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. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Arthritis Rheumatol. 2023 Dec 19;76(3):455–468. doi: 10.1002/art.42722

Atherosclerosis progression in the APPLE trial can be predicted in young people with juvenile-onset systemic lupus erythematosus using a novel lipid metabolomic signature

Junjie Peng 1,2, Pierre Dönnes 3, Stacy P Ardoin 4, Laura E Schanberg 5, Laura Lewandowski 6, George Robinson 1,2,†, Elizabeth C Jury 1,†, Coziana Ciurtin 2,†, APPLE trial investigators and Childhood Rheumatology Research Alliance (CARRA)
PMCID: PMC10922368  NIHMSID: NIHMS1933986  PMID: 37786302

Abstract

Objectives:

Patients with juvenile-onset systemic lupus erythematosus (JSLE) have increased atherosclerosis risk. This study investigated novel atherosclerosis progression biomarkers in the APPLE trial, the largest investigator-led randomised control trial of atorvastatin versus placebo for atherosclerosis progression in JSLE, using carotid intima-media thickness (CIMT) as primary outcome.

Methods:

Unsupervised clustering of baseline CIMT and CIMT-progression over 36 months was used to stratify JSLE patients. Disease characteristics, cardio-vascular risk scores and baseline serum metabolome were investigated in CIMT-stratified patients. Machine learning techniques were used to identify/validate a serum metabolomic signature of CIMT progression.

Results:

Baseline CIMT stratified JSLE patients (N=151) into three groups with distinct high, intermediate, and low CIMT trajectories irrespective of treatment allocation, despite most patients having low cardiovascular disease-risk based on recommended assessment criteria. In the placebo group (N=60), patients with high vs, low CIMT-progression had higher total (P=0.001) and low-density lipoprotein (LDL) (P=0.002) cholesterol levels, although within the normal range. Furthermore, a robust baseline metabolomic signature predictive of high CIMT-progression was identified in the placebo arm (area under the curve-AUC 80.7%). Patients treated with atorvastatin (N=61) had reduced LDL cholesterol levels after 36 months as expected, however, despite this, 36% still had high atherosclerosis progression, which was not predicted by metabolomic biomarkers, suggesting non-lipid drivers of atherosclerosis in JSLE with management implications for this subset of patients.

Conclusion:

Significant baseline heterogeneity and distinct subclinical atherosclerosis progression trajectories exist in JSLE. Metabolomic signatures can predict atherosclerosis progression in some JSLE patients with relevance for clinical trial stratification.

Graphical Abstract

graphic file with name nihms-1933986-f0001.jpg

Introduction

Juvenile-onset systemic lupus erythematosus (JSLE) accounts for approximately 15–20% of patients with SLE. JSLE is a rare disease, with ~10,000 and ~200,000 children and young people (CYP) estimated to live with the disease in the UK and the US, respectively (1, 2). JSLE is characterized by a more severe clinical phenotype compared to adults, leading to co-morbidity burden, including a significantly increased risk of developing cardiovascular disease (CVD). The impact of augmented CVD-risk from early onset of SLE has considerable individual and societal implications. In addition, there are recognised ethnic disparities in relation to SLE incidence and prevalence rates (2–3 times higher in people of Black race and Asian descent compared to White population (3)), and ethnic differences in clinical presentation and severity of JSLE (4).

Notably, JSLE patients have an estimated 100–300-fold increased CVD-related mortality compared to age-matched healthy CYP (5). 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% JSLE patients (6). 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 (7). However, despite strong evidence of increased CVD-risk in patients with JSLE, comorbidity-tailored recommendations or research directed towards stratifying/managing JSLE patients based on CVD-risk are limited (8, 9). Notably, a growing body of evidence, including data generated by our group, support that circulating biomarkers can predict CVD-risk in healthy CYP (10, 11) and CYP with JSLE (12, 13).

Carotid intima-media thickness (CIMT) is a measure of atherosclerosis which can be used to predict CVD-related events from childhood into middle-age (14) and improve the performance of traditional risk factors used for CVD-risk classification (15). Various studies have found a significant increase in CIMT in CYP with JSLE (6, 16). The Atherosclerosis Prevention in Paediatric Lupus Erythematosus (APPLE) trial was a randomized, double-blind, placebo-controlled study of atorvastatin for subclinical atherosclerosis prevention in JSLE (17). The trial failed to meet its primary end point, which was a significant decrease in the rate of CIMT progression between atorvastatin and placebo arms, although it showed rates of CIMT progression in the placebo group comparable to those reported in CYP with familial hypercholesterolemia(18). A secondary analysis identified that atorvastatin-treated post-pubertal patients with elevations in baseline high sensitivity C-Reactive Protein (hsCRP) had lower CIMT rates of progression (18), suggesting that JSLE patient heterogeneity contributed to the negative results in the primary analysis. Future clinical trials success may depend on correct patient stratification for targeted interventions.

We hypothesised that JSLE patients recruited to the APPLE trial could be stratified based on biomarkers with potential utility for tailored CVD-risk management strategies yielding better patient selection for clinical trials. To address this, we performed an in-depth analysis of patient, disease, and lipid metabolic factors that underpin CVD-risk heterogeneity in JSLE patients, using data and serum samples collected in the APPLE trial.

Patients and methods

APPLE cohort

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 the Childhood Rheumatology Research Alliance (CARRA) and APPLE trial investigators (USA). The APPLE trial was a prospective multi-centre cohort of 221 CYP with JSLE (age 10–18 at inclusion) recruited from various sites in North America and followed for 36 months (17). Subjects were randomized 1:1 to receive either placebo (N=108) or atorvastatin (N=113). All subjects met well defined inclusion/exclusion criteria as per published protocol (17).

In this study, we performed only complete case analyses, and investigated a trial sub-cohort, consisting of 151 JSLE patients (77 atorvastatin-arm; 74 placebo-arm [Table 1]) with complete baseline data and matched serum samples. In addition, we investigated CIMT progression over 36 months in another sub-cohort of 121/151 JSLE patients (60 placebo-arm [Table 2], 61 atorvastatin-arm [Table 3]) who completed the APPLE trial and had complete datasets to enable the analysis. Data related to various patient and disease related features were available as collected per the APPLE trial protocol.

Table 1:

Demographic comparison between baseline CIMT groups from APPLE cohort.

