Abstract
Keywords
INTRODUCTION
In cognitively normal (CN) older adults, carriage of the apolipoprotein E (APOE) ɛ4 allele (ɛ4) increases risk for progression to dementia due to Alzheimer’s disease (AD) (hereafter abbreviated as AD-dementia) and its prodromal stage mild cognitive impairment (MCI) [1–3]. Processes by which ɛ4 increases risk for clinical disease progression are still unclear. While studies in transgenic mice report ɛ4 acts directly to disrupt amyloid-β (Aβ) clearance [4, 5], ɛ4 is also associated with disruption to central nervous system (CNS) lipid processing, increased tau hyperphosphorylation, and increased neuroinflammation [6]. Thus, ɛ4 risk for clinical disease progression could be solely through increased Aβ accumulation or through both increased Aβ accumulation and disruption of non-Aβ CNS functions.
Clinical studies support both possibilities. First, frequency of the ɛ4 allele in the general population is approximately 25% but in older AD-dementia cohorts it can reach 60% [7, 8]. Second, compared to CN ɛ4 noncarriers, CN ɛ4 carriers show higher average Aβ levels overall and a higher proportion of individuals classified with abnormally high Aβ (Aβ+) [9, 10]. Prospective neuropsychological studies of CN ɛ4 carriers show both cognitive decline and clinical disease progression occur only if adults are also Aβ+ [11–13]. Conversely, one recent postmortem study showed low numbers of Aβ plaques but high tau levels in ɛ4 carriers who died with dementia [14]. Clinical studies of older CN adults report that glucose hypometabolism is related to ɛ4 carriage but not Aβ status [15] and also observe in CN ɛ4 carriers with low levels of amyloid (Aβ-) patterns of functional connectivity equivalent to that in AD-dementia [16]. Finally, a meta-analysis of studies investigating relationships between clinical disease stage and Aβ+ showed approximately 30% of ɛ4 carriers aged above 70 years and classified clinically with mild cognitive impairment (MCI) are Aβ- [17].
Inconsistency in findings about how ɛ4 increases risk for AD-dementia may reflect differences in study methodology. For example, most epidemiological studies of early AD-dementia, by definition include individuals with comorbid diseases which themselves are associated with CNS disease independent of Aβ, or in a manner exacerbated by ɛ4 carriage, for example, cardiovascular or cerebral vascular disease [1, 18–21]. Such comorbid conditions are also often uncontrolled in postmortem studies (e.g., [14]). Furthermore, prospective neuropsychological studies of the effect of ɛ4 on clinical disease progression in Aβ- CN adults generally use small samples (i.e., n < 33) [22, 23] limiting their ability to detect subtle ɛ4 effects independent of Aβ. Conversely, clinical studies reporting ɛ4 effects independent of Aβ on neuroimaging parameters have used cross-sectional designs with small (e.g., n < 39) convenience subsamples drawn from larger studies [15, 16].
The risk of ɛ4 for clinical disease progression should therefore be studied in a CN sample, relatively free of major or uncontrolled chronic illness followed carefully over years. With the risk of ɛ4 for clinical disease progression determined, knowledge of Aβ+ levels can be incorporated into the same models to allow estimation of the extent to which any ɛ4 risk for clinical disease progression is moderated by Aβ. The aim of this study was to determine indices of risk of clinical disease progression for ɛ4 carriage in a large CN sample for whom Aβ status was unknown, but who had satisfied rigorous medical and neuropsychiatric inclusion criteria. Risk estimates were then recomputed taking into account Aβ status. The hypothesis was that in CN older adults, for whom Aβ status was unknown, ɛ4 carriage would increase the risk of clinical disease progression over 72 months. We then explored the extent to which this risk was moderated by Aβ status.
MATERIALS AND METHODS
Participants
Data were analyzed for 765 adults from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, who were classified as CN at their baseline assessment. This sample has been described in detail [24]. Briefly, individuals aged >60 years were recruited via community advertisements, word of mouth, or were spouses of participants classified with MCI or AD-dementia also enrolled in AIBL. All individuals underwent comprehensive medical, psychiatric, and neuropsychological assessment, including a detailed medical history, assessment of vital signs (blood pressure, heart rate, waist circumference, body mass index) and a blood sample. After this assessment, the data were reviewed by a consensus clinical review panel consisting of geriatric psychiatrists, neurologists, geriatricians, and neuropsychologists blind to APOE and Aβ status. Individuals were excluded if there was a history of, or intercurrent untreated cardiovascular disease, a history of non-AD dementia, stroke, or other neurological disease, schizophrenia, bipolar disorder, current but not past clinical depression, past serious head injury, cancer other than skin cancer within the last two years, or excessive regular alcohol use exceeding two standard drinks per day for women or four for men.
