Feasibility of Predicting MCI/AD Using Neuropsychological Tests and Serum <b>β</b>-Amyloid

Luis, Cheryl A.; Abdullah, Laila; Ait-Ghezala, Ghania; Mouzon, Benoit; Keegan, Andrew P.; Crawford, Fiona; Mullan, Michael

doi:https://doi.org/10.4061/2011/786264

International Journal of Alzheimer’s Disease

On this page

Abstract Introduction Methods Results Discussion Conclusion Disclosure Acknowledgments References Copyright Related Articles

Special Issue

Biomarkers for Dementia

View this Special Issue

Research Article | Open Access

Volume 2011 | Article ID 786264 | https://doi.org/10.4061/2011/786264

Feasibility of Predicting MCI/AD Using Neuropsychological Tests and Serum β-Amyloid

Cheryl A. Luis,¹Laila Abdullah,¹Ghania Ait-Ghezala,¹Benoit Mouzon,¹Andrew P. Keegan,¹Fiona Crawford,¹and Michael Mullan¹

Academic Editor: Katsuya Urakami

Received13 Oct 2010

Revised23 Mar 2011

Accepted27 Mar 2011

Published24 May 2011

Abstract

We examined the usefulness of brief neuropsychological tests and serum Aβ as a predictive test for detecting MCI/AD in older adults. Serum Aβ levels were measured from 208 subjects who were cognitively normal at enrollment and blood draw. Twenty-eight of the subjects subsequently developed MCI () or AD () over the follow-up period. Baseline measures of global cognition, memory, language fluency, and serum Aβ_1–42 and the ratio of serum Aβ_1–42/Aβ_1–40 were significant predictors for future MCI/AD using Cox regression with demographic variables, APOE ε4, vascular risk factors, and specific medication as covariates. An optimal sensitivity of 85.2% and specificity of 86.5% for predicting MCI/AD was achieved using ROC analyses. Brief neuropsychological tests and measurements of Aβ_1–42 obtained via blood warrants further study as a practical and cost effective method for wide-scale screening for identifying older adults who may be at-risk for pathological cognitive decline.

1. Introduction

An exponential rise in Alzheimer’s disease (AD) prevalence rates is predicted to parallel the aging of baby boomers creating a potentially unsustainable economic burden to the healthcare system. Delaying the onset or progression of AD, even modestly, by earlier pharmacological intervention could substantially reduce the economic and psychosocial impact of the illness [1, 2]. Unfortunately, many AD patients remain undiagnosed or go undetected until the later stages of disease. Insights into the underlying pathological mechanisms involving beta-amyloid plaque deposition within the brain have led to the development of a host of antiamyloid agents [3] that are in various stages of clinical investigation. There is now a scientific consensus that the pathological events in AD initiate decades before clinical symptoms become apparent, and if disease modification is realized in the coming decades, the need for improved methods of early detection prior to the overt clinical signs will be accentuated.

Traditionally, neuropsychological measures, particularly those that tap cognitive abilities subsumed by the hippocampal formation such as episodic memory, have shown usefulness in identifying cognitively normal elders who subsequently develop AD [4, 5]. Decrements in semantic memory and concept formation have been shown to occur nearly a decade before the development of AD [6]. Performance on visual-spatial and verbal memory measures in midlife have also been shown to predict later memory loss [7]. Neuropsychological measures are noninvasive and generally cost effective. However, individuals with very high premorbid intellectual abilities experiencing incipient cognitive decline may go undetected, and false positives are possible in individuals with a low level of intellectual abilities. Also appropriate interpretation of extensive neuropsychological testing requires a high degree of expertise and training, which limits its use in routine clinical settings.

The advancement of molecular imaging tracers that bind to amyloid, such as Pittsburgh Compound B (PIB) or longer-lived probes (e.g., FDDNP), offers a non-invasive in vivo method to detect and quantify brain amyloid deposition [8, 9]. However, this approach for presymptomatic detection is economically impractical for routine use given the current costs and restrictions on “medically necessary” use. Similarly, biomarkers including Aβ_1–42 and phosphorylated tau (also implicated in AD pathology) in cerebral spinal fluid (CSF) can predict subsequent cognitive decline [10, 11], but lumbar puncture carries risks and is inconvenient for wide-scale use in cognitively impaired elderly subjects.