Total baseline CIMT assessment P *
High Intermediate Low
Number (N) 151 44 64 43 -
Female Sex, N, (%) 128 (84.8) 34 (77.3) 53 (82.8) 41 (95.3) 0.054
Post-puberty at baseline, N, (%) 96 (63.6) 30 (68.2) 42 (67.7) 24 (55.8) 0.375
Age, mean ± SD years 15.60 ± 2.67 16.53 ± 2.72 15.30 ± 2.55 15.11 ± 2.63 0.021
High vs Low: 0.033
High vs Intermediate: 0.045
Race, N. (%) 0.044
White 74 (49.0) 18 (40.9) 27 (42.2) 29 (67.4)
Black 39 (25.8) 16 (36.4) 16 (25.0) 7 (16.3)
Asian 10 (6.6) 3 (6.8) 7 (10.9) 0 (0.0)
Other 28 (18.5) 7 (15.9) 27 (42.2) 7 (16.3)
Annual household income, N (%) 0.44
<$25,000 42 (27.8) 10 (22.7) 22 (34.4) 10 (23.3)
$25,000–49,999 39 (25.8) 11 (25.0) 17 (26.6) 11 (25.6)
$50,000–74,999 22 (14.6) 6 (13.6) 6 (9.4) 10 (23.3)
$75,000–99,999 17 (11.3) 8 (18.2) 6 (9.4) 3 (7.0)
$100,000–150,000 14 (9.3) 4 (9.1) 6 (9.4) 4 (9.3)
>$150,000 7 (4.6) 1 (2.3) 5 (7.8) 1 (2.3)
Patient and disease characteristics at baseline
BMI, mean ± SD kg/m2 24.23 ± 5.27 24.83 ± 6.10 24.52 ± 5.29 23.20 ± 4.20 0.304
Disease duration,
mean ± SD months 		29.52 ± 29.37 39.75 ± 35.45 25.98 ± 25.44 24.30 ± 25.84 0.021
High vs Low: 0.036
High vs Intermediate: 0.042
SLEDAI, mean ± SD 4.71 ± 4.17 4.32 ± 4.12 4.91 ± 4.23 4.81 ± 4.21 0.76
SLICCDI, mean ± SD 0.35 ± 0.70 0.50 ± 0.82 0.34 ± 0.70 0.21 ± 0.56 0.156
Hypertension, no. (%) 49 (32.5) 17 (38.6) 22 (34.4) 10 (23.3) 0.282
History of smoking, N (%) 2 (1.3) 1 (2.3) 0 (0.0) 1 (2.3) 0.474
dsDNA antibody +, no. (%) 122 (80.8) 37 (84.1) 52 (81.2) 33 (76.7) 0.680
Creatinine clearance, mean ± SD ml/minute/m2 138.80 ± 31.72 139.70 (28.03) 145.27 (33.43) 128.23 (30.55) 0.023
Intermediate vs Low: 0.017
C3, mean ± SD mg/dl 102.03 ± 27.02 102.93 ± 29.34 100.03 ± 26.18 104.18 ± 26.30 0.721
C4, mean ± SD mg/dl 15.24 ± 7.47 16.02 ± 7.26 14.81 ± 6.99 15.11 ± 8.45 0.714
Medications at baseline (past 30 days)
Aspirin, no. (%) 102 (67.5) 33 (75.0) 45 (70.3) 24 (55.8) 0.133
Hydroxychloroquine, no. (%) 149 (98.7) 44 (100.0) 62 (96.9) 43 (100.0) 0.252
Multivitamin, no. (%) 111 (73.51) 33 (75.0) 45 (70.3) 33 (76.7) 0.734
Corticosteroids, no. (%) 124 (82.12) 38 (86.4) 54 (84.4) 32 (74.4) 0.287
Cyclophosphamide, no. (%) 23 (15.23) 6 (13.6) 11 (17.2) 6 (14.0) 0.848
Mycophenolate mofetil, no. (%) 34 (22.52) 11 (25.0) 15 (23.4) 8 (18.6) 0.754
Azathioprine, no. (%) 23 (15.23) 10 (22.7) 9 (14.1) 4 (9.3) 0.207
Methotrexate, no. (%) 19 (12.58) 7 (15.9) 6 (9.4) 6 (14.0) 0.573
Rituximab, no. (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) -
NSAIDs, no. (%) 46 (30.46) 15 (34.1) 19 (29.7) 12 (27.9) 0.809
ACE inhibitor, no. (%) 33 (21.85) 12 (27.3) 16 (25.0) 5 (11.6) 0.153
Serum biomarkers at baseline
hsCRP, mean ± SD mg/liter 2.53 ± 7.53 2.38 ± 5.65 3.19 ± 9.90 1.58 ± 4.03 0.57
Homocysteine, mean ± SD μmoles/liter 7.27 ± 3.32 7.41 ± 2.95 7.24 ± 3.15 7.16 ± 4.00 0.941
Lipid levels at baseline, mean ± SD mg/dl
Total cholesterol ** 153.90 ± 39.96 151.83 ± 33.71 152.73 ± 43.34 158.03 ± 41.06 0.75
HDL cholesterol ** 45.55 ± 12.60 45.38 ± 13.62 45.72 ± 12.87 45.46 ± 11.24 0.99
LDL cholesterol ** 85.76 ± 32.7 86.24 ± 28.57 84.90 ± 36.69 86.64 ± 30.74 0.961
Triglycerides 115.42 ± 74.28 101.17 ± 50.11 116.09 ± 87.63 129.67 ± 70.97 0.226
Lipoprotein A 21.68 ± 26.16 29.60 (26.92) 19.89 (28.39) 16.10 (19.21) 0.051
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*

Chi-squared test, one-way ANOVA or Tukey’s range test. Tanner Stage 4–5 are classified as post-puberty.

**

The recommended lipid levels in people younger than 18 years of age (as per APPLE trial inclusion criteria) are: total cholesterol <170 mg/dl, HDL-cholesterol>45 mg/dl and LDL-cholesterol <110 mg/dl. Lipid levels fluctuate and they are not usually monitored during puberty.

ACE – angiotensin-converting enzyme inhibitors; BMI – Body mass index; C3, C4 – complement fractions C3,C4; HDL – high-density lipoprotein; hsCRP – high sensitivity C-Reactive Protein; LDL – low-density lipoprotein; NSAIDs – non-steroidal anti-inflammatory drugs; SLEDAI – Systemic Lupus Erythematosus Disease Activity Index; SLICC DI – Systemic Lupus International Collaborating Clinics Damage Index.

Table 2:

Demographic comparison between the high CIMT progression group and low CIMT progression group in the APPLE study placebo-treated participants (N=60).

