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. 2021 Jan 13;18(1):e1003513. doi: 10.1371/journal.pmed.1003513

Plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease: A retrospective study

Lars Wallentin 1,2,*, Niclas Eriksson 2, Maciej Olszowka 1,2, Tanja B Grammer 3, Emil Hagström 1,2, Claes Held 1,2, Marcus E Kleber 4, Wolfgang Koenig 5,6,7, Winfried März 4,8,9, Ralph A H Stewart 10, Harvey D White 10, Mikael Åberg 11,12, Agneta Siegbahn 2,11,12,*
PMCID: PMC7817029  PMID: 33439866

Abstract

Background

Circulating biomarkers are associated with the development of coronary heart disease (CHD) and its complications by reflecting pathophysiological pathways and/or organ dysfunction. We explored the associations between 157 cardiovascular (CV) and inflammatory biomarkers and CV death using proximity extension assays (PEA) in patients with chronic CHD.

Methods and findings

The derivation cohort consisted of 605 cases with CV death and 2,788 randomly selected non-cases during 3–5 years follow-up included in the STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial between 2008 and 2010. The replication cohort consisted of 245 cases and 1,042 non-cases during 12 years follow-up included in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study between 1997 and 2000. Biomarker levels were measured with conventional immunoassays and/or with the OLINK PEA panels CVD I and Inflammation. Associations with CV death were evaluated by Random Survival Forest (RF) and Cox regression analyses.

Both cohorts had the same median age (65 years) and 20% smokers, while there were slight differences in male sex (82% and 76%), hypertension (70% and 78%), and diabetes (39% and 30%) in the respective STABILITY and LURIC cohorts. The analyses identified 18 biomarkers with confirmed independent association with CV death by Boruta analyses and statistical significance (all p < 0.0001) by Cox regression when adjusted for clinical characteristics in both cohorts. Most prognostic information was carried by N-terminal prohormone of brain natriuretic peptide (NTproBNP), hazard ratio (HR for 1 standard deviation [SD] increase of the log scale of the distribution of the biomarker in the replication cohort) 2.079 (95% confidence interval [CI] 1.799–2.402), and high-sensitivity troponin T (cTnT-hs) HR 1.715 (95% CI 1.491–1.973). The other proteins with independent associations were growth differentiation factor 15 (GDF-15) HR 1.728 (95% CI 1.527–1.955), transmembrane immunoglobulin and mucin domain protein (TIM-1) HR 1.555 (95% CI 1.362–1.775), renin HR 1.501 (95% CI 1.305–1.727), osteoprotegerin (OPG) HR 1.488 (95% CI 1.297–1.708), soluble suppression of tumorigenesis 2 protein (sST2) HR 1.478 (95% CI 1.307–1.672), cystatin-C (Cys-C) HR 1.370 (95% CI 1.243–1.510), tumor necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) HR 1.205 (95% CI 1.131–1.285), carbohydrate antigen 125 (CA-125) HR 1.347 (95% CI 1.226–1.479), brain natriuretic peptide (BNP) HR 1.399 (95% CI 1.255–1.561), interleukin 6 (IL-6) HR 1.478 (95% CI 1.316–1.659), hepatocyte growth factor (HGF) HR 1.259 (95% CI 1.134–1.396), spondin-1 HR 1.295 (95% CI 1.156–1.450), fibroblast growth factor 23 (FGF-23) HR 1.349 (95% CI 1.237–1.472), chitinase-3 like protein 1 (CHI3L1) HR 1.284 (95% CI 1.129–1.461), tumor necrosis factor receptor 1 (TNF-R1) HR 1.486 (95% CI 1.307–1.689), and adrenomedullin (AM) HR 1.750 (95% CI 1.490–2.056).

The study is limited by the differences in design, size, and length of follow-up of the 2 studies and the lack of results from coronary angiograms and follow-up of nonfatal events.

Conclusions

Profiles of levels of multiple plasma proteins might be useful for the identification of different pathophysiological pathways associated with an increased risk of CV death in patients with chronic CHD.

Trial registration

ClinicalTrials.gov NCT00799903.

Niclas Eriksson and colleagues report associations between 157 cardiovascular plasma biomarkers and cardiovascular death in patients.

Author summary

Why was this study done?

  • There are many reports on associations between biomarkers and outcomes in patients with chronic coronary artery disease (CAD).

  • New analytical technologies allow concurrent measurement of hundreds of protein biomarkers in small volumes of plasma.

  • The value of multiplex protein analyses has rarely been evaluated in cohorts with adequate numbers of patients and outcome events.

What did the researchers do and find?

  • We investigated the associations between the levels of multiple proteins and the occurrence of cardiovascular (CV) death during 3–12 years follow-up of 2 cohorts of 3,393 and 1,287 patients with chronic CAD.

  • The biomarkers were measured with the OLINK Proximity Extension Assay (PEA) panels CVD I and Inflammation and/or conventional immunoassays.

  • The analyses identified 18 biomarkers with confirmed independent associations with CV death.

What do these findings mean?

  • Measurements of levels of plasma protein profiles can be useful for the identification of pathophysiological pathways associated with an increased risk of CV death in patients with chronic coronary heart disease (CHD).

  • Measurements of these profiles might be useful for the identification of new treatment targets and to balance different treatments and treatment responses in patients with chronic CHD.

Introduction

Despite revascularization and optimal secondary preventive treatment, chronic coronary heart disease (CHD) is still associated with a substantial cardiovascular (CV) morbidity and mortality [1–3]. Many biomarkers have been shown to be associated with the development and clinical outcomes of CHD. However, only a few biomarkers are recommended in treatment guidelines and routinely used in clinical care [4–8]. Besides indicators of underlying metabolic disease (blood glucose, hemoglobin A1c, and low-density lipoprotein [LDL] cholesterol), the currently generally available prognostic protein biomarkers are those indicating myocardial dysfunction (N-terminal prohormone of brain natriuretic peptide, NTproBNP) [6,7], cardiac necrosis (cardiospecific high-sensitivity troponin T, cTnT-hs), inflammatory activity (high-sensitivity C-reactive protein, CRP-hs) [7], and renal dysfunction (cystatin-C, Cys-C) [4,8]. The rationale for the use of these biomarkers is their associations with underlying disease mechanisms, their incremental prognostic information on CV outcomes in addition to clinical information, and their interaction with treatment effects. There are many reports on associations between a multitude of other biomarkers and the development of CHD. However, their value has rarely been simultaneously evaluated in cohorts with adequate numbers of patients and events and including adjustment both for clinical information and for the established biomarkers.

Newly available analytical technologies allow concurrent measurement of hundreds and even thousands of protein biomarkers in small volumes of plasma [9]. One of these techniques is the proximity extension assay (PEA) technology, allowing simultaneous measurements of 92 proteins in a 1.0-μl plasma sample by PCR amplification of DNA strands from DNA-labeled antibody pairs [10,11]. In the STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY (STABILITY) trial (ClinicalTrials.gov ID NCT00799903) randomizing 15,828 patients with chronic CHD to darapladib or placebo, without any significant effect on outcomes during 3 to 5 years follow-up, we obtained plasma aliquots from all patients at baseline [12,13]. Our previous reports from this program have verified the independent prognostic importance of NTproBNP, cTnT-hs, CRP-hs, interleukin 6 (IL-6), growth differentiation factor 15 (GDF-15), and lipoprotein associated phospholipase A2 (Lp-PLA2) [14–17]. The primary aim of the present multimarker substudy was to investigate if additional biomarkers were associated with CV death in patients with chronic CHD. The findings were replicated in an observational cohort with chronic CHD followed for 12 years in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study [16].

Methods

Patients and study cohorts

The initial cohort of the present study was a substudy of the previously published international STABILITY trial, which compared darapladib, a selective inhibitor of Lp-PLA2, with placebo concerning the occurrence of CV events in 15,828 patients with chronic CHD out of whom 14,124 provided blood samples for the biomarker substudy [12,13]. The patients were recruited in 663 centers in 39 countries from December 2008 to April 2010. The patients were followed from 3 to 5 years with a median of 3.7 years. Patients were eligible if they had chronic CHD documented by prior myocardial infarction (MI) (>1 month), prior percutaneous coronary intervention (PCI) (>1 month), or prior coronary artery bypass grafting (CABG) (>3 months) or multivessel coronary artery disease (CAD) at a coronary angiogram. Outcomes were ascertained by regular follow-up visits until the end of the trial, and all study endpoints were centrally adjudicated. There were no significant effects of the randomized treatment on the outcomes. The present multimarker substudy was based on an unstratified case–cohort design consisting of a random sample from the full cohort, which was enriched with all patients suffering CV outcomes in the total biomarker cohort leading to a comparison between 2,788 patients without CV death and 605 patients with CV death.

