Circulation: Genomic and Precision Medicine最新文献

Calmodulinopathies are very rare genetic disorders associated with a high risk for sudden cardiac death. Disease-causing variants in 1 of the 3 identical CALM genes cause severe forms of long QT syndrome, catecholaminergic polymorphic ventricular tachycardia, or idiopathic ventricular fibrillation, and there are many open questions concerning management and underlying mechanisms. What is currently known depends largely on the initial publications from the International Calmodulinopathy Registry. However, progress is delayed because the accrual of patients in the International Calmodulinopathy Registry is slow. As we did long ago for long QT syndrome, this is a call for action, requesting doctors all over the world to enroll even their isolated cases in the registry. This is the only way to obtain, for an adequate number of patients, the data necessary to define the spectrum of clinical manifestations and the genotype-phenotype correlation essential for an improved risk stratification and best therapeutic management. If you are willing to contribute, please contact us.

Background: The Helix Research Network program is a large population genomics initiative that screens an all-comers population of patients for Centers for Disease Control and Prevention Tier 1 genetic conditions, including familial hypercholesterolemia (FH). We evaluated changes in clinical management and low-density lipoprotein cholesterol (LDL-C) levels among patients we identified to have FH.

Methods: Participants across 9 US health systems provided samples that underwent clinical-grade exome sequencing. Individuals with a positive screening result for a Tier 1 condition were offered no-cost genetic counseling through their health system. Using medication and laboratory testing records, we evaluated changes in patients' lipid-lowering therapies and LDL-C levels.

Results: Among 228 602 adults enrolled between 2017 to 2025, 1155 (≈1/198) had a pathogenic FH variant in LDLR (74%), APOB (25%), or PCSK9 (1%). Of the 622 with retrospective and prospective electronic health record data available (mean of 11.8 and 2.1 years, respectively), 84% lacked a prior clinical FH diagnosis. Overall, 33% received new/modified lipid-lowering therapy within the first year, but this proportion was higher in those with a newly documented FH diagnosis code (57% versus 17% for those without documentation; P<0.001). Patients with new/modified therapies had a mean LDL-C reduction of 52 mg/dL, compared with 20 mg/dL in patients with no therapeutic change (difference=32 mg/dL; P<0.001).

Conclusions: Following genetic screening, many patients with a pathogenic FH variant experienced improvements in clinical management and LDL-C levels. Electronic health record documentation of the diagnosis code was associated with a greater likelihood of therapeutic modifications, which, in turn, were associated with larger LDL-C reductions. Findings underscore the powerful potential of population genomic screening for supporting optimal lipid management in individuals with FH.

Background: Mavacamten has been shown to improve cardiac function and symptoms in patients with symptomatic (New York Heart Association [NYHA] class II-III) obstructive hypertrophic cardiomyopathy (HCM). Clinical studies suggest mavacamten monotherapy is efficacious and has a favorable safety profile, but limited evidence exists regarding monotherapy in real-world studies. This analysis aimed to describe the effectiveness and safety outcomes of mavacamten monotherapy in the real-world mavaCamten ObservationaL evIdence Global cOnsortium in HCM study (COLLIGO-HCM). Methods: Patient-level data recorded between April 2022 and February 2025 at 7 sites across 5 countries were extracted. Adult patients with a diagnosis of HCM from 2018 onwards were eligible for inclusion if they had ≥1 mavacamten prescription after the date of diagnosis. Patients were categorized based on background therapy status during mavacamten treatment: mavacamten monotherapy or mavacamten with background therapy (down-titration or no dose modification). Results: Overall, 278 patients were included and received mavacamten (mavacamten monotherapy, n=88; mavacamten with background therapy, n=190). At month 9, most patients achieved ≥1 NYHA class improvement from baseline (mavacamten monotherapy, 60.0%; mavacamten with background therapy, 61.0%). Improvements in resting and Valsalva left ventricular outflow tract gradients from baseline to month 9 were observed in both subgroups; mean left ventricular ejection fraction (LVEF) through month 9 remained ≥62.0% with mavacamten monotherapy and ≥61.4% with mavacamten with background therapy. Two patients in the mavacamten monotherapy subgroup and one patient in the mavacamten with background therapy subgroup permanently discontinued treatment owing to LVEF <50%. Conclusions: Mavacamten monotherapy was associated with improvements in cardiac function and symptoms, and positive benefits to the risk profile over a 9-month follow-up period; this was consistent with improvements observed in patients treated with mavacamten with background therapy.

