Subclinical cardiac dysfunction in idiopathic inflammatory myopathies: the role of global longitudinal strain

Subclinical cardiac dysfunction in idiopathic inflammatory myopathies: the role of global longitudinal strain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Subclinical cardiac dysfunction in idiopathic inflammatory myopathies: the role of global longitudinal strain Simone Romano, Andrea Sartorio, Chiara Dal Pont, Francesca Segatta, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7273002/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Autoimmune diseases are characterized by systemic inflammation that can affect multiple tissues. In idiopathic inflammatory myopathies (IIM), skeletal muscle is primarily involved; however, subclinical cardiac dysfunction may also occur. While left ventricular ejection fraction (EF) is commonly used to assess cardiac function, global longitudinal strain (GLS) has proven more sensitive in detecting early myocardial impairment. This study aimed to evaluate left ventricular GLS (LV GLS) in patients with IIM and no known cardiovascular disease, assessing both the prevalence of reduced GLS values and their associations with clinical and laboratory parameters. We enrolled 37 outpatients from the Department of Internal Medicine at the University Hospital of Verona, who underwent comprehensive clinical and echocardiographic assessment. The mean GLS value observed (− 17.9% ± 2.2%) was below the normal reference range (− 18.2% to − 21.2%) defined by the echocardiographic system, indicating a global reduction in longitudinal systolic function despite preserved EF. In linear regression models, GLS was significantly associated with lymphocyte count at disease onset, the presence of arthritis, and creatine kinase (CK) levels. Patients with arthritis showed significantly worse GLS values compared to those without arthritis, despite similar EF. In multivariate analysis, arthritis remained independently associated with impaired GLS and lower CK levels. Overall, our findings suggest that patients with IIM exhibit a global reduction in left ventricular longitudinal function, detectable by GLS, even in the absence of overt cardiac disease. This impairment appears particularly evident in patients presenting with arthritis. Longitudinal studies are warranted to investigate the progression of GLS alterations and their potential role in guiding therapeutic strategies. idiopathic inflammatory myopathies echocardiography GLS arthritis Figures Figure 1 Introduction Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of autoimmune diseases primarily characterized by chronic inflammation of skeletal muscle. However, due to their systemic nature, these disorders can also affect extramuscular organs, including the heart. Cardiac involvement in IIM is frequently subclinical and may remain undetected, yet it is associated with increased morbidity and mortality [ 1 ]. Left ventricular ejection fraction (EF) has traditionally been the standard echocardiographic parameter used to assess cardiac function. Although widely applied, EF is relatively insensitive to early myocardial dysfunction, particularly when the impairment affects longitudinal fibers of the left ventricle. In recent years, global longitudinal strain (GLS) has emerged as a more sensitive and reproducible echocardiographic marker for detecting early myocardial impairment, even in patients with preserved EF [ 2 – 10 ]. Several studies have demonstrated that patients with autoimmune diseases may exhibit reduced GLS values, indicating early subclinical myocardial involvement [ 9 – 16 ]. This is particularly relevant in the context of IIM, where cardiac symptoms are often absent or overshadowed by dominant muscular manifestations. GLS, therefore, may serve as a valuable tool for detecting cardiac dysfunction in its subclinical phase [ 11 – 13 ]. The aim of this study was to evaluate left ventricular GLS (LV GLS) in a cohort of patients with IIM and no known cardiovascular disease. Additionally, we sought to investigate the associations between GLS values and clinical or laboratory parameters, in order to identify potential predictors of myocardial dysfunction in this population. Methods We conducted a retrospective observational study involving 37 adult outpatients diagnosed with IIM at the Department of Internal Medicine, University Hospital of Verona. All patients had preserved LVEF and no prior history or clinical evidence of cardiovascular disease. Clinical, demographic, and laboratory data were collected during the outpatient visit and by reviewing the hospital’s electronic medical records. Transthoracic echocardiography was performed using a GE Vivid T8 ultrasound system (GE Healthcare, Arlington Heights, IL). Reference values for GLS ranged from − 18.2% to − 21.2%, as provided by the echocardiographic platform. Standard parasternal, apical, and subcostal views were obtained. GLS was measured using speckle-tracking echocardiography. Electrocardiographically gated cine loops were acquired in apical 2-, 3-, and 4-chamber views. GLS was calculated as the average strain across 18 myocardial segments (Fig. 1 ). Endocardial borders were automatically delineated at end-diastole. The tracking was visually verified and manually adjusted if necessary. Segments with poor tracking quality were excluded from the analysis. All measurements were performed by a board-certified echocardiographer blinded to the patients’ clinical and laboratory data. Statistical analyses were conducted using Jamovi software (version 2.3.18, The Jamovi project, 2022). Continuous variables were reported as mean ± standard deviation (SD) or median with interquartile range (IQR), according to their distribution. Group comparisons were performed using the Student’s t-test or the Mann–Whitney U test, as appropriate. Categorical variables were compared using the chi-square test or Fisher’s exact test. Linear regression models were used to assess associations between GLS and clinical variables, and logistic regression models were employed to identify predictors of arthritis. A two-tailed p-value < 0.05 was considered statistically significant. Results The mean GLS value in the study population was − 17.9% ± 2.2%, below the reference range provided by the echocardiographic system (− 18.2% to − 21.2%). This finding indicates a global reduction in longitudinal systolic function despite preserved EF ( Table 1 ). When stratified by sex, no significant differences in GLS were observed, although EF was slightly lower in males. Women had significantly lower body weight compared to men. Table 1 summarizes the demographic, clinical, laboratory, and echocardiographic characteristics of the overall cohort, including sex-based comparisons. Total (n = 37) Male (n = 7) Female (n = 30) p Age, m (SD) 63.8 (10.6) 61.1 (8.3) 64.4 (11.1) n.s. Age of onset, m (SD) 54.9 (11.7) 54.6 (9.21) 54.9 (12.3) n.s. Time between symptom onset and initiation of therapy, in months, me (IQR) 3 (4) 3.5 (2.5) 2 (4) n.s. Presence of diagnostic criteria, % (n) 90.9 (30) 100 (7) 88.5 (23) n.s. Weight in kg, m (SD) 70 (12.2) 83.2 (13.4) 67.3 (10.3) 0.006 Lymphocytes at onset, m (SD) 1945 (881) 2370 (1077) 1848 (823) n.s. ANAs, me (IQR) 160 (480) 320 (0) 160 (640) n.s. CK at the onset in UI/L, me (IQR) 492 (1112) 843 (789) 461 (1298) n.s. CRP at onset, me (IQR) 3 (8) 4 (2.5) 3 (4) n.s. PAH, % (n) 2.7 (1) 14.3 (1) 0 (0) 0.036 ILD, % (n) 40.5 (15) 71.4 (5) 33.3 (10) n.s. Dysphagia, % (n) 21.6 (8) 14.3 (1) 23.3 (7) n.s. Gastrointestinal symptoms, % (n) 24.3 (9) 14.3 (1) 26.7 (8) n.s. Skin manifestations, % (n)* 41.7 (15) 50 (3) 40 (12) n.s. Arthritis, % (n) 62.2 (23) 42.9 (3) 66.7 (20) n.s. LV GLS %, m (SD) − 17.9 (2.2) − 17.3 (2.41) − 18 (2.16) n.s. RV-fwLG, me (IQR) − 20 (10.7) − 20 (3.98) − 19.9 (5.20) n.s. EF, m (SD) 59.8 (4.11) 56.4 (2.15) 60.8 (4.04) 0.011 Left ventricular mass index (g/m 2 ), me (IQR) 75 (16.4) 74 (26.3) 76.5 (15) n.s. ANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients. Stratification of patients by the median GLS value (− 17.9%) did not reveal significant differences in most clinical parameters (Table 2 ). Notably, EF remained similar between the two groups, reinforcing the added diagnostic value of GLS in detecting subclinical myocardial dysfunction. Table 2 reports variables stratified by the GLS median (− 17.9%) GLS > − 17.9 (n = 18) GLS ≤ − 17.9 (n = 19) p Sex, females, % (n) 77.8 (14) 84.2 (16) n.s. Age in years, m (SD) 64.5 (9.36) 63.2 (12) n.s. Age of onset in years, m (SD) 54.3 (13) 55.4 (10.6) n.s. Time between symptom onset and initiation of therapy, in months, me (IQR) 2 (4.5) 3 (2.5) n.s. Presence of diagnostic criteria, % (n) 87.5 (14) 94.1 (16) n.s. Weight in kg, m (SD) 69.6 (16.5) 70.4 (12) n.s. Lymphocytes at onset per µL, m (SD) 2123 (892) 1789 (867) n.s. ANAs, me (IQR) 160 (240) 240 (640) n.s. CK at the onset in UI/L, me (IQR) 340 (2969) 567 (938) n.s. CRP at onset, me (IQR) 3 (7.75) 3 (5.75) n.s. PAH, % (n) 5.6 (1) 0 (0) n.s. ILD, % (n) 38.9 (7) 42.1 (8) n.s. Dysphagia, % (n) 22.2 (4) 21.1 (4) n.s. Gastrointestinal symptoms, % (n) 22.2 (4) 26.3 (5) n.s. Skin manifestations, % (n)* 50 (9) 33.3 (6) n.s. Arthritis, % (n) 66.7 (12) 57.9 (11) n.s. RV-fwLG, m (SD) − 16.5 (5.5) − 22.2 (5.7) n.s. EF, m (SD) 59.4 (3.78) 60.3 (4.54) n.s. Left ventricular mass index (g/m 2 ), me (IQR) 76 (16.3) 75 (15) n.s. GLS: Global Longitudinal Strain, ANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients. To explore clinical and laboratory correlates of GLS, we first performed univariate linear regression analyses. Both lymphocyte count at disease onset and the presence of arthritis were significantly associated with GLS. We then conducted multivariate linear regression including all variables listed in Table 3 , selected based on their clinical and pathophysiological relevance in IIM. In the adjusted model, lymphocyte count, arthritis, and CK at disease onset remained independently associated with GLS values. Table 3 presents the results of both univariate and multivariate regression analyses exploring associations between GLS and selected clinical and laboratory parameters. UNIVARIATE MULTIVARIATE Beta (C.I.95%) p Beta (95% C.I.) p Lymphocytes at onset per µL 0.357 (0.008–0.705) 0.045 0.341 (0.017–0.666) 0.04 Arthritis 0.842 (0.206–1.48) 0.011 0.971 (0.265–1.677) 0.009 CK at the onset in UI/L 0.134 (-0.217-0.485) n.s. 0.388 (0.04–0.735) 0.03 Age 0.0519 (-0.291–0.395) n.s. n.s. C.I.: confidence interval, CK: creatine kinase, n.s.: not significant. To further investigate the role of arthritis, patients were stratified according to its presence (Table 4 ). Individuals with arthritis exhibited significantly worse GLS and lower CK levels, while EF remained comparable between groups. These findings consistently support an independent association between arthritis and impaired GLS, irrespective of EF. Table 4 shows the univariate and multivariate logistic regression models using arthritis as the dependent variable, evaluating its relationship