Epicardial adipose tissue provides incremental value in predicting major adverse cardiac events in systemic sclerosis patients without pulmonary arterial hypertension beyond traditional risk factors

Introduction

Systemic sclerosis (SSc) is a connective tissue disease characterized by autoimmunity, vasculopathy, and fibrosis (1). Although categorized as a rare condition, it imposes substantial global health burdens, currently impacting over 2.5 million people (2), with reported 10-year survival probabilities ranging from 66% to 82% across population studies (3-6).

The pathogenic basis for SSc is highly complicated and remains incompletely understood (7). SSc-associated pulmonary arterial hypertension (SSc-PAH) has been reported as the most common complication associated with SSc patient mortality (8,9). It exacerbates right ventricular afterload leading to cardiac decompensation, while concurrently accelerating myocardial fibrosis via hypoxic myocardial microenvironments, thereby significantly increasing the risk of major adverse cardiovascular events (MACE) (10-12).​Under the influence of multiple factors, cardiovascular disease (CVD) accounts for 20–30% of all-cause mortality in SSc patients ​(13). Research indicates that epicardial adipose tissue (EAT) is associated with local inflammation that may involve the adjacent cardiac tissues (14), causing microvascular dysfunction and fibrosis (15), and it is considered predictive of MACE (16,17). Previous studies have shown a correlation between the thickness of EAT measured by echocardiography in front of the free right ventricular wall and the presence of SSc (18). Meanwhile, it has been shown that epicardial fat volume (EFV) is independently associated with left ventricular diastolic dysfunction and increased mortality in SSc patients (19).

Cardiac involvement among SSc patients has been tentatively linked to microvascular dysfunction and associated systolic functional disorders that are subclinical, suggesting the impairment of left ventricular longitudinal strain even before PAH onset (17). By the time SSc patients exhibit related symptoms, irreversible changes in the structural and functional properties of the heart have already taken place (20). Whether EFV can be an early predictor of MACE in SSc patients warrants further investigation​. This study aimed to investigate risk factors associated with the incidence of MACE in SSc patients without PAH, while exploring the potential prognostic utility of EFV compared to traditional risk factors. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2385/rc).

Methods

Participants

This study initially enrolled 245 SSc patients, of whom 43 were ultimately excluded. Finally, this study enrolled 202 hospitalized patients due to SSc and 202 control cases from the First Affiliated Hospital of Ningbo University between January 2017 and January 2020. All SSc patients met the 1980 American College of Rheumatology (ACR) or 2013 ACR/European League Against Rheumatism (EULAR) criteria for this disease (21,22). SSc patients were excluded from this study if they exhibited the following: (I) acute infections, (II) SSc and other overlapping syndromes, (III) any known history of MACE incidence, (IV) a previous history of coronary artery disease (CAD), or (V) the cessation of follow-up as a result of transfer or other factors. The 202 control cases were age- and sex-matched to hospitalized SSc patients. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the First Affiliated Hospital of Ningbo University (No. 2022-022A) and informed consent was provided by all the patients.

Clinical characteristics

General clinical data obtained from medical records for the participants in this study included age, gender, systolic and diastolic blood pressure, body mass index (BMI), hypertension status, hyperlipidemia status, diabetes status, smoking status, renal dysfunction status, whether elevated liver enzymes were evident, disease duration, skin thickening, whether they experience Raynaud’s phenomenon, whether swelling or stiffening of the fingers was evident, whether fingertip ulcers were observed, joint involvement, modified Rodnan skin scores, hormone use, and immunosuppressant use. The results of blood testing performed on initial hospitalization were also obtained, including levels of C-reactive protein (CRP), urea, uric acid, creatinine, blood glucose, cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and erythrocyte sedimentation rate (ESR). Antinuclear antibody testing results for anti-Scl-70, anti-RNA polymerase III, anti-U3 ribonucleoprotein, and anti-centromere protein antibodies were also obtained for SSc patients. Imaging examinations were also used to detect evidence of interstitial pneumonia, pericardial effusion, and osteoporosis, and to establish EFV and coronary artery calcium score (CACS) values. The incidence of MACE was recorded over a median follow-up period of 48 months [interquartile range (IQR), 36–60 months] after standard treatment for SSc patients. MACE diagnoses were based on the 2016 American College of Cardiology (ACC)/American Heart Association (AHA) Guidelines (23), and included cardiac death, non-fatal myocardial infarction, heart failure, malignant arrhythmia, elective coronary revascularization, and angina-related rehospitalization.

CACS calculation and EFV analyses

A 320-slice computed tomography (CT) scanner (Aquilion ONE™, Toshiba, Tokyo, Japan) was used for cardiac CT scanning at the time patients were first hospitalized when diagnosed with SSc. Images were used to calculate the calcification score values for the left main coronary artery (LMA), right coronary artery (RCA), left circumflex artery (LCX), and left anterior descending artery (LAD). Those areas exhibiting a density level ≥130 Hounsfield units (HU) were identified automatically. Scoring was as follows: 1 point for 130–199 HU, 2 points for 200–299 HU, 3 points for 300–399 HU, 4 points for ≥400 HU.