No participant had any untreated illness or disease. Clinical characteristics of the total and PET imaged sample are summarized in Table 1. Individuals were also excluded if at baseline they met clinical criteria for MCI or AD-dementia (see below). For this study, only participants with clinical data available for up to 72 months were included. A subgroup (n = 423) had also undergone Aβ neuroimaging (see below). The AIBL study was approved by the ethics committees of Austin Health, St. Vincent’s Health, Hollywood Private Hospital, and Edith Cowan University. All participants provided written informed consent prior to participation.
Measures and Procedures
Demographic and clinical characteristics
Age, sex, level of education (grouped as 0–6, 7–8, 9–12, 13–15, and >15 years), medical history, current medication use, waist circumference, and body mass index (BMI) were collected during baseline medical assessment. The Wechsler Test of Adult Reading [25] was used to estimate premorbid intelligence (IQ) and the Geriatric Depression Scale (GDS) [26] to assess severity of depressive symptoms.
APOE genotyping
An 80-ml blood sample was taken and a sample forwarded for DNA extraction using either QIAamp DNA blood Midi or Maxi kits (Qiagen) in accord with the protocol provided by the manufacturer. APOE genotype was determined through TaqMan genotyping assays (Life Technologies) for rs7412 (Assay ID: C____904973_10) and rs429358 (Assay ID: C___3084793_20) on a QuantStudio 12K-Flex real-time PCR system (Applied Biosystems) using the TaqMan GTXpress Master Mix (Life Technologies) methodology per manufacturer’s instructions. Individuals with at least one copy of the ɛ4 allele were classified as ɛ4 carriers and those without ɛ4 classified as ɛ4 noncarriers.
Aβ PET neuroimaging
Aβ levels were measured using positron emission tomography (PET) neuroimaging with 11C-Pittsburgh Compound B (11C-PiB), 18F-Florbetapir, or 18F-flutemetamol. The PET neuroimaging methodology has been described in detail [10]. For PiB and flutemetamol, PET standardized uptake value (SUV) data were summed and normalized to the cerebellar cortex SUV, producing a region-to-cerebellar ratio termed SUV ratio (SUVR). For florbetapir, SUVR was generated using the cerebellum as the reference region. Aβ levels were classified as low (Aβ-) or high (Aβ+) according to thresholds established in previous studies, which were SUVR≥1.5 for PiB [10], SUVR≥1.11 for florbetapir [27], and SUVR≥0.62 for flutemetamol [28].
Neuropsychological testing
The AIBL neuropsychological test battery assessed episodic memory, working memory, executive function, language, and attention, and has been reported in detail elsewhere [24].
Classification of clinical disease progression
At the baseline assessment and at each subsequent assessment (i.e., 18-, 36-, 54-, and 72-month), the clinical status of participants was evaluated upon completion of the comprehensive medical, clinical, and neuropsychological assessment. After each assessment, the clinical panel reviewed medical, clinical, and neuropsychological data. Where there was evidence of cognitive impairment, the panel considered classification for MCI or AD-dementia. Classification of MCI was based on the Petersen-Winblad criteria involving subjective and objective cognitive difficulties in the absence of significant functional loss [29]. Classification of AD-dementia was based on criteria from the National Institute of Neurological and Communicative Disorders and Stroke – Alzheimer’s Disease and Related Disorder (NINCDS-ADRDA) [30].
Design and Analysis
The primary clinical outcome for the study was progression from CN to MCI/AD-dementia within the 72-month study period. Progression was classified if individuals met criteria for either MCI or AD-dementia as clinical differentiation of these diagnostic entities is often difficult and based only on the presence of decline in activities of daily living, which can vary depending on informant reports [31].
Statistical analyses
Investigation of predictors of clinical disease progression was conducted in two analyses. First, data were analyzed for the total AIBL CN sample for whom Aβ status was unknown or not considered (hereafter termed total sample) and then the analyses were repeated for the Aβ PET imaged subgroup (hereafter termed PET imaged sample). Prior to analyses, distributions of demographic and clinical characteristic data for the total sample and for the PET imaged sample were screened using visual inspection of box-plots and P-P plots, as well as examination of skewness and kurtosis values. At baseline assessment, all data satisfied the assumptions of normality and homogeneity of variance. All variables at baseline were also free of extreme outlying standard scores except for premorbid IQ and GDS, which displayed two and 12 extreme outliers, respectively. A winsorizing technique [32] was used to remove outliers to allow parametric analyses.
