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The aim of the study was to compare the performance of Robust and Conventional neuropsychological norms in predicting clinical decline among healthy adults and in mild cognitive impairment (MCI). The authors developed Robust baseline cross sectional and longitudinal change norms from 113 healthy participants retaining a normal diagnosis for at least 4 years. Baseline Conventional norms were separately created for 256 similar healthy participants without follow-up. Conventional and Robust norms were tested in an independent cohort of longitudinally studied healthy (n = 223), MCI (n = 136), and Alzheimer’s disease (AD, n = 162) participants; 84 healthy participants declined to MCI or AD (NL→DEC), and 44 MCI declined to AD (MCI→AD). Compared to Conventional norms, baseline Robust norms correctly identified a higher proportion of NL→DEC with impairment in delayed memory and attention-language domains. Both norms predicted decline from MCI→AD. Change norms for delayed memory and attention-language significantly incremented baseline classification accuracies. These findings indicate that Robust norms improve identification of healthy individuals who will decline and may be useful for selecting at-risk participants for research studies and early interventions.
Improved understanding of normal cognitive functioning in an aging population is essential for recognizing the early cognitive changes associated with degenerative diseases such as Alzheimer’s disease (AD). Recognizing abnormal cognitive functioning at earlier time points will help identify individuals at risk of developing dementia (Cargin, Maruff, Collie, & Masters, 2006; Tierney, Yao, Kiss, & McDowell, 2005) and those who might benefit from receiving early treatment. Consequently, the use of valid normative information is essential for detecting very early abnormal cognitive impairment that predicts subsequent decline to a clinical diagnosis of AD or its prodrome, mild cognitive impairment (MCI).
Comparing an individual’s cognitive performance with that of a reference or normative group studied at a single time point is the conventional method to identify abnormal cognitive performance (Kendall, Marrs-Garcia, Nath, & Sheldrick, 1999; Kendall & Sheldrick, 2000). However, existing norms for older persons have been called into question because the assumption of normality was not well established (Fastenau, Denburg, & Hufford, 1999; Levine, Miller, Becker, Selnes, & Cohen, 2004; Norman, Evans, Miller, & Heaton, 2000; Sheridan et al., 2006) or because the reference groups included individuals at preclinical stages of dementia (Sliwinski, Lipton, Buschke, & Stewart, 1996). The inclusion in a normative sample of individuals who later decline to AD, lower the mean test scores, amplify the variance, and exaggerate the apparent effect of age on cognition (Ritchie, Frerichs, & Tuokko, 2007; Sliwinski et al., 1996). Such Conventional norms may underestimate the recognition of pathology (Galvin et al., 2005; Manly et al., 2005; Sliwinski et al., 1996). Robust norms, developed on normal individuals studied longitudinally to exclude individuals with future cognitive impairment, were more sensitive in detecting dementia (Sliwinski et al., 1996). Although this novel examination of normal cognition identified a potential problem with Conventional norms, the study did not experimentally test the utility of Robust norms compared to Conventional norms or their relative performance in predicting decline. More recently, Robust norms were developed in individuals with a high IQ score who remained longitudinally high cognitive performers (i.e., within 1.5 SD of published norms for all tests administered; Rentz et al., 2004), but no comparison with Conventional norms was included in the study. In another recent study Conventional and Robust norms were compared for diagnostic utility, rather than predictive value (Ritchie et al., 2007). To our knowledge, few studies predicting future clinical decline to MCI or AD have tested the performance of normative data (Chen et al., 2001; Marquis et al., 2002; Powell et al., 2006). Most prediction studies typically examine specific tests (Fleisher et al., 2007; Masur, Sliwinski, Lipton, Blau, & Crystal, 1994; Tabert et al., 2006; Tierney et al., 2005) rather than norms as predictors of decline.
Cognitive impairment has been identified in clinically normal individuals who go on to MCI (Blacker et al., 2007; Glodzik-Sobanska et al., 2007; Tierney et al., 1996) or AD (Masur et al., 1994; Meguro et al., 2001; Rubin et al., 1998;). Manly et al. (2005) incorporated a Robust normative group for operationalizing an MCI diagnosis, and identified the prevalence of MCI in an ethnically diverse urban population (Manly et al., 2005). Using Robust norms, they showed that MCI was more prevalent in those older than 75, and in those with less education (<9 years). Cognitive norms in a normal aging population may be confounded by the inclusion of cognitively abnormal individuals who would later go on to develop MCI or AD. The construction of a normative group, after excluding individuals who subsequently developed MCI (Rentz et al., 2004) or AD (Sliwinski et al., 1996), would provide a more homogeneous, clinically stable normal aging reference group to which cognitively impaired clinical groups can be compared. Although the above studies suggest that Robust norms have an advantage beyond Conventional norms, our goal was to experimentally test the utility of this concept in individuals studied longitudinally.
The primary aims of this study were to determine if Robust norms are more accurate than Conventional norms in identifying and predicting cognitive impairment and to determine if change in cognitive performance over time increments the effectiveness of baseline norms in the prediction of future decline. The first hypothesis was that Robust norms from participants with cognitive stability will provide a more accurate measure of normal cognition and distinguish cognitive impairment at baseline in participants who subsequently decline to MCI or AD. The second hypothesis was that change in memory performance over time would improve the utility of baseline performance in predicting decline. Our results support the concept that Robust norms are better than Conventional norms for the identification of current and future cognitive impairment at baseline, and such norms are more effective at later time points when longitudinal change is included.
We examined data from 890 community dwelling volunteers and patients, retrospectively studied, who were drawn from a larger pool of individuals (N = 3,987) participating in brain aging studies at the NYU Alzheimer’s Disease Center (ADC) and the affiliated NYU Center for Brain Health (CBH) since 1978. Participants were not actively recruited for this study but were selected from the preexisting database, provided that they fit the inclusion and exclusion criteria. Participants with MCI or AD were required to have at least three evaluations. The majority of participants in the preexisting database had at least 12 years of education (88%), were of middle to upper socioeconomic status, and were predominately White (89%). Participants received an extensive diagnostic evaluation that included medical, neurological, psychiatric, neuropsychological examinations, and brain computerized tomographic (CT) or magnetic resonance (MR) imaging. Informed consent, approved by the NYU School of Medicine IRB, was obtained from all participants at each evaluation.
