WO2019217714A1

WO2019217714A1 - Determination and reduction of risk of sudden cardiac death

Info

Publication number: WO2019217714A1
Application number: PCT/US2019/031569
Authority: WO
Inventors: Ashish Suresh YERI; Saumya DAS; Michael G. SILVERMAN
Original assignee: The General Hospital Corporation
Priority date: 2018-05-09
Filing date: 2019-05-09
Publication date: 2019-11-14

Abstract

Methods for identifying and optionally treating subjects to reduce the risk of sudden cardiac death, based on detection of miRNAs.

Description

Determination and Reduction of Risk of Sudden Cardiac Death

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional Application Serial No. 62/669,037, filed on May 9, 2018. The entire contents of the foregoing are incorporated herein by reference. FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Grant Nos. HL091069 and HL122547 awarded by the National Institutes of Health. The Government has certain rights in the invention.

TECHNICAL FIELD

Described herein are methods for identifying and optionally treating subjects to reduce the risk of sudden cardiac death, based on detection of miRNAs.

BACKGROUND

Sudden death accounts for nearly 50% of all coronary heart disease (CHD) related mortality. (1,2) Current prevention strategies generally focus on the use of implantable cardioverter-defibrillators (ICDs) in patients considered high risk for sudden death based on a reduced left ventricular ejection fraction (LVEF).(3)

SUMMARY

In patients with CHD, plasma levels of miR-l50-5p, miR-29a-3p, and miR-30a- 5p were each independently associated with the risk of SCD in patients not traditionally identified as high risk for SCD and thus not candidates for ICD therapy. These miRNAs, individually or as part of a multi-marker risk score, may have the potential to enhance SCD risk prediction, and warrant further study.

Thus, provided herein are methods for determining risk of sudden cardiac death (SCD) in a subject. The methods include providing a sample comprising plasma or a subfraction thereof from the subject; determining a level of microRNAs (miRNAs) comprising one, two, or all three of miR-l50 (preferably miR-l50-5p), miR-29a

(preferably miR-29a-3p), and miR-30a (preferably miR-30a-5p) in the sample; and calculating a SCD risk score based on the levels of the miRNAs.

Also provided herein are methods for selecting a treatment for, and optionally treating, a subject. The methods include providing a sample comprising plasma or a subfraction thereof from the subject; determining a level of one, two, or all three microRNAs (miRNAs) comprising miR-l50 (preferably miR-l50-5p), miR-29a

(preferably miR-29a-3p), and miR-30a (preferably miR-30a-5p) p in the sample;

comparing the levels of the miRNAs to reference levels; and selecting and optionally recommending or administering a treatment comprising a primary prevention defibrillator for a subject who has one, two, or all three of the miRNAs that are unfavorable with regard to the reference level, wherein an unfavorable level is a level of miR-l50-5p above the reference level, miR-29a-3p above the reference level, and miR-30a-5p below the reference level.

In some embodiments, the subject has had a myocardial infarction (MI), e.g., within the previous 40-90 days, or has had a revascularization procedure within the previous 90 days.

In some embodiments, the subject has a left ventricular ejection fraction (LVEF) of less than 30%, less than 35%, or less than 55%. In some embodiments, the methods include using an echocardiogram or cardiac MRI to measure LVEF.

In some embodiments, the subject has one or more symptoms of heart failure (HF) or a diagnosis of HF.

In some embodiments, determining a level of the miRNAs comprises using a sequencing based method, a hybridization based method, or a polymerase chain reaction (PCR)-based method.

In some embodiments, the sample comprises exosomes isolated from the plasma.

In some embodiments, the methods include comparing the SCD risk score to a reference risk score, wherein an SCD risk score above the reference risk score indicates that the subject is at risk of SCD.

In some embodiments, the methods include recommending that the subject be treated with a primary prevention defibrillator. In some embodiments, the methods include treating the subject with a primary prevention defibrillator, e.g., an Implantable Cardioverter Defibrillator or Cardiac Resynchronization Therapy Defibrillator if appropriate) or a wearable (e.g., wearable Cardioverter Defibrillators (e.g., ZOLL Lifevest)) defibrillator.

In some embodiments, the reference level is the median level for a reference population.

In some embodiments, the methods include measuring levels of one or more internal control miRNAs, and normalizing the levels of comprising miR-l50-5p, miR- 29a-3p, and miR-30a-5p to the internal control miRNAs.

In some embodiments, the internal control miRNAs comprise one or more of hsa- miR-l03a-3p, hsa-let-7g-5p and hsa-miR- 140-3.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting.

All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

DESCRIPTION OF DRAWINGS

Figure 1: miRNAs and Risk of Sudden Cardiac and/or Arrhythmic Death.

Conditional Logistic Regression Model adjusted for prior MI, NYHA class, and history of diabetes

Figure 2: miRNAs and Risk of Sudden Cardiac and/or Arrhythmic Death.

Conditional Logistic Regression Model adjusted for prior MI, NYHA class, history of diabetes miR-l50-5p, miR-29a-5p, and miR-30a-5p. Levels above the median for each of the miRNAs was associated with risk of SCD. When combined into a multi-marker risk score, unfavorable levels of all three miRNAs was associated with a 4.8 fold increased risk of SCD.

