Identifying risk of CVD

Abstract

The invention relates to provides methods of identifying a subject at risk of developing cardiovascular disease (CVD), as well as methods of preventing CVD in a subject identified as being at risk of developing CVD. The invention also relates to method of diagnosing subclinical atherosclerosis in a subject, methods of treating subclinical atherosclerosis or CVD, and methods of identifying a subject that is likely to benefit from treatment with one or more lipid lowering therapeutic agent. The methods of the invention relate to determining the levels of different autoantibodies.

Description

[0001] IDENTIFYING RISK OF CVD

[0002] FIELD

[0003] The invention relates to methods of identifying subjects at risk of developing cardiovascular disease (CVD) or diagnosing atherosclerosis, and associated methods of treatment. The invention also relates to methods of identifying subjects for treatment with lipid lowering therapeutic agents.

[0004] BACKGROUND

[0005] Systemic Lupus Erythematosus (SLE) is a chronic inflammatory condition driven by autoimmune dysregulation, characterised by relapsing and remitting episodes during its life-long course, usually requiring long-term immunomodulatory and immunosuppressive treatment. When SLE starts before 18 years of age, the condition is called childhood or juvenile-onset SLE (JSLE) and is distinguished by a more severe clinical phenotype compared to adult-onset SLE, leading to increased comorbidity burden, including a significantly increased risk of developing cardiovascular disease (CVD).

[0006] Notably, children and young people (CYP) with JSLE have an estimated 100 to 300-fold increased CVD-related mortality compared to age-matched healthy controls (1). Sub-clinical atherosclerosis (chronic inflammation of the large arteries with a long asymptomatic course, which is a major cause of CVD) was detected in -32% CYP with JSLE (2). A retrospective analysis of the large UK JSLE cohort (n=413) identified 12 CVD-related events, which occurred at a median age of 16 years and median disease duration of only two years (3). However, despite strong evidence of increased CVD-risk in patients with JSLE, comorbidity-tailored recommendations or research directed towards CVD-risk stratification and tailored management in JSLE are limited (4, 5). A growing body of evidence, including data generated by our group, support that serum metabolomic biomarkers can predict CVD-risk in healthy CYP (6, 7) and CYP with JSLE (8, 9).

[0007] Traditional risk factors for CVD including age, sex, family history of CVD, dyslipidaemia, high blood pressure, unhealthy diet, obesity, sedentary life, smoking and diabetes can only partially explain the increased prevalence of atherosclerosis and CVD in JSLE patents. Despite worrying trends of increased CVD-risk and higher incidenceof CVD events in younger population in the recent decades compared to the relative decline in adults aged >50 years, likely due to implementation of CVD preventative strategies later in life, the CVD-risk in young people is more difficult to estimate both in general population (10), and in the context of autoimmune diseases, such as JSLE (11). Chronic inflammation drives CVD-risk in JSLE through various disease activity-related mechanisms, including increased expression of proinflammatory cytokines such as TNF-a (12), dyslipidaemia associated with higher VLDL particles, T-cell and B-cell lipid raft activation mediating immune cell activation, increase in the pro-atherosclerotic ApoB:ApoAl ratio, and in the number and transcriptomic profile of CD8+ T-lymphocytes, characterised by upregulation of genes associated with interferon signalling and atherosclerosis processes (8, 13).

[0008] In addition to direct disease-related mechanisms, the increased obesity and sedentarism due to impact of JSLE symptoms on the functional level of an individual, and the metabolic effects of steroid treatment which is associated with increased CVD risk even at low doses (14), all play a key role in the excessive CVD-risk related morbidity and mortality in JSLE and other autoimmune rheumatic diseases (15).

[0009] Despite the recognition of the critical importance of CVD-risk identification and tailored management in JSLE, there are currently no reliable stratification tools for use in clinical practice (11) or consensus among clinicians regarding their use (16).

[0010] As statins are the most used treatments for CVD-risk management in general population, a large investigator-led clinical trial, the APPLE trial (Atherosclerosis Prevention in Pediatric Lupus Erythematosus), was designed and conducted to evaluate the efficacy and safety of atorvastatin in preventing subclinical atherosclerosis progression in children and adolescents with JSLE over 3 years (17). The APPLE trial was a randomized, double-blind, placebo-controlled, multicentre clinical trial conducted at 21 sites affiliated to the Childhood Arthritis and Rheumatology Research Alliance (CARRA) in North America. The trial investigated carotid intima media thickness (CIMT), a validated outcome measure of atherosclerosis and CVD-risk, as primary outcome. Although the APPLE trial did not meet the primary endpoint as no significant difference in the CIMT progression over 36 months has been found between the treatment arms, the secondary and subsequent analyses identified both traditional and non-traditional CVD-risk factors associated with CIMT progression in JSLE (17-20). Arecent serum biomarker analysis led to the identification of a novel metabolomic signature predictive of CIMT progression in the placebo arm (21), highlighting the JSLE patient heterogeneity in terms of subclinical atherosclerosis and need for biomarker stratification to identify individuals with high CVD-risk, most likely to need and potentially benefit from tailored CVD-risk management strategies.

[0011] Cardiovascular disease (CVD) is a major cause of morbidity and mortality in JSLE. Identifying biomarkers predictive of atherosclerosis progression in JSLE and response to available therapies are essential for personalised CVD-risk management.

[0012] SUMMARY

[0013] In a first aspect, the invention provides a method of identifying a subject at risk of developing cardiovascular disease (CVD), comprising:

[0014] i. providing a biological sample obtained from the subject;

[0015] ii. determining the levels of at least one of: anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and / or anti-NFIA antibodies in the sample;

[0016] iii. comparing the level of the antibodies in the sample with a reference level of the same antibodies;

[0017] iv. using the results of iii. to determine if the subject is at risk of developing CVD.

[0018] Step ii. of the method may comprise determining the level of two of, three of, four of, five of or all of anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and anti-NFIA antibodies in the sample.

[0019] In the method, a decrease in the level of anti-RAD23B antibodies, a decrease in the level of anti-HDAC4 antibodies, a decrease in the level of anti-STAT4 antibodies, a decrease in the level of anti-SEPTIN9 antibodies, a decrease in anti-NFIA antibodies, and / or an increase in the level of anti-STK24 antibodies compared to the reference level indicates that the subject is at risk of developing CVD.The reference level may be taken from an equivalent biological sample of a subject previously diagnosed with JSLE, and who has been determined to not be at risk, or to be at a low risk, of developing CVD.