The LURIC prospective observational study was used for replication. The LURIC study included 3,316 patients scheduled for coronary angiography between July 1997 and January 2000. Patients presenting with unstable angina, non-ST elevation MI, ST-elevation MI, and severe diseases other than chronic CHD were excluded in the current study. The patients were followed for 12 years concerning vital status, and cause of death was established by death certificates. In these analyses, 245 patients with CV death during the 12 years follow-up constituted the cases and 1,042 patients without this event the non-cases.

Ethics statement

Both studies were approved by the relevant institutional review boards and performed in accordance with the Declaration of Helsinki, and all patients provided written informed consent [16].

Biochemical analyses

Venous blood samples were obtained at inclusion in the morning after 9 hours of fasting. EDTA plasma aliquots were stored at −80°C until biochemical analyses. Established biochemical assays for NTproBNP, cTnT-hs, CRP-hs, IL-6, GDF-15, and Lp-PLA2 were centrally performed as previously published [12–17]. The proteomic analyses were performed at the Clinical Biomarkers Facility, Science for Life Laboratory, Uppsala University, Uppsala, Sweden, without information on any other data. We used the OLINK Proteomics PEA technology, which is based on pairs of antibodies equipped with DNA single-strand oligonucleotide reporter molecules. Each OLINK PEA panel contains 96 oligonucleotide-labeled antibody probe pairs that bind to their respective target if present in the sample [9,10]. Target binding by both antibodies in a pair generates double-stranded DNA amplicons, which are quantified using a Fluidigm BioMark™ HD real-time PCR platform. The analyses were run using the recommended internal control, and inter-plate variability was adjusted by intensity normalization. The resulting relative values, normalized protein expression (NPX) data, were log2 transformed. In the logarithmic phase of the curve, 1 increase of the NPX value corresponds to a doubling of the protein content, and a high NPX value corresponds to a high protein concentration. The signal specificity is exceptionally high, as binding by the 2 protein specific antibodies in close proximity is required to produce a signal. PEA assays have shown high reproducibility and repeatability with mean intra-assay and inter-assay coefficients of variation around 8% and 12%, respectively; average inter-site variation has been reported at 15% [9]. In the STABILITY cohort, we used the OLINK Proteomics Multiplex CVD I96×96 panel and the OLINK Proteomics Multiplex Inflammation96×96 panel, whereby the concentrations of a total of 157 proteins related to CV disease and inflammation were simultaneously measured. In the LURIC cohort, we used only the OLINK Proteomics Multiplex CVD I96×96 panel as only 4 of the 28 biomarkers with confirmed association to CV death in the STABILITY cohort were in the Inflammation panel and none among the 13 most important. The protein markers included in the CVD I and Inflammation panels are given in S1 Table.

Statistics

The PEA CVD I and Inflammation panels together measured the concentrations of 184 proteins, of which 27 were included in both panels. For 32 proteins, more than 10% of patient samples had levels below the lower limit of detection (LLOD). We chose to include as many proteins as possible by excluding only the 3 proteins for which more than 99.5% of all patient samples had values below LLOD, leaving 154 unique proteins for the statistical analyses (S2A Table). Values below LLOD were imputed with LLOD/2.

Baseline characteristics, established biomarkers, and PEA biomarkers were presented and compared by conventional statistics: chi-squared and Wilcoxon tests for discrete and continuous variables, respectively. Correlations between conventionally measured biomarkers and PEA biomarkers were presented as Spearman correlation coefficients using NPX data for PEA biomarkers and logarithmic transformation of biomarker concentrations measured with other assays. Because of the large number of observations, most correlation coefficients were expected to be statistically significant, and only correlation coefficients above 0.29 explaining at least 9% of the variability were considered relevant. Correlations between PEA biomarkers present on both CVD I and Inflammation panels were calculated using Pearson correlation coefficients.

Random Survival Forest (RF) analyses were performed to provide an unbiased grading of the prognostic importance of all variables including all clinical characteristics, PEA levels, and levels of other biomarkers. As the variable importance calculated by the RF could theoretically give identical results in a sample of 1,000 to 100,000 individuals, a Boruta analysis was used to confirm which of the variables in the RF analysis had a larger than random association with outcomes [18]. In short, the Boruta analysis performs multiple runs of RF comparing all variables to random variables, which are shuffled copies of the original variables. Variables performing better than the maximum random variable importance are classified as confirmed, variables performing worse are rejected, and variables that cannot be confirmed or rejected are classified as tentative. Biomarkers with confirmed associations at RF-Boruta analyses in both studies were considered to have an externally validated association with CV death. In both RF and Boruta analyses, the following settings were used: variable importance mode = permutation, mtry (randomly selected number of variables to possibly split in each node) = square root of total number of variables, minimal node size = 3, and splitrule = maxstat (maximally selected rank statistics). In the RF, we used 10,000 trees, and in the Boruta analysis, the number of trees was lowered to 1,000 due to performance issues.

The linear associations between the biomarkers and CV death were also investigated by Cox regression analyses including both clinical characteristics, established standard immunoassays, and PEA biomarkers and were presented as Forest plots. According to the study design, the Cox regression analyses included the sampling weights for each outcome and were estimated using a robust sandwich estimator. The Cox regression analyses were performed unadjusted using a predefined model [12–17] adjusting for baseline characteristics (age, sex, body mass index (BMI), current smoking, hypertension, diabetes mellitus, previous MI, previous coronary revascularization, previous stroke, previous peripheral artery disease, and randomized treatment) and adjusting also for renal function (Cys-C) and the established markers of CV risk (NTproBNP and cTnT-hs). The incremental discriminative value of each biomarker was illustrated by the C-index. If conventional as well as PEA measurements were available, only the conventional result was included in the RF and Cox analyses, considering its quantitative measurement and larger dynamic range. In the RF, Boruta, and Cox regression analyses, missing values (other than protein values below LLOD) were imputed once using the mice package for R [19,20]. The statistical analyses were predefined in a statistical analysis plan for the STABILITY PEA study in March 2017, and thereafter for replication also applied for the LURIC PEA study.

Results

PEA measurements

The PEA measurements had acceptable reproducibility with Pearson correlation coefficients 0.80 to 0.97 for 23 and a standard deviation of the difference in levels of 0.18 to 0.31 for 17 of the 27 proteins that were included in both the CVD I and Inflammation panels (S4A Table). The PEA measurements also had adequate accuracy with close correlations with conventional immunoassays with Spearman correlation coefficients of 0.87 for NTproBNP, 0.85 for GDF-15, and 0.88 for IL-6 and similar associations to CV death with both methods (S4B Table). The Spearman correlations between the immunoassays of the cardiorenal and inflammatory biomarkers Cys-C, NTproBNP, cTnT-hs, GDF-15, and IL-6 and the PEA biomarkers in the STABILITY cohort are shown in S5 Table for those PEA biomarkers with any correlation coefficient >0.29. There were significant and relevant correlations between many biomarkers, prompting multivariable adjustments when evaluating the importance of associations between biomarker levels and clinical outcomes.

STABILITY population

Baseline characteristics in the random sample and in the total STABILITY trial were almost identical (S3 Table). The baseline characteristics and the levels of established CV biomarkers in the group with CV death and in the random sample without any such event are shown in Table 1.

Table 1. Baseline characteristics and levels of established biomarkers in patients with and without CV death in the STABILITY and LURIC.

