Circulation: Genomic and Precision Medicine最新文献

Background: Inflammatory heart disease can be triggered by a variety of causes, both infectious and noninfectious in nature. We hypothesized that inflammatory cardiomyopathy is potentially related to microbial infection.

Methods: In this retrospective study, we used deep RNA sequencing on formalin-fixed paraffin-embedded heart tissue specimens to detect pathogenic agents. We first investigated 4 single-sample cases to test the feasibility of this diagnostic protocol and further 3 control-sample paired cases to improve the protocol with differential metatranscriptomics next-generation sequencing (mtNGS) analysis.

Results: We demonstrate that differential mtNGS allows identification of various microbials as potentially pathogenic, for example, Cutibacterium acnes, Corynebacterium aurimucosum, and Pseudomonas denitrificans, which are usually commensal in healthy individuals. Differential mtNGS also allows characterization of human host response in each individual by profiling alterations of gene expression, networked pathways, and inferred immune cell compositions, information of which is beneficial for us to understand different etiologies and immunity roles in each case. Additionally, differential mtNGS allows the identification of genetic variants in patients that may contribute to their susceptibility to particular microbial infections.

Conclusions: The demonstrated power of differential mtNGS in simultaneous capture of both the infectious microbial(s) and the status of human host immune response could help us better understand the pathogenesis of complex inflammatory cardiomyopathy, if conducted on a larger scale of the population. The developed differential mtNGS method could also shed light on its translation and adoption of such a laboratory test in clinic practice, allowing for a more effective diagnosis to guide therapeutic treatment of the disease.

Background: Chronic kidney disease (CKD) is highly prevalent in Central America, and genetic factors may contribute to CKD risk. To understand the influences of genetic admixture on CKD susceptibility, we conducted an admixture mapping screening of CKD traits and risk factors in US Hispanic and Latino individuals from Central America country of origin.

Methods: We analyzed 1023 participants of HCHS/SOL (Hispanic Community Health Study/Study of Latinos) who reported 4 grandparents originating from the same Central America country. Ancestry admixture findings were validated on 8191 African Americans from WHI (Women's Health Initiative), 3141 American Indians from SHS (Strong Heart Study), and over 1.1 million European individuals from a multistudy meta-analysis.

Results: We identified 3 novel genomic regions for albuminuria (chromosome 14q24.2), CKD (chromosome 6q25.3), and type 2 diabetes (chromosome 3q22.2). The 14q24.2 locus driven by a Native American ancestry had a protective effect on albuminuria and consisted of 2 nearby regions spanning the RGS6 gene. Variants at this locus were validated in American Indians. The 6q25.3 African ancestry-derived locus, encompassing the ARID1B gene, was associated with increased risk for CKD and replicated in African Americans through admixture mapping. The European ancestry type 2 diabetes locus at 3q22.2, encompassing the EPHB1 and KY genes, was validated in European individuals through variant association.

Conclusions: US Hispanic/Latino populations are culturally and genetically diverse. This study focusing on Central America grandparent country of origin provides new loci discovery and insights into the ancestry-of-origin influences on CKD and risk factors in US Hispanic and Latino individuals.

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRS_CHD) for 5 genetic ancestry groups.

Methods: We derived ancestry-specific and multi-ancestry PRS_CHD based on pruning and thresholding (PRS_PT) and ancestry-based continuous shrinkage priors (PRS_CSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRS_CHD in 176,988 individuals across 9 diverse cohorts.

Results: Multi-ancestry PRS_PT and PRS_CSx outperformed ancestry-specific PRS_PT and PRS_CSx across a range of tuning values. Two best-performing multi-ancestry PRS_CHD (ie, PRS_PTmult and PRS_CSxmult) and 1 ancestry-specific (PRS_CSxEUR) were taken forward for validation. PRS_PTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRS_CSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]).

Conclusions: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRS_CHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRS_CHD.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.