Total Placebo High CIMT progression group Low CIMT progression group P *
Number 60 35 25 -
Sex, no. (%) female 51 (85.0) 29 (82.9) 22 (88.0) 0.855
Puberty at baseline, no. (%) post puberty 38 (63.3) 21 (60.0) 17 (68.0) 0.564
Age, mean ± SD years 15.50 ± 2.48 15.46 ± 2.49 15.56 ± 2.52 0.876
Race, no. (%) 0.848
White 35 (58.33) 19 (54.29) 16 (64.0)
Black 13 (21.67) 8 (22.86) 5 (20)
Asian 4 (6.67) 3 (8.57) 1 (4.0)
Other 8 (13.33) 5 (14.29) 3 (12)
Annual household income, no. (%) 0.763
<$25,000 16 (26.67) 9 (25.71) 7 (28)
$25,000–49,999 17 (28.33) 9 (25.71) 8 (32)
$50,000–74,999 7 (11.67) 5 (14.29) 2 (8)
$75,000–99,999 8 (13.33) 6 (17.14) 2 (8)
$100,000–150,000 6 (10) 2 (5.71) 4 (16)
>$150,000 3 (5) 2 (5.71) 1 (4)
Patient and disease characteristics at baseline
BMI, mean ± SD kg/m2 24.51 ± 6.19 24.91 ± 6.60 23.94 ± 5.66 0.555
Duration of lupus, mean ± SD months 28.05 ± 30.11 27.89 ± 34.68 28.28 ± 22.88 0.961
SLEDAI, mean ± SD 4.02 ± 3.96 4.51 ± 3.98 3.32 ± 3.90 0.253
SLICC DI, mean ± SD 0.333 ± 0.774 0.457 ± 0.886 0.160 ± 0.554 0.144
Hypertension, no. (%) 23 (38.3) 16 (45.7) 7 (28.0) 0.262
History of smoking, no. (%) 0 (0) 0 (0) 0 (0) -
dsDNA antibody positive, no. (%) 45 (75.0) 24 (68.6) 21 (84.0) 0.290
Creatinine clearance, mean ± SD ml/minute/m2 133.18 ± 28.66 134.59 ± 28.24 131.21 ± 29.7 0.891
C3, mean ± SD mg/dl 106.2 ± 25.24 110.50 ± 24.53 100.05 ± 25.50 0.121
C4, mean ± SD mg/dl 16.95 ± 7.72 17.85 ± 8.18 15.63 ± 6.96 0.282
Medications at baseline (past 30 days)
Aspirin, no. (%) 43 (71.67) 24 (68.57) 19 (76) 0.735
Hydroxychloroquine, no. (%) 59 (98.33) 34 (97.14) 25 (100) 1
Multivitamin, no. (%) 42 (70) 23 (65.71) 19 (76) 0.568
Corticosteroids, no. (%) 48 (80) 29 (82.86) 19 (76) 0.743
Cyclophosphamide, no. (%) 10 (16.67) 6 (17.14) 4 (16) 1
Mycophenolate mofetil, no. (%) 11 (18.33) 8 (22.86) 3 (13.04) 0.463
Azathioprine, no. (%) 11 (18.33) 7 (20) 4 (16) 0.955
Methotrexate, no. (%) 8 (13.33) 5 (14.29) 3 (12) 1
Rituximab, no. (%) 0 (0.0) 0 (0.0) 0 (0.0) -
NSAIDs, no. (%) 19 (31.67) 9 (25.71) 10 (40) 0.373
ACE inhibitor, no. (%) 17 (28.33) 11 (31.43) 6 (24) 0.735
Serum biomarkers at baseline
hsCRP, mean ± SD mg/liter 2.88 ± 6.50 2.93 ± 6.13 2.82 ± 7.11 0.953
Homocysteine, mean ± SD μmoles/liter 7.52 ± 4.24 8.08 ± 4.97 6.76 ± 2.91 0.24
Lipid levels at baseline, mean ± SD mg/dl
Total cholesterol**** 144.59 ± 31.3 156.97 ± 32.91 127.76 ± 19.12 <0.001
HDL cholesterol**** 45.92 ± 12.71 48.38 ± 13.53 42.56 ± 10.88 0.082
LDL cholesterol** ** 74.09 ± 26.75 83.24 ± 27.98 62.00 ± 19.71 0.002
Triglycerides 128.12 ± 94.52 136.62 ± 115.75 116.56 ± 54.09 0.425
Lipoprotein A 12.25 ± 16.04 14.82 ± 17.61 8.76 ± 13.17 0.153
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*

Chi-squared test or Wilcoxon signed-rank test. Tanner Stage 4–5 are classified as post-puberty.

**

The recommended lipid levels in people younger than 18 years of age (as per APPLE trial inclusion criteria) are: total cholesterol <170 mg/dl, HDL-cholesterol>45 mg/dl and LDL-cholesterol <110 mg/dl. Lipid levels fluctuate and they are not usually monitored during puberty.

ACE – angiotensin-converting enzyme inhibitors; BMI – Body mass index; C3, C4 – complement fractions C3,C4; HDL – high-density lipoprotein; hsCRP – high sensitivity C-Reactive Protein; LDL – low-density lipoprotein; NSAIDs – non-steroidal anti-inflammatory drugs; SLEDAI – Systemic Lupus Erythematosus Disease Activity Index; SLICC DI – Systemic Lupus International Collaborating Clinics Damage Index.

Table 3:

Demographic comparison between the high, intermediate and low CIMT progression group in the APPLE study atorvastatin-treated participants (N=61).

Total CIMT progression groups P*
High Intermediate Low
Number 61 22 24 15 -
Sex, no. (%) female 49 (80.3) 17 (77.3) 21 (87.5) 11 (73.3) 0.503
Puberty at baseline.
(%) post-puberty 		35 (57.4) 13 (60.1) 13 (54.2) 9 (60.0) 0.919
Age, mean ± SD years 15.34 ± 2.72 14.87 ± 2.51 15.21 ± 2.93 16.24 ± 2.63 0.314
Race, no. (%) 0.677
White 23 (37.7) 9 (40.9) 9 (37.5) 5 (33.3)
Black 16 (26.23) 6 (27.3) 5 (20.8) 5 (33.3)
Asian 5 (8.2) 0 (0.0) 3 (12.5) 2 (13.3)
Other 17 (27.87) 7 (31.8) 7 (29.2) 3 (20.0)
Annual household income, no. (%) 0.167
<$25,000 17 (27.87) 4 (18.2) 12 (50.0) 1 (7.1)
$25,000–49,999 15 (24.59) 8 (36.4) 3 (12.5) 4 (28.6)
$50,000–74,999 6 (9.84) 3 (13.6) 3 (12.5) 0 (0.0)
$75,000–99,999 7 (11.48) 2 (9.1) 2 (8.3) 3 (21.4)
$100,000–150,000 7 (11.48) 2 (9.1) 2 (8.3) 3 (21.4)
>$150,000 4 (6.56) 1 (4.5) 1 (4.2) 2 (14.3)
Patient and disease characteristics at baseline
Body mass index, mean ± SD kg/m2 24.17 ± 4.73 22.97 ± 4.38 24.57 ± 5.40 25.31 ± 3.91 0.298
Duration of lupus, mean ± SD months 28.26 ± 29.94 25.68 ± 20.37 28.79 ± 28.34 31.20 ± 43.37 0.858
SLEDAI, mean ± SD 5.38 ± 4.74 6.55 ± 5.83 4.38 ± 3.62 5.27 ± 4.45 0.303
SLICC DI, mean ± SD 0.393 ± 0.714 0.23 ± 0.53 0.42 ± 0.72 0.60 ± 0.91 0.295
History of hypertension, no. (%) 17 (27.9) 5 (22.7) 7 (29.2) 5 (33.3) 0.766
History of smoking, no. (%) 0 (0) 0 (0) 0 (0) 0 (0) -
dsDNA antibody positive, no. (%) 51 (83.6) 18 (81.8) 19 (79.2) 14 (93.3) 0.489
Creatinine clearance, mean ± SD ml/minute/m2 147.25 ± 34.40 158.09 ± 45.41 141.95 ± 22.07 139.82 ± 29.76 0.179
C3, mean ± SD mg/dl 99.57 ± 28.05 84.28 ± 36.44 92.55 ± 41.53 96.53 ± 34.33 0.608
C4, mean ± SD mg/dl 13.87 ± 6.36 11.76 ± 5.26 14.04 ± 6.84 16.89 ± 6.25 0.058
Medications at baseline (past 30 days)
Aspirin, no. (%) 36 (59.02) 11 (50.0) 15 (62.5) 10 (66.7) 0.543
Hydroxychloroquine, no. (%) 60 (98.36) 22 (100.0) 23 (95.8) 15 (100.0) 0.457
Multivitamin, no. (%) 44 (72.13) 17 (77.3) 16 (66.7) 11 (73.3) 0.72
Corticosteroids, no. (%) 51 (83.61) 20 (90.9) 17 (70.8) 14 (93.3) 0.093
Cyclophosphamide, no. (%) 8 (13.11) 3 (13.6) 2 (8.3) 3 (20.0) 0.574
Mycophenolate mofetil, no. (%) 15 (24.59) 5 (22.7) 6 (25.0) 4 (26.7) 0.962
Azathioprine, no. (%) 8 (13.11) 3 (13.6) 4 (16.7) 1 (6.7) 0.664
Methotrexate, no. (%) 5 (8.2) 1 (4.5) 3 (12.5) 1 (6.7) 0.598
Rituximab, no. (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) -
NSAIDs, no. (%) 20 (32.79) 7 (31.8) 7 (29.2) 6 (40.0) 0.776
ACE inhibitor, no. (%) 13 (21.31) 5 (22.7) 4 (16.7) 4 (26.7) 0.744
Serum biomarkers at baseline
hsCRP, mean ± SD mg/liter 2.87 ± 9.66 2.11 ± 3.56 4.44 ± 15.00 1.48 ± 2.16 0.59
Homocysteine, mean ± SD μmoles/liter 7.17 ± 2.52 7.25 ± 2.85 6.88 ± 2.59 7.52 ± 1.95 0.731
Lipid levels at baseline, mean ± SD mg/dl
Total cholesterol** ** 158.48 ± 41.74 165.41 ± 43.55 157.88 ± 44.72 149.27 ± 34.22 0.519
HDL cholesterol**** 44.93 ± 12.68 44.00 ± 13.78 45.17 ± 12.47 45.93 ± 12.07 0.899
LDL cholesterol**** 92.21 ± 32.7 99.14 ± 37.65 91.12 ± 31.38 83.80 ± 26.18 0.373
Triglycerides 106.62 ± 55.85 111.09 ± 51.70 107.92 ± 63.33 98.00 ± 51.55 0.78
Lipoprotein A 27.15 ± 31.6 29.95 ± 33.27 23.33 ± 31.66 29.13 ± 30.52 0.754
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*

