The global prevalence of cardiometabolic disease (including coronary heart disease, stroke, hypertension, type 2 diabetes, and non-alcoholic fatty liver disease, etc) has been growing dramatically, leading to critical global health and socioeconomic burdens. The pathogenesis of these interconnected diseases is complex and highly heterogeneous, calling for multi-dimensional omics technologies, such as metagenomics and metabolomics, to disentangle this process, instead of only focusing on a few clinical parameters or biomarkers.
Recent studies have demonstrated the nonnegligible involvement of gut microbiota in the pathogenesis and development of various types of diseases, ranging from local GI diseases to remote brain disorders. While our understanding of gut bacteria or the gut microbiome in some cardiometabolic diseases has substantially increased, more in-depth research is still needed to cover more disease types and subtypes, achieve higher resolution, and obtain mechanistic insights and causal effects rather than observational findings. The prediction of cardiometabolic disease risk may also help increase the disease awareness for timely and better prevention, intervention, and treatment outcomes. In addition, the role of various fungi in the gut, or collectively termed as mycobiome, has been poorly studied in cardiometabolic diseases. The bacteria and fungi in the gut community have complicated intra-kingdom and inter-kingdom interactions, which brings another remarkable degree of complexity. Meta-omics approaches (e.g., metagenomics, metatranscriptomics) hold great potential to resolve these questions and challenges faced by researchers in this field.
The aim of this Research Topic is to decipher the role of gut microbiome and mycobiome in the development and treatment (microbiota-based therapy; personalized therapy) of different cardiometabolic diseases through the application of high-throughput sequencing technologies, preferably coupled with other high-throughput omics approaches such as metabolomics.
We welcome submissions of original research articles, reviews, methods, and perspectives focusing on but not limited to:
• Metagenomics/metatranscriptomics in human cohorts to study microbiota-disease associations and to prioritize specific disease-relevant microbes or microbial consortium.
• Relevant animal model to study the gut microbiota change through the second- or third-generation metagenomic/metatranscriptomic sequencing, and its functional consequence upon different manipulations (e.g. prebiotics, probiotics, dietary supplements), with potential mechanistic insights.
• Trans-kingdom interactions between gut bacteria and fungi in the pathogenesis of cardiometabolic diseases.
• How the microbiota composition and functionality contribute to different disease subtypes and individualized response to treatment or intervention of cardiometabolic diseases.
• Microbiota-based non-invasive diagnosis or early-stage risk assessment for different cardiometabolic diseases.
• Novel statistical or machine learning methods to annotate microbiome/mycobiome data, and their applications in the studies into cardiometabolic diseases.
The global prevalence of cardiometabolic disease (including coronary heart disease, stroke, hypertension, type 2 diabetes, and non-alcoholic fatty liver disease, etc) has been growing dramatically, leading to critical global health and socioeconomic burdens. The pathogenesis of these interconnected diseases is complex and highly heterogeneous, calling for multi-dimensional omics technologies, such as metagenomics and metabolomics, to disentangle this process, instead of only focusing on a few clinical parameters or biomarkers.
Recent studies have demonstrated the nonnegligible involvement of gut microbiota in the pathogenesis and development of various types of diseases, ranging from local GI diseases to remote brain disorders. While our understanding of gut bacteria or the gut microbiome in some cardiometabolic diseases has substantially increased, more in-depth research is still needed to cover more disease types and subtypes, achieve higher resolution, and obtain mechanistic insights and causal effects rather than observational findings. The prediction of cardiometabolic disease risk may also help increase the disease awareness for timely and better prevention, intervention, and treatment outcomes. In addition, the role of various fungi in the gut, or collectively termed as mycobiome, has been poorly studied in cardiometabolic diseases. The bacteria and fungi in the gut community have complicated intra-kingdom and inter-kingdom interactions, which brings another remarkable degree of complexity. Meta-omics approaches (e.g., metagenomics, metatranscriptomics) hold great potential to resolve these questions and challenges faced by researchers in this field.
The aim of this Research Topic is to decipher the role of gut microbiome and mycobiome in the development and treatment (microbiota-based therapy; personalized therapy) of different cardiometabolic diseases through the application of high-throughput sequencing technologies, preferably coupled with other high-throughput omics approaches such as metabolomics.
We welcome submissions of original research articles, reviews, methods, and perspectives focusing on but not limited to:
• Metagenomics/metatranscriptomics in human cohorts to study microbiota-disease associations and to prioritize specific disease-relevant microbes or microbial consortium.
• Relevant animal model to study the gut microbiota change through the second- or third-generation metagenomic/metatranscriptomic sequencing, and its functional consequence upon different manipulations (e.g. prebiotics, probiotics, dietary supplements), with potential mechanistic insights.
• Trans-kingdom interactions between gut bacteria and fungi in the pathogenesis of cardiometabolic diseases.
• How the microbiota composition and functionality contribute to different disease subtypes and individualized response to treatment or intervention of cardiometabolic diseases.
• Microbiota-based non-invasive diagnosis or early-stage risk assessment for different cardiometabolic diseases.
• Novel statistical or machine learning methods to annotate microbiome/mycobiome data, and their applications in the studies into cardiometabolic diseases.
