Choline acetyltransferase activity and cognitive domain scores of Alzheimer’s patients☆

Neurobiology of Aging 21 (2000) 11–17

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Choline acetyltransferase activity and cognitive domain scores of Alzheimer’s patients夞 Bruce A. Pappas*, Peter J. Bayleya, Barbara K. Buia, Lawrence A. Hansena, Leon J. Thala Alzheimer’s Disease Research Center, University of California at San Diego, La Jolla, CA 92093-0948, USA Received 16 September 1999; accepted 1 December 1999

Abstract Choline acetyltransferase activity and cognitive domain scores of Alzheimer’s patients. Item scores from the Mattis Dementia Rating Scale (MDRS) and the Mini-Mental State Examination (MMSE) from 389 patients with probable Alzheimer’s disease were submitted to principal component analysis with orthogonal rotation. The optimal solution identified four factors that reflected the cognitive domains of attention/registration, verbal fluency/reasoning, graphomotor/praxis and recent memory. A subgroup of patients was identified for whom both the MDRS and the MMSE had been administered within the 12 months before death. Scores were assigned to these patients for the four factors. These cognitive-domain scores were then correlated with postmortem choline acetyltransferase (ChAT) activity in the medial frontal cortex, inferior parietal cortex, and hippocampus. ChAT activity in both the medial frontal and the inferior parietal cortex significantly correlated with scores on the graphomotor/praxis factor. Medial frontal ChAT also correlated significantly with the attention/ registration scores. Hippocampal ChAT correlated significantly only with recent memory scores. These results are consistent with current animal research regarding the effect of selective cholinergic lesions on behavior. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Alzheimer’s disease; Choline acetyltransferase; Cortex hippocampus; Mattis Dementia Scale; Mini-Mental State Examination

1. Introduction Dysfunction of basal forebrain (BF) cholinergic neurons is a well-established feature of Alzheimer’s disease (AD). There is loss of cholinergic neurons in the basal forebrain nuclei that innervate the cerebral cortex and hippocampus resulting in a decrease of choline acetyltransferase (ChAT) and acetylcholinesterase in these terminal fields [7,8,28]. Furthermore, the extent of dysfunction as reflected by ChAT activity and other measures of cholinergic BF integrity correlates significantly with the depth of dementia in AD [3,10,28]. There is controversy regarding the temporal relationship between the stage of AD and cholinergic dysfunction. A recent study indicates

夞 Supported by a sabbatical research grant from Carleton University and an NSERC operating grant to B.A.P., and by NIA grant AG-05131 to L.J.T. * Corresponding author at Institute of Neuroscience, Life Sciences Research Centre, Carleton University, 1125 Colonel By Dr., Ottawa, ON K1S5B6, Canada. Tel.: ⫹1-613-520-7494; fax: ⫹1-613-520-4052. E-mail address: [email protected] (B.A. Pappas).

that ChAT activity does not decline until the dementia progresses beyond the mild and moderate stages, as reflected by scores on the Clinical Dementia Rating Scale [9]. On the other hand, there also is recent evidence suggesting that there is cholinergic dysfunction early in the evolution of Alzheimer’s dementia. A severe reduction of TrkA immunoreactive and hence most likely cholinergic [33] BF neurons has been found for both AD patients and notably, also for individuals diagnosed with mild cognitive impairment [22], which may be a preclinical antecedent of AD [1]. Although these studies unequivocally establish a relationship between BF cholinergic neuron dysfunction and AD, they have not determined if the dysfunction is causal to the dementia and if so, to what cognitive domains. We sought to determine if there is an association of specific cognitive dysfunctions with the activity of cortical and hippocampal ChAT in AD patients. This is a difficult issue to address because ChAT analyses are not routinely performed on AD patients. In addition, comprehensive neuropsychological testing carried out within an antemortem interval sufficiently recent (e.g. within 12 months) to validly reflect

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Table 1 Summary descriptions of the patient sample employed in the factor analysis and the subsamples used for the correlations between factor scores and ChAT Variable

Factor analysis sample (n ⫽ 379)

MF ChAT sample (n ⫽ 48)

IP ChAT sample (n ⫽ 19)

HP sample (n ⫽ 16)

Age Male/female % Education (years) Duration of illness MDRS score Interval before death (months) MMSE score Interval before death (months)

