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Original Contribution |

Cerebrospinal Fluid Abnormalities and Rate of Decline in Everyday Function Across the Dementia Spectrum:  Normal Aging, Mild Cognitive Impairment, and Alzheimer Disease FREE

Ozioma C. Okonkwo, PhD; Michael L. Alosco, BA; H. Randall Griffith, PhD; Michelle M. Mielke, PhD; Leslie M. Shaw, PhD; John Q. Trojanowski, MD, PhD; Geoffrey Tremont, PhD; Alzheimer's Disease Neuroimaging Initiative
[+] Author Affiliations

Author Affiliations: Departments of Neurology (Dr Okonkwo) and Psychiatry (Dr Mielke), Johns Hopkins School of Medicine, Baltimore, Maryland; Neuropsychology Program, Rhode Island Hospital (Mr Alosco and Dr Tremont), and Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University (Dr Tremont), Providence, Rhode Island; Department of Neurology, University of Alabama at Birmingham (Dr Griffith); and Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia (Drs Shaw and Trojanowski).Group Information: A complete list of Alzheimer's Disease Neuroimaging Initiative investigators is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Authorship_list.pdf.


Arch Neurol. 2010;67(6):688-696. doi:10.1001/archneurol.2010.118.
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Published online

Objective  To investigate the effect of cerebrospinal fluid (CSF) abnormalities on the rate of decline in everyday function in normal aging, mild cognitive impairment (MCI), and mild Alzheimer disease (AD).

Design  Immunoassays of total tau (t-tau), tau phosphorylated at threonine 181 (p-tau181), and β-amyloid 1-42 (Aβ42) concentrations were performed in CSF obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative. Random effects regressions were used to examine the relationship among CSF abnormalities, cognitive impairment (assessed with the Alzheimer Disease Assessment Scale–cognitive subscale [ADAS-Cog]), and functional decline (assessed with the Pfeffer Functional Activities Questionnaire) and to determine whether the impact of CSF abnormalities on functional decline is mediated by cognitive impairment.

Setting  Fifty-eight sites in the United States and Canada.

Participants  One hundred fourteen cognitively intact adults, 195 patients with MCI, and 100 patients with mild AD.

Main Outcome Measure  Decline in the Pfeffer Functional Activities Questionnaire score.

Results  Abnormalities in all CSF analytes were associated with functional decline in MCI, and all but the t-tau:Aβ42 ratio were associated with functional decline in controls. No abnormal CSF analyte was associated with functional decline in AD. Among controls, p-tau181 concentration was the most sensitive to functional decline, whereas in MCI it was Aβ42 concentration. Cerebrospinal fluid biomarkers were uniformly more sensitive to functional decline than the ADAS-Cog score among controls and variably so in MCI, whereas the ADAS-Cog score was unequivocally more sensitive than CSF biomarkers in AD. The impact of CSF abnormalities on functional decline in MCI was partially mediated by their effect on cognitive status. Across all diagnostic groups, persons with both tau and Aβ42 abnormalities exhibited the steepest rate of functional decline.

Conclusions  Abnormalities in CSF are associated with functional decline and thus with future development of AD in controls and patients with MCI. However, they do not predict further functional degradation in patients with AD. Persons with comorbid tau and Aβ42 abnormalities are at greatest risk of functional loss.

Figures in this Article

Cerebrospinal fluid (CSF) concentrations of total tau (t-tau), tau phosphorylated at threonine 181 (p-tau181), and β-amyloid 1-42 (Aβ42) have emerged as core biomarkers of Alzheimer disease (AD) owing to their intrinsic linkage to the pathognomonic features of AD (ie, neurofibrillary tangles and amyloid plaques).14 In contrast with the demonstrations of associations between CSF abnormalities and some indices of disease severity and progression such as cognitive decline,5 plaque density,6 and cerebral alterations,7,8 the relationship between CSF abnormalities and decline in everyday function has received limited attention.5,9,10 This constitutes a significant knowledge gap for several reasons.