Blood-based biomarkers have more practical applicability for routine use and are likely to be more cost effective than both CSF and imaging procedures. Consequently, measurement of Aβ_1–40 and Aβ_1–42 in blood is increasingly being explored and shows potential in identifying individuals at the preclinical stage of AD [12–14]. It has been reported that CSF Aβ levels are subject to high diurnal fluctuations with extremely high variability reported over 12 hours [15]. Over days and weeks, Aβ in blood appears more stable than CSF [16–18]. Furthermore, serum contains more Aβ than plasma [16], possibly due to the release of bound Aβ during the clotting process [19]. Hence, serum Aβ appears suitable for use in predicting MCI/AD and optimal sensitivity, and specificity is probably achievable if combined with current diagnostic procedures, such as brief neuropsychological testing.

In this study, we examined the usefulness of brief neuropsychological tests in combination with blood Aβ_1–40 and Aβ_1–42 as a predictive test for detecting MCI/AD in at-risk older adults at a pre-symptomatic stage. Such an approach will be more practical for clinical use and be germane in designing large-scale prevention trials.

2. Methods

2.1. Subjects

Participants included a subset of subjects enrolled in the Alzheimer’s Disease Anti-Inflammatory Prevention Trial (ADAPT). ADAPT was a randomized, placebo-controlled, multicenter primary prevention trial sponsored by the National Institute on Aging. The Roskamp Institute served as one of six recruitment sites located across the US. Subjects were randomly assigned to one of three groups: celecoxib (200 mg b.i.d.), naproxen sodium (220 mg b.i.d.), or placebo. The primary outcome measure of ADAPT was development of AD. Full details of data collection, measurements, and study procedures are available at http://www.jhucct.com/adapt/manall43.pdf and described elsewhere [20].

The inclusion criteria for ADAPT subjects were age of 70 or older at enrollment, a self-reported family history of AD-like dementia, and normal cognitive performance on a brief battery of neuropsychological tests. Recruitment for ADAPT began in 2002, and the study was completed in 2007. In 2005, the Roskamp Institute initiated a proteomic ancillary study (F. Crawford, PI) involving blood draw from these subjects. The inclusion criteria for this ancillary study stipulated that each subject was an active ADAPT participant and had met all the ADAPT inclusion and exclusion criteria. An approval was obtained from the ADAPT Steering Committee and a centralized IRB. A separate consent was also obtained from each subject who participated in the ancillary study.

Two hundred and fifteen subjects from the Roskamp ADAPT cohort enrolled in the proteomic ancillary study. At the time of blood draw, subjects maintained cognitively normal status as determined by their performance on an annual cognitive assessment battery. These assessments continued for an additional two years following the blood draw. Blood was collected during the semi-annual followup visits, and the cognitive assessments were performed at the baseline visit and at the annual visits. The time from baseline cognitive testing to the diagnosis of MCI/AD was 4.06 years (±1.3 SD). Timeframe from baseline cognitive testing to blood draw was 2.25 years (±0.71SD) and from blood draw to diagnosis was 1.79 years (±1.2 SD). The cognitive measures completed at baseline and annual followup included the Modified Mini-Mental State Examination (3MS) [21]; the Hopkins Verbal Learning Test-Revised (HVLT-R) [22]; Digit Span (forward and backward) from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) [23]; a Generative Verbal Fluency test (supermarket items); the narratives from the Rivermead Behavioral Memory Test (RBMT) [24]; the Brief Visuospatial Memory Test-Revised (BVMT-R) [25]. The Mini-Mental State Examination (MMSE) [26] was extracted from 3MS. Alternate forms were utilized annually for the HVLT-R, RBMT, and BVMT-R on each subsequent annual visit. Subjects also completed the 30-item Geriatric Depression Scale [27] and a self-rating scale of memory functions [28]. Collateral respondents completed the Dementia Severity Rating Scale (DSRS) [29]. Due to significant intercorrelations between these tests, analyses described below are limited to those baseline cognitive tests that were sensitive to early changes (i.e., verbal learning and memory) associated with MCI/AD [30] or tests that were similar to those previously shown to be associated with Aβ levels [31].

Normative data from the Cache County study was used to develop the standardized cut-off scores utilized in ADAPT [32]. Individuals who scored below the cut scores on annual cognitive assessments underwent further dementia workup including physical and neurological examinations, laboratory studies (i.e., CBC, chemistry count, sedimentation rate, vitamin B₁₂ and folic acid levels, thyroid test, and syphilis serological test), and neuroimaging (i.e., MRI or CT), as applicable. A more comprehensive neuropsychological assessment was also administered by a neuropsychologist as part of the dementia work-up. This battery of tests consisted of the expanded Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) battery [33]; Logical Memory I and II of the Wechsler Memory Scale-Revised [34]; Benton Visual Retention Test [35] (Benton); a generative fluency test (animals); Control Oral Word Association Test (COWAT; CFL) [36]; The Trail Making Test [37]; Symbol Digit Modalities Test (SMDT) [38]; Shipley Vocabulary [39].