[0020] An individual who has been determined to not be at risk, or to be at a low risk of developing CVD may have no thickened arteries upon CIMT assessment, for example via a vascular scan, and / or may have no atherosclerotic plaques.

[0021] If the subject is determined as at being at risk of developing CVD, the subject may be administered one or more therapy to treat or prevent the CVD.

[0022] The one or more therapy to treat or prevent the CVD may comprise or consist of one or more lipid lowering therapeutic agent.

[0023] In a second aspect, there is provided a method of preventing CVD in a subject identified as being at risk of developing CVD, comprising performing the method of the first aspect, and if the subject is determined as being at risk of developing CVD, administering one or more lipid lowering therapeutic agent to the subject.

[0024] In a third aspect, there is provided a method of diagnosing subclinical atherosclerosis in a subject, comprising:

[0025] i. providing a biological sample obtained from the subject;

[0026] ii. determining the levels of at least one of: anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and / or anti-NFIA antibodies in the sample;

[0027] iii. comparing the level of the antibodies in the sample with a reference level of the same antibodies;

[0028] iv. using the results of iii. to determine if the subject has subclinical atherosclerosis.

[0029] Step ii. of the method may comprise determining the level of two of, three of, four of, five of or all of anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and anti-NFIA antibodies in the sample.In the method, a decrease in the level of anti-RAD23B antibodies, a decrease in the level of anti-HDAC4 antibodies, a decrease in the level of anti-STAT4 antibodies, a decrease in the level of anti-SEPTIN9 antibodies, a decrease in anti-NFIA antibodies, and / or an increase in the level of anti-STK24 antibodies compared to the reference level indicates that the subject has subclinical atherosclerosis.

[0030] The reference level may be taken from an equivalent biological sample of a subject previously diagnosed with JSLE, and who has been determined to not be at risk, or to be at a low risk, of having subclinical atherosclerosis.

[0031] An individual who has been determined to not be at risk, or to be at a low risk of having subclinical atherosclerosis may have no thickened arteries upon CIMT assessment, for example via a vascular scan, and / or may have no atherosclerotic plaques.

[0032] If the subject is determined as having subclinical atherosclerosis, the subject may be administered one or more therapy to treat the subclinical atherosclerosis.

[0033] The one or more therapy to treat the subclinical atherosclerosis may comprise or consist of one or more lipid lowering therapeutic agent.

[0034] In a fourth aspect, there is provided a method of treating subclinical atherosclerosis, comprising:

[0035] i. performing the method of the third aspect, and

[0036] ii. administering one or more lipid lowering therapeutic agent to the subject if the subject is determined as having subclinical atherosclerosis.

[0037] In a fifth aspect, there is provided a method of identifying a subject that is likely to benefit from treatment with one or more lipid lowering therapeutic agent, wherein the subject has been identified as being at risk of developing CVD, or has been diagnosed with atherosclerosis according to the method of the first or third aspects, and wherein the method comprises:

[0038] i. providing a biological sample obtained from the subject;

[0039] ii. determining the levels of at least one of, such as both of: anti-ABIl and / or anti-CSNK2A2 antibodies in the sample;iii. comparing the level of the one or more antibodies in the sample with a reference level of the same antibodies;

[0040] iv. using the results of iii. to determine if the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

[0041] Both anti-ABIl and / or anti-CSNK2A2 antibodies were found to be accurate in predicting response to statin treatment across multiple signatures (Figures 2-4), with ROC values ROC values of around 0.7 individually and above 0.75 when combined. This demonstrates the utility of using these antibodies as a marker for likelihood of positive response to treatment with one or more lipid lowering therapeutic agent.

[0042] Step ii. of the method may further comprise determining the level of one of, two of, three of, four of, five of, or all of anti-PRKARlA antibodies, anti-NRIP3 antibodies, anti-PDK4 antibodies, anti-ATP5B antibodies, anti-BATF antibodies and anti-NUDT2 antibodies in the sample.

[0043] In the method, a decrease in the level of anti-ABIl antibodies, a decrease in the level of anti-CSNK2A2 antibodies, an increase in the level of anti-PRKARlA antibodies, a decrease in the level of anti-NRIP3 antibodies, an increase in the level of anti-PDK4 antibodies, a decrease in the level of anti-ATP5B antibodies, an increase in the level of anti-BATF antibodies, and / or an increase in the level of anti-NUDT2 antibodies compared to the reference level indicates that the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

[0044] Step ii. of the method may further comprise determining the level of one of, two of, three of, four of, five of, six of, seven of, eight of, nine of, 10 of, 11 of, 12 of, 13 of, 14 of, 15 of, 16 of, 17 of, 18 of, or all of anti-MLX antibodies, anti-FLIl antibodies, anti-NRF1 antibodies, anti-ERG antibodies, anti-SNRPA antibodies, anti-dsDNA antibodies, anti-RUNXITl antibodies, anti-CSNK2Al antibodies, anti-MAZ antibodies, anti-HOMER2 antibodies, anti-LIN28A antibodies, anti-NAPlL3 antibodies, anti-CD96 antibodies, anti-KCMFl antibodies, anti-EHF antibodies, anti-CLK3 antibodies, anti-PPPlR2P9 antibodies, anti-NDEl antibodies and anti-GFAP antibodies in the sample.In the method, a decrease in the level of anti-ABIl antibodies, a decrease in the level of anti-CSNK2A2 antibodies, a decrease in anti-MLX antibodies, a decrease in anti-FLI1 antibodies, a decrease in anti-NRFl antibodies, a decrease in anti-ERG antibodies, a decrease in anti-SNRPA antibodies, a decrease in anti-dsDNA antibodies, a decrease in anti-RUNXITl antibodies, a decrease in anti-CSNK2Al antibodies, a decrease in anti-MAZ antibodies, a decrease in anti-HOMER2 antibodies, a decrease in anti-LIN28A antibodies, an increase in anti-NAPlL3 antibodies, an increase in anti-CD96 antibodies, an increase in KCMF1 antibodies, an increase in anti-EHF antibodies, an increase in anti-CLK3 antibodies, an increase in anti-PPPlR2P9 antibodies, an increase in anti-NDEl antibodies and / or an increase in anti-GFAP antibodies compared to the reference level indicates that the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

[0045] In the method, the reference level may be taken from an equivalent biological sample of a subject determined to respond to lipid lowering therapeutic agents, such as statins, in treating subclinical atherosclerosis or preventing CVD.

