Abstract
Objectives
We aimed to evaluate cardiovascular (CV) risk in patients with idiopathic inflammatory myopathies (IIM) compared with healthy controls (HC) and to assess its association with disease-specific features.
Methods
Ninety IIM patients and 180 age-/sex-matched HC were included. Subjects with a history of CV disease (angina pectoris, myocardial infarction and cerebrovascular/peripheral arterial vascular events) were excluded. All participants were prospectively recruited and underwent examinations of carotid intima–media thickness (CIMT), pulse wave velocity (PWV), ankle-brachial index (ABI), and body composition. The risk of fatal CV events was evaluated by the Systematic COronary Risk Evaluation (SCORE) and its modifications.
Results
Compared with HC, IIM patients had a significantly higher prevalence of traditional CV risk factors, carotid artery disease (CARD), abnormal ABI and PWV. After propensity score matching (using traditional CV risk factors), the prevalence of CARD and pathological PWV remained significantly higher in IIM than HC. No significant difference in SCORE was observed. The most unfavourable CV risk profile was observed in patients with necrotizing myopathy, especially in statin-induced anti-HMGCR+ patients. The calculated CV risk scores by SCORE, SCORE2 and SCORE multiplied by the coefficient 1.5 (mSCORE) were reclassified according to CIMT and the presence of carotid plaques. SCORE was demonstrated to be most inaccurate in predicting CV risk in IIM. Age, disease activity, lipid profile, body composition parameters and blood pressure were the most significant predictors of CV risk in IIM patients.
Conclusion
Significantly higher prevalence of traditional risk factors and subclinical atherosclerosis was observed in IIM patients compared with HC.
Keywords: atherosclerosis, cardiovascular risk, myositis, inflammation
Rheumatology key messages.
Patients with IIM demonstrate a significantly increased prevalence of subclinical atherosclerosis compared with healthy individuals.
Increased CV risk in IIM is due to the increased prevalence of traditional risk factors.
System SCORE and its modifications underestimate the CV risk in patients with IIM.
Introduction
Cardiovascular (CV) diseases are currently the leading cause of mortality worldwide [1]. Atherosclerosis (ATS) is the main pathogenetic mechanism underlying the development of CV diseases and the immune system is the key mechanism involved in atherogenesis [2]. Many studies reported a higher prevalence of ATS in patients with autoimmune diseases compared with the general population, leading to increased CV morbidity and mortality [3, 4]. While CV risk in more common rheumatic diseases has been well described, the situation in rarer rheumatic diseases is unsatisfactory due to the lack of studies and contradictory results.
Idiopathic inflammatory myopathies (IIM) belong to a rare group of CTDs characterized by the involvement of the skeletal muscle. The traditionally recognized IIM subtypes are DM and PM [5–7]. The later recognized IBM [8], immune-mediated necrotizing myopathy (IMNM) and antisynthetase syndrome (ASS) could have been previously misdiagnosed as PM [9].
Only a few studies have assessed CV risk in patients with IIM, concluding that CV diseases are the leading cause of mortality and CV risk is higher in IIM compared with the general population [10–13]. The traditional risk factors promoting pathological vascular changes, leading to atherogenesis, are the main CV risk factors in the general population, including gender, age, dyslipidaemia, arterial hypertension, dysregulation of glucose metabolism and smoking [14]. However, CV manifestations in inflammatory rheumatic diseases are attributable to traditional risk factors only in about 75% of the cases [15]. Therefore, the increased CV risk in these patients is probably due to the inflammatory burden [16].
Systems based on traditional risk factors have been developed to assess CV risk in the general population. The most widely used in the European population are Systemic COronary Risk Evaluation (SCORE) [17], or SCORE2 [18]. The European Alliance of Associations for Rheumatology (EULAR) recommends evaluating the CV risk in patients with inflammatory arthropathies by a modified SCORE (mSCORE), i.e. SCORE multiplied by the coefficient 1.5 [19]. According to EULAR recommendations for CTDs, the same tools as those for the general population should be used for CV risk assessment [20].
Moreover, the examination of subclinical ATS can improve CV risk prediction, and includes evaluations of carotid intima–media thickness (CIMT) and plaque detection by B-mode ultrasound [21], ankle-brachial index (ABI) [19] and carotid-femoral pulse wave velocity (cf-PWV) [16, 22, 23].
This cross-sectional study aimed to prospectively recruit and evaluate CV risk in IIM patients compared with healthy controls (HC) without any history of CV disease and assess factors contributing to CV risk in IIM.
Methods
Patients and healthy controls
We conducted a cross-sectional, observational, prospective, case–control study on CV risk in 90 patients with IIM who fulfilled the classification criteria for adult IIM [24] compared with 180 HC. Two HC with the same gender and similar age to each IIM patient were selected from a pre-existing list of HC from the HC Registry of the Institute of Rheumatology in Prague (IoRP) (developed using the snowball method from a pool of employees, relatives and acquaintances) and prospectively underwent the same set of examinations over a similar period of time as IIM patients. Individuals in both groups met the inclusion criteria and had no exclusion criteria (further details in Supplementary Fig. S1, available at Rheumatology online) and signed informed consent prior to inclusion in the study.
All relevant study documentation and amendments were approved by the independent Ethics Committee of the Institute of Rheumatology Prague with reference numbers 10114/2016 and 9406/2017. The study was conducted following the principles outlined in the Declaration of Helsinki, the Guidelines of the International Council for Harmonisation (ICH) on Good Clinical Practise (GCP) Guideline E6 (R2) (EMA/CPMP/ICH/135/95) European Union (EU) Directive 95/46/EC, and other applicable regulatory requirements. All examinations were performed according to the relevant regulations and guidelines.
Body composition assessment
Body composition was measured by two methods: bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). Further details on the devices used and measurements and parameters have been described elsewhere [25, 26].