STABILITY LURIC
Baseline characteristics No event CV death p-value No event CV death p-value
(N = 2,788) (N = 605) (N = 1,042) (N = 245)
Age (years) 59/65/71 61/69/75 <0.001 57/63/70 63/69/74 <0.001
Sex: Male 82% (2,283) 83% (502) 0.527 75% (779) 80% (196) 0.085
Smoker 20% (549) 22% (130) 0.299 16% (169) 16% (40) 0.021a
Hypertension 70% (1,950) 78% (470) <0.001 76% (794) 82% (200) 0.068
Diabetes 38% (1,054) 53% (318) <0.001 26% (225) 43% (106) <0.001
Prior MI 59% (1,644) 67% (405) <0.001 43% (444) 55% (134) <0.001
Prior PCI or CABG 76% (2,114) 61% (369) <0.001 37% (382) 40% (97) 0.403
Prior stroke or TIA 9% (241) 11% (67) 0.059 7% (70) 19% (47) <0.001
Prior PAD 8% (215) 17% (105) <0.001 10% (102) 19% (47) <0.001
Randomized to darapladib 52% (1,437) 48% (290) 0.108
Medications at baseline
Aspirin 92% (2,572) 90% (542) 0.031 74% (766) 72% (176) 0.594
P2Y12 inhibitors 34% (936) 30% (183) 0.115
Beta-blockers 79% (2,203) 81% (488) 0.366 65% (675) 52% (127) <0.001
Statin treatment 97% (2,709) 98% (594) 0.159 52% (546) 48% (118) 0.233
ACE or angiotensin receptor inhibitors 76% (2,124) 80% (483) 0.054 53% (554) 70% (172) <0.001
Established biomarkers
GFR by CKD-EPI (ml/min) 62/74/86 52/66/81 <0.001 76/88/97 61/79/93 <0.001
Cys-C (mg/L) 0.86/1.00/1.16 0.98/1.18/1.44 <0.001 0.81/0.90/1.04 0.89/1.03/1.27 <0.001
NTproBNP (ng/L) 85/174/353 250/573/1,269 <0.001 94/230/616 292/762/1927 <0.001
cTnT-hs (ng/L) 6.3/9.3/14.1 10.8/16.4/26.0 <0.001 5.00/8.41/14.85 9.66/17.00/30.98 <0.001
CRP-hs (mg/L) 0.6/1.3/3.0 0.9/1.8/4.7 <0.001 1.120/2.410/5.805 1.590/4.420/10.200 <0.001
IL-6 (ng/L) 1.4/2.1/3.2 1.9/3.0/4.9 <0.001
GDF-15 (ng/L) 914/1,243/1,766 1,231/1,755/2,738 <0.001
Lp-PLA2 (μmol/min/L) 144.3/171.5/201.7 150.0/183.6/219.2 <0.001
LDL cholesterol (mmol/L) 1.64/2.10/2.61 1.65/2.20/2.91 0.015 2.43/2.98/3.60 2.51/2.95/3.57 0.909
HDL cholesterol (mmol/L) 1.00/1.18/1.40 0.97/1.16/1.37 0.149 0.83/0.98/1.14 0.80/0.96/1.19 0.221
Triglycerides (mmol/L) 1.09/1.50/2.08 1.04/1.48/2.08 0.359 1.27/1.68/2.29 1.16/1.58/2.23 0.095
Hemoglobin (g/L) 134/144/152 130/141/152 0.001 131/141/150 126/137/148 0.003
WBC count (109/L) 5.4/6.5/7.7 6.0/7.2/8.3 <0.001 5.59/6.60/7.90 5.80/6.88/8.40 0.022
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Categorical variables are reported as % (n), whereas continuous variables are reported by the percentiles 25th/50th/75th.

CKD-EPI was calculated based on creatinine, age, and gender.

a Comparison also includes the category prior smoker, which were 50% (517) for non-cases and 58% (143) for cases in the LURIC data.

ACE, angiotensin converting enzyme; CABG, coronary artery bypass grafting; CKD-EPI, chronic kidney disease epidemiology collaboration; CRP-hs, high-sensitivity C-reactive protein; cTnT-hs, high-sensitivity troponin T; CV, cardiovascular; Cys-C, cystatin-C; GDF-15, growth differentiation factor 15; GFR, glomerular filtration rate; HDL, high-density lipoprotein; IL-6, interleukin 6; LDL, low-density lipoprotein; Lp-PLA2, lipoprotein associated phospholipase A2; LURIC, Ludwigshafen Risk and Cardiovascular Health; MI, myocardial infarction; NTproBNP, N-terminal prohormone of brain natriuretic peptide; PAD, peripheral artery disease; PCI, percutaneous coronary intervention; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY; TIA, transient ischemic attack; WBC, white blood cell.

The crude bivariate comparison of the 154 biomarkers between the 605 cases and 2,788 non-cases showed significant differences with p < 0.0001 for 87 biomarkers (S6A Table). An unbiased selection of variables (biomarkers as well as clinical variables) with linear or nonlinear associations with CV death occurrence was performed by RF analyses. Prognostic importance is presented in S1A Fig. According to the corresponding Boruta analysis, 28 biomarkers had confirmed importance for the selection of patients with CV death (Fig 1, Table 2). Linear associations of biomarker levels with CV death were investigated by unadjusted (S2A Fig) and adjusted Cox regression analyses (Fig 2, Table 2). According to these analyses, NTproBNP and cTnT-hs carried most of the prognostic information, and addition of any of the other biomarkers increased C-index by less than 0.01 (Table 2).

Fig 1. Variable importance in the STABILITY cohort.

Fig 1

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Boruta analysis in the STABILITY cohort of the significance of variable importance for CV death in the RF analysis, including clinical variables as well as established and PEA biomarkers. Values are NPX values for the PEA biomarkers and log2 for the ng/L levels of NTproBNP, cTnT-hs, IL-6, GDF-15, Cys-C, CRP-hs, Lp-PLA2, and WBC measured by conventional quantitative assays. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. CRP-hs, high-sensitivity C-reactive protein; cTnT-hs, high-sensitivity troponin T; CV, cardiovascular; Cys-C, cystatin-C; GDF-15, growth differentiation factor 15; IL-6, interleukin 6; Lp-PLA2, lipoprotein associated phospholipase A2; NPX, normalized protein expression; NTproBNP, N-terminal prohormone of brain natriuretic peptide; PEA, proximity extension assay; RF, Random Survival Forest; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY; WBC, white blood cell.

Table 2. Associations between CV death and biomarkers RF analysis in the STABILITY cohort including biomarkers with confirmed importance according to the Boruta procedure and also showing results from the Cox regression analyses.

Adjusted by clinical variablesa Also for Cys-C+NTproBNP+cTnT-hsb
Order RF By SD HR CI C-index p-value HR CI C-index p-value
NTproBNP 1 1.74 2.846 (2.524–3.209) 0.802 <0.001 2.348 (2.051–2.689) 0.808 <0.001
Troponin-T 2 0.96 2.048 (1.865–2.248) 0.766 <0.001 1.483 (1.323–1.663) 0.808 <0.001
BNP 3 1.84 2.292 (2.058–2.554) 0.765 <0.001 1.089 (0.910–1.302) 0.808 0.352
VEGF-D 4 0.53 1.933 (1.673–2.234) 0.735 <0.001 1.261 (1.089–1.459) 0.810 0.002
TRAIL-R2 5 0.49 1.255 (1.149–1.370) 0.722 <0.001 1.250 (1.171–1.334) 0.811 <0.001
Cys-C 6 0.38 1.663 (1.508–1.834) 0.735 <0.001 0.983 (0.869–1.112) 0.808 0.784
SPON1 7 0.31 1.845 (1.625–2.095) 0.733 <0.001 1.247 (1.090–1.427) 0.811 0.001
U-PAR 8 0.35 1.565 (1.394–1.756) 0.721 <0.001 1.088 (0.946–1.252) 0.809 0.238
GDF-15 9 0.79 1.726 (1.547–1.925) 0.731 <0.001 1.264 (1.100–1.452) 0.811 <0.001
IL-6 10 0.95 1.524 (1.396–1.664) 0.726 <0.001 1.133 (1.021–1.257) 0.811 0.018
HGF 12 0.45 1.550 (1.410–1.704) 0.721 <0.001 1.263 (1.137–1.404) 0.813 <0.001
OPG 13 0.40 1.525 (1.360–1.710) 0.715 <0.001 1.290 (1.139–1.460) 0.811 <0.001
TGF-alpha 14 0.44 1.288 (1.201–1.381) 0.713 <0.001 1.073 (0.964–1.195) 0.808 0.198
TNF-R1 15 0.40 1.514 (1.356–1.692) 0.715 <0.001 1.012 (0.864–1.185) 0.808 0.884
MMP-12 16 0.74 1.428 (1.289–1.581) 0.711 <0.001 1.098 (0.973–1.238) 0.810 0.129
CHI3L1 18 1.17 1.432 (1.314–1.561) 0.716 <0.001 1.180 (1.065–1.307) 0.811 0.002
LIF-R 19 0.28 1.472 (1.304–1.660) 0.713 <0.001 1.102 (0.970–1.252) 0.808 0.137
FGF-23 20 0.75 1.344 (1.242–1.454) 0.716 <0.001 1.132 (1.034–1.239) 0.810 0.007
CCL25 22 0.64 1.399 (1.244–1.573) 0.710 <0.001 1.157 (1.024–1.308) 0.810 0.020
WBC (lab log2) 23 0.38 1.336 (1.211–1.473) 0.711 <0.001 1.155 (1.036–1.289) 0.811 0.009
CCL28 24 0.44 1.194 (1.103–1.293) 0.701 <0.001 1.077 (0.966–1.202) 0.809 0.181
LOX-1 25 0.76 1.184 (1.090–1.285) 0.702 <0.001 1.113 (1.015–1.220) 0.810 0.022
Lp-PLA2 (lab log2) 27 0.43 1.287 (1.160–1.428) 0.699 <0.001 1.159 (1.039–1.292) 0.810 0.008
TIM 28 0.91 1.395 (1.272–1.530) 0.713 <0.001 1.162 (1.049–1.287) 0.810 0.004
sST2 29 0.59 1.509 (1.354–1.682) 0.714 <0.001 1.199 (1.079–1.332) 0.812 <0.001
CA-125 31 0.92 1.466 (1.318–1.629) 0.713 <0.001 1.155 (1.047–1.275) 0.809 0.004
REN 36 0.99 1.237 (1.110–1.378) 0.701 <0.001 1.209 (1.089–1.343) 0.811 <0.001
GH 37 2.00 1.322 (1.201–1.455) 0.703 <0.001 1.101 (0.989–1.225) 0.808 0.079
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Values are NPX values (log2 scale) for the PEA biomarkers and log2 for the levels of NTproBNP, TroponinT-hs, IL-6, GDF-15, Cys-C, CRP-hs, Lp-PLA2 activity, and WBC measured by conventional quantitative assays. SD, standard deviation. HRs and 95% CI are calculated for increase of 1 SD. HR and C-indices are calculated after adjustment for clinical variables as above and also after adjustment for the biomarkers NTproBNP, cTnT-hs, and Cys-C.