with GLS and other clinical predictors. Absence of arthritis (n = 14) Presence of arthritis (n = 23) p Sex, females, % (n) 71.4 (10) 87 (20) n.s. Age in years, m (SD) 64 (7.62) 63.7 (12.3) n.s. Age of onset in years, m (SD) 56.6 (8.63) 53.8 (13.2) n.s. Time between symptom onset and initiation of therapy, in months, me (IQR) 2 (3.25) 3 (3) n.s. Presence of diagnostic criteria, % (n) 92.3 (12) 90 (18) n.s. Weight in kg, m (SD) 68.3 (11.9) 71.1 (12.7) n.s. Lymphocytes at onset per µL, m (SD) 1697 (771) 2095 (927) n.s. ANAs, me (IQR) 320 (320) 80 (320) 0.046 CK at the onset in UI/L, me (IQR) 901 (4308) 302 (549) 0.025 CRP at onset, me (IQR) 1.4 (2) 5 (6) n.s. PAH, % (n) 7.1 (1) 0 (0) n.s. ILD, % (n) 28.6 (4) 47.8 (11) n.s. Dysphagia, % (n) 7.1 (1) 30.4 (7) n.s. Gastrointestinal symptoms, % (n) 7.1 (1) 34.8 (8) n.s. Skin manifestations, % (n)* 28.6 (4) 50 (11) n.s. LV GLS %, m (SD) − 19 (2.04) − 17.2 (2.02) 0.011 RV-fwLG, me (IQR) − 20 (4.8) − 19.7 (5.5) n.s. EF, m (SD) 59.7 (7.75) 59.9 (4.75) n.s. Left ventricular mass index (g/m2), me (IQR) 81.5 (13.9) 74 (14.9) n.s. ANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients. Discussion Ejection fraction is a widely used global index of left ventricular function, reflecting the combined contribution of circumferential and longitudinal myocardial fibers. However, EF lacks the sensitivity to distinguish between these components and often remains within normal limits until significant myocardial dysfunction has occurred. Among the contractile components of the left ventricle, longitudinal function plays a crucial role by reducing the long-axis dimension of the LV cavity during systole, a motion primarily driven by the displacement of the mitral annulus toward the apex. This longitudinal shortening may account for up to 60% of the total stroke volume [ 17 ]. Longitudinal fibers, predominantly located in the subendocardial layer, are particularly vulnerable to a variety of pathological stimuli. This vulnerability is attributable to their high oxygen demand, greater exposure to wall stress, and relative proximity to inflammatory mediators [ 18 , 19 ]. In conditions such as systemic inflammation, these fibers are highly susceptible to damage due to oxidative stress, mitochondrial dysfunction, and impaired calcium handling [ 20 ]. Inflammatory cytokines can also induce direct subcellular alterations in cardiomyocytes, leading to early contractile impairment [ 21 ]. As a result, longitudinal dysfunction often precedes alterations in circumferential function and a decline in EF. Therefore, assessment of longitudinal strain using GLS provides a more sensitive and earlier detection method for subclinical cardiac involvement, particularly in diseases with systemic inflammatory components such as IIM. In our cohort, despite a preserved ejection fraction, the mean GLS was significantly below the lower limit of normality, indicating early and diffuse myocardial dysfunction. This observation highlights the clinical utility of GLS in identifying subclinical cardiac involvement in IIM patients, which may otherwise go undetected when using conventional parameters such as EF [ 11 – 13 ]. Since the absence of clear guidelines regarding cardiac evaluation in IIM, some studies have sought to better define the prevalence and characteristics of cardiac involvement in this setting [ 22 ]. Reported prevalence rates vary widely, ranging from 6–75%, depending on the population studied and the diagnostic methods used [ 23 , 24 ]. Among the alterations reported, ECG changes and elevated troponin levels are relatively common [ 25 ]. However, overt systolic dysfunction is rarely detected, particularly when echocardiography is used alone [ 25 ]. Advanced cardiac imaging techniques, such as cardiac magnetic resonance (CMR) and speckle-tracking echocardiography, have provided greater sensitivity in detecting early myocardial alterations. Notably, subclinical left ventricular dysfunction appears more frequently than previously thought, and in many cases is only uncovered through targeted cardiac assessment [ 23 ]. Despite the low incidence of overt heart failure, cardiovascular complications remain a leading cause of mortality in IIM patients [ 26 , 27 ]. Several echocardiographic studies have reported preserved ejection fraction in IIM patients compared with healthy controls [ 12 , 25 , 28 – 32 ], yet a subset of these patients exhibit reduced GLS values, reflecting impaired longitudinal myocardial function [ 11 – 13 ]. Moreover, lower GLS values have been shown to predict a higher risk of disease relapses and greater clinical activity in a longitudinal study [ 11 ]. Although our study lacks a control population, the finding of globally reduced GLS in our cohort is in line with these previous observations, further supporting the role of GLS in detecting early cardiac dysfunction in patients with idiopathic inflammatory myopathies. Subclinical cardiac involvement detected through GLS, even in the absence of overt cardiovascular disease, has also been reported in other autoimmune and rheumatologic conditions [ 9 , 33 ]. In particular, in patients with rheumatoid arthritis, impaired GLS has been shown to correlate with greater disease severity, increased morbidity, and higher mortality risk [ 9 , 10 , 34 ]. These findings reinforce the concept that systemic inflammation can affect myocardial function early in the disease course, making GLS a valuable non-invasive marker across different autoimmune settings. In the clinical assessment of idiopathic inflammatory myopathies, certain laboratory and immunological parameters are frequently used to estimate disease activity and severity. Among these, serum muscle enzymes (particularly CK) play a pivotal role. CK levels can be markedly elevated, often up to 50 times above the normal range, and typically reflect active muscle inflammation. Moreover, CK elevation has been associated with increased disease burden and more severe clinical presentations [ 35 – 37 ]. A recent transcriptomic study by Izuka et al [ 38 ], has expanded this understanding by linking higher CK levels with upregulation of genes involved in mitochondrial respiration, phagocytosis, and oxidative phosphorylation. These molecular signatures were also associated with increased infiltration of pro-inflammatory immune cells, such as CD-16 positive monocytes and myeloid dendritic cells [ 38 ]. In idiopathic inflammatory myopathies, muscle weakness represents the primary clinical manifestation of muscle damage. However, the systemic inflammatory nature of these diseases often leads to a wide range of extramuscular features, in which lymphocytes are thought to play a central role [ 39 ]. Given this, we included CK levels at disease onset in both univariate and multivariate analyses to explore whether higher enzyme levels might reflect cardiac involvement as well. Although CK alone was not associated with GLS in univariate analysis, we found a positive association in the multivariate model when adjusting for age, presence of arthritis, and lymphocyte count at disease onset. Infiltration of muscle tissue by lymphocytes, predominantly T lymphocytes, represents a hallmark of IIM pathology and contributes to muscle fiber damage and CK elevation [ 40 , 41 ]. Several studies have described how different subsets of lymphocytes infiltrate the muscle tissue, with specific patterns depending on the subtype of myopathy [ 40 , 41 ]. Similarly, alterations in circulating lymphocyte populations have been reported in patients with IIM [ 40 , 41 ]. For instance, an increased proportion of circulating CD4 + T cells and a reduction of CD8 + T cells have been observed, particularly in dermatomyositis [ 41 ]. Other studies have highlighted the prevalence of lymphocytopenia in these patients, suggesting a complex dysregulation of immune surveillance [ 42 , 43 ]. Regarding B cell populations, various abnormalities have been described, including alterations in the proportions of naïve and memory B cells depending on the specific form of IIM, although transitional B cell levels appear comparable to those in individuals without IIM [ 41 ]. Given the central role of lymphocytes in the immunopathology of IIM, we decided to include lymphocyte count at disease onset in our multivariate model to assess its potential association with GLS. Notably, in our cohort, mean lymphocyte values were within the normal reference range, even when stratified by GLS median values. Despite this, we found a positive association with GLS (so a worse myocardial functionality), even if with a modest beta value at linear regression when age, presence of arthritis, and lymphocyte levels at onset were considered. Arthritis, although often mild and non-destructive, is a frequent extramuscular manifestation in IIM, particularly in patients with anti-Jo1 antibodies, and may precede the onset of muscle weakness [ 44 – 47 ]. Arthritis is also commonly observed during disease relapses [ 44 ]. In our cohort, the prevalence of arthritis was 62.2%, higher than reported in previous studies, where arthritis prevalence ranged between 25% and 40% [ 44 , 46 , 47 ]. The higher prevalence observed in our cohort may reflect specific patient selection, potentially influenced by referral patterns, disease severity at presentation, or cohort characteristics. Given these observations, we included the presence of arthritis in our analyses, finding that it was independently associated with impaired GLS values. To further explore this association, we stratified patients based on the presence of arthritis, observing that those with arthritis had significantly worse GLS despite no significant difference in ejection fraction compared to patients without arthritis, as shown in Table 4 . Interestingly, patients with arthritis also exhibited lower CK levels compared to those without arthritis. This observation might reflect a clinical phenotype characterized by predominant joint involvement and relatively reduced skeletal muscle inflammation, leading to lower serum CK values. Our findings are consistent with observations in patients with rheumatoid arthritis, where impaired GLS has been associated with greater disease severity, systemic inflammation, and poorer clinical outcomes [ 9 , 10 , 14 , 16 , 48 – 51 ]. This suggests that, also in IIM, the presence of arthritis may identify a subset of patients with a more pronounced systemic inflammatory burden, contributing to subclinical myocardial dysfunction. The results of our analysis suggest that a less negative GLS value (indicating worse left ventricular function) is associated with specific clinical features of IIM, including the presence of arthritis and higher lymphocyte counts at disease onset. These findings may reflect a subset of patients with greater systemic inflammatory involvement. This finding is consistent with observations by Machado et al [ 11 ], who reported that impaired GLS was associated with more frequent relapses and higher disease activity in IIM patients. Conversely, Guerra