Evaluation of CV risk by scoring systems
CV risk was evaluated as described in our preliminary study [26]. In all participants, we applied the SCORE system [17] according to the European population chart, and the SCORE2 system [18] using the online calculator (https://u-prevent.nl/calculators) for the high-risk population (Czech Republic). In the IIM cohort, we additionally calculated mSCORE by multiplying SCORE by 1.5 [19].
Assessment of subclinical atherosclerosis
The selected non-invasive examination methods were performed in all participants: ultrasound examination of carotid arteries to evaluate the carotid intima–media thickness (CIMT) [27] and the presence of plaques (carotid artery disease [CARD]) [28], pulse wave velocity (PWV) [29] by two trained cardiologists (one for CIMT, one for PWV), and ankle-brachial index (ABI) [30] by an experienced cardiology nurse, all of whom were blinded to the group allocation. Further details were described elsewhere [26].
CV risk estimation by the calculated SCORE systems and ultrasound examination
We assessed the overall CV risk as described previously [26]. All participants were divided into three categories: low, intermediate and high risk. Categories were based on the scores and findings of the carotid ultrasound examination (Supplementary Table S1, available at Rheumatology online). Further details (baseline characteristics, laboratory methods, and statistical analysis) are available in Supplementary Data S1, available at Rheumatology online.
Results
Clinical characteristics
In total, 90 IIM patients (78% females, mean age 53.6 years) and 180 HC (72% females, mean age 56.6 years) were included in the study. The IIM group consisted of DM (32%), PM (13%), IMNM (22%) and ASS (32%). Further details are shown in Table 1. All participants underwent ultrasound of carotid arteries. ABI and PWV were performed in 81% and 70% of IIM patients, and 81% and 72% HC, respectively.
Table 1.
Baseline characteristics of patients with idiopathic inflammatory myopathies and healthy controls
| Parameter | IIM (n = 90) | HC (n = 180) | P-value |
|---|---|---|---|
| Gender, female/male, n (%) | 70 (78)/20 (22) | 130 (72)/50 (28) | 0.307 |
| Age, median (IQR), years | 58.8 (47.4–66.5) | 56.6 (44.1–63.5) | 0.126 |
| BMI, median (IQR), kg/m2 | 25.9 (23.2–31.1) | 29.6 (23.6–29.6) | 0.786 |
| Clinical features | |||
| Disease subtype, n (%) | |||
| DM | 29 (32.2) | — | |
| PM | 12 (13.3) | — | |
| Immune-mediated necrotizing myopathy | 20 (22.2) | — | |
| Antisynthetase syndrome | 29 (32.2) | — | |
| Disease duration, median (IQR), years | 5.95 (1.42–8.67) | — | |
| Disease activity (MITAX), median (IQR) | 0.16 (0.07–0.24) | — | |
| Disease damage (MDI), median (IQR) | 0.05 (0.03–0.09) | — | |
| Muscle strength (MMT-8), median (IQR) | 68 (55–75) | — | |
| IIM-associated clinical manifestations, n (%) | |||
| Muscle weakness | 64 (71) | — | |
| Oesophageal motility disorder | 16 (18) | — | |
| Skin rash | 21 (23) | — | |
| Mechanics’ hands | 24 (27) | — | |
| Raynaud’s phenomenon | 13 (14) | — | |
| Arthritis | 11 (12) | — | |
| Interstitial lung disease | 39 (43) | — | |
| Cardiac involvement | 3 (3) | — | |
| Laboratory features | |||
| CRP, median (IQR), mg/l | 2.16 (1.20–6.27) | 1.39 (0.66–2.85) | <0.001 |
| ESR, median (IQR), mm/h | 11.5 (6–21.5) | 8 (5–12) | <0.001 |
| Creatine kinase, median (IQR), µkat/l | 2.35 (1.22–7.59) | — | |
| Lactate dehydrogenase, median (IQR), µkat/l | 4.06 (3.51–5.7) | — | |
| Myoglobin, median (IQR), µg/l | 82 (57.6–250.3) | — | |
| Glucose, median (IQR), mmol/l | 5.3 (4.7–5.7) | 5.4 (5.0–5.8) | 0.042 |
| HbA1c, median (IQR), mmol/mol | 32.9 (30.4–36.6) | 33.0 (29.8–36.6) | 0.778 |
| Haemoglobin, median (IQR), g/l | 133 (124–145) | 139 (132–150) | <0.001 |
| Autoantibody (positive), n (%) | ANA: 45 (50); Mi-2: 6 (7); TIF-1γ: 4 (4); MDA-5: 1 (1); SAE: 1 (1); NXP-2: 0 (0); SRP: 5 (6); Jo-1: 22 (24); PM-Scl: 8 (9); RNP: 5 (6); Ku: 1 (1); Ro-52: 31 (34); Ro-60: 8 (9); HMGCR: 12 (13) | — | |
| Treatment | |||
| Current dose of GCa, | |||
| Median (IQR), mg/day | 7.5 (5–26.3) | — | |
| Median (IQR), mg | 11 918 (5300–29 009) | — | |
| GC exposure, median (IQR), years | 2.6 (0.61–6.96) | — | |
| Current treatment, n (%) | GC: 78 (87); MTX: 29 (32); AZA: 13 (14); CSA: 9 (10); CPA: 8 (9); LEF: 2 (2); MMF: 5 (6); TAC: 1 (1); HQ: 2 (2); RTX: 2 (2); ABA: 1 (1); IVIg: 1 (1) | — |
Prednisolone equivalent dose. Significant results (P-value <0.05) are shown in bold. ABA: abatacept; ANA: antinuclear antibodies; CPA: cyclophosphamide; CSA: ciclosporin A; GC: glucocorticoids; HbA1c: glycated haemoglobin; HC: healthy controls; HQ: hydroxychloroquine; IIM: idiopathic inflammatory myopathies; IQR: interquartile range; Jo-1: anti-histidyl-tRNA synthetase; Ku: anti-Ku (against the nuclear DNA-protein kinase subunit); LEF: leflunomide; MDA-5: anti-antigen associated with melanoma differentiation; MDI: myositis damage index; MMF: mycophenolate mofetil; Mi-2: anti-nuclear helicase 218/240 kDa; MITAX: Myositis Intention to Treat Activity Index; MMT-8: Manual Muscle Testing of eight muscle groups; NXP2: anti-nuclear matrix protein; PM-Scl: anti-Pm-Scl (anti-core complex 11–16 proteins); RNP: anti-ribonucleoprotein; Ro: anti-Ro (52/60 kDa: against cytoplasmic RNA and associated peptides); RTX: rituximab; SAE: anti-SUMO1 (small ubiquitin-like activating enzyme); SRP: anti-signal recognition particles; TAC: tacrolimus; TIF1: anti-transcription factor-1.