Figure 3: Network interactions with experimentally determined targets of miR-150-5p, miR-29a-3p, and miR-30a-5p. Gene targets (ovals) of the three miRNAs (orange rectangles) are connected by strong (thick) and weak (thin) evidence types according to miRTarBase. This subnetwork view focuses on 160 targets that either of have strong evidence for interacting with only one miRNA (green, radial layout), weak evidence to two or more miRNAs (blue, interstitial layout), or a mix of both (green, interstitial layout).

Figure 4: Enrichment analysis for ontology terms and pathways related to miR-150-5p, miR-29a-3p, and miR-30a-5p targets. Combing results for Gene

Ontology: Biological Process (gold), Jensen Disease Ontology and OMIM (blue), and WikiPathways (gray), the bar graph shows the enrichment score calculated as -log(p value) * Z score. DETAILED DESCRIPTION

70-80% of sudden cardiac deaths occur in individuals who do not have a reduced LVEF in a range that qualifies them for ICD implantation. (2) Therefore, there is an urgent need to identify patients at high risk for sudden death who do not meet these traditional clinical criteria. (3)

MicroRNAs (miRNAs) are small noncoding RNAs that regulate post- transcriptional gene expression and play a role in intercellular communication. (4) Given their stability in peripheral blood, as well as their potential role in cardiovascular physiology, circulating miRNAs have garnered enthusiasm as possible biomarkers with a functional role in disease pathogenesis. (4) MiRNAs have been implicated in regulating inflammation and cardiac fibrosis, both of which have been linked to sudden cardiac death. (5-7) Furthermore, plasma miRNAs have been found to predict cardiovascular death in patients with CHD.(8) An association was previously demonstrated between some circulating miRNAs and adverse ventricular remodeling, (5) a known risk factor for sudden death. (9) Thus, it was hypothesized that miRNAs might be promising candidate markers of SCD risk, though little investigation to date has shed light on their role in this deadly condition.

The present cohort of 5956 individuals with CHD was a carefully-phenotyped population with sudden cardiac and/or arrhythmic death (SCD) adjudicated by a clinical end point committee. (10) Recent studies have demonstrated the difficulties in ascertaining the cause of sudden death in community populations; (11) hence this cohort allows the unique opportunity to derive the association of novel miRNA biomarkers with SCD.

In this contemporary and well-phenotyped nested case-control study of individuals with CHD, the majority of whom did not have a sufficiently reduced LVEF to meet current guidelines for ICD implantation, the association between plasma miRNAs and SCD was evaluated over 3.6 years of mean follow-up. From a list of 18 candidate miRNAs curated from previous discovery efforts to identify RNA biomarkers for post- myocardial infarction remodeling, (5) or previously linked with cardiovascular disease, (13-15) 3 plasma miRNAs were identified that were independently associated with SCD; miR-l 50-5p, miR-29a-3p, and miR-30a-5p. These miRNAs were modestly correlated; as shown in figure 2, there was an incremental increase in the odds ratio for sudden cardiac arrest with the addition of each miRNA level (above or below median value). When combined into a multiple miRNA biomarker score (based on values being above vs. below the median, i.e., (miR-l50 above the median, miR-29a above the median, and miR-30a below the median)), an unfavorable level of all three miRNAs was associated with a 4.8-fold increased risk of SCD.

To the best of the present inventors’ knowledge, this study is the first to demonstrate an association between specific circulating miRNAs and SCD risk, e.g., for post-MI patients with an average FFEV of less than about 35%. The potential to more efficiently identify CHD patients at high risk of SCD is a key unmet need that has been underscored by guidelines and working groups. (3) Given that the majority of participants (94%) in the study did not fulfill current guidelines for ICDs based on FVEF and NYHA class, these miRNAs may be helpful in advancing SCD risk prediction in this broad population where SCD risk stratification measures are lacking and needed. (3) Since miRNAs may also regulate specific pathophysiologic pathways associated with ventricular arrhythmias, (27) circulating miRNAs appear to be promising candidate markers for more precise risk stratification, and may also shed light towards possible pharmacologic targets for novel preventive therapies.

Methods of Determining Risk of SCD

Described herein are methods for determining risk of SCD in subjects, and optionally selecting and administering a treatment to subjects who have a risk level above a threshold.

In some embodiments, the methods can be used in subjects who are not considered at high enough risk to warrant intervention, e.g., subjects who have apparently normal cardiac function, or subjects who have had a recent (e.g., within the previous 40 or 90 days) cardiac event such as a myocardial infarction (MI), have undergone a recent (e.g., within the previous 40 or 90 days) revascularization procedure (e.g., stent or bypass surgery), who have a left ventricular ejection fraction (LVEF) of greater than 30-35%, and/or who have symptoms and/or a diagnosis of heart failure. In these subjects, the present methods can be used to identify those who have risk levels above or below a threshold, and who should be treated accordingly. For example, these methods can be used to identify subjects who, despite having a FVEF of 30-35% or more, and optionally one or more symptoms and/or a diagnosis of heart failure, have a higher risk of SCD and thus should be treated with a primary prevention defibrillator, e.g., by implantation of a cardiac defibrillator, Cardiac Resynchronization Therapy Defibrillator (CRT-D), or use of a wearable defibrillator.