a Clinical variable adjustment includes the variables age, sex, BMI, smoking, hypertension, diabetes mellitus, prior MI, prior PCI or CABG, prior stroke/TIA, prior PAD, and randomized treatment.

b Adjustment also for Cys-C, NTproBNP, and cTnT-hs affects the HRs and C-indices of all variables. This leads to all C-indices having a minimal level of 0.808, i.e., the C-index for clinical variables and the 3 biomarkers Cys-C, NTproBNP, and cTnT-hs. The incremental discriminatory value of adding any additional biomarker can then be estimated by subtracting the corresponding C-index with 0.808, e.g., for GDF-15 the increment of the C-index is 0.003.

BMI, body mass index; BNP, brain natriuretic peptide; CA-125, carbohydrate antigen 125; CABG, coronary artery bypass grafting; CCL25, chemokine ligand 25; CCL28, chemokine ligand 28; CHI3L1, chitinase-3 like protein 1; CI, confidence interval; cTnT-hs, high-sensitivity troponin T; CV, cardiovascular; Cys-C, cystatin-C; FGF23, fibroblast growth factor 23; GDF-15, growth differentiation factor 15; HGF, hepatocyte growth factor; GH, growth hormone; HR, hazard ratio; IL-6, interleukin 6; LIF-R, leukemia inhibitory factor receptor; LOX1, lectin-like oxidized LDL receptor 1; Lp-PLA2, lipoprotein associated phospholipase A2; MI, myocardial infarction; MMP-12, matrix metalloproteinase-12; NPX, normalized protein expression; NTproBNP, N-terminal prohormone of brain natriuretic peptide; OPG, osteoprotegerin; PAD, peripheral artery disease; PCI, percutaneous coronary intervention; PEA, proximity extension assay; REN, renin; RF, Random Survival Forest; SD, standard deviation; SPON1, Spondin 1; sST2, soluble suppression of tumorigenesis 2 protein; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY; TGF-alpha, transforming growth factor alpha; TIA, transient ischemic attack; TIM, transmembrane immunoglobulin and mucin domain protein; TNF-R1, tumor necrosis factor receptor 1; TRAIL-R2, tumor necrosis factor-related apoptosis-inducing ligand receptor 2; U-PAR, urokinase plasminogen activator receptor; VEGF-D, vascular endothelial growth factor D; WBC, white blood cell.

Fig 2. Multivariable adjusted linear associations in the STABILITY cohort.

Fig 2

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Cox regression analyses in the STABILITY cohort of associations between biomarkers and CV death with adjustment for baseline characteristics. Values are the same as for Fig 1. HRs and 95% CI are calculated for increase of 1 SD. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. CI, confidence interval; CV, cardiovascular; HR, hazard ratio; SD, standard deviation; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

Replication population

The baseline characteristics of the LURIC population is shown in Table 1. In this cohort, the RF and Boruta analyses identified 21 biomarkers of confirmed importance for CV death, of which 18 also had been identified in the STABILITY cohort (Figs 3 and 4, S1B and S2B Figs, S6B Table). Also, in the LURIC cohort, the 2 strongest prognostic biomarkers were NTproBNP and cTnT-hs. At the next level of importance, another 16 biomarkers of prognostic importance were verified, in descending order: GDF-15, adrenomedullin (AM), osteoprotegerin (OPG), transmembrane immunoglobulin and mucin domain protein (TIM-1), renin (REN), Cys-C, tumor necrosis factor (TNF)-related apoptosis-inducing ligand receptor 2 (TRAIL-R2), soluble suppression of tumorigenesis 2 protein (sST2), BNP, hepatocyte growth factor (HGF), IL-6, carbohydrate antigen 125 (CA-125), spondin 1 (SPON-1), tumor necrosis factor receptor 1 (TNF-R1), fibroblast growth factor 23 (FGF-23), and chitinase-3 like protein 1 (CHI3L1). When investigating these biomarkers by Cox regression analyses, adjusting for renal function (Cys-C) and the strongest prognostic biomarkers NTproBNP and cTnT-hs, only 1 biomarker (REN) increased the C-index by more than 0.01 (Table 3, Fig 4).

Fig 3. Variable importance in the replication cohort.

Fig 3

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Boruta analysis in the LURIC cohort of the significance of variable importance for CV death in the RF analysis, including clinical variables as well as established and PEA biomarkers. Values are the same as for Fig 1. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. CV, cardiovascular; LURIC, Ludwigshafen Risk and Cardiovascular Health; PEA, proximity extension assay; RF, Random Survival Forest.

Fig 4. Multivariable adjusted linear associations in the replication cohort.

Fig 4

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Cox regression analyses in the LURIC cohort of associations between biomarkers and CV death after adjustment for baseline characteristics. Values are the same as for Fig 1. HRs and 95% CI are calculated for increase of 1 SD. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. CI, confidence interval; CV, cardiovascular; HR, hazard ratio; LURIC, Ludwigshafen Risk and Cardiovascular Health; SD, standard deviation.

Table 3. Associations between CV death and biomarkers including biomarkers with confirmed importance according to the Boruta RF analyses in both cohorts.

Biomarker Adjusted by clinical variablesa Also for Cys-C+NTproBNP+cTnT-hsb
Order RF By SD HR CI C-index p-value HR CI C-index p-value
NTproBNP 1 2.04 2.079 (1.799–2.402) 0.785 <0.001 1.779 (1.495–2.117) 0.793 <0.001
cTnT-hs 2 1.42 1.715 (1.491–1.973) 0.761 <0.001 1.266 (1.065–1.505) 0.793 0.008
GDF-15 3 0.74 1.728 (1.527–1.955) 0.765 <0.001 1.397 (1.180–1.654) 0.800 <0.001
AM 4 0.77 1.750 (1.490–2.056) 0.761 <0.001 1.352 (1.126–1.623) 0.797 0.001
OPG 5 0.45 1.488 (1.297–1.708) 0.747 <0.001 1.256 (1.087–1.452) 0.799 0.002
TIM-1 6 0.93 1.555 (1.362–1.775) 0.748 <0.001 1.294 (1.123–1.491) 0.797 <0.001
REN 7 1.09 1.501 (1.305–1.727) 0.747 <0.001 1.523 (1.329–1.745) 0.806 <0.001
Cys-C 8 0.39 1.370 (1.243–1.510) 0.747 <0.001 1.066 (0.951–1.194) 0.793 0.274
TRAIL-R2 9 0.63 1.205 (1.131–1.285) 0.740 <0.001 1.100 (0.990–1.223) 0.794 0.076
sST2 10 0.62 1.478 (1.307–1.672) 0.745 <0.001 1.250 (1.100–1.422) 0.796 <0.001
BNP 14 1.35 1.399 (1.255–1.561) 0.756 <0.001 0.860 (0.738–1.001) 0.792 0.052
HGF 17 1.31 1.259 (1.134–1.396) 0.737 <0.001 1.157 (1.031–1.299) 0.795 0.013
IL-6 18 1.24 1.478 (1.316–1.659) 0.752 <0.001 1.261 (1.110–1.432) 0.798 <0.001
CA-125 20 0.69 1.347 (1.226–1.479) 0.748 <0.001 1.101 (0.988–1.228) 0.795 0.082
SPON1 21 0.47 1.295 (1.156–1.450) 0.741 <0.001 1.072 (0.932–1.232) 0.794 0.331
TNF-R1 22 0.44 1.486 (1.307–1.689) 0.747 <0.001 1.195 (0.969–1.473) 0.794 0.096
FGF23 26 0.94 1.349 (1.237–1.472) 0.749 <0.001 1.109 (0.978–1.257) 0.795 0.107
CHI3L1 34 1.07 1.284 (1.129–1.461) 0.732 <0.001 1.110 (0.961–1.281) 0.794 0.157
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The biomarkers are ordered by the position in the RF analysis in LURIC. The Cox regression and C-index results are from the replication cohort from LURIC (n = 1,287).