First, functional restriction is a hallmark of AD and other dementias.11,12 Indeed, widely used dementia staging instruments (eg, the Clinical Dementia Rating Scale) lean heavily on reports of an individual's daily functioning in ascertaining dementia severity. Thus, decline in everyday function likely signals disease onset or progression among cognitively normal older adults and those with mild cognitive impairment (MCI), respectively. Second, everyday function is an important outcome in AD clinical trials.13 Therefore, it is useful to understand how it is related to biomarkers of AD. Third, unraveling associations between CSF abnormalities and functional decline, especially in preclinical AD, might be valuable information for patients and their care providers because they often wish to know what the future holds.

In this article, we investigate (1) whether CSF abnormalities are associated with decline in everyday function; (2) whether such associations, if existent, are comparable or differential across CSF analytes; (3) whether CSF analytes are more sensitive to functional decline than cognitive measures; (4) whether the impact of CSF abnormalities on functional decline is mediated by their effect on cognition; (5) whether the combination of abnormally high t-tau or p-tau181 and abnormally low Aβ42 concentrations confers increased risk of functional decline; and (6) whether these effects are similarly present throughout the continuum from healthy cognitive aging to AD.

PARTICIPANTS

The analyses presented herein were based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; http://www.loni.ucla.edu/ADNI/). The ADNI was launched in 2003 by the National Institute on Aging and other entities (listed in the Funding/Support section) as a 5-year public-private partnership. Enrollment target was 800 participants—200 healthy control subjects, 400 patients with amnestic MCI, and 200 patients with mild AD—at 58 sites in the United States and Canada.

Diagnosis of amnestic MCI required patient-reported memory symptoms, objective memory difficulties (impaired delayed recall of Story A from the Logical Memory Test14), essentially normal functional activities, a Clinical Dementia Rating Scale global score of 0.5, and a Mini-Mental State Examination score of 24 or more. Patients with AD met the National Institute of Neurological and Communicative Diseases and Stroke–Alzheimer's Disease and Related Disorders Association criteria12 for probable AD, had Mini-Mental State Examination scores ranging from 20 to 26 (inclusive), and had Clinical Dementia Rating Scale global scores of 0.5 or 1.0. Participants underwent evaluation at 6-month intervals for 2 (patients with mild AD) or 3 (controls and patients with MCI) years. Further details about the ADNI, including participant selection procedures and complete study protocol, have been presented elsewhere1,15,16 and may be found online at http://www.nia.nih.gov/Alzheimers/ResearchInformation/ClinicalTrials/ADNI.htm.

The present analyses included all participants—114 controls, 195 patients with MCI, and 100 patients with mild AD—who had valid test results for all CSF biomarkers (ie, t-tau, Aβ42, p-tau181, t-tau:Aβ42 ratio, and p-tau181:Aβ42 ratio) when the data download occurred in November 2008. Table 1 details the participants' baseline characteristics. Informed consent was obtained from study participants and their families, and the study was approved by the local institutional review board at the participating sites.

Table Graphic Jump LocationTable 1. Characteristics of Study Participants at Baseline
CSF COLLECTION AND ANALYSIS

Full details of the collection and analysis of CSF samples in ADNI have been provided elsewhere.1 Briefly, lumbar puncture was performed in the morning after an overnight fast. Assays of t-tau, Aβ42, and p-tau181 concentrations were performed using 0.5-mL aliquots and a multiplex platform (xMAP; Luminex Corp, Austin, Texas) with immunoassay kit–based reagents (INNO-BIA AlzBio3; Innogenetics NV, Ghent, Belgium; for research use–only reagents).

FUNCTIONAL ASSESSMENT

Everyday function was assessed with the Pfeffer Functional Activities Questionnaire (FAQ).17 The FAQ is an informant-report inventory that inquires into an older adult's ability to manage finances; complete forms; shop; perform games of skill or hobbies; prepare hot beverages; prepare a balanced meal; follow current events; attend to television programs, books, or magazines; remember appointments; and travel out of the neighborhood. Ratings range from normal (0) to dependent (3), for a total of 30 points. Higher scores indicate worse functional status. The FAQ has good reliability (item-total correlations, ≥0.80) and validity (correlations with measures of mental status, daily function, and clinical diagnosis, ≥0.70).17 Within this ADNI sample, the FAQ demonstrated excellent reliability (Cronbach α = 0.93). At baseline, with the exception of control participants who, not surprisingly, mostly had scores of 0 on the FAQ, FAQ scores in this cohort were largely devoid of floor and ceiling effects. For instance, no patient with MCI or AD had a score of 30.

