Verbal fluency in HAD - research-repository.uwa.edu.au file · Web viewTrajectories of Irregular Word Reading Ability as a Proxy for Premorbid Intelligence in Alzheimer’s Disease,

Irregular Word Reading and Dementia

Running head: Irregular Word Reading and Dementia

Trajectories of Irregular Word Reading Ability as a Proxy for Premorbid Intelligence in

Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Aging: A Longitudinal Study

MICHAEL WEINBORN1,2,3, ROMOLA S. BUCKS1, HAMID R SOHRABI2, 3, 4, 5, STEPHANIE

R RAINEY- SMITH2, 3,, BELINDA M BROWN2, 3, 7, SAMANTHA L GARDENER2, 3,,

ALEKSANDRA GOZT3, DANIEL CHRISTENSEN8, GREG SAVAGE9, SIMON M LAWS2,3,5,6,

KEVIN TADDEI2,3,5, PAUL MARUFF10, JOANNE S ROBERTSON11, KATHRYN ELLIS11, 13,

DAVID AMES12,13 , COLIN L MASTERS11, CHRISTOPHER C ROWE14, & RALPH N

MARTINS2, 3, 4, 5 for the AIBL research group15

1 School of Psychological Science, University of Western Australia, Crawley, WA, 6009

2 Australian Alzheimer's Disease Research Unit (Hollywood Private Hospital), Perth, Western

Australia, Australia

3 School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia,

Australia

4 Department of Biomedical Sciences, Macquarie University, New South Wales, Australia

5 Co-operative Research Centre for Mental Health, Carlton, VIC, Australia

6 School of Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation

Research Institute, Curtin University, Kent Street, Bentley 6102, WA, Australia

7 School of Psychology and Exercise Science, Murdoch University, 90 South Street,

Murdoch, Western Australia, Australia

8 Telethon Kids Institute, 100 Roberts Rd, Subiaco, WA 6008, Australia

9 ARC Centre of Excellence in Cognition and its Disorders, Department of Psychology,

Macquarie University, New South Wales, Australia

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10 CogState, Ltd., Melbourne, Victoria, Australia

11 Neurodegeneration Division, The Florey Institute, The University of Melbourne, Parkville,

Victoria, Australia

12 National Ageing Research Institute, Parkville, Victoria, Australia

13 Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of

Melbourne, Parkville, Victoria, Australia

14 Department of Molecular Imaging, Austin Health and Department of Medicine, University

of Melbourne

15 http://www.aibl.csiro.au (For the AIBL Research Group)

Corresponding Author:

Michael Weinborn, Ph.D., School of Psychological Science, The University of Western Australia (M304), 35 Stirling Highway, Crawley WA 6009, Australia. P: 61 8 6488 1739. Email: [email protected]

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mailto:[email protected]

http://www.aibl.csiro.au/


Abstract

The ability to read irregularly spelled words is commonly used to estimate premorbid

intelligence, as this ability has been thought to be resistant to early effects of neurodegenerative

disorders. However, studies evaluating decline of this skill in Alzheimer’s disease (AD) have

produced conflicting results. Irregular word reading was assessed three times over 36 months in

a large (N=995) sample, including healthy control, AD and Mild Cognitive Impairment (MCI)

groups. At baseline, MCI and AD groups read correctly an average of 3.01 and 7.39 fewer

words, respectively, than healthy controls. The MCI group’s performance remained stable during

the study, but the AD group declined. Importantly, the observed decline was likely an

underestimate, as significant numbers of the AD (42.6%) participants could not complete the task

at follow-up. Use of alternate (e.g., demographics-based) methods is advised to augment or

replace word pronunciation in estimating premorbid intelligence in individuals with even mild

AD.

Keywords. Premorbid Intelligence, Cognitive Reserve, Alzheimer’s Disease, Mild Cognitive

Impairment, Irregular Word Reading, WTAR.

Public Significance Statement:

This study found that irregularly spelled word-reading ability, commonly used to estimate an

individual’s cognitive function prior to onset of the dementia seen in Alzheimer’s disease (AD),

is much more adversely affected by dementia than previously thought. This is important, as

without this information, clinicians may be more likely to under-diagnose dementia in AD. We

advise caution in using this approach and provide guidance for interpreting results in the context

of other, demographics-based approaches.