Group differences at baseline assessment
To examine relationships between ɛ4 carrier status and demographic and clinical characteristics at baseline, each demographic and clinical outcome was compared between the ɛ4 carrier and noncarrier groups using independent samples t-tests for continuous variables or Chi-square tests for categorical variables. Potentially confounding effects from clinical or demographic characteristics that differed between the groups were adjusted in prospective analyses of relationships between ɛ4 carriage and clinical disease progression. To identify baseline demographic or clinical characteristics associated with clinical disease status by 72 months, bivariate associations were computed between clinical status at 72 months and ɛ4 carriage, Aβ status, and baseline demographic and clinical characteristics. Those baseline characteristics associated with clinical disease progression by 72 months were also considered in the prospective analyses of relationships between ɛ4 carriage and clinical disease progression.
Clinical and demographic data for the total and PET imaged samples were not independent and thus equivalence could not be determined by formal statistical analyses. To facilitate appreciation of equivalence between these estimates in each sample, 95% confidence intervals (CIs) were computed for each estimate of the demographic and clinical characteristics. Where the mean for each APOE ɛ4 group in the PET imaged sample was beyond the 95% CI for the same group in the total sample, the samples were classified to be different.
Prospective analyses
For both prospective analyses, the clinical status of participants who withdrew from the study or died within the 72-month period were classified as not having progressed clinically. This approach prevented overestimation of ɛ4 risk on clinical disease progression [33, 34]. Cox proportional hazard models were used to evaluate progression associated with ɛ4 carriage while adjusting for the effects of other covariates, first for the total sample and then for the PET imaged sample. Right censoring was defined by three criteria: end of study at 72-months post baseline, withdrawal from study and death [33, 34]. Participants who progressed clinically to MCI/AD-dementia were censored at the follow-up assessment in which they were diagnosed. For each Cox proportional hazard model, inspection of log(-log(Survival)) plots for ɛ4 carrier and noncarrier groups as well as analyses of models that included interactions between time-dependent predictors and time indicated that the proportional hazards assumption was met (e.g., [34]). For each model, clinical status was entered as the dependent variable, time to clinical disease progression as the time variable, and covariates (derived from the initial analyses) were entered in a hierarchical fashion to adjust models for any differences in demographic or clinical characteristics at baseline or their associations with clinical status at 72 months. In the model for the total sample, demographic and clinical characteristics were entered in block one followed by ɛ4 status in block two. In the model for the PET imaged sample, demographic and clinical characteristics were entered in block one, ɛ4 status in block two, and Aβ status in block three.
From each Cox proportional hazard model, the hazard functions for ɛ4 carrier and noncarrier groups were plotted to show cumulative hazards (i.e., cases for whom clinical status had not remained as CN over 72 months). Chi-square statistics were computed to estimate risk added to the models by ɛ4 or Aβ status. p-values and 95% CIs were reported for the adjusted hazard rates associated with individual covariates. Finally, in light of research suggesting that female ɛ4 carriers may show greater cognitive decline than male ɛ4 carriers [35, 36] and that the risk of ɛ4-related cognitive changes declines after the age of 85 [7, 37], the contributions of these factors to risk for disease progression were explored by re-computing hazard models with APOE×sex, APOE×age, age×sex, and APOE×sex×age interaction terms added.
For all comparisons, the level for statistical significance was set at p < 0.05. While use of this criterion does increase potential for familywise Type I error [38], this increased risk was considered acceptable in the current study because a) research linking genes and biomarkers to early neurodegenerative disease is a relatively new area and therefore important to foster further investigations and b) for each comparison the magnitude of the experimental effect was also used to guide interpretation of results [39].
RESULTS
During the 72-month study period, there was an average follow-up of 54 months and the total person-years at risk were 3,732. For the total sample, 43 cases (6%) of incident MCI/AD-dementia were classified by 72 months with 479 cases remaining classified as CN. There were 219 withdrawals (29%) and 24 deaths (3%). For the PET imaged sample, 28 cases (7%) of incident MCI/AD-dementia were classified by 72 months with 326 cases remaining classified as CN. There were also 61 withdrawals (14%) and 8 deaths (2%).