Prevalence of traditional risk factors
We found similar age and gender distribution in both cohorts with a significantly higher prevalence of arterial hypertension and its treatment, dyslipidaemia, and diabetes and treatment with insulin in IIM compared with HC (Table 2).
Table 2.
Comorbidities and traditional risk factors in IIM and HC before and after propensity score matching (PSM)
| IIM | HC | P-value | |
|---|---|---|---|
| Traditional cardiovascular risk factor (without PSM) | |||
| n | 90 | 180 | |
| Gender: female/male, n (%) | 70 (78)/20 (22) | 130 (72)/50 (28) | 0.307 |
| Age, median (IQR), years | 58.8 (47.4–66.5) | 56.6 (44.1–63.5) | 0.126 |
| BMI, median (IQR), kg/m2 | 25.9 (23.2–31.1) | 29.6 (23.6–29.6) | 0.786 |
| Arterial hypertension (treated or untreated), n (%) | 46 (51.1) | 53 (29.4) | 0.001 |
| Antihypertensive treatment, n (%) | 37 (41.1) | 35 (19.4) | <0.001 |
| Dyslipidaemia (treated or untreated), n (%) | 69 (76.7) | 97 (53.8) | <0.001 |
| Hypolipidaemics, current use (statins, fibrates), n (%) | 7 (7.8) | 8 (4.4) | 0.275 |
| Prediabetes, n (%) | 27 (30.0) | 46 (25.6) | 0.561 |
| Diabetes, n (%) | 16 (17.8) | 11 (6.1) | 0.005 |
| Insulin treatment, n (%) | 7 (7.8) | 1 (0.6) | 0.002 |
| Oral antidiabetic drugs, n (%) | 6 (6.7) | 7 (3.9) | 0.372 |
| Current smoker, n (%) | 5 (5.6) | 24 (13.3) | 0.060 |
| Former smoker, n (%) | 16 (17.8) | NA | NA |
| Cumulative smoking years (n = 21), mean (s.d.) | 21 (10.2) | NA | NA |
| Cumulative estimated no. of cigarettes to date (n = 21), mean (s.d.) | 62 250 (45 332) | NA | NA |
| Family history of cardiovascular diseases, n (%) | 25 (27.8) | 55 (30.6) | 0.672 |
| Alcohol (regular drinking), n (%) | 19 (21.1) | 130 (72.2) | <0.001 |
| Traditional cardiovascular risk factor (after PSM) | |||
| n | 75 | 150 | |
| Gender: female/male, n (%) | 68 (91)/7 (9) | 140 (93)/10 (7) | 0.593 |
| Arterial hypertension (treated or untreated), n (%) | 34 (45) | 51 (34) | 0.110 |
| Dyslipidaemia (treated or untreated), n (%) | 54 (72) | 91 (61) | 0.106 |
| Prediabetes, n (%) | 24 (32) | 45 (30) | 0.761 |
| Diabetes, n (%) | 7 (9) | 10 (7) | 0.593 |
| Current smoker, n (%) | 5 (7) | 12 (8) | 0.796 |
| Family history of cardiovascular diseases, n (%) | 20 (27) | 44 (29) | 0.755 |
Significant results (P-value <0.05) are shown in bold. HC: healthy controls; IIM: idiopathic inflammatory myopathies; IQR: interquartile range; NA: not available; PSM: propensity score matching.
Comparison of the cardiovascular risk
There was a significantly increased occurrence of CARD (carotid plaque presence, count, and total and maximum thickness), and pathological PWV and ABI in IIM patients compared with HC. However, PWV and ABI values did not significantly differ between the cohorts. SCORE was comparable in both cohorts, while IIM patients had a trend to higher SCORE2, which did not reach statistical significance (Table 3). To reduce the bias caused by traditional risk factors, propensity score matching (PSM) was performed (Table 2), which confirmed a significantly increased prevalence of CARD (presence, count and maximal intima–media thickness) and a higher prevalence of pathologic PWV in IIM (Table 3).
Table 3.