Chi-squared test, one-way ANOVA or Tukey's range test. Tanner Stage 4–5 are classified as post-puberty.

**

The recommended lipid levels in people younger than 18 years of age (as per APPLE trial inclusion criteria) are: total cholesterol <170 mg/dl, HDL-cholesterol>45 mg/dl and LDL-cholesterol <110 mg/dl. Lipid levels fluctuate and they are not usually monitored during puberty.

ACE – angiotensin-converting enzyme inhibitors; C3, C4 – complement fractions C3,C4; HDL – high-density lipoprotein; hsCRP – high sensitivity C-Reactive Protein; LDL – low-density lipoprotein; NSAIDs – non-steroidal anti-inflammatory drugs; SLEDAI – Systemic Lupus Erythematosus Disease Activity Index; SLICC DI – Systemic Lupus International Collaborating Clinics Damage Index.

CIMT measurements in the APPLE cohort

The APPLE investigators provided relevant CIMT measurements collected as per trial protocol (17, 18), which included assessment of the thickness of 12 vascular sites using similar ultrasound machines and a central reader (17). The mean of the mean common CIMT (MMeanIMT) measurement was the revised primary endpoint of the APPLE trial (17). CIMT measures were collected at different time points: baseline, 6 months, 12 months, and 36 months (end of trial). CIMT progression (ΔCIMT) was calculated by subtracting the mean of each of 12 CIMT measurements at 36 months from the corresponding 12 CIMT measurements at baseline.

CVD-risk scores calculation at baseline

The Framingham risk score (FRS) 2008 (19) and the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) score (20) were calculated in R; the QRISK3 score (21) was calculated using R package “QRISK3” (https://CRAN.R-project.org/package=QRISK3); and the Atherosclerotic cardiovascular disease (ASCVD) score was calculated with R package “CVrisk” (https://CRAN.R-project.org/package=CVrisk). The risk stratification cut-offs for each score are provided in Supplementary Table 1.

Metabolomics

Measures of 250 serum biomarkers were acquired with an established NMR-spectroscopy platform (Nightingale Health, https://nightingalehealth.com/) (22). Serum analysed had not been exposed to any freeze/thaw cycle and previous research showed that this platform has good accuracy in detecting metabolites in samples stored for 15+ years (23, 24) as it was the case with the APPLE trial samples. Measures included both absolute concentrations (mmol/L), ratios, and percentages (%) of lipoprotein composition of numerous metabolites (Supplementary Table 2). Data imputation was performed was performed using the Nearest Neighbor Hot Deck Imputation method after removing metabolites with more than 10% missing data (5 metabolites were removed), leaving a total of 245 metabolites/sample for analysis.

Statistical analysis

Statistical tests were performed in R and GraphPad Prism. Data were assessed for normality and analysed with parametric or nonparametric tests, as appropriate. Chi-square test and Fisher’s exact test were used for comparison between categorical variables. Details of statistical tests and parameters accounted for in the analyses are given in the figure legends. P<0.05 was considered statistically significant. Bonferroni correction was applied for multiple testing.

Unsupervised hierarchical clustering was performed with ClustVis (https://biit.cs.ut.ee/clustvis/). This method was used to stratify JSLE patients at baseline (using 12 CIMT measurements at the beginning of the trial) and based on their CIMT progression over 36 months in the atorvastatin vs. placebo arms (using the 12 CIMT progression - ΔCIMT measurements). Data analysis pipeline is detailed in Supplementary Figure 1.

Results

Baseline CIMT measurements stratify patients with JSLE into three groups, each associated with distinct CIMT trajectories irrespective of treatment allocation.

The baseline CIMT heterogeneity of JSLE patients recruited to the APPLE clinical trial was assessed in a sub-cohort of 151 patients with a mean age of 15.6 years (range 10.3–21.7 years, 85% females). A summary of baseline characteristics is depicted in Table 1.

Unsupervised hierarchical clustering was used to stratify the cohort using 12 CIMT measures at baseline. Three groups were identified with relatively high (N=44), intermediate (N=64) and low (N=43) baseline CIMT measurements (Figure 1A). Compared to patients in the low and intermediate CIMT groups, JSLE patients with high baseline CIMT were significantly older (P=0.021) and had longer disease duration (P=0.021) (Table 1). Female patients were more frequently identified in the low baseline CIMT group (95.3%) compared to the high (77.3%) and intermediate (68.2%) groups (Table 1, P=0.054). No significant differences between various patient and disease-related parameters, including lipid serum levels were found (Table 1), except for creatinine clearance estimations which were significantly higher in the intermediate compared to the low baseline CIMT groups (Table 1, P=0.017).

Figure 1:

Figure 1:

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JSLE stratification (all APPLE patients with complete baseline data, N=151) by baseline CIMT (12 measures). A) Baseline CIMT measures of patients with juvenile-onset SLE were stratified using unsupervised hierarchical clustering. All 12 CIMT measures were standardised within each row by Z score and plotted as a heat map, representing the relationship to the mean of the group (red represents relatively high CIMT measures and blue represents relatively low CIMT measures). Each column represents a patient with JSLE. Three groups of patients with distinct baseline CIMT profiles were identified. B-C) Box and whisker plots show baseline and 36-month MMeanIMT measurements (APPLE primary outcome) in the identified high, intermediate and low baseline CIMT groups. Comparisons between groups were performed using Wilcoxon signed-rank test. D) Distinct longitudinal MMeanIMT progression from baseline to 36 months of the high, intermediate, and low CIMT progression groups (Mean, 95% CI), irrespective of treatment allocation. (Only JSLE patients with completed CIMT data at 36 months were included in the panel C-D, N=121).

