Admixture Mapping of Subclinical Atherosclerosis and Subsequent Clinical Events Among African Americans in 2 Large Cohort Studies

Simplified detection of genetic background admixture using artificial intelligence

Admixture refers to the mixing of genetic ancestry from different populations. Admixture is important for genomic medicine because it can affect how an individual responds to certain medications, how they metabolize drugs, and susceptibility to certain diseases. For example, some genetic variants associated with drug metabolism and response may be more common in certain populations, and individuals with admixed ancestry may have a different frequency of these variants than individuals from the ancestral populations. Understanding the patterns of admixture in a population can also help researchers identify new genetic variants associated with diseases or traits and develop more personalized and targeted treatments. In this study, we compared and classified the known and self-reported genetic backgrounds from 1000 Genomes Project and admixed samples from GTEx projects using supervised, unsupervised and statistical classification methodologies. We developed a novel tool called Admix-AI that uses a one-dimensional convolutional neural network to understand and classify admixed genetic backgrounds using 213 DNA-marker based genetic background labels. Admix-AI can be used to discover admixed proportions in samples and ultimately aid personalized genomic medicine by identifying specific biomarker systems. We compared Admix-AI to the existing admixture categorization software and found our tool to be computationally faster with 2× speedup and streamlined usage. Admix-AI is available as open-source code under GPL version 3.0 license at https://github.com/rpauly/Admix-AI.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

PIK3CA mutations-mediated downregulation of circLHFPL2 inhibits colorectal cancer progression via upregulating PTEN

BACKGROUND

PIK3CA mutation and PTEN suppression lead to tumorigenesis and drug resistance in colorectal cancer (CRC). There is no research on the role of circular RNAs (circRNAs) in regulating PIK3CA mutation and MEK inhibitor resistance in CRC.

METHODS

The expression of circLHFPL2 in PIK3CA-mutant and wild-type cells and tissues was quantified by RNA-sequencing and qRT-PCR. CCK-8 assay and colony formation assay were used to evaluate cell viability. Annexin V/PI staining was implemented to assess cell apoptosis. Luciferase assay, biotin-coupled microRNA capture, and RIP assay were used to validate the interaction among potential targets. Western blotting and qRT-PCR assays were used to evaluate the expression of involved targets. Xenograft tumor in a nude mouse model was used to explore the role of circRNAs in vivo.

RESULTS

RNA sequencing defined downregulated expression of circLHFPL2 in both PIK3CA^H1047R (HCT116) and PIK3CA^E545K (DLD1) cells. CircLHFPL2 was also downregulated in PIK3CA-mutant CRC primary cells and tissues, which was correlated with poor prognosis. CircLHFPL2 was mainly localized in the cytoplasm and its downregulation was attributed to the PI3K/AKT signaling pathway activated by phosphorylating Foxo3a. CircLHFPL2 inhibited PI3KCA-Mut CRC progression both in vitro and in vivo. Furthermore, our work indicated that circLHFPL2 acts as a ceRNA to sponge miR-556-5p and miR-1322 in CRC cells and in turn modulate the expression of PTEN. Importantly, circLHFPL2 was able to overcome PIK3CA-mediated MEK inhibitor resistance in CRC cells.

CONCLUSIONS

Downregulation of circLHFPL2 sustains the activation of the PI3K/AKT signaling pathway via a positive feedback loop in PIK3CA-mutant CRC. In addition, downregulation of circLHFPL2 leads to MEK inhibitor resistance in CRC. Therefore, targeting circLHFPL2 could be an effective approach for the treatment of CRC patients harboring oncogenic PIK3CA mutations.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Risk Prediction in Patients With Heart Failure With Preserved Ejection Fraction Using Gene Expression Data and Machine Learning

Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients into different risk groups is a promising method to facilitate the identification of high-risk groups for further precision treatment. Here, we applied six machine learning models, namely kernel partial least squares with the genetic algorithm (GA-KPLS), the least absolute shrinkage and selection operator (LASSO), random forest, ridge regression, support vector machine, and the conventional logistic regression model, to predict HFpEF risk and to identify subgroups at high risk of death based on gene expression data. The model performance was evaluated using various criteria. Our analysis was focused on 149 HFpEF patients from the Framingham Heart Study cohort who were classified into good-outcome and poor-outcome groups based on their 3-year survival outcome. The results showed that the GA-KPLS model exhibited the best performance in predicting patient risk. We further identified 116 differentially expressed genes (DEGs) between the two groups, thus providing novel therapeutic targets for HFpEF. Additionally, the DEGs were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to HFpEF. The GA-KPLS-based HFpEF model is a powerful method for risk stratification of 3-year mortality in HFpEF patients.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Aberrantly Methylated-Differentially Expressed Genes Identify Novel Atherosclerosis Risk Subtypes

Increasing evidence has indicated that modulation of epigenetic mechanisms, especially methylation and long-non-coding RNA (lncRNA) regulation, plays a pivotal role in the process of atherosclerosis; however, few studies focused on revealing the epigenetic-related subgroups during atherosclerotic progression using unsupervised clustering analysis. Hence, we aimed to identify the epigenetics-related differentially expressed genes associated with atherosclerosis subtypes and characterize their clinical utility in atherosclerosis. Eighty samples with expression data (GSE40231) and 49 samples with methylation data (GSE46394) from a large artery plaque were downloaded from the GEO database, and aberrantly methylated-differentially expressed (AMDE) genes were identified based on the relationship between methylation and expression. Furthermore, we conducted weighted correlation network analysis (WGCNA) and co-expression analysis to identify the core AMDE genes strongly involved in atherosclerosis. K-means clustering was used to characterize two subtypes of atherosclerosis in GSE40231, and then 29 samples were recognized as validation dataset (GSE28829). In a blood sample cohort (GSE90074), chi-square test and logistic analysis were performed to explore the clinical implication of the K-means clusters. Furthermore, significance analysis of microarrays and prediction analysis of microarrays (PAM) were applied to identify the signature AMDE genes. Moreover, the classification performance of signature AMDE gene-based classifier from PAM was validated in another blood sample cohort (GSE34822). A total of 1,569 AMDE mRNAs and eight AMDE long non-coding RNAs (lncRNAs) were identified by differential analysis. Through the WGCNA and co-expression analysis, 32 AMDE mRNAs and seven AMDE lncRNAs were identified as the core genes involved in atherosclerosis development. Functional analysis revealed that AMDE genes were strongly related to inflammation and axon guidance. In the clinical analysis, the atherosclerotic subtypes were associated with the severity of coronary artery disease and risk of adverse events. Eight genes, including PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, and LSM4, were selected as the signature AMDE genes that most significantly differentiated between atherosclerotic subtypes. Ultimately, the area under the curve of signature AMDE gene-based classifier for atherosclerotic subtypes was 0.858 and 0.812 in GSE90074 and GSE34822, respectively. This study identified the AMDE genes (lncRNAs and mRNAs) that could be implemented in clinical clustering to recognize high-risk atherosclerotic patients.

NECo: A node embedding algorithm for multiplex heterogeneous networks

Complex diseases such as hypertension, cancer, and diabetes cause nearly 70% of the deaths in the U.S. and involve multiple genes and their interactions with environmental factors. Therefore, identification of genetic factors to understand and decrease the morbidity and mortality from complex diseases is an important and challenging task. With the generation of an unprecedented amount of multi-omics datasets, network-based methods have become popular to represent the multilayered complex molecular interactions. Particularly node embeddings, the low-dimensional representations of nodes in a network are utilized for gene function prediction. Integrated network analysis of multi-omics data alleviates the issues related to missing data and lack of context-specific datasets. Most of the node embedding methods, however, are unable to integrate multiple types of datasets from genes and phenotypes. To address this limitation, we developed a node embedding algorithm called Node Embeddings of Complex networks (NECo) that can utilize multilayered heterogeneous networks of genes and phenotypes. We evaluated the performance of NECo using genotypic and phenotypic datasets from rat (Rattus norvegicus) disease models to classify hypertension disease-related genes. Our method significantly outperformed the state-of-the-art node embedding methods, with AUC of 94.97% compared 85.98% in the second-best performer, and predicted genes not previously implicated in hypertension.