Total Sample
Group differences at baseline
Baseline demographic and clinical characteristics of ɛ4 carrier and noncarrier groups are shown in Table 1. ɛ4 carriers were on average 1.2 years older than noncarriers and this difference was statistically significant. Analyses of bivariate associations (Table 2) indicated that clinical status at 72 months was associated significantly with age, sex, premorbid IQ, level of education, and level of depressive symptoms. Therefore, these factors were entered as variables in block one of the Cox proportional hazard model.
Prospective analyses
Table 3 shows results of the Cox proportional hazard model for the risk of ɛ4 carriage on progression from CN to MCI/AD-dementia within 72 months. Figure 1 shows the hazard function for each APOE group. Examination of hierarchical blocks indicated that with the exception of premorbid IQ, all demographic and clinical characteristics contributed significantly to the model in block one. After adjusting risk estimates for these variables, ɛ4 status contributed significantly to the model in block two, χ 2 (1, 765) = 17.29, p < 0.001. Hazard rates indicated that progression for ɛ4 carriers was significantly higher than for noncarriers. Progression hazards for each of the demographic and clinical characteristics, except premorbid IQ, also remained statistically significant after adding ɛ4 status into the model (Table 3).
Re-computation of the hazards model with APOE, sex, and age added indicated no statistically significant contributions to risk for progression from APOE×sex (p = 0.72), APOE×age (p = 0.60), age×sex (p = 0.11), or APOE×sex×age (p = 0.72) interactions.
Aβ PET Imaged Sample
Inspection of overlap between 95% CIs associated with estimates of demographic and clinical characteristics according to APOE status indicated equivalence in these characteristics between the PET imaged sample and the total sample (Table 1). The proportion of ɛ4 carriers was also equivalent in the total sample and the PET imaged sample.
Group differences at baseline
Baseline demographic and clinical characteristics of ɛ4 carrier and noncarrier groups in the PET imaged sample are shown in Table 1. In this sample, ɛ4 carriers had significantly more Aβ+ individuals than noncarriers. Analyses of bivariate associations (Table 2) indicated that clinical status at 72 months was associated significantly with age, sex, premorbid IQ, level of education, and level of depressive symptoms. Therefore, these were entered as variables in block one of the Cox proportional hazard model.
Prospective analyses
Table 4 shows results of the Cox proportional hazard model for the risk of ɛ4 carriage on progression from CN to MCI/AD-dementia within 72 months in the PET imaged sample. Figure 2 shows the hazard functions for each APOE group. Examination of hierarchical blocks from this analysis indicated that ɛ4 status contributed significantly to the model in block two after adjusting for demographic and clinical characteristics, χ2(1, 423) = 7.35, p = 0.01. Furthermore, Aβ status contributed significantly to the model in block three after adjusting for ɛ4status, χ2(1, 423) = 4.67, p = 0.03. Inspection of hazard rates in block two indicated that the progression for ɛ4 carriers was significantly higher than for noncarriers. However, after adding Aβ status into the model in block three, this progression hazard for ɛ4 was no longer statistically significant. The hazard rate for Aβ status indicated that Aβ+ individuals had a significantly higher progression hazard than Aβ–individuals. Hazard rates for sex, premorbid IQ, level of education, and depressive symptoms also remained statistically significant after adding Aβ status into the model. Age did not contribute to the model at any block (see Table 4).
Re-computation of hazard models in the PET imaged sample with APOE, sex, and age added indicated no statistically significant contributions to risk for progression from APOE×sex (p = 0.35), APOE×age (p = 0.73), age×sex (p = 0.06), and APOE×sex×age (p = 0.32) interactions.
DISCUSSION
The results of this study support the hypothesis that in CN older adults, for whom Aβ status is unknown, ɛ4 carriage increases the risk of clinical disease progression over 72 months, even with the known risks for AD from demographic and clinical characteristics taken into account. First with Aβ status unknown, CN older adults without significant or uncontrolled cardiovascular, endocrine, or inflammatory illness who were ɛ4 carriers and noncarriers were equivalent in their proportions of females, estimated premorbid IQ, level of education, and level of depressive symptoms. While ɛ4 carriers were one year younger than noncarriers, this difference was very small in terms of disease progression models (e.g., see [40]). In this total sample, age, sex, premorbid IQ, level of education, and level of depressive symptoms were each associated with clinical disease progression by 72 months.