Abbreviations: CIMT- carotid intima-media thickness; MMeanIMT - Mean-Mean IMT common carotid artery measurement.

As a validation, the baseline MMeanIMT (primary endpoint of the trial) was significantly different between the three groups (high vs intermediate, P<0.0001; high vs low, P<0.0001; intermediate vs low, P<0.0001) (Figure 1B), thus supporting significant CIMT heterogeneity across the JSLE cohort which was maintained across the timeframe of the study (Figure 1C). In support, there were distinct CIMT trajectories over 36 months of the three patient groups which did not crossover (Figure 1D), irrespective of treatment allocation.

Together these data demonstrate significant CIMT heterogeneity at baseline and CIMT progression at 36 months, despite minimal differences in demographic and disease features, supporting further investigation of factors contributing to distinct CIMT progression rates in JSLE.

JSLE patients in the placebo arm of the APPLE trial stratified into two groups based on their CIMT trajectories over 36 months.

To examine the natural progression of subclinical atherosclerosis, the change in the 12 CIMT measures from baseline to 36 months (ΔCIMT) was assessed in all patients allocated to the placebo arm of the APPLE study (N=60) (Table 2). Unsupervised hierarchical clustering stratified patients into two groups based on ΔCIMT with high (N=35) and low (N=25) CIMT progression (Figure 2A). A significant increase in MMeanIMT, was seen in the high CIMT progression group (P<0.0001) while a significant decrease in MMeanIMT (P=0.001) characterised the low CIMT progression group (Figure 2B).

Figure 2.

Figure 2.

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JSLE stratification by delta(Δ)CIMT (12 measurements) at baseline vs 36 months in the placebo and atorvastatin arms. A) Heatmap displaying ΔCIMT (z scored) from JSLE patients from the placebo arm (full CIMT dataset, N=60) stratified by unsupervised hierarchical clustering. Each column represents a patient with JSLE. A high and low ΔCIMT progression group were discovered over 36 months. B) Box and whisker plots showing comparisons of MMeanIMT between groups from ‘A’ at baseline and 36 months. C) Heatmap displaying ΔCIMT (z scored) from JSLE patients from the atorvastatin arm (full CIMT dataset, N=61) stratified by unsupervised hierarchical clustering. Each column represents a patient with JSLE. A high, intermediate, and low ΔCIMT progression group were discovered over 36 months. D) Box and whisker plots showing comparison of MMeanIMT between groups from ‘C’ at baseline and 36 months. E) Box and whisker plots showing comparison of MMeanIMT between high/low CIMT progression groups between placebo and atorvastatin arm patients. Wilcoxon signed-rank test or t-test. F) Box and whisker plots showing comparisons of PDAY scores between baseline groups, placebo and atorvastatin progression groups.

Abbreviations: CIMT- carotid intima-media thickness; MMeanIMT- Mean-Mean IMT common carotid artery measurement. PDAY- Pathobiological Determinants of Atherosclerosis in Youth.

Importantly, there were no significant differences in age, sex, puberty stages and race between the high and low CIMT progression groups (Table 2). Unsurprisingly, routinely measured serum total cholesterol (P=0.0004) and LDL-cholesterol (P=0.002), known to be associated with atherosclerosis development, were significantly elevated in the high compared to low CIMT progression group (Table 2), although the mean values were within the recommended range for both groups (total cholesterol: 156.97±32.91 vs. 127.76±19.12 mg/dl, and LDL-cholesterol 83.24±27.98 vs. 62.00±19.71 mg/dl in the high vs. low CIMT progression groups, respectively). In addition, baseline serum total cholesterol and LDL-cholesterol and homocysteine levels positively correlated with the ΔCIMT progression in the placebo group (Supplementary Figure 2A). There were also positive correlations between CIMT progression and various JSLE-related biomarkers at baseline, such as creatinine and C4 levels, and negative correlation with the spot urine protein: creatinine ratio (PCR). Damage index (SLICC-DI) was also positively associated with CIMT progression (Supplementary Figure 2A). Taken together, these findings indicate that various measures of chronic inflammation at baseline differentially correlate with subclinical atherosclerosis progression over 36 months (as higher C4 and lower urine PCR reflect better disease control at baseline, while increased damage reflects the opposite) and altered lipid metabolism (reflected by differences in the lipid profile and homocysteine levels) may contribute in different ways to atherosclerosis progression in JSLE.

JSLE patients treated with atorvastatin in the APPLE trial stratified into 3 groups based on their CIMT trajectories over 36 months

CIMT progression over 36 months (ΔCIMT) was also assessed in the atorvastatin arm of the APPLE trial (N=61) (Table 3). Unsupervised cluster analysis (using the 12 ΔCIMT measures as described in methods) identified three distinct groups: high (N=22), intermediate (N=24) and low (N=15) CIMT progression groups (Figure 2C). Notably, 36% JSLE patients in the atorvastatin group (N=22/61) had high CIMT progression over 36 months despite treatment. Significant changes in MMeanIMT over 36 months were observed in high (increased, P<0.0001) and low (decreased, P=0.002) CIMT progression groups, while the intermediate group (P=0.51) had almost stable MMeanIMT measurements over 36 months (Figure 2D). As observed in the placebo group, no significant differences in clinical and demographic measures were observed in patients in the atorvastatin arm across the three CIMT progression groups at baseline (Table 3) and few correlations between CIMT and clinical measures were identified (Supplementary Figure 2B). Most notably there were no correlations between 12 CIMT progression measures and baseline serum lipids, likely due to the impact of atorvastatin treatment on the CIMT trajectories of some JSLE patients, even if the trial did not show overall benefit. Furthermore, the correlation analysis suggests that atorvastatin treatment disrupted the association between various biomarkers and CIMT progression observed in the placebo group (Supplementary Figure 2B). Interestingly, complement fractions C3 and C4, biomarkers of serological activity in JSLE, were inversely associated with CIMT progression similar to an independent analysis of the APPLE trial (25), indicating that disease-related factors may drive CIMT progression in JSLE despite statin treatment normalising the lipid profile.

Interestingly, when the MMeanIMT progression over 36 months (ΔMMeanIMT) was compared in the low progression groups across both treatment arms, atorvastatin-treated patients had significantly reduced CIMT progression over 36 months compared with the placebo-treated patients, (Figure 2E). This suggests that JSLE patients allocated to the statin treatment group with low CIMT progression over 36 months (24.5%, N=15, based on the unsupervised cluster analysis) benefitted from treatment with statins as their CIMT progression was significantly reduced compared to JSLE patients in the placebo arm with low CIMT progression (41.6%, N=25), despite no differences in the baseline lipid profiles between the low vs. high progression groups in the statin arm (Table 3). Conversely, no difference in ΔMMeanIMT was seen between the high CIMT progression groups in the two treatment arms suggesting that atorvastatin did not influence CIMT progression rate in these ‘high CIMT progression’ patients (58.3%, N=35/60 in the placebo arm and 36%, N=22/61 in the atorvastatin arm) (Figure 2E).