Participants whose data were included in the current analyses were between the ages of 40 and 92 with at least 12 years of education. Participants who were not native English speakers (n = 56) were included if they achieved a scaled score of at least 11 on a WAIS Vocabulary subtest, which is considered an average score in a general population (Wechsler, 1955). Participants were included if their Global Deterioration Scale (GDS; Reisberg, Ferris, de Leon, & Crook, 1982) score was ≤ 5.
Individuals with a history of stroke or significant evidence of cerebro-vascular disease (modified Hachinski score ≥ 4; Hachinski et al., 1975; Rosen, Terry, Fuld, Katzman, & Peck, 1980) were excluded, as were individuals with evidence of any neurological or psychiatric disorder other than MCI or AD, (e.g., normal pressure hydrocephalus or depression as defined by a Hamilton Depression Scale; Hamilton, 1960, score > 9).
A semistructured clinical interview using the Brief Cognitive Rating Scale (BCRS; Reisberg & Ferris, 1988) assessed the magnitude of cognitive impairment in concentration, recent and past memory, orientation, and functioning/self care and provided a numerical score for each domain. Information obtained from the BCRS was used to determine the GDS. The GDS (Reisberg et al., 1982), a seven-point rating scale uses validated descriptors to assess the global cognitive and level of functional capacity as follows: normal (NL; GDS = 1 or 2 characterized as cognitively and functionally normal; differentiated by the absence vs. presence of subjective memory complaints, respectively), MCI (GDS = 3), mild to moderate AD (GDS = 4 or 5), and severe AD (GDS ≥ 6; Reisberg et al., 1993). The GDS score was assigned independent of, and prior to, the detailed neuropsychological tests in this study. Furthermore, the clinicians performing the GDS assignment were in all cases blind to the findings from the neuropsychological testing procedures.
All diagnoses were made at a consensus meeting. The diagnosis of AD followed NINCDS-ADRDA (McKhann et al., 1984) and Diagnostic and Statistical Manual of Mental Disorders-IV (American Psychiatric Association, 1994) criteria. The NL and MCI diagnoses were based on the GDS and relevant medical data obtained at the evaluation and were not directly defined using psychometric test performance (Convit et al., 1997; de Leon et al., 2001, 1993; De Santi et al., 2001; Flicker, Ferris, & Reisberg, 1991; Kluger, Ferris, Golomb, Mittelman, & Reisberg, 1999; Prichep et al., 2006). Criteria for the diagnosis of MCI were: memory complaint documented by the patient and collateral source, normal general cognition, normal activities of daily living, no dementia (American Psychiatric Association, 1994), mild cognitive deficits (typically memory) and a GDS score of 3. Participants were included in either the normative or test groups as described below, with all groups being mutually exclusive.
The normative sample was composed of 369 diagnostically normal individuals. These individuals were divided into two mutually exclusive groups: the “Conventional Normative Group” included 256 individuals with only one diagnostic evaluation and the “Robust Normative Group” included 113 individuals who received at least three diagnostic evaluations and retained the clinical diagnosis of normal at each evaluation. The average total observation period for the Robust Normative group was 8.3 ± 4.0 years (range: 3.6 to 21.5 years) over an average of 5 ± 2 total evaluations. Using the Robust Normative group, both cross-sectional baseline and longitudinal change norms were developed. Separate cross-sectional baseline norms were developed using the Conventional Normative group. No longitudinal data was available for the Conventional Normative group as they were only studied once. Participants from this group either did not return or had not yet completed their follow-up evaluation. (See Table 1 for demographic information for these groups, Figure 1 for each group’s diagnosis at three time points, and supplemental Appendix A for group description).
The Conventional and Robust norms were tested in an independent cohort of longitudinally studied individuals called the “test sample” that was mutually exclusive of the two normative samples. All participants in the test sample had at least three evaluations except the healthy test group (HT) who had two evaluations. The test sample included 223 NL individuals, 136 MCI participants, and 162 AD participants. Of the 223 NL participants, 139 participants were diagnosed as normal at follow-up and composed the HT group. These HT participants were demographically comparable to the Conventional and Robust Normative groups and had one follow-up at the time of this report. Therefore, the time that they remained stable was shorter than the Robust group. The HT group was included within the study design as a control group for the test sample and for comparison with the normative groups (see Table 1). For the HT participants, the interval between baseline and follow-up evaluation was 3.3 ± 2.3 years (range: 1.4 to 15.3 years). The remaining 84 individuals with a normal diagnosis at baseline clinically declined by their final follow-up evaluation (NL→DEC) and received a final diagnosis of MCI (n = 48) or AD (n = 36). These participants were diagnosed as normal for 6.4 ± 4.4 years (range: 1.3 to 19.7 years) before they were first diagnosed with cognitive impairment (MCI, n = 75 or AD, n = 9). To examine a possible relationship between cognitive performance and the time to decline, this NL→DEC group was subsequently subdivided into early decliners (decline in less than 5 years after the baseline evaluation; NLEDEC, n = 41) and late decliners (decline 5 or more years after the baseline evaluation; NLLDEC, n = 43). To have a maximal number of participants in both the early and late subgroups, the NL→DEC group was split using the median decline time of 5 years (see Table 2 for demographic information for NL→DEC subgroups).
Of the 136 MCI participants, 92 remained MCI at all follow-up evaluations (stable MCI) and were studied for 3.4 ± 2.2 years (range: 1.1 to 14.5 years). The remaining 44 MCI patients declined to AD at any subsequent evaluation (MCI→AD). These 44 participants remained MCI for 3.1 ± 2.0 years (range: 1.3 to 11.4 years) before receiving a diagnosis of AD.
