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Full NameProfessor Roman Romero-Ortuno
Department:Discipline of Medical Gerontology, School of Medicine
Organisation:Trinity College Dublin
- physiology and non-communicable disease
- neuroscience and mental health
- bioengineering/medical devices
- epidemiology/population health research
Ageing Physiology, Geriatric Medicine, Frailty
- General Practice
- Public Health
- Sports and Exercise Medicine
- Community Medicine
- Geriatric Medicine
- Health Informatics
- Vascular Medicine
Sports and Exercise Medicine
The key direction of my research is towards a better understanding and modelling of the physiology of ageing across systems with particular attention to the concept of frailty in older adults. Frailty is characterised by dysregulation in multiple physiological systems and vulnerability to stressors, with risk of adverse outcomes including premature disability and mortality.
With population ageing worldwide, the early detection of frailty is crucial because some of its drivers can be intervened upon. My research has contributed new tools for the operationalisation of the clinical concept of frailty in ways that add value to practice and research.
My current research programme into early detection of frailty in older adults (FRAILMatics) has been awarded €1.5 million in funding as part of the Science Foundation Ireland President of Ireland Future Research Leaders Award programme (2019-2024). FRAILMatics is analysing very large datasets of measurements of mild physiological stresses across physiological systems in participants from The Irish Longitudinal Study on Ageing and a clinical cohort in St James’s Hospital.
FRAILMatics will pave the way towards the development of medical devices that can help clinicians identify frailty at an early stage.
Read more about FRAILMatics on: https://www.tcd.ie/news_events/articles/research-into-early-detection-of-frailty-in-older-adults-awarded-e1-5m/ and https://tilda.tcd.ie/frailmatics/
My publications are on: https://www.tcd.ie/medicine/staff/ROMEROOR
FRAILMatics’ quest is to advance, through big data analytical approaches, our understanding of the physiology of ageing and develop tools that can identify subtle dysregulated responses to stressors across physiological systems.
Big data available include computer-based neurocognitive tests such as sustained attention to response task (SART) and choice reaction time (CRT). This type of data would be of interest to prospective ICAT PhD clinicians interested in areas related to neuropsychology.
FRAILMatics has a vast cross-sectional and longitudinal dataset of automatically recorded gait parameters at the person’s preferred speed, under cognitive challenges, and at maximum speed (GAITRite® system). In the clinical cohort we also collect three-dimensional gait analysis with the Codamotion® system. The exploration of this data would be suited to those interested in Geriatric Medicine, Sports and Medicine and Movement Neuroscience, among others.
In addition, FRAILMatics has a large dataset with multiple cardiovascular parameters which are non-invasively recorded continuously (with the Finometer®) during an active stand test, which challenges the ability of our body systems to compensate for a rapid change in position. We simultaneously record brain oxygenation parameters during the active stand with the PortaLite® NIRS system. The analysis of this data would be particularly suitable for PhD clinicians in the areas of Cardiology, Vascular Medicine, Neurology, and others.
The outputs of FRAILMatics will be in the form of new models of multiple physiological dysregulation to stressors. FRAILMatics’ ambition is to produce software packages that can detect and quantify patterns of vulnerability to stressors, and therefore increase the detection of adults who are at higher risk of adverse health outcomes, with higher specificity than current identification methods.
To facilitate FRAILMatics’ research programme, SFI has funded a new dedicated High-Performance Computing (HPC) cluster (Tinney: https://www.tchpc.tcd.ie/node/1353). Excellent quantitative skills and willingness to learn advanced data science skills will be very advantageous.