Advances in Non-Invasive Myocardial Stiffness Assessment and Clinical Applications in Hypertrophic Cardiomyopathy

Abstract

Myocardial stiffness is a central determinant of diastolic dysfunction and clinical outcomes in hypertrophic cardiomyopathy; however, a non-invasive evaluation of myocardial stiffness remains challenging. Emerging techniques, such as elastography, offer direct, non-invasive quantification of myocardial stiffness, providing critical insights beyond conventional indirect surrogates. This review summarizes the principles, validation, and clinical evidence for current non-invasive techniques for assessing myocardial stiffness. We further discuss the clinical applications of these techniques in hypertrophic cardiomyopathy, including diagnostic refinement, fibrosis detection, risk stratification, and treatment monitoring, as well as the challenges and future directions required for broader clinical translation. Ultimately, the non-invasive assessment of myocardial stiffness holds promise for transforming patient management to phenotyping and therapeutic decision-making through a more precise, mechanism-based approach.

Graphical abstract

Keywords

shear wave / intrinsic velocity propagation / myocardial stiffness / hypertrophic cardiomyopathy

1. Introduction

Hypertrophic cardiomyopathy (HCM) is a genetic cardiomyopathy characterized by myocardial hypertrophy that is unexplained by abnormal loading conditions [1]. Substantial progress has been made in preventing sudden cardiac death [2, 3] and managing left ventricular outflow tract obstruction [4]. However, heart failure (HF) remains a major challenge [5]. Myocardial stiffness is the core physical determinant of ventricular diastolic filling and compliance, which is the fundamental cause of HF syndromes [6].

Despite its importance, non-invasive evaluation of myocardial stiffness remains challenging. Conventional markers such as the New York Heart Association functional class, natriuretic peptides, and standard echocardiographic indices like the E/e^′ ratio are indirect surrogates [7, 8]. These markers primarily reflect the downstream hemodynamic consequences of increased stiffness. Furthermore, they often lack the sensitivity to detect early or subtle changes in the myocardium itself [9].

In recent years, advanced imaging modalities have emerged to address this unmet need. Shear wave elastography, magnetic resonance elastography, and intrinsic cardiac elastography, each provide novel approaches for assessing myocardial stiffness. These techniques offer the potential for more direct and quantitative assessment [10, 11]. Nevertheless, all of these techniques have important limitations, including technical complexity, restricted availability, and lack of methodological standardization. These limitations currently hinder their routine clinical application.

This review provides a comprehensive overview of current non-invasive techniques to assess myocardial stiffness. We summarize their principles, validation against reference standards, and relative advantages and limitations. We further explore their emerging clinical applications in HCM, aiming to clarify their translational potential for improving diagnosis, risk stratification, and monitoring treatment.

2. Principles of Non-Invasive Assessment of Myocardial Stiffness

2.1 Defining the Target: Myocardial Stiffness

When evaluating cardiac mechanics, it is essential to distinguish between chamber stiffness and myocardial stiffness. Chamber stiffness reflects the macroscopic mechanical behavior of the entire cardiac chamber, with pressure-volume loop analysis serving as the clinical gold standard [12]. In contrast, myocardial stiffness represents the intrinsic material property of the myocardial tissue itself, typically assessed under laboratory conditions by subjecting explanted myocardial strips to uniaxial stretching and recording the stress-strain relationship [13]. Recent technological advances have enabled the non-invasive measurement of myocardial stiffness (Table 1, Ref. [14, 15, 16, 17, 18, 19, 20, 21, 22]). What is directly measured is typically the velocity of mechanical waves, which has a specific physical relationship with myocardial stiffness (elastic modulus, kPa).

2.2 Shear Wave Elastography

2.2.1 Physical Basis

Measuring shear wave velocity (SWV) allows for a direct, quantitative estimation of myocardial stiffness. The velocity (c) is fundamentally related to the tissue’s intrinsic stiffness (shear modulus, µ) and its density (\rho) by the formula Eqn. 1, and µ can be approximated to the clinically familiar Young’s modulus (E \approx 3µ). Shear waves can be generated externally by acoustic radiation force, allowing for precise control but limiting measurements to a few centimeters [23]. Alternatively, natural shear waves, created by events such as valvular closure, yield a higher signal-to-noise ratio, though their timing and location are less controlled [24].

(1)\mu =\rho c^2

2.2.2 Preclinical Validation

In animal models, shear wave elastography-derived SWV correlates strongly with myocardial stiffness measured via the end-diastolic pressure-volume relationship [25, 26] and ex vivo stress-strain testing [27, 28], all of which have confirmed its physiological relevance. However, direct validation of shear wave elastography against human myocardial stiffness using invasive pressure-volume analysis or ex vivo tissue testing is still lacking, representing a key limitation for translational application.

2.3 Intrinsic Cardiac Elastography

2.3.1 Physical Basis

Intrinsic cardiac elastography quantifies myocardial stiffness by measuring the propagation velocity of a myocardial stretch wave generated by atrial contraction [29, 30]. Because this method depends on atrial contraction to generate the stretch wave, it cannot be applied in patients with atrial fibrillation, which is a clinically important limitation [31]. The relation between myocardial stiffness (E) and wave velocity (c) can be approximated by the Moens-Korteweg equation: c = \surd (Eh / 2\rhoR), where h is the wall thickness, R is the ventricular radius, and \rho is the myocardial density [29]. This technique is accessible using high-frame-rate tissue Doppler imaging (>250 frames/s) on commercial scanners [11, 32].

2.3.2 Validation With Gold Standard

Validation studies have provided encouraging evidence. In animal models, this propagation velocity showed strong correlation with ex vivo measurements of myocardial stiffness [29], confirming its physiological basis. In humans, the propagation velocity has been shown to correlate with non-invasive indices of chamber stiffness [11], and with histopathological alterations, including myocardial fibrosis and myocyte disarray [14]. However, direct validation of intrinsic velocity propagation (iVP) against myocardial stiffness in humans using invasive pressure-volume analysis or tissue testing is still lacking, highlighting a key gap for translational application.

2.4 Magnetic Resonance Elastography

2.4.1 Physical Basis

Magnetic resonance elastography directly quantifies myocardial shear modulus by imaging low-frequency shear waves, generated externally by a mechanical actuator. Phase-contrast magnetic resonance imaging sequences capture the wave propagation, and the tissue stiffness is calculated from the wave velocity using Eqn. 1 [33, 34]. Cardiac magnetic resonance elastography remains technically challenging due to cardiac and respiratory motion, the thin and anisotropic ventricular wall, and the need for high spatial resolution and adequate signal-to-noise ratio [35].

