BACKGROUND:Dilated cardiomyopathy (DCM) is associated with shifts in cardiac metabolism. However, those shifts vary widely across patients, likely reflecting the diverse underlying causes of the disease. Identifying metabolic subtypes, or metabotypes, in DCM patients could help tailor treatments to patient needs. Hence, having a practical approach to identify these metabotypes would be a significant advance toward precision medicine in DCM. METHODS:We present a systems biology approach to uncover metabotypes directly from widely available transcriptomic data. We use in silico metabolic modeling methods that we have optimized for cardiac research to predict metabolic function activities from enzyme expression, followed by a hierarchical clustering approach. To demonstrate its power, we applied our method to publicly available cardiac data from end-stage DCM patients (N=164) and nonfailing controls (N=160). RESULTS:We identified 2 distinct metabotypes in end-stage DCM patients. These metabotypes are characterized by unique metabolic changes, notably in calcium handling, amino acid oxidation, and the pentose phosphate pathway. Strikingly, 1 DCM metabotype showed greater metabolic divergence from healthy controls, suggesting a greater metabolic contribution to its underlying etiology-even though disease severity was similar between the 2 identified DCM metabotypes. Further transcriptome-wide analysis revealed immune-related differences between metabotypes, suggesting an underlying interplay between inflammation, the immune response, and metabolism. CONCLUSIONS:Our results imply the presence of distinct metabotypes in end-stage DCM. Our systems biology approach offers an exciting opportunity to uncover novel insights into DCM, paving the way for a deeper understanding of its progression and heterogeneity.
BACKGROUND:Cardiorespiratory fitness (CRF) is a strong predictor of mortality and noncommunicable disease risk, but its underlying molecular mechanisms are poorly understood. In this study, we identified 2 signatures of CRF (1 metabolomic and 1 proteomic) from UK Biobank participants who completed a risk-stratified submaximal cycle ergometer test, with CRF estimated from the heart rate response to incremental workload. METHODS:These signatures were validated in an independent sample of UK participants with data on metabolomics (n=354 222) and proteomics (n=29 961) to investigate prospective associations with all-cause mortality and noncommunicable diseases. Prospective associations were evaluated using Cox proportional hazards models adjusted for age, sex, ethnicity, socioeconomic status, lifestyle factors (including smoking, alcohol intake, diet, and body mass index), and relevant medical history. RESULTS:Our findings reveal that higher CRF is characterized by downregulation of pathways related to inflammation, triglyceride metabolism, glycolysis, and vascular dysfunction, and upregulation of pathways related to cholesterol transport, apolipoprotein particle size, and cytoskeletal remodeling. Leveraging these insights, we developed 2 novel signatures of CRF (1 metabolomic and 1 proteomic) that robustly reflect CRF levels (R2: 0.50-0.60). Over an average of 9 years of follow-up, we observed 27 659 cases of all-cause mortality. Across the discovery and validation cohorts, we found that the metabolomic signature of CRF was strongly associated with a 39% to 54% lower risk of all-cause mortality and markedly reduced risk of type 2 diabetes (90% in both), cardiovascular disease (42%-47%), and colorectal cancer (33%-39%). Additionally, the proteomic signature of CRF was associated with a 17% lower risk of all-cause mortality, and with a 22% to 39% lower risk of type 2 diabetes and cardiovascular disease. CONCLUSIONS:Together, these findings indicate that circulating metabolites and proteins are associated with CRF and with subsequent risk of mortality and noncommunicable diseases.