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Background

Estimating premorbid intelligence is important in neuropsychological evaluation of

Alzheimer’s disease (AD) or precursor conditions (e.g., Mild Cognitive Impairment (MCI) due to

AD). The most common methods to assess premorbid intelligence require pronunciation of

irregularly spelled words (e.g., Wechsler Test of Adult Reading (WTAR; Wechsler, 2001);

National Adult Reading Test, (NART; Crawford, Parker, Stewart et al, 1989)), demographic-

based regression equations (e.g., the Barona equation; Barona, Reynolds & Chastain, 1984), or

their combination (e.g. Crawford, Nelson, Blackmore, et al, 1990).

Irregularly spelled word approaches assume this ability draws on over-learned knowledge

(e.g., pronunciation for words not conforming to typical grapheme-phoneme rules) resistant to

changes seen in typical cognitive aging and mild-moderate dementia. This contrasts with other

cognitive abilities (e.g., verbal memory) affected early in AD.

Consistent with this view, research has supported stability of

irregular word reading in healthy aging (Dykiert & Deery, 2015; Schmand, Geerlings, Jonker, et

al, 1998) with only minor declines reported in mild to moderate dementia (e.g., McGurn, Starr,

Topfer, et al, 2004; Sharpe & O’Carroll, 1991). However, others report substantial dementia-

related decline (e.g., Beardsall & Huppert, 1997; McFarlane, Welch & Rogers, 2006). In a cross-

sectional study, McFarlane et al. (2006) found significant differences in NART scores between

healthy agers and individuals with both “minimal” (defined by the authors as Mini Mental State

Examination (MMSE) scores of 24-28) and “mild” dementia (MMSE of 14-23), and differences

in WTAR scores between the healthy agers and mild dementia groups, but not the minimal

dementia group for the WTAR.

Importantly, most studies in this area, whether with healthy agers or individuals with MCI

have been cross-sectional. Such studies do not provide the best indication of the effects of AD on

irregular word reading, as general intelligence may play a role in risk factors for the development

of AD, e.g., cognitive reserve – that is, individual differences in brain resilience against

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neuropathological changes (Stern, 2006). The few longitudinal studies generally support

performance declines over time in dementia, but most have small sample sizes (e.g., Taylor,

Salmon, Rice et al, 1996 and Paque & Warrington, 1995 reported Ns < 60). In one of the few

large studies, Schmand et al. (1998) found only modest declines (3 IQ points) on the NART in

197 participants who were cognitively healthy at baseline but had “suspected” minimal/mild

dementia at six-year follow-up. Larger declines (15-18 IQ points) were found in 19 individuals

with moderate-severe dementia. Consistent with these findings, a four-year study of 78

individuals with AD (Cockburn, Keene, Hope, et al., 2000) found only minor NART declines

(2.4 words) amongst individuals with MMSE scores between 20-24, but larger declines with

greater impairment (e.g., MMSE 15-19, average decline 5.6 words; MMSE < 10, average decline

11-17 words).

In summary, the few longitudinal studies support at least minor declines in irregularly

spelled word reading in mild and, especially, moderate dementia. However, further research is

needed to describe change over time across the spectrum of AD, from cognitively healthy older

adults to MCI, and AD. The present study utilized data from the Australian Imaging, Biomarkers,

and Lifestyle (AIBL) Study of Ageing. We evaluated performance on the WTAR (Wechsler,

2001) over three years in a large well-characterized sample of cognitively healthy individuals

(HC), as well as individuals with MCI or AD. We hypothesized that:

1: At baseline, individuals with AD would have poorer WTAR performance than HC, with the

MCI group intermediate between the HC and AD groups;

2: HCs that remained healthy at follow-up would show no decline in WTAR scores. Conversely,

WTAR scores would decline amongst individuals with AD or MCI;

3: Individuals with MCI or AD with greater worsening of symptom severity over time would

show greater decline in WTAR scores relative to those with slower dementia-related decline.

Methods

Participants: 995 older adults participated in the AIBL Study in Perth and Melbourne, Australia

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(Age M±SD 71.73±7.77, range 55-96 years). For details regarding recruitment, assessment, and

inclusion/exclusion criteria, see Ellis et al (2009). Briefly, exclusion criteria were non-English

speaking, psychiatric (e.g., schizophrenia), or non-AD neurologically-relevant history (e.g.,

traumatic brain injury). HC participants were also excluded if they displayed impaired cognition

(e.g., demographics-adjusted Z scores < -1.5 on 2 or more neuropsychological tests) at baseline.

Procedures were approved by the ethics committees of Austin Health, St. Vincent’s Health,

Hollywood Private Hospital, and Edith Cowan University. All participants provided informed

consent.

