Exploring Diet/Lifestyle/Health factors as causes and modifiers of genetic determinants of healthspan, ageing and longevity
Approved Research ID: 48020
Approval date: April 18th 2019
Scientific rationale: While many of the differences we see between people are due to genetics, the exact way variations in the genome affect these differences is a hard question to answer. This is because several genes interact with each other to ultimately lead to the observed outcome (Gene-by-Gene interactions). This is further complicated by the fact that the environment and lifestyle (for example diet, exercise, stress level, etc.), also affect how these individuals look, grow, behave, get diseases, and age (Gene-by-Environment interactions). To overcome this, huge amounts of data must be recorded in a large number of humans and specialized statistical analyses must be conducted to parse out the causes and effects. Aims: our study aims at better understanding the genetics of longevity and ageing and identify modifiable lifestyle factors causally influencing longevity and ageing traits. We will apply statistical methods to explore the causal effect of longevity/ageing traits on environment and vice versa. These methods require genetic data, lifestyle information and health/phenotypic information. We will then integrate our results on aging, health, and longevity obtained through our analysis of mouse populations with the human data in order to gain a more fine-grained understanding of the intricate processes underlying aging and healthspan variation. Public health impact: Ageing is a major risk factor for several common diseases like metabolic and neurological disorders, cardiovascular diseases and cancer. These chronic illnesses are the most common diseases in developed countries, and their prevalence is expected to increase with the increase of life expectancy. However, our knowledge of the genetic factors that influence ageing, health and longevity is still rudimentary. The proposed project will allow a better understanding of how our environment and lifestyle together with our genes determines how we age, what age-related diseases we are at risk for, and how long we will live. This insight may enable the design of novel personalized preventive measures (e.g. lifestyle interventions) and eventually therapies that can slow down the aging process and extend healthy life (aka healthspan). Project duration: Given that the human analysis will be integrated with data obtained from our aging studies in mice, which by themselves take 3 years (± maximum lifespan for a mouse) for completion, we cannot set a fixed end point. We expect that the current project to run for 5 years and therefore request a rolling 3-year period, during which we will provide annual updates.
While there is no single definition of healthspan, the most common one is the period of life spent in good health, free from the chronic diseases and disabilities of aging [PMID:30084059]. Given this lack of formal definition, healthspan cannot be directly quantified. Instead, the evolution of many health parameters that typically show a strong dependence on age (cardiovascular fitness, mental health, body weight and body fat) can be routinely quantified and are available in the UK Biobank data. As such, a health score - an aggregate of these selected parameters - can serve as a proxy of healthspan. We will calculate a health score based on these parameters and estimate the contribution of genetic and environmental factors to their variation.
We will attempt to establish a causal effect of these factors on healthspan, aging or longevity (i.e. to implement preventive measures and favour healthy aging) through Mendelian randomisation. We also aim to disentangle these three different entities from the genetic standpoint. Finally, we will explore which of these environmental factors modify the effect of the genetic risk score on healthspan, ageing and longevity traits.
We will further develop a pipeline to assess the effect of genes of interest that have a particularly strong impact on healthspan or healthspan-related metabolic traits (e.g. BMI, blood pressure, etc.). More specifically, we intend to investigate the effect of eQTLs/sQTLs and rare variants for such genes as ACMSD, ACACB, SLC25A47 and others. Our project will extensively rely on the new whole genome sequencing and whole exome sequencing data made available by UK Biobank to increase the power of our REGENIE GWAS analyses.