[0046] In the method, if the subject is identified as likely to benefit from treatment with one or more lipid lowering therapeutic agent, the subject may then be administered one or more lipid lowering therapeutic agents.

[0047] In a sixth aspect, there is provided a method of treating or preventing CVD, comprising:

[0048] i. performing the method of the fifth aspect to determine if the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent: and ii. if the subject is identified as likely to benefit from treatment with one or more lipid lowering therapeutic agent, administering one or more lipid lowering therapeutic agent to the subject.

[0049] The one or more lipid lowering therapeutic agent of any aspect may be selected from statins, ezetimibe, bile acid sequestrants, PCSK9 inhibitors, adenosine triphosphatecitrate lyase (ACLY) inhibitors, fibrates, niacin, Omega-3 fatty acid ethyl esters, and marine-derived omega-3 polyunsaturated fatty acids (PUFA). Preferably, the one or more lipid lowering therapeutic agent is one or more statins, such as atorvastatin. A lipid lowering therapeutic agent refers to an agent which decreases and / or normaliseslipids serum levels, preferably to the levels suggested by a clinician which are deemed to be within a healthy range.

[0050] Bile acid sequestrants may be selected from Cholestyramine (Questran®, Questran® Light, Prevalite®, Locholest®, Locholest® Light), Colestipol (Colestid®), Colesevelam Hcl (WelChol®)

[0051] PCSK9 inhibitors may be selected from alirocumab and evolocumab.

[0052] Adenosine triphosphate -citrate lyase (ACLY) inhibitors may be selected from -Bempedoic acid (Nexletol).

[0053] A combination of bempedoic acid and ezetimibe (Nexlizet) may be used.

[0054] Fibrates may be selected from Gemfibrozil (Lopid®), Fenofibrate (Antara®, Lofibra®, Tricor®, and Triglide™), Fenofibric Acid (Fibricor® and Trilipix®).

[0055] In a seventh aspect, there is provided a statin for use in treating or preventing CVD, or for treating subclinical atherosclerosis in subject, wherein the subject has been identified as likely to benefit from treatment by the method of the fifth or sixth aspect. The statin may be atorvastatin.

[0056] In an eighth aspect, there is provided a use of a statin for the manufacture of a medicament for treating subclinical atherosclerosis or for preventing CVD in a subject, wherein the subject has been identified as likely to benefit from treatment, by the method of the fifth or sixth aspect. The statin may be atorvastatin.

[0057] In any aspect, the subject may have been previously diagnosed with JSLE.

[0058] In any aspect, the subject may be aged between about 10 and 30 years of age, such as between about 10 and 18 years of age or between about 13 and 30 years of age.

[0059] The skilled person will understand that there are a number of techniques to measure the levels of antibodies in a sample, such as fluorescence intensity, ELISA,immunoprecipitation, agglutination assays, immunoblotting (Western Blotting), radioimmunoassay, SIMOA or New functional proteomic platforms.

[0060] In any aspect, the CVD may be confirmed or assessed by measurement of CIMT progression. The skilled person will understand that there are a number of techniques to monitor CIMT progression, such as ultrasound vascular scans, cardiac MRI, arteriography, MRI or CT angiography.

[0061] In any aspect, the CVD may be caused by atherosclerosis, such as subclinical atherosclerosis.

[0062] In any aspect, the antibodies are autoantibodies.

[0063] In any aspect, the sample may be a serum, blood or plasma sample.

[0064] In any method of the invention, the subject may also receive a brain scan and / or cardiac MRI.

[0065] The inventors have identified distinct serum biomarker signatures of autoantibodies able to distinguish with high performance, individuals with JSLE at high risk for atherosclerosis progression as well as responders to statin treatment. These results, coupled with biomarker robustness and validity of the assessment of atherosclerosis progression in the context of a standardised and centrally-read CIMT measurements in the APPLE trial, provide high potential for clinical translation, addressing a critical unmet need to identify CYP with JSLE and high CVD-risk and select those more likely to respond to statins, as well as potential for identification of novel mechanisms driving atherosclerosis in JSLE which can potentially be targeted by new therapies.

[0066] Subjects who are at risk of developing CVD may see thickening of their arteries which can contribute to the CVD, which is a result of impaired circulation, which causes the symptoms.

[0067] The skilled person will understand that optional features of one embodiment or aspect of the invention may be applicable, where appropriate, to other embodiments or aspects of the invention.Embodiments of the invention will now be described in more detail, by way of example only, with reference to the accompanying drawings.

[0068] BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1. Study design and analysis plan flow diagram for the APPLE trial analyses. The JSLE cohort was stratification based on CIMT progression over 36 months in both the placebo and atorvastatin arms, as we published before (21). Abbreviations: APPLE - Atherosclerosis Prevention in Pediatric Lupus Erythematosus clinical trial; CIMT- carotid intima-media thickness; CVD - cardiovascular disease; ROC - Receiver Operating Characteristic.

[0070] Figure 2. Baseline serum autoantibodies (N=579 after data cleaning) comparisons between distinct CIMT progression groups - placebo arm and atorvastatin arm. A) Volcano plot displaying fold change of all autoantibodies and Log 10 p values comparing high (N=26) and low (N=19) CIMT progression groups - placebo arm (p<0.05; log2(fold change)>0.2). Top five significant metabolites (AUC > 60%) highlighted in red. B) Box and whisker plots showing the significant autoantibody levels of the high vs. low CIMT progression groups - placebo arm. Empirical Bayes moderated t-test. C) Individual and combined ROC analysis for discriminating high vs. low CIMT progression groups using the top five autoantibodies (individual AUC > 60%). D) Volcano plot displaying fold change of all autoantibodies and Log 10 p values comparing high (N=17) and low (N=13) CIMT progression groups - atorvastatin arm (p<0.05; log2(fold change)>0.2). E) Box and whisker plots showing the significantly distinct autoantibody levels in the high vs. low CIMT progression groups - atorvastatin arm. Empirical Bayes moderated t-test. F) Individual and combined ROC analyses for discriminating high vs. low CIMT progression groups using the top five autoantibodies (individual AUC > 60%). Abbreviations: AUC - area under the curve; CIMT- carotid intima-media thickness; ROC- Receiver Operator Curve.