75.6 ⫾ 0.4 46/54% 12.4 ⫾ 0.3 4.7 ⫾ 0.2 82.6 ⫾ 1.7 NAb 14.3 ⫾ 0.4 NAb

79.0 ⫾ 1.0 56/44% 14.0 ⫾ 0.5 7.2 ⫾ 0.6 69.5 ⫾ 5.9 6.4 ⫾ 0.5 11.6 ⫾ 1.2 6.1 ⫾ 0.5

79.2 ⫾ 1.6 58/42% 13.6 ⫾ 1.0 6.3 ⫾ 1.0 82.2 ⫾ 8.8 6.3 ⫾ 0.8 12.9 ⫾ 2.0 6.1 ⫾ 0.8

79.9 ⫾ 1.7 50/50% 13.5 ⫾ 1.0 5.5 ⫾ 0.9 91.9 ⫾ 8.3 6.2 ⫾ 0.9 14.5 ⫾ 2.1 5.9 ⫾ 0.9

NA ⫽ not applicable. The summary variables include age (at the last psychometric test occasion for the factor analysis sample and at death for the subjects for whom ChAT analyses were available), sex ratio, duration of illness (same criteria as for age), scores on the MDRS and MMSE and the interval between these tests and death (applicable only for the subjects from whom the ChAT data were obtained). b a

cognitive function at death is not always available. An analysis of our database indicated that there existed a cohort of patients whose brains had undergone ChAT analysis and who had also been cognitively evaluated with two widelyused, brief dementia scales, the Mattis Dementia Rating Scale (MDRS) [23] and the Mini-Mental State Examination (MMSE) [14]. Although both of these scales offer subscale scores purportedly reflecting several cognitive dimensions, these subscales have not been psychometrically validated. For example, the 144 items of the MDRS are used to derive five putative subscale scores: attention, initiation and perseveration, construction, conceptualization, and memory. However, factor analytic studies of the MDRS have variously reported two-factor (verbal, non-verbal), three-factor (conceptualization/organization, visual-spatial, memory/orientation), and four-factor (attention/initiation and perseveration, construction, conceptualization and memory) solutions [6,21,41]. Differences in the subject population could explain these divergent results. Similarly, the 30 items of the MMSE are collapsed to yield seven subscores putatively representing different cognitive domains, yet factor analyses typically report two- or three-factor solutions [13, 16,35,42]. The utilization of item scores from both the MDRS and the MMSE ought to provide more reliable estimates of cognition than either score alone since more data are used. In the following experiment, we sought to derive estimates of competence in several cognitive domains by factor analyzing scores from the MDRS and the MMSE for 389 patients who met ADRC-NINCDS criteria for probable AD. A substantial proportion of these patients also met postmortem neuropathological criteria for AD. To our knowledge, there is no published factor analysis combining these tests from an AD population. Subsequently, we identified a subgroup of patients who had been evaluated with the MDRS and MMSE within 12 months before their death and for whom postmortem analysis of ChAT had been carried out for the medial frontal cortex (MF CTX) and for smaller subgroups, for the inferior

parietal cortex (IP CTX) and the hippocampus (HP). Based on their MDRS and MMSE item scores, each patient was assigned a score on each of the four factors (cognitive domains) that had been identified by the factor analysis. These factor scores were then correlated with the three measures of ChAT to provide an indication of the relationships between these four cognitive domains and ChAT activity in the MF CTX, IP CTX, and HP.

2. Methods 2.1. Subjects Subjects (389) with a primary diagnosis of probable Alzheimer’s disease were identified from the archival data base of the University of California at San Diego Alzheimer’s Disease Research Center (ADRC). All were participants in ongoing longitudinal research at the ADRC through which they received annual physical, neurological, and neuropsychological evaluations. On the basis of these evaluations and a number of laboratory tests used to rule out other causes of dementia (e.g. hypothyroidism, vitamin B12 deficiency, electrolyte imbalance), the diagnosis of probable AD was made by two senior staff neurologists at the ADRC according to the criteria developed by the National Institute of Neurological and Communicative Disorders and Stroke (NINCDS) and the Alzheimer’s Disease and Related Disorders Association (ADRDA) [24]. To reduce the possibility of including subjects with multi-infarct dementia, patients with a score of five or greater on the Rosen-modified Hachinski ischemia scale [17,29] were excluded. Neuropathological confirmation of AD was obtained at autopsy for 129 of these patients by using diagnostic criteria from the NIA for Alzheimer’s disease and from CERAD for definite or probable Alzheimer’s disease [18,27]. Subgroups were identified for whom both the MDRS and MMSE had been administered within 12 months before death as part of the routine annual neuropsychological ex-

