Approved Research

The associations of genetics, plasma metabolites, and conventional exposures with health outcomes

Lay summary

Chronic non-communicable diseases (NCDs) caused 41 million deaths each year, equivalent to 74% of all deaths all over the world. Moreover, they are the main drivers of higher disability and health-care costs. It well to know that NCDs are the result of the combination of genetic, environmental, physiological, and behavioral factors. Previous studies, including results from our team, have provided some evidence on the association of genetic susceptibility and conventional risk factors with the increased risk of NCDs and mortality. However, the underlying mechanisms remains unclear yet.

Metabolomics, indicating genetic, environmental, and pathological changes during disease development, may help to clarity the underlying mechanisms of association between different exposures and health outcomes. Up to now, few studies have identified early biomarkers of common NCDs nor metabolic signatures of different exposures in relation to diseases. Additionally, health conditions often do not occur in isolation, with approximately one-third of the global population living with multimorbidity. There is rarely evidence exist on the extent to which these exposures, metabolites and metabolite signatures was related to the risk of multimorbidity.

Using the information of genetics, plasma metabolites and different exposures derived from the UK Biobank, we aimed to: 1) examine the prospective associations of baseline plasma metabolites with risk of new-onset NCDs and the mortality caused by these diseases; 2) identify metabolite signatures of different exposures, and associate these identified metabolite signatures with the risk of above NCDs among participants stratified by genetic risk; 3) assess the role of the identified exposures and their combination, metabolites and metabolite signatures in the risk of progressing from one of NCDs to multimorbidity using the multiple adjusted Cox proportional hazard regression model and elastic net regularized logistic regression model.

The results of this project will provide evidence on developing efficient strategies for the prevention, diagnosis, intervention and treatment of both single disease and multimorbidity, and as a result, may have great public health implications.