Detection of Coronary Artery Calcifications by Whole Blood Transcriptome Analyzed by Artificial InTelligence Algorithms. (CAC-TRAIT Study)
Conditions: Atherosclerosis, Atheroscleroses, Coronary, Atherosclerotic Plaque
Sponsor: Santiago Gabriel Miriuka

Study Title
Detection of Coronary Artery Calcifications by whole blood Transcriptome
analyzed by Artificial InTelligence algorithms. (CAC-TRAIT study)
The information contained herein is strictly confidential and must not be disclosed, copied, presented for
publication or used for any purpose without the prior written authorization of the participating researchers.
Research area:
Subclinical
atherosclerosis
and
prevention
of
cardiovascular events
Clinical phase:
Analytical, observational
Protocol version:
October 12, 2022
Centers:
ENERI Medical Institute, La Sagrada Familia Clinic
CABA, Argentina
Principal Investigator of the study
Santiago Miriuka
Co-principal investigators of the study
Rosana Poggio
Carlos Luzzani
Gastón Rodríguez-Granillo
Bioinformatic analysis
Alejandro La Greca
Coordinator of the on-site study
Bibiana Rubilar
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Index
Index
1
1. Contacts
3
2. Abbreviations
3
3. Abstract
4
4. Background and Rationale
6
4.1. Cardiovascular disease
6
4.2. Transcriptomics
7
4.3. Polygenic risk score and its functional derivations
8
4.4. Evidence of alterations in peripheral blood in patients with atherosclerosis:
9
4.5. Artificial intelligence
11
4.6. Use of artificial intelligence in medicine
12
5. Preliminary observations
13
5.1. In silico developments
13
5.2. Preliminary studies
17
6. Study Objectives
18
Primary Objective
18
7. Methods
19
7.1. Study Design
19
7.2. Study Population
20
7.3. Inclusion Criteria
20
7.4. Exclusion criteria.
20
8. Procedures
21
8.1. Elegibility and informed consent
21
8.2. Collection of clinical data and procedures in the baseline stage
21
Peripheral blood sample
22
8.3. Sample processing and sequencing
23
8.4. Bioinformatic and computational analysis of the transcriptomics sample
24
8.5. Analysis of cardiac CT
24
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8.7. Patient follow-up
26
8.7.1. Events of interest in the study during follow-up
26
8.7.2. Verification and adjudication of events during follow-up
27
9. Guarantee and quality control of the data
27
9.1. Confidentiality of the data
27
9.2. Data quality assurance and control Data
27
9.3. Calculation of the sample size
29
9.4. Analysis plan
29
9.5. Control of good clinical practices
30
9.6. Protocol Deviations
30
9.7. Publication policy
31
9.8. Intellectual Property
31
9.9.1. Direction of the Study
31
9.10. Financing
32
10. References
32
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1.
Contacts
●
Santiago Miriuka (Principal Investigator)
MultiplAI Health
Telephone: 1161615422
e-mail: santiago@multiplaihealth.com
●
Carlos Luzzani (Co-Investigator)
MultiplAI Health
e-mail: charlie@multiplaihealth.com
●
Rosana Poggio (Co-Investigator)
e-mail: rosana.poggio@multiplaihealth.com
●
Gastón Rodríguez-Granillo (Co-Investigator)
e-mail: grodriguezgranillo@gmail.com
2.
Abbreviations
ABC: Area Under the Curve
CAC: Coronary artery calcification
PBMC: Peripheral blood mononuclear cells
DL: Deep learning
CVD: Cardiovascular disease
AI: Artificial intelligence
RNA -Seq: Massive RNA sequencing
CT: Computed tomography
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3.
Abstract
At present, it is possible to obtain a large amount of information from a biological
sample employing technologies known collectively as omics. One of them in particular,
transcriptomics, can measure all the RNA molecules in the cells or tissues that are the
object of analysis. The set of these RNA molecules is known as the transcriptome and
represents the functional expression of the DNA of said cells or tissues. However, one of
the problems that this large mass of data presents is its interpretation: the transcriptome
is made up of tens of thousands of genes that can vary their expression and conformation
and, therefore, their understanding, visualization, and translation of these changes to the
medicine are complex. Until now, the way to interpret this molecular expression system
was with the principles of classical statistics: those RNA molecules that were differentially
expressed in the tissues were the ones that were relevant and were potential positive
markers of the differences between the cells or tissues analyzed. While this is correct, it
has several problems. First, false positives are frequent since the number of comparisons
made is very high. Secondly, these data are thousands when samples are compared, so it
is difficult for the doctor to interpret them as a whole since they cannot assess each
marker individually. Finally, minor but consistent (and possibly the most frequent)
variations of a large group of genes are not observed and not considered at the time of
interpretation.
In recent years, artificial intelligence (AI) has developed methods to help us
interpret omics. The analysis methods collectively known as machine learning or deep
learning (DL), make it possible to consider the salient characteristics (features) and their
constant variation throughout a population. In this way, algorithms can learn to classify
new samples by recognising these characteristics (features) and evaluating them as a
whole. These classification methods allow one transcriptomics to be distinguished from
another one with a high degree of accuracy.
Previous evidence suggests changes in peripheral blood transcriptomics due to
alterations predisposing to coronary heart disease. These changes are due to two main
reasons. First, exposure to coronary risk factors (known and unknown) determines
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changes in gene expression, particularly those related to metabolism, immunity, and
platelet aggregation. Second, the development of coronary heart disease (and other
complex diseases) is facilitated by predisposition represented by the inheritance of a
significant number of variants in the genome. Thus, the trait of a complex disease can be
predicted by measuring the polygenic risk score (PRS). Finally, recent studies confirm the
association of heredity with exposure, thus combining the two dimensions in a single risk
score.
In our preliminary tests, we worked on fine-tuning a measurement in a peripheral
blood sample that allows the integration of all the genetics, including heredity as
expression. We developed a novel way to analyze a biological sample's transcriptome
through convolutional neural networks (deep learning). Our findings suggest that our
analysis can classify the samples with very high precision. We applied this technology in a
preliminary study, in which we found associations between surrogate points for
cardiovascular disease and gene expression in peripheral blood. For example, we
conducted a clinical study measuring coronary artery calcification (CAC), a clear surrogate
marker of atherosclerotic disease and cardiovascular events, closely related to plaque
load, of long-established prognostic value, and amenable to accurate noninvasive
assessment by computed tomography (CT). In this study, we found clear indications of the
clinical usefulness of our methodology.
The present clinical study proposes to identify atherosclerotic transcriptomic
patterns from a whole blood sample. Likewise, as a secondary analysis, we will explore
the ability of the algorithm to determine the presence of aortic disease and
pro-inflammatory cardiometabolic alterations such as hepatic steatosis
and surrogate
markers of epicardial fat inflammation.
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4.
Background and Rationale
4.1.