Subclinical atherosclerosis and CV risk in IIM and HC before and after propensity score matching
| IIM | HC | P-value | |
|---|---|---|---|
| Parameter (without PSM) | |||
| n | 90 | 180 | |
| Coronary artery disease (carotid plaques), n (%) | 44 (48.9) | 51 (28.3) | 0.001 |
| Carotid plaque total count, mean (s.d.) | 1.01 (1.31) | 0.60 (1.06) | 0.006 |
| Carotid plaque total thickness, mean (s.d.), mm | 1.40 (1.92) | 0.82 (1.47) | 0.007 |
| Carotid plaque maximal thickness, mean (s.d.), mm | 0.94 (1.09) | 0.49 (0.81) | <0.001 |
| CIMT, mean (s.d.), mm | 0.78 (0.30) | 0.74 (0.16) | 0.167 |
| ABI (IIM: n = 73; HC: n = 164), mean (s.d.) | 0.94 (0.18) | 0.99 (0.16) | 0.040 |
| PWV (IIM: n = 63; HC: n = 130), mean (s.d.), m/s | 8.20 (2.22) | 7.81 (1.65) | 0.172 |
| Pathological CIMT (>0.9 mm), n (%) | 10 (11.1) | 19 (10.8) | 1.000 |
| Pathological ABI (<0.9) (IIM: n = 73; HC: n = 164), n (%) | 22 (30) | 45 (28) | 0.756 |
| Pathological PWV (IIM: n = 63; HC: n = 130), n (%) | 16 (25.4) | 13 (10) | 0.011 |
| SCORE, mean (s.d.) | 4.41 (3.78) | 4.03 (4.67) | 0.506 |
| SCORE2, mean (s.d.) | 9.62 (11.42) | 7.45 (8.85) | 0.088 |
| Parameter (after PSM) | |||
| n | 75 | 150 | |
| Coronary artery disease (carotid plaques), n (%) | 36 (48.6) | 47 (31.3) | 0.013 |
| Carotid plaque total count, mean (s.d.) | 1.01 (1.31) | 0.66 (1.16) | 0.037 |
| Carotid plaque total thickness, mean (s.d.), mm | 1.28 (1.68) | 0.91 (1.55) | 0.104 |
| Carotid plaque maximal thickness, mean (s.d.), mm | 0.90 (1.04) | 0.54 (0.84) | 0.007 |
| CIMT, mean (s.d.), mm | 0.77 (0.30) | 0.74 (0.16) | 0.405 |
| ABI, mean (s.d.) | 0.95 (0.19) | 0.99 (0.17) | 0.144 |
| PWV, mean (s.d.), m/s | 8.09 (2.11) | 7.97 (1.68) | 0.679 |
| Pathological CIMT (>0.9 mm), n (%) | 6 (8) | 18 (12.1) | 0.493 |
| Pathological ABI (<0.9), n (%) | 18 (28.6) | 43 (31.6) | 0.742 |
| Pathological PWV, n (%) | 14 (25) | 12 (11.1) | 0.025 |
| SCORE, mean (s.d.) | 3.96 (3.45) | 4.16 (4.86) | 0.750 |
| SCORE2, mean (s.d.) | 9.59 (12.14) | 7.59 (9.20) | 0.172 |
Significant results (P-value <0.05) are shown in bold. ABI: ankle-brachial index; CIMT: carotid intima–media thickness; HC: healthy controls; IIM: idiopathic inflammatory myopathies; PSM: propensity score matching; PWV: pulse wave velocity; SCORE: Systematic COronary Risk Evaluation.
Differences between men and women
Comparison of CV risk factors and markers of subclinical ATS in IIM showed no significant differences between women and men except for age and statin treatment (Supplementary Table S2, available at Rheumatology online). In the HC group, men had a significantly higher prevalence of pathological PWV, higher SCORE and a trend towards higher SCORE2, and overall CV risk based on SCORE and SCORE2 compared with women (Supplementary Table S3, available at Rheumatology online). Men with IIM had a significantly higher prevalence of some traditional CV risk factors, and a higher presence of carotid plaques compared with healthy men (Supplementary Table S4, available at Rheumatology online). Women with IIM were significantly older and had higher CV risk and more pronounced subclinical ATS, as well as higher calculated CV risk by SCORE and SCORE2 and overall CV risk based on SCORE2 and US examination compared with healthy women (Supplementary Table S5, available at Rheumatology online).
Differences among IIM subtypes
The gender proportion, BMI and disease activity were similar in all subtypes. Patients with IMNM had a trend towards a higher age and significantly shorter disease duration compared with other IIM subtypes. Time exposure to glucocorticoid (GC) therapy was the longest in PM and the shortest in IMNM. Furthermore, diabetes mellitus and treatment by oral antidiabetic drugs were most frequently observed in IMNM. Further details are given in Supplementary Table S6, available at Rheumatology online. We found significantly increased CIMT and CV risk based on the modifications of the SCORE system in IMNM. Additionally, IMNM had the highest prevalence, thickness and number of carotid artery plaques, and the highest (i.e. worst) PWV values, which did not reach statistical significance (Supplementary Table S7, available at Rheumatology online). Analysis on IMNM subgroups demonstrated significantly more unfavourable CV risk markers (traditional CV risk factors, markers of subclinical ATS, SCORE, SCORE2 and mSCORE) in patients with statin-induced anti-HMGCR (3-Hydroxy-3-Methylglutaryl-CoA Reductase) positive (anti-HMGCR+) IMNM compared with non-statin-induced IMNM (Supplementary Table S8, available at Rheumatology online). Similarly, the comparison of all IIM subtypes (including the two IMNM subtypes) demonstrated significant differences in traditional CV risk factors, CARD and calculated CV risk (Supplementary Tables S9 and S10, available at Rheumatology online). The post hoc analysis showed that patients with anti-HMGCR+ IMNM had the highest CIMT and calculated CV risk and overall CV risk based on SCORE and mSCORE (Supplementary Table S11, available at Rheumatology online).
Stratification of cardiovascular risk
All of the individuals were divided into four categories to assess the overall CV risk, based on the level of risk calculated by (i) SCORE (CVR-SCORE), (ii) SCORE2 (CVR-SCORE2), (iii) mSCORE (CVR-mSCORE), and (iv) carotid ultrasound findings including CIMT, presence of carotid plaques and plaque thickness (CVR-US), in the same manner as in our previously published cohorts [26]. Each category consisted of three risk strata as described in Supplementary Table S1, available at Rheumatology online. There was a trend towards increased CV risk estimated by SCORE and significantly increased SCORE2 and CVR-US in IIM compared with HC. However, subsequent PSM analysis demonstrated no significant difference (Table 4).
