Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-22T07:48:50.677Z Has data issue: false hasContentIssue false
  • English
  • Français

The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease

Published online by Cambridge University Press:  01 August 2009

Kathryn A Ellis ,
Ashley I Bush ,
David Darby ,
Daniela De Fazio ,
Jonathan Foster ,
Peter Hudson ,
Nicola T. Lautenschlager ,
Nat Lenzo ,
Ralph N. Martins  and
Paul Maruff
...Show all authors
Kathryn A Ellis*
Affiliation:
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, St. Vincent's Aged Psychiatry Service, St George's Hospital, VictoriaAustralia Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia National Ageing Research Institute, Parkville, Victoria, Australia
Ashley I Bush
Affiliation:
Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia Department of Pathology, University of Melbourne, Victoria, Australia
David Darby
Affiliation:
CogState Ltd, Melbourne, Victoria, Australia Centre for Neuroscience, University of Melbourne, Parkville, Australia
Daniela De Fazio
Affiliation:
Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia
Jonathan Foster
Affiliation:
Centre of Excellence for Alzheimer's Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia Neurosciences Unit, Health Department of Western Australia, Perth, Western Australia, Australia
Peter Hudson
Affiliation:
CSIRO, Parkville, Victoria, Australia
Nicola T. Lautenschlager
Affiliation:
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, St. Vincent's Aged Psychiatry Service, St George's Hospital, VictoriaAustralia School of Psychiatry and Clinical Neurosciences and WA Centre for Health and Ageing, University of Western Australia, Perth, Western Australia, Australia.
Nat Lenzo
Affiliation:
Centre of Excellence for Alzheimer's Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia
Ralph N. Martins
Affiliation:
Centre of Excellence for Alzheimer's Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia
Paul Maruff
Affiliation:
CogState Ltd, Melbourne, Victoria, Australia Centre for Neuroscience, University of Melbourne, Parkville, Australia
Colin Masters
Affiliation:
Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia Centre for Neuroscience, University of Melbourne, Parkville, Australia
Andrew Milner
Affiliation:
Neurosciences Australia, Parkville, Victoria, Australia
Kerryn Pike
Affiliation:
Mental Health Research Institute, The University of Melbourne, Parkville, Victoria, Australia Austin Health, Heidelberg, Victoria, Australia
Christopher Rowe
Affiliation:
Austin Health, Heidelberg, Victoria, Australia
Greg Savage
Affiliation:
Macquarie Centre for Cognitive Science, Macquarie University, NSW, Australia
Cassandra Szoeke
Affiliation:
CSIRO, Parkville, Victoria, Australia National Ageing Research Institute, Parkville, Victoria, Australia
Kevin Taddei
Affiliation:
Centre of Excellence for Alzheimer's Disease Research & Care, School of Exercise Biomedical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia Sir James McCusker Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western Australia, Australia
Victor Villemagne
Affiliation:
Austin Health, Heidelberg, Victoria, Australia
Michael Woodward
Affiliation:
Austin Health, Heidelberg, Victoria, Australia
David Ames
Affiliation:
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, St. Vincent's Aged Psychiatry Service, St George's Hospital, VictoriaAustralia National Ageing Research Institute, Parkville, Victoria, Australia
*
Correspondence should be addressed to: Kathryn A. Ellis, Academic Unit for Psychiatry of Old Age, Department of Psychiatry, University of Melbourne, St. Vincent's Aged Psychiatry Service, St George's Hospital Campus, 283 Cotham Rd, Kew, Victoria 3101, Australia. Phone: +61 3 9389 2919; Fax +61 3 9816 0477. Email: [email protected].
Get access

Abstract

Background: The Australian Imaging, Biomarkers and Lifestyle (AIBL) flagship study of aging aimed to recruit 1000 individuals aged over 60 to assist with prospective research into Alzheimer's disease (AD). This paper describes the recruitment of the cohort and gives information about the study methodology, baseline demography, diagnoses, medical comorbidities, medication use, and cognitive function of the participants.

Methods: Volunteers underwent a screening interview, had comprehensive cognitive testing, gave 80 ml of blood, and completed health and lifestyle questionnaires. One quarter of the sample also underwent amyloid PET brain imaging with Pittsburgh compound B (PiB PET) and MRI brain imaging, and a subgroup of 10% had ActiGraph activity monitoring and body composition scanning.