Cardiovascular disease
Cardiovascular disease (CVD) is the leading cause of death and disability globally. In
2019, CVD was responsible for approximately 18,600,000 deaths (32.8% of the total), and
80% occurred in low- and middle-income countries. (Mensah et al., 2019). It is possible
that after the pandemic, these numbers have increased. The early detection of individuals
at high risk of developing cardiovascular disease without symptoms has been and
continues to be a challenge for science and health authorities worldwide. Implementing
efficient and accessible methodologies for the detection of cardiovascular disease can
lead to a radical change in the management of the disease, as well as a significant
reduction in health spending associated with it and an extension in the population's life
expectancy. The traditional approach to identifying individuals at increased risk of
developing CVD or death from this cause is based mainly on the identification of single
variables (diabetes, cholesterol, smoking), a group of variables (“Framingham Risk Score”)
or biomarkers (Hs-CRP, BNP, etc.) that are associated with a higher risk of developing this
disease. Despite these advances, these approaches have a limited prediction capacity
(Area under the curve 0.60-0.65) (Y. Wang et al., 2019; Rosenberg, 2010). One possible
cause of this limited effectiveness is that numerous processes (some known and many
still unknown) are involved in developing chronic diseases that cannot be fully captured
by analysing one or a few predictor variables. On the other hand, cardiovascular imaging
is helpful for the early detection of CVD development (for example, carotid ultrasound
and detection of CAC by CT). However, it requires expensive equipment and specialised
personnel, so its use in clinical practice is scarce, and its implementation as a population
screening tool is unlikely.
In recent years, the detection of genetic variants predisposing to coronary heart
disease development has made significant progress. Like many other complex diseases,
coronary heart disease has a hereditary component in its development. Classically, the
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hereditary component was easily identifiable in two ways. Some patients develop
symptoms at an early age (for example, a myocardial infarction under 45-50 years). Other
patients have evident phenotypic traits (eg, familial hypercholesterolemia) that
predispose them to cardiovascular events. These latter cases primarily respond to specific
variants in the genome (monogenic diseases), but they are a minor part of the spectrum
of patients with cardiovascular disease. Many patients with a genetic predisposition to
cardiovascular disease inherit an extensive set of genomic variants. Although this
polygenic inheritance is recognized, many genetic variants found concerning a phenotype
do not have a clear causal relationship with the associated pathology. They are only
associated with the phenotype because they are part of a haplotype and thus relate,
either in cis or trans, to other causal variants. Coronary heart disease associated with
specific haplotypes is becoming an effective method for cardiovascular prediction.
4.2.
Transcriptomics
Cell DNA ensures that the biological information of a species is transmitted through
generations. However, the interaction of DNA with the environment is established in
different ways. The most direct and recognised is through the conversion of its code
through the expression of RNA, a process known as transcription. The set of all expressed
transcripts at a given time is known as the transcriptome. Therefore, it follows from these
concepts that transcriptomics is the intersection of that information reserve that is DNA
with modulation by signals from the ecosystem.
Within the structure of DNA are units known as genes. Until a few years ago, a gene
was considered a DNA sequence that is transcribed into RNA and then translated into a
protein. With this definition, it was found that the human genome contains
approximately 22,000 genes, not very different from the number of genes that, for
example, a fly contains (around 14,000) if we consider the phenotypic differences
between one species and another. However, the definition of gene and the number have
changed recently, possibly reaching as high as 100,000 (Salzberg, 2018). There are many
sequences in DNA that are transcribed but not translated, known globally as
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non-coding-RNA. These non-coding genes are the ones that mostly allow phenotypic
differences between species, individuals, tissues and cells. Finally, the genes represented
in the DNA sequence undergo significant changes after being transcribed, such as splicing
or circularisation, which expands the possible number of differences between biological
samples. In short, RNA represents the dynamic and active part of genetic information,
profoundly modulated by the environment.
Technology has evolved to analyse the whole transcriptomics produced in a sample
at a given time. This was partially done until a few years ago through a study known as a
microarray, but it is currently done through massive RNA sequencing (RNA-Seq). In it, all
the RNAs present at a given moment in a cell or tissue can be identified. However, the
amount of information produced is enormous and must be ordered. For this, different
software allows alignment with the human genome to identify each of the genes and
count them to determine their expression level.
The development of massive sequencing and transcriptomics must also be seen in
another context: lower costs. A few years ago, the cost per sample was well over a
thousand dollars. However, the market launch of equipment with superior capabilities has
markedly lowered the costs per sample. Therefore, this is a factor of paramount
importance for developing clinical studies with tens or hundreds of patients. The next five
years will see a continued expansion in reports of using RNA-Seq in patients. The
determination of its clinical usefulness will depend on adequate analysis, for which the
development of techniques based on artificial intelligence will facilitate its interpretation.
4.3.
Polygenic risk score and its functional derivations
As previously mentioned, heredity plays a relevant role in some patients with
coronary disease. In 2007, Samani et al. described the association of three haplotypes
with early coronary disease (Samani et al.). Since then, multiple studies have shown the
existence of specific haplotypes for different chronic diseases, including atherosclerosis
and coronary heart disease (Musunuru and Kathiresan). These haplotypes have dozens of
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genomic variants associated with a phenotype, each slightly associated with it. This
minimal association is possibly the product of the absence of a direct causal relationship,
and the existence of the minimal relationship is due to the absence of independent
segregation of the alleles (linkage disequilibrium).
Based on the knowledge of the minimum association between variants and the
possibility of massive DNA sequencing at low cost, population studies were carried out
that allowed the development of risk scores for the development of complex diseases
based on the sum of the minimum inherited risks. Thus, in recent years scores known as
PRS (Polygenic Risk Score) have been designed, which result, for example, from the
algebraic sum of the odds ratios of each of the variants. The higher the score, the higher
the risk. This way of adding minimal risks proved efficient in discriminating against
individual risks. Thus, a person in the highest risk decile has the same risk as a patient
with familial hypercholesterolemia (Inouye et al., 2018; Klarin & Natarajan, 2021).
The clinical utility of the PRS is in the phase of clinical application, and it may be
routine in the coming years. However, the PRS only measures the hereditary component
of risk. Different researchers have proposed using PRS modified by including a component
related to gene expression to establish a risk that also involves the environmental
component of the risk, which may come from the association of genetic variants with
epigenetic or transcriptome variations (Cano-Gamez & Trynka, 2020). In this way, the
concept of Transcriptome-wide Association Studies (TWAS) was born, which measures risk
based on the combination of inherited genetic variants and the expression of genes close
to the same variants (Wainberg et al., 2017; Wainberg et al., 2019; Bhattacharya et al.,
2020).
4.4.
Evidence of alterations in peripheral blood in patients with
atherosclerosis:
Atherosclerosis is known as a vascular disease caused by the deposition of
cholesterol in inflammatory cells in the middle layer of the arteries. The consequences of
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these deposits are well known: exposure to lipid-containing inflammatory cells after
plaque rupture produces most of the acute intravascular thrombotic events, including
myocardial infarction, some types of stroke, and peripheral arterial disease. (Wolf & Ley,
2019; Geovanini & Libby, 2018). Non-occlusive events eventually lead to low blood flow
ischemia.
From the preceding, the necessary interaction between inflammatory cells,
metabolism and the vascular wall in the pathophysiology of atherosclerotic disease is
deduced. This complex interaction is often measured in clinical practice by surrogate
biomarkers of vascular status and the odds of unfavourable outcomes of atherosclerotic
disease. These markers can be representative of metabolic changes (cholesterol, blood
glucose, free fatty acids, etc.), inflammatory (C-reactive protein, interleukins, leptin, etc.)
or coagulation (fibrinogen, PAI-1, etc.). Furthermore, many still unknown markers are
present in peripheral blood, originating from new cardiovascular risk factors described in
recent years, such as the intestinal microbiome (Barrington & Lusis, 2017; Komaroff,
2018) or air pollution (Jilani et al., 2020). In addition, these markers can be of different
molecular types: protein, RNAs of various shapes, or even small molecules.
