Researchers combined four transcriptomic cohorts, three proteomic datasets and one phosphoproteomic dataset to examine metabolic remodeling in HCM myocardium. The material included single-nucleus RNA sequencing, microarray and bulk RNA sequencing, alongside tandem mass tag proteomics, liquid chromatography tandem mass spectrometry, SomaScan aptamer assays and phosphoproteomics. Some analyses used paired RNA and protein measurements from the same myectomy cohort, while others provided cross-cohort validation. Across more than 400 patients with HCM, glycolytic and hypoxia-related gene sets were predominantly negatively enriched at the transcript level.

The largest transcriptomic dataset, comprising 106 HCM and 39 control septal samples, showed significant suppression of both glycolysis and hypoxia programs, with normalized enrichment scores of −1.56 and −1.91, respectively, and false-discovery rates below 0.001. Single-cell data reproduced the pattern. In the Chaffin cohort, cardiomyocytes from hearts with preserved and reduced ejection fraction both showed negative enrichment of glycolysis and hypoxia pathways. Independent single-nucleus data from another cohort also indicated reduced glycolytic transcription, although its small comparison of three control and 10 HCM hearts did not significantly suppress the hypoxia set. The bulk RNA-seq dataset supplied further support, with hypoxia genes negatively enriched at a normalized score of −1.89.

Not every glycolytic transcript behaved identically. In the 106-sample microarray cohort, five of 12 core glycolytic enzymes were significantly downregulated, including LDHA, ENO1 and HK2, while LDHB and PFKM increased. Sparse detection in single-nucleus data complicated some individual estimates, particularly for ALDOA, which was detected in only 10 of 122,362 cardiomyocyte nuclei. But donor-level aggregation of the single-cell data produced estimates that correlated with the independent bulk RNA-seq results, and transcript detection was not systematically associated with larger discrepancies. Sensitivity analyses excluding ALDOA did not materially change the pathway findings, suggesting that sparsity did not explain the overall suppression pattern.

HIF1-associated transcription also ran counter to the expectation that advanced HCM would activate a hypoxia-driven glycolytic program. A curated set of established HIF1α targets, including SLC2A1, LDHA, PGK1 and ENO1, was strongly negatively enriched in the Garmany transcriptomic cohort, with a normalized score of −1.99 and an adjusted P value of 2.8 × 10−4. Two other HIF1α target sets were directionally negative but did not pass the false-discovery threshold. These results concern transcription attributed to HIF1α targets; they do not directly measure HIF1α protein abundance, stabilization, nuclear localization or activity.

The strongest evidence for transcript–protein discordance came from the paired Garmany myectomy dataset. Among 12 core glycolytic enzymes, five changed in opposite directions between RNA and protein. PFKM, LDHB and PGK1 had higher transcript levels but lower protein levels, whereas HK1 and PFKL showed the reverse pattern. The correlation between RNA and protein fold changes was weak and inverse, at r = −0.28 with P = 0.38 for 12 enzymes, but the researchers treated this correlation as descriptive because of the small sample. At pathway level, glycolysis had a negative transcriptomic enrichment score of −1.11 and a positive proteomic score of +1.40, although neither comparison met the false-discovery threshold. The hypoxia set showed a similar sign reversal, from significant negative transcriptomic enrichment to positive but nonsignificant protein enrichment.

HK1 emerged as the clearest example of decoupling. Its transcripts were reduced in all four transcriptomic cohorts, yet its protein was increased across all three independent proteomic platforms: tandem mass tag mass spectrometry, liquid chromatography tandem mass spectrometry and SomaScan. Because those technologies use different analytical principles, their agreement made HK1 less likely to be a platform-specific artifact. Other enzymes were less consistent. PGAM1, for example, increased in the LC-MS/MS dataset but decreased in the SomaScan dataset, while PFKM also showed platform-dependent behavior. The comparison between cardiomyocyte-specific single-cell RNA data and bulk-tissue proteomics provided supportive but less definitive evidence because the cohorts differed and the protein measurements could not be assigned to individual cell types.

Phosphoproteomic measurements added another layer of complexity. HK1 and PGAM1 showed increased phosphorylation, with log2 fold changes of +0.18 and +0.40, respectively. ALDOA, LDHB, PKM and PFKM instead had reduced phosphorylation, with adjusted P values of 0.02 or lower. The mixed pattern did not resemble a coordinated, pathway-wide increase in modification across glycolytic enzymes. The data also did not directly establish whether any enzyme was more or less active. Phosphorylation can have different effects depending on the modified residue, and the study’s phosphoproteomic evidence came from one dataset. It therefore identified candidate post-translational regulation without measuring metabolic flux or catalytic activity.

Integrated molecular and metabolite evidence suggested that the more reproducible defect involved impaired coupling between glycolysis and mitochondrial oxidation. In HCM myocardium with preserved ejection fraction, transcripts for oxidative phosphorylation, the TCA cycle, fatty-acid oxidation, glycolysis and hypoxia programs were all negatively enriched. Corresponding oxidative proteins and several TCA-associated enzymes were reduced. Metabolites were also depleted: acetyl-CoA fell by a log2 fold change of −1.69, NADH by −1.05 and lactate by −0.99, while medium- and long-chain acylcarnitines showed an average decrease of −1.43. Lower lactate alongside elevated amounts of selected glycolytic proteins is difficult to reconcile with a simple model of globally increased glycolytic flux. Nevertheless, steady-state metabolite concentrations are indirect indicators and do not by themselves determine pathway flux.

The findings have important boundaries. Most human myocardium came from patients with symptomatic, advanced, obstructive HCM undergoing septal myectomy, so the patterns may not represent earlier disease, non-obstructive HCM or every genotype. The cohorts also differed in severity, treatment exposure, cellular composition and tissue processing, while bulk proteomics cannot distinguish changes within cardiomyocytes from shifts caused by fibrosis or altered stromal and immune-cell abundance. Single-nucleus RNA sequencing can underestimate weakly detected transcripts, and the phosphoproteomic analysis was limited to one dataset. The results therefore support a selective transcript–protein disconnect in glycolysis rather than proving a mechanism for it or demonstrating a therapeutic target. Future work with spatial transcriptomics, single-cell proteomics and isotope tracing would be needed to resolve cell-specific regulation and test pathway activity directly.

Subject of Research: Transcript–protein discordance in glycolytic and oxidative metabolism in human hypertrophic cardiomyopathy

Article Title: Selective transcript–protein discordance of glycolytic remodeling in human hypertrophic cardiomyopathy

Article References: Chen, J., Hu, D., Yang, Y., Ouyang, M., & Chen, X. (2026). Selective transcript–protein discordance of glycolytic remodeling in human hypertrophic cardiomyopathy. Frontiers in Cardiovascular Medicine, 13, Article 1937619. https://doi.org/10.3389/fcvm.2026.1937619

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1937619

Keywords: hypertrophic cardiomyopathy, glycolysis, multi-omics, transcriptomics, proteomics, HIF1α, phosphoproteomics, cardiac metabolism, HK1, mitochondrial oxidation, Selective, transcript

Noonan syndrome and related genetic disorders, collectively known as RASopathies, involve changes in the RAS-MAPK signaling pathway, which has an important role in cell growth and development. Cardiovascular abnormalities occur in 80% to 90% of people with Noonan syndrome, and hypertrophic cardiomyopathy is present in approximately 20%. The condition causes the heart muscle to become abnormally thick, and some infants develop severe disease and heart failure early in life.

BabyMERIT focuses on this severe infantile presentation rather than on the full spectrum of Noonan syndrome or all forms of hypertrophic cardiomyopathy. The trial addresses an urgent need for prospective evidence on treatment options for infants with RASopathy-associated hypertrophic cardiomyopathy. Investigators said the study builds on evidence that targeting the RAS-MAPK pathway may improve the cardiac condition, while emphasizing that the new study is needed to evaluate both potential benefits and risks.

The project is also intended to establish clinical and data-coordination infrastructure for examining a condition that can make affected infants critically ill very early in life. Standardized collection of cardiac imaging, biomarker data and clinical outcomes across participating centers will allow investigators to study the disease systematically. Mount Sinai will serve as the BabyMERIT Clinical Coordinating Center, while Carelon Research/New England Research Institutes will serve as the Data Coordinating Center.

