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Preliminary Communication |

Within-Person Across-Neuropsychological Test Variability and Incident Dementia FREE

Roee Holtzer, PhD; Joe Verghese, MD; Cuiling Wang, PhD; Charles B. Hall, PhD; Richard B. Lipton, MD
[+] Author Affiliations

Author Affiliations: Departments of Neurology (Drs Holtzer, Verghese, and Lipton) and Epidemiology and Population Health (Drs Wang, Hall, and Lipton), Ferkauf Graduate School of Psychology (Dr Holtzer), and Institute for Aging Research (Dr Lipton), Albert Einstein College of Medicine, Yeshiva University, Bronx, New York.


JAMA. 2008;300(7):823-830. doi:10.1001/jama.300.7.823.
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Published online

Context Neuropsychological tests are used to predict and diagnose dementia. However, to our knowledge, no studies to date have examined whether within-person across-neuropsychological test variability predicts dementia.

Objective To examine whether within-person across-neuropsychological test variability predicts future dementia.

Design The Einstein Aging Study (EAS) is a population-based longitudinal study of aging and dementia located in Bronx County, New York. We used Cox proportional hazards models using age as the time scale to estimate hazard ratios (HRs) for performance on individual neuropsychological tests (Free and Cued Selective Reminding Test, Digit Symbol Substitution subtest of the Wechsler Adult Intelligence Scale Revised, and the Vocabulary subtest of the Wechsler Adult Intelligence Scale Revised) and for within-person across-neuropsychological test variability as predictors of incident dementia. Analyses were stratified by sex, and controlled for education and medical illness.

Setting and Participants A total of 1797 participants (age ≥70 years) enrolled in the EAS between October 1993 and December 2007. Participants seen for the baseline visit only (n = 750), prevalent dementia cases (n = 72), and those with missing follow-up information (n = 78) were excluded. A total of 897 individuals were included in this investigation. Participants had follow-up visits every 12 to 18 months.

Main Outcome Measure Incident dementia.

Results Sixty-one cases of incident dementia were identified during follow-up (mean [SD], 3.3 [2.4] years), of which 26 were in the highest quartile of within-person across-neuropsychological test variability. Adjusting for sex, education, and medical illness, variability was associated with incident dementia (HR for 1-point difference in variability, 3.93 [95% confidence interval {CI}, 2.04-7.56]). The association persisted even after adjusting for level of performance on individual neuropsychological tests (HR for 1-point difference in variability, 2.10 [95% CI, 1.04-4.23]). Comparing Cox models using neuropsychological tests with and without within-person across-neuropsychological test variablity showed that the former improved the prediction of dementia. Sensitivity in a model predicting dementia at 1 year also improved when neuropsychological test variability was included.

Conclusions In this population, within-person across-neuropsychological test variability was associated with development of incident dementia independent of neuropsychological test performance. This finding needs to be confirmed in future studies.

Figures in this Article

Developing strategies to improve the prediction and diagnosis of dementia has paramount therapeutic and public health implications.1 Neuropsychological tests are used to evaluate the diagnosis of dementia,2,3 the transitional stages that precede it, such as mild cognitive impairment,4,5 and cognitive changes over time.6 Level of performance on tests of memory7,8 and executive function9,10 has been reported to predict future dementia, and memory declines more rapidly up to 7 years prior to diagnosis of dementia.11,12

When neuropsychological tests are used for diagnostic purposes, an individual's level of performance on specific tests is measured against healthy normative samples to determine cognitive impairment.13,14 However, this approach does not take into account intraindividual variability in cognitive function. The taxonomy of intraindividual variability, considered as inconsistency in cognitive performance within a person, includes the following definitions: (1) variability on the same cognitive task across multiple assessments over a long period (such variability may be high in healthy individuals across the life span15); (2) variability on repeated trials of a single cognitive task administered on 1 occasion or over short periods (cross-sectional studies show that this form of variability is increased in aging16 and dementia,17,18 but has shown variable results in individuals with mild cognitive impairment19,20); and (3) variability in performance across neuropsychological tests administered in a single session (such variability is expected in the normal population,21,22 including older adults without dementia,23 reflecting the individual's relative cognitive strengths and weaknesses). This study focused on within-person across-neuropsychological test variability because it can be estimated directly from standardized clinical neuropsychological procedures.

Increased within-person across-neuropsychological test variability has been related to poor cognitive function in normal older adults in cross-sectional studies.23 Accordingly, we examined whether increased within-person across-neuropsychological test variability was associated with future dementia after controlling for level of performance on individual neuropsychological tests. We also assessed whether variability improved the sensitivity and specificity of a model predicting development of dementia 1 year later.

Study Population

Participants were enrolled in the Einstein Aging Study (EAS), a longitudinal study of aging and dementia located at the Albert Einstein College of Medicine in Bronx County, New York. The study design, recruitment, and follow-up methods have been previously described.24,25 Briefly, the EAS used telephone-based screening procedures to recruit and follow-up a community-based cohort since 1993. The primary aim of the EAS was to identify risk factors for dementia. Eligibility criteria required that participants be aged 70 years or older, reside in the Bronx, and speak English. Exclusion criteria included severe audiovisual disturbances that would interfere with completion of neuropsychological tests, inability to ambulate even with a walking aid or in a wheelchair, and institutionalization. Potential participants older than 70 years from the Centers for Medicare & Medicaid Services population lists of Medicare-eligible individuals were first contacted by letter, then by telephone, explaining the purpose of the study. The telephone interview included verbal consent, a brief medical history questionnaire, and telephone-based cognitive screening tests.24 Following the interview, an age-stratified sample of individuals who matched on a computerized randomization procedure was invited for further evaluation at the medical center. This procedure was implemented to ensure that the individuals included in the EAS represented the Bronx population at that age stratum, and that those who agreed to participate were not different from nonresponders in terms of key demographic characteristics. Written informed consent was obtained at clinic visits according to study protocols and approved by the Committee on Clinical Investigation (institutional review board of the Albert Einstein College of Medicine).

