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. Author manuscript; available in PMC: 2017 Jul 1.
Published in final edited form as: Neuropsychology. 2015 Nov 23;30(5):624–630. doi: 10.1037/neu0000246

Biomarker Validation of a Decline in Semantic Processing in Preclinical Alzheimer's Disease

Kathryn V Papp 1, Elizabeth C Mormino 2, Rebecca E Amariglio 1,2, Catherine Munro 2, Alex Dagley 2, Aaron P Schultz 2, Keith A Johnson 1,2,4, Reisa A Sperling 1,2,3, Dorene M Rentz 1,2
PMCID: PMC4877295  NIHMSID: NIHMS732770  PMID: 26595826

Abstract

Objective

Differentially worse performance on category vs. letter fluency suggests greater semantic vs. retrieval difficulties. This discrepancy, combined with reduced episodic memory, has widespread clinical utility in diagnosing Alzheimer's disease (AD). Our objective was to investigate whether changes in semantic processing, as measured by the discrepancy between category and letter fluency, was detectable in preclinical AD: in clinically normal older adults with abnormal β-amyloid (Aβ) deposition on neuroimaging.

Methods

Clinically normal older adults (mean MMSE=29) were classified as Aβ+ (n=70) or Aβ− (n=205) using PiB-PET imaging. Participants completed Letter Fluency (FAS; word generation to letters F-A-S) and Category Fluency (CAT; word generation to animals, vegetables, fruits) annually (mean follow-up=2.42 years). The effect of Aβ status on fluency over time was examined using linear mixed models controlling for age, sex, and education. To dissociate effects related to semantic (CAT) vs. retrieval processes (CAT and FAS), models predicting CAT over time were repeated controlling for FAS and likewise for CAT controlling for FAS.

Results

At baseline, Aβ+'s performed better on FAS compared with Aβ−'s but comparably on CAT. Longitudinally, Aβ+'s demonstrated greater decline on CAT compared with Aβ−'s (p=0.0011). This finding remained significant even when covarying for FAS (p=0.0107). Aβ+'s similarly declined compared with Aβ−'s on FAS (p=0.0112) but this effect became insignificant when covarying for CAT (p=0.1607).

Conclusions

These findings provide biomarker validation for the greater specificity of declines in category vs. letter fluency to underlying AD pathology. Our results also suggest that changes in semantic processing occur earlier in the AD trajectory than previously hypothesized.

Keywords: preclinical Alzheimer's disease, verbal fluency, semantic processing, amyloid

INTRODUCTION

A current goal in Alzheimer's disease (AD) research is to identify the earliest cognitive changes associated with underlying pathology. Doing so will aide in early identification and measurement of ‘response to treatment’ at a stage when therapies may be most beneficial (Sperling et al. 2011). The earliest and most notable cognitive changes observed in AD are impairments in episodic memory (Petersen, 2004), but there is growing evidence that semantic processing may also decline relatively early in the disease process. Individuals with AD routinely exhibit deficits on neuropsychological measures which require intact semantic storage and access (for review see Salmon et al. 1999). One simple measure to assess semantic processing compares the timed generation of words to specified letters (requiring lexical access) with speeded generation of words to categories (requiring intact semantic processing). There is longstanding evidence showing that AD patients exhibit differentially worse performance on category fluency compared with letter fluency (Butters et al. 1987, for review see Laws et al. 2010). These findings are consistent with the neuroanatomical hypothesis that semantic processing is particularly reliant on temporolimbic structures (Henry & Crawford, 2004) regions preferentially affected in AD, while letter fluency may be subserved by primarily prefrontal regions (Martin & Chao, 2001).