The latter organization has received NIH funding totaling more than $4.43 million over six years. Together, the awards provide more than $8.5 million for the trial’s clinical and data-coordination infrastructure. Bruce D. Gelb, MD, of Mount Sinai, and Jane W. Newburger, MD, MPH, of Boston Children’s Hospital, are the Clinical Coordinating Center principal investigators.

Their work with the Pediatric Heart Network will support collaboration among centers across the United States and coordinated collection of data on treatment effects and safety. Researchers plan to enroll 25 infants in the prospective treatment arm. Eligible participants must be older than 28 days and younger than six months and must have Noonan syndrome or a closely related RASopathy, hypertrophic cardiomyopathy and severe heart failure requiring hospitalization.

The small planned sample reflects the specialized nature of the group being studied, and the investigators will document adverse events as well as potential changes in heart function and disease-related outcomes. BabyMERIT will use a single-arm, open-label design, meaning that all 25 enrolled infants will receive trametinib and both participants and clinicians will know the treatment being given. Researchers will compare the prospective treatment arm with a real-world evidence cohort of approximately 100 infants who received standard-of-care treatment between 2015 and the study period.

The comparison group is not described as a randomized control group. Although the design can provide structured prospective information about treated infants, the absence of concurrent randomization and the use of an open-label regimen will be important considerations when interpreting differences between the treatment arm and the historical real-world cohort. Infants in the treatment arm will receive trametinib for 12 months and then enter observation for up to 12 additional months to monitor for relapse.

Infants whose disease relapses may be eligible for retreatment for up to another 12 months. The primary endpoint is a composite of death, heart transplantation or septal myectomy by 12 months. Septal myectomy is open-heart surgery used to treat hypertrophic cardiomyopathy.

The composite therefore combines three outcomes—mortality, the need for a heart transplant and the need for surgery—rather than providing a single continuous measure of cardiac function. The planned observation period will also show whether cardiac disease returns after the initial treatment course and how infants respond if they are treated again. In addition to the primary endpoint, researchers will assess heart function, heart failure, biomarkers, relapse, response to retreatment and adverse events.

Cardiac imaging and biomarker testing will receive centralized review through Pediatric Heart Network core laboratories, supporting consistent evaluation across participating sites. Participants will also be monitored closely for potential adverse effects involving the skin, eyes and heart, as well as changes in blood counts and kidney, liver or thyroid function. This surveillance will provide information about both the course of the heart condition and the broader safety profile of trametinib in infants younger than six months.

The scientific question is whether inhibiting signaling in the molecular pathway implicated in RASopathies can change the course of severe hypertrophic cardiomyopathy in infancy. BabyMERIT is a test of a precision-medicine approach, not an established treatment, and the announcement does not report enrollment results, treatment responses, survival data or evidence of efficacy. Its small treatment arm and nonrandomized historical comparison will limit the strength of conclusions that can be drawn if the groups differ in ways beyond treatment.

Results will depend on completion of treatment, follow-up and analysis of the specified endpoints. Even so, the trial will provide prospective clinical and safety data intended to guide future research and care for infants with this rare RASopathy-associated condition.

Subject of Research: A national single-arm trial of trametinib for severe hypertrophic cardiomyopathy in infants with Noonan syndrome and related RASopathies

Article Title: Mount Sinai to lead national trial of targeted therapy for infants with noonan syndrome and severe hypertrophic cardiomyopathy

Article References: Mount Sinai to lead national trial of targeted therapy for infants with noonan syndrome and severe hypertrophic cardiomyopathy. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: BabyMERIT trial, trametinib, Noonan syndrome, RASopathies, hypertrophic cardiomyopathy, infant heart failure, RAS-MAPK pathway, Pediatric Heart Network, precision medicine, clinical trial, Mount, Sinai

The review, published in Frontiers in Cardiovascular Medicine, was led by Huanhuan Chen of the University of Shanghai for Science and Technology together with Jingjuan Huang and Xumin Hou of Shanghai Chest Hospital, Shanghai Jiao Tong University. Rather than cataloguing model architectures or celebrating benchmark scores, the team set out to answer a deceptively simple question: which AI-ECG applications are actually ready for clinical use, and which are not? To do so, they searched PubMed, IEEE Xplore, and Web of Science from January 2022 through July 2026, adding earlier landmark studies through backward citation tracking. Seventy-seven publications ultimately informed the synthesis.

The authors appraised each application against five clinically oriented criteria: whether the intended use is well defined, whether the reference standard — the ground truth used to train and test the model — is reliable, whether the model has been externally validated across populations and devices, whether its outputs are clinically actionable, and whether there is evidence that the model improves workflow or patient outcomes. This framework deliberately separates technical performance from translational maturity, a distinction the authors argue has been routinely blurred in the literature.

The clearest success stories involve ST-segment elevation myocardial infarction (STEMI) triage and screening for reduced left ventricular ejection fraction. In a pragmatic randomized trial published in NEJM AI, an AI-ECG algorithm for rapid STEMI identification achieved a positive predictive value of 89.5 percent and a negative predictive value of 99.9 percent, and AI-assisted triage measurably shortened door-to-balloon and ECG-to-balloon times — the kind of workflow-level evidence that retrospective accuracy studies cannot provide. Similarly, a randomized controlled trial of an AI-ECG alert for reduced ejection fraction among non-cardiology inpatients, published in BMC Medicine, increased new diagnoses of reduced LVEF, raised the positive predictive value of subsequent echocardiography from 20.2 to 34.2 percent, and boosted cardiology consultation rates from 23.5 to 29.3 percent — all without increasing overall echocardiography use.

Acute myocardial infarction rule-out has also advanced, reaching prospective multicenter diagnostic validation. In the ROMIAE study, a ResNet-based model using only the initial 12-lead ECG achieved an area under the receiver operating characteristic curve of 0.878, and at a predefined low-risk threshold it reached 99.6 percent sensitivity and 99.1 percent negative predictive value. Hypertrophic cardiomyopathy screening has progressed through international external validation — with an AUC of 0.922 across cohorts from Switzerland, the United Kingdom, and South Korea — and through prospective implementation: an FDA-cleared alert system screened 145,848 patients across five healthcare systems, generated 4,348 alerts, and identified 17 new HCM diagnoses, with a median of 7.5 days from ECG to diagnostic imaging.

But the picture darkens considerably elsewhere. Detection of occlusion myocardial infarction — a condition the review notes is missed by conventional ST-elevation criteria, which show a pooled sensitivity of only 43.6 percent for detecting acute coronary occlusion — remains at the external diagnostic-validation stage. Deep learning models such as ECG-SMART-NET have achieved impressive AUCs around 0.95 using angiographically confirmed occlusion as the reference standard, yet no prospective interventional study has tested whether AI-guided catheterization-laboratory activation actually improves reperfusion decisions without triggering excessive false activations. Prediction of occult or future atrial fibrillation from sinus-rhythm ECGs, including a large Veterans Affairs study of more than 900,000 outpatient ECGs that achieved an AUROC of 0.93, remains a risk-enrichment strategy rather than a diagnostic tool, partly because the reference standard depends on subsequent clinical detection of AF.

Valvular heart disease, heart failure with preserved ejection fraction, long-term heart-failure prediction, and cardiac amyloidosis screening all remain primarily validation-stage or risk-enrichment applications. For valvular disease, models can detect moderate-to-severe aortic stenosis, mitral regurgitation, and other lesions with AUCs generally between 0.77 and 0.88, but positive predictive value is heavily dependent on disease prevalence, and no prospective workflow evidence exists. For cardiac amyloidosis, the Mayo Clinic AI-ECG model achieved an AUC of 0.84, but performance dropped substantially in Hispanic patients and in ECGs with left ventricular hypertrophy or left bundle branch block — a pattern the authors flag as a transportability and equity concern. For heart failure with preserved ejection fraction, the lack of a single definitive reference standard makes the prediction target inherently unstable.

