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

By K. Ellis, Ashley I. Bush, D. Darby, Daniela De Fazio, J. Foster, P. Hudson, N. Lautenschlager, N. Lenzo, R. Martins, P. Maruff and 10 more. International Psychogeriatrics, 2009

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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.

Subfields: Physiology, Psychiatry and Mental health, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/606dec6967fe9bbfd97a582fa2b98bad4942cd42

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Citations in the corpus

The Alzheimer’s Disease Neuroimaging Initiative: Progress report and future plans (2010, background): “[15] Hampel H, Burger K, Teipel SJ, Bokde ALW, Zetterberg H, Blennow K.”
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge (2015, methodology): “AIBL study methodology has been reported previously (Ellis et al., 2009).”
Statistical normalization techniques for magnetic resonance imaging (2014, background): “…image analysis literature has emphasized the importance of intensity normalization (whichwe refer to as normalization for brevity) for registration (Hellier, 2003), cross-sectional (Wang et al., 1998; Shah et al., 2011) and longitudinal (Sweeney et al., 2013a) segmentation, longitudinal…”
Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. (2010, methodology): “The AIBL study was designed to improve nderstanding of the pathogenesis of AD, focusing on early D diagnosis, while also providing a cohort suitable for arly intervention studies (Ellis et al., 2009).”
Large-Scale Unsupervised Deep Representation Learning for Brain Structure (2018, neutral): “- for the study of AD [2].”
Longitudinal Assessment of Aβ and Cognition in Aging and Alzheimer Disease (2011, background): “participants were subsequently enrolled into the Australian Imaging, Biomarkers, and Lifestyle study of ageing.(19,25) The”
Deep Bayesian Hashing with Center Prior for Multi-modal Neuroimage Retrieval (2020, methodology): “We evaluate our method on three popular benchmark datasets: (1) Alzheimer’s Disease Neuroimaging Initiative ( ADNI1 ), (2) ANDI2 [44], and (3) Australian Imaging, Bio-markers and Lifestyle dataset ( AIBL ) [45].”
Domain Adaptation for Medical Image Analysis: A Survey (2021, neutral): “[44] employ the instance weighting strat-”