2.4.2 Preclinical Validation

Animal experiments have demonstrated that cardiac magnetic resonance (CMR) elastography can detect dynamic changes in myocardial stiffness across the cardiac cycle and differentiate normal from stiffened myocardium [36]. In humans, cardiac magnetic resonance elastography has only been reported in isolated case studies, which demonstrated its feasibility [15]. Overall, this technique remains largely preclinical or exploratory, with limited availability, high technical demands, and lack of standardized acquisition protocols.

2.5 Common Limitations of Techniques

Despite their promise, current non-invasive techniques to assess myocardial stiffness face several important limitations. First, detecting mechanical waves requires extremely high temporal resolution. Although ultrafast ultrasound imaging techniques have been developed to address this issue [37], their clinical validation and widespread adoption remain at an early stage. Magnetic resonance elastography faces similar challenges in terms of technical demand and limited availability.

Second, wave propagation in the myocardium is more complex than initially assumed. Externally generated shear waves have high frequencies, attenuate rapidly, and typically propagate only a few centimeters [23]. In contrast, intrinsically generated low-frequency mechanical waves have longer wavelengths and are strongly influenced by cardiac geometry, making interpretation less straightforward [24].

Finally, myocardial stiffness varies at different points during the cardiac cycle. Systolic stiffness appears to depend largely on active myocardial properties such as contractility, whereas passive diastolic stiffness is more influenced by tissue composition and structural remodeling [10]. This distinction is critical for interpreting elastography results from different techniques. For example, intrinsic cardiac elastography, which measures stiffness following atrial contraction, primarily reflects passive matrix remodeling [16]. In contrast, shear wave elastography can assess stiffness throughout the cardiac cycle. Measurements at end-diastole reflect intrinsic elastic properties, while systolic measurements are strongly influenced by myocardial contractility [38]. The relationship between wave velocity derived from externally induced and intrinsically generated mechanical waves is also complex. A recent study demonstrated that the two measures show similar values at the same time points in healthy individuals, but they diverge significantly under pathological conditions such as HCM [39]. Therefore, interpretation of wave velocity should be approached with caution across different physiological and disease states, and further studies are needed to elucidate the underlying cardiac physiology beyond wave velocity measurements.

2.6 Summary and Critical Comparison

In summary, the three techniques differ markedly in methodological principles, reproducibility, and readiness for clinical use. Shear wave elastography measures propagation velocity induced by acoustic radiation force or natural shear waves. It provides estimates of quantitative stiffness with good reproducibility (intraclass correlation coefficients [ICCs]: 0.77–0.90) [17, 40] but depends on operator skill and acoustic window quality. Intrinsic cardiac elastography measures atrial contraction-driven stretch waves, offering high reproducibility in sinus rhythm (ICCs: 0.91–0.95) [14] and correlation with histopathology, yet it cannot be applied in atrial fibrillation. Magnetic resonance elastography derives three dimensions (3D) stiffness maps from externally induced shear waves, achieving excellent reproducibility (ICCs: 0.92–0.96) [41] but with low feasibility due to complex setup, motion sensitivity, and cost.

Ultrasound-based methods (shear wave elastography and intrinsic cardiac elastography) are currently the most feasible for clinical application, while magnetic resonance elastography remains largely confined to specialized research centers. All three have been validated in animal models against measurements of invasive or ex vivo stiffness. In humans, intrinsic cardiac elastography shows the strongest validation through correlations with histopathology and indices of non-invasive chamber stiffness. A concise comparison of their key features is summarized in Table 2 (Ref. [11, 13, 14, 17, 25, 26, 27, 28, 29, 36, 40, 41]).

3. Mechanisms of Myocardial Stiffness in HCM

In HCM, the development of myocardial stiffness is a multiscale pathological cascade involving dynamic interactions from molecular and cellular to tissue levels (Fig. 1).

3.1 Molecular and Cellular Alterations

Sarcomeric protein mutations, primarily in myosin heavy chain 7 (MYH7) and encoding myosin-binding protein C, cardiac-type (MYBPC3), which account for 50–75% of genetically confirmed HCM cases [42], lead to a state of hypercontractility in the myocardium, increasing systolic tension but impairing diastolic relaxation due to incomplete cross-bridge detachment [43]. This hypercontractile state consumes excessive adenosine triphosphate, leading to an energy crisis that further limits active relaxation processes like Ca²⁺ reuptake [43]. In addition, the giant protein titin, which regulates passive stiffness [44], is affected by phosphorylation imbalances in HCM [45]. Reduced activity of softening kinases (e.g., Protein Kinase A, Protein Kinase G) [46, 47] and increased activity of stiffening kinases (e.g., Protein Kinase C alpha) [48] increase titin stiffness, increasing passive tension.

3.2 Tissue Structural Remodeling

Chronic hemodynamic load and metabolic-mechanical imbalances ultimately trigger irreversible structural remodeling of myocardial tissue, solidifying myocardial stiffness at the structural level [49]. The core pathological feature is myocardial fibrosis, including irreversible replacement scarring and potentially reversible diffuse interstitial fibrosis [50, 51]. Myocyte disarray is another hallmark pathological change in HCM that structurally exacerbates mechanical dysfunction [52]. The orderly alignment of cardiomyocytes is replaced by chaotic, interwoven patterns [53], disrupting tissue synergy, leading to inefficient force transmission, and further reducing diastolic efficiency [54].

4. Clinical Applications of Non-Invasive Techniques to Assess Myocardial Stiffness in HCM

4.1 Diagnosis and Phenotyping

Assessment of non-invasive stiffness is a powerful tool for diagnosing and characterizing HCM. Studies using both naturally occurring and externally induced shear waves have consistently shown that myocardial stiffness is significantly higher in HCM patients compared to healthy volunteers. Villemain et al. [17] identified a stiffness cutoff of 8 kPa, which distinguished adult HCM patients from healthy individuals with 100% specificity and 95% sensitivity. In pediatric HCM, Villemain et al. [18] also found that diastolic stiffness was significantly elevated, particularly in patients with a restrictive physiology. In a study by Strachinaru et al. [19], natural SWV was used to classify pathologic myocardium with 95% sensitivity and 90% specificity.

