The integration of epidemiological and genomic data to examine the relationship between early life exposure and aging-associated health outcomes later in life

Although life span is prolonged, health span remains a concern for the public. Aging is the single largest risk factor for chronic diseases and a complex process of progressive functional decline influenced by environmental, genetic, and stochastic factors, and aging-associated diseases are diseases that are most often seen with increasing frequency with increasing senescence. Meanwhile, the important difference between the disease status of the elderly and the young is that the elderly usually coexist with a variety of chronic diseases, known as comorbidity or complex pathology. With increasing longevity, but not necessarily health span, there is a need to determine the underlying biology of aging.
The aim of the project is to investigate whether genetic, behavioural (e.g., smoking, alcohol consumption, physical activity, sleep), physical (e.g., body mass index, blood pressure, grip strength), ill health and its indicators (e.g., diabetes, vascular problems, asthma), lifestyle (e.g., socioeconomic position), cognitive (e.g., brain structure and volume, processing speed and memory) factors, biological markers ( e.g., TG, HDL, ALT, and AST) and sensory function(e.g., hearing and vision) influence the progression and development of aging-associated diseases, including cardiovascular diseases (CVDs), chronic respiratory diseases, sense organ diseases, chronic kidney diseases (CKDs), sarcopenia, frailty, diabetes, metabolic associated fatty liver disease (MAFLD), Alzheimer’s disease, Parkinson’s disease and cancer, etc.
Rigorous analytic approaches, including Cox proportional hazard model, bivariate surface model and so on will be performed to identify biological factors, anthropometric measurements, behavior factors and lifestyle factors of aging-associated diseases. Genome wide association study (GWAS) and phenome-wide association study (PheWAS) approaches will be used to characterize genetic variation/mutation statistically associated with specific aging-associated diseases and to interrogate with which phenotypes a given genetic variant may be associated. We explore potential targets and biomarkers based on the results of UKB’s multiomic sequencing. Effect modification of individual components in the association between genetic variation/mutation and aging-associated diseases will be estimated based on effect measure. Additionally, we seek to examine how effectively genetic information, multi-omics sequencing and anthropometric measures can be used to predict the occurrence and progression of aging-associated diseases.
In the time span of 36 months, we will aim to identify the factors of aging-associated diseases. Our findings may contribute to prolong not only life span but also health span of adults that contribute to improving the quality of life of the public.