et al [ 12 ], did not find a significant association between GLS impairment and disease activity assessed by the Myositis Intention to Treat Activities Index (MITAX) or chronic damage evaluated by the Myositis Damage Index (MDI). Conclusions In patients with idiopathic inflammatory myopathies without known cardiovascular disease, we observed a global reduction in longitudinal systolic function as measured by GLS, despite preserved ejection fraction. Our findings suggest that GLS may serve as a sensitive marker for detecting early, subclinical myocardial involvement in this population. Moreover, we identified that the presence of arthritis, along with higher lymphocyte counts at disease onset, was independently associated with impaired GLS. This suggests that systemic inflammatory activity may play a pivotal role in early cardiac dysfunction among patients with IIM. Given the high prevalence of subclinical cardiac involvement, longitudinal studies are warranted to evaluate the evolution of GLS over time and to clarify its potential role in guiding clinical management and therapeutic strategies. Routine integration of GLS in the echocardiographic evaluation of IIM patients may assist in the early detection of cardiac involvement, even in asymptomatic individuals. Limitations This study has several limitations. First, the sample size was relatively small, which may limit the generalizability of our findings. Nevertheless, considering the rarity of IIM, it can be considered acceptable for an exploratory investigation. Second, the cross-sectional design prevented us from evaluating the longitudinal evolution of GLS or the effects of treatment over time. Third, the absence of a control group of healthy individuals limited the interpretability of GLS impairment in IIM, however, the GLS reference range used was derived from a healthy population and provided by the echocardiographic system manufacturer. Finally, we did not systematically assess the presence of antisynthetase or other myositis-specific antibodies, which could have offered further insights into disease phenotypes. Declarations Conflict of interest : All authors declare no conflict of interest. Human and animal rights statement: This study has been performed in accordance with the fundamental ethical principles of the Declaration of Helsinki. The study protocol was approved by the local Ethics Committee (prot. REUMABANK 1483 CESC). References Suzuki K, Kondo Y, Ishigaki S, et al (2024) Clinical characteristics of cardiac involvement in idiopathic inflammatory myopathies: A retrospective cohort study. Int J of Rheum Dis 27:e15273. https://doi.org/10.1111/1756-185X.15273 Stanton T, Leano R, Marwick TH (2009) Prediction of all-cause mortality from global longitudinal speckle strain: comparison with ejection fraction and wall motion scoring. Circ Cardiovasc Imaging 2:356–364. https://doi.org/10.1161/CIRCIMAGING.109.862334 Cikes M, Solomon SD. Beyond ejection fraction: an integrative approach for assessment of cardiac structure and function in heart failure. Eur Heart J. 2016 Jun 1;37(21):1642-50. doi: 10.1093/eurheartj/ehv510. Epub 2015 Sep 28. PMID: 26417058. Romano S, Mansour IN, Kansal M, et al (2017) Left Ventricular global longitudinal strain predicts heart failure readmission in acute decompensated heart failure. Cardiovasc Ultrasound 15:6. https://doi.org/10.1186/s12947-017-0098-3 Romano S, Judd RM, Kim RJ, et al (2017) Association of Feature-Tracking Cardiac Magnetic Resonance Imaging Left Ventricular Global Longitudinal Strain With All-Cause Mortality in Patients With Reduced Left Ventricular Ejection Fraction. Circulation 135:2313–2315. https://doi.org/10.1161/CIRCULATIONAHA.117.027740 Romano S, Judd RM, Kim RJ, et al (2018) Feature-Tracking Global Longitudinal Strain Predicts Death in a Multicenter Population of Patients With Ischemic and Nonischemic Dilated Cardiomyopathy Incremental to Ejection Fraction and Late Gadolinium Enhancement. JACC Cardiovasc Imaging 11:1419–1429. https://doi.org/10.1016/j.jcmg.2017.10.024 Sartorio A, Cristin L, Pont CD, et al (2025) Global longitudinal strain as an early marker of cardiac damage after cardiotoxic medications, a state-of-the-art review. Progress in Cardiovascular Diseases 89:92–101. https://doi.org/10.1016/j.pcad.2025.01.001 Sartorio A, Dal Pont C, Romano S (2024) Standard and New Echocardio Techniques, Such as Global Longitudinal Strain, to Monitor the Impact of Diets on Cardiovascular Diseases and Heart Function. Nutrients 16:1471. https://doi.org/10.3390/nu16101471 Cioffi G, Viapiana O, Ognibeni F, et al (2017) Prognostic Role of Subclinical Left Ventricular Systolic Dysfunction Evaluated by Speckle-Tracking Echocardiography in Rheumatoid Arthritis. Journal of the American Society of Echocardiography 30:602–611. https://doi.org/10.1016/j.echo.2017.02.001 Alexandre A, Sá-Couto D, Brandão M, et al (2024) Subclinical left ventricular dysfunction in rheumatoid arthritis: findings from the prospective Porto-RA cohort. Clin Res Cardiol. https://doi.org/10.1007/s00392-024-02548-6 Machado LSG, Oliveira ACD, Mancuso FJN, Sato EI (2023) First study demonstrating speckle tracking echocardiography has prognostic value in patients with idiopathic inflammatory myopathies. Int J Cardiovasc Imaging 39:2163–2171. https://doi.org/10.1007/s10554-023-02925-8 Guerra F, Gelardi C, Capucci A, et al (2017) Subclinical Cardiac Dysfunction in Polymyositis and Dermatomyositis: A Speckle-tracking Case-control Study. J Rheumatol 44:815–821. https://doi.org/10.3899/jrheum.161311 Zhong Y, Bai W, Xie Q, et al (2017) Cardiac function in patients with polymyositis or dermatomyositis: a three-dimensional speckle-tracking echocardiography study. Int J Cardiovasc Imaging. https://doi.org/10.1007/s10554-017-1278-9 Cioffi G, Giollo A, Orsolini G, et al (2020) Incidence and predictors of adverse clinical events in patients with rheumatoid arthritis and asymptomatic left ventricular systolic dysfunction. Clin Exp Rheumatol 38:420–427 Demirci M, Özdemir B, Zengin G, et al (2025) Cardiac Dysfunction and 5-Year Mortality in Patients With Systemic Sclerosis and Interstitial Lung Disease: A Speckle-Tracking Echocardiography Study. J Clin Ultrasound. https://doi.org/10.1002/jcu.24094 Lo Gullo A, Rodríguez-Carrio J, Aragona CO, et al (2018) Subclinical impairment of myocardial and endothelial functionality in very early psoriatic and rheumatoid arthritis patients: Association with vitamin D and inflammation. Atherosclerosis 271:214–222. https://doi.org/10.1016/j.atherosclerosis.2018.03.004 Henein MY, Gibson DG (1999) Normal long axis function. Heart 81:111–113. https://doi.org/10.1136/hrt.81.2.111 Chilian WM (1991) Microvascular pressures and resistances in the left ventricular subepicardium and subendocardium. Circulation Research 69:561–570. https://doi.org/10.1161/01.RES.69.3.561 Sanderson JE (2008) Left and right ventricular long-axis function and prognosis. Heart 94:262–263. https://doi.org/10.1136/hrt.2006.109348 Mishra S, Kass DA (2021) Cellular and molecular pathobiology of heart failure with preserved ejection fraction. Nat Rev Cardiol 18:400–423. https://doi.org/10.1038/s41569-020-00480-6 Wenzl FA, Ambrosini S, Mohammed SA, et al (2021) Inflammation in Metabolic Cardiomyopathy. Front Cardiovasc Med 8:742178. https://doi.org/10.3389/fcvm.2021.742178 Opinc A, Brzezińska O, Makowska J. Underdiagnosis of cardiopulmonary involvement in patients with idiopathic inflammatory myopathies. Reumatologia. 2021;59(5):276-284. doi: 10.5114/reum.2021.110609. Epub 2021 Nov 7. PMID: 34819701; PMCID: PMC8609382. Lundberg IE (2006) The heart in dermatomyositis and polymyositis. Rheumatology 45:iv18–iv21. https://doi.org/10.1093/rheumatology/kel311 Shah M, Shinjo SK, Day J, Gupta L (2023) Cardiovascular manifestations in idiopathic inflammatory myopathies. Clin Rheumatol 42:2557–2575. https://doi.org/10.1007/s10067-023-06599-4 Fairley JL, Wicks I, Peters S, Day J (2021) Defining cardiac involvement in idiopathic inflammatory myopathies: a systematic review. Rheumatology 61:103–120. https://doi.org/10.1093/rheumatology/keab573 Dobloug GC, Svensson J, Lundberg IE, Holmqvist M (2018) Mortality in idiopathic inflammatory myopathy: results from a Swedish nationwide population-based cohort study. Annals of the Rheumatic Diseases 77:40–47. https://doi.org/10.1136/annrheumdis-2017-211402 Limaye V, Hakendorf P, Woodman RJ, et al (2012) Mortality and its predominant causes in a large cohort of patients with biopsy‐determined inflammatory myositis. Internal Medicine Journal 42:191–198. https://doi.org/10.1111/j.1445-5994.2010.02406.x Diederichsen LP, Simonsen JA, Diederichsen AC, et al (2016) Cardiac Abnormalities in Adult Patients With Polymyositis or Dermatomyositis as Assessed by Noninvasive Modalities. Arthritis Care & Research 68:1012–1020. https://doi.org/10.1002/acr.22772 Byrnes TJ, Baethge BA, Wolf RE (1991) Noninvasive Cardiovascular Studies in Patients with Inflammatory Myopathy. Angiology 42:843–848. https://doi.org/10.1177/000331979104201010 Mugii N, Someya F (2018) Cardiopulmonary factors affecting 6-min walk distance in patients with idiopathic inflammatory myopathies. Rheumatol Int 38:1443–1448. https://doi.org/10.1007/s00296-018-4050-0 Lu Z, Wei Q, Ning Z, et al (2013) Left Ventricular Diastolic Dysfunction — Early Cardiac Impairment in Patients with Polymyositis/Dermatomyositis: A Tissue Doppler Imaging Study. J Rheumatol 40:1572–1577. https://doi.org/10.3899/jrheum.130044 Wang H, Liu H-X, Wang Y-L, et al (2014) Left Ventricular Diastolic Dysfunction in Patients with Dermatomyositis Without Clinically Evident Cardiovascular Disease. J Rheumatol 41:495–500. https://doi.org/10.3899/jrheum.130346 Chourabi S, Sayhi S, Ben Ameur S, et al (2024) Cardiac involvement in systemic lupus erythematosus: Interest of 2D global longitudinal strain. Lupus 33:1100–1108. https://doi.org/10.1177/09612033241266990 Çakmak EÖ, Fındıkçıoğlu U, Tezcan ME (2021) Disease severity affects myocardial functions in patients with treatment‐naive early rheumatoid arthritis. Int J of Rheum Dis 24:494–501. https://doi.org/10.1111/1756-185X.13992 Dalakas MC (1991) Polymyositis, Dermatomyositis, and Inclusion-Body Myositis. N Engl J Med 325:1487–1498. https://doi.org/10.1056/NEJM199111213252107 Wang ZJ, Tian ZR, Wang YQ, et al (2024) [Establishing a predictive model for the activity of idiopathic inflammatory myopathy based on MRI and clinical features]. Zhonghua Yi Xue Za Zhi 104:3409–3415. https://doi.org/10.3760/cma.j.cn112137-20240805-01790 Zhang L, Fu L, Zhang G, et al (2024) Clinico‐sero‐pathological profiles and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different perifascicular changes. CNS Neurosci Ther 30:e14882. https://doi.org/10.1111/cns.14882 Izuka S, Umezawa N, Komai T, et al (2025) Muscle Tissue Transcriptome of Idiopathic Inflammatory Myopathy Reflects the Muscle Damage Process by Monocytes and Presence of Skin Lesions. Arthritis Rheumatol 77:99–106. https://doi.org/10.1002/art.42972 Pan Z, Li M, Zhang P, et al (2025) Peripheral Blood Lymphocyte Subsets and Heterogeneity of B Cell Subsets in Patients of Idiopathic Inflammatory Myositis with Different Myositis-specific Autoantibodies. Inflammation 48:118–132. https://doi.org/10.1007/s10753-024-02052-z Franco C, Gatto M, Iaccarino L, et al (2021) Lymphocyte immunophenotyping in inflammatory myositis: a review. Current Opinion in Rheumatology 33:522–528. https://doi.org/10.1097/BOR.0000000000000831 Zhao L, Wang Q, Zhou B, et al (2021) The Role of Immune Cells in the Pathogenesis of Idiopathic Inflammatory Myopathies. Aging and disease 12:247. https://doi.org/10.14336/AD.2020.0410 Shimojima Y, Ishii W, Matsuda M, Ikeda S (2012) Phenotypes of Peripheral Blood Lymphocytes and Cytokine Expression in Polymyositis and Dermatomyositis before Treatment and after Clinical Remission. Clin MedInsightsArthritisMusculoskelet Disord 5:CMAMD.S10272. https://doi.org/10.4137/CMAMD.S10272 Viguier M, Fouéré S, de la Salmonière P, et al (2003) Peripheral blood lymphocyte subset counts in patients with dermatomyositis: clinical correlations and changes following therapy. Medicine (Baltimore) 82:82–86. https://doi.org/10.1097/00005792-200303000-00002 Klein M, Mann H, Pleštilová L, et al (2014) Arthritis in Idiopathic Inflammatory Myopathy: Clinical Features and Autoantibody Associations. J Rheumatol 41:1133–1139. https://doi.org/10.3899/jrheum.131223 Ide V, Bossuyt X, Blockmans D, De Langhe E (2018) Prevalence and clinical correlates of rheumatoid factor and anticitrullinated protein antibodies in patients with idiopathic inflammatory myopathy. RMD Open 4:e000661. https://doi.org/10.1136/rmdopen-2018-000661 Tanimoto K, Nakano K, Kano S, et al (1995) Classification criteria for polymyositis and dermatomyositis. J Rheumatol 22:668–674 Schmidt WA, Wetzel W, Friedländer R, et al (2000) Clinical and Serological Aspects of Patients with Anti-Jo-1 Antibodies – an Evolving Spectrum of Disease Manifestations. Clin Rheumatol 19:371–377. https://doi.org/10.1007/s100670070030 Naseem M, Samir S, Ibrahim IK, et al (2019) 2-D speckle-tracking assessment of left and right ventricular function in rheumatoid arthritis patients with and without disease activity. J Saudi Heart Assoc 31:41–49. https://doi.org/10.1016/j.jsha.2018.10.001 Hanvivadhanakul P, Buakhamsri A (2019) Disease activity is associated with LV dysfunction in rheumatoid arthritis patients without clinical cardiovascular disease. Adv Rheumatol 59:56. https://doi.org/10.1186/s42358-019-0100-x Midtbø H, Semb AG, Matre K, et al (2017) Disease activity is associated with reduced left ventricular systolic myocardial function in patients with rheumatoid arthritis. Ann Rheum Dis 76:371–376. https://doi.org/10.1136/annrheumdis-2016-209223 Fine NM, Crowson CS, Lin G, et al (2014) Evaluation of myocardial function in patients with rheumatoid arthritis using strain imaging by speckle-tracking echocardiography. Ann Rheum Dis 73:1833–1839. https://doi.org/10.1136/annrheumdis-2013-203314 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7273002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498220617,"identity":"e93e4c82-32fb-4582-b248-7e1c5bd11bd6","order_by":0,"name":"Simone Romano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZhReBQMDG4hOIF7LGZgWvHqQAWMbjIVHi3w7d+LjggqGfH6xw88efJx3OI9PIvcBw8MfuLUYHObdbDzjDIPlzNlp5oYztx0uZpNIN8DrMANm3m3SvG0MBga3E8ykebcdTmzjOYbfL/LNIC3/QFrSv0nzziFCC8NhkJYGkJYcoC0NQC3sbfi1gP3Cc0zCQHJ2TpnkjGPpYC0HEtLwOKz/7MbHPDU2BvzS6dskPtRYJ85vZmN8+MMGj8MgQAKVe4CghlEwCkbBKBgFeAEA9lFGLcki3HEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8623-235X","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":true,"prefix":"","firstName":"Simone","middleName":"","lastName":"Romano","suffix":""},{"id":498220618,"identity":"11225823-c9b5-49ff-8554-3bbd615399ec","order_by":1,"name":"Andrea Sartorio","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Sartorio","suffix":""},{"id":498220619,"identity":"84442b30-1b1c-4f80-b65b-cd2788b0bda2","order_by":2,"name":"Chiara Dal Pont","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Chiara","middleName":"Dal","lastName":"Pont","suffix":""},{"id":498220620,"identity":"4c297230-cbba-4dff-8fe0-20343f2efc80","order_by":3,"name":"Francesca Segatta","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Segatta","suffix":""},{"id":498220621,"identity":"0de5e305-8ce7-4dba-a0f9-62bf2930fb23","order_by":4,"name":"Marta Piazzola","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Piazzola","suffix":""},{"id":498220622,"identity":"6f59fde2-8445-4ada-a2f8-8eabf2db9ef9","order_by":5,"name":"Federico Aldegheri","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Aldegheri","suffix":""},{"id":498220623,"identity":"f85202dd-361d-422f-b672-6fa330f0485b","order_by":6,"name":"Riccardo Bixio","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Bixio","suffix":""},{"id":498220624,"identity":"4a6073a1-8116-4aa8-85a2-9c6c0a7d3c4b","order_by":7,"name":"Ombretta Viapiana","email":"","orcid":"","institution":"University of Verona: Universita degli Studi di Verona","correspondingAuthor":false,"prefix":"","firstName":"Ombretta","middleName":"","lastName":"Viapiana","suffix":""}],"badges":[],"createdAt":"2025-08-01 16:15:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7273002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7273002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89232819,"identity":"eca1b860-27d9-48c3-9702-11d62e1aa933","added_by":"auto","created_at":"2025-08-17 14:30:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":479968,"visible":true,"origin":"","legend":"\u003cp\u003eSpeckle-tracking echocardiographic strain analysis of a representative patient from the study cohort. Apical four-chamber (4CH), three-chamber (3CH), two-chamber (2CH), and long-axis (APLAX) views are presented, each displaying regional peak systolic longitudinal strain. The bull’s-eye plot integrates data from all 18 LV segments derived from these views and provides the global longitudinal strain (GLS) value. In this case, GLS is reduced, reflecting subclinical left ventricular systolic dysfunction.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7273002/v1/8e491f66f9a5889dac521a8d.png"},{"id":92533076,"identity":"f391ca07-0177-4fb5-917b-d2d9dd46e649","added_by":"auto","created_at":"2025-09-30 16:57:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7273002/v1/275e1dd9-52f3-49f3-89ee-4884e9c07e15.pdf"}],"financialInterests":"","formattedTitle":"Subclinical cardiac dysfunction in idiopathic inflammatory myopathies: the role of global longitudinal strain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic inflammatory myopathies (IIM) are a heterogeneous group of autoimmune diseases primarily characterized by chronic inflammation of skeletal muscle. However, due to their systemic nature, these disorders can also affect extramuscular organs, including the heart. Cardiac involvement in IIM is frequently subclinical and may remain undetected, yet it is associated with increased morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLeft ventricular ejection fraction (EF) has traditionally been the standard echocardiographic parameter used to assess cardiac function. Although widely applied, EF is relatively insensitive to early myocardial dysfunction, particularly when the impairment affects longitudinal fibers of the left ventricle. In recent years, global longitudinal strain (GLS) has emerged as a more sensitive and reproducible echocardiographic marker for detecting early myocardial impairment, even in patients with preserved EF [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have demonstrated that patients with autoimmune diseases may exhibit reduced GLS values, indicating early subclinical myocardial involvement [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This is particularly relevant in the context of IIM, where cardiac symptoms are often absent or overshadowed by dominant muscular manifestations. GLS, therefore, may serve as a valuable tool for detecting cardiac dysfunction in its subclinical phase [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of this study was to evaluate left ventricular GLS (LV GLS) in a cohort of patients with IIM and no known cardiovascular disease. Additionally, we sought to investigate the associations between GLS values and clinical or laboratory parameters, in order to identify potential predictors of myocardial dysfunction in this population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a retrospective observational study involving 37 adult outpatients diagnosed with IIM at the Department of Internal Medicine, University Hospital of Verona. All patients had preserved LVEF and no prior history or clinical evidence of cardiovascular disease. Clinical, demographic, and laboratory data were collected during the outpatient visit and by reviewing the hospital’s electronic medical records.\u003c/p\u003e\u003cp\u003eTransthoracic echocardiography was performed using a GE Vivid T8 ultrasound system (GE Healthcare, Arlington Heights, IL). Reference values for GLS ranged from − 18.2% to − 21.2%, as provided by the echocardiographic platform. Standard parasternal, apical, and subcostal views were obtained. GLS was measured using speckle-tracking echocardiography. Electrocardiographically gated cine loops were acquired in apical 2-, 3-, and 4-chamber views. GLS was calculated as the average strain across 18 myocardial segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEndocardial borders were automatically delineated at end-diastole. The tracking was visually verified and manually adjusted if necessary. Segments with poor tracking quality were excluded from the analysis. All measurements were performed by a board-certified echocardiographer blinded to the patients’ clinical and laboratory data.\u003c/p\u003e\u003cp\u003eStatistical analyses were conducted using Jamovi software (version 2.3.18, The Jamovi project, 2022). Continuous variables were reported as mean ± standard deviation (SD) or median with interquartile range (IQR), according to their distribution. Group comparisons were performed using the Student’s t-test or the Mann–Whitney U test, as appropriate. Categorical variables were compared using the chi-square test or Fisher’s exact test. Linear regression models were used to assess associations between GLS and clinical variables, and logistic regression models were employed to identify predictors of arthritis. A two-tailed p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean GLS value in the study population was \u0026minus;\u0026thinsp;17.9% \u0026plusmn; 2.2%, below the reference range provided by the echocardiographic system (\u0026minus;\u0026thinsp;18.2% to \u0026minus;\u0026thinsp;21.2%). This finding indicates a global reduction in longitudinal systolic function despite preserved EF \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e(\u003c/span\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When stratified by sex, no significant differences in GLS were observed, although EF was slightly lower in males. Women had significantly lower body weight compared to men.