The diagnostic application of iVP, measured by intrinsic cardiac elastography, has revealed more nuanced findings. Strachinaru et al. [16] first reported that while average iVP may not differ significantly between HCM patients and healthy controls, a large subset of HCM patients (42%) exhibit a unique non-constant wave propagation pattern, characterized by initial slow velocity followed by rapid acceleration. This suggests iVP can identify distinct biomechanical phenotypes within the HCM population. While still an emerging technique, a case study in an HCM patient by Zhao et al. [15] demonstrated a left ventricular stiffness of 21.8 kPa, substantially higher than the normal range of 7.2–9.8 kPa.

Despite encouraging diagnostic performance, most studies remain single-center with small sample sizes. Cutoff values may vary across imaging platforms and lack standardization. Therefore, it should be noted that the reported cutoff values are not yet standardized and should be applied cautiously in clinical practice. Moreover, it remains unclear whether stiffness mapping can distinguish HCM subtypes (e.g., obstructive vs. non-obstructive, apical vs. septal) or provide genotype-specific diagnostic insights. Future research should address these gaps with larger, multicenter studies and systematic phenotyping.

4.2 Assessment of Myocardial Fibrosis

A key application of these techniques is the non-invasive detection and grading of myocardial fibrosis, a critical component of HCM pathology. Myocardial stiffness measured by shear wave elastography, as shown by Villemain et al. [17], has a significant, positive correlation with markers of fibrosis on CMR, including late gadolinium enhancement (LGE) and myocardial T1 pre-contrast values. Similarly, Strachinaru et al. [20] found that natural SWV could detect local stiffness changes corresponding to scar tissue in patients after septal reduction therapy. Petrescu et al. [21] further quantified this relationship and showed that SWV increases as fibrosis progresses from interstitial to replacement types. The study identified clinically useful thresholds. An SWV below 6.0 m/s effectively rules out fibrosis, while an SWV above 8.1 m/s can distinguish severe replacement fibrosis from interstitial fibrosis with 100% specificity [21].

Studies also suggest that iVP is a sensitive marker of the underlying fibrotic burden. Naser et al. [14] first demonstrated a direct correlation between iVP and histologically confirmed myocardial fibrosis and myocyte disarray. Benz et al. [22] found that iVP was normal in early-stage, non-fibrotic HCM, suggesting its value increases with fibrosis progression. Zhao et al. [15] reported a strong spatial correlation between areas of increased myocardial stiffness on magnetic resonance elastography and regions of fibrosis identified by CMR in a patient with HCM.

Although these findings are promising, shear wave elastography thresholds require validation across different platforms and populations. Most iVP studies are cross-sectional, limiting their ability to monitor longitudinal fibrotic progression. Magnetic resonance elastography, while theoretically advantageous, remains impractical for clinical adoption due to its increased technical demands. From a pathophysiological standpoint, the initial increase in myocardial stiffness can be driven by molecular and cellular alterations, such as sarcomere hypercontractility and titin dysfunction [43, 44], which may precede the development of significant, irreversible tissue-level fibrosis [49]. Therefore, it is plausible that a detectable rise in myocardial stiffness serves as an earlier biomarker of mechanical dysfunction than the anatomical changes captured by LGE or extracellular volume (ECV). This creates an opportunity for a more nuanced, integrated assessment to stage a patient’s disease. For example, a patient presenting with elevated myocardial stiffness but with normal ECV and negative LGE may represent an earlier, “myogenic dysfunction-dominant” stage of the disease, which will be further discussed in Section 5.2. This state might signify a critical window of opportunity where proactive therapies targeting sarcomeric function, such as myosin inhibitors [55], could be most effective in preventing the progression to established fibrosis. Conversely, high stiffness coupled with extensive LGE signifies advanced, irreversible structural remodeling where therapeutic goals may shift. Thus, stiffness imaging provides complementary functional information that could refine risk stratification and guide the timing of therapeutic interventions. However, few studies have tested whether stiffness imaging provides incremental value beyond established CMR fibrosis markers. Addressing these limitations will be critical to moving fibrosis assessment from research to clinical application.

4.3 Prognosis and Risk Stratification

Measuring myocardial stiffness can provide valuable prognostic information beyond traditional risk markers. In a study by Villemain et al. [18] on pediatric HCM patients, increased myocardial stiffness measured by shear wave elastography was strongly correlated with reduced exercise capacity, a key indicator of functional limitation and prognosis. Naser et al. [14] showed that an elevated global iVP (>3.0 m/s) independently predicted adverse clinical outcomes in patients with HCM. These findings suggest that iVP’s ability to track the fibrotic burden provides important prognostic information throughout the disease course.

Current prognostic data are derived from limited, mostly single-center cohorts, with no large-scale, prospective, or multicenter validation. Whether stiffness improves established risk prediction models remains unknown. Furthermore, clinically reliable cutoff values for prognosis have not been established, and pediatric versus adult data are fragmented. Established risk models, such as the European Society of Cardiology Sudden Death Risk Model [56], are primarily designed to predict sudden cardiac death and do not adequately capture the trajectory of HF, which is a major driver of morbidity and mortality in this population [5]. This is where myocardial stiffness assessment may provide its greatest incremental value. Since increased myocardial stiffness is the fundamental mechanical basis for diastolic dysfunction and HF symptoms [6], its quantification offers a direct window into the patient’s risk for the progression of HF. Therefore, integrating stiffness into clinical practice could involve creating a novel, HF-centric risk stratification pathway. Existing multiparametric scoring systems, such as Heart Failure Association – pretest assessment, Echocardiography & natriuretic peptide, Functional testing, Final etiology (HFA-PEFF) and Heavy, Hypertensive, atrial Fibrillation, Pulmonary hypertension, Elder, Filling pressure (H₂FPEF) have shown potential value for HF risk stratification in patients with HCM [57]. These frameworks could be further refined by incorporating stiffness metrics in addition to established echocardiographic indices of diastolic function (e.g., E/e^′ ratio, left atrial volume), circulating biomarkers of wall stress and congestion (e.g., N-terminal pro-B-type natriuretic peptide), and CMR-derived fibrosis parameters (e.g., LGE extent, ECV fraction). Future studies should evaluate whether integrating stiffness into multiparametric risk models can improve patient stratification and clinical decision-making.

5. Future Perspectives

While non-invasive myocardial stiffness assessment has shown significant promise, the field is still evolving. Future advancements are expected to refine the technology, integrate it with other imaging modalities for deeper phenotyping, and ultimately guide precision medicine in HCM.