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amination of each patient. Of these patients, ChAT activity had been analyzed in the MF cortex (n ⫽ 48), IP cortex (n ⫽ 19), and hippocampus (n ⫽ 16). Table 1 summarizes the essential characteristics of the main sample and the three subsamples. 2.2. ChAT assay ChAT assays were performed on frozen tissues from the right hemisphere that had been dissected at autopsy and stored at ⫺70C until assayed. Tissue samples were from the midfrontal cortex (Brodmann areas 45, 46, or 9), inferior parietal cortex (Brodmannn areas 39 or 40) and from a cross section of the hippocampus at the level of the mammillary bodies. The assay followed previously published modifications [18] of the Fonnum [15] radioenzymatic method. 2.3. Statistical analyses Statistical analyses were performed using SPSS (version 8). The factor analyses followed the guidelines of Tabachnick and Fidel [34] and the SPSS Base 8.0 Applications Guide. The scores that were submitted to factor analysis included 17 condensed item scores from the MDRS, defined according to Colantonio et al. [6] and the 11 condensed item scores from the MMSE [36]. Statistical significance for the correlation analyses was based on one-tailed criteria because positive correlations were predicted. Bonferroni correction was not applied due to the exploratory nature of the study and the preference to reduce the likelihood of Type II error.

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Table 2 Factor loadings from the principal components extraction and varimax rotation for the 28 subscales of the MDRS and the MMSE. MDRS

Factor 1

Digit span Follow commands Counting Verbal fluency Verbal repetition Alternating movements Graphomotor Construction Similarities Priming inductive reasoning Differences Similarities (multiple choice) Identities-oddities Create sentence Verbal recall Orientation Recognition of words/designs

0.676 0.796 0.628

Factor 2

Factor 3

Factor 4

SMCs 0.713 0.726 0.812 0.746 0.379 0.584

0.573 0.564 0.617 0.763 0.802

0.746 0.824 0.682 0.645

0.726 0.736 0.578

0.658 0.665

0.594 0.642 0.609 0.785 0.738 0.63

0.726 0.512 0.638 0.786 0.666

MMSE Operation time Orientation place Word registration Word backward Word recall Naming Sentence repetition Follow commands Read and obey Write sentence Copy pentagon

0.718 0.529

0.523 0.762 0.549

0.682 0.588 0.533 0.502 0.532 0.668

0.736 0.678 0.678 0.589 0.470 0.538 0.424 0.614 0.484 0.574 0.601

a

3. Results 3.1. Factor analysis The 28 scores from the MDRS and MMSE for the 389 AD patients were submitted to principal components extraction with Varimax rotation. Four factors with eigenvalues exceeding 1.0 were extracted and accounted for 20, 17.9, 14.7, and 11.3% (sum ⫽ 63.9%) of the total variance respectively. The highest and lowest SMCs (the proportion of subscale variance accounted for by the four factors) were 0.824 and 0.379 respectively. Singularity and multicollinearity were not a problem with this data set. The four factors satisfactorily defined the variables as only four of the 28 SMCs were less than 0.5 (see Table 2). Table 2 shows the loading coefficients of the variables with the four factors as well as the communalities (SMCs). To assist the interpretation of the factors, only coefficients greater than 0.50 were considered and only these are shown in the table. All variables except verbal repetition from the MDRS (largest coefficient was 0.486, for factor 2) and

Factor loadings less than 0.50 are not shown. The last column shows SMCs (the proportion of subscale variance accounted for by the four factors).

sentence repetition (largest coefficient was 0.466, for factor 2) exceeded this cutoff loading on at least one factor. When oblique rotation for four factors was requested, only one pair of factors, factor 3 and factor 4, correlated (0.566) so orthogonal rotation was chosen to best reveal factor structure. As shown in Table 2, factor 1 loaded heavily on items testing attention, registration and recognition (item loadings in order of magnitude, from the MDRS: follow commands, digit span, identities-oddities, recognition of words/designs, counting, similarities (multiple choice); from the MMSE: word registration, naming, follow commands, read, and obey). Factor 2 loaded on items of verbal reasoning, fluency and sentence construction (in order of magnitude, from the MDRS: priming inductive reasoning, similarities, create sentence, differences, verbal fluency; from the MMSE: word backward, write sentence, orientation to place). Factor 3 loaded heavily on items reflecting graphomotor ability (in order of magnitude, from the MDRS: construction, graphomotor, alternating move-

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Fig. 1. Scatter plots showing the relationship between ChAT in MF CTX (left), IP CTX (middle), HP (right), and the scores for the four factors that were derived from the principal components analysis. The four rows of graphs depict the relationships for the four factors, respectively. The statistical significance of the correlations are indicated by asterisk: *P ⬍ 0.05, **P ⬍ 0.01.

ments, counting; from the MMSE: copy pentagon). Factor 4 clearly reflected memory (item loadings in order of magnitude, from the MDRS: verbal recall, orientation; from the MMSE: orientation time, word recall, orientation place).