The AD group consisted of 162 patients who retained the AD diagnosis (AD–AD) and who were studied for 5.1 ± 3.4 years (range: 1.1 to 18.4 years; see Table 3 for demographic information for all test groups, Figure 1 for each group’s diagnosis at three time points, and Appendix A for group description).
The cognitive test battery administered in this study included the Guild Memory Test (Gilbert, 1970; Gilbert, Levee, & Catalano, 1968) to assess several components of memory function including: paragraph immediate and delayed recall, immediate and delayed recall of verbal paired associates, and immediate recall of visual paired associates. The test battery also included several subtests from the Wechsler Adult Intelligence Scale (Wechsler, 1955); specifically, Digits Forward and Backward, the Digit Symbol Substitution Test (DSST), and the Vocabulary Test. The WAIS-R (Wechsler, 1981) version of these subtests was administered to participants in this study. In addition, we examined performance on the Object Naming Test (Flicker, Ferris, & Reisberg, 1993), the delayed trial from the NYU Shopping List Test (Flicker et al., 1993), and Perceptual Speed, a number cancellation test (PSPC; Moran & Mefferd, 1959). (See Tables 4 and and55 for baseline cognitive data for all groups). The six subtests from the Guild Memory Test have been shown to correlate with the memory quotient from the Wechsler Memory Scale (rs ranging from .55 to .67, ps < .001; Crook, Gilbert, & Ferris, 1980). Given that the repetition of a memory test may result in a learning effect, the reliability for the Guild Memory Test was not examined directly. However, the two paragraphs that comprise the paragraph recall subtests are significantly correlated (r = .62; p < .001). Moreover, the Guild Memory Test has good test–retest reliability (rs ranging from .47 to .93; ps < .01; Reisberg et al., 1989). The paragraph subtest of the Guild Memory Test, commonly known as the NYU Paragraph Recall Test, has been studied extensively (Convit et al., 1997; de Leon et al., 2001; de Leon et al., 1997; de Leon et al., 1993; Flicker et al., 1991; Kluger et al., 1999), and used in clinical drug trials (Petersen et al., 2005; Salloway et al., 2004).
We examined group differences in continuous demographic variables (e.g., age) using analysis of variance (ANOVA) with post hoc Tukey’s tests. Effect sizes for t tests and ANOVAs were measured with Cohen’s d. Using this measure, an effect size of .20 is considered a small effect, .50 is considered a medium effect, and .80 is considered a large effect. Group differences for categorical demographic variables and cognitive domains were compared using exact Pearson chi-squared analyses with Bonferroni correction for multiple comparisons. Effects sizes for chi-squared analyses were measured using Cramér’s V. With this measure a value of .10 is considered a small effect, .30 is considered a moderate effect, and .50 is considered a large effect. The McNemar test for paired binomial proportions was used to test differences in sensitivities for normative comparisons within diagnostic groups. Corrections for multiple comparisons of diagnostic group were not needed for this test because analyses were performed within group. Effects sizes for the McNemar tests were measured using the phi coefficient (). With this measure a value of .10 is considered a small effect, .30 is considered a moderate effect, and .50 is considered a large effect. Logistic regression analyses were used to test the additive value of norms for longitudinal change to norms for baseline performance. Effect sizes for logistic regression analyses were reported in terms of odds ratios. Statistical significance was defined as p values ≤ .05 except in the Pearson chi-squared analyses where statistical significance was defined as p values ≤ .01 to correct for multiple comparisons. SPSS (Version 12.0) was used for data analyses.
Norms for both the Conventional and Robust groups were developed separately based on the baseline cross-sectional test scores. Using linear regression analyses, the influence of age, years of education, and gender on cognitive scores was assessed and found to be associated with the cognitive variables. Age was associated with lower test scores on all 11 cognitive tests in the Conventional Normative group and with 7 of the 11 tests in the Robust Normative group (ts for β range from 3.4 to 6.4, ps < .05). In addition, education and gender were correlated with test performance, although not as strongly as age. Therefore, we included these three demographic variables in creating an estimated equation for cognitive performance on each test, following procedures similar to Heaton et al. (Heaton, Avitable, Grant, & Matthews, 1999). Significant two-way interactions between the variables (e.g., age–education; age–gender; gender–education) were added into the estimated equations. For each test, the intercept plus the beta weights of the demographic variables and the significant interactions were used to calculate predicted scores. We generated a z score for each test variable as follows:
where SD is the root mean squared error from the estimated equation.
Longitudinal change norms, calculated from the Robust Normative group, were based on the change in cognitive performance between baseline and the first follow-up visit. Regression models were generated accounting for age, years of education, gender, time between test administrations, baseline score, and any significant two-way interactions of the demographic variables. The resulting estimated equations were used to generate predicted scores for each test. We calculated z scores using the equation above.
Using an ROC curve we identified the cutpoint of the z score with a 90% specificity on each test for both the Conventional and Robust norms. Both sets of norms were assessed in the test sample. Participants in the test sample were then coded as impaired on a cognitive test if their score was below the cutpoint. We examined the sensitivity for each test group using both Conventional and Robust norms. After correcting for multiple comparisons, most tests could distinguish individuals with overt cognitive impairment (i.e., MCI→AD and AD–AD) compared to the HT group. However, only 6 of the 11 tests identified stable MCI patients and none of the tests for either set of norms identified NL→DEC compared to HT participants. Therefore, the test by test approach was not sensitive in detecting healthy participants who would show future cognitive decline.