Measures and Procedures:

Cognitive/Clinical Assessments: The WTAR produces estimated Full-Scale IQ scores for the

Wechsler Adult Intelligence Scale-Third edition (WAIS-III; Wechsler, 1997) based on word

reading performance. While demographics (age, education, ethnicity), and the combination of

reading score and demographics can also be used, these are not based on Australian-specific

normative data. Therefore, the primary variable used was the number of words correctly

pronounced. Of note, in the AIBL study participant education level was collected and coded in

five categories (<6, 7-8, 9-12, 13-15 and >16 years) rather than continuously.

General cognition and dementia-related symptom severity were assessed with the MMSE and

the Clinical Dementia Rating (CDR). APOE ɛ4 allele carrier status, a risk-factor for AD, was

assessed at baseline. Neuropsychological tests and self- and informant-reported memory problem

measures informed baseline and follow-up diagnoses (see Ellis et al., 2009 for detail).

Participants who had not withdrawn or died in the interim were re-assessed at 18 and 36 months.

Testing was discontinued if the participant was unable to complete the task (e.g., became

frustrated or unable to comprehend instructions). Individuals in the AD or MCI groups unable to

complete a significant subset of the battery, (e.g., only able to complete the MMSE) at the

preceding time-point were not administered the WTAR (or most other tests) at the subsequent

time-point. Worsening symptom severity was defined by an increase in global CDR score from

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baseline to either 18 or 36 months.

All HC participants were testable at all time-points, although a subset later declined on the

CDR from 0 at baseline to 0.5 (n = 40) or 1 (n = 3). Their decline was accounted for in the

analysis, and described separately in Figure 1.

Participants with previous neurologically-relevant diagnosis or current cognitive/psychiatric

symptoms were reviewed by a panel comprising geriatricians, psychiatrists, neurologists, and

clinical neuropsychologists (including co-authors DA, GS, JR, KE, MW) for consensus

classification using criteria described in Ellis et al., 2009. Those in the AD group were diagnosed

with AD at all time-points, those with MCI must have had MCI at baseline and MCI or AD at

follow-up.

Genotyping: DNA was isolated from whole blood using QIAamp DNA blood Maxi- or Midikit

(Qiagen) according to manufacturer’s protocol. APOE genotype was determined through

TaqMan® assays (Life Technologies, USA) for rs7412 (Assay ID: C____904973_10) and

rs429358 (Assay ID: C___3084793_20) performed on a QuantStudio 12K Flex real-time PCR

system (Applied Biosystems, USA).

Data Analysis: Baseline performance and trajectories of change in WTAR performance were

examined using latent growth modelling. Growth models generate intercept and slope terms,

characterizing individual starting values and change slopes. Visits 1 (Baseline), 2 (18-month) and

3 (36-month follow-up) were parameterized as 0, 1 and 2 respectively. Age was zeroed

(age_zero) at the age of the youngest participant (55 years), so that age effects can be understood

as the effect of being one year older.

All models were analyzed in Mplus v7.3 (Muthen & Muthen, 2004) using the full

information maximum likelihood estimator; thus, participants did not need to have complete data

at all time-points. Models examined included: Model 1) an intercept-only model, Model 2) a

linear, unconditional growth model, followed by a logarithmic growth model, Model 3) the

logarithmic Model 2 plus age; Model 4a) Model 3 plus sex (female = 0, male = 1), APOE ɛ4

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allele carriage status (non-carrier = 0, carrier = 1), and education (<6 = 0, 7-8 = 1, 9-12 = 2, 13-15

= 3 and >16 years = 4) Model 4b) Model 4a but without sex as it was a non-significant predictor;

Model 5) Model 4b plus clinical classification (parameterized as two dichotomous predictors:

MCI [where MCI = 1, HC or AD = 0] and AD [AD = 1, HC or MCI = 0], and Model 6) Model 5

plus symptom severity decline (decline = 1, stability in CDR score at 18 and/or 36 months from

baseline = 0). Model fit was assessed with the chi-square test, the Root Mean Square Error of

Approximation (RMSEA), which provides an absolute measure of model fit, where values < .05

are considered good, the Comparative Fit (CFI) and Tucker Lewis Indexes (TLI), where good fit

is > .95, and the Standard Root Mean Square Residual (SRMR), where scores < .05 suggest good

fit (Hu & Bentler, 1999). Chi-square difference tests were calculated to compare incremental fit

between models.