More recent work has shown that decrements in semantic processing are observed in the prodromal stages of AD, that is, at the stage of Mild Cognitive Impairment (MCI) (Joubert et al. 2010, Adlam et al. 2006). More specifically, patients diagnosed with MCI exhibit more difficulties on measures of object knowledge (Adlam et a. 2006), famous face and object naming (Joubert et al. 2010), famous building naming (Ahmed et al. 2008), and lexical decision making and priming (Duong et al. 2006). In particular, a category/letter fluency differential has been observed in MCI subjects (Murphy, Rich & Troyer, 2006) and steeper decrements in category vs. letter fluency have been associated with progression from MCI to AD dementia (Clark et al. 2009). However, other research studies have not observed differential performance between letter and category fluency in MCI participants (Nutter-Upham et al., 2008; Weakley, Schmitter-Edgecombe, & Anderson, 2013). Variability in the literature may be attributable to differences in the letters and categories administered, the number of trials completed, as well as the criteria used to classify individuals as MCI.

To date, there is only a small literature examining semantic processing at the preclinical stages of AD: individuals that are clinically normal but exhibit AD risk factors such as amyloidosis on PET imaging, apolipoprotein-E4 genotype (APOE), and/or AD-associated neurodegeneration on neuroimaging. For example, a total of 20 older APOE4+ clinically normal older adults showed a relative reduction in category fluency compared with 20 age-matched APOE4- older adults (Rosen et al. 2005). Using a clinically normal sample over the age of 80, Snitz et al. 2013 found a relationship between greater amyloid burden on PET imaging and lower category fluency. In another clinically normal older adult sample, Wirth et al. 2013 found an association between greater amyloid burden on PET imaging and longitudinal declines in semantic processing, however, semantic processing was measured in a composite that included episodic memory.

Given the importance of characterizing the earliest cognitive changes associated with preclinical AD and the literature showing decrements in semantic processing in MCI, we sought to assess semantic processing in clinically normal older adults with and without evidence of amyloidosis on PET imaging. More specifically, we examined the relationship between category and letter fluency in a baseline sample of 275 older adults participating in the Aging Brain study with annual follow-up evaluations for up to 4 years. We were interested in determining whether category fluency was selectively impaired in clinically normal older adults with high amyloid burden at baseline and hypothesized that these individuals would exhibit greater declines in category fluency over time.

METHODS

Sample characteristics

Our sample consisted of 275 clinically normal subjects participating in the Harvard Aging Brain Study. The study was conducted at the Center for Alzheimer Research and Treatment at Massachusetts General Hospital and Brigham and Women's Hospital using protocols and informed consent procedures approved by the Partners Human Research Committee. Subjects were deemed clinically normal (CN) based on proposed ADNI criteria including: 1) a global Clinical Dementia Rating score of 0, 2) scores above education-adjusted cutoffs on the 30-Minute Delayed Recall of the Logical Memory Story A (≥3 for individuals with 0 to 7 years of education, ≥ 5 for individuals with 8 to 15 years of education, and ≥9 for individuals with 16 or more years of education; Aisen et al. 2010) and 3) normal performance on the Mini Mental State Exam (MMSE) (Mungas et al. 1996, Crum et al. 1993). Participants were also required to have a Geriatric Depression Scale (GDS) score of ≤ 11. Review of medical history and physical and neurological examinations confirmed their status as clinically normal. None of the subjects had a history of alcoholism, drug abuse, head trauma, or current serious medical/psychiatric illness. Although all subjects were fluent in English, a total of 12 subjects spoke English as a second language (ESL). Given the potential confounding influence of ESL on performance on verbal fluency tasks, analyses were repeated excluding these 12 subjects.

Amyloid imaging acquisition and analysis

Amyloid burden was measured with Pittsburgh Compound B (PiB), a compound that binds to fibrillar amyloid, N-methyl-[11C]-2- (4-methylaminophenyl)-6-hydroxybenzothiazole, (Mathis et al. 2003; Klunk et al. 2004). PET scans were completed at the Massachusetts General Hospital and completed within 6 months of the baseline neuropsychological testing visit using a Siemens ECAT EXACT HR+ PET scanner. Prior to injection of 8.5-15 mCi PIB, a 10-minute transmission scan was completed for attenuation correction. Following tracer injection, 60-minutes of dynamic data were acquired in 3D acquisition mode.