The calcification integral was calculated as: calcification integral = calcification area × CT score.

All calcification intervals were summed to compute the CACS.

EFV can be obtained while calculating the CACS. EFV was determined based on the total volume of pericardial adipose tissue through the use of the vital analytical software (Aquilion ONE™). The software cut-out package was used to select the adipose tissue between the visceral pericardium and myocardium extending from the point of pulmonary artery bifurcation to the cardiac apex. The mediastinal fat, pericardial fat, myocardial tissue, and blood vessels were removed in the software via the multiplanar reconstruction (MPR) technique, and threshold value determination was performed, defining EFV as ranging from −200 to −30 HU. EFV values were then calculated automatically with the volumetry function in this program.

The measurement personnel comprised two radiology experts with more than 10 years of experience, using the relevant imaging parameters of the post-processing workstation for measurement. The two surveyors measured EFV and CACS at the same time and reached a consensus. In case of disagreement, a third radiologist was invited to measure and determine the imaging parameters.

Interstitial lung disease (ILD) and PAH diagnosis

ILD was diagnosed based on consensus between two experienced radiologists according to the presence of pulmonary fibrosis and/or negative ground-glass opacity evident in high-resolution CT images. PAH was diagnosed based on a normal mean pulmonary artery pressure (mPAP) ≥25 mmHg, pulmonary artery wedge pressure (PAWP) ≤15 mmHg, and pulmonary vascular resistance (PVR) >3 Wood units as determined through right heart catheterization. In patients who did not undergo this procedure, it was defined by a pulmonary artery systolic pressure ≥40 mmHg on echocardiogram analysis.

Statistical analysis

The software SPSS 23.0 (IBM Corp., Armonk, NY, USA) and R 3.4.3 (http://www.Rproject.org; packages: glmnet, pROC, rms, dca.R) were used for all statistical analyses. The normality of continuous variables was assessed with the Kolmogorov-Smirnov test, reporting the results as the mean ± standard deviation (SD) when normally distributed and otherwise reporting them as the median (IQR). Categorical variables were reported as numbers and percentages. Patients were separated into two groups based on whether or not they exhibited MACE incidence, and these groups were compared with t-tests or Wilcoxon rank-sum tests, with a two-sided P<0.05 being deemed significant. Using time-dependent receiver operating characteristic (ROC) curves, EFV was divided into two groups with a cutoff value of 126 cm³. Kaplan-Meier analyses and the log-rank test were used to compare rates of survival without MACE incidence. MACE-related risk factors were identified through univariate and multivariate Cox proportional hazards regression models, calculating hazard ratios (HRs) and 95% confidence intervals (CIs). Global chi-square, net reclassification index (NRI), and integrated discrimination improvement (IDI) index values were used to assess the effects of adding EFV to traditional risk factors on model discrimination when predicting MACE occurrence among SSc patients. The overall predictive efficiency observed following EFV introduction was examined based on the concordance index (C-index).

Results

SSc and control patient characteristics

The SSc patients had a mean age of 56±12 years, of whom 9.9% were male, and included 166, 34, and 2 diffuse cutaneous SSc (dcSSc), limited cutaneous SSc (lcSSc), and SSc sine scleroderma cases, respectively. The control group consisted of 202 age- and sex-matched healthy individuals who had undergone routine physical examinations. Significant differences in hypertension, diabetes, hyperlipidemia, LDL-C, ESR, CACS, and EFV were noted when comparing the control and SSc groups (Table 1).

SSc patients presented with elevated CACS scores and EFV levels

Relative to control cases, SSc patients exhibited significant increases in CACS values [33.5 (IQR, 0–128.5) vs. 0 (IQR, 0–88.25), P=0.006] and EFV [120 (IQR, 93.5–148.5) vs. 110 (IQR, 89.5–142.5) cm³, P=0.037].

MACE incidence among SSc patients

MACE incidence was evaluated in the SSc and control groups. Of the patients in the SSc group, 51 (25.2%) exhibited MACE incidence over a median follow-up period of 48 (IQR, 36–60) months. These included 10 cases of cardiac death, 6 of non-fatal myocardial infarction, 2 of revascularization (1 of percutaneous coronary intervention, 1 of coronary artery bypass grafting), 13 of angina-related rehospitalization, 18 of heart failure, and 2 of malignant arrhythmia. In contrast, just 6 of the healthy controls (3.0%) developed MACE over the follow-up period, including 2 cases of non-fatal myocardial infarction, 2 of angina-related rehospitalization, 1 of heart failure, and 1 of malignant arrhythmia.