Finally, to confirm the pharmacological effect of atorvastatin in JSLE, whilst serum LDL cholesterol did not significantly decrease in the placebo arm (Supplementary Figure 3, P=0.61), we found a significant reduction in routinely measured serum LDL cholesterol levels at 36 months in the atorvastatin-treated patients (Supplementary Figure 3, P<0.0001). Thus, despite the decrease of serum LDL cholesterol levels with atorvastatin treatment, a sizeable proportion of patients (N=22, 36.1%) continued to CIMT progression, suggesting that CIMT progression was driven by factors independent from dysregulation of lipid metabolism.

Validated CVD-risk scores misclassified JSLE patients in the APPLE trial

Since no significant differences between individual traditional CVD-risk factors (age, sex, blood pressure, diabetes, BMI, smoking) were identified between distinct CIMT trajectories in either the placebo or atorvastatin arms of the APPLE trial (with the exception of total and LDL-cholesterol levels in the placebo arm) (Tables 2–3), we explored the classification accuracy of four of the most commonly used CVD-risk scores for stratification in general population: the Framingham risk score (FRS) (19) validated from age 20; the QRISK3 score (21) validated from age 25, the only CVD-risk score that includes SLE as well as steroid treatment as individual items; and the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) score (20), the only score proposed for use in CYP from age 14, with scores >2 indicating a high risk for coronary artery calcium (CAC) progression in 25 years (26).

Applying the various CVD-risk scores to the APPLE trial JSLE cohort at baseline as per data availability, we found that very few patients were identified as high risk (Supplementary Table 1). The FRS score classified all JSLE patients as low risk (<5%, N=144) and the ASCVD score classified a large proportion of JSLE patients as low risk (<5%, N=92/99), a small proportion as borderline/moderate risk (5–19.9%, N=7/99) and none as high risk at baseline. Only the QRISK-3 and PDAY scores identified a very small number of JSLE patients as high risk (>20% and >10 points, N=2/144 and N=3/138, respectively), while the largest number of patients were classified as low risk (<5%, N=120/144 and N= 98/138, respectively) and the remaining as borderline-moderate risk. (Supplementary Table 1).

Furthermore, very few of the JSLE patients were correctly classified as high risk when compared to their stratification based on CIMT at baseline or CIMT progression pattern over 36 months in the placebo or atorvastatin arms (Supplementary Figure 4). The QRISK-3 score classified 1/63 and 1/39 JSLE patients with intermediate and low CIMT, respectively, as high risk at baseline (QRISK3 score >20%), while only 2/35 JSLE patients with high progression CIMT pattern in the placebo group were correctly identified as high risk (Supplementary Figure 4)).

Similarly, there was no conformity between the PDAY score and baseline CIMT/CIMT progression stratifications (Figure 2F); no significant difference was seen between groups stratified for high vs low baseline CIMT/CIMT progression, although patients with PDAY score >2 (at least borderline risk) were identified in all the CIMT-stratified groups (Figure 2F).

Thus, most patients with high CIMT at baseline or high CIMT progression over 36 months in the APPLE trial were misclassified by four different CVD-risk scores, suggesting that none of these tools perform well in CYP with JSLE.

Novel serum metabolomic signature predicts high CIMT progression in the placebo arm but not in the atorvastatin-arm

Since the commonly used CVD-risk scores failed to accurately classify patients with high CIMT progression, and high CIMT progression in JSLE patients in the placebo arm was positively associated with higher routinely measured serum LDL/total cholesterol levels (Table 2, Supplementary Figure 2A), a more detailed NMR metabolomic analysis was performed (250 serum lipid-based metabolites, full list Supplementary Table 2) at baseline (N=60).

Forty-eight metabolites were significantly upregulated in the high compared to the low CIMT progression group in the placebo arm (Figure 3A). The top six significantly increased metabolites selected after stringent Bonferroni correction included total esterified cholesterol, total cholesterol, phospholipids in small LDL, cholesterol in small LDL, free cholesterol in medium LDL and total lipids in small LDL (Figure 3A-red labels and 3B). This suggests that JSLE patients in the high CIMT progression group had a distinct, pro-atherogenic lipid metabolomic profile, dominated by cholesterol and LDL subsets. Using the six-metabolite signature combined, receiver operator curve (ROC) analysis in multivariate logistic regression showed an area under the curve (AUC) of 80.7%, higher than the individual metabolites alone (AUC range 74.4–75.9%) (Figure 3C). This was also higher than the AUC for total cholesterol (AUC 76.3%) and LDL-cholesterol (AUC 72.5%) levels measured in the APPLE trial (Supplementary Figure 5A), suggesting that these six metabolites could provide a biomarker signature for predicting CIMT progression in JSLE.

Figure 3.

Figure 3.

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Baseline serum metabolomics (N=245 after data cleaning) comparisons between CIMT progression groups - placebo and atorvastatin arms. A) Volcano plot displaying fold change of all metabolites and Log10 p values comparing high (N=35) and low (N=25) CIMT progression groups - placebo arm (p<0.01; log2(fold change)>0.2). Top six significant metabolites (Bonferroni correction) highlighted in red. B) Box and whisker plots showing the top six metabolite levels of the high vs low CIMT progression groups -placebo arm. Unpaired t-test. C) ROC analysis for discriminating high vs low CIMT progression groups using the top six metabolites combined and separately by AUC. D) Volcano plot displaying fold change of all metabolites and Log10 p values comparing high (N=22) and low (N=15) CIMT progression groups -atorvastatin arm (p<0.05; log2(fold change)>0.2). Top two significant metabolites (Bonferroni correction) highlighted in red. E) Box and whisker plots showing the top two metabolite levels of the high vs low CIMT progression groups -atorvastatin arm. Unpaired t-test. F) ROC analysis for discriminating high vs low CIMT progression groups using DHA% and Isoleucine by AUC.

Abbreviations: AUC - area under the curve; CIMT- carotid intima-media thickness; ROC- Receiver Operator Curve; Abbreviations/full names of all metabolites are listed in the Supplementary Appendix.

To support these findings, univariate logistic regression analysis was performed on all metabolites comparing the high and low CIMT progression groups in the placebo arm, accounting for clinical and treatment features. All six selected metabolites were increased in the high CIMT progression group (Supplementary Figure 5B). These results were further confirmed using supervised machine learning approaches. The optimized sparse partial least squares discriminant analysis showed separation between the two CIMT progression groups and identified similar metabolites (highlighted in red) in the first component of the model driving the high versus low CIMT progression stratification (Supplementary Figure 5C–D). Together, the further analysis validated the six-metabolite predictive signature of CIMT progression in the placebo arm (Figure 3A–C).

The same NMR metabolomics analysis pipeline was applied to the atorvastatin arm of the APPLE trial. Only two metabolomic markers (the ratio of docosahexaenoic acid to total fatty acids and isoleucine) were significantly different between the high and low CIMT progression groups (Figure 3D–E), with poor performance under ROC analysis (Figure 3F). Thus, no distinct baseline metabolomic signature was found between the high and low CIMT progression groups in the atorvastatin treatment arm. As neither routine serum lipid measures (Supplementary Figure 2B) nor the in depth metabolomic signature correlated with CIMT progression, these results show that in atorvastatin-treated patients, baseline lipid signatures do not predict CIMT progression, and that statin treatment abrogated the predictive signature of CIMT progression found in the placebo group.

Discussion

The current study included a novel patient stratification and biomarker identification analysis of the APPLE trial data and samples to improve CVD-risk assessment in JSLE, aiming to address the unmet clinical need for early identification and tailored CVD-risk management.