Prospective analysis showed that in the total sample, the risk of clinical disease progression increased by 1.05 times for every additional year of age, 1.62 times for males, 1.19 times for every increase in level of depressive symptoms, and decreased by 0.76 times for each increase in education level. These demographic and clinical risk factors for disease progression are consistent with those identified in other large epidemiological studies of risk for AD-dementia [41–46]. More important for the current study, however, was the observation that even with these clinical and demographic risk factors taken into account, CN ɛ4 carriers were 2.66 times more likely than noncarriers to progress to a clinical classification of MCI/AD-dementia within the 72-month study period. While ɛ4 carriage has been found previously to increase risk for classification of MCI or AD-dementia in epidemiological studies, the finding that it also predicts disease progression in the healthy AIBL sample highlights the importance of APOE ɛ4 as a risk factor for AD even in the absence of cardiovascular disease or uncontrolled systemic illness.
To evaluate the extent to which risk for progression to MCI/AD-dementia conferred by ɛ4 was due to the risk of ɛ4 for high Aβ levels, analyses were repeated in a large CN group who had also undergone Aβ PET neuroimaging. First, estimates of group demographic and clinical characteristics in the PET imaged sample were equivalent to that in the total sample when considered according to APOE status. Furthermore, proportions of ɛ4 carriers were equivalent in the total sample and the PET imaged sample. This observation is consistent with findings from previous studies and is proposed to reflect the direct relationship between ɛ4 and amyloid accumulation [10, 47–49]. As was identified for the total sample, demographic and clinical characteristics including sex, level of education, and level of depressive symptoms also conferred risk for progression to MCI/AD-dementia in the PET imaged sample and these risks remained when ɛ4 carriage was added to the predictive model. Unlike for the total sample, with Aβ status known, age did not increase risk of clinical disease progression (i.e., in the PET imaged sample). This finding is expected given the well-established and strong association between age and Aβ accumulation in CN older adults [10, 51].
The addition of Aβ status showed that high Aβ levels increased risk for clinical disease progression such that Aβ+ individuals were 2.11 times more likely than Aβ- individuals to progress to MCI/AD-dementia within 72 months. Furthermore, with Aβ status included in predictive models, ɛ4 carriage no longer predicted risk of clinical disease progression. Thus, Aβ assumed the risk for clinical disease progression that had been conferred initially by ɛ4 carriage. These data suggest strongly that ɛ4 risk for clinical disease progression is indirect, in that ɛ4 carriage increases risk for high Aβ, which in turn increases risk for clinical disease progression [6].
The finding in the total sample that ɛ4 increased risk for clinical disease progression to MCI/AD-dementia in the context of unknown Aβ status is consistent with knowledge of ɛ4 risk for dementia from epidemiological samples [1, 44], although the mechanisms by which ɛ4 acts to increase risk of disease progression are still not understood. Recent reviews of the biology of APOE in the context of AD suggest two possible mechanisms for this process. The first mechanism proposes that ɛ4 increases risk of AD through reducing Aβ clearance. The second mechanism proposes that in addition to reducing Aβ clearance, ɛ4 also affects cerebrovascular integrity and increases inflammatory processes that themselves give rise to neurodegeneration, independent of the effects of Aβ [6]. The current results support the first mechanism that risk of ɛ4 carriage for AD is mediated through the effects of ɛ4 on Aβ accumulation. The current finding that in CN adults ɛ4 does not add further predictive value for clinical disease progression once Aβ status is taken into account is consistent with findings from previous prospective experimental biomarker studies showing that in the absence of Aβ+, ɛ4 carriage is not associated with cognitive decline [22, 23]. The current results are not consistent with data from neuroimaging studies concluding that ɛ4 influences AD processes independent of Aβ [15, 16]. It is possible, however, that findings of neuronal abnormalities related to ɛ4 but independent of Aβ reflect that ɛ4 carriage doesdisrupt brain structure and function but that these effects are too subtle to manifest clinically. This hypothesis will require prospective neuroimaging and clinical studies of a relatively rare group of CN older people.
