Atrial fibrillation has been a major global challenge. Genetic factors contribute to the risk of developing atrial fibrillation but remain imprecise. combining human genetics with high-throughput metabolic data factors could help to bridge the gap between the genome and disease. Based on data from China National Survey of hypertension (CHHRS), our research group found that activity monitor factors (for example, Walking distance, SaO2 and duration of deep sleep) were the potential risk factors for a high prevalence of AF. So the integrated application of multidimensional risk factors with broad systematic characterization across numerous putative risk factors is promising. Additionally, observational epidemiological studies have shown a relationship between gut microbiota and AF. This research project aims to investigate the metabolite-modifying targets and accurate prediction model construction of atrial fibrillation based on multiomic feature spectrum. We will investigate the causal role of gut microbiome and its downstream metabolites and genetic factors in the development of AF. And we will conduct validation and monitoring of these high-risk factors in multi-omics, utilizing wearable device screening of atrial fibrillation queues. This approach aims to delve deeper into the intricacies and potential mechanisms underlying the disease. Our findings may refined disease prevention for atrial fibrillation and novel markers for early detection or intervention. The proposed program will last for three years but it might be prolonged due to advances in methodology and novel findings which may require external validations.
