeptidase enhancer 1; TFPI, tissue factor pathway inhibitor; TRAIL, TNF-related apoptosis-inducing ligand.

^{^a}

Models were calculated within the coronary plaque analysis population (n = 195). The 7 proteins that were statistically associated with statin therapy (false discovery rate–corrected P < .05) were assessed. Estimates represent per 10 mg/dL change for LDL-C (to convert to millimoles per liter, multiply by 0.0259) and per doubling for all proteins. Estimates were calculated using log₁₀-transformed plaque volume and converted back to the original scale with the resulting relative differences presented as a percentage.

Each NPX unit increase in PCOLCE was associated with a reduction in total plaque volume by 10.9% (95% CI, −20.4% to −0.2%; P = .046) (Table 2). Among the components of plaque volume, change in PCOLCE was most pronounced with respect to the change in the fibro-fatty (<130 HU) component of noncalcified plaque volume 38.5% (95% CI, −58.1% to −9.7%; P = .01) reduction per NPX unit increase in PCOLCE. In contrast, the increase in PCOLCE was associated with an increase in calcified plaque volume of 34.4%, although this did not achieve statistical significance (P = .12). Associations between LDL-C, proteins, and plaque components are presented in Table 2.

In a multivariable model including LDL-C level, ANGPTL3, MBL2, and PCOLCE, only PCOLCE retained its association with change in NCP volume, with a similar effect size (−31.2%; 95% CI, −45.3% to −13.4%, P = .002) (eTable 8 in Supplement 1). Changes in inflammatory biomarkers hs-CRP, ox-LDL and Lp-PLA2 did not relate to change in PCOLCE or NCP volume (eFigure 3 in Supplement 1).

Discussion

Although pleiotropic effects of statin therapy have been suggested, to our knowledge, prior randomized prospective studies have not investigated detailed proteomic and transcriptomic pathways in association with plaque change and atherosclerotic coronary plaque architecture, including among PWH. In this secondary analysis of the REPRIEVE randomized clinical trial mechanistic substudy, we found pitavastatin to be associated with an alteration in the abundance of several proteins. The largest effect size was observed for the procollagen enzyme PCOLCE, which was significantly associated with changes in NCP volume, independent of changes in LDL-C level. Furthermore, we showed associations between changes in PCOLCE and plaque composition that may signify plaque stabilization in this setting. Transcriptomic analyses confirmed upregulation in collagen synthesis among those on statins, providing further evidence of an association of statins with collagen pathways.

Statin therapy is a cornerstone of cardiovascular risk reduction.²⁰ Compelling evidence has established that LDL-C level is associated with future ASCVD risk and that the absolute reduction in LDL-C level results in proportional ASCVD risk reduction.^21,22 These results have formed the lipid theory of coronary atherosclerosis formation and progression. However, much of the evidence for this is based on early landmark trials that included individuals with higher LDL-C level and increased traditional ASCVD risk.

Recently, we have shown in the global REPRIEVE trial that pitavastatin therapy resulted in a 36% hazard reduction of MACE compared with placebo over a median of 5.6 years among PWH considered to be at low to moderate traditional ASCVD risk, whose baseline LDL-C level was not increased.^5,6 Critically, the observed reduction in MACE was twice as large as predicted on the basis of the achieved reduction in LDL-C levels alone, suggesting the importance of non–cholesterol-associated pathways on MACE reduction.²¹ Here, our results suggest a novel association of statin therapy with collagen pathways, which is associated with changes in plaque volume and composition.