SSc patient characteristics by MACE status

Of the SSc patients in this study, 51 and 151 did and did not develop MACEs over the course of follow-up in the MACE and non-MACE groups, respectively. Patients in the MACE group tended to be older and were more likely to be male. Elevated hypertension, diabetes, hyperlipidemia, renal dysfunction, triglyceride, total cholesterol, uric acid, HDL-C, CRP, anti-Scl-70 antibody, anti-U3 ribonucleoprotein antibody, disease duration, fingertip ulcer incidence, modified Rodnan skin score, hormone use time, maximum hormone dose, pericardial effusion, osteoporosis, and interstitial pneumonia levels were also observed among SSc patients (Table 2).

SSc patients who experienced MACEs exhibited higher CACS and EFV level

Relative to SSc patients in the non-MACE group, MACE patients exhibited a significantly higher median CACS [88 (IQR, 19–144) vs. 0 (IQR, 0–123), P=0.003]. and EFV [160 (IQR, 138–192) vs. 110 (IQR, 84–130) cm³, P<0.001] levels.

EFV offers incremental value for the prediction of MACE incidence among SSc patients

Time-dependent Youden index analyses revealed that the optimal EFV cut-off for the prediction of MACE incidence was 126 cm³ (Figure 1), and this value was therefore used to separate participants into groups with low and high EFV levels. Kaplan-Meier analyses revealed that the MACE-free survival rate of patients with high EFV levels was significantly lower than that of patients with low EFV values (log-rank P<0.01) (Figure 2). SSc patients were separated into two groups based on whether they had low or high EFV values, and rates of MACE event-free survival were compared between these groups, revealing a significantly lower rate in the high-EFV group relative to the low-EFV group (log-rank P<0.01, Figure 2). Univariate and multivariate Cox regression analyses included the following variables: age, male sex, hypertension, diabetes, hyperlipidemia, renal dysfunction, triglyceride, total cholesterol, urea, uric acid, HDL-C, CRP, anti-Scl-70 antibody, anti-U3 ribonucleoprotein antibody, duration of the disease, fingertip ulcers, modified Rodnan skin score, hormone use time, maximum dose of hormone, pericardial effusion, osteoporosis, interstitial pneumonia, CACS, and EFV. Variable that were independently associated with MACE incidence included age (HR: 1.054, 95% CI: 1.016–1.091, P=0.004), hypertension (HR: 2.253, 95% CI: 1.120–4.535, P=0.023), and EFV (HR: 1.027, 95% CI: 1.015–1.041, P=0.001), whereas CACS values were not significantly related to such risk (HR: 1.001, 95% CI: 0.999–1.004, P=0.363) (Table 3).

Figure 1 ROC curve. EFV, epicardial fat volume; ROC, receiver operating characteristic.

Figure 2 Kaplan-Meier curve. EFV, epicardial fat volume; MACE, major adverse cardiovascular events; PAH, pulmonary arterial hypertension; SSc, systemic sclerosis.

Traditional risk factors were defined as age, gender, hypertension, hyperlipidemia, BMI, diabetes, smoking, and CACS. To gain further insight into the value of EFV as a predictor of MACE together with traditional risk factors, global chi-square, C-index, IDI, and NRI values were computed (Figure 3). After the addition of EFV to traditional risk factors, global chi-square values rose significantly from 45.2 to 52.2 (P<0.01), whereas the C-index rose from 0.77 to 0.84 (P<0.01), and the IDI was 0.12 (P<0.01). These results demonstrate that this model exhibited improved discrimination following the incorporation of EFV, achieving superior accuracy and an increase in comprehensive discriminant ability by 12%. The NRI of the model was 0.46 (P<0.01), demonstrating that incorporating EFV improved the rate at which the study population was correctly identified by 46% (Figure 3). Figure 4 shows two typical cases.

Figure 3 Global chi-square and C-index. C-index, concordance index; EFV, epicardial fat volume; IDI, integrated discrimination improvement; NRI, net reclassification index.

Figure 4 Typical cases. (A) Patient 1, without traditional risk factors, had high EFV value and experienced MACE at 9-month follow-up. (B) Patients 2, with traditional risk factors, had lower EFV and did not experience MACE during follow-up. EFV, epicardial fat volume; MACE, major adverse cardiovascular events.

Discussion

MACEs are one of the most prominent risk factors related to SSc patient survival outcomes (24), with cardiac involvement accounting for upwards of 31% of mortality in these patients, as demonstrated by a study of 2,719 death certificates in France (25) Identifying high-risk patients and treating them as quickly as possible may thus help to significantly reduce MACE incidence among SSc patients. Here, correlative relationships between EFV, CACS, and MACE occurrence were evaluated among SSc patients. In this patient population, high EFV levels were independently associated with MACE risk, whereas the same was not true for CACS. Combining EFV with traditional risk factors provided incremental utility as a means of predicting the incidence of MACE among SSc patients unaffected by PAH. These results align well with similar findings reported previously in various forms of chronic inflammation characterized by higher EAT levels such as systemic lupus erythematosus and rheumatoid arthritis. This consistent link between inflammation and elevated levels of visceral fat is noteworthy.