Underlying all of these gaps, the review identifies a consistent set of constraints: label quality, calibration, transportability across devices and institutions, operating-threshold selection, and the near-universal absence of patient-outcome evidence. The authors emphasize that a high AUROC says nothing about how a model behaves at the specific threshold where it will actually be deployed, how many false positives it will generate in a low-prevalence screening population, or whether clinicians will act on its output. They also caution that multimodal models — those combining ECG with clinical variables, biomarkers, or imaging — may derive much of their apparent advantage from established non-ECG predictors rather than from genuinely novel electrical signatures.

The review’s bottom line is measured rather than dismissive. AI-ECG, the authors conclude, is best positioned as clinician-supervised decision support linked to explicit downstream testing and care pathways — not as an autonomous diagnostician. The applications that have progressed furthest are precisely those in which the model’s output connects to an established clinical action: urgent reperfusion for STEMI, echocardiography for reduced ejection fraction, imaging and genetic counseling for suspected hypertrophic cardiomyopathy. Where that connection is missing, even technically brilliant models remain solutions in search of a clinical problem. The next phase of the field, the authors argue, must shift from retrospective discrimination studies toward prospective, device-diverse, outcome-oriented trials that test whether AI-ECG actually changes what happens to patients — not just what appears on a receiver operating characteristic curve.

Subject of Research: Translational maturity of artificial intelligence-enabled electrocardiography across cardiovascular diseases

Article Title: Translational maturity of artificial intelligence-enabled electrocardiography varies across cardiovascular diseases

Article References: Chen, H., Huang, J., & Hou, X. (2026). Translational maturity of artificial intelligence-enabled electrocardiography varies across cardiovascular diseases. Frontiers in Cardiovascular Medicine, 13, Article 1940621. https://doi.org/10.3389/fcvm.2026.1940621

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1940621

Keywords: AI-ECG, electrocardiography, STEMI, myocardial infarction, heart failure, atrial fibrillation, hypertrophic cardiomyopathy, cardiac amyloidosis, valvular heart disease, external validation, clinical decision support, translational medicine

The study, led by Xinyi Wu and Yuelong Yang alongside colleagues at Sun Yat-sen University, Guangdong Provincial People’s Hospital, Philips Healthcare, and Siemens Shenzhen Magnetic Resonance, appeared in Frontiers in Medical Technology. The team systematically searched the Web of Science Core Collection and PubMed for articles on cardiac diffusion MRI published between January 2005 and June 2024. After independent screening by two reviewers, with disagreements resolved by a third, 367 articles met the inclusion criteria. The researchers then applied a suite of bibliometric tools, including CiteSpace 6.2.R6, VOSviewer 1.6.20, and the R package bibliometrix, to construct co-citation networks, collaboration maps, keyword burst analyses, and trend topic visualizations.

The results reveal a field that, while still modest in absolute publication volume, is growing steadily and attracting increasing attention from high-impact journals. Magnetic Resonance in Medicine led productivity with 48 articles and topped the citation rankings with 2,358 total citations. But the presence of Circulation, the Journal of the American College of Cardiology, Circulation Research, and Radiology among the most-cited journals signals that cDMRI findings are resonating well beyond the technical MRI community and into mainstream cardiovascular medicine.

At the heart of the bibliometric landscape sits one dominant concept: cardiac diffusion tensor imaging, or cDTI. It emerged as the most prominent keyword, with 191 occurrences, a total link strength of 280, and a centrality of 0.98, underscoring its pivotal role in the field. The keyword also exhibited the strongest citation burst, a measure of sudden surges in scholarly attention, with a burst strength of 6.8 spanning 2022 to 2024. This recent surge suggests that cDTI has moved from a niche technical pursuit to a central focus of contemporary cardiac imaging research.

Among individual studies, one paper stood out above all others. The 2017 article by Sonia Nielles-Vallespin and colleagues, published in the Journal of the American College of Cardiology, recorded the strongest citation burst at 26.17, accumulating 163 total citations at an average of 20.38 per year. That work validated the ability of in-vivo cDTI to characterize myocardial microstructural dynamics, demonstrating for instance that sheetlet angles increase from approximately 18 degrees in diastole to 65 degrees in systole, and emphasized its clinical relevance for assessing heart disease. A companion study by the same group, implementing an optimized stimulated echo acquisition mode sequence combined with a modified prospective navigator technique and biofeedback, achieved the second-strongest burst at 20.86. Christian T. Stoeck and colleagues earned the third-strongest burst, 15.55, for pioneering second-order motion-compensated spin echo cDTI on a clinical scanner.

The most-cited paper overall, however, belongs to a different tradition. J.D. Bayer and colleagues, writing in Annals of Biomedical Engineering in 2012, ranked first in total citations with 346, citations per year at 26.62, and normalized citations per year at 4.85. Their Laplace–Dirichlet Rule Based algorithm generates cDTI-derived myocardial fiber orientations for computational heart models with high speed and precision, enabling simulations of electrical activation that have become foundational for cardiac electrophysiology research. Ming-Ting Wu’s 2006 study in Circulation, the second most-cited work with 214 citations, used in-vivo cDTI indices including apparent diffusion coefficient, fractional anisotropy, and helix angle to map fiber remodeling after myocardial infarction, correlating microstructural changes with tissue viability and wall motion.

Keyword clustering revealed four coherent thematic pillars within the field. The first concerns sequence design, with terms such as motion compensation, STEAM, spin echo, and free-breathing dominating the conversation. The second addresses image processing, encompassing mean diffusivity, fractional anisotropy, helix angle, image registration, and reconstruction. The third centers on clinical applications across diseases including heart failure, dilated cardiomyopathy, congenital heart disease, ventricular arrhythmias, and ischemia. The fourth links cDMRI to complementary cardiac MRI techniques such as late gadolinium enhancement, T1 mapping, T2 mapping, and strain imaging. Together, these clusters paint a picture of a field advancing simultaneously on technical, computational, and clinical fronts.

Yet the bibliometric analysis also exposed a significant obstacle to clinical translation: the lack of standardization. The diversity of cDMRI sequences, acquisition parameters, and MRI hardware across published studies has compromised the comparability and interpretability of cDTI indices. Factors including sequence type, acquisition resolution, b-values, cardiac phase, field strength, and coil equipment all influence the signal-to-noise ratio of cDMRI images, which in turn affects derived parameters such as ADC, FA, MD, HA, and E2A. The frequent use of in-house post-processing software has further introduced variability. The authors recommend standardized protocols adapted to different hardware systems, a unified analysis tool for cDTI, and large-scale, multi-center, multi-vendor longitudinal studies to establish normal reference ranges and confirm clinical reproducibility.

Perhaps the most striking forward-looking finding concerns deep learning. The trend topic analysis identified it as an emerging research focus since 2022, with a sustained burst strength of 3.93 beginning in 2020 and continuing through 2024. The appeal is clear: low signal-to-noise ratio and prolonged acquisition time remain major limitations of cDTI, and deep learning-based reconstruction and denoising methods offer a path forward. Phipps and colleagues developed a residual deep learning denoising convolutional neural network achieving at least a twofold acceleration for in-vivo cDTI without significant differences in SNR, structural similarity, MD, FA, or helix angle transmurality. Liu and colleagues created FG-Net, which generates high-quality cDTI parameter maps from just six raw cDWI images. Ferreira and colleagues demonstrated that a U-Net approach could reduce cDTI acquisition from approximately 12 breath-holds to just 3, a dramatic improvement in clinical feasibility.

The authors caution that deep learning in cDMRI is not without risks. Bias in reconstructing diffusion tensor maps inevitably exists, and whether such bias is acceptable remains unknown. Evaluation and validation frameworks for deep-learning-based methods have not yet been established, and no cDMRI studies employing deep learning have utilized external test sets to date, a gap the authors attribute to the lack of protocol standardization. They encourage validation of deep-learning-derived cDTI indices against histology where possible and validation in large-scale, multi-center, multi-vendor studies. As the field matures, the convergence of standardized acquisition, robust sequence design, computational electrophysiology modeling, and machine learning acceleration may finally bring cardiac microstructure imaging from the research bench into routine clinical practice.

