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Predictors of cognitive decline at 18 month follow up among 1112 participants in the Australian imaging, biomarkers and lifestyle flagship study of ageing (AIBL)
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Poster Presentations P1
P1-146
PREDICTORS OF COGNITIVE DECLINE AT 18 MONTH FOLLOW UP AMONG 1112 PARTICIPANTS IN THE AUSTRALIAN IMAGING, BIOMARKERS AND LIFESTYLE FLAGSHIP STUDY OF AGEING (AIBL)
Kathryn A. Ellis1, Christopher Rowe2, Colin Masters3, Cassandra Szoeke4, Kevin Taddei5, Ralph Martins6, Victor Villemagne7, Michael Fahey8, David Ames9, Paul Maruff10, Ping Zhang11, Greg Savage12, AIBL Research Group13, Lance Macaulay14, 1University of Melbourne, Kew; 2Austin Health, University of Melbourne, Heidelberg, Melbourne; 3University of Melbourne, Melbourne, VIC; 4CSIRO, Melbourne; 5Edith Cowan University, Perth; 6Edith Cowan University, Perth; 7Austin Health, Melbourne; 8Division of Mathematics, Informatics and Statistics, CSIRO, Parkville, VIC; 9National Ageing Research Institute, Parkville; 10CogState, Melbourne; 11CSIRO, Parkville; 12Macquarie Centre for Cognitive Science, Macquarie University, Sydney, NSW; 13Mental Health Research Institute, Perth; 14CSIRO, Parkville.
Figure 1. Concept of Diagnosis matrix (D-matrix) and distribution of subjects and their characteristics according to 12 cells. A. Concept of D-matrix: the Y axis denotes the cognition spectrum, while the X axis is the severity of ischemic lessions on MRI. B.Distribution of subejcts: 3,966 subjects are classified into 12 cells of D-matrix.
factors influence the proportion of dementia subtypes. Dementia in India is expected to reach epidemic proportions in the next two decades but studies on dementia are few. We studied patients attending a memory clinic in a private hospital in South India to describe the clinical profile and compare the socio-cultural factors influencing diagnosis and dementia care in this population with studies from developed countries. Methods: Consecutive patients attending the memory clinic at Manipal hospital, Bangalore over three years were included. Socio-demographic information including income, education and linguistic background was collected. All patients underwent detailed neurological evaluation, cognitive testing, blood tests and neuroimaging. The diagnosis of MCI, dementia and the different subtypes was based on standard criteria. Results: The mean age at presentation was 65.8(9.0) years, range 4087 years. There were 127 men and 93 women. Of the total 220 patients, MCI was diagnosed in 57 (25.9%), Alzheimer’s disease (AD) in 62 (28.1%), vascular dementia (VaD) in 38 (17.3%), and mixed dementia in four patients. 37 (16.8%) patients were diagnosed as frontotemporal lobar degeneration (FTD) and its variants. Other degenerative dementias were seen in 13 and miscellaneous causes in 8 patients. One patient did not appear to have any cognitive impairment. Majority of the patients (80.4%) were bilingual or multilingual and 10 (4.9%) patients were illiterate. VaD was more common in men (3:1) and AD in women (1.5:1). Majority of the MCI patients had depression (54.4%) and seven patients developed AD during follow-up. All the patients were cared for at home by relatives except for one patient who had an appointed caretaker. Conclusions: Alzheimer’s disease was more common than vascular dementia in this study from an urban hospital in South India, in spite of the high prevalence of vascular risk factors in this population. Depression was a common cause of mild cognitive impairment. In this study of a relatively young (mean age) dementia cohort, all patients had home based care in contrast to studies from developed countries.
Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing has assembled a large cohort of individuals whose cognitive data, blood samples, imaging results, and lifestyle information is being examined longitudinally at regular 18-month intervals. This study reports data from the first 18-month follow-up, and examines predictors of decline. Methods: Participants in the AIBL inception cohort (1112 - 211 with AD, 133 with Mild Cognitive Impairment (MCI) and 768 healthy controls (HCs)) were invited to undergo reassessment 18 months after a baseline assessment. At both time points data collection included comprehensive cognitive testing, drawing of 80 ml of blood, and completion of health and lifestyle questionnaires. Those individuals who had undergone amyloid PET brain imaging with Pittsburgh compound B (PiB PET) and MRI brain imaging at baseline were invited to return for further imaging. Results: Of the 1112 participants in the inception cohort, 89.6% of the cohort was followed up at 18-months (969 participants were reassessed and 28 were recorded as deceased) and a further 115 did not return for re-assessment. The 18-month cohort comprises 969 participants and current classifications are as follows; 691 healthy controls (317 nonmemory complainers and 374 subjective memory complainers), 81 MCI cases and 197 patients with AD. Change in cognition over 18-months was greatest in the AD group compared to HC and MCI participants who did not change clinical classifications over 18-months; however HC who converted to MCI over 18-months demonstrated significantly greater decline on memory measures than stable HC volunteers and stable MCI volunteers. The magnitude of cognitive change was not different between memory complainers and non-complainers, although baseline mood measures differentiated these groups. Conclusions: Cross-sectional analysis of the AIBL dataset has already demonstrated links between cognition, brain beta-amyloid burden, structural brain changes, biomarkers, and lifestyle. Sets of neuropsychological variables were identified which predicted conversion between categories, and the neuropsychological profile at baseline differed between those who developed Mild Cognitive Impairment compared to those developing frank AD. 36 month re-assessment of the AIBL cohort is currently underway.
P1-147
NORMATIVE NEUROPSYCHOLOGY DATASETS FREE FROM PRODROMAL ALZHEIMER’S DISEASE (AD) CASES MAY IMPROVE SENSITIVITY FOR DETECTING EARLY COGNITIVE DECLINE: DATA FROM THE AIBL STUDY
Kathryn A. Ellis1, Christopher Rowe2, Olivier Salvado3, Colin Masters4, Ralph Martins5, Victor Villemagne6, Pierrick Bourgeat7, David Ames8, Paul Maruff9, Greg Savage10, AIBL research group11, Lance Macaulay12, Cassandra Szoeke13, Paul Yates14, Carolina Restrepo15, 1University of Melbourne, Kew; 2Austin Health, University of Melbourne, Heidelberg, Melbourne; 3CSIRO, Melbourne; 4University of Melbourne, Melbourne, VIC; 5Edith Cowan University, Perth; 6Austin Health, Melbourne; 7CSIRO,
Poster Presentations P1
P1-146
PREDICTORS OF COGNITIVE DECLINE AT 18 MONTH FOLLOW UP AMONG 1112 PARTICIPANTS IN THE AUSTRALIAN IMAGING, BIOMARKERS AND LIFESTYLE FLAGSHIP STUDY OF AGEING (AIBL)
Kathryn A. Ellis1, Christopher Rowe2, Colin Masters3, Cassandra Szoeke4, Kevin Taddei5, Ralph Martins6, Victor Villemagne7, Michael Fahey8, David Ames9, Paul Maruff10, Ping Zhang11, Greg Savage12, AIBL Research Group13, Lance Macaulay14, 1University of Melbourne, Kew; 2Austin Health, University of Melbourne, Heidelberg, Melbourne; 3University of Melbourne, Melbourne, VIC; 4CSIRO, Melbourne; 5Edith Cowan University, Perth; 6Edith Cowan University, Perth; 7Austin Health, Melbourne; 8Division of Mathematics, Informatics and Statistics, CSIRO, Parkville, VIC; 9National Ageing Research Institute, Parkville; 10CogState, Melbourne; 11CSIRO, Parkville; 12Macquarie Centre for Cognitive Science, Macquarie University, Sydney, NSW; 13Mental Health Research Institute, Perth; 14CSIRO, Parkville.
Figure 1. Concept of Diagnosis matrix (D-matrix) and distribution of subjects and their characteristics according to 12 cells. A. Concept of D-matrix: the Y axis denotes the cognition spectrum, while the X axis is the severity of ischemic lessions on MRI. B.Distribution of subejcts: 3,966 subjects are classified into 12 cells of D-matrix.
