Carotid Plaque Imaging Project (Identification of Vulnerable Atherosclerotic Plaques With Imaging and Biological Markers)
Conditions: Atherosclerosis, Atherosclerotic Plaque
Sponsor: Lund University

 
 
 
 
 
 
 
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Study protocol and statistical analysis plan 
(SAP) 
 
Carotid plaque imaging project (CPIP) 
Vulnerable atherosclerotic plaque: underlying mechanisms and 
novel tools for its detection 
 
 
NCT number: NCT05821894 
 
Version: 1.2 
Date: October 9th, 2024 
 
 
Principal investigator: 
Prof Isabel Goncalves 
Cardiovascular Research – Translational Studies 
 
Clinical Sciences Malmö, Lund University 
and 
Department of Cardiology, Skåne University Hospital Malmö, Sweden 
 
E-mail:  
Isabel.Goncalves@med.lu.se 
Phone:  
+46 40 331000 
Adress:  
Jan Waldenströmsg 35, house 91-12,  
Clinical Research Center, Skåne University Hospital Malmö, 20502 Malmö, 
Sweden 
 
 
 
 

 
 
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Table of Contents 
Background and objective .......................................................................................................... 3 
Study design ............................................................................................................................... 3 
Study population and inclusion .............................................................................................. 3 
Eligibility criteria ................................................................................................................... 3 
Follow-up of study subjects ................................................................................................... 3 
Outcome ................................................................................................................................. 4 
Cardiovascular events ........................................................................................................ 4 
Methods .................................................................................................................................. 4 
Study enrolment ................................................................................................................. 4 
Specimen preparation ......................................................................................................... 4 
Plaque analyses .................................................................................................................. 4 
Cytokine analysis in plasma and homogenate .................................................................... 5 
Histology ............................................................................................................................ 5 
Ethical considerations ................................................................................................................ 5 
Review approval identifiers ............................................................................................... 5 
Statistical analysis protocol (SAP) ............................................................................................. 6 
General ................................................................................................................................... 6 
Survival analysis .................................................................................................................... 6 
RNA-sequencing analysis ...................................................................................................... 6 
References .................................................................................................................................. 7 
 
 
 
 
 
 

 
 
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Background and objective 
The rupture or erosion of an atherosclerotic plaque with thrombosis or embolization often 
underlie heart attacks and strokes1. The early identification of patients with atherosclerotic 
plaques prone to rupture or erosions, vulnerable plaques (VP), and their treatment before the 
occurrence of events is, therefore, one of the greatest cardiovascular challenges today. 
Possible approaches for early detection of VP include imaging techniques allowing 
visualization of plaque structure, circulating biomarkers and better understanding of the 
pathophysiologic mechanisms of the disease.  
 
In the carotid plaque imaging project (CPIP) human atherosclerotic plaques (that were 
removed by endarterectomy) are studied to disclose their underlying structure and 
mechanisms, finding possible novel therapeutic targets or markers for VP. Plaque structure is 
also studied with imaging methods and try to develop new ways to detect VP using 
circulating or imaging markers. 
 
 
Study design 
The study uses an observational, non-randomized design. 
 
Study population and inclusion 
The study target population is all patients with carotid stenosis undergoing endarterectomy at 
the Vascular Dept. of Skåne University Hospital (SUS), Malmö, Sweden. The first patient 
was included Oct. 26th, 2005 and, at present, 1624 subjects have been enrolled and 
recruitment will be active until 3500 subjects have been included. 
 
Eligibility criteria 
Inclusion criteria: patients older than 18 years old, male or female, that are eligible for carotid 
endarterectomy due to atherosclerosis and that can provide informed consent. 
 
Exclusion criteria: patients younger than 18 years old that cannot provide informed consent. 
Patients that are pregnant. 
 
Follow-up of study subjects 
The Swedish Cause of Death and National in-patient Health Registers are used to identify 
postoperative cardiovascular events corresponding to the following codes of the 10th revision 
of the International Classification of Diseases (ICD-10): G45, G46, I20 to I25, I60 to I69, and 
I97. These are nationwide validated registers where all causes of death and more than 99% of 
all somatic (including surgery) and psychiatric hospital discharges are registered2. All deaths 
are verified against the Swedish National Population Register. Follow up of research subjects 
starts 24h after inclusion, with assessment every 2nd year until date of death or emigration 
from Sweden, whichever comes first (up to 20 years or according to ethical permits). 
 
Intraoperative events are defined as including all events occurring within the first 24 h after 
carotid endarterectomy and excluded from the follow-up analysis. 
 

 
 
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Outcome 
Patients suffering more than one episode of the same event (for example, patients with 
multiple strokes) are classified as suffering multiple events. In these cases, only the first 
chronological event is taken into account in subsequent survival analysis. 
 
Cardiovascular events 
Postoperative myocardial infarction, stroke, transient ischaemic attack, amaurosis fugax, 
cardiovascular death (according to Swedish National Registers).  
 