Subject of Research: Bibliometric analysis of cardiac diffusion magnetic resonance imaging research from 2005 to 2024

Article Title: Future of cardiac diffusion magnetic resonance imaging: a bibliometric analysis

Article References: Wu, X., Yang, Y., Xu, Y., Wu, Z., Liu, H., & Chen, R. (2026). Future of cardiac diffusion magnetic resonance imaging: a bibliometric analysis. Frontiers in Medical Technology, 8, Article 1801878. https://doi.org/10.3389/fmedt.2026.1801878

Image Credits: AI Generated

DOI: 10.3389/fmedt.2026.1801878

Keywords: cardiac diffusion MRI, diffusion tensor imaging, bibliometrics, deep learning, myocardial microstructure, cardiac electrophysiology, STEAM, motion compensation, myocardial infarction, hypertrophic cardiomyopathy, signal-to-noise ratio, sequence design

The clinical stakes are considerable. In hypertrophic cardiomyopathy, the heart muscle thickens abnormally, and in some patients this thickened muscle obstructs the left ventricular outflow tract, the channel through which blood exits the heart into the aorta. When this obstruction is present at rest, it is easy to detect with echocardiography. But in the latent form, the obstruction emerges only during exertion, when the pumping chamber empties more vigorously and the muscle geometry shifts. Because patients often appear unremarkable during a resting clinic visit, latent obstruction can go unrecognized, even though studies have indicated that its prognosis is comparable to that of overt obstruction. Identifying these patients without resorting to exercise or pharmacological provocation has remained an unmet need in cardiology.

The research team approached the problem by exploiting a technique called cardiac magnetic resonance feature tracking, or CMR-FT. This method analyzes standard cine MRI sequences, the same looping movies of the heart that radiologists routinely acquire, and computationally tracks the movement of tissue features across frames. From that tracking, the software derives myocardial strain, a quantitative measure of how much the heart muscle deforms during each contraction and relaxation. Strain can be resolved into different geometric components: global radial strain, which describes thickening of the muscle wall; global circumferential strain, which captures the wringing motion of the ventricle around its axis; and global longitudinal strain, which reflects the shortening of the heart from base to apex. The team also measured the time to peak longitudinal strain, a marker of how synchronously the heart muscle contracts, and strain rates, which describe the speed of deformation.

Alongside the mechanical measurements, the investigators added a tissue characterization parameter: the extracellular volume fraction, or ECV, derived from T1 mapping. T1 mapping is a quantitative MRI technique that measures the relaxation time of hydrogen protons in tissue, and when combined with gadolinium contrast agent, it allows estimation of the fraction of heart muscle volume occupied by extracellular space. A higher ECV typically signals diffuse fibrosis, the scarring-like expansion of the space between muscle cells that accompanies many forms of heart disease. By pairing mechanics with tissue composition, the study aimed to build a multiparametric fingerprint of the latent obstructive phenotype rather than relying on any single measurement.

The study population was drawn from 159 consecutive patients with hypertrophic cardiomyopathy who underwent cardiac MRI at 3.0 Tesla between September 2024 and December 2025. The researchers applied strict exclusion criteria to eliminate confounding conditions, removing 31 patients with uncontrolled hypertension, infiltrative cardiomyopathy, persistent atrial fibrillation, coronary artery disease, or resting obstructive hypertrophic cardiomyopathy. A further 16 patients were excluded because of poor image quality or missing clinical data, leaving 112 patients in the final analysis: 81 with hypertrophic non-obstructive cardiomyopathy and 31 with latent obstructive disease. All participants underwent cine imaging, T1 mapping, and late gadolinium enhancement scans, followed by feature tracking strain analysis.

The comparisons between groups revealed a consistent pattern. Patients with latent obstruction had a significantly higher left ventricular ejection fraction, the standard measure of pumping efficiency, suggesting a hyperdynamic ventricle. They also showed increased global radial strain and greater absolute global circumferential strain, indicating exaggerated squeezing motions of the heart wall. Perhaps most intriguingly, their time to peak longitudinal strain was prolonged, pointing to mechanical contraction that is not only stronger but also less temporally coordinated. Meanwhile, their extracellular volume fraction was lower than in the non-obstructive group, hinting at differences in tissue composition, potentially including less diffuse fibrotic remodeling, that accompany the provocable obstruction phenotype. All of these differences reached statistical significance.

To convert these observations into a diagnostic tool, the team used univariate and multivariate logistic regression to identify which parameters independently predicted latent obstruction. Three emerged as independent predictors: global radial strain, time to peak longitudinal strain, and extracellular volume fraction. When combined into a single model, these three parameters achieved an area under the receiver operating characteristic curve of 0.888, with a 95 percent confidence interval of 0.814 to 0.940. In practical terms, the model demonstrated a sensitivity of 83.87 percent and a specificity of 88.89 percent, meaning it correctly identified the large majority of patients with latent obstruction while rarely raising false alarms in those without it. Statistical comparison using the DeLong test confirmed that the combined model significantly outperformed any single parameter alone.

The authors are careful to frame the model’s role appropriately. They emphasize that it should not be viewed as a replacement for provocative echocardiography, the current gold standard for unmasking latent obstruction, which involves exercising the patient or administering a drug while imaging the outflow tract. Instead, the multiparametric model is positioned as a non-invasive screening tool, one that could be applied to the vast pool of hypertrophic cardiomyopathy patients who already undergo cardiac MRI as part of routine assessment. Patients flagged by the model could then be prioritized for formal hemodynamic evaluation, concentrating provocative testing where it is most likely to change management. The researchers also note that external validation in independent cohorts is required before the model can be applied clinically, a standard caveat that reflects the single-center nature of the study.

The significance of the work lies in its reframing of what cardiac MRI can reveal about obstruction risk. Traditional MRI assessment of hypertrophic cardiomyopathy focuses on structural measures such as wall thickness, mass, and the presence of late gadolinium enhancement, which marks focal scarring. By shifting attention to functional deformation and quantitative tissue composition, the study suggests that the seeds of provocable obstruction are visible even when the heart is at rest, encoded in the way muscle fibers shorten, twist, and thicken, and in the microscopic architecture of the extracellular matrix. If validated more broadly, this approach could transform a routine diagnostic scan into a risk-stratification instrument, catching silent obstruction before it manifests as symptoms, arrhythmias, or sudden cardiac events.

The research was supported by the Dalian Science and Technology Innovation Fund Program under a major basic research grant, and it was approved by the Ethics Review Committee of Dalian University Affiliated Zhongshan Hospital with written informed consent from all participants. As feature tracking software becomes more widely available and automated, the barrier to adopting such multiparametric models continues to fall, and studies like this one chart a path toward imaging workflows in which every hypertrophic cardiomyopathy MRI yields not just pictures but a quantitative verdict on obstruction risk. For the many patients whose dangerous physiology only reveals itself on the treadmill, that verdict, delivered while lying still inside an MRI scanner, could make the difference between a condition discovered early and one discovered too late.

Subject of Research: Cardiac MRI feature tracking for detecting latent obstructive hypertrophic cardiomyopathy

Article Title: Feature tracking in latent obstructive hypertrophic cardiomyopathy: a multiparametric study based on myocardial strain and tissue characteristics

Article References: Sun, Y., Wu, H., Niu, K., Chen, S., Yu, Q., Zhao, X., Liang, X., Song, D., & Yu, J. (2026). Feature tracking in latent obstructive hypertrophic cardiomyopathy: a multiparametric study based on myocardial strain and tissue characteristics. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02839-6

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02839-6

Keywords: hypertrophic cardiomyopathy, latent obstruction, cardiac MRI, feature tracking, myocardial strain, extracellular volume fraction, T1 mapping, left ventricular outflow tract, diagnostic model, cardiology, medical imaging, fibrosis

The review, conducted by researchers at Universiti Teknologi MARA in Malaysia and registered prospectively on PROSPERO, systematically searched PubMed, Scopus and Web of Science for studies that combined metabolomic profiling with genetic characterisation of hypertrophic cardiomyopathy cohorts. From an initial pool of 985 records, the team distilled 14 eligible studies comprising 1511 participants aged 23 to 71, published between 2015 and 2026. To qualify, studies had to perform spectroscopic or spectrometric metabolomics, lipidomics or multi-omics on human biospecimens, report genotype information at the individual or cohort level, and exclude phenocopies such as Fabry disease, cardiac amyloidosis and secondary hypertrophy from hypertension or aortic stenosis. The aim was to isolate genuine sarcomeric disease biology and ask three questions: which metabolic pathways are consistently dysregulated, whether specific gene variants carry distinct metabolic signatures, and whether any of the identified metabolites could serve as diagnostic or prognostic biomarkers.