Following completion of all components of the dementia work-up, a consensus team determined cognitive status using published diagnostic criteria. The annual battery was not utilized in diagnostic determination. The diagnosis of AD was made using NINCDS-ADRDA [40] and amnestic mild cognitive impairment (MCI) using Petersen criteria [41]. All MCI patients were considered to be amnestic MCI, as they only had memory impairment, but maintained normal activities of daily living and overall had a well-preserved cognition in other cognitive domains. Ample evidence indicates that amnestic MCI patients may be in a transitional stage between normal aging and AD with 85% of these subjects converting to AD over a 7-year period [42]. Additional evidence comes from an imaging study which demonstrated that the pattern of brain atrophy in amnestic MCI patients is typical of that observed in AD patients [43]. It is then reasonable to combine these diagnoses in a single category, thus allowing a large enough numbers to supply statistical power. Of the 215 subjects who gave blood for the ancillary study, two developed non-AD dementia, and another subject died with cognitive status unknown. Blood Aβ values were unavailable for 4 subjects. Of the remaining subject pool of 208 used in these analyses, 28 subjects met criteria for either AD () or MCI () in the two years following blood draw.

2.2. Sample Collection, Preparation, and Measurements

Blood draws for Aβ measurement and APOE genotyping were conducted by trained phlebotomists. Serum from blood was prepared and processed using standard laboratory procedures [16]. The serum Aβ content was determined, as per manufacturer’s instructions, using the ELISA kits for human Aβ_1–40 and Aβ_1–42 and the inter-assay CV, and the intraassay CV was reported to be ≤10% (Invitrogen, Calif). Additional details are provided elsewhere [16]. DNA was extracted from whole blood for APOE genotyping using Pure Gene Kits (Gentra systems, Calif), and APOE genotyping was performed using previously established methods, as described elsewhere [16]. APOE genotypes were unavailable for 4 individuals, but these were included in the analyses.

2.3. Statistical Analyses

The data set was range checked, and prior to analyses, the dependent and independent variables were examined for missing data, outliers, and violations of the normalcy assumption. Differences among groups on demographic variables, neuropsychological variables, and serum Aβ_1–40 levels were examined using either the student’s t-test or χ² analyses, depending on the type of variable measurement. The Mann-Whitney test was employed if parametric assumptions were not met.

Time-updated Cox regression modeling was used to test whether neuropsychological test scores, Aβ, or a combination of both can predict conversion to MCI/AD in individuals who were cognitively normal at baseline. Potential confounding variables shown to impact risk for cognitive decline included age, education, gender, APOE status, serum creatinine, triglycerides, presence of APOE ε4 allele, and history of vascular disease as determined by treatment with statins or antihypertensive medication which were entered as covariates. The latter variables, coded dichotomously, have been previously shown to impact Aβ levels [44]. Because previous analyses revealed a nonsignificant increase of AD risk with naproxen in this cohort [45], we also controlled for this effect.

Logistic regression modeling was employed to construct receiver operator curves (ROC) to examine the predictive performance of neuropsychological measures from the baseline visit and serum Aβ levels in diagnoses of MCI/AD. ROC curve comparisons were based on area under the curve (AUC), SE, and the associated 95% confidence interval (CI). We subsequently calculated sensitivity of the various models using the predicted probability of each subject by logistic regression modeling with specificity of at least eighty percent. Post hoc power calculations using the G-power software for multivariate regression analyses utilized here suggest a power of nearly 100% at the alpha value 0.05 for the current sample size, total number of predictors, and the observed effect size. All analyses were conducted using the SPSS version 16.0 for Macintosh.

3. Results

The mean age and education of the sample was 76.7 () and 14.6 () years, respectively. The majority of the sample was Caucasian (98.1%), and 51.9% were male. Despite the cohort’s self-report of enriched family history, less than one-third of the total sample (31.7%) carried at least one APOE ε4 allele, a frequency similar to the general population [44]. Comparisons on variables between subjects who remained cognitively normal and those who declined over the short follow-up period are reported in Table 1. Although all subjects at enrollment performed within the normal limits based on the established cut-off scores, those that ultimately declined had generally poorer scores on the 3MS, MMSE, and all memory measures. The two groups were also significantly different on serum Aβ_1–42 levels and Aβ_1–42/Aβ_1–40ratios prior to diagnoses of MCI/AD. Only 23% of the cognitively normal individuals had serum Aβ_1–42 in the lowest quartile compared to the nearly 50% of the diagnostic group (44% of MCI subjects and 50% of AD subjects).