Values are NPX values (log2 scale) for the PEA biomarkers and log2 for the levels of NTproBNP, Troponin-T, Cys-C, and CRP-hs measured by conventional quantitative assays. SD, standard deviation. HR and 95% CI are calculated for increase of 1 SD. C-indices are calculated after adjustment for clinical variables as above and also after adjustment for the biomarkers NTproBNP, troponin-T, and Cys-C.

a Clinical variable adjustment includes the variables age, sex, BMI, smoking, hypertension, diabetes mellitus, prior MI, prior PCI or CABG, prior stroke/TIA, prior PAD, and randomized treatment.

b Adjustment also for Cys-C, NTproBNP, and cTnT-hs affects the HRs and C-indices of all variables. This leads to all C-indices having a minimal level of 0.793, i.e., the C-index for clinical variables and the 3 biomarkers Cys-C, NTproBNP, and cTnT-hs. The incremental discriminatory value of adding any additional biomarker can then be estimated by subtracting the corresponding C-index with 0.793, e.g., for GDF-15 the increment of the C-index is 0.007.

AM, adrenomedullin; BMI, body mass index; CA-125, carbohydrate antigen 125; CABG, coronary artery bypass grafting; CHI3L1, chitinase-3 like protein 1; CI, confidence interval; cTnT-hs, high-sensitivity troponin T; CV, cardiovascular; Cys-C, cystatin-C; FGF23, fibroblast growth factor 23; GDF-15, growth differentiation factor 15; HGF, hepatocyte growth factor; HR, hazard ratio; IL-6, interleukin 6; LURIC, Ludwigshafen Risk and Cardiovascular Health; MI, myocardial infarction; NPX, normalized protein expression; NTproBNP, N-terminal prohormone of brain natriuretic peptide; OPG, osteoprotegerin; PAD, peripheral artery disease; PCI, percutaneous coronary intervention; PEA, proximity extension assay; REN, renin; RF, Random Survival Forest; SD, standard deviation; SPON1, Spondin 1; sST2, soluble suppression of tumorigenesis 2 protein; TIA, transient ischemic attack; TIM-1, transmembrane immunoglobulin and mucin domain protein; TNF-R1, tumor necrosis factor receptor 1; TRAIL-R2, tumor necrosis factor-related apoptosis-inducing ligand receptor 2.

Discussion

The present study investigated the associations between multiple circulating protein biomarkers, measured by immunoassays and PEA, and CV death in 2 large cohorts of optimally treated patients with chronic CHD. Employing machine learning approach, 18 biomarkers had confirmed and validated associations with CV mortality. In both cohorts, the strongest associations were found with NTproBNP and cTnT-hs, and also, the previously established independent associations with GDF-15, IL-6, and Cys-C were verified. The novel findings in the study was the identification of 13 additional proteins with an independent association to CV mortality in patients with chronic CHD, i.e., TIM-1, REN, OPG, sST2, TRAIL-R2, CA-125, BNP, HGF, SPON-1, FGF-23, CHI3L1, TNF-R1, and AM. The results indicate that myocardial strain–dysfunction–hypertrophy–fibrosis (NTproBNP, BNP, sST-2, SPON-1, and CA-125), myocyte death and apoptosis (cTnT-hs, TNF-R1, and TRAIL-R2), kidney injury (Cys-C, FGF-23, and TIM-1), hemodynamic stress, renin–angiotensin system (RAS) activation (REN and AM), oxidative stress and inflammation (GDF-15, IL-6, OPG, and CHI3L1), and angiogenesis and vascular cell proliferation (HGF) are important mechanisms associated with CV death in patients with chronic CHD (Fig 5).

Fig 5. Biomarkers and processes associated with CV death.

Fig 5

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Conceptual figure of biomarkers and processes associated with CV death in chronic CAD. CAD, coronary artery disease; CV, cardiovascular.

The current study confirmed previous findings from our group and others on the very strong importance of the cardiac biomarkers NTproBNP and cTnT-hs for prediction of CV death in patients with CHD [6,16,21]. In the present study, these biomarkers were shown to be prognostically more important than any clinical characteristic and more important than 150 other CV and inflammatory biomarkers. NTproBNP and/or BNP and cTnT-hs are related to underlying functional myocardial disturbances, such as myocardial stretch, overload, dysfunction, hypertrophy, and necrosis. However, no association has been identified between the levels of NTproBNP and cTnT-hs and disease development. Therefore, although associations between other biomarkers and CV death are attenuated by statistical adjustment for NTproBNP and/or cTnT-hs levels and renal dysfunction, still these associations might reflect important pathophysiological pathways.

Multiple biomarker profilings using PEA protein panels have previously been found useful for predicting chronological age using concentrations of 77 plasma proteins in 976 healthy individuals [22,23]. When the OLINK CVD I panel was used in 931 community-dwelling subjects, 7 proteins (OPG, TIM-1, GDF-15, matrix metalloproteinase-12 [MMP-12], REN, TNF ligand superfamily member 14 [TNFSF14], and growth hormone [GH]) were significantly related to the number of carotid arteries affected by plaques after adjustment for multiple testing [24]. From the same study, it was recently reported that 9 proteins (GDF-15, TIM-1, TRAIL-R2, SPON1, MMP-12, follistatin [FS], soluble urokinase-type plasminogen activator surface receptor [sU-PAR], OPG, and sST2) were associated with the development of heart failure [25]. It is noteworthy that 4 of these 9 proteins (GDF-15, TIM-1, OPG, and sST2) were identified and validated in the present study and that 4 others (TRAIL-R2, SPON1, MMP-12, and sU-PAR) were significant in the STABILITY cohort of the present study. The OLINK CVD I panel was also used in 847 patients with acute MI in whom the levels of GDF-15 and TRAIL-R2 were independently associated with all-cause mortality during 7 years follow-up in accordance with the present findings in the STABILITY study [26].

In 2016, a multimarker tool based on estimation of protein levels by the Somalogic modified aptamer technology was used to identify the 4-year risk of MI, stroke, heart failure, and all-cause death in 1 derivation and 1 replication cohort of patients with chronic CAD [27]. This study identified a 9-protein model providing a c-statistic of 0.71 in the replication sample, in which troponin was the only biomarker in common with the present findings. Based on the Luminex xMAP platform, another multimarker model to identify the risk of CV death, MI, or stroke was developed in a derivation cohort of 649 and replication cohort 278 patients. This model included the 4 biomarkers NTproBNP, TIM-1, osteopontin, and tissue inhibitor of metalloproteinase-1 (TIMP-1), which had a c-index of 0.79. Interestingly, NTproBNP and TIM-1 are the same markers as identified in the present study and osteopontin protein may reflect the same processes as OPG [28]. The lack of complete replication of identified markers between the studies might relate to many factors, e.g., differences in measured biomarkers, handling and age of biosamples, assay technologies, endpoints, adjustments, patient populations, treatments, and follow-up.

After the specific cardiac markers, GDF-15, a marker of oxidative stress, cellular aging, and inflammatory activity, showed the strongest association with CV death. The independent prognostic value GDF-15 [5,15,29] concerning total and CV mortality has been documented previously from the STABILITY trial as well as in several other studies. Several other proteins reflecting inflammatory activity (IL-6, CHI3L1, TIM-1, and OPG) were also significantly associated with the risk of CV death in both the present cohorts. Independent associations between IL-6 [17] and CV mortality have previously been shown in this and other studies. Also, CHI3L1 [30] and TIM-1 [31] have been found associated with the development of coronary atherosclerosis. OPG, a member of the tumor necrosis factor receptor superfamily, has pleiotropic effects on bone metabolism, endocrine functions, and vascular inflammation [32,33]. It is expressed by inflammatory stimuli, during acute MI and heart failure, and OPG plasma levels are related to outcomes in patients with these conditions [34–36]. The recently reported simultaneous reduction of inflammatory activity, e.g., as documented by simultaneous reduction of IL-6 levels and ischemic events by treatment with the anti-inflammatory agents canakinumab [37] and colchicine [38], further supports inflammatory activity as a major pathophysiological mechanism in CHD [39].

Chronic kidney disease is a well-established risk factor for CV death. Accordingly, the level of Cys-C, which reflects glomerular filtration rate, has repeatedly been demonstrated to be associated with CV events and death in patients with CHD. The renal hormone renin is a pivotal component of the RAS, which plays a key role in the maintenance of blood pressure and electrolyte-volume homeostasis. The RAS is activated in hypertension and heart failure, and reduction of its activity has become the basis for both prevention and treatment of these conditions [1,2]. AM [40] and CA-125 [41] are indicators of fluid overload and congestion and probably thereby indicators of outcome in CAD. FGF23, a hormone for regulation of phosphate hemostasis and the renin–angiotensin–aldosterone system, is an indicator of both renal and myocardial dysfunction and is associated with left ventricular hypertrophy and clinical outcomes [42]. An animal study has demonstrated Spon1 mRNA expression in different vascular tissues with the same expression in kidney and heart [43]. In this rat model, the Spon1 gene was also identified as a novel candidate gene for hypertension. In previous human studies, SPON1 has turned out to be an indicator of myocardial and renal dysfunction [25]. We also confirmed an independent association between TIM-1, a modulator of the T cell–mediated immune response, and CV outcomes [44–46]. Moreover, recently, an anti-TIM-1 antibody was shown to attenuate atherosclerosis development [31]. The identification of all these markers of CV–renal dysfunction as independent risk indicators in the current cohorts of chronic CHD is impressive not least after adjustment for the established and strong biomarkers of renal (Cys-C) and cardiac function (NTproBNP and cTnT-hs) [47].