PIB-PET data were processed as distribution volume ratio images (40-60 min interval, gray matter cerebellum reference region). Mean PiB values were extracted from an aggregate of cortical regions that typically have elevated PiB burden in AD patients including frontal, lateral temporal and parietal, and retrosplenial cortices (Hedden et al. 2012). Dichotomization into Aβ− and Aβ+ groups was determined using a Gaussian mixture modeling approach (cut-off value=1.2) given the data's bimodal distribution (Mormino et al. 2014). All analyses were also repeated using PiB-PET uptake as a continuous rather than a dichotomous measure to ensure that the results were not impacted by the selected cutoffs.

Genetic Risk

Blood samples were collected from all the participants using standard procedures to evaluate apolipoprotein E (APOE) polymorphisms. Participants were categorized as carriers (+) and non-carriers (-) based on whether or not they carried the ε4 allele.

Neuropsychological tasks

Subjects completed an extensive neuropsychological battery at baseline and yearly for a maximum of 4 post-baseline time points. Mean follow up was 2.4 years. The selected measures for fluency were the sum of the words produced in 1 minute for the letters F, A, and S (Benton et al., 1983) and the sum of the words produced in 1 minute for each of the categories animals, vegetables, and fruits (Monsch et al., 1992).

Statistical Analyses

Statistical analyses were completed using R (http://CRAN.R-project.org/doc/). In order to assess the relationship between demographic variables and different cognitive variables across the entire sample, Pearson's r correlations for continuous variables and Spearman's rho correlations for interval variables were computed for FAS, CAT, age, sex, and years of education. To test for differences in demographic variables between Aβ+/− groups, T-tests and chi-square tests were used for linear and categorical variables, respectively.

Linear mixed models were used to assess the association between baseline Aβ status and change in verbal fluency. In the first model, category fluency was the dependent variable. Effects of baseline Aβ, age, sex and education, as well as their interactions with time were modeled as covariates. Model 2 was equivalent to Model 1 except FAS was added as a covariate in order to statistically control for the predominantly retrieval-based components of verbal fluency (speeded processing, self-monitoring, and activation retrieval) and isolate semantic processing associated with CAT. Model 3 was equivalent to Model 1 with the exception that FAS was the dependent variable. Model 4 was consistent with Model 3 in that FAS was the dependent variable and CAT was included as a covariate in order to more fully isolate the purely retrieval aspects of verbal fluency while controlling for semantic processing. All models included a random intercept for each participant. These models were run on the entire sample, and subsequently repeated eliminating 12 ESL participants. The same models were also repeated while including E4 status in the model both at baseline and longitudinally to determine whether genetic risk influenced cognitive performance.

RESULTS

Demographics

Demographic characteristics are presented in Table 1 for the entire sample at baseline. At baseline, Aβ+'s were older compared with Aβ−'s [t(274)=−2.741, p=0.007] (see Table 1). There was a trend for Aβ+'s to have a higher education level [t(274)=−1.711, p=0.088]. There were no differences between groups in sex distribution [χ2 (1, 276)=0.157, p=0.692] or Logical Memory Delayed Recall scores [t(274)=−1.032, p=0.303]. Aβ+'s performed worse on the MMSE compared with Aβ−'s [t(274)=2.019, p=0.044]. This association with MMSE was no longer significant when controlling for age, sex, and years of education (p=0.091). Aβ+'s were more likely to also carry the APOE-ε4 allele in comparison with the Aβ− group [χ2 (1, 267)=46.516, p=0.000]. A total of 6 individuals (5.88%) reaching year 4 of the study met ADNI criteria for early MCI (Aisen et al. 2010). No individuals met criteria for late MCI or AD.

Table 1.

Subject Demographics, Baseline Verbal Fluency Performance By Aβ Status

n=205 Aβ− n=70 Aβ+
Age* 72 (68-77) 75 (70-79)
Sex (%M) 41.46 38.57
APOE ε4 status (%+)** 17.67 60.87
Education (years) 16 (12-18) 16 (14-18)
MMSE* 29 (29-30) 29 (28-30)
Logical Memory-II 14 (12-16) 14 (12-16)
FAS* 42 (35-51) 47 (38-56)
CAT 44 (37-51) 46 (38-49)
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Medians and Interquartile Ranges, unless otherwise stated, are listed for demographic variables by Aβ status. CAT= category fluency, FAS= letter fluency

*

p<0.05

**

p<0.001.