A total of 1797 participants were enrolled in the EAS between October 1993 and December 2007. Participants seen for the baseline visit only (n = 750), prevalent dementia cases (n = 72), and those with missing information (n = 78) were excluded. Hence, a total of 897 participants were included in this investigation. Participants had follow-up visits every 12 to 18 months, at which they underwent detailed neurological and neuropsychological evaluations.

Algorithmic Diagnosis of Dementia

To address the issue of diagnostic circularity, we derived an algorithmic diagnosis of dementia that was independent of the neuropsychological test performance. Two conditions had to be met. First, participants had to make 8 or more errors on the Blessed Information Memory Concentration test (BIMC; best score: 0 errors and worst possible score: 32 errors).26 This test has high test-retest reliability (0.86) and correlates well with the pathology of Alzheimer disease.27,28 (The BIMC test is used to screen for cognitive impairment on the telephone interview at baseline and the in-person interviews in the EAS, but the scores are not used when assigning dementia diagnosis in consensus case conferences.24) Second, impairments on basic activities on the Lawton-Brody activities of daily living,29 secondary to cognitive impairment, had to be documented using previously described procedures.30 Incident dementia using the above algorithmic procedures was used as the main outcome measure in the primary analyses reported herein. Agreement between the algorithmic and clinical diagnosis of dementia was 96% in this cohort.

Clinical Diagnosis of Dementia

The EAS participants suspected to have dementia received a complete diagnostic workup as previously described for this cohort.31 Diagnoses of dementia were assigned according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition,32 at case conferences attended by at least 1 study neurologist, a neuropsychologist, and a geriatric nurse or social worker. Alzheimer disease was diagnosed according to the criteria for probable disease detailed by the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer Disease and Related Disorders Association.3 The State of California Alzheimer Disease Diagnostic and Treatment Centers criteria was used to assign diagnoses of probable, possible, or mixed-vascular dementia.33 In individuals diagnosed with dementia, neuroimaging was used to help allocate the diagnosis of probable Alzheimer disease or probable vascular dementia. We have reported good agreement between clinical diagnoses of Alzheimer disease,34 vascular dementia,31 and dementia with Lewy bodies,35 and pathological findings in our study. For the purpose of this analysis, clinical diagnosis of dementia subtypes was used only in secondary analyses to explore whether associations with variability varied as a function of diseases processes. Hence, only pure subtypes (Alzheimer and vascular) but not mixed were considered in the exploratory analyses.

Neuropsychological Assessment

The tests included in this battery have been validated for use in the aging population in our and other aging studies.8,36,37 Our recent studies reveal that factor analysis of the neuropsychological test battery consistently yielded 3 empirically derived and statistically orthogonal cognitive domains including verbal IQ, attention/executive function, and memory.38,39 For the purpose of this study, we identified 3 tests that represented the 3 cognitive domains mentioned above, and were available for all 897 participants included in this study. The Vocabulary (total score) subtest of the Wechsler Adult Intelligence Scale Revised (WAIS-R40), considered a hold test in that it is not sensitive to the effect of aging and age-related diseases, represented the verbal IQ domain. The Digit Symbol Substitution subtest of the WAIS-R40 is commonly used to assess attention and executive function. The Free and Cued Selective Reminding Test (FCSRT; free recall) is commonly used to assess verbal memory and is sensitive to dementia.41

Estimate of Within-Person Variability Across Neuropsychological Tests

The method used herein to estimate within-person across-test variability was described in other studies as well.23,42 Assumptions of normality were met for the distribution of scores on the FCSRT, WAIS-R Vocabulary, and WAIS-R Digit Symbol Substitution tests. The raw scores of each test were z transformed on the basis of the distribution of the entire sample (n = 897). Then, the z-transformed test scores were used to calculate within-person variability across the 3 tests using this equation. Variability = 

where Zik is the kth cognitive test score for ith individual. Here, k = 1, . . . , K, K = 3 (FCSRT, WAIS-R Vocabulary, and WAIS-R Digit Symbol Substitution) and

is the individual's mean z-transformed score based on the 3 tests.

Illness Index

Consistent with our previous studies,38,39,43 dichotomous rating (present or absent) of diabetes, chronic heart failure, arthritis, hypertension, depression, stroke, Parkinson disease, chronic obstructive pulmonary disease, angina, and myocardial infarction was used to calculate an illness index summary score (range, 0-10). Medical history was obtained from multiple sources including significant others and family physicians when available. The study physician obtained the medical history during the neurological examination and trained research assistants conducted the structured clinical interview. Smoking was ascertained but did not contribute to variability in incident dementia in this study and was not included in the analyses reported herein.