Among the possible biomarkers mentioned in the previous paragraph are RNA
molecules. Blood cells are subjected to all kinds of signals from the tissues they perfuse.
These signals impact circulating cells, or even progenitors in the bone marrow, thereby
modifying gene expression. Thus, the different states of a disease can present a set of
differentially expressed genes. Various studies have confirmed the existence of genes
expressed in patients with atherosclerotic disease. Alterations in gene expression in blood
cells are evidenced in different states of predisposition to atherosclerosis (Ding et al.,
2015), prognostic evolution (Holvoet al., 2016), or response to treatment with statins
(Kini et al., 2017). Furthermore, the expression of a set of genes has been associated with
clinical variables to predict the severity of atherosclerotic disease (Y. Wang et al., 2019).
Therefore, these studies show the existence of alterations in gene expression in patients
with cardiovascular disease. It is currently claimed that these changes can be used in the
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future as markers of the disease and its evolution (Marín de Evsikova et al., 2019; Chen &
Stewart, 2016; Friede et al., 2015; Rhees & Wingrove, 2015).
4.5.
Artificial intelligence
Artificial intelligence (AI) has spread widely in recent years thanks to the
development of new algorithms that allow enormous growth in classification capabilities.
The possible applications of this in many areas of human activity have led to AI being
considered a new milestone in the evolution of humanity. In particular, for medicine, it
can mean a stage of enormous changes in how it is practised (Topol, 2019).
AI can be defined as a set of algorithms and equations that can mimic certain
cognitive behaviours of human beings. While the term suggests reasoning capabilities, AI
is currently only limited to a human (or even super-human) ability to interpret and classify
big data. For this purpose, mathematical algorithms are used, collectively known as
machine learning. One, in particular, has stood out for some years, known as the neural
network. This algorithm involves many parallel and serial linear equations, each modified
by one or more nonlinear equations, forming a network of equations, ending in a
nonlinear equation that determines a probability. The latter is used for probabilistic
classification. Although this algorithm has been known for decades, specific changes in
recent years have allowed its enormous expansion, leading them to be known as deep
learning (LeCun et al., 2015). These changes are mainly of two types: first, the ability for
the network to correct itself was developed in the 1980s by employing an algorithm
called backpropagation (Rumelhart et al., 1986); Drucker & Le Cun, 1992). This algorithm
allows the neural network to adjust in successive cycles to modifications that
progressively improve its predictive capacity (training) in successive cycles. The second
reason for the expansion of deep learning corresponds to the development of hardware
that makes it possible to carry out a large number of simple calculations (linear
regressions) in a short time, mainly through the adaptation of “GPUs” (Graphical
Processing Units) for use in AI. From these two advances, the science of deep learning has
expanded remarkably, with increasing participation in processes of daily life.
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4.6.
Use of artificial intelligence in medicine
Many examples have emerged in recent years about the potential of AI in medicine.
One of the most important studies was published in 2017, in which an algorithm was fed
photographs of skin lesions, including different forms of skin cancer. The algorithm was
able to differentiate those malignant lesions with higher accuracy than a group of
dermatologists ( Esteva et al., 2017). Another study of equal importance demonstrated a
high capacity of DL to distinguish the presence of tumour cells in histopathological images
of axillary lymph nodes of patients with breast cancer (Ehteshami Bejnordi et al., 2017).
These two examples are a sample of the impact of advances in development in this area:
a trained algorithm can automatically distinguish alterations present in an image,
regardless of the doctor. The enormous potential for diagnostic help of these algorithms
is an impetus for research on them.
Although the use of AI in medicine is in its infancy and the research stage, possible
applications of AI in medicine can be grouped into the following spaces:
Radiology: AI algorithms can be fed with imaging studies, and they can learn
characteristics associated with abnormal defects. Based on these capabilities, there
is a growing number of potential AI applications in this field, and some of them
have already been approved for clinical use. Soon, many of the most common
images in radiology will be previously scanned by an algorithm to preliminarily
identify if there is an alteration, alleviating the work of the doctor who will only
focus on those that contain a pathological alteration. Various studies have shown
the usefulness of DL in multiple areas of the cardiovascular field, from a risk
stratification and triage tool to the automatic evaluation of ventricular volumes in
magnetic resonance imaging and the coronary calcium score by CT.
Body images: Successful examples in this area are also expected due to DL's good
results in image processing. For example, the interpretation of skin lesions is
feasible with DL. Another example is the analysis of fundus images, possibly
predicting cardiovascular risk. But in addition, neural networks can recognise
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whether an eye fundus image is of a man or a woman with an accuracy of
approximately 98%, which speaks to their ability to extract imperceptible features
from an image. It is not known precisely what characteristics define the differences
in the images, but they may be related to the thickness of the arteries or another
variation imperceptible to the human eye (Poplin et al., 2018; Nent et al., 2019).
Continuous signals: This is the case of the use of AI for interpreting data coming
from continuous acquisition signals. Examples of them are, for example, the
interpretation of an electrocardiogram or electroencephalogram by a DL algorithm
(Hannun et al., 2019; Craik et al., 2019).
Language processing: The interpretation of language is being revolutionised by DL.
The automated analysis of medical records or the patient vocabulary are examples
of how it can be applied. There are multiple reports where they identify emotions,
including depressive states, from the language used by a subject (Liang et al., 2016).
5.
Preliminary observations
5.1.
In silico developments
In our laboratory, we have developed a methodology for transcriptome analysis
based on extensive bioinformatic processing of a biological sample and its subsequent
interpretation employing a deep learning algorithm. In this way, we can incorporate all
the genes read in a sequence and perform a classification using a supervised method.
This algorithm has proven robust and highly accurate in our preliminary tests. The
development comprises a pipeline that takes the RNA-seq sample in its crudest state and
analyses it entirely, including coding and non-coding RNAs, circular RNAs and the blood
microbiome. The algorithm is trained with all the samples of a cohort, previously
classified, and later, the algorithm is validated with 10 or 20% of the samples.
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The analysis of the efficiency of the algorithm was performed with different
RNA-seq databases available in digital repositories. First, we used the GTEx database,
which has taken postmortem from dozens of people and performed sequencing of them.
In total, 30 different tissues containing 11,866 sequences were analyzed. The analysis of
our algorithm achieved a correct prediction in 99.1% of the cases (figure 1). The few
errors correspond to potentially similar tissues (for example, breast with fatty tissue).
Subsequently, we analyzed the Genomic Data Commons (GDC) database, which has
RNA sequencing of biopsies from different tumors. We analyzed 10,000 biopsies
distributed in 34 tumors. Again, the predictive capacity of the algorithm was very high,
reaching 97.5% (figure 2). These two analyses show that the algorithm has a great
classification capacity between multiple samples.