Time-dependent Cox regression analyses were performed to examine the relationship between these cognitive tests and Aβ on the prediction of subsequent conversion to MCI/AD. All neuropsychological analyses were adjusted for age, gender, and education, but no adjustment for the study medications was required as these were baseline scores. Cox regression analyses show that the model using neuropsychological tests predicted MCI/AD (−2 log-likelihood = 206.51, χ² = 52.11, df = 8, ). Significant individual neuropsychological measures were 3MS (β = −0.25 ± 0.06, Wald = 17.78, ); generative verbal fluency (β = 0.12 ± 0.04, Wald = 8.09, ); HVLT-R scores (β = 0.24 ± 0.11, Wald = 4.58 ).

Cox regression analysis showed that Aβ_1–42 measured in the lowest two quartiles compared to the highest quartile was a significant individual predictor of conversion to MCI/AD in this model (−2 log-likelihood = 197.47, , df = 15, ). The regression analysis utilizing the Aβ_1–42/Aβ_1–40 ratio found similarly significant results (−2 log-likelihood = 204.69, , df = 14, ) with the lowest ratios being most predictive of subsequent conversion to MCI/AD. The final full model, adjusting for confound and the study medications, included HVLT-R, fluency, 3MS, Aβ_1–42 levels, and Aβ_1–42 quartiles (−2 log-likelihood = 166.25, χ² = 74.55, df = 18, ) with fluency, 3MS, and Aβ_1–42 in the lowest two quartiles as significant individual predictors of MCI/AD in the model. Aβ_1–40 was not a significant individual predictor. Similar results were observed when Aβ_1–40 levels and Aβ_1–42 quartiles were substituted in this model with Aβ_1–42/Aβ_1–40 ratios (−2 log-likelihood = 168.49, χ² = 72.90, df = 17, ).

Baseline values for the 3MS, HVLT-R, and generative verbal fluency scores were subtracted from those obtained at the 12-month repeat testing to determine if changes in these measures differ by Aβ_1–42 and Aβ_1–42/Aβ_1–40 ratios. In unadjusted analyses, among subjects who converted to MCI/AD, the greatest decline for HVLT-R was observed among individuals with the lowest quartile of Aβ_1–42 () and Aβ_1–42/Aβ_1–40 ratios () where individuals in the highest quartile of Aβ_1–42 () and Aβ_1–42/Aβ_1–40 ratios improved by nearly one point (0.6 ± 1.82 SD). However, these differences were not statistically significant (). For the 3MS scores, among subjects who converted to MCI/AD, those with Aβ_1–42 in the lowest quartile declined () as compared to the highest quartile (), and this difference was statistically significant (F = 3.42,). For MCI/AD subjects with the lowest quartile of the Aβ_1–42/Aβ_1–40 ratios, the 3MS values remained ultimately unchanged (), while the scores improved among those with the highest quartile of the Aβ_1–42/Aβ_1–40 ratios (), and these differences were also statistically significant (F = 3.10, ). For generative verbal fluency test, a decline was noted in both the lowest quartile () and the highest quartile () of Aβ_1–42, and these differences were marginally significant (F = 2.63, ). For Aβ_1–42/Aβ_1–40 ratios, a similar pattern was observed, but this difference was not statistically significant. Among individuals who remained cognitively normal, while a similar pattern was observed, those with lowest quartile of Aβ_1–42 and Aβ_1–42/Aβ_1–40 ratios had a larger decline than those with the highest quartile for each HVLT-R ( versus. , respectively.) and 3MS ( versus ). However, due to the small magnitude of the change in these scores, these differences were not statistically significant. No such change was observed for the generative verbal fluency test (data not shown).