Table 4.
Comparison of CV risk levels between IIM and HC before and after propensity score matching
| CVR level |
|||
|---|---|---|---|
| IIM | HC | P-value | |
| Cardiovascular risk category (without PSM) | |||
| n | 90 | 180 | |
| CVR-SCORE | 1.58 (0.70) | 1.42 (0.64) | 0.061 |
| CVR-SCORE2 | 1.94 (0.84) | 1.71 (0.79) | 0.026 |
| CVR-US | 1.82 (0.91) | 1.56 (0.85) | 0.019 |
| Cardiovascular risk category (after PSM) | |||
| n | 75 | 150 | |
| CVR-SCORE | 1.49 (0.62) | 1.43 (0.64) | 0.457 |
| CVR-SCORE2 | 1.89 (0.83) | 1.72 (0.80) | 0.130 |
| CVR-US | 1.80 (0.93) | 1.81 (0.87) | 0.126 |
Data are presented as mean (s.d.). Significant results (P-value <0.05) are shown in bold. CVR: cardiovascular risk; CVR-SCORE: cardiovascular risk estimated according to the calculated SCORE; CVR-SCORE2: cardiovascular risk estimated according to the calculated SCORE2; CVR-US: cardiovascular risk estimated according to the carotid ultrasound examination (total plaque count, plaque thickness, carotid intima–media thickness); IIM: idiopathic inflammatory myopathies; HC: healthy controls; PSM: propensity score matching; SCORE: Systematic COronary Risk Evaluation.
CV Risk re-classification
Based on CV risk determined by the modifications of SCORE (CVR-SCORE, CVR-SCORE2, CVR-mSCORE) and CVR-US, we evaluated the definitive CV risk in every individual. We observed the highest CVR when comparing SCORE, SCORE2 or mSCORE to US in each individual (Table 5). To determine the most reliable scoring system (SCORE, SCORE2, mSCORE), we used a χ2 test to compare the percentages of congruency and disparity between each pair (CVR-SCORE vs CVR-US, CVR-SCORE2 vs CVR-US, and CVR-mSCORE vs CVR-US). In IIM, the percentage of reclassifications from low or intermediate CVR-SCORE to higher CV risk according to CVR-US (32%) was significantly higher compared with the percentage of reclassifications from SCORE2-CVR (17%) and mSCORE-CVR (18%) (Table 5, Supplementary Fig. S2A–C, available at Rheumatology online).
Table 5.
Reclassification of the CV risk based on the modifications of SCORE according to the US examination
| CVR scoring system | Original CVR category | IIM, n (%) | New CVR category | Same/worse | n (%) | Total n (%) of IIM reclassified to a higher CV risk level | P-value |
|---|---|---|---|---|---|---|---|
| CVR-SCORE | 1 | 49 (54.4) | 1 | Same | 35 (38.9) | Total 29 (32.2) | 0.020 |
| 2 | Worse | 6 (6.7) | |||||
| 3 | Worse | 8 (8.9) | |||||
| 2 | 30 (33.3) | 2 | Same | 15 (16.7) | |||
| 3 | Worse | 15 (16.7) | |||||
| 3 | 11 (12.2) | 3 | Same | 11 (12.2) | |||
| CVR-SCORE2 | 1 | 35 (38.9) | 1 | Same | 28 (31.1) | Total 15 (16.7) | |
| 2 | Worse | 3 (3.3) | |||||
| 3 | Worse | 4 (4.4) | |||||
| 2 | 26 (28.9) | 2 | Same | 18 (20.0) | |||
| 3 | Worse | 8 (8.9) | |||||
| 3 | 29 (32.2) | 3 | Same | 29 (32.2) | |||
| CVR-mSCORE | 1 | 45 (50) | 1 | Same | 34 (37.8) | Total 16 (17.8) | |
| 2 | Worse | 4 (4.4) | |||||
| 3 | Worse | 7 (7.8) | |||||
| 2 | 16 (17.8) | 2 | Same | 11 (12.2) | |||
| 3 | Worse | 5 (5.6) | |||||
| 3 | 29 (32.2) | 3 | Same | 29 (32.2) |
| CVR scoring system | Original CVR category | HC, n (%) | New CVR category | Same/worse | n (%) | Total n (%) of HC reclassified to a higher CV risk level | P-value |
|---|---|---|---|---|---|---|---|
| CVR-SCORE | 1 | 120 (66.7) | 1 | Same | 93 (51.7) | Total 44 (24.4) | 0.066 |
| 2 | Worse | 8 (4.4) | |||||
| 3 | Worse | 19 (10.6) | |||||
| 2 | 45 (25) | 2 | Same | 28 (15.6) | |||
| 3 | Worse | 17 (9.4) | |||||
| 3 | 15 (8.3) | 3 | Same | 15 (8.3) | |||
| CVR-SCORE2 | 1 | 89 (49.4) | 1 | Same | 71 (39.4) | Total 29 (16.1) | |
| 2 | Worse | 7 (3.9) | |||||
| 3 | Worse | 11 (6.1) | |||||
| 2 | 54 (30) | 2 | Same | 43 (23.9) | |||
| 3 | Worse | 11 (6.1) | |||||
| 3 | 37 (20.6) | 3 | Same | 37 (20.6) |
Significant results (P-value <0.05) are shown in bold. CVR: cardiovascular risk; CVR-SCORE: cardiovascular risk estimated according to the calculated SCORE; CVR-SCORE2: cardiovascular risk estimated according to the calculated SCORE2; CVR-US: cardiovascular risk estimated according to the carotid ultrasound examination (total plaque count, plaque thickness, carotid intima–media thickness); SCORE: Systematic COronary Risk Evaluation.