One possible threat to the conclusion that ɛ4 carriage increases risk of AD-dementia by increasing Aβ levels is that most studies examining this issue utilize convenience samples, selected specifically to exclude individuals with substantial and intercurrent cardiovascular, endocrine, and inflammatory disease. Given that ɛ4 is also a risk factor for cardiovascular disease [6], the exclusion of individuals with cardiovascular disease or cardiovascular risk factors may result in the exclusion of individuals for whom ɛ4 carriage confers some risk for MCI/AD-dementia independent of Aβ. One way to test the contribution of non-Aβ processes would be to examine the risk for MCI/AD-dementia imposed by ɛ4 and Aβ in an epidemiological sample where cardiovascular disease or risk factors are not excluded. The Mayo Clinic Study of Aging (MCSA) [21] is currently the largest epidemiological study of risk for MCI/AD-dementia in CN adults whose Aβ and ɛ4 carrier status is known and where cardiovascular disease or risk factors are not excluded. For example, of the subjects providing baseline data for the MCSA, 79.9% had hypertension, 33.5% coronary heart disease, and 20% had a history of stroke, atrial fibrillation, or diabetes [21]. Despite this substantial cerebrovascular risk, studies of the MSCA observe that when Aβ levels are unknown, ɛ4 carriage is highly predictive of disease progression over an average of two years [12]. However, with both ɛ4 and Aβ added to predictive models, disease progression reflects Aβ status but not ɛ4 carriage. Thus like in the current study, MCSA investigators also concluded that the effect of ɛ4 on cognitive decline is mediated by the effect of Aβ. Further research in samples with varying degrees of cardiovascular or even cerebrovascular disease is now necessary to determine reliably how vascular risk factors moderate relationships between ɛ4, Aβ, and disease progression.
Some methodological limitations need to be considered when interpreting the current findings. The AIBL sample is not community or population based with recruitment requiring satisfaction of strict inclusion/exclusion criteria (i.e., reflected in the demographic and clinical characteristics in Table 1). This specificity was required to allow determination of early biological changes that occur in AD. Another limitation, most likely related to the sample, was the relative low rate of incident MCI/AD-dementia cases that occurred during the 72-month study period. Therefore, the hypothesis that with Aβ status known, APOE ɛ4 does not contribute risk for disease progression in CN older adults should be challenged in samples with higher comorbid cardiovascular risk factors and disease, or even frank cerebrovascular disease. Second, the Cox proportional hazards model used here assumed that censored cases, including withdrawal from study and death, were unrelated to clinical disease progression [34]. However, it is possible that individuals with AD symptoms, even mild ones, may have been biased toward dropping out of the study or even of dying. This highlights further the complex relationship between predisposing factors and the clinical outcome of AD. Additionally, while the increased risk for progression to MCI/AD-dementia associated with APOE ɛ4 after adjustment for Aβ was not statistically significant, the effect was increased and warrants further challenge in subsequent studies in other cohorts, or in the same cohort followed for a longer period.
In summary, the current study demonstrated that in a sample relatively free of cardiovascular disease and cardiovascular risk factors, ɛ4 carriage imposes risk for AD in CN older adults through its effect on increasing Aβ levels. That is, ɛ4 is a risk factor for Aβ accumulation, which in turn increases risk for clinical disease progression. Hence, APOE genotyping can help increase accurate identification of at-risk individuals and select those who are prime candidates for AD prevention trials (e.g., the TOMMORROW Study) [52].
Footnotes
ACKNOWLEDGMENTS
Funding for the study was provided in part by the study partners (Commonwealth Scientific Industrial and research Organization [CSIRO], Edith Cowan University [ECU], Mental Health Research institute [MHRI], National Ageing Research Institute [NARI], Austin Health, CogState Ltd). The study also received support from the National Health and Medical Research Council (NHMRC) and the Dementia Collaborative Research Centres program (DCRC2), as well as funding from the Science and Industry Endowment Fund (SIEF) and the Cooperative Research Centre (CRC) for Mental Health, an Australian Government Initiative.
Alzheimer’s Australia (Victoria and Western Australia) assisted with promotion of the study and the screening of telephone calls from volunteers. The AIBL team thanks the clinicians who referred patients to the study: Associate Professor Brian Chambers, Professor Edmond Chiu, Dr. Roger Clarnette, Associate Professor David Darby, Dr. Mary Davison, Dr. John Drago, Dr. Peter Drysdale,Dr. Jacqueline Gilbert, Dr. Kwang Lim, Professor Nicola Lautenschlager, Dr. Dina LoGiudice, Dr. Peter McCardle, Dr. Steve McFarlane, Dr. Alastair Mander, Dr. John Merory, Professor Daniel O’Connor, Dr. Ron Scholes, Dr. Mathew Samuel, Dr. Darshan Trivedi, and Associate Professor Michael Woodward. The authors thank all those who participated in the study for their commitment. The authors thank all those who participated in the study for theircommitment.