Statistical Analyses

We performed 3 Cox proportional hazards regression analyses to assess the relationship between neuropsychological tests and dementia. First, we examined whether level of performance on the neuropsychological tests at baseline predicted the development of dementia. Second, we examined the association between within-person across-neuropsychological test variability and the development of dementia. Third, we assessed whether within-person across-neuropsychological variability provided incremental prediction of incident dementia above and beyond what was predicted by the absolute level of performance on the neuropsychological tests by controlling for performance differences on the WAIS-R Vocabulary, FCSRT, and WAIS-R Digit Symbol Substitution. Individuals who died before developing dementia were censored at the time of their last study visit when their status as not having dementia was determined.

To assess the amount of variability of development of incident dementia explained by the models, we compared the goodness of fit of nested Cox proportional hazards models using the partial likelihood ratio test.44 We compared a model with the 3 individual neuropsychological tests with a model that added within-person across-neuropsychological test variability. Finally, using the McNemar test we examined whether within-person across-neuropsychological test variability significantly improved sensitivity for the prediction of new cases of dementia within 1 year.

We used age as the time scale because it is considered more appropriate than follow-up time in cohort studies.45 When age serves as the time scale, the hazard function can be directly interpreted as the age-specific incidence function and age is accounted for in the nonparametric term of the hazard function, providing a more flexible and effective control of age.45 Time to event was from age at the baseline assessment accounting for the left truncation at study inclusion to age at which diagnosis of dementia was ascertained or to final study contact for participants without dementia. In addition, all multivariate analyses were stratified by sex and controlled for education and medical illness. For all primary analyses, incident dementia was defined using the algorithmic procedures described earlier. Proportional hazards assumptions of the models were examined analytically and graphically and were met.

Finally, Cox proportional hazards regression analysis with age as the time scale was used to examine whether within-person across-neuropsychological test variability predicted incident dementia subtypes (eg, vascular and Alzheimer) defined by consensus diagnosis as previously described. Given the low number of incident cases of Alzheimer dementia and vascular dementia, as well as concerns for potential diagnostic circularity, these analyses were considered exploratory. All analyses were performed using SAS statistical software version 9.1 (SAS Institute Inc, Cary, North Carolina) and S-Plus version 8.0 (Insightful Corp, Seattle, Washington). The significance level was set at .05 and all tests were 2-sided.

Demographics

Participants were community residents who were relatively healthy and had normal global mental functioning based on BIMC scores that were within the normal range (Table). Of the 897 participants, there were 61 cases of incident dementia (6.8%) (defined by the algorithmic procedure described earlier) identified during the follow-up period (mean [SD], 3.3 [2.4] years). On the basis of the consensus clinical diagnostic procedures, 47 participants developed incident dementia of the Alzheimer type and 18 participants developed incident vascular dementia. During the study, 128 individuals died, as expected for the age of this cohort. Of these, 18 had developed incident dementia.

Table Graphic Jump LocationTable. Summary of Participant Characteristics per Quartile of Within-Person Across-Neuropsychological Test Variability at Baseline
Prediction of Dementia

All Cox proportional hazards regression models used age as the time scale and were adjusted for sex, education, and medical illness. In the first model, higher scores on the FCSRT (hazard ratio [HR] for 1-point difference on the test, 0.87 [95% confidence interval {CI}, 0.84-0.91]; P < .001) and WAIS-R Digit Symbol Substitution (HR for 1-point difference on the test, 0.97 [95% CI, 0.94-1.00]; P = .04) were associated with a lower risk of developing dementia. Level of performance on the WAIS-R Vocabulary test was not associated with future dementia (HR for 1-point difference on the test, 1.01 [95% CI, 0.98-1.03]; P = .67).

The second Cox proportional hazards regression analysis also used age as the time scale and adjusted for sex, education, and medical illness. Within-person across-neuropsychological test variability was significantly associated with incident dementia defined algorithmically (HR for 1-point difference in variability, 3.93 [95% CI, 2.04-7.56]; P < .001). Risk of dementia increased as a function of greater within-person variability.

The third Cox proportional hazards regression analysis controlled for each of the neuropsychological test scores to determine the independent association of variability with incident dementia. The model used age as the time scale and adjusted for sex, education, and medical illness. Similar to the previous analysis, the results revealed that higher scores on the FCSRT (HR for 1-point difference on the test, 0.89 [95% CI, 0.85-0.92]; P < .001) and WAIS-R Digit Symbol Substitution (HR for 1-point difference on the test, 0.97 [95% CI, 0.94-1.00]; P = .04) were associated with lower risk of incident dementia, and the WAIS-R Vocabulary test was not associated with it (HR for 1-point difference on the test, 1.00 [95% CI, 0.98-1.03]; P = .81). Importantly, even after controlling for individual neuropsychological test scores, within-person across-neuropsychological test variability remained significantly associated with incident dementia (HR for 1-point difference in variability, 2.10 [95% CI, 1.04-4.23]; P = .03). The HR of variability was lower after adjusting for performance level on the individual tests, as expected, but the correlations between variability and performance on the neuropsychological tests were relatively low (FCSRT, r = −0.21, P < .001; WAIS-R Digit Symbol Substitution, r = −0.12, P < .001; WAIS-R Vocabulary, r = −0.18, P < .001), suggesting that these measures were not collinear. The lower, but significant HR for within-person across-neuropsychological test variability in the third compared with the second Cox analysis, along with HRs for level of performance on the individual neuropsychological tests that were similar in the 2 analyses, are evidence that part, but not all, of the effect of variability is mediated through changes in levels of cognitive performance.