To improve on the sensitivity to detect impairment, our second approach was to combine tests based on cognitive domains. This approach has been applied by others using various methodologies to examine cognitive change associated with the onset of dementia and normal aging (Galvin et al., 2005; Palmer, Boone, Lesser, & Wohl, 1998; Rubin et al., 1998). To find subtle but genuine impairment in cognition the criteria of defining impairment on at least two tests within a domain provided a more rigorous method than impairment on individual tests within a domain. This reduced the identification of individuals as impaired who simply performed poorly on one particular test. The cognitive tests were categorized based on face validity into four separate domains: (a) working memory, (b) immediate memory, (c) delayed memory, and (d) attention-language. Although it is unconventional to combine attention and language tests into the same domain, only one language test was administered to the majority of the study cohort. Given that no one test was examined in isolation by our criteria and the Object Naming Test was more highly correlated with DSST and PSPC than with tests from other domains, we felt the combination of attention and language was acceptable. The working memory domain consisted of WAIS-R Digits Forward and Backward. The immediate recall of paragraphs, verbal paired associates, and visual paired associates comprised the immediate memory domain. The delayed recall of paragraphs, verbal paired associates, and word list comprised the delayed memory domain. DSST, PSPC, and the Object Naming Test comprised the attention-language domain.
Unlike a test by test approach, identifying cut-off levels for any combination of two or three tests within a domain did not lend itself to using the ROC curve method due to the complexity of dealing with numerous tests. The following procedure was used to define cutoff scores at baseline within a domain. The standard deviation level (e.g., < −1 SD, < −1.5 SD, < −2 SD) was selected for each test such that the combination of any two tests within a domain would classify 5 to 10% of the HT participants as impaired (i.e., 90% to 95% specificity) using Conventional norms. We chose this specificity range to reflect a conservative estimation of normal participants who may decline in the future. The z score cutoff selected for the Robust norms was the level that provided the same specificity as the respective Conventional norms (see Table 6).
By design, the specificity for the HT group using either Conventional or Robust norms was identical within each domain. This provided a fair comparison for the sensitivities between the two norm methods. The McNemar test (McNemar, 1947) was applied within each test group to evaluate the performance of the two normative procedures with respect to sensitivity.
The McNemar Test criterion is given by the equation:
where C = number of cases correctly classified by Conventional norms only and R = number of cases correctly classified using Robust norms only. The significance level is obtained by comparing the square of Z0 to a chi-squared distribution with df = 1 (Agresti, 2002). The effect size for the McNemar test was calculated using the phi coefficient:
where Z02 is equivalent to chi-squared in the standard calculation of the phi coefficient.
The Robust Norm group was used to define normal change in cognitive performance between the baseline and first follow-up evaluation. The optimal cut-off level for normal change was defined as the level that achieved specificity between 90% and 95% for the Robust Norm group. Excessive cognitive change from baseline to follow-up was defined as exceeding the standard cutoff level (z score) for at least one of the tests within a domain (see Table 6 for cutoff levels). The baseline criterion of impairment on two out of three tests within a domain was too conservative for the longitudinal data as it yielded specificities above 95%.
One of the goals of this study was to understand the change within a stable group and to characterize abnormal change. Therefore, at the first follow-up only participants who retained their baseline diagnosis were included in the analyses. For clarity, the declining groups were renamed to include their baseline, first follow-up, and final diagnoses (NL-NL→DEC; n = 52; MCI-MCI→AD; n = 12; see Appendix A). Within the NL-NL→DEC group, the effect of early (NL-NLEDEC) and late decline (NL-NLLDEC) was also examined. Decline within 5 years of the follow-up evaluation was defined as early decline and beyond that time frame decline was defined as late decline. Within the MCI groups, stability versus decline (stable MCI vs. MCI-MCI→AD) was examined. All groups were compared to the HT group.
The Conventional and Robust Norm groups did not differ from each other with respect to baseline age, education, gender, or MMSE scores (p > .05; see Table 1).
The HT group was representative of the normative groups described above, and used as the comparison group in all analyses with the other test groups. The HT group did not differ significantly from the two normative groups with respect to age, education, gender, or MMSE scores (p > .05; see Table 1), but by study design they had been followed for a shorter period of time compared to the Robust group, t(250) = 12.39; p < .001, d = 1.5.
The HT group was significantly younger than the other test groups, F(4, 516) = 38.7; p < .001, d = .94 to1.3. Although the AD–AD group received less education than the HT, NL→DEC, and stable MCI groups, F(4, 516) = 8.5; p < .001, d = .68, the mean education level for the AD–AD group was nevertheless high at 14.7 years. As expected the AD–AD group had lower MMSE scores compared to all other test groups and the MCI groups had lower MMSE scores compared to the HT group, F(4, 516) = 165.8; p < .001, d = 1.1 to 2.3. There were no gender differences between the groups. There was a significantly higher percentage of Whites in the Stable MCI group (98%) compared to the HT group (89%), χ2(1, N = 230) = 5.9, p < .05, V = .16. With respect to Apolipoprotein E genotype (ApoE), the NL→DEC group had a significantly lower proportion of participants carrying the E4 allele (19%) compared to HT (39%), χ2(1, N = 145) = 6.8, p < .05, V = .22, whereas the AD–AD group had a significantly higher proportion of E4 carriers (60%), χ2(1, N = 166) = 7.0, p < .05, V = .21 (see Table 3).
Comparing the HT and the NL→DEC group at baseline (when all participants were normal), Robust norms identified a significantly higher proportion of NL→DEC participants with impaired cognition in the delayed memory domain, χ2(1, N = 223) = 8.4, p < .01, V = .19; and the attention-language domain, χ2(1, N = 223) = 7.1, p < .01, V = .18. The Conventional norms did not identify significant group differences in any domain. Given that the HT group was significantly younger than the NL→DEC group, we reexamined our data limiting the age of both groups to those above 65 years. Our findings did not change for the delayed memory domain in that a higher proportion of NL→DEC, 65 and older, had impaired delayed memory using Robust norms, χ2(1, N = 147) = 9.3, p ≤ .01, V = .25. There was a trend toward significance in the attention-language domain using Robust norms where a higher proportion of NL→DEC, 65 and older, showed impairment, χ2(1, N = 147) = 5.7, p = .02, V = .20.