Cross-sectional Results

Examining the sample as a whole (i.e., without accounting for Aβ status), participants produced a mean of approximately 44 words over 3 minutes for both categories and letters respectively [paired sample; t(275)=−0.245, p=0.806]. Participants produced a greater number of animal exemplars (mean of 17) compared with either fruits [t(275)=16.95, p=0.000] or vegetables [t(275)=16.72, p=0.000] where approximately 13 exemplars were produced for each food type. Total number of words produced for categories was positively correlated with total words produced to letters [r(276)=0.536, p=0.000]. Participants also produced fewer words to the letter “A” (mean of 13) compared with the letters “F” and “S” (mean of 15 and 16, respectively). Education was positively correlated with both category fluency[r(276)=0.392, p=0.000] and letter fluency [r(279)=0.338, p=0.000]. At baseline, older age was associated with lower CAT [r(276)=−0.253, p=0.000] but age was not related to FAS [r(279)=0.025, p=0.679].Women outperformed men on CAT [t(274)=2.936, p=0.004] but not FAS [t(274)=0.791, p=0.430]. In terms of individual categories, women produced a greater number of fruits [t(274)=3.788, p=0.000] and vegetables [t(274)=5.771, p=0.000] compared with men; however, there were no differences between genders on number of animals produced [t(274)=−0.626, p=0.532].

While controlling for age, sex, and education, there was no effect of Aβ status on CAT [t(270)=0.638, p=0.5241] at baseline; however, Aβ+'s performed better compared to Aβ−'s on FAS [t(270)=2.135, p=0.0337].

Longitudinal Results

Greater age was associated with greater longitudinal declines in both CAT (Model 1: Age × Time: t(723)=−2.03, p=0.0427) and FAS (Model 3: Age × Time: t(728)=−3.40, p=0.0007), however, there was no association between sex or education on longitudinal performance for either CAT or FAS (see Table 2).

Table 2.

Linear Mixed Models Assessing the Interaction Between Aβ Status and Time on Verbal Fluency.

Model 1: CAT Model 2: CAT, covariate FAS
b SE t b SE t
Intercept 45.13 0.80 56.28** 33.01 1.24 26.59**
Time −0.80 0.20 −4.06** −0.74 0.19 −3.81**
Aβ+ 0.80 1.25 0.64 −0.20 1.06 −0.19
Age −0.35 0.09 −3.79** −0.39 0.08 −5.03**
Male Sex −3.65 1.09 −3.36** −3.27 0.92 −3.54**
Education 1.22 0.18 6.90** 0.80 0.15 5.22**
FAS 0.28 0.02 11.66**
Aβ+ × Time −1.01 0.29 −3.54** −0.81 0.28 −2.85*
Age × Time −0.04 0.02 −2.03* −0.02 0.02 −0.94
Male Sex × Time 0.30 0.26 1.15 0.22 0.25 0.38
Education × Time −0.03 0.04 −0.61 −0.05 0.04 −1.07
Model 3: FAS Model 4: FAS, covariate CAT
b SE t b SE t
Intercept 43.83 1.09 40.16** 26.33 1.94 13.59**
Time −0.20 0.23 −0.85 0.13 0.23 0.55
Aβ+ 3.62 1.70 2.13* 3.32 1.48 2.24*
Age 0.16 0.12 1.30 0.29 0.11 2.71*
Male Sex −1.37 1.48 −0.93 0.03 1.30 0.03
Education 1.51 0.24 6.27** 1.04 0.21 4.82**
CAT 0.39 0.04 10.37**
Aβ+ × Time −0.83 0.34 −2.46* −0.44 0.34 −1.29
Age × Time −0.09 0.03 −3.40* −0.07 0.03 −2.79*
Male Sex × Time 0.26 0.30 0.86 0.14 0.30 0.48
Education × Time 0.06 0.05 1.19 0.07 0.05 1.37
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CAT= category fluency, FAS= letter fluency

*

p<0.05

**

p<0.001.