Results: A total of 1166 volunteers were recruited, 54 of whom were excluded from further study due to comorbid disorders which could affect cognition or because of withdrawal of consent. Participants with AD (211) had neuropsychological profiles which were consistent with AD, and were more impaired than participants with mild cognitive impairment (133) or healthy controls (768), who performed within expected norms for age on neuropsychological testing. PiB PET scans were performed on 287 participants, 100 had DEXA scans and 91 participated in ActiGraph monitoring.

Conclusion: The participants comprising the AIBL cohort represent a group of highly motivated and well-characterized individuals who represent a unique resource for the study of AD. They will be reassessed at 18-month intervals in order to determine the predictive utility of various biomarkers, cognitive parameters and lifestyle factors as indicators of AD, and as predictors of future cognitive decline.

Keywords

Alzheimer's diseasemild cognitive impairmenthealthy controlscohort studylongitudinal studyPiB PET imaging
Type
Research Article
Information
International Psychogeriatrics , Volume 21 , Issue 4 , August 2009 , pp. 672 - 687
Copyright
Copyright © International Psychogeriatric Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Access Economics (2005). Dementia Estimates and Projections, Australian States and Territories. Canberra: Alzheimer's Australia.Google Scholar
American Psychiatric Association (1994). Diagnostic and Statistical Manual of Mental Disorders, 4th edn. Washington, DC: American Psychiatric Association.Google Scholar
Brink, T. L., Yesavage, J. A., Lum, O., Heersema, P., Adey, M. B. and Rose, T. L. (1982). Screening tests for geriatric depression. Clinical Gerontologist, 1, 37–44.CrossRefGoogle Scholar
Corballis, M. C. (2009). The evolution and genetics of cerebral asymmetry. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 364, 867–879.CrossRefGoogle ScholarPubMed
Corbo, R. M. and Scacchi, R. (1999). Apolipoprotein E (APOE) allele distribution in the world. Is APOE*4 a ‘thrifty’ allele? Annals of Human Genetics, 63, 301–310.CrossRefGoogle ScholarPubMed
Craig, C. L. et al. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine and Science in Sports and Exercise, 35, 1381–1395.CrossRefGoogle ScholarPubMed
Delis, D., Kramer, J., Kaplan, E. and Ober, B. (2000). California Verbal Learning Test-Second Edition. San Antonio, TX: The Psychological Corporation.Google Scholar
Delis, D. C., Kaplan, E. and Kramer, J. H. (2001). The Delis-Kaplan Executive Function System (D-KEFS). San Antonio TX: Psychological Corporation.Google Scholar
Ferri, C. P. et al. (2005). Global prevalence of dementia: a Delphi consensus study. Lancet, 366, 2112–2117.CrossRefGoogle ScholarPubMed
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle ScholarPubMed
Glodzik-Sobanska, L. et al. (2007). Subjective memory complaints: presence, severity and future outcome in normal older subjects. Dementia and Geriatric Cognitive Disorders, 24, 177–184.CrossRefGoogle ScholarPubMed
Hodge, A., Patterson, A. J., Brown, W. J., Ireland, P. and Giles, G. (2000). The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle-aged women in a study of iron supplementation. Australian and New Zealand Journal of Public Health, 24, 576–583.CrossRefGoogle Scholar
Jonker, C., Geerlings, M. I. and Schmand, B. (2000). Are memory complaints predictive for dementia? A review of clinical and population-based studies. International Journal of Geriatric Psychiatry, 15, 983–991.3.0.CO;2-5>CrossRefGoogle ScholarPubMed
Jorm, A. F. and Jacomb, P. A. (1989). The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE): socio-demographic correlates, reliability, validity and some norms. Psychological Medicine, 19, 1015–1022.CrossRefGoogle ScholarPubMed
Jost, B. C. and Grossberg, G. T. (1996). The evolution of psychiatric symptoms in Alzheimer's disease: a natural history study. Journal of the American Geriatrics Society, 44, 1078–1081.CrossRefGoogle ScholarPubMed
Kryscio, R. J., Schmitt, F. A., Salazar, J. C., Mendiondo, M. S. and Markesbery, W. R. (2006). Risk factors for transitions from normal to mild cognitive impairment and dementia. Neurology, 66, 828–832.CrossRefGoogle ScholarPubMed
Larrieu, S. et al. (2002). Incidence and outcome of mild cognitive impairment in a population-based prospective cohort. Neurology, 59, 1594–1599.CrossRefGoogle Scholar
Lautenschlager, N. T. et al. (2008). Effect of physical activity on cognitive function in older adults at risk for Alzheimer disease: a randomized trial. JAMA, 300, 1027–1037.CrossRefGoogle ScholarPubMed