Traditional risk factors independently associated with MACE incidence among individuals with coronary heart disease (CHD) include age, gender, smoking status, hyperlipidemia, diabetes, and BMI. Large vessel involvement has recently been suggested to affect CVD and overall morbidity among patients with SSc (26). In a meta-analysis performed by Tyndall et al., SSc patients were confirmed to exhibit higher atherosclerosis risk (27). In a study of a population of inpatients in the USA, CHD prevalence was examined among 308,452 patients hospitalized for SSc, among whom 5.4% were determined to be hospitalized for cardiovascular-related reasons (28) Significantly higher rates of SSc patient mortality among individuals hospitalized for CVD were noted as compared to inpatient mortality rates for other forms of rheumatic disease including rheumatoid arthritis and systemic lupus erythematosus. Traditional cardiovascular risk factors thus warrant consideration when evaluating SSc patients. In this study, traditional MACE-related risk factors such as age, smoking history, gender, hypertension, dyslipidemia, and hyperglycemia differed significantly when comparing SSc patients who did and did not experience MACE.

In a limited number of studies, researchers have noted no marked differences in traditional CHD-related risk factors when comparing SSc patients and the general population (29). Disordered coronary microvascular circulation and inflammatory response in cardiac tissue are hallmarks of SSc-associated myocardial dysfunction (30). EFV is closely related to cardiac tissue inflammation. Therefore, we investigated whether EFV could surpass traditional risk factors and become an effective predictor of MACE in SSc patients.

CACS values determined through cardiac CT scanning can predict MACE risk obtained by cardiac CT examination is an independent predictor of MACE (31). Coronary artery calcification is firmly established as being linked to CHD onset and progression, and guidelines for clinical practice in Europe and the USA have established CACS as a tool to more effectively assess cardiovascular risk (32). Many prior studies have confirmed that CACS severity is significantly associated with CHD prognosis, and it can be used to accurately assess various forms of cardiovascular and cerebrovascular disease (33,34). Coronary artery lesions are commonly reported in SSc patients, with the Australian Scleroderma Cohort study having revealed CHD to be three times more common among 850 SSc patients relative to control cases (35). Here, SSc patients were found to exhibit CACS levels significantly higher than those of normal controls, and CACS values were also significantly higher among patients who experienced MACEs relative to those who did not. The higher rates of mortality among SSc patients may be linked to both microvascular and macrovascular cardiac involvement and damage, contributing to a greater overall risk of CVD.

Despite the above evidence, there are also some studies in which traditional risk factors for CHD have been found not to differ significantly when comparing SSc patients and the general population (36). SSc-related cardiac dysfunction is characterized by myocardial fibrosis and dysregulated coronary microvascular circulation (37,38). No strong data supporting the greater prevalence of traditional CHD-related risk factors among SSc patients that are associated with higher rates of cardiac complications and death have been published. Here, although CACS was related to MACE incidence in univariate analyses, this did not remain true following adjustment for other risk factors. These discrepancies among studies may be related to population-specific differences, including ethnicity. A multi-ethnic study of atherosclerosis revealed that Chinese individuals exhibited the lowest CACS values for both males and females (39). Yamamoto et al. noted that, following adjustment for traditional (40) risk factors, CACS was unable to reliably predict MACE risk among Asian populations. These differences may also be associated with the pathogenesis of SSc, which is characterized by the presence of non-inflammatory occlusive vascular lesions that involve arterioles and multivessel beds, capillary loss, and fibrotic activity (41). The degree of large vessel involvement in SSc patients remains somewhat controversial, with coronary angiography having revealed rates of coronary artery lesions that were similar in SSc patients and healthy controls (36). Myocardial Raynaud’s phenomenon is a cause of myocardial ischemia among patients with SSc, and the associated microangiopathy results in ischemic changes. Myocardial contractile band necrosis and the formation of fibrous scars around sites of myocardial ischemic injury have been observed independently of the associated coronary artery supply area (42). Whether CACS, which cannot effectively assess microvascular function, can predict MACE in SSc patients may require further study.