For linear mixed models, age is centered at 75 and education is centered at 16 years.

Aβ+'s exhibited greater decline over time compared with Aβ−'s on CAT, (Model 1: t(723)=−3.54, p=0.0004) and this association remained significant after adding FAS as a covariate (Model 2: t(722)=−2.85, p=0.0044) (see Figure 1). On FAS, Aβ+'s also exhibited greater decline over time compared with Aβ−'s (Model 3: t(728) = −2.46, p=0.0142), however, this association became insignificant when CAT was added to the model as a covariate (Model 4: t(722)=−1.29, p=0.1967). Findings were equivalent after excluding the 12 ESL participants. When we added E4 status to the model, there were no baseline or longitudinal associations between genetic risk and verbal fluency performance. In addition, findings were comparable when repeating all analyses using PiB-PET as a continuous rather than dichotomous variable.

Figure 1. Visual Representation of Rates of Change in Verbal Fluency in Relation to Aβ Status with and without Letter Fluency (FAS) or Category Fluency (CAT) as covariates.

Figure 1

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Aβ+ (red), Aβ− (green). Solid lines represent models 1 and 2 (without covarying for FAS or CAT, respectively). Dashed lines represent models 3 and 4, covarying for CAT (left) and FAS (right). Slopes change significantly for FAS when controlling for CAT, but do not change for CAT when controlling for FAS.

DISCUSSION

This study suggests that changes in semantic processing are occurring very early in the AD trajectory, that is, in individuals who have evidence of AD pathology on neuroimaging, but who are otherwise clinically normal. More specifically, we found that older adults with high levels of Aβ deposition at baseline declined at a greater rate on category fluency compared with their Aβ− peers and these findings remained significant when controlling for the largely retrieval-based aspects of verbal fluency (e.g., letter fluency). Aβ+'s appeared to also decline more rapidly on letter fluency compared with Aβ−'s, however, this association became insignificant when controlling for category fluency. Taken together, this suggests that semantic-based aspects of verbal fluency are preferentially affected in preclinical AD in contrast with purely retrieval-based demands. These findings are consistent with work in MCI subjects who routinely show decrements in semantic processing (Joubert et al. 2010) and in particular, greater declines in category vs. letter fluency (Murphy et al. 2006, Clark et al. 2009).

To our knowledge, this is one of the first prospective studies examining semantic processing longitudinally in relation to biomarker-defined evidence of preclinical AD. Our results are consistent with a study that examined clinically normal older adults (82+) for whom greater PET amyloid burden was associated with retrospective declines in category fluency (Snitz et al. 2013). Similarly, Wirth et al. 2013 found an association between greater PET amyloid burden and longitudinal declines in semantic processing. However, neither study dissociated pure semantic knowledge decline versus inefficiencies in verbal retrieval processes. Event-related fMRI studies indicate differential brain activation between Ɛ4 carriers vs. non-carriers during semantic processing tasks (Rao et al. 2015). The increased activity in bilateral prefrontal cortex and temporo-parietal areas during a semantic task has been hypothesized to reflect compensatory activation in the setting of declines in semantic processing.

Category fluency is a particularly useful means of assessing semantic processing in clinically normal older adults because it is not limited by ceiling effects common in this population or characteristic of other semantic measures such as the Boston Naming Test (Rentz et al. 2013). Letter fluency, which shares a number of features comparable with category fluency, can be used as a statistical control for retrieval-based difficulties and/or inefficiencies (Reverberi et al. 2014). Category fluency may also be less susceptible to education or lifetime exposure effects observed in other item/stimulus-based measures of semantic processing (Brickman et al. 2004). However, we do observe sex-based differences in performance whereby women are generally better performers on category fluency compared with men and on the naming of fruits and vegetables in particular.