We used the partial likelihood ratio test44 to examine whether adding within-person across-neuropsychological test variability to neuropsychological test performance improved prediction of dementia compared with using neuropsychological test performance alone. The results were significant (log partial likelihood ratio with and without variability, respectively, −200.5 and −202.6; χ21 = 4.19, P = .04), indicating that prediction of incident dementia was improved by adding within-person across-neuropsychological test variability. We also examined whether within-person across-neuropsychological test variability improved the sensitivity for the prediction of dementia within 1 year. In a model that included the 3 individual neuropsychological tests and controlled for age, sex, education, and disease illness, a score combining the coefficients of all covariates in the model that resulted in 80% specificity for detecting dementia yielded 83% sensitivity for predicting dementia within 1 year. Including within-person across-neuropsychological test variability in this model, maintaining specificity at 80%, significantly increased the sensitivity for predicting dementia within 1 year to 88% (McNemar test, P = .01).

Kaplan-Meier curves were used to delineate the cumulative risk of incident dementia using age as the time scale. Within-person across-neuropsychological test variability dichotomized into the highest (ie, worst) quartile vs the rest revealed that (consistent with the previous results) higher variability was associated with increased risk of incident dementia (HR, 2.25; 95% CI, 1.32-3.86) (Figure, A). Additionally, Kaplan-Meier curves for the FCSRT (HR, 4.77; 95% CI, 2.80-8.10), WAIS-R Digit Symbol Substitution (HR, 2.43; 95% CI, 1.44-4.09), and WAIS-R Vocabulary tests (HR, 1.51; 95% CI, 0.88-2.57) dichotomized into the lowest (ie, worst) quartile vs the rest are illustrated in the Figure, B-D, respectively.

Place holder to copy figure label and caption
Figure. Kaplan-Meier Curves for the Cumulative Risk of Incident Dementia
Graphic Jump Location

Variability denotes within-person across-neuropsychological test variability. Increased variability denotes worse cognitive function. CI indicates confidence interval; HR, hazard ratio; WAIS-R, Wechsler Adult Intelligence Scale Revised.

Exploratory Analyses Using Clinical Consensus Diagnosis of Dementia Subtypes

Cox proportional hazards regression analyses with age serving as the time scale, adjusting for sex, education, and illness index, were used to examine whether associations between variability and clinical diagnosis of dementia using consensus case conference procedures varied as a function of dementia subtype. Variability predicted development of both incident dementia of the Alzheimer type (n = 47; HR, 3.63 [95% CI, 1.79-7.37]), and incident vascular dementia (n = 18; HR, 5.26 [95% CI, 1.70-16.30]). The HR for Alzheimer disease after adjusting for neuropsychological test results was 1.68 (95% CI, 0.80-3.55; P = .17); these results are comparable with dementia overall but power was limited to demonstrate a significant difference. The disease processes underlying pure subtypes of Alzheimer dementia and vascular dementias are different. Thus, it appears that variability is sensitive to dementia irrespective of disease subtype.

Our assessment of intraindividual variability addresses within-person differences in cognitive function and its use as a marker of pathology.46 The taxonomy of intraindividual variability offers several operational definitions for this construct.47 To our knowledge, this is the first study to demonstrate that within-person across-neuropsychological test variability is associated with incident dementia in a population-based cohort aged 70 years or older, even after adjusting for level of performance on each individual neuropsychological test.

The premise guiding the study was that variability or inconsistency in performance across neuropsychological tests would increase in the preclinical stages of dementia compared with normal aging. This, in part, is attributed to the decline in memory function years before the diagnosis of dementia.11 As opposed to summary scores or intraindividual indices that estimate function within 1 cognitive domain, within-person across-neuropsychological test variability may be conceptualized as a single representation of variability across multiple domains subserved by several cortical regions and networks. In this context, this form of cognitive variability may be considered a signature of decline in cerebral integrity in the early stages of dementing illnesses.

In choosing the individual tests used to calculate within-person across-neuropsychological test variability, we aimed to maximize the number of participants, represent different cognitive domains, and increase generalizability by including a few easily administered tests that are commonly used in other studies and by clinicians assessing cognitive impairment in elderly individuals. Within-person across-neuropsychological test variability can be computed using tests other than those used in this study. Further, this form of cognitive variability can be estimated using standard and widely used clinical and neuropsychological assessment procedures that are typically given in 1 testing session. Hence, the potential clinical utility of this aspect of cognitive function is quite appealing because it requires no changes to standard assessment procedures in aging studies or assessment of cognitive disorder in clinic settings. It is noteworthy that variability on subdomains of the Mini-Mental State Examination48 was related to short-term decline on a global measure of cognitive function in a small cohort of centenarians.42 Hence, examining across-cognitive domain variability in screening measures, including those currently used to support the diagnosis of mild cognitive impairments,49 is of interest.

Research and theories concerning within-person variability on single measures, although relatively recent, suggest that increased inconsistency represents impaired top-down executive control processes50 subserved by frontal regions.51,52 However, at present, little is known about within-person across-cognitive test variability, the theoretical and neuroanatomical basis for this putative construct. Our findings suggest that even though substantial in older adults without dementia,23 within-person across-neuropsychological test variability may be pathological when a certain threshold is exceeded. It is noteworthy that variability was sensitive to both Alzheimer and vascular dementia subtypes, which vary in terms of their etiology and cognitive profile.33,53,54 Therefore, as a signature of early decline in global cerebral integrity, variability may capture the summation of differing sensitivities of brain regions and networks to various disease processes as opposed to estimating the effect of disease on a single brain region. The relatively low correlations between variability and the level of performance on the individual neuropsychological tests appear to support this notion.