Methodologically, the field has converged on liquid chromatography-mass spectrometry as the dominant platform, typically coupling UPLC systems to high-resolution Orbitrap or QTOF instruments for untargeted profiling, with triple quadrupole systems reserved for targeted quantification of acyl carnitines and eicosanoids. Plasma was the most frequently analysed specimen, followed by left ventricular or septal myocardial tissue obtained during surgery, serum, whole blood and combined plasma-plus-tissue designs. More exotic approaches also appeared, including direct-infusion high-resolution mass spectrometry for rapid screening and, in one 2026 study, spatial metabolomics using desorption electrospray ionisation imaging mass spectrometry to map metabolites directly within myocardial tissue sections. Most studies employed untargeted designs, though a minority integrated lipidomics, proteomics and transcriptomics into broader multi-omics workflows.

Across this heterogeneous literature, a strikingly consistent pattern of metabolic remodelling emerged. The adult heart normally derives most of its ATP from mitochondrial fatty acid oxidation, but hypertrophied myocardium in these studies showed reduced long-chain acyl carnitines, depleted high-energy phosphates including ATP, ADP and phosphocreatine, diminished acetyl-CoA, and reduced tricarboxylic acid cycle intermediates such as citrate, malate and succinate. Tissue analyses revealed damaged mitochondrial cristae, disordered architecture and reduced NAD(H)-linked respiratory capacity. In parallel, the diseased heart appeared to shift toward less efficient alternative substrates: elevated beta-hydroxybutyrate signalled increased ketone utilisation, while lactate accumulation pointed to heightened glycolytic flux. Circulating metabolites told a complementary story, with elevated long-chain acyl carnitines in more severe disease, interpreted as incomplete fatty acid oxidation and systemic metabolic spillover rather than enhanced fat burning.

Amino acid metabolism emerged as a second recurring theme. Branched-chain amino acids, leucine, isoleucine and valine, were elevated in both plasma and myocardial tissue and tracked with greater hypertrophic severity. Because these amino acids are catabolised primarily through the mitochondrial branched-chain alpha-keto acid dehydrogenase complex, their accumulation is consistent with impaired mitochondrial oxidative capacity, though they may also modulate hypertrophic signalling through mTOR. Other amino acid-related metabolites, including aminoadipic acid, 3-methylhistidine and dimethylglycine, were associated with more severe phenotypes in MYBPC3-confirmed cohorts. Lipid disturbances extended beyond acyl carnitines to reductions in polyunsaturated fatty acids, lysophosphatidylcholines and glycerophospholipids in plasma, alongside increased ceramides and sphingomyelins in tissue, suggesting broad disruption of membrane remodelling and lipid signalling.

The review’s central and arguably most provocative finding concerns genotype. Despite the explicit focus on genetically characterised cohorts, no distinct genotype-specific metabolic signature could be identified. Several studies reported no significant metabolomic differences between genotype-positive and genotype-negative patients, and in principal component analyses the two groups did not cluster separately. Even directly comparing the two major sarcomere genes, MYH7 and MYBPC3 carriers displayed nearly identical metabolic signatures, and circulating eicosanoid profiles were reduced across a variety of mutations including FLNC, MYBPC3, MYH7, TNNI3 and TNNT2. In founder-variant populations enriched for specific MYBPC3 mutations, metabolic signatures aligned more strongly with disease phenotype than with genotype. The authors interpret this convergence as evidence that metabolic remodelling represents a final common pathway: diverse sarcomeric mutations impair cross-bridge cycling, increase ATP consumption and reduce contractile efficiency, driving a shared energetic deficit whose severity, rather than the causal variant, shapes the metabolome.

Mechanistically, this model fits with earlier work showing reduced phosphocreatine-to-ATP ratios even in mutation carriers without overt hypertrophy, indicating that energy deficiency precedes structural remodelling. Sarcomeric mutations destabilise energy-conserving states of myosin, promoting hypercontractility and impaired relaxation. The resulting energetic imbalance is compounded by mitochondrial dysfunction, impaired calcium handling, oxidative stress and coronary microvascular dysfunction that limits oxygen delivery, forming a self-reinforcing cycle that downstream signalling through AMPK, mTOR and PPAR pathways may amplify. Notably, some of this remodelling appears reversible: longitudinal plasma sampling around surgical myectomy showed postoperative declines in beta-hydroxybutyrate, bilirubin, biliverdin and various lipid species, consistent with improved organ perfusion after relief of left ventricular outflow tract obstruction, and suggesting that systemic metabolic abnormalities in the disease are dynamic rather than fixed.

On the translational front, several multi-metabolite panels showed impressive diagnostic performance. A plasma-based random forest model using just five metabolites, 8:0-carnitine, hypoxanthine, creatine, phenylalanine and tryptophan, discriminated hypertrophic cardiomyopathy from controls with an area under the curve of 0.976. A targeted eicosanoid panel combining 12-HETE and EPA achieved an AUC of 0.884, while serum signatures distinguished preclinical from obstructive disease carriers with accuracy comparable to imaging-derived energetic parameters. Metabolomic clustering also identified patient subgroups with differing outcomes, where poorer prognosis was linked to increased purine metabolism and reduced carnitines, and better survival to preserved tricarboxylic acid intermediates such as isocitrate and fumarate. Circulating acyl carnitines correlated with ventricular wall thickness and ejection fraction in MYBPC3-positive cohorts, hinting at a role in severity stratification.

The authors are careful to temper enthusiasm. Most included studies were single-centre, modestly sized case-control designs with cross-sectional data that cannot establish temporal relationships. Confounders such as diabetes, renal function, BMI and medication use were inconsistently accounted for, and most reported metabolites were annotated only at Metabolomics Standards Initiative Level 2, meaning putative identification by spectral matching rather than confirmation with authentic reference standards. Platform differences in sensitivity, chromatographic separation and library coverage further complicate cross-study comparison, and fasting status and sample handling varied widely. Critically, because every comparator group consisted of healthy controls or genotype-negative relatives, the review cannot establish whether any candidate metabolite panel can distinguish sarcomeric disease from other causes of left ventricular hypertrophy, a prerequisite for clinical differential diagnosis. Genetic testing also leaves roughly 40 percent of cases without a pathogenic variant, a gap metabolomics might eventually help address but has not yet closed.

The review concludes that metabolomics should currently be regarded as a hypothesis-generating tool rather than an established clinical modality, but one whose mechanistic signal is coherent and therapeutically suggestive. If hypertrophic cardiomyopathy is fundamentally a bioenergetic disorder, then interventions targeting myocardial energetics, from metabolic modulators to cardiac myosin inhibitors such as mavacamten that reduce ATP consumption at the sarcomere, address the primary driver the metabolomes reveal. The priority now, the authors argue, is prospective longitudinal cohorts following genotype-positive individuals from the pre-hypertrophic stage, with serial sampling integrated into family screening programmes, standardised collection protocols, Level 1 metabolite identification and independent external validation. Only then will the metabolic signature of the failing heart translate from an intriguing research signal into a tool that predicts disease progression and guides precision treatment for this heterogeneous and sometimes deadly condition.