EAT is the adipose tissue directly surrounding the coronary arteries, and EFV values provide a method for quantifying EAT levels (43). In the study of CHD, the value of EAT has been confirmed. EAT is associated with an increased risk of hemodynamically significant CAD (44). EAT is also associated with elevated coronary calcium levels, higher levels of vascular stiffness, and poorer cardiovascular outcomes. EAT can also affect vascular function, alter plaque characteristics, and promote microvascular disease (18,45,46). However, there is currently limited research on whether EFV can be used to predict MACE events in patients with SSc. In this study, the EFV levels of SSc patients were significantly higher than those of the healthy control group, and the EFV levels of SSc patients in the MACE group were also significantly higher than those in the non-MACE group. EFV is an independent risk factor for MACE events in SSc patients, and EFV has a beneficial effect on traditional risk factors. EAT secretes protective cytokines under physiological conditions (47), but its function shifts from physiological anti-inflammatory to pathological pro-inflammatory when patients are experiencing immune abnormalities or inflammatory responses (48-50). In addition to being a source of various inflammatory cytokines such as interleukin (IL)-1β, IL-6, MCP-1, tumor necrosis factor (TNF)-α, and so on (51), one study has shown that the cytokines released by EAT diffuse from the outside to the inside, from the outer membrane, middle membrane to the inner membrane, and gradually diffuse into the coronary artery lumen through paracrine pathways (52). Another study has shown that cytokines and free fatty acids (FFAs) released by EAT enter nutrient vessels through the vascular endocrine pathway and are then transported to the arterial wall to interact with the myocardium, leading to dysfunction of myocardial microvasculature and vasomotor dysfunction (53). In addition, EAT can also affect myocardial perfusion by affecting coronary artery blood flow and inducing endothelial injury (54). In summary, vascular damage in SSc patients involves endothelial dysfunction, vascular remodeling, and severe immune-mediated responses. In the future, we will further analyze the relationship between EAT and MACE in SSc patients.

Several models have been developed for the prediction of cardiovascular event incidence among SSc patients. Long et al. noted an association between EFV and the severity of SSc [adjusted odds ratio (OR): 1.010, 95% CI: 1.003–1.018, P=0.007], while also highlighting the independence of EFV from cardiovascular risk factors or ILD (55). Gieszczyk-Strózik et al. further noted the ability of the CHLD (chronic kidney disease, hypertension, hyperlipidaemia, diabetes mellitus) score to independently predict MACE risk (unit HR per 1 point 3.46; 95% CI: 2.06–5.82, P<0.0001). Combined analyses of multiple risk factors, as in the case of the CHLD score, may be better suited to predicting long-term prognostic outcomes for SSc patients unaffected by PAH (56). In this study, the link between conventional risk factors and cardiovascular event incidence was reemphasized among SSc patients. Moreover, EFV was found to offer incremental value in combination with these traditional risk factors when seeking to predict MACE incidence, emphasizing the consistency between inflammation and elevated levels of visceral fat in these patients. These results emphasize the relevance of EFV levels and offer preliminary insights to support improved risk stratification such that patients facing higher levels of cardiovascular risk can be identified.

Unfortunately, some limitations of this retrospective study need to be addressed. First, all data were from a single center with a small sample size, and potential lack of diversity (e.g., ethnicity, SSc subtype, etc.) may limit the external validity of the findings. Second, we cannot rule out that treatment received (e.g., corticosteroids, immunosuppressants, etc.) may confound the association between EFV and MACE in SSc patients. Besides, the SSc patients in the sample had different antibody expression, and further stratification study is necessary. Therefore, prospective studies should be conducted to minimize the interference of these factors on the results. Finally, we should explore more fully automated quantitative markers derived from CT scans to better predict future cardiovascular events and overall survival in SSc patients.

In summary, EFV is independently associated with MACE risk among SSc, even among patients with normal pulmonary artery pressure. When combined with traditional risk factors, EFV offers incremental utility for MACE prediction. There is also clear value to conduct further studies focused on traditional risk factors, CACS, and EFV values among SSc patients.

Conclusions

High EFV levels are independently associated with MACE risk among SSc patients, with poorer prognostic outcomes being evident among patients with SSc even if they were unaffected by PAH. The introduction of EFV offers incremental utility over traditional risk factors alone for the prediction of MACE incidence.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2385/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2385/dss

