Improving prediction of ageing outcomes by integrating social, environmental, and genetic information

The Advanced Care Research Centre at the University of Edinburgh is a new £20m interdisciplinary research collaboration aiming to transform later life with person centred integrated care

The vision of the ACRC is to play a vital role in addressing the Grand Challenge of ageing by transformational research that will support the functional ability of people in later life so they can contribute to their own welfare for longer. With fresh and diverse thinking across interdisciplinary perspectives our academy students will work to creatively embed deep understanding, data science, artificial intelligence, assistive technologies and robotics into systems of health and social care supporting the independence, dignity and quality-of-life of people living in their own homes and in supported care environments.

The ACRC Academy will equip future leaders to drive society’s response to the challenges of later life care provision; a problem which is growing in scale, complexity and urgency. Our alumni will become leaders in across a diverse range of pioneering and influential roles in the public, private and third sectors.

Genome-wide association studies can reliably identify genetic variants associated with traits/disease; these can be combined into a single polygenic score and used for prediction. This project integrates polygenic scores for ageing-related diseases (e.g., frailty, stroke, dementia) with environmental predictors (e.g., SES, social support, exercise) to test whether positive aspects of the environment can lower a person’s genetic risk of adverse ageing. By considering genes and environment, we can refine prediction of ageing outcomes and estimate environmental effects free from genetic bias, thus identifying optimal intervention targets. The project’s findings will inform a Citizens’ Jury on the topic of personalised genomics.

Specific Aims:

1. To more fully understand how different ageing outcomes (e.g., wellbeing, frailty, and dementia) and social/individual predictors of these outcomes are related using a network-and-clustering based approach in the English Longitudinal Study of Ageing (ELSA). This enables identification of variables that more strongly share aetiology and will guide variable selection for objective 2 by focusing on a smaller set of indicative measures.
2. To identify environmental variables (e.g., SES, social isolation, life events, exercise) that protect/exacerbate any genetic risk of poor ageing outcomes. Polygenic risk scores of diseases like frailty, stroke, and Alzheimer’s disease will be modelled in ELSA alongside demographic, social, psychological, and medical predictors of quality of life ageing indicators and admission to care homes. This method produces unbiased estimates controlled for a person’s polygenic risk of disease and can include interaction effects.
3. To better establish causality between significant predictors (e.g. educational attainment) and ageing outcomes (e.g., dementia) from objective 2, Mendelian Randomization (MR) will be used. Existing publicly available genome-wide association summary statistics for the variables of interest will be used for analysis.
4. To understand how elderly people themselves view personalised genomics and whether they see value in our healthcare system having access to such sensitive genetic data. Here we will hold a Citizens’ Jury using select findings from our prior objectives as a case example, and will invite experts in clinical genetics, bio-ethics, medicine, and social justice as speakers.