Robust norms had significantly higher sensitivity than the Conventional norms in the delayed memory domain, 20% versus 10%; McNemar: Z0 = 3.0; p < .01, = .29, and the attention-language domain, 18% versus 7%; McNemar: Z0 = 3.0, p < .01, = .29, (see Table 7 and Figure 2). We confirmed our results in the sample limited to participants 65 and older group, such that Robust norms outperformed Conventional norms in these domains.
The NLEDEC (decline in less than 5 years after the baseline evaluation, n = 41) group was significantly older, F(2, 220) = 20.6; p ≤ .01, d = .80, and had a lower proportion of ApoE4 carriers, χ2(1, N = 180) = 3.5, p ≤ .05, V = .19, compared to the HT (see Table 2).
A significantly greater percentage of the NLEDEC group compared to HT were classified as impaired in delayed and immediate memory using Robust norms, χ2(1, N = 180) = 17.1, p < .01, V = .31; χ2(1, N = 180) = 10.8, p ≤ .01, V = .24, respectively. Only immediate memory showed significant group differences using Conventional norms, χ2(1, N = 180) = 8.4, p ≤ .01, V = .22.
There was a significant difference in sensitivity between Robust and Conventional norms for delayed memory (32% vs. 15%, respectively) McNemar: Z0 = 2.7; p ≤ .05, = .24, but not for the immediate memory domain (24% and 22%, respectively), (see Table 7 for McNemar results).
As compared to HT the NLLDEC (decline 5 or more years after the baseline evaluation, n = 43) group were significantly older, F(2, 220) = 20.6; p < .01, d = 1.1, and had a lower proportion of ApoE4 carriers, χ2(1, N = 182) = 4.6, p < .05, V = .20 (see Table 3).
There was no significant difference for any domain between the proportion of NLLDEC and HT group participants classified as impaired using Conventional norms. Using Robust norms, we found a trend toward significance, χ2(1, N = 182) = 5.7, p = .02, V = .18, for the proportion of NLLDEC participants classified as having impaired attention-language compared to HT.
There were no significant differences in sensitivity between Robust and Conventional norms for attention-language domain (19% vs. 7%, respectively), (see Table 7 for McNemar results).
Comparing the stable MCI and the HT groups, both sets of norms identified significantly more stable MCI as showing impairment in delayed memory, Robust norms: χ2(1, N = 231) = 26.6, p <.01, V = .34; Conventional norms: χ2(1, N = 231) = 15.7, p < .01, V = .26; and attention-language, Robust norms: χ2(1, N = 231) = 25.3, p < .01, V = .33; Conventional norms: χ2 (1, N = 231) = 13.3, p < .01, V = .24 domains. Our findings were confirmed when we limited the sample to participants 65 and older. A higher proportion of stable MCI participants had impaired delayed memory, χ2(1, N = 155) = 20.8, p < .001, V = .37 and attention-language, χ2(1, N = 155) = 16.3, p < .001, V = .33 domains.
The Robust norms had significantly higher sensitivity relative to Conventional norms in the delayed memory (34% vs. 26%; McNemar: Z0 = 2.7; p < .05, = .24), and attention-language (32% vs. 22%; McNemar: Z0 = 3.0, p < .05, = .28) domains (see Table 8 for McNemar results). We confirmed our results in the sample limited to participants 65 and older group, such that Robust norms outperformed Conventional norms in these domains (McNemar p < .05).
Comparing MCI→AD and HT groups with respect to the percentage of participants classified as impaired, the Robust norms showed a significant group difference in all domains, χ2(1, N = 183) range = 10.2 to 76.9, p ≤ .01, V = .24 to .65, whereas the Conventional norms showed a significant difference in all but the working memory domain χ2(1, N = 183) range = 24.4 to 68.7, p ≤ .01, V = .37 to .61.
The sensitivity for the Robust norms did not significantly differ from that of the Conventional norms for any domain (see Table 8 for McNemar results).
For both norms, the AD–AD group had a significantly higher proportion of individuals classified with impaired cognitive performance in all domains as compared to the HT group, Robust norms: χ2(1, N = 301) range = 28.8 to 222.5, p < .01, V =.31 to .86; Conventional norms: χ2(1, N = 301) range = 20.9 to 222.5, p < .01, V = .26 to .86.
The sensitivity for the Robust norms did not significantly differ from that of the Conventional norms for any domain (see Table 8 for McNemar results).
In summary, at baseline the NL→DEC and the MCI groups were more impaired on delayed memory and attention-language domains compared to the HT group. The AD group showed impairment in all domains. See Figure 3 for performance in the delayed memory domain for all groups. Robust norms outperformed Conventional norms in identifying NL→DEC and Stable MCI in the delayed memory and attention-language domains. In NL→DEC, cognitive impairment was more readily identified the closer an individual was to the time of decline.
As stated in the Method section, NL and MCI individuals who declined at the first follow-up and progressed to a different diagnostic group were removed from the longitudinal analysis. For example, out of the 84 participants at baseline in the NL→DEC group, 32 declined to MCI or AD at their first follow-up. Therefore, they were removed from all further analyses and the remaining group was renamed NL-NL→DEC, indicating the diagnosis of normal at baseline and at first follow-up, and a future follow-up diagnosis reflecting clinical decline. The same labeling principal was applied to the MCI→AD group, which had 12 participants remaining with an MCI diagnosis at first follow-up (MCI-MCI→AD).
The demographic variables for the individuals remaining in the test groups were reexamined. As compared to the HT group the NL-NL→DEC group (n = 52) was significantly older, F(3, 290) = 34.0; p < .001, d = .90, but there were no group differences for MMSE and gender distribution (see Table 9).
As with the baseline results we examined if the point at which decline occurred affected cognitive change. To maximize the size of the subgroups, the median time to decline from first follow-up (5 years) was used to subdivide the group into early (NL-NLEDEC) and late decliners (NL-NLLDEC). As compared to the HT group, these two groups were significantly older, F(2, 188) = 12.9, p < .01, d = 1.0 than the HT group. There were no group differences with respect to education, gender, ApoE, and MMSE for the NL-NLEDEC (n = 28), and NL-NLLDEC (n = 24) groups (see Table 9).
