Association Analysis of Cardiovascular and Nervous System Diseases and Intestinal Microbiome Based on Multi-omics Big Data and Related Applications

Overview

Research purpose 1. Combined with multi-omics data and advanced data mining methods, we explored the pathogenesis and potential application pathway of intestinal microbiome mediated by specific cardiovascular diseases (such as idiopathic ventricular tachycardia, stenosis after coronary artery stenting injury, etc.) and nervous system diseases (such as carotid atherosclerosis, moyamoya disease, etc.). 2. The secondary goal of this study is the construction of risk prediction model. Based on the pathogenesis identified by multi-omics association analysis, detailed dietary information and clinical information related to cardiovascular and nervous system diseases, the risk of cardiovascular and nervous system diseases was assessed and the disease risk model was constructed. 3. Based on the key genes and microorganisms excavated, disease-related machine learning models can be built, and models can be built to prevent and treat diseases. Research background Cardiovascular and nervous system diseases such as arrhythmias (atrial fibrillation, ventricular tachycardia, ventricular fibrillation, postoperative vascular stenosis injury, etc.), heart failure, atherosclerosis (coronary heart disease, stroke, peripheral vascular disease, carotid atherosclerosis, etc.), epilepsy, moyamoya disease, etc., are currently leading to the main diseases affecting the health and death of residents in China. Through the unremitting efforts of many scientists, the research on the association between intestinal flora and cardiovascular diseases (ventricular tachycardia/atrial fibrillation, carotid atherosclerosis, etc.) and nervous system diseases (Parkinson's disease, epilepsy, carotid atherosclerosis, etc.) has made breakthrough progress. However, the study of gut microbiota is still in its infancy, and it is not possible to deeply understand the complex regulatory processes between heart disease and nervous system diseases and gut microbiota, involving a large number of host genes, host metabolites, and associated bacteria and bacteria-related metabolites. Based on multi-omics data, the data integration method combined with machine learning analyzes the connection between cardiovascular and nervous system and gut microbes, helping to deepen the research on the mechanism related to heart disease and nervous system under the regulation of gut microbes and providing new ideas for the prevention and treatment of related diseases. This study will also promote the implementation of clinical interventions with precise flora and provide new ideas for the treatment of cardiovascular diseases and neurological diseases.

A microbial ecosystem is a complex community of interacting bacteria. The potential role of intestinal flora in human health has attracted extensive attention. Imbalances in the gut microbiome have been implicated in a variety of chronic diseases. Cardiovascular diseases (CVDs) are the leading cause of morbidity worldwide and are influenced by both genetic and environmental factors. Recent advances have provided scientific evidence that cardiovascular disease may also be attributable to gut microbiota. In many literatures, we have found complex interactions between microorganisms, their metabolites, and potential impacts on the development and progression of cardiovascular disease. The use of intestinal flora in the treatment of cardiovascular diseases is the latest research direction. Gut microbes are likely to be used in the clinical treatment of cardiovascular diseases in the near future. Gut flora plays an important role in human health and disease transformation. It not only participates in many physiological processes of the host, but also affects the function of the central nervous system (CNS) through the activity of the microbiota - gut - brain axis, which may be closely related to neurotransmitter, immune, endocrine and metabolite pathways. When the intestinal flora is dysfunctional, it can affect the occurrence and development of CNS diseases such as cerebral ischemia, Parkinson's disease, Alzheimer's disease, multiple sclerosis, hepatic encephalopathy and mental disorders. Fecal microbial transplantation, exercise training, acupuncture and massage and other therapies can improve intestinal flora disorders, and are expected to become effective measures to treat and prevent some nervous system diseases. 1. Subject selection basis Based on electrocardiogram, blood test or B-ultrasonography and other examination methods, combined with clinical manifestations, patients judged by professional physicians as meeting the inclusion criteria of this experimental protocol will be included in the study cohort for study. 2. Dosage selection/administration plan/dosage adjustment basis This study did not involve intervention experiments, and there was no dose selection/dosing regimen/dose adjustment. 3. Basis for destination selection This experiment is not an intervention experiment, so the end point of this experiment is chosen after the subjects are diagnosed with related diseases or the control group, and the end point is taken after the completion of sampling. 4. Risk and benefit basis For patients with specific cardiovascular and cerebrovascular diseases, the benefits of this trial far outweigh the risks. This experiment will collect fecal samples in vitro, which is a non-invasive sample collection, which will not cause trauma to patients and has high safety. This project will also collect peripheral blood samples from patients for study. The process of collecting blood samples may involve pain and other reactions, but the operations involved in this study are all carried out by experienced nurses and nurses to minimize such risks. This experiment will contribute to the research of cardiovascular and nervous system-related diseases. By elucidating the molecular mechanism of intestinal microorganisms regulating cardiovascular and nervous system diseases, disease-related genes and biomarkers can be found, promoting the accurate diagnosis of diseases, providing targets for the precise treatment and prevention of diseases, and providing targeted regulation of the structure and metabolite composition of microorganisms. Prevention and treatment of related diseases. Research content 1. Test population This project plans to use multi-omics big data (host genome, transcriptome, metabolome, metagenomic and metabetomic data), combined with cardiovascular disease cohort (idiopathic ventricular tachycardia, restenosis after coronary artery stenting injury) and nervous system disease cohort (carotid atherosclerosis, Moyamoya disease), and use machine learning to integrate data from different omics. To delve deeper into the mechanisms of cardiovascular disease and the role of gut microbes in it. The entire process is expected to involve nearly 500 sequencing objects, different omics data (totaling tens of thousands of samples), and yield a total of hundreds of terabytes of omics data. 2. Sample size calculation Through literature research and clinical big data analysis, reliable conclusions can be obtained with an experimental cohort of more than 200 people and a control cohort. So the experimental data were selected from a disease cohort of 250 people and a control cohort of 240 people. 3. Specific research content First, data, including blood and stool samples, need to be collected from cohorts of patients with cardiovascular and neurological diseases and from healthy people. Based on the amount of data in published articles and the statistical adjustment results of the trial, an estimated disease cohort of 250 people and a healthy control cohort of 240 people are expected. At the same time, detailed sample information (including physical examination data and phenotypic data such as diet data) was recorded so that multi-omics data could be combined to gain insight into factors affecting cardiovascular disease. Second, the genome and transcriptome of the collected blood samples were sequenced. For fecal samples, metagenomic sequencing and metometabolic and proteomic sequencing were performed. Thirdly, for multi-omics data processing, data processing of sequenced genes, transcripts, metagenomes and metabetomes is carried out, and combined with disease cohort, key genes potentially causing corresponding diseases and corresponding microbial data are selected. Fourthly, for the data analysis step, based on the cohort of cardiovascular diseases and neurological diseases, the association analysis of these candidate host genes and bacteria is conducted by using machine learning methods, and the construction and interpretation of relevant pathways are carried out in combination with previous studies. Mechanisms and pathways rise to the recognition of patterns for the construction of predictive models for early disease prevention and disease diagnosis.