It is of further interest to examine whether and the extent to which different aspects of intraindividual variability in cognitive functions are related in terms of the theory, underlying mechanism, and utility in predicting outcomes of interest. For instance, research suggests that individuals are not consistently diagnosed with mild cognitive impairment across multiple sessions.55,56 Although some fluctuations may be attributed to error in measurement and limitations of this construct, it would be of interest to examine the relationship between across-testing and within-testing session variability and whether the latter improves understanding and prediction of transitional stages in cognitive aging.

The limitations of the study should be considered. First, the sample, although representative of the Bronx, consisted of volunteers who resided in the community and who were relatively healthy and willing to travel to the medical center. Hence, the generalizability of the findings may potentially be limited. Second, the diagnosis of incident dementia was based on an algorithmic procedure that was independent of neuropsychological test scores and consensus diagnosis. Hence, diagnostic circularity is less likely to confound the findings. However, to examine longitudinal associations between this form of variability and clinical diagnosis of dementia, within-person across-neuropsychological test variability ideally should be derived from tests that are not part of the clinical diagnostic procedures. Third, assignment of subtypes of dementia is fallible. Although the diagnoses were made according to standardized criteria, some misclassification is inevitable. Further, differential associations between variability and dementia subtypes may be demonstrated, perhaps when using neuropsychological tests that are more sensitive to either vascular or Alzheimer diseases.33,53,54 Fourth, attrition is a major issue of concern in any longitudinal study. A significant number of individuals enrolled in the EAS were not eligible to participate in this investigation because they had only the baseline evaluation and were awaiting the next yearly visit. However, the subsample included in this study was not different from the entire EAS cohort in terms of age, sex distribution, and level of education (data available from author on request). Nonetheless, the potential for selection bias should be considered. We emphasize that a single variability measure cannot replace a complete neuropsychological examination, nor do we advocate that 3 neuropsychological tests are sufficient to provide adequate assessment of an individual's cognitive function. Instead, we propose that measures of within-person across-test variability be viewed as complementary to standard assessment procedures that are used to predict future risk of dementia. Finally, our results were identified in a single population; these results should be validated in a new population and their value in discriminating between populations that will and will not develop dementia should be defined before intraperson across-neuropsychological test variability is used routinely in clinical settings.

In summary, within-person across-neuropsychological test variability was associated with development of dementia independently of performance of the neuropsychological tests. This finding needs to be replicated in different populations before it is applied in a clinical setting.

Corresponding Author: Roee Holtzer, PhD, Albert Einstein College of Medicine, Yeshiva University, Rousso Bldg, Room 306, 1165 Morris Park Ave, Bronx, NY 10461 (rholtzer@aecom.yu.edu).

Authors Contributions: Dr Holtzer had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Holtzer, Verghese.

Acquisition of data: Lipton, Verghese.

Analysis and interpretation of data: Holtzer, Verghese, Hall, Wang, Lipton

Drafting of the manuscript: Holtzer, Verghese

Critical revision of the manuscript for important intellectual content: Holtzer, Verghese, Hall, Wang, Lipton,

Statistical analysis: Wang, Hall, Holtzer, Verghese.

Obtained funding: Lipton.

Administrative, technical, or material support: Verghese, Lipton.

Study supervision: Holtzer, Verghese

Financial Disclosures: None reported.

Funding/Support: The Einstein Aging Study is supported by the National Institute on Aging program project grant AGO3949. Dr Holtzer is supported by the National Institute on Aging Paul B. Beeson Award K23 AG030857. Dr Verghese is supported by the National Institute on Aging grant AG025119.

Role of the Sponsor: The National Institute on Aging did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