Results

Groups did not differ with regard to sex, however, the MCI and AD groups were older (p

<.001), with lower education levels (p <.001) than the HC group (Table 1). APOE ɛ4 status

varied across groups with the AD group having the highest percentage of carriers (63.4%)

followed by the MCI (50.4%), and HC (27.0%) groups. Descriptive data for basic cognitive

functioning (MMSE) and dementia symptom severity (CDR) are provided in Table 2.

Results summarizing all models are presented in Table 3. An intercept-only model provided

poor fit to the data. Whilst fit was improved with a linear growth model (not shown), a

logarithmic model produced excellent, and significantly better fit, than the intercept-only model.

All subsequent models built on this logarithmic growth model, and all displayed good fit, which

did not differ significantly across models. The logarithmic model was scaled such that the final

time step was equal to 1 (time steps were set to 0, 0.631, and 1) for ease of interpretation, so that

the loss by the third assessment was equal to the slope term plus the predictor by slope effects.

Demographic factors: Models 3, 4a and 4b assessed the initial associations between

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demographics and baseline WTAR performance1, as well as WTAR performance over time.

Model 3 (age alone) demonstrated that greater age was a very modest predictor of both poorer

baseline performance and greater decline. Conversely, once diagnostic status was considered (in

Model 5), greater age was associated with a very small increase in baseline performance (0.13 of

a word) and unrelated to decline.

Sex was included, along with APOE ɛ4 status and education in Model 4a, but was not a

significant predictor, and was removed from subsequent models. For each increase in education

group level participants read an additional 2.88 words at baseline, e.g., those with 16+ years of

education correctly read 4Educ level*2.88Intercept = 11.52 more words at baseline than those with 6 or

fewer years of education (Model 4b). In addition, the rate of change in word reading over time

was also marginally greater with higher levels of education (about 0.20 to 0.28 of a word across

models, although not significant in the final model), e.g. those with 16+ years of education

improved in their word reading between 0.20Educ x slope*4Educ level to 0.28Educ x slope*4Educ level (0.80 to

1.12) over 36 months. Taken together, these effects indicate that increasing levels of education

attenuate the general loss of word reading across assessments (-0.94 by 36 months), but only in

those with 16+ years of education is the reduction in word reading entirely cancelled out (i.e [-

0.94Slope] + [0.25Educ x slope * 4Educ level] = 0.06 more words read at Visit 3 by those with 16+ years of

education). APOE ɛ4 allele carriers scored on average 0.77 of a point lower than non-carriers, but

this was only a trend. However, carriers displayed greater decline (0.86 more words over 36

months) than non-carriers. This last finding is likely due to the higher frequency of APOE ɛ4

allele carriers in MCI/AD, and must be considered in the context of Model 5.

Diagnostic Status: Model 5 evaluated the contribution of AD and MCI after considering the

above factors. Individuals with AD at baseline scored, on average, 7.39 words lower than HC

and lost 3.07 words over the 36 months. Conversely, the MCI group read 3.01 fewer words at

1Note: Due to initial diagnostic group differences for age and education, the final model was also run with the AD and MCI groups and a subsample of the HC group (n=165) well-matched on age, education and gender (all group differences ps > 30). Results were substantively unchanged and did not change interpretations.

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baseline, but their trajectory of decline was not different from the HC group. With regard to

diagnostic decline, of the HC group who continued with the study for at least one follow-up

assessment (n=660), 1.7% were classified as MCI and 0.5% as AD at the 18-month follow-up,

and by 36 months, 3.2% and 0.7% were classified as MCI and AD, respectively. For the n=93

baseline MCI participants followed up at least once, 34.4% and 54.7% had converted to AD at 18

and 36 months, respectively.

Worsening symptom severity: Model 6 assessed the contribution of cognitive and functional

decline over time. Supporting Hypothesis 3, worsening symptom severity as indicated by the

CDR was associated with a steeper decline in WTAR scores (average loss of 1.03 words over 36

months). WTAR mean scores by group are illustrated in Figure 1. Those in the HC group who

remained cognitively healthy displayed stability in WTAR scores over time. Conversely, healthy

controls that declined on the CDR had lower baseline WTAR scores (approximately 2 points)

compared to the stable-HC group, and marginal decline over 36 months (~1 point). Individuals

with stable MCI had baseline WTAR scores 2 words lower than HC but their performance did not

change substantially. Whereas, those with MCI displaying CDR-decline scored 2 words lower

than the stable MCI group at baseline, then declined by nearly 2 further words over time.

Ability to complete the WTAR: At 18- and 36-month follow-up, 30 (18.5%) and 67 (41.4%) of

the 162 initial AD participants had died or withdrawn. Of the 132 remaining AD participants who

attempted the WTAR at 18 months, 35 (26.5%) failed to complete the task (and most other tests).

