Circulation: Genomic and Precision Medicine最新文献

Background: Pathogenic variants in the lamin A/C (LMNA) gene cause an aggressive form of dilated cardiomyopathy (DCM), marked by higher rates of advanced conduction disease, malignant ventricular tachyarrhythmias, and advanced heart failure compared with other causes of nonischemic cardiomyopathy. However, the mechanisms that drive the development and progression of LMNA DCM are incompletely understood.

Methods: To identify proteins and biological pathways associated with likely pathogenic/pathogenic LMNA variants, we measured ≈3000 plasma proteins using the OLINK platform in a genetic DCM cohort consisting of LMNA (n=41) and sarcomeric (n=18) DCM, along with phenotype-negative individuals from family-based cascade screening (n=55) with (LMNA, n=16; sarcomere, n=12) or without the family variant (genotype negative, n=27).

Results: We identified several novel proteins associated with LMNA DCM compared with sarcomeric DCM, including EDA2R (ectodysplasin A2 receptor; per log2 fold change in relative protein abundance, β=3.0; P=4×10^-³) and MYL4 (myosin light chain 4; β=2.32; P=5×10^-³). Among the proteins associated with LMNA DCM, 26 showed concordant differential gene expression from single-cell sequencing in cardiomyocytes from myocardial biopsies in advanced LMNA heart failure compared with control hearts (false discovery rate, <5%). We performed principal component analyses on these 26 proteins to identify proteomic signatures of LMNA DCM and found the first principal component to be associated with left ventricular ejection fraction and complete heart block in the LMNA DCM cohort. Six proteins-EDA2R, MYL4, CRIM1 (cysteine-rich transmembrane bone morphogenetic protein regulator 1), TPR (translocated promoter region), FSTL3 (follistatin-like 3), and NFYA (nuclear transcription factor Y)-were associated with LMNA pathogenic variants across phenotype-negative individuals, DCM, and their respective cardiomyocyte RNA expression profiles in advanced heart failure.

Conclusions: Proteomic profiling in individuals with likely pathogenic/pathogenic LMNA variants illuminated integral pathways across the spectrum of LMNA DCM. These findings may help advance genotype-driven biomarker discovery and tailored therapeutic development in LMNA DCM.

Background: Clinical factors discriminate incident atrial fibrillation (AF) risk with moderate accuracy, with only modest improvement after incorporation of polygenic risk scores. Whether emerging large-scale proteomic profiling can augment AF risk estimation is unknown.

Methods: In the UK Biobank cohort, we derived and validated a machine learning model to predict incident AF risk using serum proteins (Pro-AF). We compared Pro-AF to a validated clinical risk score (Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation, CHARGE-AF) and an AF polygenic risk score. Models were evaluated in a multiply resampled test set from nested cross-validation (internal test set), and a sample of UK Biobank participants separate from model development (hold-out test set). Metrics included discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic curve and net reclassification.

Results: Trained in 32 631 UK Biobank participants, Pro-AF predicts incident AF using 121 protein levels (out of 2911 protein analytes). When assessed in the internal test set comprising 30 632 individuals (mean age 57±8 years, 54% women, 2045 AF events) and hold-out test set comprising 13 998 individuals (mean age 57±8 years, 54% women, 870 AF events), discrimination of 5-year incident AF was highest using Pro-AF (area under the receiver operating characteristic curve internal: 0.761 [95% CI, 0.745-0.780], hold-out: 0.763 [0.734-0.784]), followed by CHARGE-AF (0.719 [0.700-0.737]; 0.702 [0.668-0.730]) and the polygenic risk score (0.686 [0.668-0.702]; 0.682 [0.660-0.710]). AF risk estimates were well-calibrated, and the addition of Pro-AF led to substantial continuous net reclassification improvement over CHARGE-AF (eg, internal test set 0.410 [0.330-0.492]). A simplified Pro-AF including only the 5 most influential proteins (NT-proBNP [N-terminal pro-brain natriuretic peptide], EDA2R [ectodysplasin A2 receptor], NPPB [B-type natriuretic peptide], BCAN [brevican core protein], and GDF15 [growth/differentiation factor 15]), retained favorable discriminative value (area under the receiver operating characteristic curve internal: 0.750 [0.733-0.768]; hold-out: 0.759 [0.732-0.790]).

Conclusions: A machine learning-based protein score discriminates 5-year incident AF risk favorably compared with clinical and genetic risk factors. Large-scale proteomic analysis may assist in the prioritization of individuals at risk for AF for screening and related preventive interventions.

