Hostname: page-component-745bb68f8f-s22k5 Total loading time: 0 Render date: 2025-02-06T12:35:29.131Z Has data issue: false hasContentIssue false
  • English
  • Français

Amnestic mild cognitive impairment: Diagnostic outcomes and clinical prediction over a two-year time period

Published online by Cambridge University Press:  22 March 2006

H. RANDALL GRIFFITH ,
KELLI L. NETSON ,
LINDY E. HARRELL ,
EDWARD Y. ZAMRINI ,
JOHN C. BROCKINGTON  and
DANIEL C. MARSON
H. RANDALL GRIFFITH
Affiliation:
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama Alzheimer's Disease Research Center, University of Alabama at Birmingham, Birmingham, Alabama
KELLI L. NETSON
Affiliation:
Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama
LINDY E. HARRELL
Affiliation:
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama Alzheimer's Disease Research Center, University of Alabama at Birmingham, Birmingham, Alabama
EDWARD Y. ZAMRINI
Affiliation:
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama Alzheimer's Disease Research Center, University of Alabama at Birmingham, Birmingham, Alabama
JOHN C. BROCKINGTON
Affiliation:
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama Alzheimer's Disease Research Center, University of Alabama at Birmingham, Birmingham, Alabama
DANIEL C. MARSON
Affiliation:
Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama Alzheimer's Disease Research Center, University of Alabama at Birmingham, Birmingham, Alabama
Rights & Permissions [Opens in a new window]

Abstract

Amnestic mild cognitive impairment (MCI) has been defined as a precursor to Alzheimer's disease (AD), although it is sometimes difficult to identify which persons with MCI will eventually convert to AD. We sought to predict MCI conversion to AD over a two-year follow-up period using baseline demographic and neuropsychological test data from 49 MCI patients. Using a stepwise discriminant function analysis with Dementia Rating Scale (DRS) Initiation/Perseveration and Wechsler Memory Scale, third edition (WMS-III) Visual Reproduction Percent Retention scores, we correctly classified 85.7% of the sample as either AD converters or MCI nonconverters, with 76.9% sensitivity and 88.9% specificity. Adding race, the presence of vascular risk factors, or cholinesterase inhibitor use to the analysis did not greatly change the classification rates obtained with neuropsychological test data. Examining neuropsychological test cutoff scores revealed that DRS Initiation/Perseveration scores below 37 and Visual Reproduction Percent Retention scores below 26% correctly identified AD converters with 76.9% sensitivity and 91.7% specificity. These results demonstrate that commonly administered neuropsychological tests identify persons with MCI at baseline who are at risk for conversion to AD within 1–2 years. Such methods could aid in identifying MCI patients who might benefit from early treatment, in providing prognostic information to patients, and identifying potential clinical trial participants. (JINS, 2006, 12, 166–175.)

Keywords

NeuropsychologyAlzheimer's diseaseMemoryPsychomotor performanceDementiaDemography
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 12 , Issue 2 , March 2006 , pp. 166 - 175
Copyright
© 2006 The International Neuropsychological Society

INTRODUCTION

The focus on persons with amnestic mild cognitive impairment (MCI) has been intensified with the introduction of the Quality Standards Subcommittee practice parameter criteria for MCI in 2001 (Petersen et al., 2001; Winblad et al., 2004). These criteria, proposed by Peterson and colleagues and employed by the Mayo Clinic research group in their studies of MCI (Petersen et al., 1999; Petersen, 2000), define a clinical entity distinct from typical cognitive complaints of older adults and conditions resulting from psychometric and clinical evidence of any form of cognitive impairment (e.g., cognitively impaired–not demented (CIND)) (Ingles et al., 2003; Kurz et al., 2003; Tuokko et al., 2003). The criteria define persons displaying subjective complaints of memory loss, psychometric evidence of memory loss, otherwise normal cognitive functioning, generally normal everyday activities of daily life, and no evidence of dementia.

The diagnosis of MCI made with these criteria has been useful in identifying persons at risk for eventual dementia, with studies demonstrating an annual risk for conversion to dementia of 15% in persons with MCI over a range of 6 years in clinically-based populations (Petersen et al., 1999). In one retrospective epidemiological study that applied MCI criteria to participants over a 10-year follow-up period, 27% of the participants with MCI at study entry developed dementia within the 10-year period, with cumulative rates of dementia conversion in subsequent participants reaching as high as 21.7% for all dementias (Ganguli et al., 2004). While a considerable number of persons remained diagnosed with MCI throughout the study (up to 21.2%), nearly 23% of persons with MCI had returned to normal (i.e., no longer fulfilled criteria for MCI). The estimated number of cumulative conversions over time in this epidemiological study is considerably below that estimated within clinic-based samples (Bowen et al., 1997; Petersen et al., 1999; Bozoki et al., 2001), although limitations in the retrospective application of the Mayo criteria to this epidemiological study could explain differences in the findings.

