Our research project entitled “Multiomic characterisation of sepsis and related diseases” aims to provide insights into the diagnosis, prognosis and treatment of sepsis and related diseases. These diseases, such as sepsis, septic shock, septic cardiomyopathy and sepsis combined with acute kidney injury, are often characterised by heterogeneity, complex mechanisms, multi-organ dysfunction and high mortality. Our goal is to deepen our understanding of the mechanisms of sepsis and related diseases, to distinguish their heterogeneity, analyse the differences between young and elder sepsis patients, and to mine potential underlying biomarkers for early detection and improved patient prognosis through multiomic analysis.
In our previous studies, we performed comprehensive analyses of various biomarkers (e.g. routine clinical examinations, immunological examinations, gene and metabolites, etc.) in sepsis patients and its related diseases to search for differentially expressed biomarkers as well as key markers. Through enrichment analysis and other methods, we will find relevant pathways to deepen our understanding of the mechanisms of sepsis and its related diseases, differentiate their heterogeneity through differentially expressed biomarkers, analyse the impact of age on sepsis patients at multiple levels, and establish different and diverse predictive models through multi-omics biomarkers for early detection and improved prognosis.
We plan to evaluate these biomarkers using the large amount of data in the UK Biobank dataset. To do this, we will need Tier 2 datasets including but not limited to (i) routine clinical investigations; (ii) available diagnostic and medical history data including ICD codes; (iii) genomics, transcriptomics and metabolomics data; (iv) patient reported outcome data; and (v) outcome data such as mortality.
The results of this project will expand our knowledge of sepsis and related diseases and deepen our understanding of their mechanisms. In addition, the identification of molecules with histological signatures under relevant exposures will help to identify biomarkers predictive of sepsis and related diseases and enable more accurate clinical decisions for early prevention, diagnosis and precision medicine of sepsis and related diseases. Moreover, as medical care continues to evolve, the proportion of the population that is elderly continues to increase and the demand for medical resources continues to grow. Age-stratified studies of sepsis patients, particularly in the elderly population, can help us better understand the causes of the problem of sepsis in the elderly and provide a basis for developing more effective prevention and treatment strategies.