JSLE patients recruited to the APPLE trial, despite being young, already had different degrees of subclinical atherosclerosis. This study further explored subclinical atherosclerosis heterogeneity by stratifying patients into distinct groups and by defining distinct CIMT progression rates over 36 months, irrespective of treatment allocation. The only significant predictors of baseline CIMT unsupervised patient stratification were age, disease duration and creatinine clearance, supporting previous findings that longer SLE duration is associated with increased CVD-risk (27, 28). However, the other predictors of baseline CIMT identified by the multivariable analysis of the APPLE trial (29) (minority status, higher BMI, male sex, higher lipoprotein A, proteinuria, azathioprine use, and prednisone dose) did not differ between the baseline CIMT patient groups derived from this current unsupervised cluster analysis. No patient or disease-related significant differences were identified between the high versus low CIMT progression groups in the placebo arm either, apart from the increased levels of total and LDL-cholesterol in the high progression group. These findings are difficult to appreciate at individual patient level, as most of JSLE patients had normal lipid profiles, even if there were statistically significant differences between the high vs. low CIMT progression groups.

Although the previous second analysis of APPLE trial showed that hsCRP and pubertal status predicted response to atorvastatin, our unsupervised cluster analysis did not identify these markers as being different between JSLE patients stratified on CIMT at baseline or according to the rate of their progression over 36 months (18), which could be explained by the limitations posed by the available sample size, as well as differences in the methodological approach of our analyses. Our approach allowed us to identify predictors directly related to the APPLE trial primary outcome while accounting for the CIMT patient heterogeneity at baseline as well as heterogeneity in their CIMT progression pattern, which in turn ensures a more comprehensive investigation of potential biomarkers of subclinical atherosclerosis progression. Conversely, the previous secondary analysis categorized patients based on markers such as hsCRP and pubertal status, while not taking into account their CIMT heterogeneity at the beginning of the trial, which was the most significant predictor of treatment response in our analysis (Figure 2E shows that only patients with low CIMT progression benefitted from statin treatment, suggesting that CIMT stratification at baseline could have led to a positive outcome of the APPLE trial)

JSLE patients allocated to the placebo arm provided the opportunity to examine untreated CIMT progression, as a validated measure for CVD-risk (30, 31), and led to the identification of two patterns of CIMT progression and a robust serum lipid signature which defined the JSLE patients who progressed at a higher rate, despite routinely measured lipid profiles being within the normal limits in both groups. Not surprisingly, none of the validated CVD-risk scores used in general population performed well in the APPLE trial cohort as almost all JSLE patients were classified as low risk. Five conventional CVD-risk scores underestimated the CVR in adult-onset SLE by 50%, while three “lupus adapted” scores (QRISK3, and modified FRS/SCORE risk scores) misclassified 25% SLE patients as low risk (32). This emphasizes the need for additional, high-performance biomarkers for CVD-risk identification in SLE across age.

Lipid metabolomics is extensively used for atherosclerosis risk prediction in SLE as it provides more in-depth information that routinely measured lipids (including particle size, and components). A machine learning model using the same metabolomic platform we employed in this study identified a lipidomic signature which distinguished adult-onset SLE patients with vs without atherosclerosis plaques on vascular scans with a good performance (AUC=80%) (33), while a high apolipoprotein-B:A1 ratio, linked with high CD8+ T cell phenotyping and transcriptomic profile was identified as potential marker for atherogenic progression in JSLE (12). In our study, the 6-biomarker lipid signature outperformed the LDL-cholesterol and total cholesterol (used in routine practice) in identifying JSLE patients with high rates of natural CIMT progression. This metabolomic signature provides an opportunity to explore future validation in external JSLE cohorts, which we will be pursuing.

Three out of six metabolites defining the CIMT progression signature in the placebo arm are lipid components of small and dense LDL particles. Thus, as well as predictive power, our signature also provides mechanistic insight, as the association between the size of LDL particles and atherosclerosis has been explored before, with studies providing evidence for prolonged retention in plasma and enhanced ability to penetrate the arterial wall of small LDL particles (34–36). Lipid lowering drugs with smaller LDL targeted reduction properties, such as rosuvastatin, may represent a better targeted treatment choice for atherosclerosis prevention (37) for patients with JSLE, highlighting the need for more precise patient stratification to address the statin response heterogeneity found in JSLE.

Although accelerated atherosclerosis has been linked to many autoimmune rheumatic diseases, the association between JSLE disease activity and CIMT progression remains controversial, with some studies finding an association (6), while others did not (38). In our analysis, the untreated CIMT progression correlated positively with a pro-atherogenic lipid profile and presence of SLICC JSLE damage, suggesting that JSLE severity contributes to atherosclerosis, similar to previous reports (39).However, we acknowledge the limitations of our correlation analyses between CIMT progression and baseline biomarkers, due to the exploratory nature of these analyses and lack of multiple testing correction despite the use of a more stringent P value cut-off (<0.01), as well as inability to account for the potential impact of the variation of these biomarkers over 36 months, which is also likely to have influenced the CIMT progression in both the placebo and atorvastatin arms. This suggests a limited predictive value of individual baseline biomarkers for a disease that is recognised to fluctuate significantly over time. The observed differences between the direction of correlation of various JSLE markers reflecting disease activity and CIMT progression in both the placebo and atorvastatin arms highlight the need for a more comprehensive understanding of the interplay between lipid regulation, chronic inflammation, JSLE treatment, and traditional CVD factors in determining the pattern of CIMT progression in JSLE.

One possible explanation for the APPLE trial not meeting its primary endpoint is offered by the CIMT progression stratification in the atorvastatin arm, which identified a subgroup of JSLE patients that progressed at a high rate despite atorvastatin successfully lowering their pro-atherogenic lipid profile. This indicates alternative mechanisms underpinning their atherosclerosis progression, as the high CIMT progressors receiving statin treatment were not defined at baseline by the metabolomic signature which characterised the high progressors in the placebo group. Together, these findings support the hypothesis of complementary atherosclerosis mechanisms in JSLE, very likely related to dysregulated lipid metabolism, chronic inflammation, and endothelial dysfunction, possibly modulated in distinct ways in the high vs. low CIMT progression groups. The investigation of molecular mechanisms of atherosclerosis in JSLE or that of anti-inflammatory and metabolic therapeutic benefits of atorvastatin are beyond the scope of this paper.

As with many other CVD measures, CIMT alone is not an ideal measure for predicting CVD-risk in CYP because of challenges of standardisation across age. Factors contributing to the heterogeneity of the CIMT measures include variable ultrasound probe positioning, and potential individual heterogeneity in the context of pubertal growth during the trial, despite the use of a standardised vascular ultrasound protocol and that of a central reader in the APPLE trial. These factors, in addition to lifestyle advice provided to all patients and other unidentified factors might explain why some patients surprisingly experienced CIMT regression over time in both the low progression groups in the placebo and statin arms. There is an increasing body of evidence that atherosclerosis can be regressed in both human and animal studies, with the most accepted possible mechanisms being related to mobilisation of apoB-lipoproteins from the arterial wall, combined with efflux of cholesterol, other lipids and foam cells, as well as influx of healthy phagocytes that remove necrotic debris and macrophage phenotypic changes, all potentially leading to atherosclerosis lesions reversal (40, 41). Despite no convincing evidence that a specific therapy can promote atherosclerosis regression, there are increasing efforts in targeting the inflammatory mechanisms of atherosclerosis (42).