Funding: This study was supported by a grant from the Public Welfare Science and Technology Program Project of Ningbo (No. 2022S028).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2385/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by ethics committee of the First Affiliated Hospital of Ningbo University (No. 2022-022A) and informed consent was provided by all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Nikpour M, Baron M. Mortality in systemic sclerosis: lessons learned from population-based and observational cohort studies. Curr Opin Rheumatol 2014;26:131-7. [Crossref] [PubMed]
2. Barnes J, Mayes MD. Epidemiology of systemic sclerosis: incidence, prevalence, survival, risk factors, malignancy, and environmental triggers. Curr Opin Rheumatol 2012;24:165-70. [Crossref] [PubMed]
3. Steen VD, Medsger TA. Changes in causes of death in systemic sclerosis, 1972-2002. Ann Rheum Dis 2007;66:940-4. [Crossref] [PubMed]
4. Al-Dhaher FF, Pope JE, Ouimet JM. Determinants of morbidity and mortality of systemic sclerosis in Canada. Semin Arthritis Rheum 2010;39:269-77. [Crossref] [PubMed]
5. Elhai M, Meune C, Avouac J, Kahan A, Allanore Y. Trends in mortality in patients with systemic sclerosis over 40 years: a systematic review and meta-analysis of cohort studies. Rheumatology (Oxford) 2012;51:1017-26. [Crossref] [PubMed]
6. Arias-Nuñez MC, Llorca J, Vazquez-Rodriguez TR, Gomez-Acebo I, Miranda-Filloy JA, Martin J, Gonzalez-Juanatey C, Gonzalez-Gay MA. Systemic sclerosis in northwestern Spain: a 19-year epidemiologic study. Medicine (Baltimore) 2008;87:272-80. [Crossref] [PubMed]
7. Rosendahl AH, Schönborn K, Krieg T. Pathophysiology of systemic sclerosis (scleroderma). Kaohsiung J Med Sci 2022;38:187-95. [Crossref] [PubMed]
8. Khanna D, Tashkin DP, Denton CP, Renzoni EA, Desai SR, Varga J. Etiology, Risk Factors, and Biomarkers in Systemic Sclerosis with Interstitial Lung Disease. Am J Respir Crit Care Med 2020;201:650-60. [Crossref] [PubMed]
9. Jiang Y, Turk MA, Pope JE. Factors associated with pulmonary arterial hypertension (PAH) in systemic sclerosis (SSc). Autoimmun Rev 2020;19:102602. [Crossref] [PubMed]
10. Zhang J, Hu X, Wang T, Xiao R, Zhu L, Ruiz M, Dupuis J, Hu Q. Extracellular vesicles in venous thromboembolism and pulmonary hypertension. J Nanobiotechnology 2023;21:461. [Crossref] [PubMed]
11. Naeije R, Richter MJ, Rubin LJ. The physiological basis of pulmonary arterial hypertension. Eur Respir J 2022;59:2102334. [Crossref] [PubMed]
12. Tan R, You Q, Yu D, Xiao C, Adu-Amankwaah J, Cui J, Zhang T. Novel hub genes associated with pulmonary artery remodeling in pulmonary hypertension. Front Cardiovasc Med 2022;9:945854. [Crossref] [PubMed]
13. Mok MY, Lau CS. The burden and measurement of cardiovascular disease in SSc. Nat Rev Rheumatol 2010;6:430-4. [Crossref] [PubMed]
14. Baichoo R, Fu J, Xiong X, Zhou Q, Guan X, Xu X. The role of cardiac adipose tissue in coronary artery disease: a narrative review of cardiac imaging studies. Quant Imaging Med Surg 2024;14:9685-97. [Crossref] [PubMed]
15. Packer M. Epicardial Adipose Tissue May Mediate Deleterious Effects of Obesity and Inflammation on the Myocardium. J Am Coll Cardiol 2018;71:2360-72. [Crossref] [PubMed]
16. Yang L, Yu W, Wan P, Wang J, Shao X, Zhang F, Yang X, Chen Y, Li Q, Jiang D, Wang Y, Jiang Q, Wang J, Wang Y. Epicardial fat volume, an independent risk factor for major adverse cardiovascular events, had an incremental prognostic value to myocardial perfusion imaging in Chinese populations with suspected or known coronary artery disease with a normal left ventricular ejection fraction. Front Cardiovasc Med 2023;10:1261215. [Crossref] [PubMed]
17. Goeller M, Achenbach S, Marwan M, Doris MK, Cadet S, Commandeur F, Chen X, Slomka PJ, Gransar H, Cao JJ, Wong ND, Albrecht MH, Rozanski A, Tamarappoo BK, Berman DS, Dey D. Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects. J Cardiovasc Comput Tomogr 2018;12:67-73. [Crossref] [PubMed]
18. Hirata Y, Tabata M, Kurobe H, Motoki T, Akaike M, Nishio C, Higashida M, Mikasa H, Nakaya Y, Takanashi S, Igarashi T, Kitagawa T, Sata M. Coronary atherosclerosis is associated with macrophage polarization in epicardial adipose tissue. J Am Coll Cardiol 2011;58:248-55. [Crossref] [PubMed]