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003esummarizes the demographic, clinical, laboratory, and echocardiographic characteristics of the overall cohort, including sex-based comparisons.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.8 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.1 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.4 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.9 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.6 (9.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.9 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime between symptom onset and initiation of therapy, in months, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of diagnostic criteria, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.9 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.5 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight in kg, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.2 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.3 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes at onset, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1945 (881)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2370 (1077)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1848 (823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANAs, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160 (480)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160 (640)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK at the onset in UI/L, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e492 (1112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e843 (789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e461 (1298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP at onset, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.036\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILD, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.5 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.4 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.3 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDysphagia, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.6 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.3 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal symptoms, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.3 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.7 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin manifestations, \u003cem\u003e% (n)*\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.7 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.2 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.9 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.7 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLV GLS %, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;17.9 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;17.3 (2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;18 (2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRV-fwLG, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;20 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;20 (3.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;19.9 (5.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.8 (4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.4 (2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.8 (4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular mass index (g/m\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.5 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients.\u003c/p\u003e\u003cp\u003eStratification of patients by the median GLS value (\u0026minus;\u0026thinsp;17.9%) did not reveal significant differences in most clinical parameters (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, EF remained similar between the two groups, reinforcing the added diagnostic value of GLS in detecting subclinical myocardial dysfunction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ereports variables stratified by the GLS median (\u0026minus;\u0026thinsp;17.9%)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLS\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;17.9\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGLS\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;17.9\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, females, % \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77.8 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.2 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.5 (9.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset in years, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.3 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.4 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime between symptom onset and initiation of therapy, in months, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of diagnostic criteria, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.5 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.1 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight in kg, m (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.6 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.4 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes at onset per \u0026micro;L, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2123 (892)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1789 (867)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANAs, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160 (240)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (640)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK at the onset in UI/L, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340 (2969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e567 (938)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP at onset, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.6 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILD, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.9 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.1 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDysphagia, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.2 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.1 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal symptoms, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.2 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.3 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin manifestations, % (n)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.3 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.7 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.9 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRV-fwLG, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;16.5 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;22.2 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.4 (3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.3 (4.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en.s.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular mass index (g/m\u003csup\u003e2\u003c/sup\u003e),\u003c/p\u003e\u003cp\u003e\u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGLS: Global Longitudinal Strain, ANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients.\u003c/p\u003e\u003cp\u003eTo explore clinical and laboratory correlates of GLS, we first performed univariate linear regression analyses. Both lymphocyte count at disease onset and the presence of arthritis were significantly associated with GLS. We then conducted multivariate linear regression including all variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, selected based on their clinical and pathophysiological relevance in IIM. In the adjusted model, lymphocyte count, arthritis, and CK at disease onset remained independently associated with GLS values.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003epresents the results of both univariate and multivariate regression analyses exploring associations between GLS and selected clinical and laboratory parameters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUNIVARIATE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMULTIVARIATE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003cp\u003e(C.I.95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBeta\u003c/p\u003e\u003cp\u003e(95% C.I.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes at onset per \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.357 (0.008\u0026ndash;0.705)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.341 (0.017\u0026ndash;0.666)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.842 (0.206\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.971 (0.265\u0026ndash;1.677)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK at the onset in UI/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.134 (-0.217-0.485)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003cp\u003e(0.04\u0026ndash;0.735)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0519 (-0.291\u0026ndash;0.395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eC.I.: confidence interval, CK: creatine kinase, n.s.: not significant.\u003c/p\u003e\u003cp\u003eTo further investigate the role of arthritis, patients were stratified according to its presence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Individuals with arthritis exhibited significantly worse GLS and lower CK levels, while EF remained comparable between groups. These findings consistently support an independent association between arthritis and impaired GLS, irrespective of EF.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eshows the univariate and multivariate logistic regression models using arthritis as the dependent variable, evaluating its relationship with GLS and other clinical predictors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsence of arthritis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePresence of arthritis\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, females, % \u003cem\u003e(n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.4 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (7.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.7 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge of onset in years, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.6 (8.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.8 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime between symptom onset and initiation of therapy, in months, me (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of diagnostic criteria, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.3 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight in kg, m (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.3 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.1 (12.