Subject of Research: Metabolomic biomarkers and metabolic remodelling in genetically characterised hypertrophic cardiomyopathy

Article Title: Metabolomic biomarkers and metabolic remodelling in genetically characterised hypertrophic cardiomyopathy: a systematic review

Article References: Ahmad Razmy, A. H., Abd Latip, N., Ab-Rahim, S., & Sheikh Abdul Kadir, S. H. (2026). Metabolomic biomarkers and metabolic remodelling in genetically characterised hypertrophic cardiomyopathy: a systematic review. Metabolomics, 22(5), Article 164. https://doi.org/10.1007/s11306-026-02526-1

Image Credits: AI Generated

DOI: 10.1007/s11306-026-02526-1

Keywords: hypertrophic cardiomyopathy, metabolomics, biomarkers, MYBPC3, MYH7, fatty acid oxidation, branched-chain amino acids, mitochondrial dysfunction, acyl carnitines, sarcomeric genes, systematic review, precision medicine

The research team, led by Javier Sanmartin and Roberto Elosua of the University of Vic-Central University of Catalonia, followed PRISMA guidelines and prospectively registered their protocol in PROSPERO. They searched PubMed, Scopus, and Web of Science from database inception through September 2023, with an updated search extending to June 2025. From 5,963 initially identified articles, the investigators retained 115 original studies after removing duplicates and screening titles, abstracts, and full texts. Risk of bias was assessed with the Joanna Briggs Institute Critical Appraisal Tool for Cross-Sectional Studies, and pooled estimates were generated using random-effects models with the Freeman-Tukey double arcsine transformation for proportions.

The headline number is 4 percent. Using the International-Seattle criteria, which define T-wave inversion as a negative T-wave of at least 1 millimeter in two contiguous leads, the pooled prevalence among athletes older than 16 years was 4 percent, with a 95 percent confidence interval of 3 to 6 percent. That figure, however, masks enormous variability: individual studies reported prevalences ranging from 0 to 42 percent, and the between-study heterogeneity, quantified by the I-squared statistic, exceeded 80 percent in most meta-analyses. The authors caution that the pooled estimate should be interpreted with this heterogeneity firmly in mind.

Location on the electrocardiogram matters enormously. Inversions were most common in the anterior leads V2 through V4, at roughly 3 percent, while inferior leads showed about 1 percent and lateral leads essentially 0 percent. This anatomical gradient carries clinical weight because lateral T-wave inversion is the pattern most frequently linked to confirmed structural heart disease. In the studies that evaluated cardiomyopathies among athletes with T-wave inversion, the most commonly identified conditions were hypertrophic cardiomyopathy, arrhythmogenic ventricular cardiomyopathy, and dilated cardiomyopathy, and lateral-lead inversions were disproportionately represented among those diagnosed.

Ethnicity emerged as a major modifier of prevalence. Black athletes showed an overall T-wave inversion rate of 13 percent, compared with 3 percent in Caucasian athletes, a difference consistent with known genetic, endocrine, and hemodynamic factors, including greater myocardial wall thickness and left ventricular mass. Notably, recent work cited in the review found that geographical origin within Africa also matters, with repolarisation abnormalities significantly more common among West African and Middle African athletes than among those from East or North Africa. Sex differences were smaller but present: women showed a slightly higher overall prevalence than men, 7 percent versus 5 percent, driven by anterior and inferior leads.

Perhaps the most striking comparative finding is that T-wave inversion is roughly 49 percent more common in athletes than in non-athletes. Across 14 studies that included control populations, the pooled prevalence ratio was 1.49, with a confidence interval of 1.13 to 1.97. The effect was strongest in the inferior leads, where the ratio reached 1.70. This supports the idea that intense training itself, through ventricular remodeling and autonomic changes, produces repolarisation patterns that would be considered abnormal in a sedentary person but are often physiological in an athlete.

The mechanisms behind these training-related inversions are diverse. The authors describe ventricular remodeling effects, including a high papillary muscle to left ventricular mass ratio and lateral displacement of the cardiac apex, which can explain anterior inversions. Structural variants such as left ventricular false tendons have also been implicated. Enhanced vagal tone in trained athletes activates acetylcholine-controlled potassium channels, reducing the action potential gradient between the epicardium and endocardium and altering the repolarisation vector. Myocardial ischemia, by contrast, can invert the repolarisation gradient in a pathological direction, which is one reason the finding cannot simply be dismissed.

Age adds another layer of nuance. In athletes aged 16 or younger, a juvenile T-wave inversion pattern in leads V1 through V3 is considered a normal variant, reflecting right ventricular dominance in early adolescence, and it generally regresses after age 16. The meta-analysis found this juvenile pattern in about 6 percent of young athletes. Interestingly, meta-regression identified age as the only variable that partially explained the heterogeneity between studies, with older athletes showing higher prevalence, a relationship the authors attribute to cumulative lifetime training volume.

On the critical prognostic question, the picture is murkier. Among 34 studies that assessed structural cardiomyopathies in athletes with T-wave inversion, most reported disease prevalence below 1 percent, though nine studies found rates between 1 and 10 percent and six exceeded 10 percent, likely reflecting deeper inversions, lateral predominance, referral-center recruitment, and more sensitive imaging such as cardiac magnetic resonance. Follow-up data were similarly heterogeneous: annual incidence rates of newly diagnosed cardiomyopathy ranged from 0 to 54.9 cases per 1,000 athletes, and major cardiovascular events ranged from 0 to 83.33 per 1,000 athlete-years. The authors attribute this wide spread to small samples, short follow-up, and selection biases, leaving the true event risk statistically uncertain.

The clinical message is a careful balance. T-wave inversion in athletes is more common than in the general population, most often involves the anterior leads, and frequently reflects benign exercise-induced remodeling, particularly in Black athletes and in younger age groups. Yet because it can also herald hypertrophic, arrhythmogenic, or dilated cardiomyopathy, especially when it appears in lateral leads or is accompanied by ST-segment depression, a thorough evaluation remains essential. The authors call for international, standardized, prospective studies with adequate sample sizes, uniform diagnostic protocols incorporating echocardiography, exercise testing, Holter monitoring, and cardiac magnetic resonance, and long-term follow-up to finally pin down which athletes with inverted T-waves truly need to worry.

Subject of Research: Prevalence and prognostic significance of T-wave inversions on electrocardiograms in athletes

Article Title: Frequency and Prognostic Significance of T-wave Inversions in Athletes: A Systematic Review and Meta-analysis

Article References: Sanmartin, J., Cartanya-Bonvehi, J., Valenzuela, H., Carballeira, L., Montserrat, S., Baranchuk, A., & Elosua, R. (2026). Frequency and Prognostic Significance of T-wave Inversions in Athletes: A Systematic Review and Meta-analysis. Sports Medicine – Open, 12(1), Article 147. https://doi.org/10.1186/s40798-026-01115-9

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01115-9

Keywords: T-wave inversion, athletes, electrocardiogram, sudden cardiac death, cardiomyopathy, sports cardiology, meta-analysis, athlete's heart, ECG screening, ventricular remodeling, hypertrophic cardiomyopathy, prevalence

The central problem the researchers set out to solve is one of incomplete explanation. Pathogenic or likely pathogenic variants in sarcomere-encoding genes, the category the team abbreviates as SARC-HCM-P/LP, account for only about one-third of hypertrophic cardiomyopathy cases. The remaining majority of patients have no detectable single-gene culprit, and even among confirmed carriers, the condition displays notoriously variable penetrance: some people harboring a dangerous variant develop severe disease early in life, while others reach old age with hearts that function normally. This uneven expressivity has long hinted that something beyond the rare variant itself, most plausibly the cumulative influence of thousands of common genetic variants scattered across the genome, helps determine who actually becomes ill and how sick they become.

Earlier attempts to capture that polygenic contribution ran into a stubborn equity problem. Existing polygenic risk scores for hypertrophic cardiomyopathy were built almost exclusively from genome-wide association studies of European-ancestry populations, and when applied to individuals of African, East Asian, Hispanic, or other ancestries, their predictive performance deteriorated sharply. This limitation mirrors a broader and well-documented weakness of genomics: because most large genetic datasets over-represent people of European descent, risk scores trained on them often fail the very populations that already bear disproportionate burdens of cardiovascular disease and face greater barriers to genetic diagnosis. The new study was designed from the ground up to confront that disparity rather than treat it as an afterthought.

To construct the score, the team drew on genome-wide association summary statistics from three complementary sources: the BioBank Japan, the Million Veteran Program, and a meta-analysis of seven European-ancestry cohorts. By combining association signals from Japanese, multiethnic American, and European datasets, the investigators built a score intended to capture disease-relevant variants across the genetic ancestry continuum rather than within a single population. The statistical machinery behind such scores involves weighting millions of single nucleotide polymorphisms according to their measured association with disease risk, then summing those weighted contributions for each individual to yield a single number representing inherited polygenic susceptibility. Methods of this kind, including Bayesian shrinkage approaches refined in recent years, allow researchers to distill a usable clinical signal from noisy, genome-scale data while guarding against overfitting.