5.1 Technical Improvements

Future technical advancements in myocardial elastography are aimed at improving accuracy, expanding clinical applicability, and addressing the inherent complexities of cardiac mechanics.

5.1.1 Overcoming Dependency: From Doppler to Speckle-Tracking

Doppler-based techniques, such as shear wave elastography and intrinsic cardiac elastography, while offering high temporal resolution, are angle-dependent, although this can be mitigated by careful alignment [31]. The next step will be the development of robust, angle-independent tracking algorithms, most likely based on advanced speckle-tracking. The analysis workflow can be time-consuming; future refinements, including semiautomatic tracking, will be essential to facilitate clinical adoption.

5.1.2 Spatial Advancements: From 2D to 3D/4D

The propagation of elastic waves in the myocardium is complicated by the active properties of muscle fibers, the tissue’s anisotropy due to complex fiber orientations, and the ventricle’s geometry [37]. These factors induce a complex wave propagation pattern in three dimensions, which complicates the direct relationship between measured wave velocity and tissue stiffness. Recent pioneering work has utilized four dimensions (4D) ultrafast ultrasound to study this natural wave propagation in 3D, allowing for better determination of wave excitation sources and direction [58]. While this represents a major leap forward, both the imaging sequences and post-processing algorithms require further optimization before 4D elastography can be applied on a large scale.

5.1.3 Artificial Intelligence for Quantification of Myocardial Stiffness

Artificial intelligence (AI) is transforming myocardial stiffness assessment by enabling fully automated post-processing, which mitigates operator dependency and poor reproducibility of conventional strain and elastography methods. Deep learning models, such as U-Net for cardiac segmentation and optical flow networks for tissue motion tracking, allow fast, robust estimation of displacement and strain directly from standard CMR or ultrasound images [59]. End-to-end architectures can even reconstruct elasticity maps from raw data, bypassing error-prone intermediate steps [60]. This automation lays the foundation for standardized, scalable stiffness quantification in clinical practice.

AI also shows promise in accelerating physics-based approaches, such as finite element analysis combined with CMR. Finite element analysis infers stiffness through inverse problems but is often computationally intensive, operator-dependent, and sensitive to model assumptions [61, 62, 63, 64]. Deep learning offers a data-driven alternative to replace iterative optimization, improving computational efficiency, however, these methods remain exploratory [65]. More immediately translatable is the integration of AI with radiomics, which extracts quantitative texture features from routine images such as CMR cine or ultrasound B-mode to detect patterns associated with myocardial fibrosis [66]. For example, radiomics models based on non-contrast CMR cine have accurately predicted LGE [67], and ultrasound radiomics enables contrast-free, semi-quantitative fibrosis assessment, offering an alternative for patients ineligible for CMR [68, 69].

Despite these advancements, a significant challenge remains due to the scarcity of large, high-quality labeled datasets needed for model training and validation. Nevertheless, AI is undoubtedly a core driving force poised to propel the non-invasive assessment of myocardial stiffness into mainstream clinical practice.

5.2 Multimodality Imaging for Advanced Phenotyping

The true potential of myocardial stiffness measurement is realized when it is integrated with other imaging techniques to decode the specific contributors to stiffness (Fig. 2). The use of multimodality imaging holds promise for creating detailed “imaging phenotypes” of individual patients. Techniques such as LGE [70] and T1 mapping/ECV [71] provide core methods for assessing myocardial fibrosis, which is a primary structural source of increased myocardial stiffness [22]. Furthermore, breakthroughs have been achieved in assessing myocyte disarray using cardiac diffusion tensor imaging, an advanced technique that can depict the microstructural architecture of myocardial fiber bundles [72]. In a study by Naser et al. [14], the correlation of iVP with myocardial disarray suggests that the underlying causes of stiffness are themselves heterogeneous.

By integrating quantitative stiffness data from elastography with structural data from CMR, a more precise “myocardial stiffness phenotyping” can be developed. This would allow for the classification of patients into distinct subtypes, such as “fibrosis-dominant”, “myogenic dysfunction-dominant”, or “mixed” phenotypes. Identifying these dominant drivers could enable more precise risk stratification, identifying patients most likely to benefit from early, aggressive interventions.

5.3 Future Therapeutic Directions: Guiding Novel Therapies With Stiffness Phenotyping

While not yet part of routine clinical practice, emerging therapies hold promise for phenotype-guided management in HCM, with myocardial stiffness serving as a potential biomarker for patient stratification (Table 3, Ref. [73, 74, 75, 76]).

5.3.1 Targeting Sarcomere Hyperfunction

For patients with the “myogenic dysfunction-dominant” phenotype, cardiac myosin inhibitors, such as Mavacamten and Aficamten, are tailored for this phenotype [55]. These agents selectively stabilize myosin in a super-relaxed state, reducing the number of available myosin heads and thereby decreasing intrinsic myocardial contractility and calcium sensitivity [55]. Clinical trials (e.g., EXPLORER-HCM, VALOR-HCM) have confirmed that Mavacamten significantly reduces the E/e^′ ratio and improves left atrial volume, indicators that reflect ventricular filling pressures and diastolic function [73, 74], suggesting attenuation of functional stiffness.

5.3.2 Targeting Fibrosis

For fibrosis-dominant phenotypes, treatment should focus on slowing or reversing structural remodeling to prevent permanent loss of myocardial elasticity. Mineralocorticoid receptor antagonists, such as spironolactone, theoretically exert antifibrotic effects by inhibiting the aldosterone pathway. Although they are beneficial in other HF types [75], there is currently a lack of large-scale randomized controlled trials specifically evaluating the impact of mineralocorticoid receptor antagonists on myocardial fibrosis or clinical outcomes in HCM patients.

5.3.3 Broad-Acting Therapy

Sodium-glucose cotransporter 2 inhibitors exhibit pleiotropic cardioprotective effects, including improving myocardial energy metabolism, inhibiting the sodium-hydrogen exchanger, and providing anti-inflammatory, anti-oxidative stress, and anti-fibrotic benefits [77, 78]. These mechanisms are highly consistent with the downstream pathological pathways of HCM. Real-world data suggest reduced mortality and hospitalizations in HCM patients [76], and ongoing trials (e.g., SOTA-CROSS HCM [NCT06433050], SONATA-HCM [NCT06481891]) will clarify their role in stiffness-informed therapeutic strategies.