3.2. Correlations Z scores for each of the four factors were calculated for each subject by utilizing the linear equation derived from

B. A. Pappas et al. / Neurobiology of Aging 21 (2000) 11–17 Table 3 Pearson correlations between the scores for the four factors, the total scores for the MDRS and the MMSE, and MF cortex and hippocampal ChAT Score

MF CTX ChAT (n ⫽ 48)

IP CTX ChAT (n ⫽ 19)

HP ChAT (n ⫽ 16)

MDRS total MMSE total

0.502** 0.421**

0.804** 0.631**

0.600** 0.607*

* P ⬍ .01, **P ⬍ .001, all one tail.

principal components extraction. Multiple regression analyses were then performed to determine Pearson correlation coefficients between these psychometric scores and the three ChAT variables. Despite the small ratios of cases to independent variables, particularly for IP CTX and HP ChAT, exploratory multiple regression analyses were also carried out to further assess the unique contributions of the psychometric variables to the three measures of ChAT. Step-wise, backward and forward analyses were determined for all three measures and the results of these analyses were consistent with one another. Fig. 1 (the four left panels) show the scatter plots relating MF CTX ChAT and factors 1 through 4. ChAT significantly correlated with factor 1 (r ⫽ 0.319, P ⬍ 0.014) and factor 3 (r ⫽ 0.402, P ⬍ 0.002). The multiple regression analysis indicated that only factor 3 contributed significantly to MF CTX ChAT (F(1,46) ⫽ 8.86, P ⬍ 0.005). Thus, although factor 1 also correlated with MF CTX ChAT, its unique contribution failed to reach significance (P ⬍ 0.08). The four middle panels of Fig. 1 show the scatter plots relating IP CTX ChAT and Factors 1 through 4. IP ChAT and factor 3 were significantly correlated (r ⫽ 0.559, P ⬍ 0.014). The multiple regression analysis indicated that only factor 3 contributed significantly to IP CTX ChAT (F(1,17) ⫽ 7.73, P ⬍ 0.014). The scatter plots relating HP ChAT and factors 1– 4 are

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shown in the right panels of Fig. 1. Only factor 4 correlated significantly (r ⫽ 0.514, P ⬍ 0.021) with HP ChAT and the multiple regression analyses indicated that this factor alone was a significant contributor to the variation in HP ChAT (F(1,14) ⫽ 5.04, P ⬍ 0.041). As shown in Table 3, the total scores on both the MDRS and the MMSE significantly correlated with all three measures of ChAT. A composite score for these two tests was derived for each subject by determining the standardized (Z) score for each on each test, adding these two scores together, and dividing by two. The derivation of these composite scores was based only on the scores of the 48 subjects for whom neurochemical data were available. These composite scores also correlated with both MF CTX (r ⫽ 0.471, P ⬍ 0.001), IP ChAT (r ⫽ 0.738, P ⬍ 0.001) and HP ChAT (r ⫽ 0.618, P ⬍ 0.005). The three scatter plots for these variables are shown in Fig. 2.

4. Discussion Factor analysis of item scores from the MDRS and MMSE yielded an optimum solution of four orthogonal factors. Factor 1 loaded on digit span, on items from both tests that required that the patient follow simple commands, word registration and recognition, counting of written letters, naming of objects, word and design recognition, and the detection of similarities and oddities among words and visual objects. One common feature of these test items is that they are reflective of the attention to and the registration of verbal and non-verbal information. Scores on this factor can be assumed therefore to be a measure of attention/ registration processes. Factor 2 is a composite of sub-tests reflecting verbal fluency and reasoning and seems best summarized as verbal fluency/ reasoning. Factor 3 is an obvious composite of items measuring graphomotor ability and praxis and may be summarized as

Fig. 2. Scatterplots showing the relationship between the ChAT in MF CTX (left), IP CTX (middle), and HP (right) and the composite z score for the MDRS and MMSE. The statistical significance of the correlations are indicated by asterisk: **P ⬍ 0.01, ***P ⬍ 0.001.