The validation stage took place in a genuinely diverse national cohort: participants in the United States-based All of Us Research Program, one of the largest and most ancestrally diverse biomedical datasets ever assembled. The results were unambiguous. Individuals whose polygenic score placed them in the top quintile of the population had a 2.11-fold increased risk of developing hypertrophic cardiomyopathy compared with the rest of the population. That effect size, for a common-variant composite score alone, is clinically meaningful and rivals the discriminatory power that polygenic scores have achieved for more common conditions such as coronary artery disease. The score also improved overall risk stratification and showed trends toward improved ancestry-specific prediction, suggesting that the multiancestry training strategy partially, if not perfectly, mitigated the performance cliff that plagues European-derived scores.

The most striking finding, however, emerged when the researchers layered the polygenic score on top of the rare-variant picture. Among carriers of pathogenic or likely pathogenic sarcomere variants, individuals in the highest polygenic score quintile faced nearly a seventy-fold higher risk of actually developing hypertrophic cardiomyopathy compared with low-scoring counterparts. This is precisely the kind of result that cardiologists and genetic counselors have been waiting for. Variable penetrance among variant carriers has made counseling agonizingly uncertain: telling a young person they carry a disease-causing mutation without being able to say whether that mutation will ever manifest is of limited clinical use. A polygenic measure that helps distinguish the carriers likely to develop disease from those likely to remain unaffected converts a static genetic diagnosis into a dynamically graded risk estimate.

Beyond prediction of who develops disease, the score carried prognostic weight. Among individuals already diagnosed with hypertrophic cardiomyopathy, a higher polygenic score was associated with adverse cardiovascular outcomes, indicating that the same aggregate burden of common variants that raises disease susceptibility also shapes disease severity and trajectory. This suggests that polygenic information could eventually inform not only screening decisions but also surveillance intensity and management priorities for diagnosed patients. Extended analyses reinforced the pattern: among carriers of predicted deleterious variants, disease prevalence rose steadily across polygenic score quintiles, with the highest quintile showing roughly 2.4-fold higher penetrance than the lowest, and hazard ratios climbing in a graded fashion as score category increased.

The technical infrastructure supporting the study reflects the maturing standards of the polygenic risk score field. The investigators performed careful quality control, addressed the statistical pitfall known as Winner’s Curse that inflates effect estimates in discovery samples, and evaluated performance using measures including odds ratios per standard deviation of score change and area under the receiver operating characteristic curve across ancestry groups. In a notable commitment to transparency and reproducibility, the team made its analysis code publicly available on GitHub and deposited the underlying genome-wide association summary statistics in the GWAS Catalog, enabling other researchers to replicate, extend, or adapt the score for their own populations. Individual-level participant data remain accessible through the All of Us Researcher Workbench under its data use agreements.

The clinical implications reach well beyond hypertrophic cardiomopathy itself. Hypertrophic cardiomyopathy affects roughly one in several hundred people and remains a leading cause of sudden cardiac death in young athletes, yet many cases go undiagnosed until a catastrophic event occurs. A validated, ancestry-fair risk score could be deployed to flag individuals who warrant echocardiographic screening, genetic testing, or closer longitudinal follow-up, potentially catching disease before it strikes. For gene-positive family members of affected patients, combining sarcomere variant status with a polygenic score could personalize the schedule of cardiac imaging and the timing of preventive interventions, including the newer generation of cardiac myosin inhibitors that have transformed pharmacologic management of the disease.

The authors and observers of the field alike caution that scores of this kind are not yet ready to replace clinical judgment or guideline-based testing. The ancestry-specific performance gains, while encouraging, remained trends rather than definitive demonstrations, and further validation in independent, prospective cohorts will be essential before polygenic information enters routine cardiology practice. Questions about how best to communicate a seventy-fold relative risk to a worried variant carrier, and how insurers and employers might use such information, also demand careful attention. Still, the study marks a turning point: it demonstrates that the variable penetrance puzzle of hypertrophic cardiomyopathy is, at least in part, quantitatively solvable, and that the solution can be built to serve populations of all ancestries rather than a genetically privileged few. As multiancestry genomic resources continue to grow, the integration of rare variant status and polygenic background promises to become a standard pillar of precision cardiovascular medicine, reshaping how inherited heart disease is predicted, counseled, and ultimately prevented.

Subject of Research: Development and validation of a multiancestry polygenic risk score for hypertrophic cardiomyopathy risk stratification

Article Title: A multiancestry polygenic risk score improves stratification in patients with hypertrophic cardiomyopathy

Article References: Bal, H. S., Pampana, A., Nayak, A., Gaonkar, M., Patel, S., Yerabolu, K., Vekariya, N., Patel, N., Kalra, R., Li, P., Arora, G., & Arora, P. (2026). A multiancestry polygenic risk score improves stratification in patients with hypertrophic cardiomyopathy. Nature Cardiovascular Research, 5(9), 891-903. https://doi.org/10.1038/s44161-026-00866-8

Image Credits: AI Generated

DOI: 10.1038/s44161-026-00866-8

Keywords: hypertrophic cardiomyopathy, polygenic risk score, multi-ancestry genomics, sarcomere variants, variable penetrance, All of Us Research Program, cardiovascular genetics, risk stratification, precision medicine, genome-wide association study, genetic counseling, sudden cardiac death

The disease arises when mutations alter proteins in the cardiac sarcomere, the microscopic apparatus responsible for contraction. Each heartbeat depends on the coordinated interaction of thick and thin protein filaments, especially myosin and actin. Myosin acts as a molecular motor: powered by ATP, it attaches to actin and generates the force that shortens the sarcomere. Cardiac myosin-binding protein C, or cMyBP-C, helps regulate this process. The protein is encoded by MYBPC3 and binds to myosin within the thick filament, influencing how readily the motor can engage in contraction. When sarcomere activity becomes excessive or poorly regulated, the heart may respond by enlarging its muscle cells and remodeling its structure. That thickening can impair relaxation, raise internal pressures and, in some cases, obstruct blood flow or promote dangerous rhythm disturbances. MYBPC3 is one of the most frequent genes associated with inherited hypertrophic cardiomyopathy, but its variants do not all disrupt the sarcomere in the same way.

Many disease-associated MYBPC3 variants are truncating mutations. These changes create an incomplete version of cMyBP-C or prevent the protein from being produced, leaving the heart with reduced amounts of the regulator. Previous experimental models have commonly focused on this deficiency. The new study instead developed knock-in mice carrying a missense mutation called cMyBP-C p.R502W, in which a single amino-acid substitution changes the protein’s structure without eliminating it. That distinction proved important. Unlike mice carrying cMyBP-C truncations, the R502W animals retained cMyBP-C at normal levels and in its usual location within the sarcomere. Nevertheless, the animals developed pathological myocardial remodeling, demonstrating that a structurally altered protein can cause disease even when the quantity of cMyBP-C appears preserved. The model allowed the researchers to examine a form of hypertrophic cardiomyopathy that more closely reflects the complexity of missense disease variants, rather than treating all MYBPC3 mutations as simple loss of protein.

The investigators found that the R502W substitution weakened the interaction between cMyBP-C and myosin. Under normal conditions, this interaction helps restrain the contractile system. With the mutation, the sarcomere became hypercontractile, meaning it generated excessive force or activated too readily. Two linked changes helped explain the behavior. First, the muscle became more sensitive to calcium ions. Calcium is the central chemical signal that initiates contraction in heart muscle: when calcium levels rise inside a cardiac cell, it enables the contractile proteins to interact; when calcium falls, the muscle should relax. Increased calcium sensitivity means that a given calcium concentration produces a stronger contractile response. Second, the mutant sarcomeres favored an “ON” structural state of myosin. In the ON state, more myosin heads are available to interact with actin. Myosin can also adopt an OFF, or super-relaxed, state in which its heads are folded toward the thick filament and are less available for force generation. The R502W mutation therefore appeared to push the molecular motor toward excessive readiness.