In HC, there was only a trend towards a higher proportion of reclassifications to higher CV risk levels from CVR-SCORE (24%) compared with proportions of reclassified individuals from CVR-SCORE2 (16%) (Table 5, Supplementary Fig. S2D and E, available at Rheumatology online).
Association of CV risk and markers of subclinical atherosclerosis
Parameters of subclinical ATS were strongly associated with all modifications of SCORE and the corresponding CVR categories in IIM patients. In addition, increased PWV correlated positively with the presence of carotid plaques and their thickness (Supplementary Table S12, available at Rheumatology online).
Associations of CV risk and IIM-specific features
In IIM, potential associations between markers of CV risk and subclinical ATS with disease-specific features, parameters of body composition, nutrition and systemic inflammation, which were selected on a priori clinical judgement, were analysed. Several parameters were most frequently associated with CV risk markers in univariate analysis (Supplementary Table S13, available at Rheumatology online). Age and mean arterial pressure (MAP) correlated positively with CARD, PWV, and modifications of SCORE, CVR-SCOREs and CVR-US. Disease activity (assessed by MITAX) correlated negatively with carotid plaque presence and thickness, SCORE2, CVR-SCORE2, CVR-mSCORE and CVR-US. Longer administration of GC was associated only with an increased number of carotid plaques. Other frequently associated parameters included body composition parameters, such as percentage of body fat (BF%), lean body mass (LBM%), visceral fat (VF) assessed by DXA, and markers of nutritional status, such as extracellular mass/body cell mass ratio (ECM/BCM) and phase angle measured by BIA. Higher ECM/BCM and lower phase angle (both negative predictors of nutritional status) correlated with increased carotid plaque count, thickness and overall CV risk estimated by SCORE systems. Higher BF%, VF and lower LBM% were associated with higher, i.e. worse values of, PWV, SCORE, mSCORE and CVR-SCORE. Markers of atherogenic lipid profile (total cholesterol, low-density lipoprotein cholesterol, non-high density lipoprotein cholesterol, apo-B and triglyceride) and levels of glycated haemoglobin (HbA1c) were increased in patients with unfavourable markers of subclinical ATS (CIMT, PWV, CARD) and higher CV risk based on SCORE systems. In addition, positive correlations were observed between macrophage inflammatory protein-1β (MIP-1β; CCL4) and carotid plaque total thickness, platelet-derived growth factor (PDGF)-bb and ABI, and IL-4 and SCORE2. On the contrary, IFN-γ inducible protein-10 (IP-10; CXCL10) correlated negatively with SCORE and SCORE2. Finally, IL-8 was positively associated with carotid plaque thickness, CVR-SCORE2 and CVR-mSCORE. Further details are shown in Supplementary Table S13, available at Rheumatology online.
Traditional risk factors in IIM
The presence of arterial hypertension (51%), dyslipidaemia (69%), prediabetes (27%) and type 2 diabetes (16%) and a history of smoking (21%) were significantly associated with higher SCORE and overall CV risk based on modifications of SCORE and US examinations. Moreover, patients with arterial hypertension and dyslipidaemia had significantly increased PWV and risk of CARD (Supplementary Table S14, available at Rheumatology online).
Autoantibodies in IIM
Only the most prevalent autoantibodies in IIM patients at baseline were included in the analysis: antinuclear antibodies (ANA; 50%), anti-Jo-1 (24%) and anti-Ro52 (34%) (Table 1). There was no significant difference between ANA negative and positive patients regarding CV risk and markers of subclinical ATS. On the contrary, anti-Jo-1 negative (anti-Jo-1–) patients had significantly worse calculated CV risk (higher SCOREs) and more frequent CARD compared with anti-Jo-1 positive (anti-Jo-1+) patients. Anti-Ro52 positivity (Anti-Ro52+) was associated with increased CVR-mSCORE (Supplementary Table S14, available at Rheumatology online).
Anti-Jo-1+ patients had significantly lower age (P = 0.010), a trend towards a lower BMI (P = 0.058), a significantly decreased prevalence of arterial hypertension and antihypertensive therapy (P < 0.001 for both), diabetes mellitus (P = 0.028) and treatment with peroral antidiabetic drugs (P = 0.003). On the other hand, anti-Jo-1+ patients had a significantly higher prevalence of interstitial lung disease (ILD) and arthritis compared with anti-Jo-1− (P < 0.001 for both), and were less often treated with methotrexate (MTX; P = 0.004).
Subgroup analysis based on anti-Jo-1 and anti-Ro52 positivity revealed significant differences in all traditional CV risk factors, presence of carotid plaques, pathological CIMT and ABI, and calculated CV risk by SCORE and mSCORE (Supplementary Tables S15 and S16, available at Rheumatology online). The post hoc analysis showed, that the majority of differences were found between the anti-Jo-1+/anti-Ro52+ group and the anti-Jo-1−/anti-Ro52− or the anti-Jo-1−/anti-Ro52+ group (Supplementary Table 17, available at Rheumatology online).
Clinical manifestations of IIM
Parameters selected a priori for analysis were ILD (43%), skin rash (23%), mechanics’ hands (27%), and Raynaud’s phenomenon (14%) (Table 1). Patients with ILD had significantly decreased (worse) ABI, whereas patients without Raynaud’s phenomenon had higher SCORE and mSCORE (Supplementary Table S14, available at Rheumatology online).
Treatment of IIM
Apart from GC (87%), most patients were treated with MTX (32%) and azathioprine (AZA; 14%) (Table 1). Only a positive association between higher PWV and current treatment with MTX was demonstrated (Supplementary Table S14, available at Rheumatology online).
Multivariate analysis models
Variables with P < 0.25 from univariate analyses were subsequently tested in the multivariate models. Variables with significant association (P < 0.05) are shown in Table 6. Age was the most frequent factor affecting all parameters of CV risk based on calculated scores, PWV and carotid plaque thickness. Other predictors included MAP, BF% assessed by DXA, and MITAX. The total duration of GC therapy appeared to be a significant factor for carotid plaque count and overall CVR-US. Levels of MIP-1β and age were the most significant variables affecting the total thickness of carotid plaques (Table 6).