[0071] Figure 3. Performance of autoantibody predictive models and two-step risk stratification. A) ROC curve showing the performance of placebo autoantibody signature model under 10-fold cross-validation. B) ROC curve showing the performance of atorvastatin autoantibody signature model under 10-fold cross-validation. C) Proposal of a two-step risk stratification and treatment strategy for atherosclerosis riskin JSLE. Flow chart shows the potential clinical implementation of a two-step strategy to first identify CYP with JSLE at risk of high atherosclerosis progression who can be targeted by any type of CVD-risk management strategy, and secondly, select with high accuracy the individuals with JSLE more likely to benefit from statin treatment as CVD-risk management strategy.

[0072] Figure 4. Baseline serum autoantibodies (N=579 after data cleaning) comparisons between distinct CIMT progression groups (High vs. Low and Intermediate, identifying non-responders vs. responders and partial responders to statin) -atorvastatin arm. A) Volcano plot displaying fold change of all autoantibodies and LoglO p values comparing high (N=17) vs. low and intermediate combined (N=32) CIMT progression groups - atorvastatin arm (p<0.05; log2(fold change)>0.2). B) Box and whisker plots showing the significantly different autoantibody levels of the high vs. low CIMT progression groups - atorvastatin arm. Empirical Bayes moderated t-test. C) Combined and separate ROC analyses for discriminating high vs. low CIMT progression groups, using the top five autoantibodies (ranked by individual AUC, all with AUC>69%). Abbreviations: AUC - area under the curve; CIMT- carotid intima-media thickness; ROC- Receiver Operator Curve.

[0073] Figure 5. Juvenile-onset systemic lupus erythematosus (JSLE) stratification by ACIMT (12 measurements) at baseline versus 36 months in the placebo arm. A) Heat map displaying ACIMT (Z scored) from patients with JSLE from the placebo arm (full CIMT data set, N = 60) stratified by unsupervised hierarchical clustering. Each column represents a patient with JSLE. High and low ACIMT progression groups were discovered over 36 months. B) Box and whisker plots showing comparisons of MMeanIMT between groups from (A) at baseline and 36 months. C) Correlations between various patient / disease related factors at baseline and ACIMT at 36 months (Placebo arm, N=60). Pearson correlation coefficients are represented as connecting lines between the 12 ACIMT section and the clinical characteristic section. Only correlations with p value below 0.01 are shown (not adjusted). Red line represents a positive correlation and blue line a negative correlation. The width and colour shade of the connecting lines indicate the strength of the correlation. Abbreviations: CIMT, carotid intima-media thickness; MMeanIMT, mean of the mean common CIMT.Figure 6. Juvenile-onset systemic lupus erythematosus (JSLE) stratification by ACIMT (12 measurements) at baseline versus 36 months in the atorvastatin arm. A) Heat map displaying ACIMT (Z scored) from patients with JSLE from the atorvastatin arm (full CIMT data set, N = 61) stratified by unsupervised hierarchical clustering. Each column represents a patient with JSLE. High and low ACIMT progression groups were discovered over 36 months. B) Box and whisker plots showing comparisons of MMeanIMT between groups from (A) at baseline and 36 months. C) Correlations between various patient / disease related factors at baseline and ACIMT at 36 months (Atorvastatin arm, N=61). Pearson correlation coefficients are represented as connecting lines between the 12 ACIMT section and the clinical characteristic section. Only correlations with p value below 0.01 are shown (not adjusted). Red line represents a positive correlation and blue line a negative correlation. The width and colour shade of the connecting lines indicate the strength of the correlation. Abbreviations: CIMT, carotid intima-media thickness; MMeanIMT, mean of the mean common CIMT.

[0074] Figure 7. Combined and separate ROC analyses for discriminating High (N=17) vs. Low (N=13) CIMT progression groups in the atorvastatin group, using the two overlapping autoantibodies (ABI1 and CSNK2A2) in both atorvastatin group comparisons. Abbreviations: AUC - area under the curve; CIMT- carotid intima-media thickness; ROC- Receiver Operator Curve.

[0075] Figure 8. Combined and separate ROC analyses for discriminating High vs. Low and Intermediate (N=32) CIMT progression groups in the atorvastatin group, using the two overlapping autoantibodies (ABI1 and CSNK2A2) in both atorvastatin group comparisons. Abbreviations: AUC - area under the curve; CIMT- carotid intima-media thickness; ROC- Receiver Operator Curve.

[0076] EXAMPLES

[0077] The inventors conducted a biomarker discovery study investigating baseline autoantibody profiles in a sub-cohort of the APPLE (Atherosclerosis Prevention in Pediatric Lupus Erythematosus) trial, a large, multi-centre, randomized, double-blind, clinical trial of atorvastatin versus placebo (1:1) for atherosclerosis progression in JSLE, using carotid intima-media thickness (CIMT) as the primary outcome, conducted across 21 sites in North America.Ninety -four ISLE patients (mean [SD] age =15.3 [2.4] years; 73 [78%] female) recruited to the APPLE trial (45 randomized to the placebo and 49 to the atorvastatin arm) with matched baseline serum samples and complete longitudinal CIMT measurements over the duration of the APPLE trial (36 months) were included in the analysis. Unsupervised cluster analysis based on patterns on CIMT progression over 36 months identified patients with high and low CIMT progression placebo and statin arms.

[0078] Differential expression of serum autoantibody profiles at baseline predictive of high vs. low atherosclerosis progression patterns in both the placebo and atorvastatin arms of the APPLE trial, was assessed using Empirical Bayes moderated t-test and Receiver Operator Curve (ROC) analysis with logistic regression model.

[0079] Six autoantibodies (STK24, RAD23B, HDAC4, STAT4, SEPTIN9, NFIA) were significantly associated with high vs. low CIMT progression in the placebo arm (combined AUC=87%). In the atorvastatin arm, eight autoantibodies (ABI1, ATP5B, CSNK2A2, NRIP3, PRKAR1A, PDK4, BATF, NUDT2) distinguished high from low CIMT progression groups (combined AUC=96%).