The need for better prediction of persons at risk within the MCI group is thus apparent (Storandt et al., 2002). While satisfaction of the MCI criteria might be adequate to detect persons on entry into MCI, other clinical (Daly et al., 2000; Marquis et al., 2002), genetic (Petersen et al., 1995, 1996; Lange et al., 2002), neuroimaging (Jack et al., 1999), and neuropsychological investigations (Masur et al., 1994; Tierney et al., 1996) may be needed to determine who out of the larger group of MCI will likely convert to dementia. Of these methods, neuropsychological test data might be useful, as it has been shown to independently predict risk of dementia above and beyond hippocampal volume and apolipoprotein E status (Marquis et al., 2002) and is often completed in persons with a question of MCI due to the inclusion of the criterion of psychometric memory impairment in the diagnostic criteria for MCI (Petersen et al., 2001).

Previously identified neuropsychological predictors of conversion to dementia in memory disordered older adults have included measures of immediate and delayed memory recall (Tierney et al., 1996; Albert et al., 2001), a measure of mental control (Tierney et al., 1996), and measures of set-shifting and sequencing (Albert et al., 2001). Other researchers have found that the asymmetry between verbal and nonverbal abilities is predictive of dementia in memory-disordered patients (Jacobson et al., 2002). However, these prior studies were not conducted in persons who had received a clinical diagnosis of amnestic MCI using Mayo criteria, and thus may overlap with other conditions such as age-associated memory impairment (AAMI) and CIND.

The purpose of the current study was to examine the clinical, demographic, and neuropsychological predictors of conversion to probable AD dementia in persons with MCI over a two-year follow-up period.

METHOD

Research Participants

Participants in this study were 49 individuals diagnosed with amnestic MCI and 49 normal controls drawn from the participant pool of the Alzheimer's Disease Research Center (ADRC) at the University of Alabama at Birmingham (UAB). Participants with MCI were community-dwelling older adults who either presented for clinical evaluation to the UAB Memory Disorders Clinic or were volunteers recruited from the community into the ADRC from March 2000 to September 2003. All MCI participants received either a baseline consensus diagnosis of amnestic MCI or were diagnosed with amnestic MCI upon a follow-up evaluation, and underwent comprehensive ADRC assessment and consensus diagnosis for at least one year following diagnosis of amnestic MCI. The most recent comprehensive assessment was completed as of December 2004. Controls were volunteers recruited by the ADRC who underwent comprehensive neuropsychological and neurological evaluation and were assigned “normal control” status at the consensus conference (i.e., no dementia and no evidence of MCI). All participants gave informed consent for the procedures outlined as a part of this Institution Review Board (IRB)-approved research.

Procedures

As a part of the participation in the UAB ADRC, all participants underwent a comprehensive medical and neurological history and examination. Some participants underwent other medical procedures, including laboratory evaluation for reversible causes of cognitive impairment (complete blood count (CBC), Chem 18, B12, thyroid, rapid plasma regain (RPR)), review of prior medical records, review of neuroimaging and repeat neuroimaging if clinically indicated, and apolipoprotien E (APOE) genotyping. Assignment of staging of dementia was determined using the Clinical Dementia Rating scale (CDR) (Morris, 1997) and the Global Deterioration Scale (GDS) (Reisberg et al., 1982). Overall cognitive severity was determined using the Mini-Mental State Examination (MMSE) (Folstein et al., 1975).

Participants also underwent neuropsychological testing using a battery of tests devised to detect cognitive impairment in older adults (Butters et al., 1994; Lezak, 1995; Pasquier, 1999). The battery specifically consisted of the Dementia Rating Scale (DRS) (Mattis, 1988), the Digit Symbol subtest of the WAIS-III (Wechsler, 1997a), the Digit Span, Spatial Span, Letter-Number Sequencing, Logical Memory I and II, and Visual Reproduction I and II subtests of the WMS-III (Wechsler, 1997b), a 15-item version of the Boston Naming Test (BNT) (Kaplan et al., 1983), measures of phonemic word fluency (CFL) (Benton & Hamsher, 1983) and semantic word fluency (Spreen & Strauss, 1991), the Hopkins Verbal Learning Test (HVLT) (Brandt, 1991), the Executive Clock Drawing Task (CLOX) (Royall et al., 1998), Trail Making Test A from the Halstead Reitan test battery (Reitan & Wolfson, 1993), and a short form of the Token Test (Spellacy & Spreen, 1969). A measure of self-reported depression symptoms was also administered (Geriatric Depression Scale) (Yesavage, 1983).