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graphomotor/praxis. Factor 4 reflects verbal recall and the orientation to time and place that both depend upon recent memory. Accordingly, scores on this factor can be assumed to measure recent memory function. Because to our knowledge there are no other published studies that involved the common factor analysis of both the MDRS and the MMSE, it is impossible to compare our results with those of others. Two previous factor analytic studies of only the MDRS have utilized data solely from patients with diagnoses of possible or probable AD [6,41]. There is also one published factor analysis of the MMSE with a very small sample of AD patients [13]. Collectively, those three studies have four points of agreement with the current one. First, there is no support for the putative seven cognitive domains of the MMSE and five domains of the MDRS. Rather, all four studies indicate that a smaller number of cognitive domains are assessed with these tests. Second, one common feature of those studies and the current one is the identification of a memory factor. There is substantial agreement among studies as to which items contribute to this factor. Third, the two analyses of the MDRS and the current study extract a graphomotor/praxis factor, again, with very similar item loadings. Fourth, a reasoning/conceptualization factor emerges from all three studies with some variation in item loading, most notably the inclusion of verbal fluency items in the present analysis. The current study also identified a fourth factor attention/registration and the item loadings bear some similarity to a factor for the MDRS that was identified by Woodard et al. [41] as “attentioninitiation-perseveration.” As factor analysis yields a linear equation for each factor with coefficients that weight the scores on individual test items, it was possible to assign to each subject a single score on each of the four factors. These scores were then correlated to available archival data for ChAT for subjects who met the criterion that their MDRS and MMSE scores were obtained within 12 months before death to ensure that the factor scores would be reasonably representative of the patients’ cognitive status at the time of death. One potential problem with this assignment of scores to the subgroup for whom ChAT data were available, is the possibility that these patients differed sufficiently from the larger sample on whom the factor analysis was derived so as to render invalid the assignation of factor scores. The most obvious expected difference is that this subgroup would be older and would have had a longer disease duration because they were most likely to have been in the terminal state of AD at the time of death. This turned out to be the case but the difference in duration was probably immaterial. The best measures of dementia stage would be the mean sample scores on the MDRS and the MMSE and the largest differences between the mean scores for the main sample and the subsamples were 13.1 for the MDRS and ⫺2.7 for the MMSE. It seems unlikely that such small differences would invalidate the assignation of factor scores to the subsample of patients. Significant correlations were found between MF CTX ChAT and factor 1, attention/registration, and factor 3, graphomotor/praxis, scores. Despite the small sample sizes,

IP CTX ChAT also correlated with factor 3, graphomotor/ praxis, and HP ChAT significantly correlated with factor 4, recent memory. It is remarkable that these correlations emerged despite the expected imprecision inherent in generating scores for individual subjects from the factor analysis and the variability inherent in the ChAT assays due to uncontrollable factors such as death-brain removal interval. Furthermore, the correlations are consistent with current conceptualization of the respective functions of cortical and hippocampal cholinergic pathways. The cerebral cortex receives its cholinergic innervation primarily from the nucleus basalis of Meynert whereas the hippocampus is cholinergically innervated from the medial septum and the vertical diagonal band [26,31]. There are differing behavioral effects of selective lesions of cortical versus hippocampal cholinergic terminal fields, effected in the rat by intraparenchymal administration of the immunotoxin 192 IgG-saporin, suggesting that they are components of two different cognitive systems [12]. Current research indicates that lesions to the cortical system do not directly affect memory but rather they impair attentional processes [4,5,25,37]. The precise nature of the impairment remains to be clarified. Similarly, excitotoxic lesions of the monkey basal forebrain affect attention but not memory [38,39]. On the other hand, 192 IgG-saporin lesion of the rat hippocampus impairs spatial learning/memory, although this is not a universal finding [2,11,32,40]. Thus, this research with infrahumans suggests that attentional (and perhaps other cognitive domains) and mnemonic processes seem differentially related to cortical and hippocampal cholinergic function respectively. The results of the current experiment indicate that this is mirrored in the human brain. In conclusion, the factor analysis of the MDRS and MMSE reported here, which is based on the largest sample of AD patients to date, confirms that these two tests assess a small number of cognitive domains. More importantly perhaps, the performance of Alzheimer’s patients in these cognitive domains seems to correlate differentially to cortical and hippocampal cholinergic function. This conclusion is based on a relatively small sample, however, and should be viewed as a preliminary finding until it is confirmed and or extended with much larger samples. Acknowledgments We thank Michael Alford for technical assistance. REFERENCES [1] Almkvist O, Basun H, Backman L, Herlitz A, Lannfelt L, Small B, Viitanen M, Wahlund LO, Winblad B. Mild cognitive impairment—an early stage of Alzheimer’s disease? J Neural Transm 1998; (suppl.):54:21–9. [2] Baxter MG, Gallagher M. Intact spatial learning in both young and aged rats following selective removal of hippocampal cholinergic input. Behav Neurosci 1996;110:460 –7.

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