[0080] Results

[0081] Participant Characteristics

[0082] A total of 94 participants to the APPLE trial were included into this biomarker subcohort study based on CIMT data and serum sample availability. CYP with JSLE previously stratified based on distinct CIMT progression patterns were analysed comparatively (21). In the placebo arm, the high CIMT progression group (N=26) was compared with the low CIMT progression group (N=19) stratified based on unsupervised clustering analysis of CIMT progression over 3 years (Fig. 4 and (21)).

[0083] Similarly, in the atorvastatin arm, the high CIMT progression group (non-responders to statin, N=17) was compared either with the low CIMT progression group (responders to statin, N=13), or with the combined group with low (N=13) and intermediate (N=19) CIMT progression (defined as responder and partial-responders to statin) (Fig. 5 and (21)).

[0084] A summary of baseline characteristics, including demographics, disease duration, disease activity (assessed using the validated SLEDAI - Systemic Lupus ErythematosusDisease Activity Index) and damage (assessed using the validated SLICC DI Systemic Lupus International Collaborating Clinics Damage Index), and baseline JSLE markers and lipid profile, as well as information about relevant JSLE treatment at baseline is depicted in Table 1. The previous comparative analyses of the JSLE patients stratified based on distinct CIMT progression patterns in either the placebo and atorvastatin groups did not identify any statistically significant differences, apart from increased total cholesterol and low-density lipoprotein (LDL)-cholesterol in the high vs. low CIMT progression groups in the placebo arm, with the caveat that most CYP with JSLE had normal lipid profile at baseline (21). This highlights the difficulty clinicians have in identifying JSLE patients with high CVD-risk in the absence of CIMT measurements.

[0085] The primary analyses were focused on autoantibody profiling and comparisons between individuals with distinct CIMT progression to identify novel biomarkers predictive of high subclinical atherosclerosis progression in the placebo and atorvastatin arms in the APPLE trial. Data analysis pipeline is detailed in Figure 1.

[0086] Distinct autoantibody signature predicts high vs. low CIMT progression in the placebo arm of the APPLE trial

[0087] To assess whether the autoantibody signature can identify different patterns of natural CIMT progression over 36 months in JSLE, the autoantibody profile of the high CIMT progression group (N=26) was compared to the low CIMT progression group (N=19) in the placebo arm of the APPLE trial. Six autoantibodies were significantly different, with one upregulated (STK24) and five downregulated (RAD23B, HDAC4, STAT4, SEPTIN9, NFIA) in the high vs. low CIMT progression groups in the placebo arm of the APPLE trial (Figure 2A-B). Full names and functions of the novel autoantibodies identified by the ROC analysis are listed in the Table 2.

[0088] ROC analysis in univariate logistic regression generated individual autoantibody AUCs ranging from 56% to 69% (Table 3 and Figure 2C). Multivariate ROC analysis combing the top five autoantibodies (with an individual AUC cutoff above 60%) demonstrated a combined AUC of 87%, outperforming, as expected, the accuracy of each autoantibody alone in predicting atherosclerosis progression in JSLE.Distinct autoantibody profile predicts response to statin treatment in the APPLE trial

[0089] The same analysis pipeline was employed to examine the autoantibody profiling of the atorvastatin arm of the APPLE trial, by comparing the high CIMT progression (statin non-responders, N=17) against the low CIMT progression group (statin responders, N=13). Eight autoantibodies were significantly differentially expressed (p<0.05; log2(fold change)>0.2) with four upregulated (ABI1, ATP5B, CSNK2A2, NRIP3) and four downregulated (PRKAR1A, PDK4, BATF, NUDT2) in the high vs. low CIMT progression group in the statin arm (Figure 2D-E). The full names and known functions of these autoantibodies are listed in Table 4. ROC analysis in univariate logistic regression generated AUC values for the individual autoantibodies ranging from 47% to 74%, while multivariate ROC analysis using the top five performing autoantibodies (with individual AUCs >60%) yielded a combined AUC of 96% (Table 5 and Figure 2F).

[0090] To comprehensively examine the autoantibody signature relevant for atherosclerosis progression despite statin treatment in the atorvastatin arm, the low and intermediate CIMT progression groups (defining statin responders and partial responders) were combined as a new moderate atherosclerosis progression group (N=32). This moderate progression group was then compared to the high CIMT progression group (statin non-responders) (Figure 4A-B). 21 autoantibodies were differentially expressed, 13 were upregulated (ABI1, CSNK2A2, MLX, FLU, NRF1, ERG, SNRPA, dsDNA, RUNX1T1, CSNK2A1, MAZ, H0MER2, LIN28A) and 8 were downregulated (NAP1L3, CD96, KCMF1, EHF, CLK3, PPP1R2P9, NDE1, GFAP) in the high vs. moderate CIMT progression groups (AUCs ranging from 59% to 75%) (Figure 4A-C, Table 4-5).

[0091] Multivariate ROC analysis using the top five performing autoantibodies (with individual AUC >69%) yielded a combined AUC of 88% (Figure 4C). As expected, combing the low and intermediate CIMT progression groups increased heterogeneity of the groups and led to reduced performance in the ROC analysis.

[0092] Notably, ABI1 and CSNK2A2 emerged as shared predictive markers in both atorvastatin group comparisons (predicting both response and partial response to statin).These results highlight the importance of both lipid-related and lipid-independent disease-specific biomarkers potentially implicated in the pathogenesis of atherosclerosis in JSLE

[0093] Two-step risk stratification strategy for predicting atherosclerosis progression and atorvastatin response in JSLE

[0094] The comprehensive analysis of distinct autoantibody profiles between high and low CIMT progression groups in both placebo and atorvastatin arms (Figure 2) led to the development of two predictive models, trained with the top-performing autoantibodies using logistic regression ROC analysis. These logistic regression models were validated using 10-fold cross-validation to assess their robustness.

[0095] The placebo arm model showed adequate predictive power with an AUC of 79% (Specificity: 80%; Sensitivity: 58%) under 10-fold cross-validation (Figure 3A). The atorvastatin arm model (derived from the comparison between the high vs. low CIMT progression groups in the atorvastatin arm showed stronger predictive performance with an AUC of 86% (Specificity: 78%; Sensitivity: 76%) under 10-fold cross-validation (Figure 3B). The robust performance of these internally validated autoantibody signature models highlights their potential application in clinical trial design, to enable the selection of individuals with JSLE most likely to benefit from atorvastatin (e.g. biomarker enriching trials to maximise the chance of trial success).