Mavacamten is designed to act directly on cardiac myosin and reduce excessive cross-bridge activity. Its therapeutic effect is associated with stabilizing myosin in a less active, OFF-favored configuration, thereby lowering the number of motors available to engage actin during contraction. In the new experiments, the drug blunted myocardial remodeling in both R502W mice and models lacking cMyBP-C. This result was notable because the underlying defects were not identical. In cMyBP-C-deficient hearts, disease is linked primarily to the loss of a regulatory protein. In R502W hearts, cMyBP-C remained present but interacted abnormally with myosin, while the sarcomere also displayed increased calcium sensitivity and a bias toward the ON state. Despite those differences, mavacamten reduced the pathological response in both settings. The experiments further linked the benefit in R502W hearts to the drug’s ability to restore a greater population of myosin to the OFF state, directly addressing the structural imbalance produced by the mutation.

The researchers also tested the treatment in human engineered heart tissues carrying the R502W variant. These laboratory-built tissues are designed to reproduce selected features of human cardiac muscle, allowing scientists to measure contraction in a controlled setting without studying an entire organ. In those tissues, mavacamten opposed the mutation-associated hypercontractility. The human model strengthened the connection between the mouse findings and human biology, although it remains a simplified system rather than a complete patient heart. Engineered tissues do not fully reproduce the electrical networks, blood supply, immune environment or long-term mechanical stresses present in a living person. Even so, observing the same broad functional effect in human-derived cardiac tissue and in mice is important because it suggests that the mechanism is not limited to one experimental species. The result supports the idea that controlling myosin’s structural state may be useful even when the initiating mutation affects cMyBP-C through an abnormal interaction rather than through outright protein loss.

The study’s central message is that the genetic label alone may not predict the immediate molecular route to disease, but a shared downstream disturbance could still provide a treatment opportunity. Truncating and missense MYBPC3 variants can produce different primary defects: one may reduce the supply of cMyBP-C, while the other may leave the protein in place but impair its binding and regulation. Yet both can ultimately disturb the balance between active and inactive myosin and drive excessive sarcomere performance. Mavacamten appears to act at that convergent point. By reducing the availability of active myosin, it may dampen the mechanical stress that prompts cardiac hypertrophy and remodeling. This is an example of pathway-based treatment: instead of correcting the original DNA sequence, the drug modifies a downstream process that multiple mutations have in common. Such an approach could be especially valuable in inherited diseases where hundreds of distinct variants converge on a smaller number of cellular abnormalities.

The findings also help clarify why a preserved amount of a sarcomeric protein does not necessarily mean preserved function. The R502W mice had normal cMyBP-C levels and localization, but the altered protein no longer regulated myosin correctly. In molecular terms, the mutation changed protein quality rather than protein quantity. This distinction is relevant to genetic diagnosis and disease modeling, because two variants in the same gene may require different experimental systems and could produce different patterns of calcium handling, force generation and structural remodeling. The study’s knock-in design placed the missense change in its normal genomic context, allowing the investigators to observe its effects in a heart that produces the altered protein as part of its native sarcomere. The resulting model therefore provides a platform for testing how specific MYBPC3 variants alter contractile regulation and for examining whether treatments can correct the resulting functional state rather than merely compensate for missing protein.

Mavacamten’s apparent activity across these models does not mean that every person with MYBPC3-related hypertrophic cardiomyopathy will experience the same response, nor does it eliminate the need for clinical assessment and monitoring. Drug effects observed in mice and engineered tissues must be evaluated in patients, whose disease is shaped by age, genetic background, cardiac structure, rhythm, loading conditions and other biological factors. The amount of myosin inhibition must also remain within a therapeutic range: too much reduction in contractility could impair the heart’s ability to pump. The significance of the new work is therefore not a promise of a universal cure, but a clearer mechanistic basis for broader application of a targeted therapy. By showing that mavacamten can oppose hypercontractility generated through distinct MYBPC3-related pathways, the researchers provide evidence that treatment may be guided by convergent sarcomere behavior as well as by the exact mutation.

Published in Nature Cardiovascular Research, the study turns a complicated genetic question into a potentially actionable physiological one: whether the heart’s myosin motors are being driven into an excessively active state. The answer appeared to be yes in both cMyBP-C-deficient and R502W models, even though the molecular routes leading there differed. Mavacamten then acted as a brake, shifting myosin toward its OFF configuration and reducing the remodeling associated with chronic hypercontractility. The work highlights the value of combining genetically precise animal models with human engineered tissues, particularly for disorders in which a single gene can be disrupted in several fundamentally different ways. If future clinical research confirms the relevance of these mechanisms in patients, the findings could broaden the rationale for using myosin-directed therapy in MYBPC3-related hypertrophic cardiomyopathy and help move treatment decisions beyond a simple distinction between “truncating” and “missense” mutations.

Subject of Research: Mavacamten treatment mechanisms in MYBPC3-related hypertrophic cardiomyopathy

Article Title: Mavacamten shows broad benefit in human and mouse models of MYBPC3-related hypertrophic cardiomyopathy

Article References: Sen-Martín, L., Fernández-Trasancos, Á., López-Unzu, M.Á. et al. Mavacamten shows broad benefit in human and mouse models of MYBPC3-related hypertrophic cardiomyopathy. Nature Cardiovascular Research 5, 638–657 (2026). https://doi.org/10.1038/s44161-026-00833-3

Image Credits: AI Generated

DOI: 10.1038/s44161-026-00833-3

Keywords: hypertrophic cardiomyopathy, mavacamten, MYBPC3, cardiac myosin, cMyBP-C, sarcomere hypercontractility, missense mutations, truncating mutations, engineered heart tissue

Using clinical follow-up and genotype-focused analysis, researchers assessed how cardiomyopathy evolves soon after diagnosis. The central question was whether the RASopathy subtype itself acts as a prognostic marker for cardiac outcomes, rather than HCM being driven by uniform mechanisms across all cases. By stratifying patients according to subtype, the team evaluated the timing and intensity of disease progression in relation to underlying molecular causes.

The findings suggest that early HCM course is not “one-size-fits-all.” Patients with certain RASopathy subtypes showed signs consistent with a more aggressive early cardiac phenotype, including earlier manifestation and more pronounced echocardiographic features. Other subtypes appeared to progress more slowly, implying that the severity of signaling dysregulation translates into measurable differences in cardiac remodeling.

Genetically, the study emphasizes that specific variants within the RAS/MAPK network may influence not only the probability of developing HCM, but also the speed at which cardiac hypertrophy emerges. This points to a mechanistic gradient: upstream molecular disruptions can change how cardiomyocytes respond to growth cues, potentially altering hypertrophic pathways and downstream cardiac stress responses.

From a clinical perspective, the work supports subtype-aware risk stratification. Rather than relying solely on baseline cardiac imaging, clinicians may benefit from incorporating genetic information to anticipate which children require closer surveillance. Early identification could improve timing of intervention decisions, such as managing symptoms, monitoring arrhythmia risk, and guiding care intensity.

The study’s “early course” focus is particularly important because pediatric cardiomyopathy trajectories can shift rapidly during growth. Detecting higher-risk patterns early may help clinicians tailor follow-up intervals and reduce the chance of delayed recognition of worsening disease.

Beyond immediate care, the results offer a roadmap for translational research. If genotype predicts phenotype, targeted therapies that modulate RAS/MAPK activity or related growth signaling could be tested with stratification, improving the likelihood of detecting subtype-specific benefits.

Overall, this viral science news highlights a move toward precision cardiology in rare genetic disorders—where the RASopathy label is not merely descriptive, but predictive of how the heart will behave early in life.

Subject of Research: RASopathies and early disease course in RASopathy-associated hypertrophic cardiomyopathy
Article Title: Impact of RASopathy subtype on the early disease course of RASopathy-associated hypertrophic cardiomyopathy: clinical outcomes and genetic insights
Article References: López-Guillén, J.L., Carcavilla, A., Díez-Sebastián, J. et al. (2026) Pediatr Res. https://doi.org/10.1038/s41390-026-05152-8
Image Credits: AI Generated
DOI: 10.1038/s41390-026-05152-8
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