In some embodiments, the methods can be used in subjects who typically would be considered at high enough risk, e.g., subjects who have a FVEF of 30-35% or less, and symptoms and/or a diagnosis of heart failure. In these subjects, the methods can be used to identify those who have risk levels above or below a threshold, and who should be treated accordingly, e.g., those at higher risk should be treated with a primary prevention defibrillator, e.g., implantation of a primary prevention defibrillator such as a cardiac defibrillator or use of a wearable defibrillator, while those at lower risk should be treated with regimens that do not include a primary prevention defibrillator. For example, these methods can be used to identify subjects who, despite having a FVEF of 30-35% or less, and optionally one or more symptoms and/or a diagnosis of heart failure, have a low risk of SCD and thus can be treated without a primary prevention defibrillator, or use of a primary prevention defibrillator can be delayed. The methods can include re-testing the subject to monitor their risk over time, and if the levels of the miRNAs increase over time, the decision to delay use of a primary prevention defibrillator can be revisited and a primary prevention defibrillator used.

Symptoms of heart failure can include one or more of Exertional dyspnea and/or dyspnea at rest; Orthopnea; Acute pulmonary edema; Chest pain/pressure and

palpitations; Tachycardia; Fatigue and weakness; Nocturia and oliguria; Anorexia, weight loss, nausea; Exophthalmos and/or visible pulsation of eyes; Distention of neck veins; Weak, rapid, and thready pulse; Rales, wheezing; S3 gallop and/or pulsus alternans; Increased intensity of P₂ heart sound; Hepatojugular reflux; Ascites, hepatomegaly, and/or anasarca; or Central or peripheral cyanosis or pallor; see, e.g., Dumitru,“Heart Failure,” available at emedicine.medscape.com/article/l63062-overview; Updated: May 07, 2018). Heart failure can be diagnosed using the Framingham criteria (see, e.g., Ho et al, J Am Coll Cardiol. 1993; 22(4 Suppl A):6A-l3A). In some embodiments, the subjects have heart failure characterized using the New York Heart Association (NYHA) classification system I, II, III, or IV; or American College of Cardiology/ American Heart Association (ACC/ AHA) staging system stage A, B, C, or D (see, e.g., Yancy et al, Circulation. 2013 Oct 15. l28(l6):e240-327).

Methods of Treatment

In a subject who is determined to have a risk level above a threshold using a method described herein, the methods can include treating the subject to reduce their risk of sudden cardiac death. The treatments can include, for example, use of a primary prevention defibrillator such as an implantable (e.g., Implantable Cardioverter

Defibrillator or Cardiac Resynchronization Therapy Defibrillator if appropriate) or a wearable (e.g., wearable Cardioverter Defibrillators (e.g., ZOFF Fifevest)) defibrillator. Alternatively or in addition, the treatments can include administration of a

pharmacological agent that reduces risk of SCD, e.g., Anti-arrhythmics such as amiodarone, lidocaine, mexiletine, or sotalol; Beta-blockers such as Metoprolol, carvedilol, or nebivolol; Mineralocorticoid antagonists such as spironolactone or eplerenone; Angiotensin receptor/neprilysin inhibitors such as sacubitril/valsartan (Entresto); or SGLT2-inhibitors such as empagliflozin, dapagliflozin, canagliflozin, or ertugliflozin. In some embodiments, a subject who is determined not to have elevated risk of SCD is not treated with a primary prevention defibrillator, but is treated with a pharmacological agent.

Risk Determination

The present methods can include obtaining a sample comprising plasma or a subfraction thereof, and determining levels of miR-l50 (eg., miR-l50-5p), miR-29a (e.g., miR-29a-3p), and miR-30a (e.g., miR-30a-5p) in the sample. Subfractions of plasma can be obtained using, e.g., microfluidics, bulk isolation, precipitating agents, filtration, or centrifugation. In some embodiments, a subfraction comprising exosomes is used, and a step of isolating the exosomes is included in the methods. For example, exosomes can be isolated with one of several methods such as differential gradient ultracentrifugation (e.g., fractions 6-10 together or individually), size exclusion columns (sized for exosomes in the 30-200 nanometer diameter range), or bulk isolation (such as EXOQUICK isolation reagent).

The sequence of human miR-l50-5p is UCUCCCAACCCUUGUACCAGUG (SEQ ID NO: l). The sequence of human miR-l50-3p (or miR-l50*) is

CUGGUACAGGCCUGGGGGACAG (SEQ ID NO:2).

The sequence of human miR-29a-3p is UAGCACCAUCUGAAAUCGGUUA (SEQ ID NO:3). The sequence of human miR-29a-5p (or miR-29a*) is

ACU GAUUU CUUUU GGU GUU C AG (SEQ ID NO:4).

The sequence of human miR-30a-5p is UGUAAACAUCCUCGACUGGAAG (SEQ ID NO:5). The sequence of human miR-30a-3p (or miR-30a*) is

CUUU C AGU CGGAU GUUU GC AGC (SEQ ID NO:6).

Alternatively, an isomiR of the above can be used, e.g., an isomer with 5’ trimming, 3’ trimming, 3’ nucleotide addition, or nucleotide substitution (e.g., up to 1, 2, 3, 4, or 5 nucleotide differences). The present methods can be performed, e.g., on mammalian subjects, e.g., on humans or non-human mammals, e.g., veterinary subjects. For subjects of species other than humans, the sequence of the corresponding miRNA from that species should be used.