[0096] The data demonstrates that the models can be used for the development of a two-step CVD-risk stratification strategy for predicting both the high atherosclerosis progression risk and the efficacy of atorvastatin treatment in JSLE (Figure 3C). The placebo autoantibody model can stratify the JSLE cohorts into those with high or low natural CIMT progression. The cohorts identified as having high natural CIMT progression can then be further stratified into low CIMT progression (statin responders and partialresponders) and high CIMT progression (non-responders to statins) using the atorvastatin autoantibody model. As statins have similar mechanism of action across all the class drugs, this model is adequate to predict response to other types of statin treatments. The two-step risk stratification strategy tackles the highly heterogeneous nature of JSLE by providing tailored treatment approaches through precision patient stratification.Summary

[0097] The inventors have successfully identified a distinct autoantibody signature that differentiates between high and low CIMT progression groups in the placebo arm (Figure 2). Six identified key autoantibodies (STK24, RAD23B, HDAC4, STAT4, SEPTIN9, NFIA) showed robust performance (Top markers combined AUC of 87%) in multivariate ROC analysis, outperforming the previously identified metabolomic markers of atherosclerosis progression using the same APPLE trial cohort (21). Moreover, autoantibody profiling analysis led to the first identification of biomarkers predictive of atherosclerosis progression response to statin in JSLE (Top markers combined AUC of 96%, Figure 3), despite the lack of a predictive metabolomic signature in the same cohort (21). The recognised functions of the best performing autoantibody signatures, along with the enrichment pathway analysis, suggest that lipid-independent pathways play a significant role in atherosclerosis development in JSLE. This sub-cohort analysis also provides a potential explanation for the lack of efficacy of the atorvastatin treatment in the APPLE trial, highlighting, in addition to the subclinical atherosclerosis heterogeneity reported before (21), that JSLE-associated atherosclerosis likely involves both lipid-dependent and independent (autoimmune-related) mechanisms.

[0098] Additionally, the autoantibody predictive models developed for assessing risk of atherosclerosis progression in JSLE achieved an overall AUC of 79% and 86% in the placebo and atorvastatin arms, respectively. The robust performance of the predictive autoantibody models can be used for clinical trial design, through facilitating more precise patient selection strategy for CVD-risk management interventions. The development of a two-step stratification strategy based on the identified autoantibody signatures of atherosclerosis progression in both the placebo and atorvastatin arms of the APPLE trial further highlights the potential for clinical implementation of these markers in first identifying individuals at risk, and secondly selected from those at risk, the ones likely to benefit from statin treatment.

[0099] Table 1 Demographic / clinical table of the APPLE trial sub-cohort (N=94) in the autoantibody profiling study.

[0100]

[0101]

[0102]

[0103] Disease Activity Index; SLICC / ACR-DI - Systemic Lupus International Collaborating Clinics / American College of Rheumatology Damage Index; dsDNA antibody - Anti- double-stranded DNA antibody; C3, C4 - complement fractions C3, C4; HDL - high- density lipoprotein; LDL - low-density lipoprotein.

[0104] Table 2 Names and functions of the top significantly differentially expressed autoantigens (proteins) identified in the High vs. Low CIMT progression groups in the placebo arm

[0105]

[0106]

[0107] Functional information was obtained from UniProt and KEGG databases. Autoantibodies with significantly elevated levels (p<0.05; log2(fold change)>0.2) in the high CIMT progression group, and the ones with significantly decreased levels (p<0.05; log2(fold change)>0.2) are shown.

[0108] Table 3 - Performance of the top significantly differentially expressed autoantibodies in stratifying atherosclerosis progression risk in the placebo arm

[0109]

[0110]

[0111] Summary of the performance metrics for six autoantibodies (RAD23B, HDAC4, STAT4, SEPTIN9, STK24, NFIA) differentially expressed in the high (N=26) vs. low (N=19) atherosclerosis progression groups in the placebo arm. The AUC were calculated using logistics regression ROC analysis. The optimal sensitivity, specificity and cut-off values were determined using Youden index method. Autoantibodies with significantly elevated levels (p<0.05; log2(fold change)>0.2) in the high CIMT progression, and the ones with significantly decreased levels (p<0.05; log2(fold change)>0.2) are shown.

[0112] Table 4 - Performance of the top significantly differentially expressed autoantibodies in stratifying atherosclerosis progression risk in the atorvastatin arm

[0113]

[0114]

[0115]

[0116]

[0117] Functional information was obtained from UniProt and KEGG databases (128, 129). Autoantibodies with significantly elevated levels (p<0.05; log2(fold change)>0.2) in the high CIMT progression group, and the ones with significantly decreased levels (p<0.05; log2(fold change)>0.2) are shown.

[0118] Table 5 Performance of the top significantly differentially expressed autoantibodies in stratifying atherosclerosis progression risk in the atorvastatin arm.

[0119]

[0120]

[0121] Summary of the performance metrics of the 29 novel autoantibodies identified in the atorvastatin arm, by comparing the high (N=17) vs. low (N=13) atherosclerosis progression groups (8 antibodies) or by comparing the high (N=17) vs. low and intermediate / moderate (N=32) atherosclerosis progression groups (21 antibodies, with two overlapping between the two signatures). The AUC were calculated using logistics regression ROC analysis. The optimal sensitivity, specificity and cut-off values were determined using Youden index method.

[0122] MATERIAL AND METHODS

[0123] Participants

[0124] This biomarker cohort study included a subset of CYP with JSLE recruited to the APPLE trial selected based on serum sample availability at baseline and matched complete datasets collected over the duration of the APPLE trial (N=94, 45 in the placebo arm, 49 in the atorvastatin arm). The APPLE trial enrolled 221 CYP with JSLE (age 10-18 at inclusion) who met the ACR 1997 revised classification criteria (22), recruited from 21 sites in North America, and followed for 36 months (17). Subjectswere randomized 1:1 to receive either placebo (N=108) or atorvastatin (N=113) upon informed consent / assent as age / developmentally-appropriate. All subjects met well defined inclusion / exclusion criteria as per published protocol (17).