BACKGROUND:GDF2 (encoding BMP9) variants have been described in pulmonary arterial hypertension (PAH) and hereditary hemorrhagic telangiectasia (HHT), as well as a few BMP10 variants in PAH. The purpose of the present study was to develop a functional assay capable of discriminating benign from pathogenic variants and to characterize the underlying molecular mechanisms responsible for their loss of function. METHODS:We developed a single-step functional assay in which C2C12 cells stably expressing a BMP (bone morphogenetic protein)-responsive element upstream of a firefly luciferase reporter would be stimulated by the autocrine secretion of BMP9 or BMP10 variants produced by transfected expression plasmids. RESULTS:Using this functional assay, we reclassified all GDF2 variants and 2 out of 5 BMP10 variants identified in patients with PAH as likely pathogenic. In contrast, only 2 of the 4 GDF2 variants identified in suspected patients with HHT were found to be likely pathogenic; nevertheless, none of the patients met the diagnostic criteria for hereditary hemorrhagic telangiectasia. We also showed, using ELISAs and Western blots, that the loss of function of GDF2 and BMP10 variants was mostly due to altered processing (folding/stability defects). Moreover, we found that loss-of-function GDF2 variants impaired the secretion of BMP10, suggesting a potential dominant-negative mechanism. CONCLUSIONS:We developed a functional assay for GDF2 and BMP10 variants, enabling the reclassification of variants of unknown significance. Together, this study further supports the involvement of GDF2 and BMP10 as predisposing genes in PAH. This single-step assay will be transferable to clinical genetic laboratories and will improve diagnosis of patients with PAH and HHT.
BACKGROUND:Performance and transferability of contemporary polygenic risk scores (PRS) for atherosclerotic cardiovascular disease phenotypes may vary across PRS methods, training data, and trait ascertainment. METHODS:We aimed to investigate the performance and transferability of contemporary PRS for atherosclerotic cardiovascular disease subtypes: coronary heart disease (CHD), abdominal aortic aneurysm (AAA), ischemic stroke (IS), and peripheral artery disease (PAD), using the All of Us Workbench, which consists of a large, diverse cohort with whole-genome sequence data. We also developed and evaluated a multi-trait PRS for each subtype. Performance of PRS for 4 atherosclerotic cardiovascular disease subtypes was compared across genetic similarity groups in 245 388 All of Us participants. Groups genetically similar to European, African, admixed American, and remaining groups (combined as other) were used to assess PRS for CHD, IS, AAA, PAD, and multi-trait. RESULTS:PRS for CHD and AAA performed better than IS and PAD. For CHD, CHDPGS003725 performed the best (hazard ratio per SD increase [95% CI]), across genetic ancestry groups, European, and African (1.72 [1.67-1.78], 1.23 [1.17-1.29]), with CHDPGS004696 being best for admixed American (1.91 [1.70-2.15]), and CHDPGS003356 for other (1.75 [1.58-1.95]). The best performing PRS for AAA was AAAMulti for European, other, and admixed American (1.71 [1.52-1.92], 1.59 [1.07-2.37], 1.50 [0.90-2.52]) and AAAPGS003972 for African (1.39 [1.19-1.63]). For IS, ISMulti performed best for other and European (1.49 [1.17-1.89], 1.33 [1.25-1.42]), and ISPGS000039 performed best in admixed American and African (1.17 [1.07-1.27], 1.09 [1.04-1.15]). For PAD, PADMulti performed best for all groups (other, 1.51 [1.19-1.92]; European, 1.32 [1.24-1.41]; admixed American, 1.23 [1.05-1.45]; and African, 1.18 [1.04-1.34]). CONCLUSIONS:Multi-trait and multi-ancestry PRS performed better than individual trait and/or single ancestry PRS for each atherosclerotic cardiovascular disease phenotype across ancestrally diverse and admixed individuals, with minimal change including adjustment for conventional risk factors.
BACKGROUND:GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance. METHODS:We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis. RESULTS:This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare variant-based risk score derived from the framework demonstrated distinct clinical characteristics compared with a conventional common variant-based PRS. The rare variant-based risk score significantly discriminated CAD cases and predicted cardiovascular mortality in an independent cohort. Furthermore, combining the rare variant-based risk score with the traditional PRS improved CAD prediction compared with the PRS alone (area under the curve, 0.66 versus 0.61; P=0.007). CONCLUSIONS:These findings underscore the distinct and complementary value of the rare variant-based risk score compared with the conventional PRS, highlighting the enhanced predictive power achieved through their integration. This comprehensive approach proposes broader genetic profiling, offering substantial potential for improved clinical risk stratification and personalized prevention strategies.