6. Conclusions

Increased myocardial stiffness is a core pathophysiological feature of HCM, and is responsible for its diverse clinical manifestations [17]. Non-invasive assessment of myocardial stiffness has emerged as a promising frontier in cardiac imaging. This review summarizes the principles and validation of current technologies, while also acknowledging their limitations. Emerging clinical evidence highlights their potential to advance the management of HCM. These techniques not only facilitate diagnosis and phenotyping by identifying distinct stiffness patterns, but also provide crucial insights into myocardial fibrosis, risk stratification, and prognosis. By integrating stiffness measurements with complementary imaging modalities, a more refined “myocardial stiffness phenotyping” can be achieved, which holds the key to guiding novel therapies. Despite these advances, significant challenges remain. Future research must address the lack of technical standardization, validate findings in large multicenter cohorts, and establish clinically accurate thresholds for diagnosis and prognosis.

References

[1] Maron BJ, Maron MS. Hypertrophic cardiomyopathy. Lancet (London, England). 2013; 381: 242–255. https://doi.org/10.1016/S0140-6736(12)60397-3.

[2] Maron BJ, Desai MY, Nishimura RA, Spirito P, Rakowski H, Towbin JA, et al. Management of Hypertrophic Cardiomyopathy: JACC State-of-the-Art Review. Journal of the American College of Cardiology. 2022; 79: 390–414. https://doi.org/10.1016/j.jacc.2021.11.021.

[3] Maron MS, Rowin EJ, Maron BJ. The Paradigm of Sudden Death Prevention in Hypertrophic Cardiomyopathy. The American Journal of Cardiology. 2024; 212: S64–S76. https://doi.org/10.1016/j.amjcard.2023.10.076.

[4] Ommen SR, Ho CY, Asif IM, Balaji S, Burke MA, Day SM, et al. 2024 AHA/ACC/AMSSM/HRS/PACES/SCMR Guideline for the Management of Hypertrophic Cardiomyopathy: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2024; 149: e1239–e1311. https://doi.org/10.1161/CIR.0000000000001250.

[5] Ho CY, Day SM, Ashley EA, Michels M, Pereira AC, Jacoby D, et al. Genotype and Lifetime Burden of Disease in Hypertrophic Cardiomyopathy: Insights from the Sarcomeric Human Cardiomyopathy Registry (SHaRe). Circulation. 2018; 138: 1387–1398. https://doi.org/10.1161/CIRCULATIONAHA.117.033200.

[6] Aurigemma GP, Zile MR, Gaasch WH. Contractile behavior of the left ventricle in diastolic heart failure: with emphasis on regional systolic function. Circulation. 2006; 113: 296–304. https://doi.org/10.1161/CIRCULATIONAHA.104.481465.

[7] Chevalier C, Wendner M, Suling A, Cavus E, Muellerleile K, Lund G, et al. Association of NT-proBNP and hs-cTnT with Imaging Markers of Diastolic Dysfunction and Focal Myocardial Fibrosis in Hypertrophic Cardiomyopathy. Life (Basel, Switzerland). 2022; 12: 1241. https://doi.org/10.3390/life12081241.

[8] Țica O, Țica O. Molecular Diagnostics in Heart Failure: From Biomarkers to Personalized Medicine. Diagnostics (Basel, Switzerland). 2025; 15: 1807. https://doi.org/10.3390/diagnostics15141807.

[9] Mordi IR, Singh S, Rudd A, Srinivasan J, Frenneaux M, Tzemos N, et al. Comprehensive Echocardiographic and Cardiac Magnetic Resonance Evaluation Differentiates Among Heart Failure With Preserved Ejection Fraction Patients, Hypertensive Patients, and Healthy Control Subjects. JACC. Cardiovascular Imaging. 2018; 11: 577–585. https://doi.org/10.1016/j.jcmg.2017.05.022.

[10] Caenen A, Bézy S, Pernot M, Nightingale KR, Vos HJ, Voigt JU, et al. Ultrasound Shear Wave Elastography in Cardiology. JACC. Cardiovascular Imaging. 2024; 17: 314–329. https://doi.org/10.1016/j.jcmg.2023.12.007.

[11] Pislaru C, Ionescu F, Alashry M, Petrescu I, Pellikka PA, Grogan M, et al. Myocardial Stiffness by Intrinsic Cardiac Elastography in Patients with Amyloidosis: Comparison with Chamber Stiffness and Global Longitudinal Strain. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography. 2019; 32: 958–968.e4. https://doi.org/10.1016/j.echo.2019.04.418.

[12] Abraham D, Mao L. Cardiac Pressure-Volume Loop Analysis Using Conductance Catheters in Mice. Journal of Visualized Experiments: JoVE. 2015; 52942. https://doi.org/10.3791/52942.

[13] Arunachalam SP, Arani A, Baffour F, Rysavy JA, Rossman PJ, Glaser KJ, et al. Regional assessment of in vivo myocardial stiffness using 3D magnetic resonance elastography in a porcine model of myocardial infarction. Magnetic Resonance in Medicine. 2018; 79: 361–369. https://doi.org/10.1002/mrm.26695.

[14] Naser JA, Anupraiwan O, Adigun RO, Maleszewski JJ, Pislaru SV, Pellikka PA, et al. Myocardial Stiffness by Cardiac Elastography in Hypertrophic Cardiomyopathy: Relationship With Myocardial Fibrosis and Clinical Outcomes. JACC. Cardiovascular Imaging. 2021; 14: 2051–2053. https://doi.org/10.1016/j.jcmg.2021.05.024.

[15] Zhao R, Wang J, Yu Y, Li X. Cardiac magnetic resonance elastography imaging in hypertrophic cardiomyopathy. European Heart Journal. Cardiovascular Imaging. 2024; 25: e259. https://doi.org/10.1093/ehjci/jeae180.

[16] Strachinaru M, Geleijnse ML, de Jong N, van den Bosch A, Michels M, Schinkel AFL, et al. Myocardial Stretch Post-atrial Contraction in Healthy Volunteers and Hypertrophic Cardiomyopathy Patients. Ultrasound in Medicine & Biology. 2019; 45: 1987–1998. https://doi.org/10.1016/j.ultrasmedbio.2019.04.031.

[17] Villemain O, Correia M, Mousseaux E, Baranger J, Zarka S, Podetti I, et al. Myocardial Stiffness Evaluation Using Noninvasive Shear Wave Imaging in Healthy and Hypertrophic Cardiomyopathic Adults. JACC. Cardiovascular Imaging. 2019; 12: 1135–1145. https://doi.org/10.1016/j.jcmg.2018.02.002.