Inclusion/Exclusion Criteria

Patients with MCI

Patients with MCI were included in this study if they were an ADRC participant receiving a diagnosis of amnestic MCI or normal control from March 2000 to September 2003 and had received at least one follow-up ADRC Consensus Diagnosis by December 2004. Amnestic MCI diagnosis was assigned based upon Mayo criteria (Petersen et al., 2001), which were operationalized as: (1) subjective memory complaint by the patient and/or an informant, (2) objective memory impairment falling approximately 1.5 standard deviations or more below age and education equivalent control performance on a neuropsychological measure of memory, (3) relatively normal performance in other cognitive domains, (4) relatively normal activities of daily living, and (5) lack of dementia, as reflected by a failure to meet NINCDS-ADRDA criteria for dementia (McKhann et al., 1984). Although no psychometric cutoff scores were employed to define “objective impairment,” the neuropsychologists on the ADRC consensus committee determined evidence of objective memory impairment using appropriate age-matched norms and in reference to a patient's educational and socioeconomic background. All MCI patients in this study received a Clinical Dementia Rating (CDR) staging of 0.5 (Morris, 1993) (Table 1). Thirteen of the 49 MCI patients were taking a cholinesterase inhibitor (9 donepezil, 3 galantamine, 1 rivastigmine) for the purpose of memory loss treatment at the time of the study.

Comparison of demographic information between normal control participants and MCI patients

Persons receiving a primary diagnosis of nonamnestic MCI and who were not subsequently diagnosed as amnestic MCI were excluded from the study. Other exclusion criteria for the total sample included suffering from a potentially treatable form of dementia, another neurodegenerative disease, another chronic debilitating neurological illness (i.e., cerebral palsy), cancer (except skin cancer), severe pulmonary, renal, or liver disease, cardiac disease, autoimmune disease, alcoholism, or conditions expected to cause death within 1 year. Patients suffering from untreated major depression (but not mild depression), any other severe psychiatric disorder, and/or severe behavioral problems were also excluded. Patients were not excluded for undergoing pharmacological treatment for memory loss. Patients receiving a Hachinski Ischemia Index score greater than 4 were excluded (Hachinski et al., 1975). This index score is derived from the number of vascular risk factors present, such as hypertension and prior stroke, and a score greater than 4 indicates significant risk for cognitive problems related to vascular changes as opposed to AD.

Normal controls

Normal controls were included if they were determined to have no memory or other cognitive impairment at consensus and did not fulfill criteria for MCI or dementia. Controls were closely matched to the MCI patient sample on demographic variables of age, gender, race, and education. Controls in the ADRC were excluded based upon the previously described exclusion criteria for MCI participants.

ADRC Consensus Diagnosis

Diagnostic characterization was assigned to all participants annually as a result of an ADRC Consensus Diagnostic conference. Diagnoses were assigned based upon the judgment of three neurologists (L.E.H., E.Y.Z., J.C.B.) and two clinical neuropsychologists (D.C.M., H.R.G.), all of whom have expertise in diagnoses of dementia and MCI. Within the consensus conference, all relevant clinical, laboratory, neuroimaging, and neuropsychological findings were discussed in order to assign diagnoses. In the cases of diagnostic disagreement, a majority rule was used to assign diagnoses.

Follow-up ADRC consensus diagnoses were assigned within the ADRC Consensus Conference based upon the results of ADRC assessments at one year and two years subsequent to initial diagnosis with amnestic MCI. All follow-up assessments were completed within a reasonable window from the anniversary date of the initial baseline ADRC assessment, with an average follow-up interval of 13 months (range 6–19 months). The follow-up consensus diagnostic outcomes of interest in MCI patients consist of the following:

  1. Static amnestic MCI (“MCI Nonconverters”). Participants with static amnestic MCI were determined to continue to meet criteria for amnestic MCI on follow-up evaluation.
  2. Conversion to Alzheimer's disease (“AD Converters”). Participants were felt to have converted from amnestic MCI to AD if sufficient declines in cognition and function were observed such that a person no longer met criteria for amnestic MCI but met NINCDS-ADRDA (McKhann et al., 1984) criteria for probable or possible AD.

Statistical Analyses

Initial analyses were performed to compare normal controls to individuals with MCI for purposes of sample characterization. Baseline group differences in demographic variables were compared using one-way analysis of variance (ANOVA) (age, education) with a Bonferroni correction or chi square (gender, race). Baseline group differences in ratings of cognitive severity, disease staging, and neuropsychological test performance were compared using one-way ANOVA with a Bonferroni correction at p ≤ .002.

Subsequently, the MCI group was examined with analyses comparing baseline characteristics of AD converters and MCI nonconverters. For purposes of these analyses, “baseline” refers to the assessment at which participants were identified as having amnestic MCI. Again, baseline group differences in demographic variables were compared using one-way ANOVA (age, education) with a Bonferroni correction or chi square (gender, race). Baseline group differences in ratings of cognitive severity, disease staging, and neuropsychological test performance were compared using one-way ANOVA with a Bonferroni correction at p ≤ .002. Prediction of eventual conversion to AD at either one- or two-year follow-up evaluations using baseline neuropsychological measures was computed using stepwise discriminant function analyses (DFA) (F in = 3.84; F out = 2.71).