Table 6.
Significant associations of CV risk and markers of subclinical atherosclerosis with selected characteristics of IIM—multivariate regression analysis
| Correlated parameters | Disease features, CV risk factors, body composition, chemokines | β | P-value |
|---|---|---|---|
| SCORE | Age | 0.138 | <0.001 |
| BF-DXA (%) | −0.251 | 0.045 | |
| MAP | 0.096 | 0.003 | |
| SCORE2 | Age | 0.395 | <0.001 |
| BMI | −0.636 | 0.033 | |
| mSCORE | Age | 0.208 | <0.001 |
| BF-DXA (%) | −0.377 | 0.045 | |
| MAP | 0.144 | 0.003 | |
| CIMT | — | — | — |
| ABI | — | — | — |
| PWV | Age | 0.057 | 0.020 |
| Carotid plaques (total count) | GC-exposure time | 0.0003 | 0.015 |
| Carotid plaques (total thickness) | Age | 0.048 | 0.009 |
| MIP-1β | 0.008 | 0.028 | |
| Carotid plaques (maximum thickness) | Age | 0.026 | 0.006 |
| MITAX | −2.078 | 0.018 | |
| CVR-SCORE | Age | 0.021 | <0.001 |
| MAP | 0.017 | 0.020 | |
| CVR-SCORE2 | Age | 0.034 | <0.001 |
| CVR-mSCORE | Age | 0.035 | <0.001 |
| MITAX | −1.349 | 0.016 | |
| BF-DXA (%) | −0.068 | 0.033 | |
| CVR-US | Age | 0.020 | 0.014 |
| MITAX | −1.570 | 0.044 | |
| GC-exposure time | 0.0002 | 0.020 |
ABI: ankle-brachial index; β: regression β-coefficient; BF-DXA (%): body fat percentage measured by dual-energy X-ray absorptiometry; CIMT: carotid intima–media thickness; CV: cardiovascular; CVR: cardiovascular risk; CVR-mSCORE: cardiovascular risk estimated according to the calculated mSCORE; CVR-SCORE: cardiovascular risk estimated according to the calculated SCORE; CVR-SCORE2: cardiovascular risk estimated according to the calculated SCORE2; CVR-US: cardiovascular risk based on carotid ultrasound examination (plaques, CIMT); GC: glucocorticoids; MAP: mean arterial pressure; MIP-1β: macrophage inflammatory protein-1β; MITAX: Myositis Intention to Treat Activity Index; mSCORE: modified Systematic COronary Risk Evaluation; PWV: pulse wave velocity; SCORE: Systematic COronary Risk Evaluation; US: ultrasound (examination).
Discussion
This cross-sectional study on CV risk in IIM compared with HC with similar age and gender distribution included 90 IIM patients and 180 HC. To our knowledge, this is the first study in IIM to include lipid profile, body composition by two methods (BIA and DXA), three independent non-invasive examinations of subclinical ATS, and assessment of CV risk (SCORE, SCORE2 and mSCORE). Unlike in the more frequent rheumatic diseases, there is scant evidence on CV risk, mortality and morbidity in IIM. Accelerated ATS of coronary arteries and myocarditis in IIM leads to increased CV mortality [13], which is the leading cause of mortality [11]. A meta-analysis reported ∼2.37 times increased CV risk in PM and DM [31], while the CV risk was accelerated during the first 5 years of the disease course.
In our study, we estimated the CV risk using SCORE and SCORE2 recommended for the general population in accordance with the recent EULAR recommendation for evaluating the CV risk in CTDs [20]. In addition, we included mSCORE, recommended for patients with autoimmune inflammatory arthropathies [19]. Our findings on carotid ultrasound examinations demonstrated that none of the used scoring systems was accurate in IIM or HC; SCORE in particular was the least accurate. This has been reflected in the most recent guidelines for CV disease prevention where the new SCORE2 system was introduced [32]. To date, the only study validating CV risk tools in IIM is the RI.CAR.D.A study, which also proved that both SCORE and mSCORE underestimate the CV risk in ASS [33]. However, other routinely used CV risk scoring systems might be even more suitable than SCORE2 or mSCORE for IIM patients. Selection of SCORE, SCORE2 and mSCORE in our IIM patients and HC was due to the ethnically homogeneous population of the Czech Republic (predominantly Caucasian of Slavic ancestry). Nevertheless, other CV risk assessment tools should be tested by further studies in IIM cohorts according to the suitability of these tools in various populations.
Significantly increased prevalence of CARD and abnormal PWV were demonstrated in our IIM cohort, even after PSM aimed to reduce the bias caused by the traditional CV risk factors. Despite CIMT not being significantly increased in our IIM patients, there have been contradictory reports in previous studies, including our preliminary study [26, 33–35]. Regarding the traditional CV risk factors, our results confirmed their increased prevalence as reported earlier [36–40]. The analyses on gender-related differences between IIM and HC cohorts demonstrated, among others, that men with IIM had a worse traditional CV risk profile and significantly more frequent CARD compared with healthy men, while women with IIM had significantly higher calculated CV risk and more serious subclinical ATS compared with healthy women.
The most unfavourable CV risk profile was seen in IMNM, which was as expected due to the statin-induced aetiology and a history of dyslipidaemia before the onset of IMNM in the majority of our patients (60%). Notably, age, arterial hypertension, dyslipidaemia, an unfavourable body composition profile and HbA1c were associated with markers of subclinical ATS. Both comparisons of statin-induced (anti-HMGCR+) and non-statin-induced (anti-HMGCR−) IMNM patients and other subtypes with post hoc analysis confirmed the worst CV risk profile in statin-induced anti-HMGCR+ IMNM. Nevertheless, this analysis is limited by a small number of patients.