Examination of sensitivity and specificity using ROC analysis revealed the AUC for neuropsychological testing with age, education, and gender as covariates was 0.83 (95% CI [0.75–0.91], ). For Aβ_1–42 (adjusted for presence of APOE ε4 allele, vascular risk factors, and associated medications), the AUC was 0.79 (95% CI [0.70–0.88], ). When neuropsychological testing (3MS, HVLT-R, and Generative Verbal Fluency) and Aβ_1–42 were combined, the AUC was increased to 0.91 (95% CI [0.86–0.95], ). For the adjusted (as above) Aβ_1–42/Aβ_1–40 ratios alone, the AUC was 0.79 (95% CI [0.71–0.88], ), and when combined with the neuropsychological measures, AUC was 0.91 (95%CI [0.87–0.96], ). The various ROC curves are displayed in Figure 1. Optimal sensitivities with specificity of at least 80% predicted probabilities are shown in Table 2. The highest sensitivity and specificity was achieved using a combination of cognitive scores and Aβ_1–42/Aβ_1–40 ratio, but this finding was driven by Aβ_1–42.

4. Discussion

The pathogenesis of AD is initiated before the clinical symptoms of cognitive impairment and functional decline become apparent in its victims. A simple and pragmatic method for identifying older adults at an increased risk for MCI/AD who may benefit from targeted prevention is therefore of importance in reducing the burden of AD. The combination of brief neuropsychological tests along with blood-based biomarkers of AD represents a reasonable approach with a potential for wide-scale use. Our findings here provide support for this notion and demonstrate that early prediction of risk for developing MCI/AD may be feasible via a combination of brief neuropsychological tests and biomarkers in an at-risk cohort. In this subcohort from ADAPT, measures of global cognitive function (3MS), episodic memory (HVLT-R Trial 4), language fluency, and serum Aβ_1–42/Aβ_1–40 ratio achieved an excellent accuracy of 91%. Furthermore, sensitivity with specificity of at least 80% for the combined measures was superior to neuropsychological measures or to serum Aβ levels alone.

We have recently shown that Aβ levels alone can predict MCI/AD [14], but Aβ levels are influenced by vascular disease and associated medications [44] and require adjustment to observe the full impact of Aβ in predictive modeling. We have also shown that in subjects diagnosed with AD, there is an association between measures of language tests of fluency and object naming and Aβ_1–40 and that memory performance is associated with serum Aβ_1–42[31]. An association between serum Aβ_1–40 and cognitive measures of memory and language has also been reported in cognitively normal older adults [46]. High baseline Aβ_1–42 and Aβ_1–40 with stable Aβ_1–42 over time is shown to be associated with diminishing cognition [47]. More recently, Yaffe and colleagues demonstrated that low Aβ_1–42/Aβ_1–40 ratios predict cognitive decline over 9 years [48]. In our study, we demonstrate that low Aβ_1–42 and Aβ_1–42/Aβ_1–40 ratios are associated with cognitive decline even within one year. This is extremely valuable from the clinical perspective, as the ability to identify at-risk individuals within a year prior to the onset can significantly improve the quality of care and the recruitment strategy for prevention trials by redirecting those individuals who may not benefit from preventive therapies towards more suitable clinical intervention. This is demonstrated by recent ADAPT findings, which suggest that individuals with low baseline cognitive scores converted soon after the trial initiated and that neither naproxen nor celecoxib intervention was beneficial to these individuals [49]. Collectively, these findings suggest that combining cognitive tests with blood Aβ may be useful for predicting future MCI/AD, which to date has not been explored, particularly as either Aβ or the cognitive tests alone may not have the desired sensitivity or specificity for prediction of future MCI/AD.

This current work presented here provides evidence that the combination of brief neuropsychological tests and blood Aβ has potential utility in predicting MCI/AD at least 2 to 4 years prior to the clinical classification of MCI or diagnosis of AD. In addition, our findings also demonstrate the importance of accounting for factors such as APOE, vascular risk factors, and medications when using Aβ in predicting MCI/AD. Although at present no studies have reported sensitivity and specificity of CSF Aβ_1–42 in predicting MCI/AD conversion from normal cognition, a large multicenter study has shown that CSF Aβ_1–42 predicts transition from MCI to AD [50], while tau alone achieved a high sensitivity (83%) with acceptable specificity (72%). It is interesting to note that our findings using blood and cognitive tests, a far less invasive method, resulted in higher sensitivities and specificities for predicting cognitive decline in at-risk cognitively normal older adults. Despite the limitation that blood sampling was not conducted at the same time point as the cognitive testing, our data provide strong support for further evaluation of this approach, particularly as we have not seen significant fluctuations in Aβ levels over a one-year period (pers. Comm.).

5. Conclusion

Our study provides support that blood-based Aβ levels may have diagnostic utility when combined with neuropsychological measures. This proposed method warrants further investigation to determine its practical applicability in specialized clinic setting by allied health personal and in routine primary care clinics.