RESULTS

Group Characterization at Study Entry

Forty-nine MCI participants from the UAB ADRC met all inclusion/exclusion criteria for this study. One person included in the study who received a baseline diagnosis of amnestic MCI, was recharacterized as nonamnestic MCI at one year follow-up, and then rediagnosed as amnestic MCI at two-year follow-up. However, this participant's outcome was considered to be stable given that the latest consensus diagnosis was in agreement with the amnestic MCI diagnosis at study entry.

These MCI participants were matched with healthy older adult controls from the ADRC for age, gender, race, and education. The results of matching are presented in Table 1, which demonstrates that there were no significant group differences for the basic demographic variables. The CDR staging, GDS, and MMSE scores are also presented in Table 1. Both the CDR and GDS demonstrated higher ratings of impairment in the MCI patients compared to controls, and the mean MMSE score was significantly lower in MCI versus controls. The Geriatric Depression Scale (Table 1) was on average higher in the MCI group versus controls, although the mean score of the MCI patients was below clinical cutoff for depression (Sheikh & Yesavage, 1986).

Neuropsychological performance of MCI and controls at study entry are presented in Table 2. After correcting for multiple comparisons, significantly lower average performance was observed for MCI patients versus controls on the DRS Total Score (p < .001), a semantic word fluency composite score (p < .001), Logical Memory Immediate and Delayed Recall and Percent Retention (p < .001), Visual Reproduction Immediate and Delayed Recall and Percent Retention (p < .001), HVLT Recall (p < .001), HVLT Discrimination Index (p = .001), and CLOX 2 (p = .002).

Neuropsychological performance comparison between normal control participants and MCI patients

Diagnostic Outcomes

All 49 MCI participants received an ADRC Consensus follow-up diagnosis for at least one year following their initial diagnosis with amnestic MCI, and 17 of the 49 (34.69%) received follow-up diagnoses for two years following initial MCI diagnosis. At one-year follow-up, eleven were characterized as having converted to AD (22.45% of the sample). Of the 17 participants with two-year follow-up data, two were characterized as having converted to AD (11.77% of the sample). Thus, the average annual rate of conversion to dementia in the MCI participants was 17.11%.

Comparison of demographic and clinical variables between AD converters and MCI nonconverters

Table 3 illustrates the comparison between AD converters and MCI nonconverters for baseline demographic variables, ratings of staging, MMSE, depression, APOE status, use of anti-dementia medications, Hachinski Ischemia Index scores, and histories of hypertension and diabetes. There were no demographic differences between AD converters and MCI nonconverters, although the majority of AD converters were White (12 of 13) (χ2 = 2.70, p = .10). All MCI participants had baseline CDR staging of 0.5 regardless of their follow-up consensus characterization status. The distribution of GDS ratings did not differ at baseline between converters and nonconverters (p > .10). MMSE scores were very similar at baseline between the converters and nonconverters (p > .10). Depression, as measured by the Geriatric Depression Scale, showed no significant group differences at baseline (p > .10). The APOE genotyping also showed no significant group differences in presence or absence of the epsilon-4 (ε4) gene. Anti-dementia medications were used more frequently at baseline by AD converters when compared to MCI nonconverters (p < .001). The distributions of Hachinski Ischemia Index scores and histories of hypertension and diabetes at baseline were not different between AD converters and MCI nonconverters (p > .10). No differences were found between converters and nonconverters on follow-up assessment interval.

Comparison of demographic and clinical information between MCI nonconverters and AD converters

Neuropsychological differences at baseline between AD converters and MCI nonconverters

Table 4 presents the baseline neuropsychological test performance differences for the AD converters and MCI nonconverters. After correction for multiple comparisons, none of the group comparisons on neuropsychological test measures demonstrated significance at p ≤ .002 (Bonferroni corrected). However, several comparisons demonstrated trends towards significance (p < .01) including lower mean performance at baseline in the AD converters on DRS Memory, Visual Reproduction II and Percent Retention, and DRS Initiation/Perseveration.

Neuropsychological performance comparison between MCI nonconverters and AD converters

Predictor Models of AD Conversion

A stepwise DFA was computed predicting group status (AD converter, MCI nonconverter) using baseline neuropsychological test performance. Potential predictors included measures on which a trend was observed for group differences (DRS Memory, Visual Reproduction II, Visual Reproduction Percent Retention, DRS Initiation/Perseveration). The overall DFA model was significant (p < .001) and included DRS Initiation/Perseveration (Step 1) and Visual Reproduction Percent Retention (Step 2). The model had a sensitivity of 76.9% and a specificity of 88.9% and correctly classified 85.7% of the MCI sample as either AD Converters or MCI Nonconverters.