[0125] Access to clinical, serological and vascular scan data, as well as matched serum samples from the JSLE cohort enrolled in the APPLE trial was facilitated by an international collaboration with CARRA and APPLE trial investigators (USA).

[0126] CYP with JSLE have been stratified based on CIMT progression over 36 months in both the placebo and atorvastatin arms, using unsupervised hierarchical clustering, as we published before (21). This analysis stratified the CYP with JSLE recruited to the placebo-arm into two distinct groups with high (N=35) and low (N=25) CIMT progression over 36 months (Figure 5), and the patients enrolled into the atorvastatin-arm into three distinct groups with high (N=22), intermediate (N=24) and low (N=15) CIMT progression over 36 months (Figure 6).

[0127] Biomarker analysis

[0128] Baseline serum samples were analysed using a sensitive testing / discovery platform (Sengenics, htips / / sengen. :s;<:<;nU &n;.e. .!.S£oyery-a?niyZ), which facilitates the identification of over 1,800 serum autoantibodies relevant to immune responses, which bind with high specificity to their corresponding autoantigens.

[0129] Only autoantibodies identified as true signal (showing no cross-reactivity and false positive in the Internal QC Pooled Normal group (N=6)), and were subsequently included in downstream analyses.

[0130] Net intensities (Netl) were computed by subtracting local background fluorescence from foreground fluorescence for each antigen spot, measured in relative fluorescence units (RFUs). Data were analysed under Log2 transformation and Loess normalisation for consistency across array. To ensure the normalisation has been performed successfully, six internal QC pooled normals were run in the experiment, and the correlation between these biological replicates were investigated. To ensure true autoantibody signal detection, non-specific binding controls were used as references. Negative correlation filter (NCF) method was applied to identify true autoantibody signal, excluding antigenshighly correlated with background intensities, retaining only the least correlated. A total 579 autoantibodies were selected as true autoantibody signals for downstream analysis.

[0131] Statistical Analyses

[0132] Statistical tests were performed in R. Data were assessed for normality and analysed with parametric or nonparametric tests, as appropriate. Chi-square test was used for comparison between categorical variables. One-way ANOVA and Tukey’s range test were applied for comparisons among more than two groups. Details of statistical tests and parameters accounted for in the analyses are given in the figure legends. P < 0.05 was considered statistically significant. False Discovery Rate (FDR) correction or Bonferroni correction was applied for multiple testing.

[0133] Unsupervised hierarchical clustering was performed with ClustVis (htps: / / biit.cs.ut.ee / ciustvas / ) (23). This method was used to stratify JSLE patients based on their CIMT progression over 36 months in the atorvastatin vs. placebo arms in the APPLE trial (using the difference between 12 distinct CIMT measurements at 36 months vs. baseline for each individual - ACIMT measurements).

[0134] The autoantibody profiles of distinct CIMT progression groups identified in both the placebo and atorvastatin arms (as described and published before (21)) were compared to identify differentially expressed antigens using Empirical Bayes moderated t-test and Receiver Operator Curve (ROC) analysis with logistic regression model.

[0135] Logistic regression was used as a predictive model to evaluate biomarker performance in distinguishing distinct JSLE groups with different CIMT progression patterns. Individual biomarkers were analysed with univariate logistic regression, while combined markers were assessed with multivariate logistic regression through ROC analysis.

[0136] For model performance evaluation, the ROC plot and the area under the curve (AUC) of each model were computed with the pROC package in R (24). Youden method was applied to determine the optimal balance between sensitivity and specificity, providing the optimal model accuracy of the model.Model validation

[0137] Ten-fold cross-validation was applied for all ML models with the caret package in R (25). Data were randomly partitioned into 10 groups of almost equal size. Nine groups were used as training data for model construction and the remaining group was used as validation data. The process was repeated for all 10 folds until each observation in the data is used for validation purposes once. The average performance of the 10 models was used as the result of the 10-fold cross-validation.

[0138] Pathway enrichment analysis

[0139] Pathway enrichment analysis was performed using Metascape

[0140]

[0141] with default settings (26). The statistically significant autoantibodies identified in comparisons between high and low CIMT progression in the statin arm were analysed to reveal enriched biological pathways.

[0142] Functional annotations

[0143] The functional information for the identified autoantibodies (see Chapter IV) was obtained from the UniProt and KEGG databases (27, 28).

[0144] References

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Claims

CLAIMS1. A method of identifying a subject at risk of developing cardiovascular disease (CVD), comprising:i. providing a biological sample obtained from the subject;ii. determining the levels of at least one of: anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and / or anti-NFIA antibodies in the sample;iii. comparing the level of the antibodies in the sample with a reference level of the same antibodies;iv. using the results of iii. to determine if the subject is at risk of developing CVD.

2. The method of claim 1, wherein the method comprises determining the level of two of, three of, four of, five of or all of anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and anti-NFIA antibodies in the sample.

3. The method of claim 1 or claim 2, wherein a decrease in the level of anti-RAD23B antibodies, a decrease in the level of anti-HDAC4 antibodies, a decrease in the level of anti-STAT4 antibodies, a decrease in the level of anti-SEPTIN9 antibodies, a decrease in anti-NFIA antibodies, and / or an increase in the level of anti-STK24 antibodies compared to the reference level indicates that the subject is at risk of developing CVD.

4. The method of any of claims 1-3, wherein the reference level from an equivalent biological sample of a subject previously diagnosed with JSLE, and who has been determined to not be at risk, or to be at a low risk, of developing CVD.

5. The method of any of claims 1-4, wherein if the subject is determined as at being at risk of developing CVD, the subject is to be administered or more lipid lowering therapeutic agent.

6. A method of preventing CVD in a subject identified as being at risk of developing CVD, comprising:i. performing the method of any of claims 1-4; andii. administering one or more lipid lowering therapeutic agent to the subject if the subject is determined as being at risk of developing CVD.

7. A method of diagnosing subclinical atherosclerosis in a subject, comprising:i. providing a biological sample obtained from the subject;ii. determining the levels of at least one of: anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and / or anti-NFIA antibodies in the sample;iii. comparing the level of the antibodies in the sample with a reference level of the same antibodies;iv. using the results of iii. to determine if the subject has subclinical atherosclerosis.

8. The method of claim 7, wherein the method comprises determining the level of two of, three of, four of, five of or all of anti-RAD23B antibodies, anti-HDAC4 antibodies, anti-STAT4 antibodies, anti-SEPTIN9 antibodies, anti-STK24 antibodies, and anti-NFIA antibodies in the sample.