Disclosure

The authors report no conflict of interests.

Acknowledgments

The ADAPT study was supported by the National Institute of Aging through the National Institute of Health Grant no. NIH 7U01AG15477-02. Portions of this study were also supported by the Alzheimer’s Association (NIRG-09-131751) and through the generous support from the Robert and Diane Roskamp Foundation.

References

R. Brookmeyer, E. Johnson, K. Ziegler-Graham, and H. M. Arrighi, “Forecasting the global burden of Alzheimer's disease,” Alzheimer's and Dementia, vol. 3, no. 3, pp. 186–191, 2007.
View at: Publisher Site | Google Scholar
D. L. Weimer and M. A. Sager, “Early identification and treatment of Alzheimer's disease: social and fiscal outcomes,” Alzheimer's and Dementia, vol. 5, no. 3, pp. 215–226, 2009.
View at: Publisher Site | Google Scholar
C. Holmes, D. Boche, D. Wilkinson et al., “Long-term effects of Abeta42 immunisation in Alzheimer’s disease: follow-up of a randomized, placebo-controlled phase 1 trial,” Lancet, vol. 372, pp. 216–223, 2008.
View at: Google Scholar
D. Blacker, H. Lee, A. Muzikansky et al., “Neuropsychological measures in normal individuals that predict subsequent cognitive decline,” Archives of Neurology, vol. 64, no. 6, pp. 862–871, 2007.
View at: Publisher Site | Google Scholar
M. W. Bondi, D. P. Salmon, D. Galasko, R. G. Thomas, and L. J. Thal, “Neuropsychological function and apolipoprotein E genotype in the preclinical detection of Alzheimer's disease,” Psychology and Aging, vol. 14, no. 2, pp. 295–303, 1999.
View at: Publisher Site | Google Scholar
H. Amieva, M. Le Goff, X. Millet et al., “Prodromal Alzheimer's disease: successive emergence of the clinical symptoms,” Annals of Neurology, vol. 64, no. 5, pp. 492–498, 2008.
View at: Publisher Site | Google Scholar
G. W. Small, A. La Rue, S. Komo, A. Kaplan, and M. A. Mandelkern, “Predictors of cognitive change in middle-aged and older adults with memory loss,” American Journal of Psychiatry, vol. 152, no. 12, pp. 1757–1764, 1995.
View at: Google Scholar
W. E. Klunk, H. Engler, A. Nordberg et al., “Imaging brain amyloid in Alzheimer's disease with pittsburgh compound-B,” Annals of Neurology, vol. 55, no. 3, pp. 309–319, 2004.
View at: Publisher Site | Google Scholar
G. W. Small, V. Kepe, L. M. Ercoli et al., “PET of brain amyloid and tau in mild cognitive impairment,” New England Journal of Medicine, vol. 355, no. 25, pp. 2652–2663, 2006.
View at: Publisher Site | Google Scholar
P. Vemuri, H. J. Wiste, S. D. Weigand et al., “MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change,” Neurology, vol. 73, no. 4, pp. 294–301, 2009.
View at: Publisher Site | Google Scholar
A. M. Fagan, C. M. Roe, C. Xiong, M. A. Mintun, J. C. Morris, and D. M. Holtzman, “Cerebrospinal fluid tau/β-amyloid (42) ratio as a prediction of cognitive decline in nondemented older adults,” Archives of Neurology, vol. 64, no. 3, pp. 343–349, 2007.
View at: Publisher Site | Google Scholar
N. R. Graff-Radford, J. E. Crook, J. Lucas et al., “Association of low plasma Aβ42/Aβ40 ratios with increased imminent risk for mild cognitive impairment and Alzheimer' disease,” Archives of Neurology, vol. 64, no. 3, pp. 354–362, 2007.
View at: Publisher Site | Google Scholar
O. I. Okereke, W. Xia, D. J. Selkoe, and F. Grodstein, “Ten-year change in plasma amyloid β levels and late-life cognitive decline,” Archives of Neurology, vol. 66, no. 10, pp. 1247–1253, 2009.
View at: Publisher Site | Google Scholar
L. Abdullah, C. Luis, D. Paris et al., “Serum Aβ levels as predictors of conversion to mild cognitive impairment/Alzheimer disease in an ADAPT subcohort,” Molecular Medicine, vol. 15, no. 11-12, pp. 432–437, 2009.
View at: Publisher Site | Google Scholar
R. J. Bateman, G. Wen, J. C. Morris, and D. M. Holtzman, “Fluctuations of CSF amyloid-β levels: implications for a diagnostic and therapeutic biomarker,” Neurology, vol. 68, no. 9, pp. 666–669, 2007.