At 36 months, approximately 75 of the remaining 95 participants attempted the WTAR whilst 32

(42.6%) were unable to complete it. AD participants unable to complete the WTAR at 36 months

were more cognitively impaired (t (93) = -5.25, p < .001) at baseline (MMSE M = 17.00+4.27)

than those who remained testable (MMSE M = 21.55+3.19).

For MCI participants, 23 (18.4% at 18 months) and 51 (40.8% at 36 months) of the initial 125

had died or withdrawn. At 18 months, 99 of the remaining 102 attempted the WTAR but 6

(5.8%) failed to complete it. At 36 months, 71 of the remaining 74 MCI participants attempted

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the WTAR but 7 (9.9%) failed to complete it. All of these participants had converted to AD.

Comparison to a demographics-predicted estimated IQ score: While there is not a well-validated

demographics-only prediction method specific to the Australian population, an adapted version of

the WTAR demographics-based predicted WAIS-III Full Scale IQ (FSIQ) score was calculated

(using USA norms) to provide an illustrative clinical comparison to estimates obtained by

irregular word reading. As stated above, education level was coded in five categories (<6, 7-8. 9-

12, 13-15 and >16 years) rather than continuously. These grouping ranges differ slightly from

the six used by the WTAR demographics-based equation (<8, 9-11, 12, 13-15, 16, and >16).

To allow calculation of the demographics based FSIQ estimate: 1) the first two groups (<6

and 7-8) from the current study were collapsed to be equivalent to the <8 WTAR group, 2)

individuals in the 9-12 years group in this study were assigned the mean of the two estimated IQ

scores from WTAR 9-11 and 12 years groups, 3) the 13-15 years group needed no change, and 4)

individuals in the >16 years group from this study were assigned the mean of the two predicted

FSIQ scores from the WTAR 16 and > 16 groups. Due to the baseline group differences in age

and education, the sub-sample of 165 HCs well-matched on demographic variables was utilized

for this comparison. A one-way ANOVA conducted on the WTAR demographics-predicted

FSIQ score for the HC (M=102.28+8.79), MCI (M=102.67+8.76) and AD (M=101.94+8.90) was

not significant F (2, 440) = .23 p = .79. This was an expected outcome as the HC group was

well-matched to the MCI and AD groups on relevant demographic factors. Conversely, the

ANOVA conducted on participants’ baseline word reading performance-predicted FSIQ (using

USA norms), [HC (M=111.26+8.79), MCI (M=108.76+8.87) and AD (M=104.18+12.44)]

revealed a significant difference [F (2, 438) = 20.99 p = .01].

Further, to obtain an estimate of potential misclassification rates that may influence

clinical decision-making, the difference between each participant’s baseline word-reading vs.

demographics-predicted FSIQ score was calculated and used to classify participants into 1 of 3

groups based on whether: 1) the difference was within 10 points (that is, roughly similar

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estimates), 2) the word-based method produced a substantially higher FSIQ estimate (> 10

points) than the demographics method, or 3) the word-based method produced a substantially

lower FSIQ estimate (> 10 points) than the demographics method. While the percent within the

first group (obtaining roughly similar estimates regardless of method) was generally similar

across diagnostic groups (HC= 55.5%, MCI=56.8% and AD= 64.1%), this obscures significant

patterns of over- and under-prediction across groups (X2 (4) = 25.97, p < .001). Follow-up group

comparisons revealed significant differences between the HC and AD groups (X2 (2) = 26.36, p

< .001) and the HC and MCI groups (X2 (2) = 9.14.97, p = .01), but not the MCI and AD groups

(X2 (2) = 5.70, p = .06).

Specifically, the word reading method tended to produce much higher estimates in the HC

and MCI groups (43.9% and 36.4%, respectively), compared with the AD group (only 24.2%).

Therefore, reliance on demographic approaches would potentially overestimate premorbid FSIQ

by more than 10 points for many in the HC and MCI groups. Importantly, however, the more

significant problem here is potential underestimation of FSIQ in the AD group. Specifically, AD

participants were more than ten times as likely to have a substantially lower reading-predicted vs.

demographics-predicted FSIQ than demographically-matched HC (11.8% for AD vs. 0.6% for

HC). The MCI group displayed lower, but also elevated risk (6.8%) of misclassification with the

reading based method compared with the HC group. Of note, however, these comparisons must

be interpreted with caution as formal validated measures of IQ were not administered prior to

diagnosis and the comparisons described above are based on estimated rather than true IQ scores.