Lyketsos, C. G. and Olin, J. (2002). Depression in Alzheimer's disease: overview and treatment. Biological Psychiatry, 52, 243–252.CrossRefGoogle ScholarPubMed
Martins, R. N. et al. (1995). ApoE genotypes in Australia: roles in early and late onset Alzheimer's disease and Down's syndrome. Neuroreport, 6, 1513–1516.CrossRefGoogle ScholarPubMed
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D. and Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology, 34, 939–944.CrossRefGoogle ScholarPubMed
Mega, M. S., Cummings, J. L., Fiorello, T. and Gornbein, J. (1996). The spectrum of behavioral changes in Alzheimer's disease. Neurology, 46, 130–135.CrossRefGoogle ScholarPubMed
Meyers, J. E. and Meyers, K. R. (1995). Rey Complex Figure Test and Recognition Trial.Professional Manual: Psychological Assessment Resource, Inc.Google Scholar
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.CrossRefGoogle ScholarPubMed
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G. and Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56, 303–308.CrossRefGoogle ScholarPubMed
Pike, K. E. et al. (2007). Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer's disease. Brain, 130, 2837–2844.CrossRefGoogle ScholarPubMed
R Development Core Team (2005). R: A Language and Environment for Statistical Computing, reference index version 2.8.1. Vienna: R Foundation for Statistical Computing.Google Scholar
Reisberg, B. (2007). Global measures: utility in defining and measuring treatment response in dementia. International Psychogeriatrics, 19, 421–456.CrossRefGoogle ScholarPubMed
Reisberg, B. and Gauthier, S. (2008). Current evidence for subjective cognitive impairment (SCI) as the pre-mild cognitive impairment (MCI) stage of subsequently manifest Alzheimer's disease. International Psychogeriatrics, 20, 1–16.CrossRefGoogle ScholarPubMed
Ritchie, C. W., Ames, D., Masters, C. L. and Cummings, J. (2007). Therapeutic Strategies in Dementia. Oxford: Clinical Publishing.Google Scholar
Rozzini, R., Gozzoli, M. P., Indelicato, A., Lonati, F. and Trabucchi, M. (2008). Patterns of antidepressants prescriptions in a large Italian old population. International Journal of Geriatric Psychiatry, 23, 872–873.CrossRefGoogle Scholar
Rowe, C. C. et al. (2007). Imaging beta-amyloid burden in aging and dementia. Neurology, 68, 1718–1725.CrossRefGoogle ScholarPubMed
Saxton, J. et al. (2000). Normative data on the Boston Naming Test and two equivalent 30-item short forms. Clinical Neuropsychology, 14, 526–534.CrossRefGoogle ScholarPubMed
Sheikh, J. I. and Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. In Brink, T. L. (ed.), Clinical Gerontology: A Guide to Assessment and Intervention (pp. 165–173). New York: The Haworth Press.Google Scholar
Snaith, R. P. and Zigmond, A. S. (1986). The hospital anxiety and depression scale. British Medical Journal, 292, 344.CrossRefGoogle ScholarPubMed
Solfrizzi, V. et al. (2004). Vascular risk factors, incidence of MCI, and rates of progression to dementia. Neurology, 63, 1882–1891.CrossRefGoogle ScholarPubMed
Strauss, E., Sherman, and Spreen, O. (2006). A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary (3rd edn). New York: Oxford University Press.Google Scholar
Takeda, M., Okochi, M., Tagami, S., Tanaka, T. and Kudo, T. (2007). Biological markers as outcome measures for Alzheimer's disease interventions – real problems and future possibilities. International Psychogeriatrics, 19, 391–400.CrossRefGoogle ScholarPubMed
Wechsler, D. (1945). A standardised memory scale for clinical use. Journal of Psychology, 19, 87–95.CrossRefGoogle Scholar
Wechsler, D. (1997). Wechsler Adult Intelligence Scale, 3rd edn(WAIS-III). San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D. (2001). Wechsler Test of Adult Reading: Examiner's Manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Winblad, B. et al. (2004). Mild cognitive impairment – beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240–246.CrossRefGoogle Scholar
World Health Organization (1992). The ICD-10 Classification of Mental and Behavioural Disorders. Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organization.Google Scholar
Yesavage, J. A. et al. (1982). Development and validation of a geriatric depression screening scale: a preliminary report. Journal of Psychiatric Research, 17, 37–49.CrossRefGoogle Scholar
Zigmond, A. S. and Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67, 361–370.CrossRefGoogle ScholarPubMed