Compared to the HT group the Stable MCI and MCI-MCI→AD groups were significantly older, F(3, 290) = 34.0, p < .01, d = 1.24, and had lower MMSE scores, F(3, 290) = 25.1, p < .01, d = 1.22. The Stable MCI group had a higher percentage of Whites (98%) than the HT group (89%), χ2(1, N = 230) = 5.9, p < .05, V = .16 (see Table 10).
Using logistic regression analyses, we examined whether longitudinal change improved the prediction of outcome group beyond that of the cross-sectional baseline performance. Longitudinal change significantly added to baseline performance for delayed memory and attention-language domains in all groups compared to HT (see Table 11 for chi-squared results). The effect sizes were measured in odds ratios (OR) and ranged from 2.6 to 25.2, which indicated large effects. Furthermore, longitudinal change in immediate memory significantly added to baseline performance in stable MCI (OR = 2.5) and MCI-MCI→AD (OR = 4.6) groups compared to HT (see Table 11).
When early and late decline groups were examined, longitudinal change significantly added to baseline prediction for delayed memory (OR = 3.9) and attention-language (OR = 9.1) domains for the NL-NLEDEC group but not for the NL-NLLDEC (see Table 11 for chi-squared results). For the NL-NLLDEC group excessive change was more prevalent than baseline impairment in delayed memory and attention-language domains. For the delayed memory domain within the Stable MCI group, the same proportion of participants had baseline impairment as had excessive change, whereas, a lower proportion showed excessive change than baseline impairment in the attention-language domain. In addition, excessive change significantly added to baseline impairment for both domains in the prediction of Stable MCI. Similarly, the MCI-MCI→AD group had the same proportion of participants with baseline impairment and excessive change for the delayed memory domain. Within the attention-language domain a higher proportion of these participants had excessive change than baseline impairment.
Three main findings emerge from this study. First, Robust norms were more sensitive than Conventional norms in identifying early cognitive impairment in normal individuals who eventually decline to MCI or AD, and in stable MCI. In MCI participants who eventually decline to AD and in AD participants, both Robust and Conventional norms performed equally well at identifying individuals with cognitive impairment. Second, normal individuals who declined within 5 years demonstrated impairment at baseline in immediate and delayed memory domains using Robust norms and in immediate memory using Conventional norms. However, those who declined after 5 years did not show baseline impairment in any domain using either norm method. Third, longitudinal change significantly added to the prediction of decline accuracies of the baseline scores in all groups compared to the HT group in the delayed memory and attention-language domains. Each of these conclusions is discussed in greater detail below.
The present study demonstrates that Robust norms are more sensitive than Conventional norms at identifying normal individuals who will decline in the future. In the delayed memory and attention-language domains, Robust norms show a significant improvement over Conventional norms in predicting decline. Our findings add to the previous studies of Robust norms. To date Robust norms have been provided for memory tests (Marcopulos & McLain, 2003; Rentz et al., 2004; Ritchie et al., 2007; Sliwinski, Buschke, Stewart, Masur, & Lipton, 1997), language tests (Marcopulos & McLain, 2003; Rentz et al., 2004; Zec, Burkett, Markwell, & Larsen, 2007), measures of general cognition (Marcopulos & McLain, 2003), and executive function (Marcopulos & McLain, 2003) and separately for these domains in those with high IQ scores (Rentz et al., 2004). When used, Robust norms have been shown to reduce the variability in older individuals’ cognitive scores (Ritchie et al., 2007; Sliwinski et al., 1996), and to be useful in the diagnosis of MCI (Manly et al., 2005; Marcopulos, Gripshover, Broshek, McLain, & Brashear, 1999; Sliwinski et al., 1996). Of the few previous studies that reported data for Robust norms, only one assessed the prediction of future cognitive decline. Marcopulos and McLain generated Robust norms in a rural population and showed that these norms can be used to predict decline. Decline was defined as a drop of 1 SD on three or four measures in the group of community dwelling individuals who passed a screening test but not a rigorous diagnostic evaluation. In the current study we show for the first time that Robust norms in comparison to Conventional norms improved prediction of clinically significant cognitive decline, defined as a change in diagnostic category, in individuals diagnosed as normal or with MCI.
At first glance the sensitivity of the Robust norms in the normal decline group seems low at 20% for delayed memory domain and 18% for attention-language domain. However, this is an expected result given that all our participants in this comparison had normal cognitive function at baseline. It has been shown that cognition remains relatively stable until the onset of dementia, but one study showed that at baseline, test completion time was predictive of decline to AD (Galvin et al., 2005). Others have demonstrated that with a diagnosis of dementia Conventional and Robust norms work equally well (Ritchie et al., 2007). Our data supported this finding in that when patients approach a clear-cut AD diagnosis, Conventional or Robust norms are equally useful. Our study demonstrates the benefit of Robust norms in predicting decline to the prodromal and early stages of AD.
The present study demonstrates that normal individuals who eventually decline to MCI or AD within 5 years have impaired immediate and delayed memory at baseline. The predictive value of memory performance at the MCI stage is well-documented in the AD literature (Artero, Tierney, Touchon, & Ritchie, 2003; Backman, Small, & Fratiglioni, 2001; Kluger et al., 1999; Tierney et al., 1996; Tierney et al., 2005). The present study adds to these findings by showing that memory impairment can be identified prior to the clinical transition to MCI. These findings support previous observations that longitudinally normal individuals who eventually develop MCI or AD show impaired delayed memory years prior to decline (Elias et al., 2000; Fox, Warrington, Seiffer, Agnew, & Rossor, 1998; Masur et al., 1994; Powell et al., 2006; Rubin et al., 1998). Robust and Conventional norms were not significantly different in terms of identifying those who show normal early decline. We believe that with greater statistical power from a larger sample, Robust norms will outperform Conventional norms for identifying future impairment in the delayed memory domain. Currently our group size is small (n = 41), but with 77 participants, 80% power is achieved in detecting differences in sensitivity of this domain.
