Cognitive impairment no dementia (CIND) is conceptualized as a stage of cognitive decline between normal aging and onset of dementia. As persons with CIND are at high risk of developing dementia, efforts to determine early predictors of cognitive decline are warranted to advance both clinical knowledge and practice. Recent evidence suggests persons with CIND may have changes in vascular function compared to non-impaired peers, which may have clinical potential to differentiate those with and without CIND. The purpose of this study is to determine whether vascular functioning, examined both by individual indicators and as an aggregate vascular factor, will be associated with cognitive impairment. It is expected that the individual vascular indicators of hypertension, diabetes, stroke, and heart problems will be related to cognitive status classification, with poorer vascular function being more strongly associated with CIND as compared to the control group. Further, it is expected that examining the aggregate vascular factor in a multivariate approach will be more strongly associated with cognitive status than examining the vascular indicators individually. Data for this study were collected in the Victoria Longitudinal Study (VLS), a large-scale longitudinal, sequential study of community-dwelling older adults in Victoria, British Columbia. Cognitive group status was determined by a distributional approach based on scores on 5 cognitive reference measures. The associations between all vascular factors and cognitive status groups were assessed using chi-square analyses. Univariate analyses were then carried out using ordinal logistic analysis. A multivariate approach using discriminant analysis was then used to determine if cognitive status group membership was associated with vascular function based on linear combinations of vascular indicators. Contrary to expected results, we did not find a significant association between any of the vascular indicators (i.e., blood pressure classification, severity of stroke, severity of heart troubles, and severity of diabetes) and the cognitive status classifications. Further, group membership was not associated with any of the individual vascular markers, or by a multivariate combination of the indicators. Several reasons for this study’s findings include discrepant definitions of cognitive impairment in the literature, sample characteristics (i.e. high education, low base rate of vascular problems), and methodological considerations. Future research objectives should address the longitudinal association of vascular function and cognitive status. / Graduate

The absence of gold standard criteria for mild cognitive impairment (MCI) impedes the comparison of research findings and the development of primary and secondary prevention strategies addressing the possible conversion to dementia. The objective of Study 1 was to compare the predictive ability of different MCI models as markers for incipient dementia in a longitudinal population-based Canadian sample. The utility of well-documented MCI criteria using data from persons who underwent a clinical examination in the second wave of the Canadian Study of Health and Aging (CSHA) was examined. Demographic characteristics, average neuropsychological test performance, and prevalence and conversion rates were calculated for each classification. Receiver operating characteristic (ROC) analyses were employed to assess the predictive power of each cognitive classification. The highest prevalence and conversion rates were associated with case definitions of multiple-domain MCI. The only diagnostic criteria to significantly predict dementia five years later was the Cognitive Impairment, No Dementia (CIND) Type 2 case definition. It is estimated that more restrictive MCI case definitions fail to address the varying temporal increases in decline across different cognitive domains in the progression from normal cognitive functioning to dementia. Using data from the CSHA, the objective of Study 2 was to elucidate the clinical correlates that best differentiate between cognitive classifications. A machine learning algorithm was used to identify the symptoms that best discriminated between: 1) not cognitively impaired (NCI) and CIND; 2) CIND & demented; and 3) converting and non-converting CIND participants. Poor retrieval was consistently a significant predictor of greater cognitive impairment across all three questions. While interactions with other predictors were noted when differentiating CIND from NCI and demented from non-demented participants, retrieval was the sole predictor of conversion to dementia over five years. Importantly, the limited specificity and predictive values of the respective algorithms caution against their use as clinical markers of CIND, dementia, or conversion. Rather, it is recommended that the predictors serve as markers for ongoing monitoring and assessment. Overall, the results of both studies suggest that the architecture of pathological cognitive decline to dementia may not be captured by a single set of diagnostic criteria.

The absence of gold standard criteria for mild cognitive impairment (MCI) impedes the comparison of research findings and the development of primary and secondary prevention strategies addressing the possible conversion to dementia. The objective of Study 1 was to compare the predictive ability of different MCI models as markers for incipient dementia in a longitudinal population-based Canadian sample. The utility of well-documented MCI criteria using data from persons who underwent a clinical examination in the second wave of the Canadian Study of Health and Aging (CSHA) was examined. Demographic characteristics, average neuropsychological test performance, and prevalence and conversion rates were calculated for each classification. Receiver operating characteristic (ROC) analyses were employed to assess the predictive power of each cognitive classification. The highest prevalence and conversion rates were associated with case definitions of multiple-domain MCI. The only diagnostic criteria to significantly predict dementia five years later was the Cognitive Impairment, No Dementia (CIND) Type 2 case definition. It is estimated that more restrictive MCI case definitions fail to address the varying temporal increases in decline across different cognitive domains in the progression from normal cognitive functioning to dementia. Using data from the CSHA, the objective of Study 2 was to elucidate the clinical correlates that best differentiate between cognitive classifications. A machine learning algorithm was used to identify the symptoms that best discriminated between: 1) not cognitively impaired (NCI) and CIND; 2) CIND & demented; and 3) converting and non-converting CIND participants. Poor retrieval was consistently a significant predictor of greater cognitive impairment across all three questions. While interactions with other predictors were noted when differentiating CIND from NCI and demented from non-demented participants, retrieval was the sole predictor of conversion to dementia over five years. Importantly, the limited specificity and predictive values of the respective algorithms caution against their use as clinical markers of CIND, dementia, or conversion. Rather, it is recommended that the predictors serve as markers for ongoing monitoring and assessment. Overall, the results of both studies suggest that the architecture of pathological cognitive decline to dementia may not be captured by a single set of diagnostic criteria.