Background: DM1 is caused by a (CTG)n trinucleotide repeat expansion in the 3'UTR of the DMPK gene. Once expressed, repeat RNA form toxic hairpins that sequester the muscle blind-like (MBNL) family of splicing factors. This disrupts tissue alternative splicing landscape, triggering multisystemic manifestations - myotonia, muscle weakness, cardiac contractile defects, arrhythmia, and neurologic disturbances. While impaired mitochondrial function has been reported in brain, skeletal muscle, and fibroblasts of DM1 patients, they have not been reported in the heart, nor have their contribution to the DM1 cardiac pathogenesis been explored. Here, we probed the bioenergetic profile of DM1-afflicted heart tissues and explored the mechanistic basis of DM1-induced cardiac bioenergetic defects. Methods: Using an inducible, heart-specific DM1 mouse model, we performed extracellular flux analyses, measured total ATP and NAD(H) concentrations, and performed immunofluorescence staining and transmission electron microscopy to characterize DM1-induced cardiac bioenergetics and mitochondrial structural defects. We analyzed eCLIP-Seq data to identify mitochondria-related missplicing events, which we validated in human and mouse DM1 heart tissues. Finally, we used antisense oligonucleotides (ASO) to replicate these events and to test the recapitulation of DM1-like bioenergetic and structural defects in vitro. Results: DM1 induced a multi-state decrease in oxygen consumption rate (OCR) with a corresponding reduction in ATP and NAD(H) concentrations, indicating impaired oxidative phosphorylation in DM1-afflicted mouse hearts. We also found significant cardiac mitochondria fragmentation, which correlated with the missplicing of transcripts encoding mitochondria fission factor (Mff, encodes MFF protein) and dynamin related protein 1 (Dnm1l, encodes DRP1 protein) in DM1-afflicted human and mouse hearts. ASO-mediated redirection of Dnm1l alternative splicing reproduced DM1-like impairment in cardiac bioenergetics and mitochondrial dynamics in wild type HL-1 cardiomyocytes. Conclusions: Together, these findings reveal that expanded (CUG)n RNA toxicity in DM1 disrupts cardiac bioenergetics through missplicing of critical mitochondrial fission transcripts. These misspliced transcripts represent potential therapeutic targets for improving mitochondrial function and cardiac symptoms of DM1.

Background: Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown.

Methods: We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by International Classification of Diseases-Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, n=16 671) and validation (30%, n=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices.

Results: The study sample had a mean age of 56.1 years; 44% were men, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73-2.03]; P<0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72-0.74] versus 0.71 [0.69-0.72]; ΔC-index, 0.03 [0.02-0.04]).

Conclusions: A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.

Background: Hereditary hemorrhagic telangiectasia (HHT) is a near-fully penetrant autosomal dominant disorder characterized by nosebleeds, anemia, and arteriovenous malformations. The great majority of HHT cases are caused by heterozygous loss-of-function mutations in ACVRL1 or ENG, which encode proteins that function in bone morphogenetic protein signaling. HHT prevalence is estimated at 1 in 5000 and is accordingly classified as rare. However, HHT is suspected to be underdiagnosed.

Methods: To estimate the true prevalence of HHT, we summed allele frequencies of predicted pathogenic variants in ACVRL1 and ENG using 3 methods. For method 1, we included Genome Aggregation Database (gnomAD v4.1) variants with ClinVar annotations of pathogenic or likely pathogenic, plus unannotated variants with a high probability of causing disease. For method 2, we evaluated all ACVRL1 and ENG gnomAD variants using threshold filters based on accessible in silico pathogenicity prediction algorithms. For method 3, we developed a machine learning-based classification system to improve the classification of missense variants.

Results: We calculated an HHT prevalence of between 2.1 in 5000 and 11.9 in 5000, or 2 to 12× higher than current estimates. Application of our machine learning-based classification method revealed missense variants as the greatest contributor to pathogenic allele frequency and similar HHT prevalence across genetic ancestries.

Conclusions: Our results support the notion that HHT is underdiagnosed and that HHT prevalence may be above the threshold of a rare disease.

Coronary artery disease remains the leading cause of death worldwide. One of the greatest developments in preventive cardiology has been the identification and treatment of standard modifiable risk factors associated with coronary artery disease. However, despite advances in the management of standard modifiable risk factors, there is an escalating number of patients who continue to present with acute coronary syndromes, a trend that is particularly concerning given the decreasing age-adjusted incidence rates of these conditions. This persistent clinical challenge underscores the urgency to explore alternative approaches for early detection and improved risk stratification. In recent years, the emergence of proteomics technologies has brought forth promising avenues for the discovery of novel biomarkers that hold the potential to revolutionize the timely detection and management of coronary artery disease. Proteomics enables the high throughput and often unbiased analysis of protein abundance, modifications, and interactions within pathways relevant to cardiovascular disease pathogenesis. Of particular importance is the capability to detect low-abundance proteins including those with currently unknown functions. While the functional assessment of these proteins aligns more with mechanistic studies, their role in biomarker discovery is equally important. Such detection may provide new insights into cardiac pathophysiology, including potential new markers for early disease detection and risk assessment. Although the latest proteomics technology and bioinformatic approaches do provide the opportunity for novel discoveries, understanding the limitations of each technology platform is important. This review provides an updated overview of major proteomic platforms and discusses their methodological strengths, constraints, and applications, using recent coronary artery disease studies as illustrative examples. By integrating proteomics data with clinical information, including advanced noninvasive imaging techniques and other omics disciplines, such as genomics and metabolomics, we can deepen our understanding of disease mechanisms and improve risk stratification. Although the discovery of novel biomarkers represents a significant step forward in the field, their true clinical value is contingent upon their rigorous validation in clinical trials and implementation studies. With our current capabilities and emerging advancements, we are well-positioned to advance proteomics-guided precision medicine in cardiovascular care over the coming decade.