Normal participants who eventually decline to MCI or AD in 5 or more years had no cognitive impairment at baseline. This finding is in contrast with results from studies that examined cases diagnosed as normal who declined to AD or other dementias, and found that at baseline, naming and DSST, two tests used within the language-attention domain in the current study, were impaired (Rubin et al., 1998). Similar to our findings, others did not find naming to be sensitive to early AD (Testa et al., 2004). In healthy aging on the other hand, language, as measured using naming, showed slight performance decline in persons over the age of 70 (Zec, Markwell, Burkett, & Larsen, 2005).
The finding that individuals with a normal diagnosis who decline within 5 years have cognitive impairment and those who decline at a later time point have no cognitive impairment prompts discussion about diagnostic criteria for normality. To date few studies have concentrated on operationalizing a definition of normality. Edwards, Lindquist, and Yaffe (2004) reported on a normal cohort of participants from an Alzheimer’s disease Research Center followed for approximately 2 years. Although a higher proportion of those normal participants that declined to MCI or AD at follow-up had memory symptoms at baseline, this proportion did not differ statistically from those who remained normal (Edwards et al., 2004). Blacker and colleagues (2007) followed 107 normal individuals for approximately 5 years and found that poorer memory performance at baseline predicted decline to MCI in 5 years and that these problems were not reported by the participant or an informant during a clinical interview (Blacker et al., 2007). Clinical interviews are traditionally used to stage individuals and the two most popular instruments are the Clinical Dementia Rating Scale (CDR; Morris et al., 1997) and the GDS (Reisberg et al., 1982) described above. With the exception of the classification of Age Associated Memory Impairment (Crook et al., 1986), psychometric cut-off levels are not used in an operationalized definition of normality in older persons. Moreover, Petersen and colleagues (2001) suggested that although neuropsychological information can be helpful for diagnostic purposes, it cannot be used alone for an MCI or AD diagnosis. Rather, clinical judgment is also required. Petersen et al. reported that many factors (e.g., age, education, ethnic background, etc.) can affect test performance and that distinguishing different subtypes of MCI and dementia can be challenging due to overlapping neuropsychological scores. With this in mind, our findings and those of Blacker et al. (2007) suggest that it might be possible to operationally define cognitive performance in normal individuals and to separate those likely to decline in the future from those who will remain stable. With a more careful operational definition of normality (i.e., Robust norms), it is possible that psychometric discrimination of cognitively impaired normal and MCI populations may be more accurately defined. Our data suggest that in normal individuals the lack of impairment in any domain cross-sectionally and the lack of change over time may indicate continued cognitive stability for at least 5 more years.
We found that for both normal and MCI participants who decline, there is excessive change in the delayed memory and attention-language domains. This finding is consistent with the observation that older individuals who do not develop dementia have stable cognition, whereas those that do develop dementia fail to show a practice effects on repeated cognitive testing (Galvin et al., 2005).
Clinical methods for predicting which normal individuals will show cognitive decline in the future have not been well studied and require further attention. One major obstacle to identifying those at risk for future cognitive impairment is that there is a low incidence of decline and slow progression of normal older persons to dementia. For example, in a prior longitudinal study of more than 200 participants in our patient population, results over a mean interval on 3.8 years indicated that of 21 GDS = 1 normal participants, none declined to a dementia diagnosis. Of 105 GDS = 2 normal participants, approximately 4% per year declined to dementia (Kluger et al., 1999). However, when decline to MCI or dementia has been studied, GDS Stage 2 normal participants with subjective memory decline have been found to decline at a rate of approximately 7% per year (Prichep et al., 2006; Reisberg & Gauthier, in press). PET imaging studies have been shown to be predictive of decline from NL to MCI and AD (de Leon et al., 2001; Jagust et al., 2006; Mosconi et al., 2007). However, these techniques are invasive and expensive. Electroencephalographic changes have also been found to be useful in predicting cognitive decline in normal GDS Stage 2 participants with subjective impairment (Prichep et al., 2006). However, additional predictive methods are clearly necessary. Although impaired baseline cognitive performance among normal individuals has previously provided information about risk of AD (Masur et al., 1994), the current study shows that it is also informative for predicting future MCI. A cognitive predictor is very much needed to identify individuals at increased risk for MCI and to identify those who may be candidates for early intervention. As MCI is of interest in clinical trials, knowing who is destined to develop MCI is a first step in studying treatment of preclinical MCI.
Robust norms, as conceptualized by Sliwinski et al., (1996) were generated by removing future decliners to AD from the normative group. Such Robust norms more accurately estimate the common effect of age on cognition in the absence of very early disease. We refined the methods used by Sliwinski et al. by retrospectively excluding individuals from the Robust normative group who eventually declined to MCI as well as those who eventually declined to AD. Manly et al. (2005) previously suggested that Robust norms may be useful in studies with MCI because such norms should be more sensitive in detecting MCI. Our data are the first to show that Robust norms are in fact more sensitive than Conventional norms for identifying which normal individuals decline to MCI or AD. MCI is often considered a prodromal stage of AD (Flicker et al., 1991; Morris et al., 2001; Petersen et al., 2001; Petersen et al., 1999; Reisberg, 1986), and these individuals show detectable cognitive change (Manly et al., 2005; Reisberg et al., 1988). Excluding normal participants who subsequently decline to MCI from the normative group resulted in a more conservative estimation (higher cut scores) of normal cognitive performance.