We then performed a sensitivity analysis of the algorithm. To do this, we started
with 100 random samples taken from brain tumors from the GDC database, 50 of them
corresponding to GBM and 50 of them to GL. With them, new training was carried out in
which we achieved a precision of 100%, which translates into an area under the curve
(ROC) of 1. Successively, we removed samples at random, performing effective training
with only 30 samples (15 of each group). Therefore, the algorithm has a very efficient
behaviour with a low number of clinical cases. This reflects the fundamental differences
in gene expression between the two groups of tumors.
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Figure 1: Confusion matrix for normal tissue samples. The graph shows the intersection of the algorithm's
prediction and the actual sample. Thus, the descending central diagonal from left to right shows the
coincidence between the real and the predicted. Outside this diagonal, the numbers represent the
algorithm's classification errors. As can be seen, they are infrequent.
The above results were obtained with RNA-Seq samples from solid tissues
(biopsies). As mentioned above, the cells in the blood can present modifications in their
transcriptomics in the presence of a solid tumor or systemic disease. Therefore, below we
perform analyzes of different datasets in different contexts.
First, we analyzed the samples obtained by Best et al., performed on patients with
different solid tumors, including breast, lung, colon, and glioblastoma multiforme cancer
(Best et al., 2015). The transcriptomics of these patients was performed on platelets. In all
the cases analyzed, the AUC for detecting the presence of the tumor was 1.
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Figure 2: Confusion matrix for tumor tissue samples. The graph has the same meaning as in the previous
figure. Again, the numbers of mistakes outside the diagonal line are very few
We then analyzed the samples from Cassetta and collaborators on monocytes from
patients with breast or endometrial cancer, compared to healthy subjects (Cassetta et al.,
2019). Once again, the prediction capacity of the algorithm was 100%. Finally, we
analyzed the dataset generated by Scharer et al. on patients with lupus compared to
healthy subjects (Scharer et al., 2019). Again, classification was excellent (AUC of 1).
In summary, the pipeline generated and the analyses performed to demonstrate its
ability to classify transcriptomics samples. The algorithm proves to be robust and
extremely sensitive, possibly for two reasons. First, the number of variants it considers is
enormous: approximately 200,000 different forms of genes can be evaluated. Secondly,
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the analysis capacity of neural networks makes it possible to consider this great mass of
information with an unprecedented capacity to interpret this data. These findings then
lead us to propose the hypothesis that our pipeline may be useful in detecting tumour
pathologies in peripheral blood transcriptomics.
5.2.
Preliminary studies
We previously developed a clinical protocol with the aim of perfecting the
methodology that will be used in this study. The initial goal was to develop blood
collection, RNA isolation, library generation for sequencing, and RNA sequencing. Briefly,
the preliminary results of these steps, performed on 540 samples from 3 different studies,
are described below.
First, a robust methodology was established to obtain an RNA sample from
peripheral whole blood (without cell separation). Samples were collected in PaxGene RNA
tubes through antecubital vein puncture. No adverse effects were observed in this
procedure. Samples were stored at -20°C until RNA extraction. This step was performed
through the use of a specific kit (Thermo-Fischer). As a result, it was possible to
consistently obtain RNA samples in sufficient quantities for sequencing in all cases. Then,
the library was built, a process in which the RNA is transformed into cDNA
(complementary DNA), from which the RNA corresponding to ribosomes and globin are
extracted, fragmented, and finally the adapters are added at the ends. Again, the library
construction was developed in a robust way for all the analyzed samples.
Subsequently, RNA sequencing was performed at a depth of 100 million reads.
Between 20 and 25 thousand genes expressed in peripheral blood were detected in all
the samples (n=540). In a series of patients, computed tomography was performed to
determine the degree of calcification of the coronary arteries (Coronary Calcium Score,
CCS). An analysis of gene expression in peripheral blood was performed and genes related
to the degree of coronary calcification were found.
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Figure 3: Preliminary results. We analyzed 38 samples from patients who had coronary calcium
measured. The left panel shows the result of a PCA analysis where a moderate but significant reduction in
dimensionality is found based on the extent of coronary calcium. The heatmap shows the differentially
expressed genes between the two groups.
6.
Study Objectives
Primary Objective
The primary objective of the study is to evaluate the diagnostic accuracy of
peripheral whole blood transcriptomes analyzed by an artificial intelligence algorithm to
detect the presence and extent of coronary calcification in individuals without a history of
known cardiovascular disease.
Specific Primary Objectives
1. To classify transcriptomes according to the presence and extent of coronary
calcification by noncontrast cardiac CT.
2. Train artificial intelligence algorithms with information from the transcriptome to
recognize the presence and extent of calcification in coronary arteries.
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3. To determine the degree of diagnostic accuracy of the method to detect the presence
and extent of coronary calcification non-invasively.
Secondary objectives
1. To determine the degree of diagnostic accuracy of the method to detect the
presence and extent of:
-
Aortic
-
calcification Aortic valve calcification
-
Hepatic steatosis
-
Volume of epicardial fat
-
Volume of high-density epicardial fat (upper quartile)
-
Enlargement of cardiac cavities To
2. To explore differences in transcriptomics of peripheral blood between patients
who did and did not develop a CV event during follow-up.
3. To explore different electrocardiographic patterns according to the results of
cardiac CT and peripheral blood transcriptomics.
4. Generate a polygenic risk score (PRS) from the transcriptome and/or exome
sequencing and associate it with the transcriptome and coronary calcification.
7.
Methods
7.1.
Study Design
This trial will be a prospective observational study. A convenience sample will be
carried out to include 800 patients who attend the ENERI Medical Institute, La Sagrada
Familia Clinic, to undergo a cardiac CT without contrast (coronary calcium score), either
for a medical indication or volunteers to an assessment of cardiovascular risk. The study
will have a baseline stage in which a clinical evaluation will be performed, blood samples
will be drawn for transcriptome analysis and laboratory analysis. Then, a DNA sample
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obtained by swabbing the buccal mucosa will be taken. Subsequently, a non-contrast
gated cardiac CT will be performed to assess the presence and extent of coronary
calcification and other results of interest. At the end of patient enrollment, biological
samples will be sequenced for in silico evaluation of the results. Finally, a 5-year clinical
(telephone) follow-up will be carried out to collect data on the incidence of fatal and
non-fatal cardiovascular events.
7.2.
Study Population
7.3.
Inclusion Criteria
●
Men and women between 30 and 75 years attending the institution for a
non-contrast cardiac CT scan (coronary calcium score) either by medical indication
or voluntary for cardiovascular risk assessment.
●
Signature of informed consent form
7.4.
Exclusion criteria.
●
Previously known chronic renal or hepatic insufficiency.
●
Active chronic lung disease, defined as: exacerbated asthma, exacerbated COPD,
or pulmonary fibrosis.
●
Active and/or previous cardiovascular disease, defined as: previous acute
myocardial infarction, stable or unstable angina, cerebrovascular accident, history
of vascular interventions (coronary or other territory), heart failure, severe
cardiomyopathies or valvulopathies.
●
Uncontrolled hyper or hypothyroidism.
●
Suprarrenal insufficiency.
●
Previous surgeries in the last 3 months.
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●
Severe trauma in the last 6 months, defined as one that involved bone fractures
and/or surgical interventions.
●
Known active cancer disease or under treatment (acute or preventive), or history
of cancer disease without criteria for cure.