View at: Publisher Site | Google Scholar
L. Abdullah, D. Paris, C. Luis et al., “The influence of diagnosis, intra- and inter-person variability on serum and plasma Aβ levels,” Neuroscience Letters, vol. 428, no. 2-3, pp. 53–58, 2007.
View at: Publisher Site | Google Scholar
D. R. Lachno, H. Vanderstichele, G. De Groote et al., “The influence of matrix type, diurnal rhythm and sample collection and processing on the measurement of plasma beta-amyloid isoforms using the INNO-BIA plasma Abeta forms multiplex assay,” The Journal of Nutrition, Health and Aging, vol. 13, pp. 220–225, 2009.
View at: Google Scholar
N. Ertekin-Taner, L. H. Younkin, D. M. Yager et al., “Plasma amyloid β protein is elevated in late-onset Alzheimer' disease families,” Neurology, vol. 70, no. 8, pp. 596–606, 2008.
View at: Publisher Site | Google Scholar
P. W. Thompson and A. Lockhart, “Monitoring the amyloid beta-peptide in vivo—caveat emptor,” Drug Discovery Today, vol. 14, no. 5-6, pp. 241–251, 2009.
View at: Publisher Site | Google Scholar
C. Meinert , L. McCaffrey, and J. Breitner, “Alzheimer's disease anti-inflammatory prevention trial: design, methods and baseline results,” Alzheimer’s Dement, vol. 5, pp. 93–104, 2009.
View at: Google Scholar
E. L. Teng and H. C. Chui, “The modified mini-mental state (MMS) examination,” Journal of Clinical Psychiatry, vol. 48, no. 8, pp. 314–318, 1987.
View at: Google Scholar
J. Brandt and R. Benedict, “The Hopkins verbal learning test: development of a new memory test with six equivalent forms,” Clinical Neuropsychologist, vol. 5, no. 2, pp. 125–142, 1991.
View at: Google Scholar
D. Wechsler, Wechsler Adult Intelligence Scale—Revised. Manual, Psychological Corporation, New York, NY, USA, 1981.
B. Wilson, J. Cockburn, A. Baddeley, and R. Hiorns, “The development and validation of a test battery for detecting and monitoring everyday memory problems,” Journal of Clinical and Experimental Neuropsychology, vol. 11, no. 6, pp. 855–870, 1989.
View at: Google Scholar
R. Benedict, C. Schrectlen, L. Groninger et al., “Revision of the brief visual spatial memory tests: studies of normal performance, reliability and validity,” Psychological Assessment, vol. 10, pp. 31–39, 1996.
View at: Google Scholar
M. F. Folstein, S. E. Folstein, and P. R. McHugh, “Mini mental state. A practical method for grading the cognitive state of patients for the clinician,” Journal of Psychiatric Research, vol. 12, no. 3, pp. 189–198, 1975.
View at: Publisher Site | Google Scholar
J. Yesavage, T. Brink, T. Rose et al., “Development and validation of a geriatric depression screening scale: a preliminary report,” Journal of Psychiatric Research, vol. 17, pp. 37–49, 1983.
View at: Google Scholar
L. Squire, C. Wetzel, and P. Slater, “Memory complaints after electroconvulsive therapy: assessment with a new self-rating scale instrument,” Biological Psychiatry, vol. 14, pp. 791–801, 1979.
View at: Google Scholar
C. M. Clark and D. C. Ewbank, “Performance of the dementia severity rating scale: a caregiver questionnaire for rating severity in Alzheimer' disease,” Alzheimer Disease and Associated Disorders, vol. 10, no. 1, pp. 31–39, 1996.
View at: Publisher Site | Google Scholar
C. A. de Jager, A. C. M. C. Schrijnemaekers, T. E. M. Honey, and M. M. Budge, “Detection of MCI in the clinic: evaluation of the sensitivity and specificity of a computerised test battery, the Hopkins verbal learning test and the MMSE,” Age and Ageing, vol. 38, no. 4, pp. 455–460, 2009.
View at: Publisher Site | Google Scholar
C. A. Luis, L. Abdullah, D. Paris et al., “Serum β-amyloid correlates with neuropsychological impairment,” Aging, Neuropsychology, and Cognition, vol. 16, no. 2, pp. 203–218, 2009.
View at: Publisher Site | Google Scholar
