Improving decision reliability in transport safety engineering through a machine learning-based model.

Considered a global epidemic, obesity impacts approximately 30% of Canadian adults with prevalence rates continuing to rise. Obesity is associated with greater risk of a number of chronic conditions such as hypertension, diabetes and depression. Obesity has also been shown to be a risk factor for Alzheimer's disease (AD) and related dementias, independent of vascular and diabetes comorbidities. Mild cognitive impairment (MCI), considered a transitional state between normal aging and dementia, has also been shown to be more common among moderately to severely obese older adults (up to 50%) compared to the general population (6%). Obesity is most commonly defined using body mass index (BMI). The relationship between the entire range of BMI classes (underweight, normal, overweight, obesity I, II and III) and cognitive function is complex and influenced by factors such as age, sex, and race. For example, obesity has been shown to be more negatively associated with cognition among women than men. Also, beyond age 75, evidence has been shown for the “obesity paradox” where higher BMI is more protective with regards to cognitive function, particularly for executive function. Low BMI/being underweight has also been shown to be a risk factor for dementia, especially among Asian older adults. The primary research question that this current study aims to answer is the nature of the relationship between classes of BMI and cognitive function among Canadian older adults using data from the Canadian Longitudinal Study on Aging (CLSA). The CLSA is a prospective cohort study of over 50,000 Canadian adults aged between 45 and 85 followed for at least 20 years. Being the first database of this kind in Canada, it offers a unique opportunity to examine the complex relationship between the range of BMI classifications and cognitive function among a large sample of Canadian older adults. The CLSA database also includes variables that can be used to classify participants according to indication of MCI, thus also allowing a unique opportunity to examine the relationship between BMI classification and presence of MCI. In order to answer the primary research question, this study had two main objectives. For Objective 1, we aimed to determine the cross-sectional relationship between BMI classification and (a) memory performance, and (b) indication of MCI, using baseline data from the CLSA. We hypothesized that there will be presence of an inverted U relationship with participants classified as underweight and obese (Class I, II and III) showing lower level of memory performance and higher rates of MCI compared to normal BMI. For Objective 2, we aimed to determine whether 3-year changes in BMI (from baseline to first follow-up) were associated with changes in memory performance. We hypothesized that 3-year decreases in BMI will be associated with improvements in cognitive function.