19. Wang X, Butcher SC, Myagmardorj R, Liem SIE, Delgado V, Bax JJ, De Vries-Bouwstra JK, Marsan NA. Epicardial adipose tissue in patients with systemic sclerosis. Eur Heart J Imaging Methods Pract 2023;1:qyad037.
20. Nie LY, Wang XD, Zhang T, Xue J. Cardiac complications in systemic sclerosis: early diagnosis and treatment. Chin Med J (Engl) 2019;132:2865-71. [Crossref] [PubMed]
21. van den Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A, et al. 2013 classification criteria for systemic sclerosis: an American college of rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis 2013;72:1747-55. [Crossref] [PubMed]
22. Preliminary criteria for the classification of systemic sclerosis (scleroderma). Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee. Arthritis Rheum 1980;23:581-90. [Crossref] [PubMed]
23. Correction to: Duration of Dual Antiplatelet Therapy: A Systematic Review for the 2016 ACC/AHA Guideline Focused Update on Duration of Dual Antiplatelet Therapy in Patients With Coronary Artery Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 2016;134:e195-7. [Crossref] [PubMed]
24. Simeón-Aznar CP, Fonollosa-Plá V, Tolosa-Vilella C, Espinosa-Garriga G, Campillo-Grau M, Ramos-Casals M, et al. Registry of the Spanish Network for Systemic Sclerosis: Survival, Prognostic Factors, and Causes of Death. Medicine (Baltimore) 2015;94:e1728. [Crossref] [PubMed]
25. Elhai M, Meune C, Boubaya M, Avouac J, Hachulla E, Balbir-Gurman A, et al. Mapping and predicting mortality from systemic sclerosis. Ann Rheum Dis 2017;76:1897-905. [Crossref] [PubMed]
26. Nussinovitch U, Shoenfeld Y. Atherosclerosis and macrovascular involvement in systemic sclerosis: myth or reality. Autoimmun Rev 2011;10:259-66. [Crossref] [PubMed]
27. Tyndall AJ, Bannert B, Vonk M, Airò P, Cozzi F, Carreira PE, et al. Causes and risk factors for death in systemic sclerosis: a study from the EULAR Scleroderma Trials and Research (EUSTAR) database. Ann Rheum Dis 2010;69:1809-15. [Crossref] [PubMed]
28. Dave AJ, Fiorentino D, Lingala B, Krishnan E, Chung L. Atherosclerotic cardiovascular disease in hospitalized patients with systemic sclerosis: higher mortality than patients with lupus and rheumatoid arthritis. Arthritis Care Res (Hoboken) 2014;66:323-7. [Crossref] [PubMed]
29. Ngian GS, Sahhar J, Proudman SM, Stevens W, Wicks IP, Van Doornum S. Prevalence of coronary heart disease and cardiovascular risk factors in a national cross-sectional cohort study of systemic sclerosis. Ann Rheum Dis 2012;71:1980-3. [Crossref] [PubMed]
30. Chhikara S, Kanda A, Ogugua FM, Rouf R, Nouraee C, Bawaskar P, Molitor JA, Shenoy C. The primary cardiomyopathy of systemic sclerosis on cardiovascular magnetic resonance imaging. Eur Heart J Cardiovasc Imaging 2023;24:1661-71. [Crossref] [PubMed]
31. Cheong BYC, Wilson JM, Spann SJ, Pettigrew RI, Preventza OA, Muthupillai R. Coronary artery calcium scoring: an evidence-based guide for primary care physicians. J Intern Med 2021;289:309-24. [Crossref] [PubMed]
32. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Atherosclerosis 2016;252:207-74. [Crossref] [PubMed]
33. Oei HH, Vliegenthart R, Hak AE, Iglesias del Sol A, Hofman A, Oudkerk M, Witteman JC. The association between coronary calcification assessed by electron beam computed tomography and measures of extracoronary atherosclerosis: the Rotterdam Coronary Calcification Study. J Am Coll Cardiol 2002;39:1745-51. [Crossref] [PubMed]
34. Schmermund A, Möhlenkamp S, Stang A, Grönemeyer D, Seibel R, Hirche H, Mann K, Siffert W, Lauterbach K, Siegrist J, Jöckel KH, Erbel R. Assessment of clinically silent atherosclerotic disease and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: rationale and design of the Heinz Nixdorf RECALL Study. Risk Factors, Evaluation of Coronary Calcium and Lifestyle. Am Heart J 2002;144:212-8. [Crossref] [PubMed]
35. Ngian GS, Sahhar J, Wicks IP, Van Doornum S. Cardiovascular disease in systemic sclerosis--an emerging association? Arthritis Res Ther 2011;13:237. [Crossref] [PubMed]
36. Akram MR, Handler CE, Williams M, Carulli MT, Andron M, Black CM, Denton CP, Coghlan JG. Angiographically proven coronary artery disease in scleroderma. Rheumatology (Oxford) 2006;45:1395-8. [Crossref] [PubMed]
37. Denton CP, Khanna D. Systemic sclerosis. Lancet 2017;390:1685-99. [Crossref] [PubMed]
38. Butt SA, Jeppesen JL, Torp-Pedersen C, Sam F, Gislason GH, Jacobsen S, Andersson C. Cardiovascular Manifestations of Systemic Sclerosis: A Danish Nationwide Cohort Study. J Am Heart Assoc 2019;8:e013405. [Crossref] [PubMed]