Influence of Genetic Ancestry on Human Serum Proteome

Disease risk varies significantly between ethnic groups, however, the clinical significance and implications of these observations are poorly understood. Investigating ethnic differences within the human proteome may shed light on the impact of ancestry on disease risk. We used admixture mapping to explore the impact of genetic ancestry on 237 cardiometabolic biomarkers in 2,216 Latin Americans within the Outcomes Reduction with an Initial Glargine Intervention (ORIGIN) study. We developed a variance component model in order to determine the proportion of variance explained by inter-ancestry differences, and we applied it to the biomarker panel. Multivariable linear regression was used to identify and localize genetic loci affecting biomarker variability between ethnicities. Variance component analysis revealed that 5% of biomarkers were significantly impacted by genetic admixture (p < 0.05/237), including C-peptide, apolipoprotein-E, and intercellular adhesion molecule 1. We also identified 46 regional associations across 40 different biomarkers (p < 1.13 × 10^-6). An independent analysis revealed that 34 of these 46 regions were associated at genome-wide significance (p < 5 × 10^-8) with their respective biomarker in either Europeans or Latin populations. Additional analyses revealed that an admixture mapping signal associated with increased C-peptide levels was also associated with an increase in diabetes risk (odds ratio [OR] = 6.07 per SD, 95% confidence interval [CI] 1.44 to 25.56, p = 0.01) and surrogate measures of insulin resistance. Our results demonstrate the impact of ancestry on biomarker levels, suggesting that some of the observed differences in disease prevalence have a biological basis, and that reference intervals for those biomarkers should be tailored to ancestry. Specifically, our results point to a strong role of ancestry in insulin resistance and diabetes risk.

Inhibition of PRMT3 activity reduces hepatic steatosis without altering atherosclerosis susceptibility in apoE knockout mice

The nuclear receptor liver X receptor (LXR) impacts on cholesterol metabolism as well as hepatic lipogenesis via transcriptional regulation. It is proposed that inhibition of the protein arginine methyltransferase 3 (PRMT3) uncouples these two transcriptional pathways in vivo by acting as a specific lipogenic coactivator of LXR. Here we validated the hypothesis that treatment with the allosteric PRMT3 inhibitor SGC707 will diminish the hepatic steatosis extent, while leaving global cholesterol metabolism, important in cholesterol-driven pathologies like atherosclerosis, untouched. For this purpose, 12-week old hyperlipidemic apolipoprotein E knockout mice were fed a Western-type diet for six weeks to induce both hepatic steatosis and atherosclerosis. The mice received 3 intraperitoneal injections with SGC707 or solvent control per week. Mice chronically treated with SGC707 developed less severe hepatic steatosis as exemplified by the 51% reduced (P < 0.05) liver triglyceride levels. In contrast, the extent of in vivo macrophage foam cell formation and aortic root atherosclerosis was not affected by SGC707 treatment. Interestingly, SGC707-treated mice gained 94% less body weight (P < 0.05), which was paralleled by changes in white adipose tissue morphology, i.e. reduction in adipocyte size and browning. In conclusion, we have shown that through PRMT3 inhibitor treatment specific functions of LXR involved in respectively the development of fatty liver disease and atherosclerosis can be uncoupled, resulting in an overall diminished hepatic steatosis extent without a negative impact on atherosclerosis susceptibility. As such, our studies highlight that PRMT3 inhibition may constitute a novel therapeutic approach to limit the development of fatty liver disease in humans.

Identification of potential 'lifestyle-responsive' epigenomic biomarkers in healthy women aged 18-40

CONTEXT

Human health is complex and multifaceted; there is a need for biomarkers that reflect the multidimensional nature of health.

OBJECTIVE

To identify potential epigenomic biomarkers of health in women aged 18-40 participating in a six-month lifestyle intervention, next level health.