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes at onset per \u0026micro;L, m (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1697 (771)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2095 (927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANAs, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e320 (320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (320)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.046\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK at the onset in UI/L, me (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e901 (4308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e302 (549)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.025\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP at onset, me (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAH, \u003cem\u003e% (n)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eILD, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.6 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.8 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDysphagia, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.4 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal symptoms, % (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.8 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin manifestations, % (n)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.6 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLV GLS %, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;19 (2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;17.2 (2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.011\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRV-fwLG, \u003cem\u003eme (IQR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;20 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;19.7 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF, \u003cem\u003em (SD)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.7 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.9 (4.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft ventricular mass index (g/m2), me (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.5 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en.s.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eANAs: Antinuclear antibodies, CK: creatine kinase, CRP: C-reactive protein, PAH: pulmonary arterial hypertension, ILD: Interstitial Lung Disease, LV GLS: Left Ventricle Global Longitudinal Strain, RV-fwLG: Right ventricular-free wall longitudinal strain, EF: ejection fraction, m: mean, SD: standard deviation, me: median, IQR: interquartile range, n.s.: not significant, * = data available for 36 patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEjection fraction is a widely used global index of left ventricular function, reflecting the combined contribution of circumferential and longitudinal myocardial fibers. However, EF lacks the sensitivity to distinguish between these components and often remains within normal limits until significant myocardial dysfunction has occurred. Among the contractile components of the left ventricle, longitudinal function plays a crucial role by reducing the long-axis dimension of the LV cavity during systole, a motion primarily driven by the displacement of the mitral annulus toward the apex. This longitudinal shortening may account for up to 60% of the total stroke volume [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Longitudinal fibers, predominantly located in the subendocardial layer, are particularly vulnerable to a variety of pathological stimuli. This vulnerability is attributable to their high oxygen demand, greater exposure to wall stress, and relative proximity to inflammatory mediators [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In conditions such as systemic inflammation, these fibers are highly susceptible to damage due to oxidative stress, mitochondrial dysfunction, and impaired calcium handling [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Inflammatory cytokines can also induce direct subcellular alterations in cardiomyocytes, leading to early contractile impairment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a result, longitudinal dysfunction often precedes alterations in circumferential function and a decline in EF. Therefore, assessment of longitudinal strain using GLS provides a more sensitive and earlier detection method for subclinical cardiac involvement, particularly in diseases with systemic inflammatory components such as IIM. In our cohort, despite a preserved ejection fraction, the mean GLS was significantly below the lower limit of normality, indicating early and diffuse myocardial dysfunction. This observation highlights the clinical utility of GLS in identifying subclinical cardiac involvement in IIM patients, which may otherwise go undetected when using conventional parameters such as EF [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Since the absence of clear guidelines regarding cardiac evaluation in IIM, some studies have sought to better define the prevalence and characteristics of cardiac involvement in this setting [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Reported prevalence rates vary widely, ranging from 6\u0026ndash;75%, depending on the population studied and the diagnostic methods used [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Among the alterations reported, ECG changes and elevated troponin levels are relatively common [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, overt systolic dysfunction is rarely detected, particularly when echocardiography is used alone [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Advanced cardiac imaging techniques, such as cardiac magnetic resonance (CMR) and speckle-tracking echocardiography, have provided greater sensitivity in detecting early myocardial alterations. Notably, subclinical left ventricular dysfunction appears more frequently than previously thought, and in many cases is only uncovered through targeted cardiac assessment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite the low incidence of overt heart failure, cardiovascular complications remain a leading cause of mortality in IIM patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Several echocardiographic studies have reported preserved ejection fraction in IIM patients compared with healthy controls [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], yet a subset of these patients exhibit reduced GLS values, reflecting impaired longitudinal myocardial function [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, lower GLS values have been shown to predict a higher risk of disease relapses and greater clinical activity in a longitudinal study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although our study lacks a control population, the finding of globally reduced GLS in our cohort is in line with these previous observations, further supporting the role of GLS in detecting early cardiac dysfunction in patients with idiopathic inflammatory myopathies. Subclinical cardiac involvement detected through GLS, even in the absence of overt cardiovascular disease, has also been reported in other autoimmune and rheumatologic conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In particular, in patients with rheumatoid arthritis, impaired GLS has been shown to correlate with greater disease severity, increased morbidity, and higher mortality risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings reinforce the concept that systemic inflammation can affect myocardial function early in the disease course, making GLS a valuable non-invasive marker across different autoimmune settings.\u003c/p\u003e\u003cp\u003eIn the clinical assessment of idiopathic inflammatory myopathies, certain laboratory and immunological parameters are frequently used to estimate disease activity and severity. Among these, serum muscle enzymes (particularly CK) play a pivotal role. CK levels can be markedly elevated, often up to 50 times above the normal range, and typically reflect active muscle inflammation. Moreover, CK elevation has been associated with increased disease burden and more severe clinical presentations [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A recent transcriptomic study by Izuka et al [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], has expanded this understanding by linking higher CK levels with upregulation of genes involved in mitochondrial respiration, phagocytosis, and oxidative phosphorylation. These molecular signatures were also associated with increased infiltration of pro-inflammatory immune cells, such as CD-16 positive monocytes and myeloid dendritic cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In idiopathic inflammatory myopathies, muscle weakness represents the primary clinical manifestation of muscle damage. However, the systemic inflammatory nature of these diseases often leads to a wide range of extramuscular features, in which lymphocytes are thought to play a central role [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Given this, we included CK levels at disease onset in both univariate and multivariate analyses to explore whether higher enzyme levels might reflect cardiac involvement as well. Although CK alone was not associated with GLS in univariate analysis, we found a positive association in the multivariate model when adjusting for age, presence of arthritis, and lymphocyte count at disease onset. Infiltration of muscle tissue by lymphocytes, predominantly T lymphocytes, represents a hallmark of IIM pathology and contributes to muscle fiber damage and CK elevation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Several studies have described how different subsets of lymphocytes infiltrate the muscle tissue, with specific patterns depending on the subtype of myopathy [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Similarly, alterations in circulating lymphocyte populations have been reported in patients with IIM [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. For instance, an increased proportion of circulating CD4\u0026thinsp;+\u0026thinsp;T cells and a reduction of CD8\u0026thinsp;+\u0026thinsp;T cells have been observed, particularly in dermatomyositis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Other studies have highlighted the prevalence of lymphocytopenia in these patients, suggesting a complex dysregulation of immune surveillance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Regarding B cell populations, various abnormalities have been described, including alterations in the proportions of na\u0026iuml;ve and memory B cells depending on the specific form of IIM, although transitional B cell levels appear comparable to those in individuals without IIM [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given the central role of lymphocytes in the immunopathology of IIM, we decided to include lymphocyte count at disease onset in our multivariate model to assess its potential association with GLS. Notably, in our cohort, mean lymphocyte values were within the normal