Background: Dilated cardiomyopathy (DCM) is a heart muscle disease in which the left ventricle is enlarged, resulting in systolic dysfunction. Pathogenic variants in genes encoding proteins involved in cardiac contractility, cytoskeleton structure, and Ca²⁺ handling have been associated with DCM. TNNC1 (cTnC [cardiac troponin C]) variants are implicated in DCM, hypertrophic, and restrictive cardiomyopathies. Unlike other sarcomere genes, most reports of TNNC1 variants lack segregation or pedigree data, partly because the majority of the variants described, to date, have been reported as de novo. Therefore, a critical need is warranted to further understand the mechanisms by which TNNC1 variants could impact myofilament function, especially in response to PKA (protein kinase A)-mediated phosphorylation as this posttranslational modification modulates sarcomere function in response to β-adrenergic stimulation.

Methods: Probands with the novel TNNC1-c.404A>C variant (cTnC-E135A) and family members were identified and consented. cTnC-depleted donor human cardiac muscle preparations were reconstituted with recombinant exogenous human cTnC-E135A. Steady-state isometric force and crossbridge kinetics were measured before and after PKA incubation. We used in silico modeling to further investigate crossbridge cycling kinetics.

Results: We identified a multigenerational family carrying the TNNC1-c.404A>C variant with autosomal dominant DCM with both systolic and diastolic dysfunctions. Using reconstituted human cardiac muscle preparations, we showed that the cTnC-E135A abolishes the myofilament response to PKA-mediated phosphorylation. Furthermore, in silico mathematical modeling showed that this variant affects crossbridge kinetics by decreasing both Ca²⁺ k_OFF-rate constant and myosin detachment rate, which could result in increased ventricular stiffness and reduced ejection fraction.

Conclusions: Our clinical and genetics data, combined with the in silico modeling and functional assays, suggest that cTnC-E135A is associated with DCM and disrupts kinetics of Ca²⁺ and crossbridge cycling by abolishing the myofilament response to PKA phosphorylation.

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. The need for a new taxonomy of HF is recognized; up to now, such phenomapping efforts have primarily used clinical data. Proteomics offers potential for more precise phenotypic identification and mechanistic insights. However, few phenomapping studies have used this approach, and all have focused on targeted cardiovascular proteomics panels and a restricted HF ejection fraction group.

Methods: We measured over 7000 plasma proteins in a population-based cohort of 1351 patients with HF, used k-means clustering to identify distinct phenogroups, and compared their clinical characteristics and all-cause mortality.

Results: Three proteomics-defined phenogroups were identified, with substantial differences in survival (phenogroup 1 5-year survival probability, 65% [95% CI, 61%-68%]; phenogroup 2, 45% [40%-51%]; phenogroup 3, 26% [22%-30%]), independent of clinical characteristics. Phenogroups also exhibited differences in several measures suggesting poorer health, including NT-proBNP (N-terminal pro-B-type natriuretic peptide), kidney function, and Meta-Analysis Global Group in Chronic Heart Failure scores, but did not differ by ejection fraction or New York Heart Association class.

Conclusions: Our study demonstrates that molecular phenomapping can stratify patients with HF into distinct subgroups that go beyond predefined clinical classifications.

Cardiovascular disease remains the leading cause of death worldwide, calling for the development of novel therapeutics. Over the past 3 decades, substantial investments in human genetic research have unveiled the genetic architecture of cardiovascular disease, offering promising novel therapeutic targets. These discoveries have been instrumental in the development of several cardiovascular drug development programs, such as those targeting proprotein convertase subtilisin/kexin type 9, lipoprotein (a), apo C₃, and angiopoietin-like 3. Large-scale resources such as population-based biobanks and data repositories, now enable human genetic data to be leveraged at scale and inform not only target selection, but also clinical drug development. This review highlights the transformative potential of human genetics in cardiovascular drug development, focusing on IL (interleukin)-6 signaling as a case study. Specifically, we discuss how IL-6 signaling was pinpointed as a key causal mediator of atherosclerosis by genetic data, shaping the current development landscape for anti-IL-6 therapeutics in cardiovascular disease. Recent genetic studies employing innovative methodologies have provided key insights into prioritizing indications for clinical testing, informing repurposing strategies, optimizing clinical trial design for population selection, and assessing safety signals. Despite this progress, methodological challenges, such as pleiotropic effects of genetic variants, extrapolation of small genetic associations to large interventional effects, and the predominance of European-derived data, highlight the need for careful interpretation. Continued methodological advances, coupled with the emergence of high-throughput omics data and detailed cardiovascular phenotyping, promise unprecedented opportunities to refine drug discovery and development.