●
Known autoimmune disease active or in treatment.
●
Ongoing pregnancy, postpartum period of less than 12 months or breastfeeding.
●
Other serious illness with an estimated life expectancy of less than 12 months
(according to the investigators).
●
Temperature greater than 37.5°C recorded by a thermometer or any acute
infection caused by viruses or bacteria confirmed by a health professional in the
previous 30 days.
●
Pathologies under immunosuppressive treatment
8.
Procedures
8.1.
Elegibility and informed consent
Patients will be recruited at the ENERI Medical Institute and at the outpatient
clinics of the “La Sagrada Familia” clinic. Patients who meet the eligibility criteria will be
approached by the main investigator or one of the study collaborators and the
characteristics of the procedure to be performed, the risks of the associated procedures
and the scope of the research to be carried out will be explained to them. After providing
the necessary information and answering the questions of the participants, they will be
formally invited to sign the informed consent.
8.2.
Collection of clinical data and procedures in the baseline stage
At the time of inclusion in the study, the following epidemiological, clinical and
complementary study data will be collected using questionnaires specially designed for
this study:
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Clinical data and medical history
●
Contact data: Cell phone , landline, phone of an alternative contact, and E-mail
address.
●
Epidemiological data: date of birth, gender, ethnicity.
●
Clinical history: Presence of any of the following pathologies: dyslipidemia,
diabetes, high blood pressure, smoking, previous relevant diseases.
●
Medications at the time of admission.
●
Level of physical activity and diet.
Peripheral blood sample
2.5 milliliters of peripheral blood will be obtained by puncture of the antecubital
vein in a PAXgene or Tempus tube (commercial tubes that contain a solution for the
preservation of RNA) to obtain RNA. The patient must be fasting for 8 hours.
Subsequently, the blood will be kept at room temperature for up to 48 hours, or at 4°C (in
a refrigerator) for up to two weeks, until it is transferred to the laboratory, where it will be
frozen at -20°C until it is processed (RNA extraction). .
Subsequently, the second additional peripheral blood sample (10 milliliters in EDTA)
will be obtained to measure the complete blood count, glycosylated hemoglobin,
hepatogram, lipid profile, among others. The primary tube for clinical laboratory analysis
will be sent for analysis to a local laboratory (Stambulián).
Oral mucose swab (mouth swab)
To take the sample, the professional will be asked to rub a buccal swab against the
inside of the patient's cheeks and gums, and to place it in a dry sterile 1.5ml
eppendorf-type tube or with lysis buffer containing it will be labeled with the
participant's ID and then place it in the refrigerator at 4°c waiting to be sent to the
laboratory, where the DNA extraction will be performed. Subsequently, it will be stored in
a freezer at -20°C.
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Non-contrast cardiac computed tomography
ECG-triggered non-contrast cardiac CT ("coronary calcium score") will be performed
using the same equipment in all patients, a spectral multidetector tomograph (IQon
Spectral CT, Philips Healthcare, Best, The Netherlands). Non-contrast cardiac CT,
associated with effective radiation doses of between 1.0 and 1.5 mSv (compared to
approximately 5-7 mSv in a conventional chest CT), will be acquired using the following
parameters: collimation 32 x 0. 625 mm; voltage 120 kV; mAs adjusted according to
weight (<60 kg= 30 mAs; 60 to 80 kg= 50 mAs; >80 kg= 80 mAs); rotation time 270
milliseconds; thickness 2.5 mm, with an acquisition time of approximately 0.18 seconds.
Iterative reconstruction algorithms (iDose3) will be used for dose reduction.
12-lead electrocardiogram
All participants will undergo a 12-lead electrocardiogram, which involves the use of 10
electrodes for a correct reading of the electrical potential of the heart during a cardiac
cycle. To carry it out,
an electrode will be placed on each limb and 6 on the chest,
generating 6 leads of the limbs that observe the heart from a frontal plane and 6
precordial leads that observe the heart from a horizontal plane. The risks of an EKG are
minimal. Mild skin discomfort or irritation may be felt after the electrodes are removed.
There is no risk of electric shock.
8.3.
Sample processing and sequencing
Blood samples will be processed after extraction using commercial kits for RNA
extraction. Subsequently, the library, a procedure by which the transformation of the RNA
sample into short cDNA chains for sequencing is known. For this procedure, the
commercial
TruSeq-RiboZero-GlobinFree
kit
(Illumina)
will
be
used. Subsequent
sequencing (without ribosomal RNA and globin) will be performed by an external
sequencing service provider. A sequencing with a read depth of at least 100 million reads
will be requested.
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8.4.
Bioinformatic and computational analysis of the transcriptomics
sample
The bioinformatic analysis of the sequenced samples will be performed from the
.fastq files containing the raw data. These files will be fed to an pipeline analysis It
comprises a successive series of bioinformatic procedures, including sample quality
control, alignment with the human genome and count of reads per identified gene. The
transcripts to be identified correspond to messenger RNAs, non-coding RNAs, circular
RNAs, and RNAs corresponding to the blood microbiome. The expression quantification of
the transcripts is then transformed using a series of proprietary routines, to then be
transformed into an image (US patent 62/944,063 (pending)). This image represents both
the expression of the transcripts as a function of their expression level and the genomic
position as well as the RNA subtype. The set of images, which represent the set of gene
expression in peripheral blood, is subsequently entered into a neural network to generate
training that classifies the samples according to it. All these bioinformatic and artificial
intelligence procedures are carried out using free use programs (“open access software”)
under a routine created by the study researchers, mainly using python for coding, and
pyTorch for training the neural network.
8.5.
Analysis of cardiac CT
The presence of CAC and its extension will be evaluated using both ordinal variables
(number of segments with CAC and number of affected vessels, and qualitatively:
absence, mild, moderate, and severe; and the number of coronary segments with
calcification) as continuous variables (Agatston units). The presence and extension of
calcifications at the level of the aortic valve and the thoracic aorta (Agatston units) will
also be evaluated independently. The extent of aortic calcification will be evaluated in
sections that include from the bifurcation of the pulmonary artery as the upper limit to
the diaphragm as the lower limit.
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Quantitative analysis of SCC will be performed using dedicated software
(HeartBeat-CS, Philips Healthcare, Best, The Netherlands). Finally, the volume and density
of epicardial fat will be calculated by evaluating the volume of tissue with a density
between -190 and -30 HU located in the area between the epicardium and the
pericardium, the bifurcation of the pulmonary artery as the upper limit, and the
diaphragm as lower limit.
Finally, the density of the hepatic parenchyma will be calculated through a region of
interest >100 mm2 at the level of the right and splenic lobes. From these measurements,
the presence of hepatic steatosis will be defined from a quotient of less than 1.
At least 20% of the participants (randomly selected) will have a double reading of the
main outcome of the study (presence or extent of CACs). This reading will be carried out
by an expert external to the group of researchers of the study and blind to the first
reading. In case of disagreement, consensus will be sought between the two experts. The
aim of this process is to enhance the internal validity of the study.
The epicardial fat volume analysis will be performed by experts from the University of
Palermo (Italy).
8.6.