COGNITIVE ASSESSMENT

Global cognition was assessed with the Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog).18 The ADAS-Cog is the most widely used cognitive measure in AD clinical trials. It is brief and structured and assesses verbal learning and memory, language, orientation, ideational praxis, and constructional praxis. Scores range from 0 to 70, with higher scores reflecting poorer cognitive function.

DATA ANALYSES

Group differences on the CSF measures were tested using single degrees of freedom contrast tests, corrected for inequality of variance. To examine the association among CSF abnormalities, cognitive impairment, and functional decline within each diagnostic group, we fitted a series of random coefficient regressions19,20 that modeled change in FAQ scores as a function of baseline values on CSF biomarkers and the ADAS-Cog score. Abnormality on CSF biomarkers was defined using previously established ADNI thresholds (t-tau, 93 pg/mL; Aβ42, 192 pg/mL; p-tau181, 23 pg/mL; t-tau:Aβ42 ratio, 0.39; and p-tau181:Aβ42 ratio, 0.10).1 For the ADAS-Cog, we modeled the effect of performance that is 1 SD above (ie, worse than) group-specific means.21 The biomarker × time terms were the primary effects of interest because they would reveal the impact of CSF abnormality or cognitive impairment on the rate of change in FAQ.

To quantify and compare the variation in functional decline accounted for by each CSF biomarker or the ADAS-Cog score, we calculated the proportional reduction—a pseudo-R2 statistic—in the FAQ score's rate of change residual variation that was attained when each biomarker and its interaction with time was introduced into a model that only contained age, baseline FAQ score, and their interactions with time.20 Higher R2 values indicated that the variable being modeled accounted for a larger proportion of the unexplained variation in—and, thus, is more sensitive to—rate of change in FAQ.

To examine whether the effect of CSF biomarkers on functional decline is mediated by their effect on cognition, we tested a series of random coefficient regressions that added terms for ADAS-Cog and ADAS-Cog × time to each CSF biomarker model. Full mediation was assumed when a previously significant biomarker × time interaction became nonsignificant. Partial mediation was indicated when the biomarker × time effect was attenuated but remained significant. The percentage of the relationship between the CSF biomarker and functional decline that was mediated by cognition was computed as (original estimate − ADAS-Cog–adjusted estimate)/original estimate. Because mediation requires that the substantive and mediator variables be associated with the outcome, these analyses were performed only within diagnostic groups in which CSF biomarkers and ADAS-Cog were both significantly related to functional decline.

Finally, we examined whether individuals with a combination of abnormal tau and Aβ42 findings experience a faster rate of functional decline relative to those with no or 1 CSF abnormality by fitting a series of random coefficient regressions in which the rate of functional decline among persons in the normal tau/normal Aβ42 group was contrasted with the rate of decline in the abnormal tau/normal Aβ42, normal tau/abnormal Aβ42, and abnormal tau/abnormal Aβ42 groups.

As a precondition for examining the effects of CSF abnormalities and ADAS-Cog scores on functional decline, we first examined the temporal course and rate of functional decline within each group by fitting group-specific random effects regressions that modeled change in FAQ scores as a function of time.20 To determine the temporal course of functional decline, we compared the relative fit of linear (time) and curvilinear (time × time) polynomials for time using the Bayesian information criterion.22 On the Bayesian information criterion, lower values indicate better fit. The polynomial specification for time (ie, linear or quadratic) that emerged as optimal was used in all subsequent analyses.

All random coefficient regressions outlined included random intercept and random slope terms to account or test for potential interindividual variability in baseline scores and rate of change, respectively.20 In addition, they all included age, baseline FAQ scores, and their interactions with time as covariates; to further adjust for variations in baseline FAQ scores, analyses were begun at the 6-month assessment. Data analyses were performed using commercially available statistical software (SPSS, version 16.0; SPSS Inc, Chicago, Illinois).