Background: Prior work suggests modifiable cardiovascular risk factors (CRFs) account for 80% to 90% of the risk for incident myocardial infarction. The contributions of genetic and other novel CRFs have not been simultaneously assessed in contemporary data sets.

Methods: In the United Kingdom Biobank, CRFs were identified and Cox proportional hazards models with traditional CRFs (hypertension, diabetes, dyslipidemia, waist-to-hip ratio, diet, exercise, alcohol, and socioeconomic deprivation) and contemporary/genetic CRFs (Lp(a) [lipoprotein(a)], hsCRP [high-sensitivity C-reactive protein], familial hypercholesterolemia variants, and polygenic risk score for coronary artery disease) were constructed for coronary artery disease. Coronary artery disease was defined as a first-time myocardial infarction diagnosis or coronary revascularization. R² was calculated for each model, and the percent contribution of each individual CRF was calculated by the R² percent decrease after its removal.

Results: Among 299 707 individuals, the mean (SD) age was 56.2 (8.1) years, and 166 533 (55.6%) were women. Over a median (interquartile range) follow-up of 11.0 (9.6-12.5) years, 17 409 (5.8%) of participants developed myocardial infarction. R² increased from the base model (R², 0.021 [0.020-0.022]), to the clinical model (R², 0.045 [0.043-0.046]), to the contemporary/genetic model (R², 0.053 [0.052-0.055]). The most powerful individual CRFs were hypertension (R² loss, 15.2% [14.5-17.1]) and polygenic risk score for coronary artery disease (R² loss, 12.4% [10.8-13.3]), followed by dyslipidemia (R² loss, 3.4% [2.6-3.5]), diabetes (R² loss, 2.2% [1.5-2.0]), hsCRP (R² loss, 1.8% [1.5-2.0]), and Lp(a) (R² loss, 1.5% [1.2-1.8]).

Conclusions: Novel CRFs like polygenic risk score for coronary artery disease, hsCRP, and Lp(a) have similar importance, comparable to traditional CRFs such as hypertension, dyslipidemia, and diabetes, for incident myocardial infarction, highlighting important identifiable residual risk factors.

Background: To better define the importance of the amyloidogenic p.V142I TTR allele across the life span of a carrier, we leveraged data from All of Us to provide a generalizable assessment of the population-level burden of cardiovascular risk and estimate the age at disease onset.

Methods: We included self-identifying Black participants in All of Us who provided genomic data (N=77 767). The exposure of interest was p.V142I TTR carrier status (N=2213). Outcomes included incident heart failure (HF), atrial fibrillation, and carpal tunnel syndrome.

Results: The median (interquartile range) age at enrollment was 56 (42-64) years. For the subset with genetic ancestry data (N=50 516), the p.V142I TTR carrier frequency was 3.5% (N=1771) among those with African ancestry. After adjustment for age and traditional risk factors, p.V142I TTR carrier status was associated with a greater risk of HF (odds ratio, 1.56 [95% CI, 1.22-1.99]; P=0.001), atrial fibrillation (odds ratio, 1.3 [95% CI, 1.08-1.90]; P=0.013), and carpal tunnel syndrome (odds ratio, 1.94 [95% CI, 1.43-2.63]; P<0.001).The risks increased in the sixth decade of life. In carriers, the attributable risk of the variant for HF, atrial fibrillation, and carpal tunnel syndrome was 27%, 26%, and 43%, respectively. While traditional HF risk factors did not modify the association of carrier status with HF (P-interaction >0.05 for all), their presence substantially augmented the risk of HF over a lifetime.

Conclusions: p.V142I TTR carriers are at an increased risk of HF and atrial fibrillation, beginning during the sixth decade of life. HF risk rises in a dose-dependent manner with other nonamyloid-related HF risk factors, highlighting the importance of aggressive treatment of HF risk factors among carriers. These observations also confirm the clinical relevance of the p.V142I TTR variant for individuals of African ancestry and underscore the importance of efforts to increase diagnoses, implement TTR-targeted therapies, and evaluate screening strategies for variant transthyretin cardiac amyloidosis.