In order to determine the influence of race on the DFA neuropsychological predictor model, a DFA was performed that entered the two neuropsychological predictor variables of conversion and race into the model. The model with race included correctly classified 85.7% of the participants, but sensitivity decreased to 69.2% while specificity increased to 91.7%. A discriminant function testing the influence of anti-dementia medication use on the prediction of dementia with the neuropsychological variables correctly classified 81.3% of the participants, with a sensitivity of 69.2% and specificity of 85.7%. Discriminant functions including history of hypertension and history of diabetes with the neuropsychological test predictors were identical to the discriminant function with the neuropsychological test predictors alone.

Neuropsychological Test Cut Scores for Predicting AD Conversion in MCI

Cut scores for the DRS Initiation/Perseveration and Visual Reproduction Percent Retention were determined by selecting raw scores at which 76.9% of the AD converters were identified and at which less than 20% of the MCI nonconverters were identified (approximating the sensitivity/specificity statistics for the DFA analysis). A combination of cut scores of 36 on the DRS Initiation/Perseveration and 26 on the Visual Reproduction Percent Retention resulted in a sensitivity of 76.9% and a specificity of 91.7%, with an overall correct classification of 87.8% of the sample. Using the Visual Reproduction cut score alone resulted in a sensitivity of 76.9%, a specificity of 91.2%, and overall correct classification of 83.7% (due to an increased false positive rate of 33.33%).

DISCUSSION

The current study investigated the ability of baseline neuropsychological measures to predict eventual risk for conversion to dementia in newly diagnosed MCI patients. We successfully modeled predictors of eventual “AD conversion” using baseline neuropsychological data, and were able to classify the vast majority of MCI patients using the Percent Retention score from the WMS-III Visual Reproduction subtest and the DRS Initiation/Perseveration Subscale score. In comparison, potential clinical and demographic predictor factors such as race, use of cholinesterase inhibitors at baseline, and histories of hypertension and diabetes at baseline did not improve the predictor model above and beyond the predictor findings with the neuropsychological tests. These findings have potential relevance regarding the utility of using neuropsychological test results at baseline to determine risk of eventual dementia conversion.

Our findings are in accord with previous studies using neuropsychological measures to predict subsequent dementia in memory-disordered patients. Tierney and colleagues (1996) found that delayed memory and mental control predicted dementia over two-year follow-up in a group of memory disordered persons not diagnosed with Mayo MCI criteria. Albert and colleagues (2001) later predicted dementia over a three-year follow up in memory disordered patients using memory and executive function test scores, generally in keeping with our findings. Jacobson and colleagues (2002) reported that asymmetry between verbal and visual abilities predicted dementia despite the lack of significant group differences in either measure alone. We instead, preferred to enter measures into the model that showed trends towards group differences. However, none of these prior studies investigated prediction of dementia for persons diagnosed with MCI using Mayo criteria. Thus, to our knowledge this study represents the first attempt to predict eventual dementia in MCI patients diagnosed with the Mayo criteria. Given the prevalence of the Mayo criteria for MCI (Petersen et al., 2001), these findings are likely of clinical utility for practitioners who evaluate and treat persons with MCI.

The findings of the current study offer potentially useful clinical data in determining risk of dementia conversion in MCI patients. At the time of clinical diagnosis, persons with amnestic MCI are considered to be at greater risk for developing clinically probable Alzheimer's disease (Petersen et al., 1999). In keeping with prior studies of MCI patients treated at tertiary care medical centers (Bowen et al., 1997; Bozoki et al., 2001; Petersen et al., 1999), we observed a 17.11% average rate of conversion to AD, with the bulk of conversions occurring within the first year following diagnosis (22.45% conversion rate within the first year). The neuropsychological variables that were used in the conversion classification equation were the DRS Initiation/Perseveration subscale and WMS-III Visual Reproduction Percent Retention. The DRS is a neuropsychological measure commonly used to reliably track progression of dementia stage (Mattis, 1988). Likewise, the WMS-III is a widely used battery of memory measures that is commonly used in assessment of dementia (Wechsler, 1997b). While the discriminant function can be used to compute centroid scores for predicting conversion, a more clinically useful approach is offered by using cut scores for predicting risk of conversion. Using a cutoff of 26% or less of the Percent Retention score for Visual Reproduction alone yielded 76.9% sensitivity and 91.2% specificity, although false positives were somewhat elevated (greater than 33%). Adding a cut score on the DRS Initiation/Perseveration of 36 reduces the false positive rate to 23.1%.

There is potential clinical benefit to identifying persons who are at risk of conversion to dementia using neuropsychological measures. Offering prognostic information to patients and their family members can aid in preparation of estate arrangements, consideration of issues of long-term care, and enhance vigilance for potential changes in instrumental activities of daily life (Marson, 2001; Marson et al., 2001). Furthermore, recent evidence has indicated that early treatment of MCI patients with donepezil has the potential to delay the progression to dementia within the first year of treatment, although after three years the delaying effect of donepezil was not observed (Petersen et al., 2005). Thus, psychometric approaches to detecting early risk of dementia such as the approach presented here have the potential to aid in early decision-making regarding treatment of MCI with a cholinesterase inhibitor.