39. McClelland RL, Chung H, Detrano R, Post W, Kronmal RA. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2006;113:30-7. [Crossref] [PubMed]
40. Yamamoto H, Kitagawa T, Kihara Y. Clinical implications of the coronary artery calcium score in Japanese patients. J Atheroscler Thromb 2014;21:1101-8. [Crossref] [PubMed]
41. Allanore Y, Simms R, Distler O, Trojanowska M, Pope J, Denton CP, Varga J. Systemic sclerosis. Nat Rev Dis Primers 2015;1:15002. [Crossref] [PubMed]
42. Mavrogeni S, Karabela G, Koutsogeorgopoulou L, Stavropoulos E, Katsifis G, Plastiras SC, Kitas GD, Panopoulos S, Pentazos G, Tzatzaki E, Markousis-Mavrogenis G, Kolovou G, Sfikakis PP. Pseudo-infarction pattern in diffuse systemic sclerosis. Evaluation using cardiovascular magnetic resonance. Int J Cardiol 2016;214:465-8. [Crossref] [PubMed]
43. Iacobellis G. Epicardial adipose tissue in contemporary cardiology. Nat Rev Cardiol 2022;19:593-606. [Crossref] [PubMed]
44. Yu W, Chen Y, Zhang F, Liu B, Wang J, Shao X, Yang X, Shi Y, Wang Y. Association of epicardial adipose tissue volume with increased risk of hemodynamically significant coronary artery disease. Quant Imaging Med Surg 2023;13:2582-93. [Crossref] [PubMed]
45. Mazurek T, Zhang L, Zalewski A, Mannion JD, Diehl JT, Arafat H, Sarov-Blat L, O’Brien S, Keiper EA, Johnson AG, Martin J, Goldstein BJ, Shi Y. Human epicardial adipose tissue is a source of inflammatory mediators. Circulation 2003;108:2460-6. [Crossref] [PubMed]
46. Sinha SK, Thakur R, Jha MJ, Goel A, Kumar V, Kumar A, Mishra V, Varma CM, Krishna V, Singh AK, Sachan M. Epicardial Adipose Tissue Thickness and Its Association With the Presence and Severity of Coronary Artery Disease in Clinical Setting: A Cross-Sectional Observational Study. J Clin Med Res 2016;8:410-9. [Crossref] [PubMed]
47. Marchington JM, Pond CM. Site-specific properties of pericardial and epicardial adipose tissue: the effects of insulin and high-fat feeding on lipogenesis and the incorporation of fatty acids in vitro. Int J Obes 1990;14:1013-22.
48. Agra RM, Teijeira-Fernández E, Pascual-Figal D, Sánchez-Más J, Fernández-Trasancos A, González-Juanatey JR, Eiras S. Adiponectin and p53 mRNA in epicardial and subcutaneous fat from heart failure patients. Eur J Clin Invest 2014;44:29-37. [Crossref] [PubMed]
49. Iacobellis G, Pistilli D, Gucciardo M, Leonetti F, Miraldi F, Brancaccio G, Gallo P, di Gioia CR. Adiponectin expression in human epicardial adipose tissue in vivo is lower in patients with coronary artery disease. Cytokine 2005;29:251-5. [Crossref] [PubMed]
50. Sacks HS, Fain JN, Cheema P, Bahouth SW, Garrett E, Wolf RY, Wolford D, Samaha J. Depot-specific overexpression of proinflammatory, redox, endothelial cell, and angiogenic genes in epicardial fat adjacent to severe stable coronary atherosclerosis. Metab Syndr Relat Disord 2011;9:433-9. [Crossref] [PubMed]
51. Chaldakov GN, Beltowsky J, Ghenev PI, Fiore M, Panayotov P, Rančič G, Aloe L. Adipoparacrinology--vascular periadventitial adipose tissue (tunica adiposa) as an example. Cell Biol Int 2012;36:327-30. [Crossref] [PubMed]
52. Vural B, Atalar F, Ciftci C, Demirkan A, Susleyici-Duman B, Gunay D, Akpinar B, Sagbas E, Ozbek U, Buyukdevrim AS. Presence of fatty-acid-binding protein 4 expression in human epicardial adipose tissue in metabolic syndrome. Cardiovasc Pathol 2008;17:392-8. [Crossref] [PubMed]
53. Nakamura K, Fuster JJ, Walsh K. Adipokines: a link between obesity and cardiovascular disease. J Cardiol 2014;63:250-9. [Crossref] [PubMed]
54. Iacobellis G, Corradi D, Sharma AM. Epicardial adipose tissue: anatomic, biomolecular and clinical relationships with the heart. Nat Clin Pract Cardiovasc Med 2005;2:536-43. [Crossref] [PubMed]
55. Long BD, Stojanovska J, Brown RKJ, Attili AK, Jackson EA, Ognenovski V. Increased Epicardial Fat Volume Is Independently Associated with the Presence and Severity of Systemic Sclerosis. Acad Radiol 2017;24:1473-81. [Crossref] [PubMed]
56. Gieszczyk-Strózik K, Wybraniec MT, Widuchowska M, Brzezińska-Wcisło L, Kotyla P, Kucharz E, Mizia-Stec K. CHLD score, a new score based on traditional risk factor evaluation and long-term cardiovascular outcomes in patients with systemic sclerosis. Sci Rep 2021;11:19598. [Crossref] [PubMed]