Discussion

To our knowledge, this is the first study to evaluate irregular word reading, a commonly used

method of predicting premorbid intelligence longitudinally in a large, well-characterized sample

of older adults. This study is vital, as previous research has produced conflicting results regarding

whether, and how significantly, irregular word reading is affected in MCI and early AD.

Firstly, results support previous findings of stability of irregular word reading in typical aging

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(Deery & Brett, 2015; Schmand, Geerlings, Jonker, et al, 1998). Specifically, individuals who

remained cognitively healthy over time displayed no WTAR decline. Interestingly, while the

most-educated individuals were on average able to pronounce about 11.5 more words at baseline

than those with the least education, higher levels of education were not a completely protective

factor against decline in WTAR scores, except in those with at least 16 years of education.

Consistent with Hypotheses 1 and 2, results did not support the robustness of irregular word

reading ability to the effects of even relatively mild AD (cf. McGurn et al, 2004; McFarlane et al,

2006). At baseline, individuals with AD scored an average of more than seven points lower than

the HC group, and lost 3 further words over 36 months. This is notable as 1) additional analyses

supported that this was not explained by lower levels of education for the AD group at baseline,

and 2) the AD group was relatively high functioning (mean baseline MMSE score of 20.11,

median CDR of 1). It should also be noted that only individuals with AD who were capable of

completing the WTAR at least once during the study were included, and more than 40% became

unable to complete the WTAR (or much of the cognitive battery) within three years. Therefore,

these results may underestimate the steepness of decline in irregular word reading ability once an

individual meets diagnostic criteria for AD.

As expected, WTAR performance for individuals with MCI was less affected - on average

reading about three words fewer than HC at baseline – a level less likely to result in large

premorbid IQ underestimates, but still important for clinicians and researchers to consider.

Contrary to Hypothesis 2, there was no evidence of decline in WTAR scores for the MCI group

as a whole over time. However, this is likely due to the heterogeneous nature of our MCI group,

and of the MCI construct in general (e.g., Han, Kim, et al, 2012). AIBL uses MCI criteria

(Winblad et al, 2004) requiring objectively impaired cognitive scores, but no functional

impairment. This inevitably combines those with MCI due to AD pathology (Albert et al, 2011)

and a declining course, and those with MCI due to other (e.g., vascular) pathology that may or

may not result in decline. This heterogeneity is seen in the trajectory of estimated WTAR scores

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for the MCI-Stable vs. MCI-Declined (Figure 1), where the MCI-Declined group both performed

lower than the MCI-Stable group at baseline, and further declined such that their estimated

performance was three points lower than the MCI-Stable group at 36 months, and nearly as low

as the AD group at baseline. This intriguing finding could be further explored in future studies of

MCI with subgroups selected based on AD biomarker positivity status.

The current study also extends previous research by characterizing the WTAR performance of

a small subgroup (n=43) of HC who displayed cognitive decline during the course of the study.

At baseline, these individuals demonstrated poorer WTAR performance than the non-declining

HC group. They also showed a marginal decline of about one word over time, ultimately

performing similarly to the MCI groups at the 18-month follow-up. This suggests that poorer

irregular word reading may be evident earlier than suggested in previous studies; that is, evident

in MCI but also in undiagnosed older individuals likely to show future cognitive decline.

Many participants with AD and MCI declined cognitively and functionally over the course of

the study, and CDR decline was a further independent predictor of WTAR decline (an average

loss of one word over 36 months). More compellingly, however, the role of symptom severity

was evident when investigating whether a participant was able meaningfully to complete the

irregular word reading task at all. Specifically, at the 36-month follow-up, approximately 10% of

the baseline MCI and 42% of the baseline AD group could not validly complete the WTAR. The

most notable factor contributing to whether a participant could complete the WTAR was

symptom severity, with the concurrent CDR scores of those who were untestable typically in the

moderate to severe range.

While the present study provided several important contributions to the literature, there were

some limitations. Longitudinal study of cognition amongst individuals with MCI and AD is

challenging and prone to selection bias. As described above, many individuals with AD at

baseline were unable to complete the WTAR at follow-up. This, in concert with the inclusion of

only those who had been able to complete the task, raises the likelihood that the degree of decline

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on such tasks is likely greater than indicated. We also had to treat education as a categorical

variable, rather than continuous, due to data collection practices in the study at the time. While

this limits the precision of coding educational achievement, it should be noted that collecting

reliable information about educational level is notoriously challenging, particularly in

communities similar to the present sample with older individuals from a variety of countries and

educational backgrounds. In such instances, a single number representing educational

achievement may provide only an illusion of precision, and use of ranges may be more accurate.