Despite the known relationship between systemic inflammation in rheumatic diseases and atherogenesis [41], disease activity in our patients was negatively correlated to CV risk and subclinical ATS. Meanwhile, one study has reported more pronounced myocardial dysfunction in IIM with a polyphasic course of the disease 2 years after its onset. In addition, early signs of myocardial dysfunction were detected in IIM patients without the presence of traditional CV risk factors soon after the disease manifestation [42]. In our study, the possible explanation could be a mild mean disease activity, similar to our preliminary study [26], and pharmacotherapy-induced decreasing disease activity during the disease course with an increasing GC cumulative dose and prevalence of the traditional risk factors.
In line with our preliminary study [26], anti-Jo-1− patients showed significantly worse CV risk compared with anti-Jo-1+ patients. Higher CV risk in anti-Jo-1− patients was attributable rather to the increased prevalence of traditional CV risk factors, although a significantly higher prevalence of ILD in anti-Jo-1+ patients could negatively influence the physical condition and activity and probably worsen CV status [43]. The positivity of anti-Ro52 autoantibody in anti-Jo-1− patients was not a protective factor, as both anti-Jo-1−/anti-Ro52− and anti-Jo-1−/anti-Ro52+ patients had significantly worse SCORE, mSCORE and an overall CV risk category (based on SCORE, SCORE2 and mSCORE) compared with anti-Jo-1+/anti-Ro52+ patients. ILD in the whole cohort was associated only with worse ABI, a marker of occlusive artery disease.
Several cytokines and chemokines appeared to be associated with subclinical ATS and CV risk: IL-4, IL-8, MIP-1β, PDGF-bb and IP-10. The association of these cytokines/chemokines with CV risk has been reported in previous studies [44–47].
Regarding the therapy, the total duration of GC therapy was a significant predictor of carotid plaque count and overall CVR-US. The potential cardioprotective effect of MTX could not be confirmed in our study, which is in line with the well-designed CIRT trial [48].
In conclusion, our cross-sectional cohort study in IIM patients demonstrated a significantly increased risk of subclinical ATS and thus increased CV risk compared with HC with comparable age and gender distribution. A significantly increased prevalence of traditional CV risk factors in IIM was observed. The most unfavourable findings were seen in IMNM patients, especially in statin-induced anti-HMGCR+ IMNM. Nevertheless, our results are limited due to a small number of samples in each IIM subtype and should be verified by further studies on larger cohorts of individual IIM subtypes.
All scoring systems for CV risk screening underestimated the CV risk in IIM. SCORE2 appeared to be the most accurate tool in both IIM and HC. Nevertheless, due to the inaccuracy of these tools, an examination of subclinical ATS should be considered for assessing CV risk in IIM patients, especially when there are no established tools for CV risk screening in IIM. While treatment with methotrexate did not appear to be beneficial, long-term therapy with glucocorticoids was associated with carotid artery disease and should be therefore as short as possible with the preferable use of corticoid-sparing agents.
Supplementary Material
Acknowledgements
The authors would like to thank all patients and healthy controls who participated in the study, and Xiao Svec for language editing.
Contributor Information
Sabina Oreska, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Hana Storkanova, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Aneta Pekacova, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Jaroslav Kudlicka, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Vladimir Tuka, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Ondrej Mikes, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Zdislava Krupickova, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Martin Satny, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Eva Chytilova, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Jan Kvasnicka, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Maja Spiritovic, Institute of Rheumatology, Prague, Czech Republic; Department of Physiotherapy, Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic.
Barbora Hermankova, Department of Physiotherapy, Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic.
Petr Cesak, Department of Human Movement Laboratory, Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic.
Marian Rybar, Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic.
Karel Pavelka, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Ladislav Senolt, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Herman Mann, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Jiri Vencovsky, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Michal Vrablik, 3rd Department of Internal Medicine, General University Hospital and 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Michal Tomcik, Institute of Rheumatology, Prague, Czech Republic; Department of Rheumatology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.
Supplementary material
Supplementary material is available at Rheumatology online.
Data availability
Individual anonymized participant data will not be shared. Pooled study data, protocol or statistical analysis plan can be shared upon request at oreska@revma.cz.
Contribution statement
S. Oreska, H. Storkanova, L. Senolt, J. Vencovsky, M. Vrablik and M. Tomcik designed the study. S. Oreska, H. Storkanova, A. Pekacova, J. Kudlicka, V. Tuka, O. Mikes, Z. Krupickova, M. Satny, E. Chytilova, J. Kvasnicka, M. Spiritovic, B. Hermankova, K. Pavelka, L. Senolt, H. Mann, M. Vrablik and M. Tomcik collected patient data. A. Pekacova and H. Storkanova performed the laboratory analysis. S. Oreska, M. Spiritovic and B. Hermankova performed the body composition analysis. J. Kudlicka, V. Tuka, O. Mikes, Z. Krupickova, M. Satny, E. Chytilova and J. Kvasnicka performed the cardiovascular examination. P. Cesak and M. Rybar performed the statistical analysis. S. Oreska and M. Tomcik prepared the original draft of the manuscript. All authors critically interpreted the results, reviewed the draft version and approved the final manuscript.
Funding
This work was supported by the Ministry of Health of the Czech Republic [023728, NV18-01-00161A; NU21-01-00146]; Ministry of Education Youth and Sports of the Czech Republic [SVV 260638; BBMRI.cz-LM2023033], Charles University Grant Agency [GAUK 312218]. There is no financial support or other benefits from commercial sources for the work reported in the manuscript.
Disclosure statement: The authors have declared no conflicts of interest.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Individual anonymized participant data will not be shared. Pooled study data, protocol or statistical analysis plan can be shared upon request at oreska@revma.cz.