Other clinical factors potentially related to memory loss in MCI were not as useful in predicting conversion to dementia. Use or nonuse of cholinesterase inhibitors did not add to prediction of conversion status in our MCI group. Interestingly, in our MCI patient sample, converters were more likely to have been taking a cholinesterase inhibitor at baseline—a logic that runs contrary to this class of medication having a benefit in terms of delaying onset of dementia. However, the intent of this study was not to evaluate the potential benefit of cholinesterase inhibitor use, and as such this finding should be considered with caution. This finding does suggest that patients nearing conversion are likely to have a more aggressive form of clinical memory loss that will result in cholinesterase use earlier on.

The race of persons with MCI also did not aid in prediction of conversion, although the majority of patients in the AD converter group were White, while a greater proportion of African Americans were present in the MCI nonconverter group (although this group difference did not reach significance). This disproportionate representation of African Americans in the nonconverter group did not appear to be related to a higher frequency of hypertension or diabetes in the nonconverter group [or the so-called “vascular cognitive impairment” presentation (Malouf & Birks, 2004)]. Furthermore, the over-representation of African-Americans in the nonconverter group did not appear to be due to misclassification of African-Americans as MCI on the basis of nonrepresentative neuropsychological test norms; comparison of African-American MCI patients to African-American controls revealed a very similar pattern of neuropsychological test deficits in the MCI patients as seen in the overall control versus MCI group comparisons. Furthermore, using the cut-score method for predicting group membership resulted in a very similar number of false positives in African American (9%) and White (8%) MCI patients. It would appear important to investigate the issue of potential differences in AD conversion risk in Whites and African Americans further.

Lastly, conversion to dementia in MCI appeared to occur with equal frequency in persons with and without vascular risk factors of diabetes and hypertension. Although these risk factors have been associated with vascular dementia (Hachinski et al., 1975) and can occur in conjunction with AD (McDowell, 2001; Mungas et al., 2002), in MCI these factors are less likely to contribute to dementia risk than memory loss. For instance, an earlier study demonstrated very similar findings to ours but in a more heterogeneous group of MCI participants defined by CDR scores = 0.5 without fulfillment of other Mayo criteria (DeCarli et al., 2004). The intent of our study was to present patients without significant vascular disease, and thus we employed an Ischemia index cutoff score of 4. It is possible that for patients with more significant vascular risk factors, these factors will have greater prognostic value for a dementia.

Findings from this study are limited due to a lack of autopsy and neuropathological findings to confirm our cases of probable AD. However, no reversions or recharacterizations have occurred in our sample following a consensus diagnosis of probable AD. Nonetheless, the current findings might not hold in samples where all cases of AD have been pathologically verified. It would also be desirable to perform retrospective analyses in samples that have undergone long-term clinical follow-up to determine the ultimate group status of all participants (Ganguli et al., 2004).

In conclusion, this study demonstrated that baseline neuropsychological measures are capable of discriminating persons with MCI who are likely to convert to probable AD over a two-year time-span of clinical follow-up. Such data could prove useful for identifying participants for prevention studies, for pharmacological interventions, and for clinical counseling regarding prognosis and planning. Future studies should employ longer follow-up periods, a broader array of neuropsychological measures, and other potential markers for conversion such as neuroimaging (Jack et al., 1999). There also appears to be a specific need to further investigate possible differences in MCI conversion to dementia in Whites and African Americans.

ACKNOWLEDGMENTS

This research was supported by grants from the National Institute on Aging (Alzheimer's Disease Research Center) (1P50 AG16582-01) and the National Institute of Mental Health (1R01 MH55247-01).