Storage of biological samples
The remainder of the RNA samples and all of the DNA samples will be stored in a freezer
at -20° C to be used in future research. The biological samples will be coded (see
Confidentiality of the data) to maintain the anonymity of the participants and will be
stored at FLENI Escobar Headquarters. The storage place has the necessary equipment to
ensure their proper preservation. The temperature and other storage conditions will be
permanently monitored as part of the quality assurance of the biological samples. It also
has an emergency plan with trained personnel with defined responsibilities to attend to
alarms and take actions to restore the system in the event of a failure. The freezer that
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preserves the biological samples and the monitoring systems are connected to a
permanent electrical source, with an emergency generator with enough autonomy to
ensure its uninterrupted operation.
Since the stored samples will not be used in this study, specific consent will be requested
for their storage and subsequent analysis..
8.7.
Patient follow-up
Patients included in the study will be contacted annually by phone call to collect the
following data:
●
Vital status.
●
Cardiovascular events: New episodes of hospitalization due to acute ischemic
syndrome, cerebrovascular event, heart failure or revascularization.
●
New diagnoses of relevant chronic diseases.
If they cannot be contacted, the "alternative means of contact" provided by the patient
will be used, in order to know their vital status or obtain a new contact number.
8.7.1.
Events of interest in the study during follow-up
●
Death from any cause
●
Acute ischemic syndrome: Diagnosis of acute myocardial infarction or unstable
angina.
●
New diagnosis of heart failure with preserved or impaired ventricular function.
●
Vascular revascularization: performance of myocardial revascularization surgery,
coronary angioplasty or peripheral vascular angioplasty.
●
Cerebrovascular event: Diagnosis of cerebrovascular accident (“stroke”) or
transient ischemic attack.
●
Cancer: new diagnosis of any type of cancer
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8.7.2.
Verification and adjudication of events during follow-up
When an event of interest is reported during study follow-up, the "phase of
verification” that consists of obtaining data on: type of event, date, place where it was
treated
and
its
corresponding
source
document
(death
certificate,
epicrisis,
complementary study, evolution of clinical history, etc.) where the diagnosis of the event
appears with a doctor's signature. The study event adjudication committee will be made
up of three medical investigators (RP, SGM, GG). Each of the members will analyze the
documentation sent for the case separately and will adjudicate the event. If there is no
agreement, it will be awarded by majority.
9.Guarantee and quality control of the data
9.1.
Confidentiality of the data
The data of the patients will be collected anonymously from the moment of signing
the informed consent. To do this, a representative code of the patient will be created,
which will include the initials of the study, followed by a hyphen and the inclusion
number. The clinical documentation of the patient and the tube containing the blood of
the patients or control subjects will contain only this information. The informed consent
will be kept in a different location from the clinical information and under lock and key.
The transcriptomics data will remain anonymized and will receive the name
designated at the time of inclusion in the study. The data will be deposited and protected
on outsourced servers in compliance with the European regulations of the General Data
Protection Regulation (GDPR). AWS is currently used to safeguard the information.
9.2.
Data quality assurance and control
a. Data entry and storage
electronic platform RedCap. This electronic data capture platform is covered by security
systems, in compliance with current regulations on data protection. The access control will
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be configured in such a way that there are two owners with the project administrator role,
who will be in charge of authorizing the access of the study personnel, as necessary, and
with access levels according to the roles and responsibilities. The electronic forms that will
be used for data upload will be specifically designed for this study. The data will be entered
by the researcher at the recruitment center, via the web. The data will be stored on a
central outsourced server (AWS).
b. Data quality control monitoring
Data storage and processing will be centralized in xx. The personnel in charge of entering
the data will be trained in the use of RedCap for this purpose.
The following quality controls will be carried out:
- Validation rules generated according to the nature of the variables,
programmed through the data validation modules available in RedCap.
- Automatic query generation (queries) according to pre-established
validation rules, to be answered by researchers on the sites.
- Manual design of explanatory queries, to be answered by the researchers
on the sites.
- Registration system, history and query control (queries).
- Periodic reports on the status of data entry, query generation and
response, on a weekly basis.
c.
Elaboration of the manual of procedures
A manual of procedures will be developed where the procedures for the
implementation of the baseline data collection questionnaire, blood extraction and
follow-up will be described.
d. Training and certification
In order to participate in the study, all staff must attend a training session prior to
the start of fieldwork. Training sessions will include all aspects of the protocol and
procedures manual regarding enrollment, follow-up, protocol, and measurement
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procedures. Those who have acquired the expected skills will be certified to carry out the
study procedures. Staff will be retrained if necessary.
e. Site Monitoring Visits and Audits
Monitoring visits and audits will be conducted at the health centre in order to
follow up on the progress of the fieldwork, identify problems and carry out retraining if
necessary.
9.3.
Calculation of the sample size
The calculation of the number of patients to demonstrate a positive effect for the
use of peripheral blood transcriptomics for the diagnosis of the presence of severe
coronary calcification is carried out based on previous experiences by the group of
researchers. There is still no standardized methodology in the medical literature to
perform the sample calculation of this type of analysis, since it is very recent and is in the
exploration phase.studies silico carried out by this group of researchers and the
preliminary clinical studies carried out previously, it was observed that at least a sample
of 800 patients is necessary for a robust training of the artificial intelligence algorithm
that detects transcriptional differences between patients with or without a solid tumor.
(for example, breast tumor). Therefore, 800 patients will be included based on the
minimum number necessary to perform an adequate training of an artificial intelligence
algorithm.
9.4.
Analysis plan
For the description of the general characteristics of the study population, descriptive
statistics such as means, medians and proportions will be used. To calculate the
diagnostic performance of the method, the following will be calculated:
●
True Positives (TP) or the probability that the test is positive since the disease is
present
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●
True Negatives (TN) or the probability that the test is negative since the disease is
not present.
●
False Positives (FP) or Type I Error
●
False Negatives (FN) or Type II Error
●
Sensitivity: VP/VP+FN
●
Specificity: VN/ VN+FP
●
Precision or accuracy: VP/VP+FP or sensitivity + (1-specificity)
The diagnostic precision can vary between 0.5 and 1, where 1 represents a perfect
diagnostic value and 0.5 is a test without diagnostic discriminatory capacity. An area
under the curve (AUC) will also be performed to determine the diagnostic capacity of the
trained algorithm.
9.5.
Control of good clinical practices
The proposed trial will be conducted following the recommendations of the
International Council for Harmonization (ICH) for clinical studies (E6R2) (ICH Official Web
Site : ICH, nd). In particular, the control of good clinical practices will be supervised in
those elements that concern this type of evaluation study of new technologies, including
patient confidentiality and informed consent. In addition, the standards suggested by the
same regulations will be met (E18, (ICH Official Web Site : ICH, nd)).
All study investigators have been certified in GCP guidelines.
9.6.
Protocol Deviations
Investigators in this study will strictly monitor compliance with good clinical
practice. Any deviation from the protocol will be reported to the ethics committee and
the case will be individually analyzed in order to determine the procedures to follow.
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9.7.
Publication policy
Once the study is finished, it will be published in different ways. First, it will be
released to the scientific and medical community in the form of a conference abstract, in
order to rapidly communicate the results of the conference to those interested. It will
subsequently be published as a paper in an international peer-reviewed journal.
9.8.