In our current targeted discovery proteomics approach of 255 immune, inflammatory, cardiovascular, and cardiometabolic proteins, results suggest that pitavastatin was associated with an alteration in the abundance of several important proteins. These findings from this large multicenter randomized clinical trial corroborate results from smaller trials in PWH, showing effects of different statins on MBL2, TFPI, and PCOLCE.^10,23 In a propensity score–matched analysis of statin use among the general population assessing almost 5000 proteins, PCOLCE and ANGPTL3 were similarly identified as being upregulated and downregulated, respectively; other complement and tumor necrosis factor (TNF)–related superfamilies were associated with statin use, lending credence to our findings beyond HIV.²⁴

Of the assessed proteins decreased by pitavastatin, MBL2 is a lectin involved in innate immune defense through complement activation and inflammation modulation.²⁵ ANGPTL3, an endogenous inhibitor of lipoprotein lipase and endothelial lipase, has been well studied in the context of atherosclerosis where loss-of-function variants were associated with lower odds of coronary artery disease, and its inhibition with the monoclonal antibody evinacumab led to a decrease in atherosclerotic lesion area and necrotic content.²⁶ That MBL2 and ANGPTL3 were no longer associated with NCP changes after adjustment for LDL-C level might suggest that the statin effects on these proteins may be primarily functioning through effects on improved cholesterol indices. We have previously observed that TFPI is lowered by statin use and may be considered a compensatory change given existing in vitro and in vivo data.^10,23 Finally, TRAIL or TNF ligand superfamily member 10 (TNFSF10) is primarily produced by immune cells.²⁷

Pitavastatin treatment also led to increased levels of NRP-1, MIC-A and MIC-B, and PCOLCE. NRP-1 is a vascular endothelial growth factor coreceptor involved in angiogenesis, cell migration, and signal transduction.²⁸ MIC-A and MIC-B are nonclassical MHC class I polypeptides that do not participate in antigen presentation but instead act as ligands, most importantly for NK cells through the type II integral membrane protein (NKG2D) receptor.²⁹

Notably, the largest outcome of pitavastatin was an associated increase in PCOLCE, the rate-limiting enzyme expressed by fibroblasts involved in collagen deposition in interstitial tissue. PCOLCE accelerates procollagen maturation and triggers the assembly of collagen molecules into fibrils.³⁰ Data on the role of PCOLCE in atherosclerosis is limited though may be relevant given that types I and III collagen, the major forms processed by PCOLCE, are the predominant forms of collagen in the vascular extracellular matrix.³¹ Our current observation significantly extends data from a prior smaller trial²³ with atorvastatin in PWH, in which we showed that a statin-related increase in PCOLCE was associated with a decrease in total plaque volume. As studies in PWH and in the general population have shown PCOLCE upregulation in response to various statin therapies, we believe this is a consistent statin effect in vivo.^10,23,24 Nonetheless, increases in PCOLCE with statin therapy may be a relevant mechanism for plaque modification in the HIV population, which is especially enriched in noncalcified vulnerable plaque.

The significant association of PCOLCE with differential outcomes on plaque composition has not been shown previously, to our knowledge. Specifically, the increase in PCOLCE showed the largest association with reduction in the more lipid-rich components (lower HU) of NCP. At the same time, PCOLCE tended to be associated with increases in calcified plaque volume. Increased PCOLCE abundance may result in increased collagen deposition in the matrix of atherosclerotic lesions, which we observed as a reduction in the lower attenuation components of noncalcified plaque tissue. Further, the collagen fibrils may act not only as a scaffold for calcium deposition in the atherosclerotic matrix but also may promote the deposition and aggregation of calcium within the vessel walls through collagen disc–domain receptors and promote the transformation of vascular smooth muscle cells into osteochondral-like cells.^32,33 Transcriptomic analyses performed in this study confirm for the first time enrichment of individual genes involved in collagen formation and gene sets previously curated to represent formation of collagen and collagen fibrils. This work extends prior data demonstrating statin effects to increase fibrous cap thickness,³⁴ although we could not measure such effects using CTA. This shift toward higher attenuation plaque components and an increase in calcified plaque volumes seen in the REPRIEVE trial is considered as a process of plaque stabilization, a well-known attribute of statin therapies, the exact mechanism of which has remained unclear.^16,34