Of note, Aβ+'s performed better compared with Aβ−'s at baseline on letter fluency but equivalently on category fluency. Although somewhat surprising, this has been observed in other clinically normal older adults samples with Aβ+ biomarkers (Mormino et al. 2012). One explanation for this may be that Aβ+'s may already be exhibiting breakdowns in semantic networks at baseline and are therefore relying more heavily on phonemic-based strategies for verbal fluency (Schwartz et al. 2003). Phonemic-based strategies are particularly useful on letter fluency but are unlikely to confer the same benefits when required to produce items from categories. In the future, we plan on analyzing verbal fluency performance through semantic clustering in order to address this possibility (Troyer et al. 1998). Alternatively, although Aβ+'s in our study are not statistically more highly educated compared with Aβ−'s, it may be that Aβ+'s enrolled in the study on the basis of normal cognitive performance, may be cognitively more robust and/or effective in using compensatory strategies for their given amount of pathological burden. Similarly of interest is our finding of a negative relationship between age and category fluency but a lack of a relationship between age and letter fluency. It may be that category fluency is simply a more demanding cognitive task given that it involves both executive components (strategy development, cognitive flexibility, self-monitoring and speeded processing) and semantic components (clustering by subcategory, extracting exemplars from semantic memory) while letter fluency requires primarily executive components (Reverberi et al. 2014). It is important to note that although this study shows that the semantic component of verbal fluency appears to be differentially related to decline in preclinical AD, does not mean that executive functioning as a whole is spared early in the AD trajectory. While we have examined a measure classified under the domain of executive functioning (letter fluency), this study does not comprehensively explore executive functioning in preclinical AD.

Results of this study must also be interpreted in the context of the stimuli used. Our findings of a difference in better performance in animals versus fruits and vegetables and worse performance on the letter ‘A’ versus the letters ‘F’ and ‘S’ suggests that which stimuli are administered and the number of trials of administration may affect performance and thus the interpretation of differences in performance between letter vs. category fluency.

Future analyses may further tease apart the integrity of semantic networks by examining letter vs. category performance on an item-by-item basis. For example, Weingartner et al. (1993) found that AD patients produced fewer uncommon vs. common exemplars to categories. It would be useful to determine if this pattern is similarly observed at the preclinical stage of AD. If so, additional metrics relating to types of exemplars may have utility as early cognitive markers. Furthermore, quantifying clustering and switching techniques, as has been done in other studies (Troyer et al. 1998), will help provide insight into how performance is changing differentially in letter versus category fluency and in older adults with and without AD pathology.

In addition, recent findings suggest that amyloidosis, in combination with signs of neurodegeneration, is associated with greater cognitive decline in comparison to the presence of either amyloidosis or neurodegeneration alone (Mormino et al, 2014b). It will be important to examine the combined effect of Aβ status and other neuroimaging markers of brain integrity on semantic processing. In particular, given that poorer semantic memory has been associated with smaller entorhinal cortex/hippocampal volume (Hirni et al. 2013), it will be important to determine the extent to which amyloid burden and hippocampal volume interact in determining changes in semantic processing. Finally, measurement of tau deposition using radiotracer 18F-T807 is currently underway for participants in the Aging Brain Study and examining the combined effects of tau and Aβ deposition will surely provide us with more clues about changes in semantic processing early in the AD trajectory (Johnson et al., 2014).

One limitation of our sample is the relatively high level of overall educational achievement (e.g., the majority of our participants are college-educated) which necessitates future work to determine if our findings are generalizable to a more educationally diverse cohort. In addition, while these results provide information at the group level, the overall changes in verbal fluency are subtle enough to make development of clinical cut-offs premature (e.g., only 6% of participants met criteria for Mild Cognitive Impairment at four year follow-up). Differential performance on letter vs. category fluency is not sensitive to amyloid status at the cross sectional level in clinically normal older adults.

In sum, there is longstanding evidence that AD patients produce fewer exemplars to specific categories (Weingartner et al. 1993) while often continuing to produce more exemplars to specific letters. Differentially worse category vs. letter fluency, in combination with reduced episodic memory performance, has served as a “rule of thumb” for AD diagnosis by neurologists and neuropsychologists alike for more than 30 years. Neuroimaging methods, such as PiB-PET, have afforded the opportunity to examine AD pathology much earlier in the disease process. This study shows that the category/letter fluency cognitive signature associated with AD is not only supported by biomarker evidence, but also observed earlier in the disease trajectory than previously considered.

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