Visiting Professor: Alessandro Gialluisi
Untangling biological aging and the risk of age-related brain disorders in the Moli-sani cohort: current findings and future perspectives for population studies
Alessandro Gialluisi
Department of Medicine and Surgery, University of Insubria, Varese, Italy
Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy
Short Biography:
Alessandro Gialluisi holds a BSc in Biotechnologies (University of Bologna, 2007), and a MSc in Molecular Biology (University of Pisa, 2010). After spending a pre-doctoral research period at the Medical Genetics Unit of the Sant’Orsola-Malpighi hospital, University of Bologna (2010-2011), he started a PhD in the Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands, working on the genetics of reading and language abilities, and being awarded a PhD in Science (Radboud University Nijmegen, 2015). He continued investigating learning-related skills in his first post-doc (2015-2016) in the Statistical Genetics group, Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany. Then, he moved back to Italy to widen his spectrum of research interests to the molecular and genetic epidemiology of neurodegenerative and psychiatric disorders, at the Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy. He successfully completed a residency program in Medical Statistics and Biometry (Sapienza University of Rome, 2020), through a research project on the development and analysis of biological aging markers based on machine learning algorithms.
Abstract:
In this talk, I will provide a brief overview of our most recent research activity, focused on the computation and analysis of deep learning markers of biological aging and on the identification of risk predictors and factors influencing age-related neurodegenerative and neuropsychiatric risk. We do this through combined approaches using classical epidemiology analyses, machine learning algorithms and statistical genetics methods, using both data from an Italian population cohort – the Moli-sani study – and publicly available summary statistics from previous genomic studies like GWAS.
In the first part, I will describe our work aimed at the development and validation of biological aging estimators as novel and cost-effective public health tools in the Moli-sani cohort, to characterize x their genetic underpinnings and potential non-genetic influences. These tools may help predicting clinical risks and designing personalized anti-ageing strategies in the future.
In the second part, I will present our analyses modelling incident neurodegenerative risk – prominently for Alzheimer’s disease/dementia and Parkinson’s disease/parkinsonisms – as a function of different modifiable risk factors like exposure to air pollution, lifestyles, socioeconomic status and others, in the same population setting. Similarly, I will provide preliminary evidence of how supervised machine learning algorithms may help identifying novel shared predictive biomarkers for these disorders, which is very important in light of the current lack of risk-predictive markers which can predict their onset probability well in advance.
In the last part, I will show the road ahead for our study and, more in general, for population cohorts in the fields of biogerontology and of epidemiology of common neurodegenerative disorders.
Keywords:
neuroepidemiology, health data science, statistical genetics, molecular psychiatry
Date: Tuesday, February 7th
Time: 4pm-5pm
Location: PHFM 3015 (Western Centre for Public Health and Family Medicine) or Zoom (please email epibio@schulich.uwo.ca for Zoom link)