MATERIALS AND METHODS

Methylation data were obtained by reduced representation bisulphite sequencing of 21 female intervention participants as well as three non-participants. The Differential Methylation Analysis Package (DMAP) was used to investigate inter- and intra-individual variability and to identify potential targets of transient epigenetic control in the population studied.

RESULTS

Eleven genes were identified as significantly differentially methylated post- intervention in all 21 participants. 1884 genomic locations were found to be differentially methylated amongst the total female population studied representing potential epigenomic biomarkers.

CONCLUSIONS

The ability to demonstrate epigenetic changes arising from a lifestyle intervention can provide key information on the relationship between gene regulation, human behaviour and health.

Genome-wide admixture and association study of subclinical atherosclerosis in the Women's Interagency HIV Study (WIHS)

Cardiovascular disease (CVD) is a major comorbidity among HIV-infected individuals. Common carotid artery intima-media thickness (cCIMT) is a valid and reliable subclinical measure of atherosclerosis and is known to predict CVD. We performed genome-wide association (GWA) and admixture analysis among 682 HIV-positive and 288 HIV-negative Black, non-Hispanic women from the Women’s Interagency HIV study (WIHS) cohort using a combined and stratified analysis approach. We found some suggestive associations but none of the SNPs reached genome-wide statistical significance in our GWAS analysis. The top GWAS SNPs were rs2280828 in the region intergenic to mediator complex subunit 30 and exostosin glycosyltransferase 1 (MED30 | EXT1) among all women, rs2907092 in the catenin delta 2 (CTNND2) gene among HIV-positive women, and rs7529733 in the region intergenic to family with sequence similarity 5, member C and regulator of G-protein signaling 18 (FAM5C | RGS18) genes among HIV-negative women. The most significant local European ancestry associations were in the region intergenic to the zinc finger and SCAN domain containing 5D gene and NADH: ubiquinone oxidoreductase complex assembly factor 1 (ZSCAN5D | NDUF1) pseudogene on chromosome 19 among all women, in the region intergenic to vomeronasal 1 receptor 6 pseudogene and zinc finger protein 845 (VN1R6P | ZNF845) gene on chromosome 19 among HIV-positive women, and in the region intergenic to the SEC23-interacting protein and phosphatidic acid phosphatase type 2 domain containing 1A (SEC23IP | PPAPDC1A) genes located on chromosome 10 among HIV-negative women. A number of previously identified SNP associations with cCIMT were also observed and included rs2572204 in the ryanodine receptor 3 (RYR3) and an admixture region in the secretion-regulating guanine nucleotide exchange factor (SERGEF) gene. We report several SNPs and gene regions in the GWAS and admixture analysis, some of which are common across HIV-positive and HIV-negative women as demonstrated using meta-analysis, and also across the two analytic approaches (i.e., GWA and admixture). These findings suggest that local European ancestry plays an important role in genetic associations of cCIMT among black women from WIHS along with other environmental factors that are related to CVD and may also be triggered by HIV. These findings warrant confirmation in independent samples.

Association of variant in the ADIPOQ gene and functional study for its role in atherosclerosis

The burden of atherosclerosis is heritable and associated with elevated risk of developing CVDs. Here, we evaluated genetic variants of adiponectin (ADIPOQ) gene, which has important role in anti- atherosclerosis, with risk of atherosclerosis among a large Chinese population. Our results show that rs74577862 was significantly associated with risk of atherosclerosis (OR=2.08; 95%CI=1.48-2.91; P=2.2×10-5). When stratified by atherosclerosis site, rs74577862 was associated with increased risk of both carotid atherosclerosis (OR=2.03; 95%CI=1.35-3.06; P=6.3×10-4) and coronary atherosclerosis (OR=2.11; 95%CI=1.44-3.09; P=1.1×10-4). In addition, we also carried out site-directed mutagenesis and dual-luciferase reporter assay to confirm the positive finding, which presents a significant decrease in luciferase expression for the reconstructed plasmid with rs74577862 A allele in comparison to the one with G allele (P<0.001). Real-time PCR also confirmed the findings above. These results strongly suggest that the functional SNP, ADIPOQ rs74577862 might contribute to atherosclerosis susceptibility.