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Schretlen DJ, Munro CA, Anthony JC, Pearlson GD. Examining the range of normal intraindividual variability in neuropsychological test performance.  J Int Neuropsychol Soc. 2003;9(6):864-870
PubMed   |  Link to Article
Lipton RB, Katz MJ, Kuslansky G,  et al.  Screening for dementia by telephone using the memory impairment screen.  J Am Geriatr Soc. 2003;51(10):1382-1390
PubMed   |  Link to Article
Verghese J, Katz MJ, Derby CA, Kuslansky G, Hall CB, Lipton RB. Reliability and validity of a telephone-based mobility assessment questionnaire.  Age Ageing. 2004;33(6):628-632
PubMed   |  Link to Article
Blessed G, Tomlinson BE, Roth M. The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects.  Br J Psychiatry. 1968;114(512):797-811
PubMed   |  Link to Article
Fuld P. Psychological testing in the differential diagnosis of the dementias. In: Katzman TR, Bick KL, eds. Alzheimer's Disease: Senile Dementia and Related Disorders. Vol 7. New York, NY: Raven Press; 1978:185-193
Grober E, Dickson D, Sliwinski MJ,  et al.  Memory and mental status correlates of modified Braak staging.  Neurobiol Aging. 1999;20(6):573-579
PubMed   |  Link to Article
Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living.  Gerontologist. 1969;9(3):179-186
PubMed   |  Link to Article
Sarazin M, Berr C, De Rotrou J,  et al.  Amnestic syndrome of the medial temporal type identifies prodromal AD: a longitudinal study.  Neurology. 2007;69(19):1859-1867
PubMed   |  Link to Article
Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H. Abnormality of gait as a predictor of non-Alzheimer's dementia.  N Engl J Med. 2002;347(22):1761-1768
PubMed   |  Link to Article
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994
Chui HC, Victoroff JI, Margolin D, Jagust W, Shankle R, Katzman R. Criteria for the diagnosis of ischemic vascular dementia proposed by the state of California Alzheimer's disease diagnostic and treatment centers.  Neurology. 1992;42(3 pt 1):473-480
PubMed   |  Link to Article
Crystal HA, Dickson D, Davies P, Masur D, Grober E, Lipton RB. The relative frequency of “dementia of unknown etiology” increases with age and is nearly 50% in nonagenarians.  Arch Neurol. 2000;57(5):713-719
PubMed   |  Link to Article
Verghese J, Crystal HA, Dickson DW, Lipton RB. Validity of clinical criteria for the diagnosis of dementia with Lewy bodies.  Neurology. 1999;53(9):1974-1982
PubMed   |  Link to Article
Katzman R, Aronson M, Fuld P,  et al.  Development of dementing illnesses in an 80-year-old volunteer cohort.  Ann Neurol. 1989;25(4):317-324
PubMed   |  Link to Article
Sliwinski M, Buschke H, Stewart WF, Masur D, Lipton RB. The effect of dementia risk factors on comparative and diagnostic selective reminding norms.  J Int Neuropsychol Soc. 1997;3(4):317-326
PubMed
Holtzer R, Friedman R, Lipton RB, Katz M, Xue X, Verghese J. The relationship between specific cognitive functions and falls in aging.  Neuropsychology. 2007;21(5):540-548
PubMed   |  Link to Article
Holtzer R, Verghese J, Xue X, Lipton RB. Cognitive processes related to gait velocity: results from the Einstein Aging Study.  Neuropsychology. 2006;20(2):215-223
PubMed   |  Link to Article
Wechsler D. Wechsler Adult Intelligence Scale-Revised. New York, NY: Psychological Corp; 1981
Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing.  Neurology. 1988;38(6):900-903
PubMed   |  Link to Article
Kliegel M, Sliwinski M. MMSE cross-domain variability predicts cognitive decline in centenarians.  Gerontology. 2004;50(1):39-43
PubMed   |  Link to Article
Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia.  J Neurol Neurosurg Psychiatry. 2007;78(9):929-935
PubMed   |  Link to Article
Silvey SD. Statistical Inference. London, England: Chapman & Hall; 1975
Thiébaut AC, Benichou J. Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study.  Stat Med. 2004;23(24):3803-3820
PubMed   |  Link to Article
Stuss DT, Murphy KJ, Binns MA, Alexander MP. Staying on the job: the frontal lobes control individual performance variability.  Brain. 2003;126(pt 11):2363-2380
PubMed   |  Link to Article
Li SC, Huxhold O, Schmiedek F. Aging and attenuated processing robustness: evidence from cognitive and sensorimotor functioning.  Gerontology. 2004;50(1):28-34
PubMed   |  Link to Article
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed   |  Link to Article
Nasreddine ZS, Phillips NA, Bedirian V,  et al.  The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.  J Am Geriatr Soc. 2005;53(4):695-699
PubMed   |  Link to Article
West R, Murphy KJ, Armilio ML, Craik FI, Stuss DT. Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control.  Brain Cogn. 2002;49(3):402-419
PubMed   |  Link to Article
Bellgrove MA, Hester R, Garavan H. The functional neuroanatomical correlates of response variability: evidence from a response inhibition task.  Neuropsychologia. 2004;42(14):1910-1916
PubMed   |  Link to Article
Stuss DT, Toth JP, Franchi D, Alexander MP, Tipper S, Craik FI. Dissociation of attentional processes in patients with focal frontal and posterior lesions.  Neuropsychologia. 1999;37(9):1005-1027
PubMed   |  Link to Article
Jokinen H, Kalska H, Mantyla R,  et al.  Cognitive profile of subcortical ischaemic vascular disease.  J Neurol Neurosurg Psychiatry. 2006;77(1):28-33
PubMed   |  Link to Article
McPherson SE, Cummings JL. Neuropsychological aspects of vascular dementia.  Brain Cogn. 1996;31(2):269-282
PubMed   |  Link to Article
Bischkopf J, Busse A, Angermeyer MC. Mild cognitive impairment—a review of prevalence, incidence and outcome according to current approaches.  Acta Psychiatr Scand. 2002;106(6):403-414
PubMed   |  Link to Article
Collie A, Maruff P, Currie J. Behavioral characterization of mild cognitive impairment.  J Clin Exp Neuropsychol. 2002;24(6):720-733
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure. Kaplan-Meier Curves for the Cumulative Risk of Incident Dementia
Graphic Jump Location

Variability denotes within-person across-neuropsychological test variability. Increased variability denotes worse cognitive function. CI indicates confidence interval; HR, hazard ratio; WAIS-R, Wechsler Adult Intelligence Scale Revised.

