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Our Research

Our research mission is to expand and strengthen our expertise and focus on the advancement of excellence in health care governance and planning for the ageing and older person.

With a long and well established history in ageing research, the Department of Gerontology is currently involved in a diverse range of research projects. These research programmes will enable health professionals and academics to consider the full range of activities and aspects that affect and shape the ageing process as well as deliver new ways to advance the concept of successful ageing and promote preventative health care.

Trinity’s Discipline of Medical Gerontology is well known internationally for its strong research environment and thriving history of academic achievement across basic, translational and clinical studies, as well as wider gerontological research.

We support the ‘4P research’ principles:

  • improving prediction tools
  • preventive methods and interventions
  • personalised solutions
  • participation of citizens to assure that the health and quality of life outcome is relevant to the person

Important research strands in the Discipline Medical Gerontology are:


The increased understanding of human ageing and joint efforts on analysis of multiomics and other big data needs to be translated to well defined, standardised and feasible biomarkers to provide scores that monitor individual ageing trajectories that can be used for risk assessment and personalised interventions in human and model systems, and to monitor the individual and often heterogeneous response to interventions.

The challenge for future research is to gain a better understanding of the genetic and environmental determinants of human lifespan and health span, and to translate the results of discoveries in model organisms into health improvements for ageing humans. In the first place, this requires an improvement of biological age indication. At older ages, calendar age is not very informative on the holistic health status or intrinsic capacity of individuals. Biomarkers of the ageing process (often referred to as biological age indicators) ideally reflect the physiological state of the systems that form a common denominator to the risk of multiple age-related diseases. Such whole-system indicators need to be an early reflection of deficits and/or decline in intrinsic capacity.


Ongoing and future research should shed more light on the mechanisms — in particular modifiable mechanisms — by which socioeconomic conditions early in life affect health outcomes later in life. These mechanisms can be part of a prevention policy agenda aimed at mitigating the impact of adverse early life conditions on health outcomes over the life-cycle.


Applications of machine learning algorithms (artificial intelligence, or AI) in the field of ageing research offer enormous possibilities. Such methods applied to data, acquired at a single time point or longitudinally, can be employed to generate predictors of disease and identify moments for early intervention. AI-derived biomarkers of ageing enable a holistic view of biological processes and allow for the development of new methods to build causal models, extracting the most important features and identifying biological targets.

The Discipline has a thriving record of scholarly publications, examples of which are available on TARA.

Research Areas

  • Brain health, stroke, cognitive impairment, delirium and dementia
  • Falls and syncope
  • Multimorbidity, frailty and resilience
  • Nutrition
  • Osteoporosis and bone health
  • Continence
  • Population Health in Ageing
  • Health services research and quality improvement
  • Technology Research for Independent Living
  • Asset mapping and social prescribing
  • Creative Life and the Humanities
  • Elder abuse and gerontological advocacy