9. The method of claim 7 or 8, wherein a decrease in the level of anti-RAD23B antibodies, a decrease in the level of anti-HDAC4 antibodies, a decrease in the level of anti-STAT4 antibodies, a decrease in the level of anti-SEPTIN9 antibodies, a decrease in anti-NFIA antibodies, and / or an increase in the level of anti-STK24 antibodies compared to the reference level indicates that the subject has subclinical atherosclerosis.

10. The method of any of claims 7-9, wherein the reference level from an equivalent biological sample of a subject previously diagnosed with JSLE, and who has been determined to not be at risk, or to be at a low risk, of having subclinical atherosclerosis.

11. The method of any of claims 7-10, wherein if the subject is determined as having subclinical atherosclerosis, the subject is to be administered one or more lipid lowering therapeutic agent.

12. A method of treating subclinical atherosclerosis, comprising:i. performing the method of any of claims 7-10; andii. administering one or more lipid lowering therapeutic agent to the subject if the subject is determined as having subclinical atherosclerosis.

13. A method of identifying a subject that is likely to benefit from treatment with one or more lipid lowering therapeutic agent, comprising:i. providing a biological sample obtained from the subject;ii. determining the levels of at least one of, such as both of: anti-ABIl and / or anti-CSNK2A2 antibodies in the sample;iii. comparing the level of the one or more antibodies in the sample with a reference level of the same antibodies;iv. using the results of iii. to determine if the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

14. The method of claim 13, wherein the subject has been identified as being at risk of developing CVD according to the method of any of claims 1-4, or has been diagnosed with atherosclerosis according to the method of any of claims 7-10.

15. The method of claim 13 or 14, wherein the method further comprises determining the level of one of, two of, three of, four of, five of, or all of anti-PRKARlA antibodies, anti-NRIP3 antibodies, anti-PDK4 antibodies, anti-ATP5B antibodies, anti-BATF antibodies and anti-NUDT2 antibodies in the sample.

16. The method of claim 15, wherein a decrease in the level of anti-ABIl antibodies, a decrease in the level of anti-CSNK2A2 antibodies, an increase in the level of anti-PRKAR1A antibodies, a decrease in the level of anti-NRIP3 antibodies, an increase in the level of anti-PDK4 antibodies, a decrease in the level of anti-ATP5B antibodies, an increase in the level of anti-BATF antibodies, and / or an increase in the level of anti-NUDT2 antibodies compared to the reference level indicates that the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

17. The method of claim 13 or 14, wherein the method further comprises determining the level of one of, two of, three of, four of, five of, six of, seven of, eight of, nine of, 10 of, 11 of, 12 of, 13 of, 14 of, 15 of, 16 of, 17 of, 18 of, or all of anti-MLX antibodies, anti-FLIl antibodies, anti-NRFl antibodies, anti-ERG antibodies, anti-SNRPA antibodies, anti-dsDNA antibodies, anti-RUNXITl antibodies, anti-CSNK2Alantibodies, anti-MAZ antibodies, anti-HOMER2 antibodies, anti-LIN28A antibodies, anti-NAPlL3 antibodies, anti-CD96 antibodies, anti-KCMFl antibodies, anti-EHF antibodies, anti-CLK3 antibodies, anti-PPPlR2P9 antibodies, anti-NDEl antibodies and anti-GFAP antibodies in the sample.

18. The method of claim 17, wherein a decrease in the level of anti-ABIl antibodies, a decrease in the level of anti-CSNK2A2 antibodies, a decrease in anti-MLX antibodies, a decrease in anti-FLIl antibodies, a decrease in anti-NRFl antibodies, a decrease in anti-ERG antibodies, a decrease in anti-SNRPA antibodies, a decrease in anti-dsDNA antibodies, a decrease in anti-RUNXITl antibodies, a decrease in anti-CSNK2Al antibodies, a decrease in anti-MAZ antibodies, a decrease in anti-HOMER2 antibodies, a decrease in anti-LIN28A antibodies, an increase in anti-NAPlL3 antibodies, an increase in anti-CD96 antibodies, an increase in KCMF1 antibodies, an increase in anti-EHF antibodies, an increase in anti-CLK3 antibodies, an increase in anti-PPP 1R2P9 antibodies, an increase in anti-NDEl antibodies and / or an increase in anti-GFAP antibodies compared to the reference level indicates that the subject is likely to benefit from treatment with one or more lipid lowering therapeutic agent.

19. The method of any of claims 13-18, wherein the reference level is taken from an equivalent biological sample of a subject determined to respond to lipid lowering therapeutic agents, in treating subclinical atherosclerosis or preventing CVD.

20. The method of any of claims 13-19, wherein if the subject is identified as likely to benefit from treatment with one or more lipid lowering therapeutic agent, the subject is to be administered one or more lipid lowering therapeutic agents.

21. A method of treating or preventing CVD, comprising:i. performing the method of any of claims 13-19; andii. administering one or more lipid lowering therapeutic agent to the subject if the subject is identified as likely to benefit from treatment with one or more lipid lowering therapeutic agent.

22. The method of any of claims 5, 6, 11 or 12-21, wherein the lipid lowering therapeutic agent is selected from statins, ezetimibe, bile acid sequestrants, PCSK9 inhibitors,adenosine triphosphate -citrate lyase (ACLY) inhibitors, fibrates, niacin, Omega-3 fatty acid ethyl esters, and marine-derived omega-3 polyunsaturated fatty acids (PUFA).

23. The method of any of claims 5, 6, 11 or 12-21, wherein the lipid lowering therapeutic agent is a statin, optionally wherein the statin is atorvastatin.

24. A statin for use in treating or preventing CVD, or for treating subclinical atherosclerosis in subject, wherein the subject has been identified according to the method of any of claims 1-5, 7-11, 13-20 or 22-23.

25. Use of a statin for the manufacture of a medicament for treating subclinical atherosclerosis or for preventing CVD in a subject, wherein the subject has been identified according to the method of any of the previous claims 1-5, 7-11, 13-20 or 22-23.

26. The method of any of claims 1-23, wherein the subject has previously been diagnosed with JSLE; optionallywherein the subject is between about 10 and 18 years of age.

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