Intellectual Property
The methodology to be applied in this study has been submitted to the Patent Office for
legal protection (preliminary patent US 62/944,063). The intellectual property of the
study remains in the hands of MultiplAI Health.
9.9.
Study Organization
9.9.1.
Direction of the Study
The principal investigator of the project is Dr. Santiago Miriuka from the FLENI
institute. Dr. Rosana Poggio and Dr. Carlos Luzzani will be the co-principal investigators.
Dr. Gastón Granillo will be the principal investigator of the center (La Sagrada Familia)
who will provide logistical support and will be responsible for the entire implementation
of the study on site. A CONICET fellow will be the coordinator of the logistics and
processing of the biological samples of RNA and DNA. Post-doctoral fellow Alejandro La
Greca will be in charge of bioinformatic analysis. María Olivera will be the general
coordinator and Monitor of the study and Bibiana Rubilar the coordinator of the study on
site.
Field Coordinating Center
The researchers will be in charge of supervising and controlling that all the
procedures
are
carried
out
following the recommended standards during the
implementation of the study, as well as the follow-up of the patients.
Communications
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Continuous communication will be maintained between the group of study
researchers by telephone, email and whatsapp.
9.10.
Financing
The financing to cover the processes and materials necessary to carry out this study
will be provided entirely by MultiplAI Health LTD.
10.
References
Barrington, WT, & Lusis, AJ (2017). Association between the gut microbiome and
atherosclerosis. Nature Reviews Cardiology, 14(12), 699–700.
https://doi.org/10.1038/nrcardio.2017.169
Best, MG, Sol, N., Kooi, I., Tannous, J., Westerman, BA, Rustenburg, F., Schellen, P.,
Verschueren, H., Post, E., Koster, J., Ylstra, B., Ameziane, N., Dorsman, J., Smit, EF,
Verheul, HM, Noske, DP, Reijneveld, JC, Nilsson, RJA, Tannous, BA, … Wurdinger, T.
(2015). RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer,
Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell, 28(5),
666–676. https://doi.org/10.1016/j.ccell.2015.09.018
Bhattacharya, A., Li, Y., & Love, MI (2020). MOSTWAS: Multi-Omic strategies for
transcriptome-wide association studies. Cold Spring Harbor Laboratory.
http://dx.doi.org/10.1101/2020.04.17.047225
Cano-Gamez, E., & Trynka, G. (2020). From GWAS to function: Using functional genomics
to identify the mechanisms underlying complex diseases. Frontiers in Genetics, 11.
https://doi.org/10.3389/fgene.2020.00424
Cassetta, L., Fragkogianni, S., Sims, AH, Swierczak, A., Forrester, LM, Zhang, H., Soong,
DYH, Cotechini, T., Anur, P., Lin, EY, Fidanza, A., Lopez-Yrigoyen, M., Millar, MR,
Urman, A., Ai, Z., Spellman, PT, Hwang, ES, Dixon, JM, Wiechmann, L., … Pollard,
JW (2019). Human Tumor-Associated Macrophage and Monocyte Transcriptional
Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic
Targets. Cancer Cell, 35(4), 588-602.e10.
https://doi.org/10.1016/j.ccell.2019.02.009
Chen, H.-H., & Stewart, AFR (2016). Transcriptomic signature of atherosclerosis in the
peripheral blood: Fact or fiction? Current Atherosclerosis Reports, 18(12).
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
32

.
https://doi.org/10.1007/s11883-016-0634-x
Craik, A., He, Y., & Contreras-Vidal, JL (2019). Deep learning for electroencephalogram
(EEG) classification tasks: a review. Journal of Neural Engineering, 16(3), 031001.
https://doi.org/10.1088/1741-2552/ab0ab5
Ding, J., Reynolds, LM, Zeller, T., Müller, C., Lohman, K., Nicklas, BJ, Kritchevsky, SB, Huang,
Z., de la Fuente, A., Soranzo, N., Settlage, RE, Chuang, C.-C., Howard, T., Xu, N.,
Goodarzi, MO, Chen, Y.-DI, Rotter, JI, Siscovick, DS, Parks, JS, … Liu, Y. (2015).
Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature
of Obesity-Related Type 2 Diabetes and Cardiovascular Disease. Diabetes, 64(10),
3464–3474. https://doi.org/10.2337/db14-1314
Drucker, H., & Le Cun, Y. (1992). Improving generalization performance using double
backpropagation. IEEE Transactions on Neural Networks, 3(6), 991–997.
https://doi.org/10.1109/72.165600
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer,
N., Litjens, G., van der Laak, JAWM, Hermsen, M., Manson, QF, Balkenhol, M.,
Geessink, O., Stathonikos, N., van Dijk, MC, Bult, P., Beca, F., Beck, AH, Wang, D.,
Khosla, A., Gargeya, R., … Venâncio, R. (2017). Diagnostic Assessment of Deep
Learning Algorithms for Detection of Lymph Node Metastases in Women With
Breast Cancer. JAMA, 318(22), 2199. https://doi.org/10.1001/jama.2017.14585
Esteva, A., Kuprel, B., Novoa, RA, Ko, J., Swetter, SM, Blau, HM, & Thrun, S. (2017).
Dermatologist-level classification of skin cancer with deep neural networks.
Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Friede, KA, Ginsburg, GS, & Voora, D. (2015). Gene Expression Signatures and the
Spectrum of Coronary Artery Disease. Journal of Cardiovascular Translational
Research, 8(6), 339–352. https://doi.org/10.1007/s12265-015-9640-6
Geovanini, GR, & Libby, P. (2018). Atherosclerosis and inflammation: overview and
updates. Clinical Science, 132(12), 1243–1252.
https://doi.org/10.1042/cs20180306
Hannun, AY, Rajpurkar, P., Haghpanahi, M., Tison, GH, Bourn, C., Turakhia, MP, & Ng, AY
(2019). Cardiologist-level arrhythmia detection and classification in ambulatory
electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69.
https://doi.org/10.1038/s41591-018-0268-3
Holvoet, P., Vanhaverbeke, M., Bloch, K., Baatsen, P., Sinnaeve, P., & Janssens, S. (2016).
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
33

.
Low      MT‐CO1      in Monocytes and Microvesicles Is Associated With Outcome in
Patients With Coronary Artery Disease. Journal of the American Heart Association,
5(12). https://doi.org/10.1161/jaha.116.004207
ICH Official web site : ICH. (nd). Retrieved June 26, 2022, from
https://www.ich.org/page/efficacy-guidelines
Inouye, M., Abraham, G., Nelson, CP, Wood, AM, Sweeting, MJ, Dudbridge, F., Lai, FY,
Kaptoge, S., Brozynska, M., Wang, T., Ye, S., Webb, TR, Rutter, MK, Tzoulaki, I.,
Patel, RS, Loos, RJF, Keavney, B., Hemingway, H., Thompson, J., … Samani, NJ
(2018). Genomic risk prediction of coronary artery disease in 480,000 adults.
Journal of the American College of Cardiology, 72(16), 1883–1893.
https://doi.org/10.1016/j.jacc.2018.07.079
Jilani, MH, Simon-Friedt, B., Yahya, T., Khan, AY, Hassan, SZ, Kash, B., Blankstein, R., Blaha,
MJ, Virani, SS, Rajagopalan, S., Cainzos-Achirica, M., & Nasir, K. (2020).