Additionally, only a single measure of irregular word reading (the WTAR) was used, and

there may be some differences between measures. For example, variability in task difficulty

and/or sensitivity to dementia-related changes in word reading performance has been found.

McFarlane et al. (2006) noted poorer NART performance in those with “mild” dementia

compared to healthy agers, with no differences observed between these groups on the WTAR.

Conversely, in a study of healthy young Australian adults, Mathias et al. (2007) found smaller

discrepancies between the estimated IQ score from the NART and actual Full Scale IQ scores

compared with WTAR-based estimates. It is possible that the words used across tests may vary

inadvertently in ways that affect task difficulty, including cultural relevance or word-frequency.

Conclusions: The ability to read irregularly spelled words appears to be resistant to cognitive

changes seen in healthy aging, and measures of this ability can be of great utility. However, this

study contributes to the literature by identifying poorer performance for such tasks early in the

Alzheimer’s disease process. Deficits seen in MCI appear to be relatively modest, but should still

be considered a risk, potentially producing a small degree of underestimation of premorbid

function. Importantly, however, the current results indicate that once even mild AD becomes

clinically manifest (e.g., when even mild functional impairment emerges), these measures are

more likely to underestimate premorbid function. Indeed, in the current study individuals with

AD obtained estimated FSIQs an average of seven points lower than a demographically similar

healthy control group at baseline. Other indicators (e.g., educational and occupational history) of

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premorbid ability should added to the assessment.

Specifically, alternate methods to augment or verify estimates provided by irregular word

reading approaches are advised in assessing this population. The use of demographic-based

regression approaches is not without its limitations, including lower accuracy at the tails of the IQ

distribution (i.e., underestimating FSIQ in high-functioning individuals and overestimating FSIQ

in lower-functioning individuals). Further, there is no well-validated demographics-based

approach to estimating FSIQ in an Australian population.

However, we believe that an application of an adaption of one of these methods (the WTAR

Demographics-only based prediction) in this sample provided a useful illustration of how such

approaches can inform clinical interpretation of word-reading based estimates. Specifically, we

found that individuals with early AD were more than 10 times as likely as HCs to have

significant (greater than 10 points) underestimates of their FSIQ using word-based compared with

demographic approaches (11.8% vs. 0.6%). This does not mean that irregular word-based

approaches should be abandoned by clinicians working with older adults. However, results from

this study support the need for caution where even very early AD is suspected. Specifically,

clinicians are advised to check estimates provided by word reading methods against

demographic-based approaches. When demographic approaches provide higher estimates, they

are likely to be more accurate and useful in clinical decision-making. Indeed, the WTAR

provides additional information useful in this regard, including demographics-based regression

equations to evaluate the validity of an individual’s obtained word-reading performance (i.e.,

whether their reading performance is unusually low given educational history, Wechsler, 2001).

Unfortunately, these approaches are only available for some measures, and have only been

normed in the USA and UK, and therefore could not be applied to this sample. These and other

approaches to estimating premorbid function/cognitive reserve more robust to cognitive decline

need development and further norming in Australia and beyond.

Acknowledgments

16


Funding for the AIBL study was provided by the CSIRO Flagship Collaboration Fund and the

Science and Industry Endowment Fund in partnership with Edith Cowan University, The Florey

Institute of Neuroscience and Mental Health, Alzheimer's Australia, National Ageing Research

Institute, Austin Health, CogState Ltd., Hollywood Private Hospital, and Sir Charles Gairdner

Hospital. The study also received funding from the National Health and Medical Research

Council, the Dementia Collaborative Research Centres, the Australian Alzheimer's Research

Foundation, and Operational Infrastructure Support from the Government of Victoria.

17


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Table 1: Descriptive statistics for the Alzheimer’s disease, Mild Cognitive Impairment and Cognitively Healthy Control groups

Diagnosis at baseline

Cognitively Healthy Control(n=718)

Mild Cognitive Impairment

(n=117)

Alzheimer’s disease(n=162)

p-values

Mean Age at Baseline (SD) 69.83 ± 6.82 75.96 ± 7.63 77.11 ± 8.12 <.001Years of Education (%)0-67-89-1213-1516+

0.4%7.8%38.1%19.7%34.0%

3.4%12.0%41.9%17.9%24.8%

5.0%15.0%36.9%20.6%22.5%

<.001

Gender (% female) 58.2% 57.3% 61.1% .760APOE ɛ4 allele (%positive) 27.0% 50.4% 63.4% <.001Abbreviations: APOE = apolipoprotein, SD = standard deviation, n= number of participants, Bold indicates statistical significance (p < .05). Characteristics compared using independent samples t-test for continuous variables and χ2 for categorical variables.