 
Methods 
 
Study enrolment 
Patients are preoperatively assessed by an independent accredited neurologist. Indications for 
surgery are as described by Asciutto et al3, briefly, plaques associated with ipsilateral 
symptoms (stroke, transitory ischemic attack, stroke or amaurosis fugax) and stenosis, 
measured by ultrasound based on flow velocities as previously validated4, >70% or plaques 
not associated with symptoms and stenosis >80% in the 6 months prior to surgery. The 
decision on eligibility for carotid endarterectomy is proposed on a multidisciplinary meeting 
where neurologists, radiologists, vascular surgeons and angiologists participate. 
 
Information about comorbidities and past medical history are obtained through standardized 
preoperative interviews and review of the medical records. Cardiovascular risk factors are 
recorded, namely hypertension (systolic blood pressure >140 mm Hg), diabetes and smoking 
history, as well as use of medications (anti-hypertensive, anti-coagulants, diabetes treatment 
and lipid-lowering medication).  
 
Blood samples are collected one day before endarterectomy. White blood cell (WBC) counts 
and plasma levels of high sensitive C-reactive protein (hsCRP), triglycerides, high-density 
lipoprotein (HDL) and low-density lipoprotein (LDL) and creatinine/GFR are determined by 
routine laboratory methods. The reminder (EDTA-plasma, serum) is aliquoted and frozen -
80°C. 
 
Specimen preparation 
Plaques are snap-frozen in liquid nitrogen immediately after surgical removal. One fragment 
(~2 mm) from the most stenotic region is used for histology. Plaques are homogenized in a 
buffer consisting of 50 mmol/L Tris-HCl (pH 7.5), 0.25 mol/L sucrose, 2 mmol/L tris(2-
carboxyethyl)phosphine HCl, 50 mmol/L NaF, 1 mmol/L Na-orthovanadate, 10 mmol/L Na-
glycerophosphate, 5 mmol/L Na-pyrophosphate, protease inhibitor cocktail (Roche Complete, 
EDTA-free,), 1 mmol/L benzamidine, and 10 mmol/L phenylmethylsulfonyl fluoride 
according to Gonçalves et al5.  
 
Plaque analyses 
In homogenates, elastin is measured using the Fastin Elastin assay was used (Biocolor, 
Carrickfergus, Northern Ireland, UK). Collagen is measured using a Sircol soluble Collagen 
assay (Biocolor, Carrickfergus, Northern Ireland, UK). MMPs (1, 2, 3, 9 and 10) are analysed 
using the Mesocale human MMP ultra sensitive kit (Mesoscale, Gaithersburg, MD, USA). 
TIMPs (1 and 2) are analysed using MILLIPLEX MAP Human TIMP Magnetic Bead Panel 

 
 
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(Milliplex, MA, USA). All kits are used according to manufacturer’s instructions. Absorbance 
measurements are performed using a Tecan Elisa plate reader. 
 
RNA is prepared using standard Trizol method and cleared of Ribosomal RNA using Ribo- 
Zero™ Magnetic Kit (Epicentre). Strand specific paired end RNAseq libraries are prepared 
with ScriptSeq™ v2 RNA-Seq Library v2 Preparation Kit (Epicentre) and paired end libraries 
sequenced using high-output kit version 2, HiSeq2000 platform, Illumina, USA. 
 
Further analyses are performed as covered by the ethical permit. 
 
Cytokine analysis in plasma and homogenate 
A multiplex assay is used for measuring cytokines according to the manufacturer’s 
instructions (incl. fractalkine, interferon-γ (IFN-γ), interleukin-(IL)-6, monocyte 
chemoattractant protein-1 (MCP-1), monocyte inflammatory protein-1β, (MIP-1β), Platelet 
derived growth factor (PDGF)-AB/BB, Regulated on Activation Normal T Cell Expressed 
and Secreted (RANTES), and tumour necrosis factor-α (TNF-α; the Human 
Cytokine/chemokine Immunoassay, Millipore Corporation, MA, USA) with analysis by 
Luminex 100 IS 2.3 (Austin, Texas, USA). 
 
Histology 
Plaque fragments are embedded in optimal cutting temperature (OCT) compound and 
cryosectioned and/or fixed in 4% formaldehyde, dehydrated, embedded in paraffin and 
sections with a microtome. Stained slides are scanned using ScanScope Console Version 8.2 
(LRI imaging AB, Vista CA, USA). Measurements of the area of plaque (% area) for 
histology stainings were quantified blindly using BiopixiQ 2.1.8 (Gothenburg, Sweden) and 
QuPath Version: 0.3.06. 
 
 
Ethical considerations 
The study was approved by ‘the Regional Ethical Review Board in Lund’ (Sweden), which 
has since been re-designated ‘the Swedish Ethical Review Authority’ (under the Swedish 
Ministry of Education) and conformed to the principles of the Declaration of Helsinki. 
Written informed consent is given by all patients. 
 