In the present study, sST2 emerged as an independent biomarker contributing to risk stratification for CV death. ST2 is the receptor for interleukin-33, a cytokine with antihypertrophic and antifibrotic effects on the myocardium. Serum levels of the soluble form of sST2 is a biomarker for myocardial strain and is well established to provide prognostic information in patients with heart failure [48] but also in patients with chronic CHD [49], acute coronary syndrome [50], and in population-based cohorts (Dallas Heart) [51]. The prognostic importance of sST2 in the LURIC cohort has been shown earlier by using a conventional immunoassay, which strengthens the current findings with PEA technology [52].

HGF also appeared as a factor that significantly contributed to the identification of the risk of CV death in the current study. HGF has pleitropic cell functions including angiogenesis, anti-apoptosis, proliferation, and differentiation. Based on high levels in the early phase of MI and in heart failure, HGF has been suggested as a prognostic and diagnostic biomarker of CV disease [53]. HGF gene therapy has also been used in the treatment of ischemic heart disease and for tissue regeneration [54]. In accordance with our findings, the level of HGF has previously been found to be independently associated with the progression of atherosclerosis and clinical events in patients with CHD and heart failure and with long-term mortality in the general population [55–59].

Finally, TNF-R1 and TRAIL-R2 are members of the TNF receptor superfamily and involved in the processes of apoptosis. Probably, these processes are activated by several of the other mechanisms associated with a raised risk for CV death and their incremental importance most likely limited [25,60].

Limitations and strengths

Although being a large trial with global recruitment, the STABILITY cohort might not be fully representative for all patients with chronic CHD. For example, less than 20% of patients were women, smokers, or had prior multivessel disease. Also, the LURIC observational cohort has limitations, for example, because of recruitment in association with a coronary angiogram and a lack of systematic follow-up concerning secondary preventive treatment and nonfatal events. Therefore, some biomarkers appearing to have prognostic importance in one setting might not be possible to verify because of the differences in design and size of the studies. However, the differences in design might also be considered an advantage when searching for more robust prognostic biomarkers with relevance in the broad real-life patient population. In the LURIC trial, the associations using 4 years or 12 years follow-up were similar, although with weaker significances at 4 years because of fewer events. Evaluation of shorter time follow-up was avoided because of low event rates and thereby too low statistical power for reliable multivariate evaluation of multiple biomarkers. Some biomarkers had low availability, making them impossible to evaluate in this setting.

Implication and next steps

The employed multiplex PEA methodology provides a profile of the relative protein concentrations, which is appropriate for screening of the most useful proteins to include in a multiplex assay. However, for final evaluation of the usefulness of protein profiling, a multiplex assay allowing simultaneous and precise quantification across the complete dynamic range of all included proteins will be needed.

Conclusions

In patients with chronic CHD, 18 out of 157 circulating biomarkers had internally confirmed and externally validated independent significant associations with CV death. The results indicate that myocardial strain–dysfunction–hypertrophy–fibrosis (NTproBNP, BNP, sST2, SPON1, and CA-125), myocyte death and apoptosis (cTnT-hs, TNF-R1, and Trail-R2), kidney injury (Cys-C, FGF-23, and TIM-1), hemodynamic stress, RAS activation (REN and AM), oxidative stress (GDF-15 and OPG), vascular inflammation and immune modulation (IL-6 and CHI3L1), and angiogenesis and vascular cell proliferation (HGF) are important mechanisms associated with CV death in patients with chronic CHD (Fig 5). Profiles of levels of multiple plasma proteins can be useful for the identification of different pathophysiologic pathways associated with an increased risk of CV death in patients with chronic CHD. Measurements of these profiles and their pathways might be useful for the identification of new treatment targets and balancing different treatments in patients with chronic CHD.

Supporting information

S1 STROBE Checklist. STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

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S1 Text. Statistical analysis plan.

(PDF)

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S1 Table. Proteins included on the Olink CVD I and Inflammation panels.

(PDF)

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S2 Table. PEA biomarker NPX values.

(A) Data availability in all 4,127 patients in the STABILITY cohort. (B) Data availability in all 1,331 patients in the LURIC cohort. LURIC, Ludwigshafen Risk and Cardiovascular Health; NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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S3 Table. Baseline characteristics in the randomly selected subcohort and in all patients in the STABILITY trial.

STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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S4 Table

(A) Reproducibility of biomarkers determined in both the CVD1 and Inflammation OLINK PEA panels. (B) Comparisons between biomarkers associations with CV death when using measurements with conventional (lab log2) and PEA assays (NPX) as evaluated by univariate Cox regression analyses. CV, cardiovascular; NPX, normalized protein expression; PEA, proximity extension assay.

(PDF)

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S5 Table. Spearman correlation between the NPX values of relevant PEA biomarkers (rows with any correlation >0.29) and the logarithmic transformation of the ng/L levels of established biomarkers (including only the random cohort in the STABILITY cohort).

NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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S6 Table. PEA biomarker NPX values in patients with and without CV death in (A) the STABILITY cohort and (B) the LURIC cohort (numbers are percentiles 25th/50th/75th).

CV, cardiovascular; LURIC, Ludwigshafen Risk and Cardiovascular Health; NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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S1 Fig. Random Forest analysis in (A) the STABILITY cohort and (B) the LURIC cohort of prognostic variables for CV death including clinical variables as well as established and PEA biomarkers.

Each estimate of variable importance is colored by the Boruta analysis result. CV, cardiovascular; LURIC, Ludwigshafen Risk and Cardiovascular Health; PEA, proximity extension assay; RF, Random Survival Forest; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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S2 Fig. Multivariable unadjusted linear associations by Cox regression analyses in (A) the LURIC cohort and (B) the STABILITY cohort of associations between biomarkers and CV death.

Values are the same as for Fig 1. HR and 95% CI are calculated for increase of 1 SD. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. Variables colored white were not included in the Boruta analysis. CI, confidence interval; CV, cardiovascular; HR, hazard ratio; LURIC, Ludwigshafen Risk and Cardiovascular Health; PEA, proximity extension assay; SD, standard deviation; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

(PDF)

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Abbreviations

AM

adrenomedullin

BMI

body mass index

BNP

brain natriuretic peptide

CA-125

carbohydrate antigen 125

CABG

coronary artery bypass grafting

CAD

coronary artery disease

CHD

coronary heart disease

CHI3L1

chitinase-3 like protein 1

CI

confidence interval

CRP-hs

high-sensitivity C-reactive protein

cTnT-hs

high-sensitivity troponin T

CV

cardiovascular

Cys-C

cystatin-C

FGF-23

fibroblast growth factor 23

GDF-15

growth differentiation factor 15

HGF

hepatocyte growth factor

HR

hazard ratio

IL-6

interleukin 6

LDL

low-density lipoprotein

LLOD

lower limit of detection

Lp-PLA2

lipoprotein associated phospholipase A2

LURIC

Ludwigshafen Risk and Cardiovascular Health

maxstat

maximally selected rank statistics

MI

myocardial infarction

NPX

normalized protein expression

NTproBNP

N-terminal prohormone of brain natriuretic peptide

OPG

osteoprotegerin

PCI

percutaneous coronary intervention

PEA

proximity extension assay

RAS

renin–angiotensin system

RF

Random Survival Forest

SD

standard deviation

sST2

soluble suppression of tumorigenesis 2 protein

STABILITY

STabilization of Atherosclerotic plaque By Initiation of darapladIb TherapY

TIM-1

transmembrane immunoglobulin and mucin domain protein

TIMP-1

tissue inhibitor of metalloproteinase-1

TNF-R1

tumor necrosis factor receptor 1

TRAIL-R2

tumor necrosis factor-related apoptosis-inducing ligand receptor 2

Data Availability

Anonymized individual participant data and study documents can be requested for further research from www.clinicalstudydatarequest.com.