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Table 2: Descriptive data for cognitive measures the Alzheimer’s disease, Mild Cognitive Impairment and Cognitively Healthy Control groups.

MMSE (Mean (SD)) CDR (median (IQR))Baseline diagnosis

Cognitively Healthy ControlBaseline (n=716) 28.92 (1.15) 0 (0, 0)18 months (n=658) 28.76 (1.37) 0 (0, 0)36 months (n=593) 28.75 (1.36) 0 (0, 0)Mild Cognitive ImpairmentBaseline (n=125) 26.19 (2.65) 0.5 (0.5, 0.5)18 months (n=102) 25.35 (3.13) 0.5 (0.5, 0.5)36 months (n=74) 23.27 (5.06) 0.5 (0.5, 1)Alzheimer’s diseaseBaseline (n=162) 20.11 (4.42) 1 (0.5, 1)18 months (n=129) 16.38 (6.31) 1 (1, 2)36 months (n=87) 13.15 (7.18) 2 (1, 3)

Abbreviations: MMSE= Mini Mental State Examination, CDR = Clinical Dementia Rating Scale, SD = standard deviation, IQR = interquartile range, n= number of participants

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Table 3: Latent growth curve models assessing WTAR baseline performance and trajectories of change over 36 months

Model 1:(Intercept only)

Model 2: (unconditional logarithmic growth)

Model 3:(Model 2 plus Age_zero)

Model 4a: (Model 1 plus Age_zero, Gender, APOE status, Education)

Model 4b: (Model 1 plus Age_zero, APOE status, Education)

Model 5: (Model 4a plus clinical classification (HC, MCI or AD)

Model 6 (Model 5 plus CDR decline status)

Number of participants 995 995 995 992 992 992 897Initial status Intercept 41.06*** 41.67*** 42.67*** 34.16*** 34.16*** 33.59*** 33.88***

Age_zero -.06* 0 0 0.13*** 0.12***Sex (Male=1) 0.01APOE status(ε4 carrier = 1)

-0.77# -0.77# 0.75# 0.76#

Education (≤12y = 1) 2.88*** 2.88*** 2.70*** 2.68***AD Status (AD=1) -7.39*** -6.30***MCI status (MCI=1) -3.01*** -2.62***CDR decline (decline = 1) -0.94

Rate ofChange

Slope -1.27*** -0.45 -0.90# -0.94# -1.23* -1.16*

Intercept x slope 3.33 2.88 1.84 1.93 1.10 1.04Age_zero x slope -.05** -0.04* -0.05* -0.01 -0.01Sex x slope -0.36APOE status x slope -0.84** -0.86** -0.40 -0.34Education x slope 0.28* 0.25* 0.24# 0.20AD status x slope -3.07*** -2.55***MCI status x slope -0.70 -0.49CDR decline x slope -1.03*

Goodnessof Fit

2 (df) 122.21 (4)*** 2.67 (1) 2.52 (2) 4.59 (5) 3.06 (4) 7.88 (6) 7.36 (7)

RMSEA (90% CI) .17 (.15-.20) .04 (0-.10) .02 (0-.07) 0 (0-.04) 0 (0-.04) .02 (0-.05) 0.01 (0-.04)

CFI .95 1.0 1.0 1.0 1.0 1.0 1.0TLI .96 1.0 1.0 1.0 1.0 1.0 1.0SRMR ..20 .01 .01 .01 .01 .01 .01

Note: Age_zero is participant age zeroed at the youngest participant’s age (55). Bold indicates statistical significance (#p < .10, * p < .05, ** p < .01, *** p < .001).Abbreviations: AD = Alzheimer’s disease, APOE = Apolipoprotein E, CDR = Clinical Dementia Rating scale, HC = Healthy Control, MCI = Mild cognitive impairment, WTAR = Wechsler Test of Adult Reading. RMSEA = Root Mean Square Error of Approximation, CFI = Comparative Fit Index, TLI = Tucker Lewis Index, SRMR = Standard Root Mean Square Residual.

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Irregular Word Reading and Dementia 25

Documents

Verbal fluency in HAD - research-repository.uwa.edu.au file · Web viewTrajectories of Irregular Word Reading Ability as a Proxy for Premorbid Intelligence in Alzheimer’s Disease,