References

REFERENCES

Albert, M.S., Moss, M.B., Tanzi, R., & Jones, K. (2001). Preclinical prediction of AD using neuropsychological tests. Journal of the International Neuropsychological Society, 7, 631–639.CrossRefGoogle Scholar
Benton, A.L. & Hamsher, K. (1983). Multilingual Aphasia Examination. Iowa City, IA: AJA Associates.
Bowen, J., Teri, L., Kukull, W., McCormick, W., McCurrry, S., & Larson, E. (1997). Progression to dementia in patients with isolated memory loss. Lancet, 349, 763–765.Google Scholar
Bozoki, A., Giordani, B., Heidebrink, J.L., Berent, S., & Foster, N.L. (2001). Mild cognitive impairments predict dementia in nondemented elderly patients with memory loss. Archives of Neurology, 58, 411–416.Google Scholar
Brandt, J. (1991). The Hopkins Verbal Learning Test: Development of a new verbal memory test with six equivalent forms. The Clinical Neuropsychologist, 5, 124–142.Google Scholar
Butters, M., Salmon, D., & Butters, N. (1994). Neuropsychological assessment of dementia. In M. Storandt & G. VandenBos (Eds.), Neuropsychological assessment of dementia and depression in older adults: A clinician's guide (pp. 33–59). Washington, DC: American Psychological Association.
Daly, E., Zaitchik, D., Copeland, M., Schmahmann, J., Gunther, J., & Albert, M. (2000). Predicting conversion to Alzheimer disease using standardized clinical information. Archives of Neurology, 57, 675–680.CrossRefGoogle Scholar
DeCarli, C., Mungas, D., Harvey, D., Reed, B., Weiner, M., Chui, H., & Jagust, W. (2004). Memory impairment, but not cerebrovascular disease, predicts progression of MCI to dementia. Neurology, 63, 220–227.CrossRefGoogle Scholar
Folstein, M., Folstein, S., & McHugh, P. (1975). Mini-Mental State: A practical guide for grading the cognitive state of the patient for the physician. Journal of Psychiatry Research, 12, 189–198.CrossRefGoogle Scholar
Ganguli, M., Dodge, H., Shen, C., & DeKosky, S.T. (2004). Mild cognitive impairment, amnestic type: An epidemiological study. Neurology, 63, 115–121.Google Scholar
Hachinski, V.C., Iliff, L.D., Zilhka, E., Du Boulay, G.H., McAllister, V.L., Marshall, J., Russell, R.W., & Symon, L. (1975). Cerebral blood flow in dementia. Archives of Neurology, 32, 632–637.Google Scholar
Ingles, J.L., Fisk, J.D., Merry, H.R., & Rockwood, K. (2003). Five-year outcomes for dementia defined solely by neuropsychological test performance. Neuroepidemiology, 22, 172–178.Google Scholar
Jack, C.R., Jr., Petersen, R.C., Xu, Y.C., O'Brien, P.C., Smith, G.E., Ivnik, R.J., Boeve, B.F., Waring, S.C., Tangalos, E.G., & Kokmen, E. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology, 52, 1397–1403.Google Scholar
Jacobson, M.W., Delis, D.C., Bondi, M.W., & Salmon, D.P. (2002). Do neuropsychological tests detect preclinical Alzheimer's disease: Individual-test versus cognitive-discrepancy score analyses. Neuropsychology, 16, 132–139.CrossRefGoogle Scholar
Kaplan, E., Goodglass, H., & Weintraub, S. (1983). Boston Naming Test. Philadelphia: Lea & Febiger.
Kurz, X., Scuvee-Moreau, J., Vernooij-Dassen, M., & Dresse, A. (2003). Cognitive impairment, dementia and quality of life in patients and caregivers. Acta Neurologica Belgica, 103, 24–34.Google Scholar
Lange, K.L., Bondi, M.W., Salmon, D.P., Galasko, D., Delis, D.C., Thomas, R.G., & Thal, L.J. (2002). Decline in verbal memory during preclinical Alzheimer's disease: Examination of the effect of APOE genotype. Journal of the International Neuropsychological Society, 8, 943–955.Google Scholar
Lezak, M.D. (1995). Neuropsychological assessment (3rd ed.). New York: Oxford University Press.
Malouf, R. & Birks, J. (2004). Donepezil for vascular cognitive impairment. Cochrane Database System Review (1), CD004395.Google Scholar
Marquis, S., Moore, M.M., Howieson, D.B., Sexton, G., Payami, H., Kaye, J.A., & Camicioli, R. (2002). Independent predictors of cognitive decline in healthy elderly persons. Archives of Neurology, 59, 601–606.Google Scholar
Marson, D. (2001). Loss of financial capacity in dementia: Conceptual and empirical approaches. Aging, Neuropsychology and Cognition, 8, 164–181.CrossRefGoogle Scholar
Marson, D., Dymek, M., & Geyer, J. (2001). Informed consent, competency, and the neurologist. Neurologist, 7, 317–326.Google Scholar
Masur, D., Sliwinski, M., Lipton, R., Blau, A., & Crystal, H. (1994). Neuropsychological prediction of dementia and the absence of dementia in healthy elderly persons. Neurology, 44, 1427–1432.Google Scholar
Mattis, S. (1988). Dementia Rating Scale (DRS). Odessa, FL: Psychological Assessment Resources.
McDowell, I. (2001). Alzheimer's disease: Insights from epidemiology. Aging (Milano), 13, 143–162.Google Scholar
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. (1984). Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA work group under the auspices of the Department of Health and Human Services Task Force on Alzheimer's disease. Neurology, 34, 939–944.CrossRefGoogle Scholar