Tables

Table Graphic Jump LocationTable. Summary of Participant Characteristics per Quartile of Within-Person Across-Neuropsychological Test Variability at Baseline

References

DeCarli C. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment.  Lancet Neurol. 2003;2(1):15-21
PubMed   |  Link to Article
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 4th ed, text revision. Washington, DC: American Psychiatric Association; 2000
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease.  Neurology. 1984;34(7):939-944
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Petersen RC, Doody R, Kurz A,  et al.  Current concepts in mild cognitive impairment.  Arch Neurol. 2001;58(12):1985-1992
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Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome.  Arch Neurol. 1999;56(3):303-308
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Potter GG, Attix DK. An integrated model for geriatric neuropsychological assessment. In: Attix DK, Welsh-Bohmer KA, eds. Geriatric Neuropsychology: Assessment and Intervention. New York, NY: Guilford Publications; 2006:5-26
Grober E, Lipton RB, Hall C, Crystal H. Memory impairment on free and cued selective reminding predicts dementia.  Neurology. 2000;54(4):827-832
PubMed   |  Link to Article
Masur DM, Sliwinski M, Lipton RB, Blau AD, Crystal HA. Neuropsychological prediction of dementia and the absence of dementia in healthy elderly persons.  Neurology. 1994;44(8):1427-1432
PubMed   |  Link to Article
Albert MS, Moss MB, Tanzi R, Jones K. Preclinical prediction of AD using neuropsychological tests.  J Int Neuropsychol Soc. 2001;7(5):631-639
PubMed   |  Link to Article
Bäckman L, Jones S, Berger AK, Laukka EJ, Small BJ. Cognitive impairment in preclinical Alzheimer's disease: a meta-analysis.  Neuropsychology. 2005;19(4):520-531
PubMed   |  Link to Article
Hall CB, Ying J, Kuo L,  et al.  Estimation of bivariate measurements having different change points, with application to cognitive ageing.  Stat Med. 2001;20(24):3695-3714
PubMed   |  Link to Article
Hall CB, Ying J, Kuo L, Lipton RB. Bayesian and profile likelihood change point methods for modeling cognitive function over time.  Computational Statistics Data Analysis. 2003;42:91-109
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Lezak MD, Diane BH, Loring DW. Neuropsychological Assessment. 4th ed. New York, NY: Oxford University Press; 2004
Mitrushina M, Boone KB, Razani J, D’Elia L. Handbook of Normative Data for Neuropsychological Assessment. 2nd ed. New York, NY: Oxford University Press; 2005
Salthouse TA. Implications of within-person variability in cognitive and neuropsychological functioning for the interpretation of change.  Neuropsychology. 2007;21(4):401-411
PubMed   |  Link to Article
West R. The transient nature of executive control processes in younger and older adults.  Eur J Cognitive Psychol. 2001;13(1-2):91-105
Link to Article
Hultsch DF, MacDonald SW, Hunter MA, Levy-Bencheton J, Strauss E. Intraindividual variability in cognitive performance in older adults: comparison of adults with mild dementia, adults with arthritis, and healthy adults.  Neuropsychology. 2000;14(4):588-598
PubMed   |  Link to Article
Knotek PC, Bayles KA, Kaszniak AW. Response consistency on a semantic memory task in persons with dementia of the Alzheimer type.  Brain Lang. 1990;38(4):465-475
PubMed   |  Link to Article
Christensen H, Dear KB, Anstey KJ, Parslow RA, Sachdev P, Jorm AF. Within-occasion intraindividual variability and preclinical diagnostic status: is intraindividual variability an indicator of mild cognitive impairment?  Neuropsychology. 2005;19(3):309-317
PubMed   |  Link to Article
Dixon RA, Garrett DD, Lentz TL, MacDonald SW, Strauss E, Hultsch DF. Neurocognitive markers of cognitive impairment: exploring the roles of speed and inconsistency.  Neuropsychology. 2007;21(3):381-399
PubMed   |  Link to Article
Matarazzo JD, Daniel MH, Prifitera A, Herman DO. Inter-subtest scatter in the WAIS–R standardization sample.  J Clin Psychol. 1988;44(6):940-950
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Matarazzo JD, Prifitera A. Subtest scatter and premorbid intelligence: Lessons from the WAIS–R standardization sample.  Psychol Assess. 1989;1(3):186-191
Link to Article
Schretlen DJ, Munro CA, Anthony JC, Pearlson GD. Examining the range of normal intraindividual variability in neuropsychological test performance.  J Int Neuropsychol Soc. 2003;9(6):864-870
PubMed   |  Link to Article
Lipton RB, Katz MJ, Kuslansky G,  et al.  Screening for dementia by telephone using the memory impairment screen.  J Am Geriatr Soc. 2003;51(10):1382-1390
PubMed   |  Link to Article
Verghese J, Katz MJ, Derby CA, Kuslansky G, Hall CB, Lipton RB. Reliability and validity of a telephone-based mobility assessment questionnaire.  Age Ageing. 2004;33(6):628-632
PubMed   |  Link to Article
Blessed G, Tomlinson BE, Roth M. The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects.  Br J Psychiatry. 1968;114(512):797-811
PubMed   |  Link to Article