Funding Statement

The present study was supported by the Swedish Foundation for Strategic Research (project RB13-0197) to LW and AS, and by Science for Life Laboratory, Uppsala, Sweden (https://www.scilifelab.se/). Funding from own institution, Uppsala Clinical Research Center, Uppsala, Sweden (Sweden Organisation number 232100-0024) was given to LW and AS. OLINK Proteomics and Roche Diagnostics provided their respective assays at a reduced cost. GlaxoSmithKline sponsored the main STABILITY trial and the biobanking of the samples but provided no specific support for this sub-study. None of the sponsoring companies had any input on the study design, analyses, interpretation or manuscript preparation. The sponsor was given the opportunity to review and comment on the manuscript. LURIC received funding from the European Union’s Horizon 2020 research and innovation programme under the ERA-Net Cofund action N° 727565 (OCTOPUS project) and the German Ministry of Education and Research (grant number 01EA1801A), from the 7th Framework Program (integrated projects AtheroRemo, Grant Agreement number 201668 and RiskyCAD, Project Number 305739) of the European Union, and the Competence Cluster of Nutrition and Cardiovascular Health (nutriCARD) which is funded by the German Federal Ministry of Education and Research (grant number 01EA1411A). None of the sponsors had any input on the study design, analyses, interpretation, the decision to publish, or preparation of the manuscript.

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Decision Letter 0

Adya Misra

3 Sep 2020

Dear Dr Wallentin,

Thank you for submitting your manuscript entitled "Profiling of plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review.

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Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Adya Misra, PhD,

Senior Editor

PLOS Medicine

Decision Letter 1

Adya Misra

1 Oct 2020

Dear Dr. Wallentin,

Thank you very much for submitting your manuscript "Profiling of plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease" (PMEDICINE-D-20-04160R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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We look forward to receiving your revised manuscript.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Comments from Academic Editor

Tone down the causal nature of the observations. There is too much emphasis on 'mechanisms' and I'd say that these are potential mechanisms. In cardiovascular research, there is little evidence to suggest that risk factors differ for primary or secondary prevention. Can they discuss the implications of restricting the population to those with CHD only? Would one expect a different pattern when investigated in a different at-risk group?

Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

Abstract- please provide brief participant demographics

Abstract-please provide 95% CI and p-values when describing association

Abstract-The last sentence of the methods and findings section should include 2-3 limitations of your study design/methodology

Abstract conclusions

Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful. * Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions. * Please avoid vague statements such as "these results have major implications for policy/clinical care". Mention only specific implications substantiated by the results. * Please avoid assertions of primacy ("We report for the first time....")

Author summary

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please use square brackets for references, providing punctuation after a single space. For example xxxx [1,2].

Methods

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b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Please ensure that the study is reported according to the [STROBE] guideline, and include the completed [STROBE or other] checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

Please report your study according to the relevant guideline, which can be found here: http://www.equator-network.org/

Please introduce Lp-PLA2 on first view

Please provide participant details for both cohorts- including details of where they were recruited, dates of recruitment and follow up.

Please provide p-values of up to three decimal places throughout the text. Please ensure these are accompanied by 95%CI

Page 17 Line 216 suggest replacing “confirmed importance for CV death” to “associated with CVD death” or similar

Discussion

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

I suggest tempering some of the language in this section, “definitely established as key risk indicators” and “demonstrated to outperform” for example. Since this is an observational study please rephrase to remove causal language

Line 268- please remove the word “causal”

You may wish to rephrase optimal treatment here or explain, that patients were treated with xxx medication. You may wish to include this explicit information in the methods.

Comments from the reviewers:

Reviewer #1: "Profiling of plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease" aims to characterize significant biomarkers with an association to coronary heart disease (CHD). Two cohorts were involved, a derivation cohort consisting of 605 cases with CV death and 2788 non-death cases from the STABILITY trial, and a validation cohort of 245 cases with CV death and 1042 non-death cases from the LURIC study, with mass measurement of protein biomarkers achieved using Proximity Extension Assay (PEA) technology.

The main methodology was based on statistical analysis via the Boruta method on random survival forests (RF), with RF intended to estimate the prognostic importance of variables, and Boruta to determine their significance. Cox regression analyses were also performed. Known prognostic biomarkers such as NTproBNP & cTnT-hs were confirmed as associated with CHD, together with a number of other proteins, for a total of 18 biomarkers of 157 examined.

These findings are interesting in that there appears to be relatively weak consensus on protein biomarker associations with CHD, from prior work. However, while mostly comprehensively described, some points might be clarified, particularly pertaining to data availability and the methodology used:

1. The STABILITY cohort and LURIC cohorts appear to have widely varying follow-up periods (median 3.7 years, and up to 12 years respectively). It might be commented on whether this warrants additional treatment (e.g. short-term vs. long-term CHD mortality)

2. The Pearson/Spearman correlation coefficients for the PEA biomarker measurements (as referenced in Line 117) might be provided if possible in the supplementary.

3. The relatively low availability of a large number of biomarkers (Table S2a), together with the minimal availability criteria required to be included in further analysis, might be supported in greater detail. For example, the TSLP biomarker is listed as having 99.5% of values below the lower limit of detection, which appears to imply that it qualifies to be considered for assessment of association with CHD, despite having only 1 in 200 subjects having data available for it. While there is no objection against having such biomarker values reported in principle, the qualification for further analyses might be justified (especially as such values appear to be automatically imputed from Line 179, despite the scant basis for such imputation)

4. Further on this, certain biomarkers such as BNP (39.3% availability in STABILITY) appear to be presented as significant (BNP is the third top variable, in Figure S1a), despite relatively low availability. It is not immediately evident whether there might be any bias involved towards the subset of patients with detectable BNP, given that this value was not available for nearly 40% of patients. Moreover, is data availability accounted for in determining significance for the statistical analysis? This might be further commented on.

5. On the C-index, while it is stated that "...the incremental discriminative value of each biomarker was illustrated by the C-index" (Line 175), it is not immediately evident as to how this is arrived at. For example, the C-index for NTproBNP is given as 0.802, in Table 2. Does this C-index reflect the performance of the model given all variables (including/excluding NTproBNP?), and if so, does the C-index of Troponin-T reflect the performance give all variables including/excluding Troponin-T, and possibly also including/excluding NTproBNP? Given the relative novelty of the RF-Boruta analysis, it might be appropriate to devote slightly more text to explaining it (possibly in the supplementary material)

6. The "By SD" column for Tables 2 & 3/Figures 2 & 4 might be initially defined, together with abbreviations such as those for hazard ratio & confidence interval.

7. The biomarker unadjusted values for HR, CI & C-index (as referenced in Line 171) might also be provided as for the adjusted values in Tables 2 & 3.

8. From what could be understood, the derivation STABILITY cohort was first used to develop an RF-Boruta model (and its variable importance analyzed), while the validation LURIC cohort was then used to develop a separate/independent RF-Boruta model, though with input in the form of biomarkers of confirmed importance (Line 216) as determined from the derivation cohort model. The associations for both the derivation and validation cohorts are as presented in Table 3.

However, validation might be more commonly understood as applying a model obtained from the training (derivation) data, to the validation data, and reporting the performance (possibly C-index in this case). This might be considered.

Reviewer #2: The authors Lars Wallentin et al, proposed to reviewer an interesting publication entitled "Circulating biomarkers are associated with development of coronary heart disease (CHD) and its complications by reflecting pathophysiological pathways and/or organ dysfunction".

They explored the associations between cardiovascular (CV) and inflammatory biomarkers and CV death using proximity extension assays (PEA) in 605 cases with CV death and 2,788 randomly selected non-cases during 3 - 5 years follow-up in the STABILITY trial

They seek for validation of their findings in in the LURIC cohort consisted of 245 cases and 1,042 non-cases during 12 years follow-up.

Authors to conclude that Protein profiles based on clusters of biomarkers reflecting different pathophysiologic mechanisms might be useful for identification of new treatment targets and balancing different treatments in patients with chronic CHD.

While the manuscript is really pleasant to read, there is several points the reviewer which to be seen addressed.

Minor points:

- Trial registration is given for STABILITY study and not for the LURIC one

- The methods previously used to measure NTproBNP, cTnT-hs, CRP-hs, IL-6, GDF-15 and Lp-PLA2 should be mentioned

- Table 1 in the legend "For BMI, weight, and biomarkers…" this is not relevant as BMI, weight are not presented in this table. However, this remark stands for the Age of the patients

- Table S3, presentation by percentils 25th/50th/75th are also relevant for the age

- Table 1, 2, 3 & 4: a color code should be givent to understand the meaning of the red, yellow, blue and green boxes

- Page 23, line 300-303: "The lack of complete replication … relate to many factors e.g…." important to add "handling and age of the biosamples" as the stability overtime of the biosamples could be critical.

Major points:

- The authors detailed within TableS1 the panel from which the measurements have been used for the 27 redundant biomarkers. It is important to have this description as well in the main tables. Furthermore, it is also important to have a comparison of the results obtained on both panels for those 27 proteins present. What are the reproducibility & variability between the two panels while using the same samples?