Individual clinical trial participant-level data (IPD) may not be shared due to General Data 
Protection Regulation (GDPR; (EU)2016/679) legislation as sensitive personal data is 
included. 
 
Review approval identifiers 
From the Swedish Ethical Review Authority: 472/2005; 472/2005; 2010/480; 2014/904; 
2017/89; 2018/63; 27-2020/3.1; 60/2008; 2012/209, 2023-05910-01. 
 
Swedish Data Inspection Board approval: 1572-2005. 
 
Regional Biobank Register [Regionalt Biobanksregister Södra sjukvårdsregionen]: SC392; 
BD48. 
 
 
 

 
 
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Statistical analysis protocol (SAP) 
 
General 
All analyses based on plaque homogenate measurements are normalized against wet weight of 
the plaque. Presentation of values are as mean and standard deviation (SD) if normally 
distributed and as median and interquartile range (IQR) if non-normally distributed. 
 
Two-group comparisons are performed with Mann-Whitney test if variables are non-normally 
distributed, and with students t-test if variables are normally distributed. Pearson’s χ2 test is 
used for categorical data. Spearman’s rho is used for correlation analysis. The Benjamini-
Hochberg (BH) procedure is applied to the P values to control for false discovery rate. If not 
stated otherwise P values less than 0.05 are considered significant. 
 
Survival analysis 
Simple and multiple linear regressions are used to explore the relationship between two or 
more continuous variables, while logistic regression is used in case of dichotomous variables. 
Freedom from postoperative events is calculated by life-tables according to Kaplan–Meier 
survival analysis. Correction for potential confounders is done through Cox regression 
analysis. Values are presented as hazard ratio (HR) with 95% confidence intervals (CI) and 
statistical significance (p). 
 
RNA-sequencing analysis 
Gene expression is quantified using Salmon with gene annotations from Gencode v.27 and 
imported using tximport. Using count data, differential expression analysis is examined using 
DESeq2 and counts normalized using DESeq2´s median of ratios and log2 transformed. For 
orthogonal partial least squares discriminant analysis (OPLS-DA), expression of 
genes and transcription factors are pulled out from a whole dataset and analysed in SIMCA-P 
software package (version 14.1, Umetrics, Umeå, Sweden). Prior to analysis, the gene 
expressions are mean centred and scaled to unit variance. R2Y and Q2Y metrics describe the 
percentage of variation explained by the model and predictive ability of the model, 
respectively. Furthermore, overall contribution of each gene to group discrimination is ranked 
by VIP values (variable influence on projection). Gene with VIP value greater than 1 is 
considered important in group discrimination. For network analysis, counts are normalized 
between samples using a trimmed mean of M values (TMM) by the edgeR, resulting in gene 
expressions as log2-counts per million7. Network analysis of human plaque expression data is 
conducted using the weighted gene co-expression network analysis (WGCNA). Genes with 
low-expression variance (<1.5) across samples are filtered out8. Gene co-expression networks 
are constructed, and clusters of genes (modules) are identified consequently. Pearson 
correlation between the first principal component of gene expressions in each module and 
variable (such as symptomatic) status are calculated. Pathway enrichment in each module is 
performed using Enrichr on 1721 high-quality pathways from the Elsevier Pathway 
Collection9. 
 
More advanced analyses are performed by the team bioinformatician in R.  
 
 
 

 
 
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References 
 
1. 
Sakakura, K., et al. Pathophysiology of atherosclerosis plaque progression. Heart, 
lung & circulation 22, 399-411 (2013). 
2. 
Fredrikson, G.N., et al. Identification of immune responses against aldehyde-modified 
peptide sequences in apoB associated with cardiovascular disease. Arterioscler 
Thromb Vasc Biol 23, 872-878 (2003). 
3. 
Asciutto, G., et al. Long-term progression of contralateral carotid artery disease after 
endarterectomy: is there a need for Duplex surveillance? Int Angiol 31, 361-367 
(2012). 
4. 
Hansen, F., et al. Accuracy of duplex sonography before carotid endarterectomy--a 
comparison with angiography. European journal of vascular and endovascular 
surgery : the official journal of the European Society for Vascular Surgery 12, 331-
336 (1996). 
5. 
Goncalves, I., et al. Changes related to age and cerebrovascular symptoms in the 
extracellular matrix of human carotid plaques. Stroke 34, 616-622 (2003). 
6. 
Bankhead, P., et al. QuPath: Open source software for digital pathology image 
analysis. Scientific reports 7, 16878 (2017). 
7. 
Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for 
differential expression analysis of digital gene expression data. Bioinformatics 26, 
139-140 (2010). 
8. 
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation 
network analysis. BMC Bioinformatics 9, 559 (2008). 
9. 
Chen, E.Y., et al. Enrichr: interactive and collaborative HTML5 gene list enrichment 
analysis tool. BMC Bioinformatics 14, 128 (2013).