[18] Villemain O, Correia M, Khraiche D, Podetti I, Meot M, Legendre A, et al. Myocardial Stiffness Assessment Using Shear Wave Imaging in Pediatric Hypertrophic Cardiomyopathy. JACC. Cardiovascular Imaging. 2018; 11: 779–781. https://doi.org/10.1016/j.jcmg.2017.08.018.

[19] Strachinaru M, Bosch JG, van Gils L, van Dalen BM, Schinkel AFL, van der Steen AFW, et al. Naturally Occurring Shear Waves in Healthy Volunteers and Hypertrophic Cardiomyopathy Patients. Ultrasound in Medicine & Biology. 2019; 45: 1977–1986. https://doi.org/10.1016/j.ultrasmedbio.2019.04.004.

[20] Strachinaru M, Bosch JG, Schinkel AFL, Michels M, Feyz L, de Jong N, et al. Local myocardial stiffness variations identified by high frame rate shear wave echocardiography. Cardiovascular Ultrasound. 2020; 18: 40. https://doi.org/10.1186/s12947-020-00222-1.

[21] Petrescu A, Cvijic M, Wouters L, Youssef A, Bézy S, Orlowska M, et al. Ultrasound shear wave elastography for detection of myocardial fibrosis. European Heart Journal. Cardiovascular Imaging. 2025; 26: 1537–1545. https://doi.org/10.1093/ehjci/jeaf200.

[22] Benz DC, Sadeghi A, Rafter PG, Clerc OF, Canseco Neri J, Taylor A, et al. Echocardiographic Characterization of Myocardial Stiffness in Healthy Volunteers, Cardiac Amyloidosis, and Hypertrophic Cardiomyopathy: A Case-Control Study Using Multimodality Imaging. Circulation. Cardiovascular Imaging. 2025; 18: e017475. https://doi.org/10.1161/CIRCIMAGING.124.017475.

[23] Couade M, Pernot M, Messas E, Bel A, Ba M, Hagege A, et al. In vivo quantitative mapping of myocardial stiffening and transmural anisotropy during the cardiac cycle. IEEE Transactions on Medical Imaging. 2011; 30: 295–305. https://doi.org/10.1109/TMI.2010.2076829.

[24] Keijzer LBH, Caenen A, Voorneveld J, Strachinaru M, Bowen DJ, van de Wouw J, et al. A direct comparison of natural and acoustic-radiation-force-induced cardiac mechanical waves. JACC. Scientific Reports. 2020; 10: 18431. https://doi.org/10.1038/s41598-020-75401-1.

[25] Caenen A, Keijzer L, Bézy S, Duchenne J, Orlowska M, Van Der Steen AFW, et al. Continuous shear wave measurements for dynamic cardiac stiffness evaluation in pigs. Scientific Reports. 2023; 13: 17660. https://doi.org/10.1038/s41598-023-44588-4.

[26] Bézy S, Duchenne J, Orlowska M, Caenen A, Amoni M, Ingelaere S, et al. Impact of Loading and Myocardial Mechanical Properties on Natural Shear Waves: Comparison to Pressure-Volume Loops. JACC. Cardiovascular Imaging. 2022; 15: 2023–2034. https://doi.org/10.1016/j.jcmg.2022.07.011.

[27] Pislaru C, Urban MW, Pislaru SV, Kinnick RR, Greenleaf JF. Viscoelastic properties of normal and infarcted myocardium measured by a multifrequency shear wave method: comparison with pressure-segment length method. Ultrasound in Medicine & Biology. 2014; 40: 1785–1795. https://doi.org/10.1016/j.ultrasmedbio.2014.03.004.

[28] Pernot M, Lee WN, Bel A, Mateo P, Couade M, Tanter M, et al. Shear Wave Imaging of Passive Diastolic Myocardial Stiffness: Stunned Versus Infarcted Myocardium. JACC. Cardiovascular Imaging. 2016; 9: 1023–1030. https://doi.org/10.1016/j.jcmg.2016.01.022.

[29] Pislaru C, Pellikka PA, Pislaru SV. Wave propagation of myocardial stretch: correlation with myocardial stiffness. Basic Research in Cardiology. 2014; 109: 438. https://doi.org/10.1007/s00395-014-0438-5.

[30] Espeland T, Aakhus S. The Stiffer, the Faster: Echocardiographic Evaluation of Myocardial Properties. Circulation. Cardiovascular Imaging. 2025; 18: e018058. https://doi.org/10.1161/CIRCIMAGING.125.018058.

[31] Pislaru C, Alashry MM, Thaden JJ, Pellikka PA, Enriquez-Sarano M, Pislaru SV. Intrinsic Wave Propagation of Myocardial Stretch, A New Tool to Evaluate Myocardial Stiffness: A Pilot Study in Patients with Aortic Stenosis and Mitral Regurgitation. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography. 2017; 30: 1070–1080. https://doi.org/10.1016/j.echo.2017.06.023.

[32] De Jesus T, Alashry MM, Padang R, Pislaru SV, Nkomo VT, Pellikka PA, et al. Intrinsic cardiac elastography in patients with primary mitral regurgitation: predictive role after mitral valve repair. European Heart Journal. Cardiovascular Imaging. 2021; 22: 912–921. https://doi.org/10.1093/ehjci/jeaa117.

[33] Khan S, Fakhouri F, Majeed W, Kolipaka A. Cardiovascular magnetic resonance elastography: A review. NMR in Biomedicine. 2018; 31: e3853. https://doi.org/10.1002/nbm.3853.

[34] Akkaya HE, Erden A, Kuru Öz D, Ünal S, Erden İ. Magnetic resonance elastography: basic principles, technique, and clinical applications in the liver. Diagnostic and Interventional Radiology (Ankara, Turkey). 2018; 24: 328–335. https://doi.org/10.5152/dir.2018.18186.

[35] Castelein J, Borra RJH, Hansen AE. Cardiac Magnetic Resonance Elastography: Why Multiplex Approaches Hold the Key to Clinical Translation. Journal of Magnetic Resonance Imaging: JMRI. 2025; 62: 842–843. https://doi.org/10.1002/jmri.70015.