Fuld P. Psychological testing in the differential diagnosis of the dementias. In: Katzman TR, Bick KL, eds. Alzheimer's Disease: Senile Dementia and Related Disorders. Vol 7. New York, NY: Raven Press; 1978:185-193
Grober E, Dickson D, Sliwinski MJ,  et al.  Memory and mental status correlates of modified Braak staging.  Neurobiol Aging. 1999;20(6):573-579
PubMed   |  Link to Article
Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living.  Gerontologist. 1969;9(3):179-186
PubMed   |  Link to Article
Sarazin M, Berr C, De Rotrou J,  et al.  Amnestic syndrome of the medial temporal type identifies prodromal AD: a longitudinal study.  Neurology. 2007;69(19):1859-1867
PubMed   |  Link to Article
Verghese J, Lipton RB, Hall CB, Kuslansky G, Katz MJ, Buschke H. Abnormality of gait as a predictor of non-Alzheimer's dementia.  N Engl J Med. 2002;347(22):1761-1768
PubMed   |  Link to Article
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994
Chui HC, Victoroff JI, Margolin D, Jagust W, Shankle R, Katzman R. Criteria for the diagnosis of ischemic vascular dementia proposed by the state of California Alzheimer's disease diagnostic and treatment centers.  Neurology. 1992;42(3 pt 1):473-480
PubMed   |  Link to Article
Crystal HA, Dickson D, Davies P, Masur D, Grober E, Lipton RB. The relative frequency of “dementia of unknown etiology” increases with age and is nearly 50% in nonagenarians.  Arch Neurol. 2000;57(5):713-719
PubMed   |  Link to Article
Verghese J, Crystal HA, Dickson DW, Lipton RB. Validity of clinical criteria for the diagnosis of dementia with Lewy bodies.  Neurology. 1999;53(9):1974-1982
PubMed   |  Link to Article
Katzman R, Aronson M, Fuld P,  et al.  Development of dementing illnesses in an 80-year-old volunteer cohort.  Ann Neurol. 1989;25(4):317-324
PubMed   |  Link to Article
Sliwinski M, Buschke H, Stewart WF, Masur D, Lipton RB. The effect of dementia risk factors on comparative and diagnostic selective reminding norms.  J Int Neuropsychol Soc. 1997;3(4):317-326
PubMed
Holtzer R, Friedman R, Lipton RB, Katz M, Xue X, Verghese J. The relationship between specific cognitive functions and falls in aging.  Neuropsychology. 2007;21(5):540-548
PubMed   |  Link to Article
Holtzer R, Verghese J, Xue X, Lipton RB. Cognitive processes related to gait velocity: results from the Einstein Aging Study.  Neuropsychology. 2006;20(2):215-223
PubMed   |  Link to Article
Wechsler D. Wechsler Adult Intelligence Scale-Revised. New York, NY: Psychological Corp; 1981
Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing.  Neurology. 1988;38(6):900-903
PubMed   |  Link to Article
Kliegel M, Sliwinski M. MMSE cross-domain variability predicts cognitive decline in centenarians.  Gerontology. 2004;50(1):39-43
PubMed   |  Link to Article
Verghese J, Wang C, Lipton RB, Holtzer R, Xue X. Quantitative gait dysfunction and risk of cognitive decline and dementia.  J Neurol Neurosurg Psychiatry. 2007;78(9):929-935
PubMed   |  Link to Article
Silvey SD. Statistical Inference. London, England: Chapman & Hall; 1975
Thiébaut AC, Benichou J. Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study.  Stat Med. 2004;23(24):3803-3820
PubMed   |  Link to Article
Stuss DT, Murphy KJ, Binns MA, Alexander MP. Staying on the job: the frontal lobes control individual performance variability.  Brain. 2003;126(pt 11):2363-2380
PubMed   |  Link to Article
Li SC, Huxhold O, Schmiedek F. Aging and attenuated processing robustness: evidence from cognitive and sensorimotor functioning.  Gerontology. 2004;50(1):28-34
PubMed   |  Link to Article
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed   |  Link to Article
Nasreddine ZS, Phillips NA, Bedirian V,  et al.  The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.  J Am Geriatr Soc. 2005;53(4):695-699
PubMed   |  Link to Article
West R, Murphy KJ, Armilio ML, Craik FI, Stuss DT. Lapses of intention and performance variability reveal age-related increases in fluctuations of executive control.  Brain Cogn. 2002;49(3):402-419
PubMed   |  Link to Article
Bellgrove MA, Hester R, Garavan H. The functional neuroanatomical correlates of response variability: evidence from a response inhibition task.  Neuropsychologia. 2004;42(14):1910-1916
PubMed   |  Link to Article
Stuss DT, Toth JP, Franchi D, Alexander MP, Tipper S, Craik FI. Dissociation of attentional processes in patients with focal frontal and posterior lesions.  Neuropsychologia. 1999;37(9):1005-1027
PubMed   |  Link to Article
Jokinen H, Kalska H, Mantyla R,  et al.  Cognitive profile of subcortical ischaemic vascular disease.  J Neurol Neurosurg Psychiatry. 2006;77(1):28-33
PubMed   |  Link to Article
McPherson SE, Cummings JL. Neuropsychological aspects of vascular dementia.  Brain Cogn. 1996;31(2):269-282
PubMed   |  Link to Article
Bischkopf J, Busse A, Angermeyer MC. Mild cognitive impairment—a review of prevalence, incidence and outcome according to current approaches.  Acta Psychiatr Scand. 2002;106(6):403-414
PubMed   |  Link to Article
Collie A, Maruff P, Currie J. Behavioral characterization of mild cognitive impairment.  J Clin Exp Neuropsychol. 2002;24(6):720-733
PubMed   |  Link to Article
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