The presence and/or level of a nucleic acid can be evaluated using methods known in the art, e.g., Sequencing based methods such as small RNA sequencing (e.g., Bio Scientific Nextflex, NEB, Illumina, Sanger, pyrosequencing, or NextGeneration Sequencing); hybridization based methods such as FIREPLEX particle-based multiplex miRNA assays (ABCAM), Edgeseq (HTG) and nCounter (Nanostring); or polymerase chain reaction (PCR)-based methods such as (SYBR, Tacman, microarrays reverse transcriptase polymerase chain reaction (RT-PCR), quantitative or semi-quantitative real time RT-PCR, digital PCR i.e. BEAMing ((Beads, Emulsion, Amplification, Magnetics) Diehl (2006) Nat Methods 3:551-559); RNAse protection assay; Northern blot; see, e.g., Srinivasan et al., 2019, Cell 177, 446-462; Sambrook, et al, Molecular Cloning: A Laboratory Manual (3. Sup.rd Edition, 2001); Bernard (2002) Clin Chem 48(8): 1178-

1185; Miranda (2010) Kidney International 78: 191-199; Bianchi (2011) EMBO Mol Med 3:495-503; Taylor (2013) Front. Genet. 4: 142; Yang (2014) PLOS One 9(1 l):el 10641); Nordstrom (2000) Biotechnol. Appl. Biochem. 31(2): 107-112; Ahmadian (2000) Anal Biochem 280: 103-110. In some embodiments, high throughput methods, e.g., chips as are known in the art (see, e.g., Ch. 12, Genomics, in Griffiths et al., Eds. Modern genetic Analysis, l999,W. H. Freeman and Company; Ekins and Chu, Trends in Biotechnology, 1999, 17:217-218; MacBeath and Schreiber, Science 2000, 289(5485): 1760-1763;

Simpson, Proteins and Proteomics: A Laboratory Manual , Cold Spring Harbor

Laboratory Press; 2002; Hardiman, Microarrays Methods and Applications: Nuts & Bolts, DNA Press, 2003), can be used to detect the presence and/or level of a miRNA described herein. Measurement of the level of a biomarker can be direct or indirect.

In some embodiments, the methods are performed using an internal standard to normalize levels of the miRNAs. For example, hsa-miR-l03a-3p, hsa-let-7g-5p and hsa- miR- 140-3 can be used.

The methods can include comparing the levels to reference or threshold levels.

The methods can include calculating a risk score based on levels of one, two, or all three miRNAs. As shown in figure 2, there is incremental increase in the odds ratio for sudden cardiac arrest with the addition of each miRNA level (above or below median value).

In some embodiments, the risk is calculated based on values for all three miRNAs being above vs. below a selected threshold or reference level, e.g., the median. As noted herein, an unfavorable level of one, two, or all three miRNAs (i.e., a level of miR-l50-5p above the reference level, miR-29a-3p above the reference level, and miR-30a-5p below the reference level) was associated with a 4.8-fold increased risk of SCD. In some embodiments, the methods can include assigning an elevated level of risk to a subject if two of the three miRNAs are below the median, and treating the subject accordingly. In some embodiments, the methods include calculating a score based on the reference levels being unfavorable, with the range of the score being from 0 to 3, with 1 point for each miRNA that is unfavorable with regard to the reference level, i.e., a level of miR-l 50-5p above the reference level, miR-29a-3p above the reference level, and miR-30a-5p below the reference level. In some embodiments, calculating a score can include determining one or more additional variables such as LVEF, recent MI or revascularization procedure, QRS duration, or left bundle branch block (LBBB), and an additional point can be included for an unfavorable result for each of the variables (e.g., one point for each of any one or more of LVEF < 30%, 35%, or 55%; MI or revascularization, e.g., within 40- 90 days; QRS of >120, 130, 140, or 150 msec; or presence of LBBB).

Suitable reference values can be determined using methods known in the art, e.g., using standard clinical trial methodology and statistical analysis. The reference values can have any relevant form. In some cases, the reference comprises a predetermined value for a meaningful level of the miRNAs, e.g., a control reference level that represents a normal level of the miRNAs, e.g., a level in a subject who is not at risk of SCD, and/or a disease reference that represents a level of the miRNAs associated with subjects who succumb to SCD. In some embodiments, the risk is risk of SCD within a selected time period, e.g., within 30 days, 60 days, 120 days, 1 year, 2 years, 3 years, 3.5 years, 4 years, or 5 years.

The predetermined level can be a single cut-off (threshold) value, such as a median or mean, or a level that defines the boundaries of an upper or lower quartile, tertile, or other segment of a clinical trial population that is determined to be statistically different from the other segments. It can be a range of cut-off (or threshold) values, such as a confidence interval. It can be established based upon comparative groups, such as where association with risk of developing disease or presence of disease in one defined group is a fold higher, or lower, (e.g., approximately 2-fold, 4-fold, 8-fold, 16-fold or more) than the risk or presence of disease in another defined group. It can be a range, for example, where a population of subjects (e.g., control subjects) is divided equally (or unequally) into groups, such as a low-risk group, a medium-risk group and a high-risk group, or into quartiles, the lowest quartile being subjects with the lowest risk and the highest quartile being subjects with the highest risk, or into n-quantiles (i.e., n regularly spaced intervals) the lowest of the n-quantiles being subjects with the lowest risk and the highest of the n-quantiles being subjects with the highest risk.

In some embodiments, the predetermined level is a level or occurrence in the same subject, e.g., at a different time point, e.g., an earlier time point.