This novel analysis of the APPLE trial provides evidence for the limitations of restricting CVD-risk factor assessment to traditional CVD variables in JSLE patients who have distinct trajectories of subclinical atherosclerosis progression. In addition, demographic, and disease characteristics, as well as routine lipid profiling did not identify JSLE patients with increased CVD-risk, and although effective in lowering serum lipids, atorvastatin did not prevent subclinical atherosclerosis progression in many at risk JSLE patients. Further research into the mechanisms driving the unique lipidomic signature predictive of CIMT progression we identified in the untreated patients, as well as investigation of other pro-inflammatory and metabolic pro-atherosclerotic mechanisms not influenced by statins may potentially support future personalised therapeutic strategies to address the increased CVD-risk in JSLE.

Supplementary Material

Supinfo

Acknowledgements

We would like to thank all participants and hospital sites that recruited patients for the APPLE study. We would like to thank CARRA for support for this project and review of the manuscript.

Funding

The APPLE study was supported by the NIH (National Institute of Arthritis and Musculoskeletal and Skin Diseases contract N01-AR-2-2265), the Edna and Fred L. Mandel Jr. Center for Hypertension and Atherosclerosis, and Pfizer, which provided atorvastatin and matching placebo.

This work was supported by a Versus Arthritis PhD Studentship (22908) and Career Development Fellowship (22856), as well as grants from the National Institute of Heath Research (NIHR) - University College London Hospital (UCLH) Biomedical Research Centre grant, BRC4/III/CC and BRC773/III/CC/101350, and Lupus UK and was performed within the Centre for Adolescent Rheumatology Versus Arthritis at University College London (UCL), UCLH and Great Ormond Street Hospital (GOSH) supported by grants from Versus Arthritis (21593 and 20164), Great Ormond Street Children’s Charity, and the NIHR-Biomedical Research Centres at both GOSH and UCLH. The views expressed are those of the authors and not necessarily those of the National Health System (NHS), the NIHR or the UK Department of Health. Dr Lewandowski was funded by the NIAMS Intramural Program. The APPLE study was funded by the National Institutes of Health (NIAMS N01-AR-2-2265).

The APPLE study was supported by the NIH (National Institute of Arthritis and Musculoskeletal and Skin Diseases contract N01-AR-2-2265), the Edna and Fred L. Mandel Jr. Center for Hypertension and Atherosclerosis, and Pfizer, which provided atorvastatin and matching placebo.

Footnotes

Competing interests

The authors declared no relevant conflicts of interest.

Collaborators: The following participated in this study by enrolling patients at sites or by performing study procedures at sites: Stacy Ardoin, Esi Morgan Dewitt, C Egla Rabinovich, Janet Ellis, Kelly Mieszkalski, Janet Wootton (Duke University Medical Center, Durham, North Carolina), Peter Chira, Joyce Hsu, Tzielan Lee, Christy Sandborg, Jan Perea (Stanford University School of Medicine, Palo Alto, California), Beth Gottlieb, Patricia Irigoyen, Jennifer Luftig, Shaz Siddiqi, Zhen Ni, Marilynn Orlando, Eileen Pagano (Cohen Children’s Medical Center, New Hyde Park, New York), Andrew Eichenfield, Lisa Imundo, Deborah Levy, Philip Kahn, Candido Batres, Digna Cabral (Morgan Stanley Children’s Hospital of New York–Presbyterian, New York, New York), Kathleen A. Haines, Yukiko Kimura, Suzanne C. Li, Jennifer Weiss, Mary Ellen Riordan, Beena Vaidya (Hackensack University Medical Center, Hackensack, New Jersey), Emily von Scheven, Michelle Mietus-Snyder (University of California at San Francisco Medical Center, San Francisco, California), Earl Silverman, Lawrence Ng (Hospital for Sick Children, Toronto, Ontario, Canada), Suzanne Bowyer, Susan Ballinger, Thomas Klausmeier, Debra Hinchman, Andrea Hudgins (Indiana University School of Medicine, Indianapolis, Indiana), Marilynn Punaro, Shirley Henry, Shuzen Zhang (Texas Scottish Rite Hospital for Children, Dallas, Texas), Nora G. Singer, Elizabeth B. Brooks, Stacy Miner, Nancy Szabo, Lisabeth Scalzi (University Hospitals/ Case Medical Center, Cleveland, Ohio), David Sherry, Libby Dorfeld, Sarajane Wilson, Jenna Tress (Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania), Deborah McCurdy, Tatiana Hernandez, Jyotsna Vitale (University of California Los Angeles Medical Center, Los Angeles, California), Marisa Klein-Gitelman, Angela Kress, Nicole Lowe, Falguni Patel (Children’s Memorial Hospital, Chicago, Illinois), Carol Wallace, Stephanie Hamilton (Seattle Children’s Hospital and Regional Medical Center, Seattle, Washington), Richard Silver, Katie Caldwell, Diane Kamen (Medical University of South Carolina, Charleston, South Carolina), Linda Wagner-Weiner, Becky Puplava, Atanas Lonchev (University of Chicago, Chicago, Illinois), Gloria Higgins, Monica Bacani (Nationwide Children’s Hospital, Columbus, Ohio), Hermine Brunner, Cynthia Rutherford, Jamie Meyers-Eaton, Shannen Nelson, Alexei Grom (Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio), Larry Jung, Teresa Conway, Lacey Frank, Lori Kuss (Creighton University Medical Center, Omaha, Nebraska), Jenny Soep, Hazel Senz (University of Colorado, Aurora, Colorado), Ann Reed, Thomas Mason, Jane Jaquith, Diana E. Paepke-Tollefsrud (Mayo Clinic, Rochester, Minnesota).

Ethical approval information, institution(s) and number(s):

The Duke Clinical Research Institute (Durham, NC) served as the data-coordinating center and provided oversight of all aspects of the study’s conduct, management, and statistical analysis. The study was conducted at 21 Childhood Arthritis and Rheumatology Research Alliance (CARRA) sites in North America. Local institutional review board approval was obtained, and all patients or their guardians gave informed consent and assent following local guidelines. The ClinicalTrials.gov Identifier is: NCT00065806. Chief Investigator: Prof. Laura Schanberg, The Duke University

Data sharing statement:

The APPLE clinical trial study protocol and results are publicly available - Use of atorvastatin in systemic lupus erythematosus in children and adolescents - PubMed (nih.gov). Preliminary analyses of this study are also available at SSRN SSRN: https://ssrn.com/abstract=4336159 or http://dx.doi.org/10.2139/ssrn.4336159. The study has been reported according to the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines (PMID: 22031171). Data used for all the complementary analyses included in this manuscript and the analytic codes are available on request.

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Associated Data

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

Supplementary Materials

Supinfo
NIHMS1933986-supplement-Supinfo.docx (1.3MB, docx)

Data Availability Statement

The APPLE clinical trial study protocol and results are publicly available - Use of atorvastatin in systemic lupus erythematosus in children and adolescents - PubMed (nih.gov). Preliminary analyses of this study are also available at SSRN SSRN: https://ssrn.com/abstract=4336159 or http://dx.doi.org/10.2139/ssrn.4336159. The study has been reported according to the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines (PMID: 22031171). Data used for all the complementary analyses included in this manuscript and the analytic codes are available on request.

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