[36] da Silveira JS, Scansen BA, Wassenaar PA, Raterman B, Eleswarpu C, Jin N, et al. Quantification of myocardial stiffness using magnetic resonance elastography in right ventricular hypertrophy: initial feasibility in dogs. Magnetic Resonance Imaging. 2016; 34: 26–34. https://doi.org/10.1016/j.mri.2015.10.001.

[37] Villemain O, Baranger J, Friedberg MK, Papadacci C, Dizeux A, Messas E, et al. Ultrafast Ultrasound Imaging in Pediatric and Adult Cardiology: Techniques, Applications, and Perspectives. JACC. Cardiovascular Imaging. 2020; 13: 1771–1791. https://doi.org/10.1016/j.jcmg.2019.09.019.

[38] Saloux E, Simard C, Ruello P, Lemaitre A, Hodzic A, Lebrun A, et al. Impact of loading, heart rate, and short episodes of ischaemia on myocardial stiffness assessed using shear wave elastography in an open-chest animal model. European Heart Journal. Imaging Methods and Practice. 2025; 3: qyaf015. https://doi.org/10.1093/ehjimp/qyaf015.

[39] Malik A, Villalobos Lizardi JC, Baranger J, Venet M, Pernot M, Mital S, et al. Comparison Between Acoustic Radiation Force-Induced and Natural Wave Velocities for Myocardial Stiffness Assessment in Hypertrophic Cardiomyopathy. JACC. Cardiovascular Imaging. 2024; 17: 223–225. https://doi.org/10.1016/j.jcmg.2023.07.015.

[40] Santos P, Petrescu AM, Pedrosa JP, Orlowska M, Komini V, Voigt JU, et al. Natural Shear Wave Imaging in the Human Heart: Normal Values, Feasibility, and Reproducibility. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2019; 66: 442–452. https://doi.org/10.1109/TUFFC.2018.2881493.

[41] Castelein J, Duus AS, Bække PS, Sack I, Anders MS, Kettless K, et al. Reproducibility of Cardiac Multifrequency MR Elastography in Assessing Left Ventricular Stiffness and Viscosity. Journal of Magnetic Resonance Imaging: JMRI. 2025; 61: 2146–2154. https://doi.org/10.1002/jmri.29640.

[42] Gartzonikas IK, Naka KK, Anastasakis A. Current and emerging perspectives on pathophysiology, diagnosis, and management of hypertrophic cardiomyopathy. Hellenic Journal of Cardiology: HJC = Hellenike Kardiologike Epitheorese. 2023; 70: 65–74. https://doi.org/10.1016/j.hjc.2022.11.002.

[43] Ušaj M, Moretto L, Månsson A. Critical Evaluation of Current Hypotheses for the Pathogenesis of Hypertrophic Cardiomyopathy. International Journal of Molecular Sciences. 2022; 23: 2195. https://doi.org/10.3390/ijms23042195.

[44] Granzier HL, Labeit S. Discovery of Titin and Its Role in Heart Function and Disease. Circulation Research. 2025; 136: 135–157. https://doi.org/10.1161/CIRCRESAHA.124.323051.

[45] Loescher CM, Hobbach AJ, Linke WA. Titin (TTN): from molecule to modifications, mechanics, and medical significance. Cardiovascular Research. 2022; 118: 2903–2918. https://doi.org/10.1093/cvr/cvab328.

[46] Krüger M, Kötter S, Grützner A, Lang P, Andresen C, Redfield MM, et al. Protein kinase G modulates human myocardial passive stiffness by phosphorylation of the titin springs. Circulation Research. 2009; 104: 87–94. https://doi.org/10.1161/CIRCRESAHA.108.184408.

[47] LeWinter MM, Granzier H. Cardiac titin: a multifunctional giant. Circulation. 2010; 121: 2137–2145. https://doi.org/10.1161/CIRCULATIONAHA.109.860171.

[48] Kötter S, Gout L, Von Frieling-Salewsky M, Müller AE, Helling S, Marcus K, et al. Differential changes in titin domain phosphorylation increase myofilament stiffness in failing human hearts. Cardiovascular Research. 2013; 99: 648–656. https://doi.org/10.1093/cvr/cvt144.

[49] Wolf CM. Hypertrophic cardiomyopathy: genetics and clinical perspectives. Cardiovascular Diagnosis and Therapy. 2019; 9: S388–S415. https://doi.org/10.21037/cdt.2019.02.01.

[50] Bing R, Dweck MR. Myocardial fibrosis: why image, how to image and clinical implications. Heart (British Cardiac Society). 2019; 105: 1832–1840. https://doi.org/10.1136/heartjnl-2019-315560.

[51] Zhu L, Wang Y, Zhao S, Lu M. Detection of myocardial fibrosis: Where we stand. Frontiers in Cardiovascular Medicine. 2022; 9: 926378. https://doi.org/10.3389/fcvm.2022.926378.

[52] Ariga R, Tunnicliffe EM, Manohar SG, Mahmod M, Raman B, Piechnik SK, et al. Identification of Myocardial Disarray in Patients With Hypertrophic Cardiomyopathy and Ventricular Arrhythmias. Journal of the American College of Cardiology. 2019; 73: 2493–2502. https://doi.org/10.1016/j.jacc.2019.02.065.

[53] Finocchiaro G, Sheikh N, Leone O, Westaby J, Mazzarotto F, Pantazis A, et al. Arrhythmogenic potential of myocardial disarray in hypertrophic cardiomyopathy: genetic basis, functional consequences and relation to sudden cardiac death. Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology. 2021; 23: 985–995. https://doi.org/10.1093/europace/euaa348.

[54] Ciampi Q, Betocchi S, Losi MA, Lombardi R, Villari B, Chiariello M. Effect of hypertrophy on left ventricular diastolic function in patients with hypertrophic cardiomyopathy. Heart International. 2006; 2: 106. https://doi.org/10.4081/hi.2006.106.

[55] Savsin H, Tokarek T. Comprehensive Review: Mavacamten and Aficamten in Hypertrophic Cardiomyopathy. Biomedicines. 2025; 13: 1619. https://doi.org/10.3390/biomedicines13071619.

[56] Authors/Task Force members, Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). European Heart Journal. 2014; 35: 2733–2779. https://doi.org/10.1093/eurheartj/ehu284.

[57] Gao YP, Liu HY, Bi XJ, Sun J, Zhu Y, Zhou W, et al. H₂FPEF and HFA-PEFF scores for heart failure risk stratification in hypertrophic cardiomyopathy patients. ESC Heart Failure. 2025; 12: 2225–2238. https://doi.org/10.1002/ehf2.15247.