- A comparison of the data obtained with the different methods (conventional & PEA) for the previously used NTproBNP, cTnT-hs, CRP-hs, IL-6, GDF-15 and Lp-PLA2 would be nice to have. The manuscript suggest an interest in using the multiplex method, thus methods should be compared side by side for the above listed biomarkers.

- Discussion looks very much like other publications using the PEA or other multiplex technologies (Somalogic, …) even if figure 5 gives a nice resume of the findings. The conclusion on Page 27, line 393 suggesting the" identification of potential treatment targets and balancing different treatments, in patients with CHD" is not enough developed. Even if the topic of the journal is Medicine, important information coming from experimental studies at least on some of the listed biomarkers and all listed pathways could enriched the discussion.

Reviewer #3: In this work Wallentin et al. study the association between 157 cardiovascular and inflammatory biomarkers and CV death using proximity extension analysis in patients with stable coronary heart disease.

The derivation cohort consisted of 605 cases (who experienced CV death) and 2788 non-cases (stable CHD patients that survived the 3-5 year follow up).

The validation cohort consisted of 245 cases and 1042 non-cases but the follow up was shorter (1 year).

Biomarker levels were measured using conventional laboratory immunoassays and/or OLINK panels.

28 biomarkers were prognostic in the derivation cohort (most belonged to the CVD panel and only 4 to the inflammation panel).

After validation, a total of 18 biomarkers were found to associate with CV death in both cohorts, of these the strongest predictors were ntProBNP and cTNT-hs.

This is an interesting and data-rich study which highlights the potential clinical utility of multimarker plasma biomarker panels for personalised risk stratification in CHD, yet I have some concerns.

#The abstract focuses on chronic CHD, while the STABILITY and LURIC methods section refers to stable CHD; chronic and stable CHD are not exactly the same thing, so stick to one term.

#How was stable CHD confirmed in the STABILITY/LRIC studies, did all participants have baseline invasive coronary angiography? (describe this in main text to avoid sending readers to the original papers to find out).

#Define CV death and explain how was it ascertained - death certificates, GP records, national statistics office etc?

#Discuss the potential impact of having differential followup duration in the derivation and validation cohorts - consider running a sensitivity analysis on the derivation cohort to check whether the significant biomarkers persist if the follow up duration is whittled to 1year to match that used in the validation cohort.

#The author's description of PEA technology in the introduction can be better clarified. It is key to understanding the paper so would be good to explain it at the outset and not later in the discussion.

"Proximity Extension Assay (PEA) technology, allowing simultaneous measurements of 92 proteins in a 1.0 μl plasma sample by PCR amplification of DNA strands from DNA-labeled antibody pairs"

(from OLINK site: A pair of oligonucleotide-labeled antibodies ("probes") are allowed to pair-wise bind to the target protein present in the sample in a homogeneous assay, with no need for washing. When the two probes are in close proximity, a new PCR target sequence is formed by a proximity-dependent DNA polymerization event...the resulting sequence is subsequently detected and quantified using standard real-time PCR.)

#The section about reproducibility of assays (ln 117) should be moved to the results section, it is a result.

#Did you consider adjusting for socio-economic position in the regression analyses?

#You randomised for the administration of darapladib in the regressions, but could you explain to the reader what is the expected effect of darapladib Rx in these patients? Are we to expect lower death rates in darapladib-treated patients?

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 2

Adya Misra

1 Dec 2020

Dear Dr. Wallentin,

Thank you very much for re-submitting your manuscript "Screening of plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease – a retrospective study" (PMEDICINE-D-20-04160R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

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If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Dec 08 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Title: please revise to “Plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease: a retrospective study”

Please provide p-values up to three decimal places only

Please use square brackets for references and place the bracket before full stop.

Please add brief participant demographics in the abstract

Please provide full details for ref 19, 31,33

Discussion- please emphasize the low availability of some markers versus the significant ones to provide context. Along with noting which findings confirm previous work, please also highlight the novel aspects of this study

STROBE checklist- please use paragraph and sections instead of page numbers as these are likely to change

Please add study dates in abstract

At line 304, "hypothesis-free"? Please revise

At line 429, perhaps "megatrial" can be avoided (e.g., "large trial")

Comments from Reviewers:

Reviewer #1: We thank the authors for addressing most of the concerns from the previous review round. However, the issue of (extremely) low availability of some PEA markers (including significant ones such as BNP) might be explicitly recognized as a limitation/discussed in the text, to help place the findings in context.

Reviewer #2: The reviewer read the revised manuscript and is aknowledging that its concerns were addressed.

Few minor comments howerever:

-the authors stated to have been using an "agnostic approach by machine learning" (p41/65 line 304). This is partially true, knowing that the proteomic approach was performed on a set of preselected targets known to play a role in cardiovascular diseases and inflammation (Olink PEA-CVD1 & Inflammation panels). Obvisously, theexperimental setting was oriented while the analyses were using machine learning.

- p42/65 line 324 : "inflammatory biomarkers .. NTproBNP" remove one dot

- the authors have not been relating FGF23 to Renin angiotensin aldosterone system while FGF23 is a known regulator of Renin via its control of the 1-12(OH)2D

Reviewer #3: The revised manuscript is improved and I have no further suggested changes.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 3

Richard Turner

5 Jan 2021

Dear Dr. Wallentin,

I am writing concerning your manuscript submitted to PLOS Medicine, entitled “Plasma proteins associated with cardiovascular death in patients with chronic coronary heart disease – a retrospective study.”

We have now completed our final technical checks and have approved your submission for publication. You will shortly receive a letter of formal acceptance from the editor.

Kind regards,

PLOS Medicine

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 STROBE Checklist. STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

    (DOCX)

    Click here for additional data file. (31.9KB, docx)
    S1 Text. Statistical analysis plan.

    (PDF)

    Click here for additional data file. (691.6KB, pdf)
    S1 Table. Proteins included on the Olink CVD I and Inflammation panels.

    (PDF)

    Click here for additional data file. (124.4KB, pdf)
    S2 Table. PEA biomarker NPX values.

    (A) Data availability in all 4,127 patients in the STABILITY cohort. (B) Data availability in all 1,331 patients in the LURIC cohort. LURIC, Ludwigshafen Risk and Cardiovascular Health; NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (120.4KB, pdf)
    S3 Table. Baseline characteristics in the randomly selected subcohort and in all patients in the STABILITY trial.

    STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (106.3KB, pdf)
    S4 Table

    (A) Reproducibility of biomarkers determined in both the CVD1 and Inflammation OLINK PEA panels. (B) Comparisons between biomarkers associations with CV death when using measurements with conventional (lab log2) and PEA assays (NPX) as evaluated by univariate Cox regression analyses. CV, cardiovascular; NPX, normalized protein expression; PEA, proximity extension assay.

    (PDF)

    Click here for additional data file. (141.7KB, pdf)
    S5 Table. Spearman correlation between the NPX values of relevant PEA biomarkers (rows with any correlation >0.29) and the logarithmic transformation of the ng/L levels of established biomarkers (including only the random cohort in the STABILITY cohort).

    NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (114.9KB, pdf)
    S6 Table. PEA biomarker NPX values in patients with and without CV death in (A) the STABILITY cohort and (B) the LURIC cohort (numbers are percentiles 25th/50th/75th).

    CV, cardiovascular; LURIC, Ludwigshafen Risk and Cardiovascular Health; NPX, normalized protein expression; PEA, proximity extension assay; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (153.8KB, pdf)
    S1 Fig. Random Forest analysis in (A) the STABILITY cohort and (B) the LURIC cohort of prognostic variables for CV death including clinical variables as well as established and PEA biomarkers.

    Each estimate of variable importance is colored by the Boruta analysis result. CV, cardiovascular; LURIC, Ludwigshafen Risk and Cardiovascular Health; PEA, proximity extension assay; RF, Random Survival Forest; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (38.1KB, pdf)
    S2 Fig. Multivariable unadjusted linear associations by Cox regression analyses in (A) the LURIC cohort and (B) the STABILITY cohort of associations between biomarkers and CV death.

    Values are the same as for Fig 1. HR and 95% CI are calculated for increase of 1 SD. Color coding according to the Boruta analysis result: green = confirmed, yellow = tentative, and red = rejected. Variables colored white were not included in the Boruta analysis. CI, confidence interval; CV, cardiovascular; HR, hazard ratio; LURIC, Ludwigshafen Risk and Cardiovascular Health; PEA, proximity extension assay; SD, standard deviation; STABILITY, STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY.

    (PDF)

    Click here for additional data file. (45.8KB, pdf)
    Attachment

    Submitted filename: Answer to Comments from the Editor and Reviewer.docx

    Click here for additional data file. (51.5KB, docx)
    Attachment

    Submitted filename: Answer to Comments from the Editor and Reviewer.docx

    Click here for additional data file. (51.5KB, docx)

    Data Availability Statement

    Anonymized individual participant data and study documents can be requested for further research from www.clinicalstudydatarequest.com.

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