Circulation: Genomic and Precision Medicine最新文献

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.

Background: Congenital heart disease (CHD) is the most common heterogeneous birth defect, with prevalence varying across populations. A comprehensive meta-analysis could refine the genetic risk estimates and enhance our understanding of CHD susceptibility.

Methods: We conducted a meta-analysis of 175 case-control studies investigating 107 genetic variants across 72 gene regions. Pooled odds ratios were calculated using 6 genetic models, with subgroup analyses by ethnicity and CHD subtype. Gene Ontology and network analyses elucidated the functional significance of implicated genes.

Results: Thirty-six variants were significantly associated with CHD (P<0.05), including 7 missense mutations in NRP1, MTHFR, MTRR, NOS3, and DNMT1. Ten variants, including rs1531070 in MAML3 (odds ratio, 1.52; P=5.9×10^-15), surpassed genome-wide significance. Ethnicity-specific analyses identified 13 significant variants, including MTHFR-rs1801131 in Chinese (P=1.71×10^-10), STX18-AS1-rs870142 in Europeans (P=7.13×10^-16), and MTRR-rs1801394 in Middle Eastern populations (P=9.8×10^-8). Subtype analyses revealed 25 variants associated with specific CHD subtypes, such as STX18-AS1-rs16835979 with atrial septal defect (P=2.1×10^-16) and variants in MTHFR, NRP1, and PTPN11 with tetralogy of Fallot (P=3.0×10^-17-2.33×10^-10). The rs1801133 variant was linked to double-outlet right ventricle (P=3.0×10^-11) and patent ductus arteriosus (P=6.5×10^-9). Gene Ontology and network analyses highlighted genes involved in cardiac development and folate metabolism in CHD pathogenesis.

Conclusions: This meta-analysis refines CHD risk estimates across diverse ancestries and subtypes, underscoring the complex genetic architecture of the disease. Variants involved in cardiac development and metabolic pathways represent promising targets for precision medicine in CHD.

Background: Cardiac remodeling occurs in the mature heart and is a cascade of adaptations in response to stress, which are primed in early life. A key question remains as to the processes that regulate the geometry and motion of the heart and how it adapts to stress.

Methods: We performed spatially resolved phenotyping using machine learning-based analysis of cardiac magnetic resonance imaging in 47 549 UK Biobank participants. We analyzed 16 left ventricular spatial phenotypes, including regional myocardial wall thickness and systolic strain in both circumferential and radial directions. In up to 40 058 participants, genetic associations across the allele frequency spectrum were assessed using genome-wide association studies with imputed genotype participants, and exome-wide association studies and gene-based burden tests using whole-exome sequencing data. We integrated transcriptomic data from the GTEx project and used pathway enrichment analyses to further interpret the biological relevance of identified loci. To investigate causal relationships, we conducted Mendelian randomization analyses to evaluate the effects of blood pressure on regional cardiac traits and the effects of these traits on cardiomyopathy risk.

Results: We found 42 loci associated with cardiac structure and contractility, many of which reveal patterns of spatial organization in the heart. Whole-exome sequencing revealed 3 additional variants not captured by the genome-wide association study, including a missense variant in CSRP3 (minor allele frequency 0.5%). The majority of newly discovered loci are found in cardiomyopathy-associated genes, suggesting that they regulate spatially distinct patterns of remodeling in the left ventricle in an adult population. Our causal analysis also found regional modulation of blood pressure on cardiac wall thickness and strain.

Conclusions: These findings provide a comprehensive description of the pathways that orchestrate heart development and cardiac remodeling. These data highlight the role that cardiomyopathy-associated genes have on the regulation of spatial adaptations in those without known disease.

Background: Previous studies have suggested that the associations between ambient air pollution and atherosclerotic cardiovascular diseases (ASCVD) differ by genotype. A genome-wide approach provides a more comprehensive understanding of this relationship on a genomic scale.

Methods: Using data from ≈300 000 UK Biobank participants, we conducted a genome-wide interaction analysis on 10 745 802 variants. We examined the interactions between fine particulate matter (PM_2.5) and genetic variants across 3 ASCVD subtypes: coronary artery disease, ischemic stroke, and peripheral artery disease. A polygenic risk score was constructed, and functional annotation identified potential genes at loci interacting with air pollution. In vivo studies explored how genome-wide interaction analysis-identified genes interacting with PM_2.5 might contribute to atherosclerotic plaque progression.

Results: During 12.55 years of follow-up, 42 696 ASCVD events were observed. Genome-wide interaction analysis identified 12 loci shared across the ASCVD subtypes related to PM_2.5 exposure. Functional annotation suggested these loci and colocalized genes are involved in pathways such as cell-cell adhesion, deoxyribonucleotide biosynthesis, RNA metabolism, and calcium homeostasis. High genetic risk combined with PM_2.5 exposure was associated with coronary artery disease, ischemic stroke, and peripheral artery disease, with hazard ratios and 95% CIs of 1.35 (1.32-1.37), 1.53 (1.47-1.58), and 1.68 (1.62-1.75), respectively. Animal studies confirmed that adenosine kinase gene expression might interact with PM_2.5, potentially influencing atherosclerotic plaque development through inflammation.

Conclusions: Our study identified genome-wide loci interacting with PM_2.5 and linked adenosine kinase expression in response to PM_2.5 exposure to the formation of atherosclerotic plaques, highlighting potential pathways that connect PM_2.5 to ASCVD.

Background: There is a higher prevalence of heart rate corrected QT (QTc) prolongation in patients with diabetes and metabolic syndrome. QT interval genome-wide association studies have identified candidate genes for cardiac energy metabolism, and experimental studies suggest that polyunsaturated fatty acids have direct effects on ion channel function. Despite this, there has been limited study of metabolite concentration relationships with QT intervals.

Methods: In 21 056 UK Biobank participants with same-day electrocardiograms and plasma profiling of 100 metabolites, per-metabolite regression analyses with the QTc were performed adjusting for clinically relevant variables. Participants with ischemic heart disease or heart failure were excluded. Significant metabolites (P<5×10^-4) that replicated in an independent UK Biobank sample (N=5304), underwent Least Absolute Shrinkage and Selection Operator regression with clinical variables to identify top predictors and calculate the QTc variance explained. Two-sample Mendelian randomization and locus-level colocalization analyses were performed to test for causal relationships and shared genetic etiologies, respectively.

Results: Twenty-two metabolites were associated with the QTc in main and replication regression analyses, including ketone bodies, fatty acids, glycolysis-related molecules, and amino acids. Top associations were 3-hydroxybutyrate (8.9 ms), acetone (7.9 ms), and polyunsaturated fatty acids (-7.3 ms), when comparing the highest versus lowest deciles. A combined metabolite and clinical variables Least Absolute Shrinkage and Selection Operator model significantly increased the QTc variance explained compared with the clinical-only model (11.2% versus 7.7%; P=0.002). There was support for a causal relationship between Linoleic acid to fatty acid ratio and the QTc, and evidence for colocalization for 15 metabolites at 7 QT loci, including CASR for citrate and glutamine.

Conclusions: In the largest study of metabolite-QTc relationships, we identify 22 associated metabolites and clinically relevant effect sizes, with evidence for genetic support. For the first time, we report a potentially protective effect of polyunsaturated fatty acids in humans. These metabolites may be risk factors in acquired and congenital long-QT syndrome and warrant additional investigation for arrhythmia risk stratification.