Thus, in some cases the level of one, two, or all three of the miRNAs in a subject being less than or equal to a reference level of the miRNAs is indicative of a clinical status (e.g., indicative of a high risk of SCD). In other cases the level of one, two, or all three of the miRNAs in a subject being greater than or equal to the reference level of the miRNAs is indicative of the absence of disease or normal risk of the disease. In some embodiments, the amount by which the level in the subject is the less than the reference level is sufficient to distinguish a subject from a control subject, and optionally is a statistically significantly less than the level in a control subject. In cases where the level of the miRNAs in a subject being equal to the reference level of the miRNAs the“being equal” refers to being approximately equal (e.g., not statistically different).

The predetermined value can depend upon the particular population of subjects (e.g., human subjects) selected. For example, an apparently healthy population will have a different‘normal’ range of levels of the miRNAs than will a population of subjects which have, are likely to have, or are at greater risk to have, SCD, or who have heart failure. Accordingly, the predetermined values selected may take into account the category (e.g., sex, age, health, risk, presence of other diseases) in which a subject (e.g., human subject) falls. Appropriate ranges and categories can be selected with no more than routine experimentation by those of ordinary skill in the art. In characterizing likelihood, or risk, numerous predetermined values can be established.

In some embodiments, the methods include the use of imaging modalities, e.g., an echocardiogram or cardiac magnetic resonance imaging (MRI), e.g., to determine LVEF. Presently, subjects who have an EF < 30-35%, can get a defibrillator, but only 30% will actually need it. Thus, the methods can include determining FVEF and identifying subjects who have an FVEF of below 30-35%, or below 55%, and who have one or more miRNAs below a reference level, as being at increased risk of SCD and optionally recommending or administering a treatment comprising a primary prevention

defibrillator.

In some embodiments, e.g., where the subject patient meets independent criteria for CRT (e.g., QRS duration of greater than 120 msec or 150 msec, and left bundle branch block (FBBB) the methods can include recommending or implanting a CRT-D. The methods can include determining QRS duration and detecting FBBB configuration on an electrocardiogram (see, e.g., Katritsis, Arrhythm Electrophysiol Rev. 2016 Aug; 5(2): 80-81).

In some embodiments, in subjects who have had an MI, the methods include measuring the infarct scar size using cardiac MRI, and identifying subjects who have a scar that is larger than a reference size (e.g., more than 10%, 12%, 15%, 20%, or 25%), and who have one or more miRNAs below a reference level, as being at increased risk of SCD and optionally recommending or administering a treatment comprising a primary prevention defibrillator.

EXAMPLES

The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

Methods

The following materials and methods were used in the Examples below.

Study Population

We performed a nested case-control study within a multi-center cohort of 5956 patients with CHD who were followed prospectively for SCD. (10) Each participant with incident SCD during follow-up (case) was matched (on age, sex, race, LVEF, time of blood draw, and fasting status) to two randomly selected participants without incident SCD during follow up (control) using risk set sampling. Of the initial 129 cases and 258 controls, samples from 35 participants (11 cases, 24 controls) were excluded from analysis (33 had significant hemolysis, and 2 had undetectable miRNA levels). This study complies with the declaration of Helsinki, the locally appointed ethics committee has approved the research protocol, and all subjects gave written informed consent.

Data Collection and Endpoint Classification

A comprehensive baseline assessment was performed at the time of enrollment, which included a comprehensive medical history, electrocardiogram, and venous blood sampling for laboratory analysis. During the baseline evaluation, venous blood was collected via peripheral venipuncture, centrifuged, and stored at -80°C until analysis.

The baseline LVEF was recorded as the most recent clinically indicated assessment of LVEF at the time of study entry.

Following enrollment, participants were followed for clinical endpoints by a central Clinical Coordinating Center at Brigham and Women’s Hospital through questionnaires administered by mail or telephone. Medical records pertaining to all deaths and cardiac arrests were requested to confirm study endpoints. Endpoints were confirmed using data from emergency service reports, emergency room and other medical records, autopsies, and witness reports. Due to the known limitations for sudden death determination, information from the death certificate was not used in determination of sudden or arrhythmic death. All deaths were adjudicated by a clinical endpoints committee at Brigham and Women’s Hospital.

The primary endpoint was sudden cardiac and/or arrhythmic death (SCD). Details of the endpoint classification have been published previously, but are summarized here. (10)

Definite sudden cardiac death was defined as death or fatal cardiac arrest occurring within 1 hour of symptom onset without evidence for a non-cardiac cause by history or autopsy. Probable sudden cardiac death was defined as an unwitnessed death or death that occurred during sleep where the participant was observed to be symptom- free within the preceding 24 hours. Arrhythmic death was defined as an abrupt spontaneous loss of pulse without evidence of preceding circulatory impairment or neurological dysfunction. (12) Out-of- hospital cardiac arrests due to ventricular fibrillation requiring external electrical defibrillation for resuscitation were considered aborted arrhythmic deaths and included as an arrhythmic death.

Laboratory Methods: miRNA and cytokine measurements

We quantified plasma levels of 18 candidate miRNAs that were identified from 2 sources: 1) Previously identified miRNAs in our laboratory using next-generation sequencing from plasma in post MI patients;(5) and 2) published literature.(l3-l5) Plasma miRNA levels were measured using the FirePlex® miRNA assay (Abeam, Cambridge, MA).(16,17)

Plasma levels of Interleukin (IL)— 6, IL-10, Tumor Necrosis Factor-alpha (TNF- a), and Monocyte Chemoattractant Protein- 1 (MCP-l), chosen based on prior data,(l8,l9) were measured using the FirePlex® immunoassay(20) in a subset of controls with adequate sample availability (n=57).

miRNA data normalization

A total of 5 plates were used for the FirePlex® miRNA assay. To help normalize the intensity data between plates, replicates of the same pooled human serum (PHS) sample were run on each plate (ranging from 2-4 samples). The geometric mean of all PHS samples was used to calculate the scaling factors for each miRNA within each sample across all plates. The geometric mean of the scaling factors for each plate for each miRNA was then computed. Finally, the distinct scaling factor for each plate was then computed as the median of scaling factors across all miRNAs for each individual plate (thus yielding 1 scaling factor per plate).