This study showing changes in PCOLCE level associated with changes in plaque composition helps to corroborate and highlight one potential mechanism of early statin studies showing effects to increase collagen and decrease lipid content as a mechanism of plaque stabilization in animal studies.³⁵ Other collagen genes as suggested in our transcriptomic analysis may be increased by statin therapy and thus other mechanisms are possible. Further research is needed to identify the sources of the proteomic and gene transcripts to better understand the biological pathways associated with plaque progression and regression. In this regard, change in PCOLCE was strongly associated with noncalcified plaque controlling for LDL-C level, whereas the change in LDL-C was less strongly associated with noncalcified plaque, in contrast to some prior studies.^36,37 Differences with prior studies may relate to the modest baseline levels of LDL in our study, relative low risk of our study population compared with studies assessing LDL-C to plaque, and study duration. Taken together, these studies suggest that statins may contribute to complex changes in local coronary plaque architecture, resulting from simultaneous delipidation of the atheroma and effects to upregulate specific collagen pathways in relation to changes in plaque architecture and more stabilized plaque.

Limitations

Our study has limitations. First, we used a targeted discovery proteomics approach using a predefined set of proteins. Association of pitavastatin with the proteome may be broader and mediated by more than 1 or several proteins not examined, although our results assessing the transcriptome supported the unique aspects of statin effects on collagen pathways. Additionally, the relationship between plasma protein levels and relevant in vivo tissue activity is unknown. Further analyses using tissue proteomics and mechanistic bench studies are warranted. Although our results were from a randomized clinical trial, we measured change in PCOLCE and plaque at the same time points, limiting definitive conclusions on causality. In this regard, it is possible that statin association with local plaque composition leads to biological upregulation of collagen pathways and PCOLCE. Also, the REPRIEVE mechanistic substudy was powered to identify changes in noncalcified plaque. We did not see a relationship between change and LDL and noncalcified plaque, in contrast to other studies, and we may be underpowered on this and other secondary analyses. The study population includes a cohort of PWH from the US with low to moderate ASCVD risk, which may not be generalizable to other populations with known disease or at higher ASCVD risk.

Conclusions

In conclusion, in a post hoc analysis of the large, US-based REPRIEVE randomized clinical trial of pitavastatin treatment, we identified a novel pathway that may be associated with the protective effects of statin therapy on coronary plaque. By increasing the abundance of the rate-limiting enzyme of collagen deposition, PCOLCE may play a role in the atherosclerotic plaque stabilization effects of statins by transforming lipid-rich components of noncalcified plaque to more stable fibrous and calcified components. Further studies are now needed to assess potential targeting of collagen pathways for reduction of vulnerable coronary plaque phenotypes.

Supplement 1.

eMethods.

eTable 1. Proteomic Changes in Response to Pitavastatin Treatment

eTable 2. Clinical Characteristics of the Protein Analysis Population at Baseline and Follow-Up

eTable 3. Biological Processes Associated With the Clusters Present in the Imputed Protein-Protein Interaction Network

eTable 4. Transcriptomic Changes in Response to Pitavastatin Treatment

eTable 5. Gene Set Enrichment Analysis of Collagen-Associated Reactome Pathways

eTable 6. Baseline Characteristics of the Coronary Plaque Analysis Population

eTable 7. Clinical Characteristics of the Coronary Plaque Analysis Population at Baseline and Follow-Up

eTable 8. Multivariable Linear Regression for Association Between Change in LDL, Plasma Proteins, and Noncalcified Plaque Volume

eFigure 1. Flow Diagram of Protein Exclusions

eFigure 2. Volcano Plot on the Effects of Pitavastatin on Protein Abundance in the Coronary Plaque Analysis Population

eFigure 3. Correlation Heatmap Between Temporal Changes in Lipids, Biomarkers, Significant Proteins, and Noncalcified Plaque Volumes

jamacardiol-e244115-s001.pdf^{(745.7KB, pdf)}

Supplement 2.

Data Sharing Statement.

jamacardiol-e244115-s002.pdf^{(15.4KB, pdf)}

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