J. T. Tschanz, K. A. Welsh-Bohmer, I. Skoog et al., “Dementia diagnoses from clinical and neuropsychological data compared: the cache county study,” Neurology, vol. 54, no. 6, pp. 1290–1296, 2000.
View at: Google Scholar
J. C. Morris, A. Heyman, R. C. Mohs et al., “The consortium to establish a registry for Alzheimer's disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease,” Neurology, vol. 39, no. 9, pp. 1159–1165, 1989.
View at: Google Scholar
D. Wechsler, Wechsler Memory Scale—Revised, Psychological Corporation, San Antonio, Tex, USA, 1987.
A. Benton, K. Hamsher, and A. Siven, Multilingual Aphasia Examination, AJA Associates, Iowa City, Calif, USA, 3rd edition, 1994.
S. W. Sumerall, P. L. Timmons, A. L. James, M. J. M. Ewing, and M. E. Oehlert, “Expanded norms for the controlled oral word association test,” Journal of Clinical Psychology, vol. 53, no. 5, pp. 517–521, 1997.
View at: Publisher Site | Google Scholar
R. Reitan, Trail Making Test: Manual for Administering and Scoring, Reitan Neuropsychological Laboratory, Tucson, Ariz, USA, 1986.
A. Smith, Symbol Digit Modalities Test—Manual, Western Psychological Services, Los Angeles, Calif, USA, 1982.
R. Zachary, Shipley Institute of Living Scale—Revised, Western Psychological Services, Los Angeles, Calif, USA, 1991.
G. McKhann, D. Drachman, M. Folstein et al., “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, vol. 34, pp. 939–944, 1984.
View at: Google Scholar
R. C. Petersen, G. E. Smith, S. C. Waring, R. J. Ivnik, E. G. Tangalos, and E. Kokmen, “Mild cognitive impairment: clinical characterization and outcome,” Archives of Neurology, vol. 56, no. 3, pp. 303–308, 1999.
View at: Google Scholar
R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in mild cognitive impairment,” Archives of Neurology, vol. 58, no. 12, pp. 1985–1992, 2001.
View at: Google Scholar
J. L. Whitwell, R. C. Petersen, S. Negash et al., “Patterns of atrophy differ among specific subtypes of mild cognitive impairment,” Archives of Neurology, vol. 64, no. 8, pp. 1130–1138, 2007.
View at: Publisher Site | Google Scholar
L. Abdullah, C. Luis, D. Paris et al., “High serum Aβ and vascular risk factors in first-degree relatives of Alzheimer's disease patients,” Molecular Medicine, vol. 15, no. 3-4, pp. 95–100, 2009.
View at: Publisher Site | Google Scholar
C. G. Lyketsos, J. C. S. Breitner, R. C. Green et al., “Naproxen and celecoxib do not prevent AD in early results from a randomized clinical trial,” Neurology, vol. 68, no. 21, pp. 1800–1808, 2007.
View at: Publisher Site | Google Scholar
J. Gunstad, M. B. Spitznagel, E. Glickman et al., “β-amyloid is associated with reduced cognitive function in healthy older adults,” Journal of Neuropsychiatry and Clinical Neurosciences, vol. 20, no. 3, pp. 327–330, 2008.
View at: Publisher Site | Google Scholar
S. Cosention, Y. Stern, E. Sokolov et al., “Plasma {beta}-amyloid and cognitive decline,” Archives of Neurology. In press.
View at: Google Scholar
K. Yaffe, A. Weston, N. R. Graff-Radford et al., “Association of plasma beta-amyloid level and cognitive reserve with subsequent cognitive decline,” Journal of the American Medical Association, vol. 305, pp. 261–266, 2011.
View at: Google Scholar
J. C. S. Breitner, “Onset of Alzheimer's dementia occurs commonly without prior cognitive impairment: results from the Alzheimer's disease anti-inflammatory prevention trial (ADAPT),” Alzheimers Dement, vol. 4, no. 4, supplement 1, pp. T130–T131, 2008.
View at: Google Scholar
N. Mattsson, H. Zetterberg, O. Hansson et al., “CSF biomarkers and incipient Alzheimer' disease in patients with mild cognitive impairment,” Journal of the American Medical Association, vol. 302, no. 4, pp. 385–393, 2009.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2011 Cheryl A. Luis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1634

Downloads

1011

Citations