To account for sample input volume and other technical factors, three miRNAs, namely hsa-miR-l03a-3p, hsa-let-7g-5p and hsa-miR-l40-3p, were chosen as normalizers based on the expression and their low coefficient of variation (CV) with relative invariance in healthy subjects and those with cardiovascular disease in prior work. (5, 17) Levels of these miRNAs were measured, and the geometric mean for each of these three miRNAs across all samples was used to compute three scaling factors for each individual sample. The geometric mean of the three scaling factors was then used to normalize each sample. Following normalization, expression values for each miRNA were then log transformed for analysis.

Statistical analysis

The baseline characteristics of the study population were presented as means (+/- standard deviation [SD]), medians (interquartile range), and proportions. The associations between miRNAs and risk of SCD were evaluated using conditional logistic regression (to account for the matched case-control study design) adjusted for prior MI, NYHA class, and history of diabetes. MiRNA levels were evaluated across tertiles based upon the distribution of values in the control group. A linear test for trend was performed across tertiles using the median value in each tertile. Significant miRNAs were also evaluated as dichotomous variables: above vs below the median (based on the median value in controls) and combined into a multi-miRNA score. Spearman correlation coefficients were used to evaluate relationships between miRNAs among the controls, and between significant miRNAs and inflammatory cytokines in a subset of controls. Analyses were performed with SAS version 9.4. Data normalization was performed using R version 3.4.2.

Bioinformatics based approach: Network Reconstruction and Enrichment Analysis

Targets of the 3 SCD-associated miRNAs were extracted from miRTarBase version 6.1 (21) using Target Interaction Finder(22) as integrated into the Genboree Workbench. (23) The xGMML output was imported into Cytoscape,(24) revealing a network of 1402 edges connecting 3 miRNA nodes to 1352 target gene nodes. The network was filtered in Cytoscape based on evidence type (“Functional MTI”, excluding “Non-Functional”) or topology (targets of at least 2 of 3 candidate miRNAs), producing a subnetwork of 160 target gene nodes and 210 edges.

The set of 160 target genes defined by network reconstruction were used as input for enrichment analysis using Enrichr.(25) Tabular results were downloaded for Gene Ontology: Biological Process, OMDVI, Jensen Disease and WikiPathways. (26) Ontology and pathway terms were selected from the significant results based on low redundancy and relevance. Example 1. Plasma miRNAs associated with SCD

A total of 118 SCD cases and 234 matched controls were included in the nested case-control study. Baseline characteristics are presented in Table 1. Mean age of the cases and controls was 66 years and the majority (80%) were men. Most (86%) had a prior MI and more than one-third had diabetes. The median LVEF was 45% (IQR =

13%; range =10-75%), and slightly more than one in four participants had symptomatic heart failure. Participants were followed for 3.6 (± 1.6) years.

The results of the conditional logistic regression models (adjusted for prior MI, NYHA class, and diabetes) for each of the miRNAs tested can be found in Table 1. Of the 18 miRNAs evaluated, miR-l50-5p, miR-29a-3p, and miR-30a-5p were each significantly associated with an increased risk of SCD (p trend <0.05, Figure 1) with ORs of 2.03 (95% Cl, 1.12-3.67), 1.93 (95% Cl, 1.07-3.50) and 0.55 (95% Cl, 0.31-0.97), respectively, for individuals with miRNA levels in the 3^rd versus I^st tertile (Figure 1). When miRNA levels were modeled as above vs. below the median, all three miRNAs were each independently associated with risk of SCD (Figure 2). When combined as part of a multiple miRNA biomarker score, an unfavorable level of all three miRNAs (above the median for miR-l50-5p and miR-29a-3p; below the median for miR-30a-5p) was associated with a 4.8-fold increased risk of SCD (95% Cl 1.59 - 14.51 ; p=0.006).

Spearman correlation coefficients were calculated to evaluate the relationship between the three significant miRNAs in the 234 controls. Mir-l50-5p and miR-29a-3p were moderately correlated (R²=0.43, p<0.000l), and miR-29a-3p and miR-30a-5p were modestly correlated (R²=0.30, p<0.000l), whereas miR-l50-5p and miR-30a-5p were not (R²=0.06, p=0.38).

Table 1: Baseline Characteristics

*mean (standard deviation)

**median (quartile 1 - quartile 3)

Example 2. Bioinformatics based approach: Network Reconstruction and Enrichment Analysis

Targets of the 3 SCD-associated miRNAs produced a subnetwork of 160 target gene nodes and 210 edges (Figure 3). The set of 160 target genes defined by network reconstruction were used as input for enrichment analysis. Ontology and pathway terms were selected from the significant results based on low redundancy and relevance. These selected terms were visualized as a bar graph (Figure 4). The key pathways identified are related to fibrosis, inflammation, and apoptosis/cell death; each of which have been related to ventricular arrhythmias and/or SCD.