[58] Papadacci C, Finel V, Villemain O, Tanter M, Pernot M. 4D Ultrafast Ultrasound Imaging of Naturally Occurring Shear Waves in the Human Heart. IEEE Transactions on Medical Imaging. 2020; 39: 4436–4444. https://doi.org/10.1109/TMI.2020.3020147.

[59] Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, et al. Deep learning in ultrasound elastography imaging: A review. Medical Physics. 2022; 49: 5993–6018. https://doi.org/10.1002/mp.15856.

[60] Patel D, Tibrewala R, Vega A, Dong L, Hugenberg N, Oberai AA. Circumventing the solution of inverse problems in mechanics through deep learning: Application to elasticity imaging. Computer Methods in Applied Mechanics and Engineering. 2019; 353: 448–466. https://doi.org/10.1016/j.cma.2019.04.045.

[61] Zhang Y, Wang VY, Morgan AE, Kim J, Ge L, Guccione JM, et al. A Novel MRI-Based Finite Element Modeling Method for Calculation of Myocardial Ischemia Effect in Patients With Functional Mitral Regurgitation. Frontiers in Physiology. 2020; 11: 158. https://doi.org/10.3389/fphys.2020.00158.

[62] Gao H, Li WG, Cai L, Berry C, Luo XY. Parameter estimation in a Holzapfel-Ogden law for healthy myocardium. Journal of Engineering Mathematics. 2015; 95: 231–248. https://doi.org/10.1007/s10665-014-9740-3.

[63] Zhang X, Liu ZQ, Campbell KS, Wenk JF. Evaluation of a Novel Finite Element Model of Active Contraction in the Heart. Frontiers in Physiology. 2018; 9: 425. https://doi.org/10.3389/fphys.2018.00425.

[64] Li W. Biomechanics of infarcted left Ventricle-A review of experiments. Journal of the Mechanical Behavior of Biomedical Materials. 2020; 103: 103591. https://doi.org/10.1016/j.jmbbm.2019.103591.

[65] Bracamonte JH, Saunders SK, Wilson JS, Truong UT, Soares JS. Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications. Applied Sciences (Basel, Switzerland). 2022; 12: 3954. https://doi.org/10.3390/app12083954.

[66] Hassani C, Saremi F, Varghese BA, Duddalwar V. Myocardial Radiomics in Cardiac MRI. AJR. American Journal of Roentgenology. 2020; 214: 536–545. https://doi.org/10.2214/AJR.19.21986.

[67] Zhang H, Zhao L, Wang H, Yi Y, Hui K, Zhang C, et al. Radiomics from Cardiovascular MR Cine Images for Identifying Patients with Hypertrophic Cardiomyopathy at High Risk for Heart Failure. Radiology. Cardiothoracic Imaging. 2024; 6: e230323. https://doi.org/10.1148/ryct.230323.

[68] Kagiyama N, Shrestha S, Cho JS, Khalil M, Singh Y, Challa A, et al. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine. 2020; 54: 102726. https://doi.org/10.1016/j.ebiom.2020.102726.

[69] Yu F, Huang H, Yu Q, Ma Y, Zhang Q, Zhang B. Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy. Annals of Translational Medicine. 2021; 9: 108. https://doi.org/10.21037/atm-20-4891.

[70] Meier C, Eisenblätter M, Gielen S. Myocardial Late Gadolinium Enhancement (LGE) in Cardiac Magnetic Resonance Imaging (CMR)-An Important Risk Marker for Cardiac Disease. Journal of Cardiovascular Development and Disease. 2024; 11: 40. https://doi.org/10.3390/jcdd11020040.

[71] Swoboda PP, McDiarmid AK, Page SP, Greenwood JP, Plein S. Role of T1 Mapping in Inherited Cardiomyopathies. European Cardiology. 2016; 11: 96–101. https://doi.org/10.15420/ecr/2016:28:2.

[72] Khalique Z, Ferreira PF, Scott AD, Nielles-Vallespin S, Firmin DN, Pennell DJ. Diffusion Tensor Cardiovascular Magnetic Resonance Imaging: A Clinical Perspective. JACC. Cardiovascular Imaging. 2020; 13: 1235–1255. https://doi.org/10.1016/j.jcmg.2019.07.016.

[73] Olivotto I, Oreziak A, Barriales-Villa R, Abraham TP, Masri A, Garcia-Pavia P, et al. Mavacamten for treatment of symptomatic obstructive hypertrophic cardiomyopathy (EXPLORER-HCM): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet (London, England). 2020; 396: 759–769. https://doi.org/10.1016/S0140-6736(20)31792-X.

[74] Hegde SM, Lester SJ, Solomon SD, Michels M, Elliott PM, Nagueh SF, et al. Effect of Mavacamten on Echocardiographic Features in Symptomatic Patients With Obstructive Hypertrophic Cardiomyopathy. Journal of the American College of Cardiology. 2021; 78: 2518–2532. https://doi.org/10.1016/j.jacc.2021.09.1381.

[75] Sethi R, Vishwakarma P, Pradhan A. Evidence for Aldosterone Antagonism in Heart Failure. Cardiac Failure Review. 2024; 10: e15. https://doi.org/10.15420/cfr.2024.10.

[76] Aglan A, Fath AR, Eldaly AS, Anderson AS, Phillips JS, Maron BJ, et al. Impact of Sodium-Glucose Cotransporter 2 Inhibitors on Mortality in Hypertrophic Cardiomyopathy. JACC. Advances. 2024; 3: 100843. https://doi.org/10.1016/j.jacadv.2024.100843.

[77] Piperis C, Marathonitis A, Anastasiou A, Theofilis P, Mourouzis K, Giannakodimos A, et al. Multifaceted Impact of SGLT2 Inhibitors in Heart Failure Patients: Exploring Diverse Mechanisms of Action. Biomedicines. 2024; 12: 2314. https://doi.org/10.3390/biomedicines12102314.

[78] Wijnker PJM, Dinani R, van der Laan NC, Algül S, Knollmann BC, Verkerk AO, et al. Hypertrophic cardiomyopathy dysfunction mimicked in human engineered heart tissue and improved by sodium-glucose cotransporter 2 inhibitors. Cardiovascular Research. 2024; 120: 301–317. https://doi.org/10.1093/cvr/cvae004.

Funding

National Natural Science Foundation of China(82472010)