reference range, even when stratified by GLS median values. Despite this, we found a positive association with GLS (so a worse myocardial functionality), even if with a modest beta value at linear regression when age, presence of arthritis, and lymphocyte levels at onset were considered. Arthritis, although often mild and non-destructive, is a frequent extramuscular manifestation in IIM, particularly in patients with anti-Jo1 antibodies, and may precede the onset of muscle weakness [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Arthritis is also commonly observed during disease relapses [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In our cohort, the prevalence of arthritis was 62.2%, higher than reported in previous studies, where arthritis prevalence ranged between 25% and 40% [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The higher prevalence observed in our cohort may reflect specific patient selection, potentially influenced by referral patterns, disease severity at presentation, or cohort characteristics. Given these observations, we included the presence of arthritis in our analyses, finding that it was independently associated with impaired GLS values. To further explore this association, we stratified patients based on the presence of arthritis, observing that those with arthritis had significantly worse GLS despite no significant difference in ejection fraction compared to patients without arthritis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Interestingly, patients with arthritis also exhibited lower CK levels compared to those without arthritis. This observation might reflect a clinical phenotype characterized by predominant joint involvement and relatively reduced skeletal muscle inflammation, leading to lower serum CK values. Our findings are consistent with observations in patients with rheumatoid arthritis, where impaired GLS has been associated with greater disease severity, systemic inflammation, and poorer clinical outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This suggests that, also in IIM, the presence of arthritis may identify a subset of patients with a more pronounced systemic inflammatory burden, contributing to subclinical myocardial dysfunction. The results of our analysis suggest that a less negative GLS value (indicating worse left ventricular function) is associated with specific clinical features of IIM, including the presence of arthritis and higher lymphocyte counts at disease onset. These findings may reflect a subset of patients with greater systemic inflammatory involvement. This finding is consistent with observations by Machado et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], who reported that impaired GLS was associated with more frequent relapses and higher disease activity in IIM patients. Conversely, Guerra et al [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], did not find a significant association between GLS impairment and disease activity assessed by the Myositis Intention to Treat Activities Index (MITAX) or chronic damage evaluated by the Myositis Damage Index (MDI).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn patients with idiopathic inflammatory myopathies without known cardiovascular disease, we observed a global reduction in longitudinal systolic function as measured by GLS, despite preserved ejection fraction. Our findings suggest that GLS may serve as a sensitive marker for detecting early, subclinical myocardial involvement in this population. Moreover, we identified that the presence of arthritis, along with higher lymphocyte counts at disease onset, was independently associated with impaired GLS. This suggests that systemic inflammatory activity may play a pivotal role in early cardiac dysfunction among patients with IIM. Given the high prevalence of subclinical cardiac involvement, longitudinal studies are warranted to evaluate the evolution of GLS over time and to clarify its potential role in guiding clinical management and therapeutic strategies. Routine integration of GLS in the echocardiographic evaluation of IIM patients may assist in the early detection of cardiac involvement, even in asymptomatic individuals.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, the sample size was relatively small, which may limit the generalizability of our findings. Nevertheless, considering the rarity of IIM, it can be considered acceptable for an exploratory investigation. Second, the cross-sectional design prevented us from evaluating the longitudinal evolution of GLS or the effects of treatment over time. Third, the absence of a control group of healthy individuals limited the interpretability of GLS impairment in IIM, however, the GLS reference range used was derived from a healthy population and provided by the echocardiographic system manufacturer.\u003c/p\u003e\u003cp\u003eFinally, we did not systematically assess the presence of antisynthetase or other myositis-specific antibodies, which could have offered further insights into disease phenotypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: All authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and animal rights statement:\u003c/strong\u003e This study has been performed in accordance with the fundamental ethical principles of the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe study protocol was approved by the local Ethics Committee (prot. REUMABANK 1483 CESC).\u003c/strong\u003e\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSuzuki K, Kondo Y, Ishigaki S, et al (2024) Clinical characteristics of cardiac involvement in idiopathic inflammatory myopathies: A retrospective cohort study. Int J of Rheum Dis 27:e15273. https://doi.org/10.1111/1756-185X.15273\u003c/li\u003e\n\u003cli\u003eStanton T, Leano R, Marwick TH (2009) Prediction of all-cause mortality from global longitudinal speckle strain: comparison with ejection fraction and wall motion scoring. Circ Cardiovasc Imaging 2:356\u0026ndash;364. https://doi.org/10.1161/CIRCIMAGING.109.862334\u003c/li\u003e\n\u003cli\u003eCikes M, Solomon SD. Beyond ejection fraction: an integrative approach for assessment of cardiac structure and function in heart failure. Eur Heart J. 2016 Jun 1;37(21):1642-50. doi: 10.1093/eurheartj/ehv510. Epub 2015 Sep 28. PMID: 26417058.\u003c/li\u003e\n\u003cli\u003eRomano S, Mansour IN, Kansal M, et al (2017) Left Ventricular global longitudinal strain predicts heart failure readmission in acute decompensated heart failure. Cardiovasc Ultrasound 15:6. https://doi.org/10.1186/s12947-017-0098-3\u003c/li\u003e\n\u003cli\u003eRomano S, Judd RM, Kim RJ, et al (2017) Association of Feature-Tracking Cardiac Magnetic Resonance Imaging Left Ventricular Global Longitudinal Strain With All-Cause Mortality in Patients With Reduced Left Ventricular Ejection Fraction. Circulation 135:2313\u0026ndash;2315. https://doi.org/10.1161/CIRCULATIONAHA.117.027740\u003c/li\u003e\n\u003cli\u003eRomano S, Judd RM, Kim RJ, et al (2018) Feature-Tracking Global Longitudinal Strain Predicts Death in a Multicenter Population of Patients With Ischemic and Nonischemic Dilated Cardiomyopathy Incremental to Ejection Fraction and Late Gadolinium Enhancement. JACC Cardiovasc Imaging 11:1419\u0026ndash;1429. https://doi.org/10.1016/j.jcmg.2017.10.024\u003c/li\u003e\n\u003cli\u003eSartorio A, Cristin L, Pont CD, et al (2025) Global longitudinal strain as an early marker of cardiac damage after cardiotoxic medications, a state-of-the-art review. Progress in Cardiovascular Diseases 89:92\u0026ndash;101. https://doi.org/10.1016/j.pcad.2025.01.001\u003c/li\u003e\n\u003cli\u003eSartorio A, Dal Pont C, Romano S (2024) Standard and New Echocardio Techniques, Such as Global Longitudinal Strain, to Monitor the Impact of Diets on Cardiovascular Diseases and Heart Function. Nutrients 16:1471. https://doi.org/10.3390/nu16101471\u003c/li\u003e\n\u003cli\u003eCioffi G, Viapiana O, Ognibeni F, et al (2017) Prognostic Role of Subclinical Left Ventricular Systolic Dysfunction Evaluated by Speckle-Tracking Echocardiography in Rheumatoid Arthritis. Journal of the American Society of Echocardiography 30:602\u0026ndash;611. https://doi.org/10.1016/j.echo.2017.02.001\u003c/li\u003e\n\u003cli\u003eAlexandre A, S\u0026aacute;-Couto D, Brand\u0026atilde;o M, et al (2024) Subclinical left ventricular dysfunction in rheumatoid arthritis: findings from the prospective Porto-RA cohort. Clin Res Cardiol. https://doi.org/10.1007/s00392-024-02548-6\u003c/li\u003e\n\u003cli\u003eMachado LSG, Oliveira ACD, Mancuso FJN, Sato EI (2023) First study demonstrating speckle tracking echocardiography has prognostic value in patients with idiopathic inflammatory myopathies. Int J Cardiovasc Imaging 39:2163\u0026ndash;2171. https://doi.org/10.1007/s10554-023-02925-8\u003c/li\u003e\n\u003cli\u003eGuerra F, Gelardi C, Capucci A, et al (2017) Subclinical Cardiac Dysfunction in Polymyositis and Dermatomyositis: A Speckle-tracking Case-control Study. J Rheumatol 44:815\u0026ndash;821. https://doi.org/10.3899/jrheum.161311\u003c/li\u003e\n\u003cli\u003eZhong Y, Bai W, Xie Q, et al (2017) Cardiac function in patients with polymyositis or dermatomyositis: a three-dimensional speckle-tracking echocardiography study. Int J Cardiovasc Imaging. https://doi.org/10.1007/s10554-017-1278-9\u003c/li\u003e\n\u003cli\u003eCioffi G, Giollo A, Orsolini G, et al (2020) Incidence and predictors of adverse clinical events in patients with rheumatoid arthritis and asymptomatic left ventricular systolic dysfunction. Clin Exp Rheumatol 38:420\u0026ndash;427\u003c/li\u003e\n\u003cli\u003eDemirci M, \u0026Ouml;zdemir B, Zengin G, et al (2025) Cardiac Dysfunction and 5-Year Mortality in Patients With Systemic Sclerosis and Interstitial Lung Disease: A Speckle-Tracking Echocardiography Study. J Clin Ultrasound. https://doi.org/10.1002/jcu.24094\u003c/li\u003e\n\u003cli\u003eLo Gullo A, Rodr\u0026iacute;guez-Carrio J, Aragona CO, et al (2018) Subclinical impairment of myocardial and endothelial functionality in very early psoriatic and rheumatoid arthritis patients: Association with vitamin D and inflammation. Atherosclerosis 271:214\u0026ndash;222. https://doi.org/10.1016/j.atherosclerosis.2018.03.004\u003c/li\u003e\n\u003cli\u003eHenein MY, Gibson DG (1999) Normal long axis function. Heart 81:111\u0026ndash;113. https://doi.org/10.1136/hrt.81.2.111\u003c/li\u003e\n\u003cli\u003eChilian WM (1991) Microvascular pressures and resistances in the left ventricular subepicardium and subendocardium. Circulation Research 69:561\u0026ndash;570. https://doi.org/10.1161/01.RES.69.3.561\u003c/li\u003e\n\u003cli\u003eSanderson JE (2008) Left and right ventricular long-axis function and prognosis. Heart 94:262\u0026ndash;263. https://doi.org/10.1136/hrt.2006.109348\u003c/li\u003e\n\u003cli\u003eMishra S, Kass DA (2021) Cellular and molecular pathobiology of heart failure with preserved ejection fraction. Nat Rev Cardiol 18:400\u0026ndash;423. https://doi.org/10.1038/s41569-020-00480-6\u003c/li\u003e\n\u003cli\u003eWenzl FA, Ambrosini S, Mohammed SA, et al (2021) Inflammation in Metabolic Cardiomyopathy. Front Cardiovasc Med 8:742178. https://doi.org/10.3389/fcvm.2021.742178\u003c/li\u003e\n\u003cli\u003eOpinc A, Brzezińska O, Makowska J. Underdiagnosis of cardiopulmonary involvement in patients with idiopathic inflammatory myopathies. Reumatologia. 