Given the potential associations between the miRNAs and pathways known to be associated with ventricular arrhythmias, an additional exploratory analysis was performed to evaluate the relationship between the three miRNAs and SCD in a limited subset of 21 cases where ventricular tachycardia (VT) or ventricular fibrillation (VF) was documented at the time of SCD. When compared with 40 matched controls, miR-l50-5p and miR- 30a-5p were each significantly associated with risk of VT/VF (respective ORs of 5.18 [95% Cl 1.27-21.09; p=0.02] and 0.05 [95% Cl 0.004 - 0.62; p=0.02] per unit increase), whereas miR-29a-3p was not (OR 0.97 [95% Cl 0.33-2.84; p=0.95] per unit increase).

Example 3. Plasma miRNA associations with cytokines

In a subset of controls (n=57), miR-l50-5p was inversely correlated with IL-10 (R²=-0.32, p=0.03), whereas miR-29a-3p and miR-30a-5p were each correlated with IL- 6: R²=0.32, p=0.0l; and R²=0.28, p=0.03 respectively. None of the miRNAs were correlated with TNF-a or MCP-l (Table 2).

Table 2: Association between miRNAs and SCD

*Test for linear trend across tertiles

Table 3: Spearman Correlation Coefficients of miRNAs and cytokines

IL, interleukin; TNF, tumor necrosis factor; MCP-l, Monocyte Chemoattractant Protein- 1

References:

1. Wellens HJ, Schwartz PJ, Lindemans FW et al. Risk stratification for sudden cardiac death: current status and challenges for the future. Eur Heart J

2014;35: 1642-51.

2. Myerburg RJ, Goldberger JJ. Sudden Cardiac Arrest Risk Assessment: Population Science and the Individual Risk Mandate. JAMA Cardiol 2017;2:689-694.

3. Al-Khatib SM, Stevenson WG, Ackerman MJ et al. 2017 AHA/ACC/HRS Guideline for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2017.

4. Santovito D, Weber C. Zooming in on microRNAs for refining cardiovascular risk prediction in secondary prevention. Eur Heart J 2017;38:524-528.

5. Danielson KM, Shah R, Yeri A et al. Plasma circulating extracellular RNAs in left ventricular remodeling post-myocardial infarction. EBioMedicine 2018. 6. Hussein AA, Gottdiener JS, Bartz TM et al. Inflammation and sudden cardiac death in a community-based population of older adults: the Cardiovascular Health Study. Heart Rhythm 2013;10: 1425-32.

7. Halliday BP, Gulati A, Ali A et al. Association Between Midwall Late Gadolinium Enhancement and Sudden Cardiac Death in Patients With Dilated

Cardiomyopathy and Mild and Moderate Left Ventricular Systolic Dysfunction.

Circulation 2017; 135 : 2106-2115.

8. Karakas M, Schulte C, Appelbaum S et al. Circulating microRNAs strongly predict cardiovascular death in patients with coronary artery disease-results from the large AtheroGene study. Eur Heart J 2017;38:516-523.

9. Phan D, Aro AL, Reinier K et al. Left Ventricular Geometry and Risk of Sudden Cardiac Arrest in Patients With Severely Reduced Ejection Lraction. J Am Heart Assoc 2016;5.

10. Chatterjee NA, Moorthy MV, Pester J et al. Sudden death in coronary heart disease patients without severe systolic dysfunction. JAMA Cardiol 2018.

11. Myerburg RJ. Cardiac and Noncardiac Causes of Apparent Sudden Arrhythmic Deaths: Shadows in a Spectrum. Circulation 2018;137:2701-2704.

12. Hinkle LE, Thaler HT. Clinical classification of cardiac deaths.

Circulation 1982;65:457-64.

13. Bronze-da-Rocha E. MicroRNAs expression profiles in cardiovascular diseases. Biomed Res Int 20l4;20l4:985408.

14. Kuwabara Y, Ono K, Horie T et al. Increased microRNA-l and microRNA-l33a levels in serum of patients with cardiovascular disease indicate myocardial damage. Circ Cardiovasc Genet 2011;4:446-54.

15. Marques FZ, Vizi D, Khammy O, Mariani JA, Kaye DM. The transcardiac gradient of cardio-microRNAs in the failing heart. Eur J Heart Fail 2016;18: 1000-8.

16. Chapin SC, Appleyard DC, Pregibon DC, Doyle PS. Rapid microRNA profiling on encoded gel microparticles. Angew Chem Int Ed Engl 2011;50:2289-93.

17. Shah R, Ziegler O, Yeri A et al. MicroRNAs Associated With Reverse Left Ventricular Remodeling in Humans Identify Pathways of Heart Failure Progression. Circ Heart Fail 20l8;l l :e004278. 18. Luo NG, T.; Wang, D.; Fu, Y.; . MicroRNA-l 50 regulates lipid metabolism and inflammatory response. J Metabolic Syndrome 20l3;2.

19. Jiang X, Xu C, Lei F et al. MiR-30a targets IL-lalpha and regulates islet functions as an inflammation buffer and response factor. Sci Rep 20l7;7:5270.

20. Lefrancais E, Mallavia B, Zhuo H, Calfee CS, Looney MR. Maladaptive role of neutrophil extracellular traps in pathogen-induced lung injury. JCI Insight 2018;3.

21. Hsu SD, Lin FM, Wu WY et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res

201 l;39:Dl63-9.

22. Riutta A, Hanspers K, Pico AR. Target interaction finder. GitHub

Repository, 2017.