Associations between particulate matter air pollution, presence and progression
of subclinical coronary and carotid atherosclerosis: A systematic review.
Atherosclerosis, 306, 22–32.
https://doi.org/10.1016/j.atherosclerosis.2020.06.018
Kini, AS, Vengrenyuk, Y., Shameer, K., Maehara, A., Purushothaman, M., Yoshimura, T.,
Matsumura, M., Aquino, M., Haider, N., Johnson, KW, Readhead, B., Kidd, BA, Feig,
JE, Krishnan, P., Sweeny, J., Milind, M., Moreno, P., Mehran, R., Kovacic, JC, …
Sharma, S. (2017). Intracoronary Imaging, Cholesterol Efflux, and Transcriptomes
After Intensive Statin Treatment. Journal of the American College of Cardiology,
69(6), 628–640. https://doi.org/10.1016/j.jacc.2016.10.029
Klarin, D., & Natarajan, P. (2021). Clinical utility of polygenic risk scores for coronary artery
disease. Nature Reviews Cardiology, 19(5), 291–301.
https://doi.org/10.1038/s41569-021-00638-w
Komaroff, AL (2018). The Microbiome and Risk for Atherosclerosis. JAMA, 319(23), 2381.
https://doi.org/10.1001/jama.2018.5240
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
https://doi.org/10.1038/nature14539
Liang, C., Li, Y., & Luo, J. (2016). A Novel Method to Detect Functional microRNA
Regulatory Modules by Bicliques Merging. IEEE/ACM Transactions on
Computational Biology and Bioinformatics, 13(3), 549–556.
https://doi.org/10.1109/tcbb.2015.2462370
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
34

.
Marín de Evsikova, C., Raplee, ID, Lockhart, J., Jaimes, G., & Evsikov, AV (2019). The
Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data
within a Precision Medicine Context for Metabolic Disease Atherosclerosis.
Journal of Personalized Medicine, 9(2), 21. https://doi.org/10.3390/jpm9020021
Mensah, GA, Roth, GA, & Fuster, V. (2019). The global burden of cardiovascular diseases
and risk factors. Journal of the American College of Cardiology, 74(20),
2529–2532. https://doi.org/10.1016/j.jacc.2019.10.009
Musunuru, K., & Kathiresan, S. (2019). Genetics of Common, Complex Coronary Artery
Disease. Cell, 177(1), 132–145. https://doi.org/10.1016/j.cell.2019.02.015
Nent, E., Nozaki, C., Schmöle, A.-C., Otte, D., & Zimmer, A. (2019). CB2 receptor deletion
on myeloid cells enhanced mechanical allodynia in a mouse model of neuropathic
pain. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-43858-4
Poplin, R., Varadarajan, AV, Blumer, K., Liu, Y., McConnell, MV, Corrado, GS, Peng, L., &
Webster, DR (2018). Prediction of cardiovascular risk factors from retinal fundus
photographs via deep learning. Nature Biomedical Engineering, 2(3), 158–164.
https://doi.org/10.1038/s41551-018-0195-0
Rhees, B., & Wingrove, JA (2015). Developing Peripheral Blood Gene Expression-Based
Diagnostic Tests for Coronary Artery Disease: a Review. Journal of Cardiovascular
Translational Research, 8(6), 372–380.
https://doi.org/10.1007/s12265-015-9641-5
Rosenberg, S. (2010). Multicenter Validation of the Diagnostic Accuracy of a Blood-Based
Gene Expression Test for Assessing Obstructive Coronary Artery Disease in
Nondiabetic Patients. Annals of Internal Medicine, 153(7), 425.
https://doi.org/10.7326/0003-4819-153-7-201010050-00005
Rumelhart, DE, Hinton, GE, & Williams, RJ (1986). Learning representations by
back-propagating errors. Nature, 323(6088), 533–536.
https://doi.org/10.1038/323533a0
Salzberg, SL (2018). Open questions: How many genes do we have? BMC Biology, 16(1).
https://doi.org/10.1186/s12915-018-0564-x
Samani, NJ, Erdmann, J., Hall, AS, Hengstenberg, C., Mangino, M., Mayer, B., Dixon, RJ,
Meitinger, T., Braund, P., Wichmann, H.-E., Barrett, JH, König, IR, Stevens, SE,
Szymczak, S., Tregouet, D.-A., Iles, MM, Pahlke, F., Pollard, H., Lieb, W., …
Schunkert, H. (2007). Genomewide Association Analysis of Coronary Artery
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
35

.
Disease. New England Journal of Medicine, 357(5), 443–453.
https://doi.org/10.1056/nejmoa072366
Scharer, CD, Blalock, EL, Mi, T., Barwick, BG, Jenks, SA, Deguchi, T., Cashman, KS, Neary,
BE, Patterson, DG, Hicks, SL, Khosroshahi, A., Eun-Hyung Lee, F., Wei, C., Sanz, I., &
Boss, JM (2019). Epigenetic programming underpins B cell dysfunction in human
SLE. Nature Immunology, 20(8), 1071–1082.
https://doi.org/10.1038/s41590-019-0419-9
Topol, EJ (2019). High-performance medicine: the convergence of human and artificial
intelligence. Nature Medicine, 25(1), 44–56.
https://doi.org/10.1038/s41591-018-0300-7
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, AN, Knowles, DA, Golan, D.,
Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., Björkegren, JL, Kyung Im, H.,
Pasaniuc, B., Rivas, MA, & Kundaje, A. (2017). Transcriptome-wide association
studies: Opportunities and challenges. Cold Spring Harbor Laboratory.
http://dx.doi.org/10.1101/206961
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, AN, Knowles, DA, Golan, D.,
Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., Björkegren, JLM, Im, HK,
Pasaniuc, B., Rivas, MA, & Kundaje, A. (2019). Opportunities and challenges for
transcriptome-wide association studies. Nature Genetics, 51(4), 592–599.
https://doi.org/10.1038/s41588-019-0385-z
Wang, X., Cui, N., Liu, X., & Ming, L. (2019). Identification of a blood-based 12-gene
signature that predicts the severity of coronary artery stenosis: An integrative
approach based on gene network construction, Support Vector Machine
algorithm, and multi-cohort validation. Atherosclerosis, 291, 34–43.
https://doi.org/10.1016/j.atherosclerosis.2019.10.001
Wang, Y., Lv, Q., Wu, H., Chen, K., Hong, X., Gu, C., Fu, G., & Zhang, W. (2019). Comparison
of MESA of and Framingham risk scores in the prediction of coronary artery
disease severity. Herz, 45(S1), 139–144.
https://doi.org/10.1007/s00059-019-4838-z
Wolf, D., & Ley, K. (2019). Immunity and Inflammation in Atherosclerosis. Circulation
Research, 124(2), 315–327. https://doi.org/10.1161/circresaha.118.313591
Zhang, D., Liu, S., Li, Z., & Wang, R. (2022). Global, regional and national burden of
gastroesophageal reflux disease, 1990–2019: update from the GBD 2019 study.
Annals of Medicine, 54(1), 1372–1384.
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
36

.
https://doi.org/10.1080/07853890.2022.2074535
Protocol RNA-coronary CT study-2-Version 1.0
Date:12/10/2022
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