factors influence the proportion of dementia subtypes. Dementia in India is expected to reach epidemic proportions in the next two decades but studies on dementia are few. We studied patients attending a memory clinic in a private hospital in South India to describe the clinical profile and compare the socio-cultural factors influencing diagnosis and dementia care in this population with studies from developed countries. Methods: Consecutive patients attending the memory clinic at Manipal hospital, Bangalore over three years were included. Socio-demographic information including income, education and linguistic background was collected. All patients underwent detailed neurological evaluation, cognitive testing, blood tests and neuroimaging. The diagnosis of MCI, dementia and the different subtypes was based on standard criteria. Results: The mean age at presentation was 65.8(9.0) years, range 4087 years. There were 127 men and 93 women. Of the total 220 patients, MCI was diagnosed in 57 (25.9%), Alzheimer’s disease (AD) in 62 (28.1%), vascular dementia (VaD) in 38 (17.3%), and mixed dementia in four patients. 37 (16.8%) patients were diagnosed as frontotemporal lobar degeneration (FTD) and its variants. Other degenerative dementias were seen in 13 and miscellaneous causes in 8 patients. One patient did not appear to have any cognitive impairment. Majority of the patients (80.4%) were bilingual or multilingual and 10 (4.9%) patients were illiterate. VaD was more common in men (3:1) and AD in women (1.5:1). Majority of the MCI patients had depression (54.4%) and seven patients developed AD during follow-up. All the patients were cared for at home by relatives except for one patient who had an appointed caretaker. Conclusions: Alzheimer’s disease was more common than vascular dementia in this study from an urban hospital in South India, in spite of the high prevalence of vascular risk factors in this population. Depression was a common cause of mild cognitive impairment. In this study of a relatively young (mean age) dementia cohort, all patients had home based care in contrast to studies from developed countries.
Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing has assembled a large cohort of individuals whose cognitive data, blood samples, imaging results, and lifestyle information is being examined longitudinally at regular 18-month intervals. This study reports data from the first 18-month follow-up, and examines predictors of decline. Methods: Participants in the AIBL inception cohort (1112 - 211 with AD, 133 with Mild Cognitive Impairment (MCI) and 768 healthy controls (HCs)) were invited to undergo reassessment 18 months after a baseline assessment. At both time points data collection included comprehensive cognitive testing, drawing of 80 ml of blood, and completion of health and lifestyle questionnaires. Those individuals who had undergone amyloid PET brain imaging with Pittsburgh compound B (PiB PET) and MRI brain imaging at baseline were invited to return for further imaging. Results: Of the 1112 participants in the inception cohort, 89.6% of the cohort was followed up at 18-months (969 participants were reassessed and 28 were recorded as deceased) and a further 115 did not return for re-assessment. The 18-month cohort comprises 969 participants and current classifications are as follows; 691 healthy controls (317 nonmemory complainers and 374 subjective memory complainers), 81 MCI cases and 197 patients with AD. Change in cognition over 18-months was greatest in the AD group compared to HC and MCI participants who did not change clinical classifications over 18-months; however HC who converted to MCI over 18-months demonstrated significantly greater decline on memory measures than stable HC volunteers and stable MCI volunteers. The magnitude of cognitive change was not different between memory complainers and non-complainers, although baseline mood measures differentiated these groups. Conclusions: Cross-sectional analysis of the AIBL dataset has already demonstrated links between cognition, brain beta-amyloid burden, structural brain changes, biomarkers, and lifestyle. Sets of neuropsychological variables were identified which predicted conversion between categories, and the neuropsychological profile at baseline differed between those who developed Mild Cognitive Impairment compared to those developing frank AD. 36 month re-assessment of the AIBL cohort is currently underway.
P1-147
NORMATIVE NEUROPSYCHOLOGY DATASETS FREE FROM PRODROMAL ALZHEIMER’S DISEASE (AD) CASES MAY IMPROVE SENSITIVITY FOR DETECTING EARLY COGNITIVE DECLINE: DATA FROM THE AIBL STUDY
Kathryn A. Ellis1, Christopher Rowe2, Olivier Salvado3, Colin Masters4, Ralph Martins5, Victor Villemagne6, Pierrick Bourgeat7, David Ames8, Paul Maruff9, Greg Savage10, AIBL research group11, Lance Macaulay12, Cassandra Szoeke13, Paul Yates14, Carolina Restrepo15, 1University of Melbourne, Kew; 2Austin Health, University of Melbourne, Heidelberg, Melbourne; 3CSIRO, Melbourne; 4University of Melbourne, Melbourne, VIC; 5Edith Cowan University, Perth; 6Austin Health, Melbourne; 7CSIRO,