23. Subramanian SL, Kitchen RR, Alexander R et al. Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies. J Extracell Vesicles 20l5;4:27497.

24. Shannon P, Markiel A, Ozier O et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498- 504.

25. Kuleshov MV, Jones MR, Rouillard AD et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 20l6;44:W90- 7.

26. Kutmon M, Riutta A, Nunes N et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res 20l6;44:D488-94.

27. Yang B, Lu Y, Wang Z. Control of cardiac excitability by microRNAs. Cardiovasc Res 2008;79:571-80.

28. Wang M, Li S, Zhou X et al. Increased inflammation promotes ventricular arrhythmia through aggravating left stellate ganglion remodeling in a canine ischemia model. International Journal of Cardiology 2017;248:286-293.

29. Zhang Y, Liu D, Chen X et al. Secreted monocytic miR-l 50 enhances targeted endothelial cell migration. Mol Cell 2010;39: 133-44. 30. Li X, Kong M, Jiang D, Qian J, Duan Q, Dong A. MicroRNA-l 50 aggravates H202-induced cardiac myocyte injury by down-regulating c-myb gene. Acta Biochim Biophys Sin (Shanghai) 2013;45:734-41.

31. Roncarati R, Viviani Anselmi C, Losi MA et al. Circulating miR-29a, among other up-regulated microRNAs, is the only biomarker for both hypertrophy and fibrosis in patients with hypertrophic cardiomyopathy. J Am Coll Cardiol 2014;63:920-7.

32. Zile MR, Mehurg SM, Arroyo JE, Stroud RE, DeSantis SM, Spinale FG. Relationship between the temporal profile of plasma microRNA and left ventricular remodeling in patients after myocardial infarction. Circ Cardiovasc Genet 2011;4:614-9.

33. Melman YF, Shah R, Danielson K et al. Circulating MicroRNA-30d Is

Associated With Response to Cardiac Resynchronization Therapy in Heart Failure and Regulates Cardiomyocyte Apoptosis: A Translational Pilot Study. Circulation

2015;131 :2202-2216.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for determining risk of sudden cardiac death (SCD) in a subject, the

method comprising:

providing a sample comprising plasma or a subfraction thereof from the subject; determining a level of microRNAs (miRNAs) comprising one, two, or all three of miR-l50 (preferably miR-l 50-5p), miR-29a (preferably miR-29a-3p), and miR-30a (preferably miR-30a-5p) in the sample; and

calculating a SCD risk score based on the levels of the miRNAs.

2. The method of claim 1, wherein the subject has had a myocardial infarction (MI) within the previous 40-90 days, or has had a revascularization procedure within the previous 90 days.

3. The method of claim 1, wherein the subject has a left ventricular ejection fraction (LVEF) of less than 30-35%.

4. The method of claim 1, further comprising using an echocardiogram or cardiac MRI to measure LVEF.

5. The method of claim 1, wherein the subject has one or more symptoms of heart failure (HF) or a diagnosis of HF.

6. The method of claim 1 , wherein determining a level of the miRNAs comprises using a sequencing based method, a hybridization based method, or a polymerase chain reaction (PCR)-based method.

7. The method of claim 1, wherein the sample comprises exosomes isolated from the plasma.

8. The method of claim 1, further comprising:

comparing the SCD risk score to a reference risk score, wherein an SCD risk score above the reference risk score indicates that the subject is at risk of SCD.

9. The method of claim 8, further comprising recommending that the subject be treated with a primary prevention defibrillator.

10. The method of claim 9, further comprising treating the subject with a primary

prevention defibrillator.

11. A method for selecting a treatment for a subject, the method comprising:

providing a sample comprising plasma or a subfraction thereof from the subject; determining a level of one, two, or all three microRNAs (miRNAs) comprising miR- 150 (preferably miR-l 50-5p), miR-29a (preferably miR-29a-3p), and miR-30a (preferably miR-30a-5p) p in the sample;

comparing the levels of the miRNAs to reference levels; and

selecting and optionally recommending or administering a treatment comprising a primary prevention defibrillator for a subject who has one, two, or all three of the miRNAs that are unfavorable with regard to the reference level, wherein an unfavorable level is a level of miR-l50-5p above the reference level, miR-29a-3p above the reference level, and miR-30a-5p below the reference level.

12. The method of claim 11, wherein the subject has had a myocardial infarction (MI) within the previous 40-90 days, or has had a revascularization procedure within the previous 90 days.

13. The method of claim 11, wherein the subject has (i) a LVEF of less than 30-35% and (ii) one or more symptoms of HF or a diagnosis of HF.

14. The method of claim 13, further comprising using an echocardiogram or cardiac MRI to measure FVEF

15. The method of claim 11, wherein determining a level of the miRNAs comprises using a sequencing based method, a hybridization based method, or a polymerase chain reaction (PCR)-based method.

16. The method of claim 11, wherein the sample comprises exosomes isolated from the plasma.

17. The method of claim 11, further comprising calculating an SCD risk score based on the levels of the miRNAs in the sample.

18. The method of claim 11, wherein the reference level is the median level for a

reference population.

19. The method of any of claims 1-18, further comprising measuring levels of one or more internal control miRNAs, and normalizing the levels of comprising miR-l50- 5p, miR-29a-3p, and miR-30a-5p to the internal control miRNAs.

20. The method of claim 19, wherein the internal control miRNAs comprise one or more of hsa-miR-l03a-3p, hsa-let-7g-5p and hsa-miR- 140-3.