Seminar Series: Aayushi Joshi and Patricia Nistor

PhD Thesis Proposal Defense Public Lectures

Understanding frailty in type 2 diabetes mellitus: From epidemiological evidence to a longitudinal analysis in a national cohort

Aayushi Joshi, PhD Candidate

Program: Epidemiology and Biostatistics
Supervisors: Dr. Kristin Clemens and Dr. Saverio Stranges
Department of Epidemiology and Biostatistics
Schulich School of Medicine & Dentistry
Western University

Short Biography:
Aayushi Joshi, is a PhD Candidate in Epidemiology and Biostatistics at Western University, supervised by Dr. Kristin K. Clemens and Dr. Saverio Stranges. Her doctoral research examines the longitudinal relationship between type 2 diabetes mellitus and frailty in middle-aged and older adults integrating a cohort analysis of the Canadian Longitudinal Study on Aging (CLSA). She previously completed a MSc in Experimental Medicine at McGill University and her research interests broadly include frailty, chronic disease epidemiology, and aging-related population health.

Abstract:
Frailty is highly prevalent among individuals with type 2 diabetes mellitus (T2DM) and independently associated with mortality, hospitalization, and disability, yet existing evidence is largely cross-sectional and frailty is rarely assessed in routine T2DM care. This dissertation addresses this gap through four integrated chapters. The first is a scoping review mapping longitudinal evidence on T2DM and frailty and predictors of frailty progression. The second and third are original analyses using three waves of the Canadian Longitudinal Study on Aging (CLSA); one examining the prospective association between T2DM and incident frailty and frailty severity over six years, and the other investigating osteoporosis as a mediator of the T2DM–frailty relationship. The fourth is a review article articulating the clinical rationale for integrating frailty assessment into T2DM and cardiometabolic care. Together, this work will generate key Canadian longitudinal evidence on T2DM and frailty and inform more individualized care for older adults with T2DM.

Area of research:
Type 2 diabetes, frailty, chronic disease epidemiology, CLSA

Risk stratification algorithms to predict dementia

Patricia Nistor, PhD Candidate

Program: Epidemiology and Biostatistics
Supervisors: Dr. Shehzad Ali and Dr. Osvaldo Espin-Garcia
Department of Epidemiology and Biostatistics
Schulich School of Medicine & Dentistry
Western University

Short Biography:
Patricia Nistor is a fourth-year PhD student in Epidemiology under the supervision of Dr. Shehzad Ali and Dr. Osvaldo Espin-Garcia. Patricia completed both her MSc in Epidemiology and BSc with Honours Specialization in Neuroscience at Western University. Previously, her research has been focused on individuals with spinal cord injury while working in a specialized primary care clinic, and older adults with multimorbidity at risk of social isolation during her MSc. Currently, her research investigates ageing populations with respect to dementia, particularly, how to predict at risk individuals for prevention interventions so that with longer life expectancy individuals can continue to live healthy lives.

Abstract:
Dementia burden continues to increase as populations age, particularity in Canada. Therefore, it is important to identify individuals with high risk of developing dementia early, so that targeted resources can be directed towards the most appropriate preventative interventions, reducing illness burden individually and systemically by preventing progression to dementia. Three groups of risk algorithms will be developed, genetics only, lifestyle/clinical risk factors only, and a combination of genetics and lifestyle/clinical risk factors. From these models a simplified algorithm suitable for application in Canadian primary care will be derived and evaluated. CanPath data will be used for model development and internal validation. Model performance will be assessed used using the c-statistic, calibration, and sensitivity and specificity analyses. Internal validation will be conducted with a sample of data set aside for internal validation, along with bootstrapping, with 100 repetitions, will be employed, alongside the AUC, the F score, and the Brier Score. Development of a risk stratification algorithm for dementia for the Canadian context applicable at a stage when preventive interventions are most effective may help reduce the burden of dementia in later life for individuals, their caregivers, and the healthcare system.

Area of research:
Dementia; aging, population health; risk stratification; risk prediction

Date: Friday, October 30
Time: 1:30 pm - 2:30 pm
Location: P HFM 3015 (Western Centre for Public Health and Family Medicine) or via Zoom (Zoom link may be requestet at  EpiBio@uwo.ca )