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43, 2412–2414.Google Scholar
Morris, J.C. (1997). Clinical Dementia Rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. International Psychogeriatrics, 9 Suppl. 1, 173–176; discussion 177–178.CrossRefGoogle Scholar
Mungas, D., Reed, B.R., Jagust, W.J., DeCarli, C., Mack, W.J., Kramer, J.H., Weiner, M.W., Schuff, N., & Chui, H.C. (2002). Volumetric MRI predicts rate of cognitive decline related to AD and cerebrovascular disease. Neurology, 59, 867–873.Google Scholar
Pasquier, F. (1999). Early diagnosis of dementia: Neuropsychology. Journal of Neurology, 246, 6–15.CrossRefGoogle Scholar
Petersen, R.C. (2000). Mild cognitive impairment: Transition between aging and Alzheimer's disease. Neurologia, 15, 93–101.Google Scholar
Petersen, R.C., Smith, G.E., Ivnik, R.J., Tangalos, E.G., Schaid, D.J., Thibodeau, S.N., Kokmen, E., Waring, S.C., & Kurland, L.T. (1995). Apolipoprotein E status as a predictor of the development of Alzheimer's disease in memory-impaired individuals. Journal of the American Medical Association, 273, 1274–1278.Google Scholar
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56, 303–308.CrossRefGoogle Scholar
Petersen, R.C., Stevens, J.C., Ganguli, M., Tangalos, E.G., Cummings, J.L., & DeKosky, S.T. (2001). Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 56, 1133–1142.Google Scholar
Petersen, R.C., Thomas, R.G., Grundman, M., Bennett, D., Doody, R., Ferris, S., Galasko, D., Jin, S., Kaye, J., Levey, A., Pfeiffer, E., Sano, M., van Dyck, C.H., & Thal, L.J. (2005). Vitamin E and donepezil for the treatment of mild cognitive impairment. New England Journal of Medicine, 352, 2379–2388.Google Scholar
Petersen, R.C., Waring, S.C., Smith, G.E., Tangalos, E.G., & Thibodeau, S.N. (1996). Predictive value of APOE genotyping in incipient Alzheimer's disease. Annals of the New York Academy of Science, 802, 58–69.CrossRefGoogle Scholar
Reisberg, B., Ferris, S., deLeon, M., & Crook, T. (1982). The Global Deterioration Scale for assessment of primary degenerative dementia. American Journal of Psychiatry, 139, 1136–1139.Google Scholar
Reitan, R.M. & Wolfson, D. (1993). The Halstead-Reitan Neuropsychological Test Battery: Theory and clinical interpretation. Tuscon, AZ: Neuropsychology Press.
Royall, D.R., Cordes, J.A., & Polk, M. (1998). CLOX: An executive clock drawing task. Journal of Neurology, Neurosurgery and Psychiatry, 64, 588–594.Google Scholar
Sheikh, J. & Yesavage, J. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. In T. Brink (Ed.), Clinical gerontology: A guide to assessment and intervention (pp. 165–173). New York: Haworth Press.
Spellacy, F. & Spreen, O. (1969). A short form of the Token Test. Cortex, 5, 390–397.CrossRefGoogle Scholar
Spreen, O. & Strauss, E. (1991). A compendium of neuropsychological tests. New York: Oxford University Press.
Storandt, M., Grant, E.A., Miller, J.P., & Morris, J.C. (2002). Rates of progression in mild cognitive impairment and early Alzheimer's disease. Neurology, 59, 1034–1041.Google Scholar
Tierney, M.C., Szalai, J.P., Snow, W.G., Fisher, R.H., Nores, A., Nadon, G., Dunn, E., & St George-Hyslop, P.H. (1996). Prediction of probable Alzheimer's disease in memory-impaired patients: A prospective longitudinal study. Neurology, 46, 661–665.CrossRefGoogle Scholar
Tuokko, H., Frerichs, R., Graham, J., Rockwood, K., Kristjansson, B., Fisk, J., Bergman, H., Kozma, A., & McDowell, I. (2003). Five-year follow-up of cognitive impairment with no dementia. Archives of Neurology, 60, 577–582.CrossRefGoogle Scholar
Wechsler, D. (1997a). Wechsler Adult Intelligence Scale—Third Edition. New York: Psychological Corporation.
Wechsler, D. (1997b). Wechsler Memory Scale—Third Edition. New York: Psychological Corporation.
Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.O., Nordberg, A., Backman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., de Leon, M., DeCarli, C., Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., van Duijn, C., Visser, P., & Petersen, R.C. (2004). Mild cognitive impairment—beyond controversies, towards a consensus: Report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240–246.Google Scholar
Yesavage, J. (1983). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17, 37–49.Google Scholar
Figure 0

Comparison of demographic information between normal control participants and MCI patients

Figure 1

Neuropsychological performance comparison between normal control participants and MCI patients

Figure 2

Comparison of demographic and clinical information between MCI nonconverters and AD converters

Figure 3

Neuropsychological performance comparison between MCI nonconverters and AD converters