2021;59(5):276-284. doi: 10.5114/reum.2021.110609. Epub 2021 Nov 7. PMID: 34819701; PMCID: PMC8609382.\u003c/li\u003e\n\u003cli\u003eLundberg IE (2006) The heart in dermatomyositis and polymyositis. Rheumatology 45:iv18\u0026ndash;iv21. https://doi.org/10.1093/rheumatology/kel311\u003c/li\u003e\n\u003cli\u003eShah M, Shinjo SK, Day J, Gupta L (2023) Cardiovascular manifestations in idiopathic inflammatory myopathies. Clin Rheumatol 42:2557\u0026ndash;2575. https://doi.org/10.1007/s10067-023-06599-4\u003c/li\u003e\n\u003cli\u003eFairley JL, Wicks I, Peters S, Day J (2021) Defining cardiac involvement in idiopathic inflammatory myopathies: a systematic review. Rheumatology 61:103\u0026ndash;120. https://doi.org/10.1093/rheumatology/keab573\u003c/li\u003e\n\u003cli\u003eDobloug GC, Svensson J, Lundberg IE, Holmqvist M (2018) Mortality in idiopathic inflammatory myopathy: results from a Swedish nationwide population-based cohort study. Annals of the Rheumatic Diseases 77:40\u0026ndash;47. https://doi.org/10.1136/annrheumdis-2017-211402\u003c/li\u003e\n\u003cli\u003eLimaye V, Hakendorf P, Woodman RJ, et al (2012) Mortality and its predominant causes in a large cohort of patients with biopsy‐determined inflammatory myositis. Internal Medicine Journal 42:191\u0026ndash;198. https://doi.org/10.1111/j.1445-5994.2010.02406.x\u003c/li\u003e\n\u003cli\u003eDiederichsen LP, Simonsen JA, Diederichsen AC, et al (2016) Cardiac Abnormalities in Adult Patients With Polymyositis or Dermatomyositis as Assessed by Noninvasive Modalities. Arthritis Care \u0026amp; Research 68:1012\u0026ndash;1020. https://doi.org/10.1002/acr.22772\u003c/li\u003e\n\u003cli\u003eByrnes TJ, Baethge BA, Wolf RE (1991) Noninvasive Cardiovascular Studies in Patients with Inflammatory Myopathy. Angiology 42:843\u0026ndash;848. https://doi.org/10.1177/000331979104201010\u003c/li\u003e\n\u003cli\u003eMugii N, Someya F (2018) Cardiopulmonary factors affecting 6-min walk distance in patients with idiopathic inflammatory myopathies. Rheumatol Int 38:1443\u0026ndash;1448. https://doi.org/10.1007/s00296-018-4050-0\u003c/li\u003e\n\u003cli\u003eLu Z, Wei Q, Ning Z, et al (2013) Left Ventricular Diastolic Dysfunction \u0026mdash; Early Cardiac Impairment in Patients with Polymyositis/Dermatomyositis: A Tissue Doppler Imaging Study. J Rheumatol 40:1572\u0026ndash;1577. https://doi.org/10.3899/jrheum.130044\u003c/li\u003e\n\u003cli\u003eWang H, Liu H-X, Wang Y-L, et al (2014) Left Ventricular Diastolic Dysfunction in Patients with Dermatomyositis Without Clinically Evident Cardiovascular Disease. J Rheumatol 41:495\u0026ndash;500. https://doi.org/10.3899/jrheum.130346\u003c/li\u003e\n\u003cli\u003eChourabi S, Sayhi S, Ben Ameur S, et al (2024) Cardiac involvement in systemic lupus erythematosus: Interest of 2D global longitudinal strain. Lupus 33:1100\u0026ndash;1108. https://doi.org/10.1177/09612033241266990\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;akmak E\u0026Ouml;, Fındık\u0026ccedil;ıoğlu U, Tezcan ME (2021) Disease severity affects myocardial functions in patients with treatment‐naive early rheumatoid arthritis. Int J of Rheum Dis 24:494\u0026ndash;501. https://doi.org/10.1111/1756-185X.13992\u003c/li\u003e\n\u003cli\u003eDalakas MC (1991) Polymyositis, Dermatomyositis, and Inclusion-Body Myositis. N Engl J Med 325:1487\u0026ndash;1498. https://doi.org/10.1056/NEJM199111213252107\u003c/li\u003e\n\u003cli\u003eWang ZJ, Tian ZR, Wang YQ, et al (2024) [Establishing a predictive model for the activity of idiopathic inflammatory myopathy based on MRI and clinical features]. Zhonghua Yi Xue Za Zhi 104:3409\u0026ndash;3415. https://doi.org/10.3760/cma.j.cn112137-20240805-01790\u003c/li\u003e\n\u003cli\u003eZhang L, Fu L, Zhang G, et al (2024) Clinico‐sero‐pathological profiles and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different perifascicular changes. CNS Neurosci Ther 30:e14882. https://doi.org/10.1111/cns.14882\u003c/li\u003e\n\u003cli\u003eIzuka S, Umezawa N, Komai T, et al (2025) Muscle Tissue Transcriptome of Idiopathic Inflammatory Myopathy Reflects the Muscle Damage Process by Monocytes and Presence of Skin Lesions. Arthritis Rheumatol 77:99\u0026ndash;106. https://doi.org/10.1002/art.42972\u003c/li\u003e\n\u003cli\u003ePan Z, Li M, Zhang P, et al (2025) Peripheral Blood Lymphocyte Subsets and Heterogeneity of B Cell Subsets in Patients of Idiopathic Inflammatory Myositis with Different Myositis-specific Autoantibodies. Inflammation 48:118\u0026ndash;132. https://doi.org/10.1007/s10753-024-02052-z\u003c/li\u003e\n\u003cli\u003eFranco C, Gatto M, Iaccarino L, et al (2021) Lymphocyte immunophenotyping in inflammatory myositis: a review. Current Opinion in Rheumatology 33:522\u0026ndash;528. https://doi.org/10.1097/BOR.0000000000000831\u003c/li\u003e\n\u003cli\u003eZhao L, Wang Q, Zhou B, et al (2021) The Role of Immune Cells in the Pathogenesis of Idiopathic Inflammatory Myopathies. Aging and disease 12:247. https://doi.org/10.14336/AD.2020.0410\u003c/li\u003e\n\u003cli\u003eShimojima Y, Ishii W, Matsuda M, Ikeda S (2012) Phenotypes of Peripheral Blood Lymphocytes and Cytokine Expression in Polymyositis and Dermatomyositis before Treatment and after Clinical Remission. Clin MedInsightsArthritisMusculoskelet Disord 5:CMAMD.S10272. https://doi.org/10.4137/CMAMD.S10272\u003c/li\u003e\n\u003cli\u003eViguier M, Fou\u0026eacute;r\u0026eacute; S, de la Salmoni\u0026egrave;re P, et al (2003) Peripheral blood lymphocyte subset counts in patients with dermatomyositis: clinical correlations and changes following therapy. Medicine (Baltimore) 82:82\u0026ndash;86. https://doi.org/10.1097/00005792-200303000-00002\u003c/li\u003e\n\u003cli\u003eKlein M, Mann H, Ple\u0026scaron;tilov\u0026aacute; L, et al (2014) Arthritis in Idiopathic Inflammatory Myopathy: Clinical Features and Autoantibody Associations. J Rheumatol 41:1133\u0026ndash;1139. https://doi.org/10.3899/jrheum.131223\u003c/li\u003e\n\u003cli\u003eIde V, Bossuyt X, Blockmans D, De Langhe E (2018) Prevalence and clinical correlates of rheumatoid factor and anticitrullinated protein antibodies in patients with idiopathic inflammatory myopathy. RMD Open 4:e000661. https://doi.org/10.1136/rmdopen-2018-000661\u003c/li\u003e\n\u003cli\u003eTanimoto K, Nakano K, Kano S, et al (1995) Classification criteria for polymyositis and dermatomyositis. J Rheumatol 22:668\u0026ndash;674\u003c/li\u003e\n\u003cli\u003eSchmidt WA, Wetzel W, Friedl\u0026auml;nder R, et al (2000) Clinical and Serological Aspects of Patients with Anti-Jo-1 Antibodies \u0026ndash; an Evolving Spectrum of Disease Manifestations. Clin Rheumatol 19:371\u0026ndash;377. https://doi.org/10.1007/s100670070030\u003c/li\u003e\n\u003cli\u003eNaseem M, Samir S, Ibrahim IK, et al (2019) 2-D speckle-tracking assessment of left and right ventricular function in rheumatoid arthritis patients with and without disease activity. J Saudi Heart Assoc 31:41\u0026ndash;49. https://doi.org/10.1016/j.jsha.2018.10.001\u003c/li\u003e\n\u003cli\u003eHanvivadhanakul P, Buakhamsri A (2019) Disease activity is associated with LV dysfunction in rheumatoid arthritis patients without clinical cardiovascular disease. Adv Rheumatol 59:56. https://doi.org/10.1186/s42358-019-0100-x\u003c/li\u003e\n\u003cli\u003eMidtb\u0026oslash; H, Semb AG, Matre K, et al (2017) Disease activity is associated with reduced left ventricular systolic myocardial function in patients with rheumatoid arthritis. Ann Rheum Dis 76:371\u0026ndash;376. https://doi.org/10.1136/annrheumdis-2016-209223\u003c/li\u003e\n\u003cli\u003eFine NM, Crowson CS, Lin G, et al (2014) Evaluation of myocardial function in patients with rheumatoid arthritis using strain imaging by speckle-tracking echocardiography. Ann Rheum Dis 73:1833\u0026ndash;1839. https://doi.org/10.1136/annrheumdis-2013-203314\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"idiopathic inflammatory myopathies, echocardiography, GLS, arthritis","lastPublishedDoi":"10.21203/rs.3.rs-7273002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7273002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutoimmune diseases are characterized by systemic inflammation that can affect multiple tissues. In idiopathic inflammatory myopathies (IIM), skeletal muscle is primarily involved; however, subclinical cardiac dysfunction may also occur. While left ventricular ejection fraction (EF) is commonly used to assess cardiac function, global longitudinal strain (GLS) has proven more sensitive in detecting early myocardial impairment.\u003c/p\u003e\u003cp\u003eThis study aimed to evaluate left ventricular GLS (LV GLS) in patients with IIM and no known cardiovascular disease, assessing both the prevalence of reduced GLS values and their associations with clinical and laboratory parameters. We enrolled 37 outpatients from the Department of Internal Medicine at the University Hospital of Verona, who underwent comprehensive clinical and echocardiographic assessment.\u003c/p\u003e\u003cp\u003eThe mean GLS value observed (\u0026minus;\u0026thinsp;17.9% \u0026plusmn; 2.2%) was below the normal reference range (\u0026minus;\u0026thinsp;18.2% to \u0026minus;\u0026thinsp;21.2%) defined by the echocardiographic system, indicating a global reduction in longitudinal systolic function despite preserved EF. In linear regression models, GLS was significantly associated with lymphocyte count at disease onset, the presence of arthritis, and creatine kinase (CK) levels. Patients with arthritis showed significantly worse GLS values compared to those without arthritis, despite similar EF. In multivariate analysis, arthritis remained independently associated with impaired GLS and lower CK levels.\u003c/p\u003e\u003cp\u003eOverall, our findings suggest that patients with IIM exhibit a global reduction in left ventricular longitudinal function, detectable by GLS, even in the absence of overt cardiac disease. This impairment appears particularly evident in patients presenting with arthritis. Longitudinal studies are warranted to investigate the progression of GLS alterations and their potential role in guiding therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Subclinical cardiac dysfunction in idiopathic inflammatory myopathies: the role of global longitudinal strain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:30:03","doi":"10.21203/rs.3.rs-7273002/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54b02488-8817-4077-bd76-30181772c886","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:30:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7273002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7273002","identity":"rs-7273002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}