Our Robust normative group and the healthy test group had a higher representation of apoE 4 carriers compared to the NL→DEC group. This was surprising given that apoE 4 is a risk factor for AD. One reason to account for this disparity is that cognitively normal individuals who participate in research studies on AD tend to have a family member who had AD and therefore they have a higher probability of being apoE 4 carriers. The normal decline group in our study, however, did not reflect the normative population. When we separated the declining group into those that declined to MCI versus AD, we found that the NL-MCI group had proportions of E4 carriers that were not significantly different from the stable MCI group although the NL-AD participants were similar to the healthy test group and both normative groups. There was a lower proportion of NL→DEC who declined to AD (39%), and therefore the overall group resembled the stable MCI group. Some have reported that cognitive performance is lower in normal individuals who are apoE 4 carriers (Bondi et al., 1995; Flory, Manuck, Ferrell, Ryan, & Muldoon, 2000; O’Hara et al., 1998) however, Jorm et al. (2007) showed that between the ages of 20 to 65 apoE genotype did not influence cognition. Given that our normative groups had similar proportions of carriers and noncarriers we are confident that our results are not biased. We might in fact have underestimated the cognitive impairment in our NL→DEC group.
Given that the healthy test group was significantly younger than the other test groups, we reexamined our data limiting the age of all test groups to those above 65 years. Our baseline findings did not change. However, as expected, a higher proportion of normal decliners and stable MCI participants had impaired delayed memory and attention-language compared to the healthy test group. Robust norms outperformed Conventional norms in these domains. This was particularly important with respect to comparing the healthy test and NL→DEC groups as older age is a risk factor for decline (Gao, Hendrie, Hall, & Hui, 1998; Kukull et al., 2002; Ritchie & Kildea, 1995).
With the specificity of the Robust and Conventional norms equally constrained, the sensitivity of the Robust norms was significantly higher than for the Conventional norms for the normal decline and stable MCI groups. For longitudinal change over time, the results were also unaffected in that the normal early decline group showed excessive change in delayed memory and attention-language domains compared to the healthy test group. Therefore, we are confident that the age distribution of the healthy test group and the other test groups did not bias our results.
Our Conventional and Robust Normative samples were mutually exclusive of each other and exclusive of the test sample. Therefore, we could more independently assess the utility of the norms. We were able to test the efficiency of using Robust and Conventional norms in identifying cognitive impairment in normal, MCI and AD groups. Few previous studies have compared these two types of norms. In these few studies, the diagnostic groups either were not mutually exclusive (Ritchie et al., 2007; Sliwinski et al., 1996) or the study did not evaluate the utility of these norms in predicting decline from NL (Manly et al., 2005; Ritchie et al., 2007). The literature on Robust norms is growing and with increased availability of data from longitudinal normal cohorts, we suggest that Robust norms be generated for published neuropsychological tests so as to increase the sensitivity of identifying those at high risk for early decline.
The current study must be examined in light of its limitations. The participant samples used in this study were not random population samples. All participants were volunteers participating in brain aging studies at a research center. The resulting samples were better educated, healthier, and ethnically less heterogeneous than the general population. Thus, we do not know if Robust norms would be equally useful when applied to the general population and those with less education. We anticipate, however, that Robust norms would be applicable to the general population as the literature has shown the utility of these norms in a random sample of older participants and in those with less education (Manly et al., 2005; Sliwinski et al., 1996). Further, because we limited the study to normal, MCI, and AD participants, the generalizability of our findings to other dementing disorders is unknown. This study was specifically designed to examine the sensitivity of our cognitive measures to predict decline in longitudinally validated clinical groups so as to establish the utility of these norms for diagnostically homogenous normal, MCI, and AD populations. Future studies are warranted to explore the specificity of Robust norms for predicting progression to AD. Finally, with the exception of the immediate and delayed paragraph recall tests, the version of the measures used in this study are specific to our ADC and may not be commonly used by other centers or clinicians. However, our measures are, of course, available for use by other investigators, and they are similar to other tests of relevant cognitive domains that are commonly used. We acknowledge that many of these measures (e.g., digits forward and backward) have been updated. However, given the ongoing longitudinal research of our center, the older versions remain in use. Nevertheless, Robust norms outperform Conventional norms and we believe this generalization will hold true for diverse versions of these or similar tests.
The results of this study highlight the importance of using a longitudinally stable normal cohort to develop cognitive test norms. Not only are cross-sectional Robust norms more useful than Conventional cross-sectional norms but longitudinal norms provide additional value for predicting future cognitive performance. Some normal individuals who will decline to MCI or AD have baseline impairment in memory function and, depending on the interval between baseline and decline, this impairment may also include other cognitive domains. Together, baseline performance and longitudinal change provide unique information about which normal individuals will decline. Our findings should be useful for augmenting participant selection for research protocols.
This research was supported by NIH-NIA Grants AG12101, AG13616, AG08051 and AG022374. We thank the NYUSoM ADC Clinical Core and Datacore for their assistance in this work, especially Alok Vedvyas. Special thanks to Juan Li for her statistical assistance.
Supplemental materials: http://dx.doi.org/10.1037/0894-4126.96.36.1999.supp
Susan De Santi, Department of Psychiatry, New York University School of Medicine.
Elizabeth Pirraglia, Department of Psychiatry, New York University School of Medicine.
William Barr, Departments of Neurology and Psychiatry, New York University School of Medicine.
James Babb, Department of Radiology, New York University School of Medicine.
Schantel Williams, Department of Psychiatry, New York University School of Medicine.
Kimberley Rogers, Department of Psychiatry, New York University School of Medicine.
Lidia Glodzik, Department of Psychiatry, New York University School of Medicine.
Miroslaw Brys, Department of Psychiatry, New York University School of Medicine.
Lisa Mosconi, Department of Psychiatry, New York University School of Medicine.
Barry Reisberg, Department of Psychiatry, New York University School of Medicine.
Steven Ferris, Department of Psychiatry, New York University School of Medicine and The Nathan S. Kline Institute for Psychiatric Research.
Mony J. de Leon, Department of Psychiatry, New York University School of Medicine and The Nathan S. Kline Institute for Psychiatric Research.