GROUP DIFFERENCES IN BASELINE CSF ANALYTES

As reported in previous studies,1,2,5 CSF levels of t-tau and p-tau181 and the t-tau:AB42 and p- tau181:AB42 ratios were significantly higher, whereas Aβ42 levels were significantly lower, in patients with MCI and those with AD compared with controls, and in patients with AD compared with those with MCI (Table 2).

Table Graphic Jump LocationTable 2. CSF Biomarker Concentrations and Ratios at Baseline
TEMPORAL PATTERN OF CHANGE IN FAQ SCORE

Within each diagnostic group, the model that examined change in FAQ score as a function of linear time had a lower Bayesian information criterion statistic compared with the model that specified a quadratic function for time. For example, within the MCI group, the Bayesian information criterion statistic was 3618.45 for the linear model, whereas it was 3628.87 for the quadratic model. This was taken as evidence that, within each group, change in the FAQ score was better characterized as proceeding linearly. All subsequent analyses were performed using a linear function for time.

RATE OF CHANGE IN FAQ SCORE

Score on the FAQ increased (ie, worsened) at a mean (SE) biannual rate of 0.04 (0.04) (P = .28) among controls, 1.23 (0.16) (P < .001) among patients with MCI, and 1.77 (0.19) (P < .001) among patients with AD. Although the mean rate of deterioration in FAQ scores among controls was nonsignificant, inspection of the random slope term revealed that there was significant interindividual variability around this mean value (estimate, 0.10 [SE, 0.02]; P < .001). Together these findings suggest that the FAQ duly captures longitudinal decline in everyday function across the dementia spectrum, albeit potentially less so among controls. Furthermore, the observed interindividual variability in slope trajectory, which was seen within each group, provided the basis for examining the impact of predictors (ie, CSF measures and the ADAS-Cog score) on rate of change in the FAQ.20

CSF BIOMARKERS, ADAS-Cog SCORES, AND RATE OF CHANGE IN FAQ SCORES

Among controls, only t-tau, Aβ42, p-tau181, and p-tau:Aβ42 abnormalities were associated with a faster rate of functional decline. In MCI, all CSF measures and the ADAS-Cog score were significantly associated with the rate of functional decline. Finally, within the AD group, no CSF measure predicted rate of decline on the FAQ score. In contrast, the ADAS-Cog score significantly predicted FAQ decline (Table 3; Figures 1, 2, and 3). Of note, the random slope term in these analyses was significant (P < .001), indicating substantial between-person deviations from the mean/prototypical rate of change. The plots (Figures 1-3) present the prototypical change trajectories for illustrative purposes (eg, the t-tau graph in Figure 1 displays trajectories for the prototypical control with normal t-tau levels vs the prototypical control with abnormal t-tau levels).20

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Figure 1.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among control subjects. Aβ42 indicates β-amyloid 1-42; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

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Figure 2.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among patients with mild cognitive impairment. Aβ42 indicates β-amyloid 1-42; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

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Figure 3.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among patients with Alzheimer disease. For other abbreviations, see Figure 2.

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Table Graphic Jump LocationTable 3. Trajectories of Functional Change Across AD Spectrum as a Function of CSF Biomarkers and ADAS-Cog Scoresa
VARIANCE IN FUNCTIONAL DECLINE EXPLAINED BY CSF BIOMARKERS AND ADAS-Cog

Among controls, p-tau181 concentration emerged as the most sensitive to decline in FAQ score (R2 = 9.57), and ADAS-Cog score was the least sensitive. In the MCI group, Aβ42 level accounted for the most variance in FAQ score (R2 = 11.84), although the t-tau:Aβ42 ratio was virtually as sensitive (R2 = 11.39). Among patients with AD, ADAS-Cog score accounted for 34% of the variance, whereas no CSF measure accounted for more than 3% (Table 3).

COGNITION AS A MEDIATOR OF CSF BIOMARKERS’ EFFECT ON RATE OF DECLINE

The mediation analyses were performed only in the MCI group because it was the only group in which CSF biomarkers and ADAS-Cog scores significantly predicted the rate of functional decline. Adjustment for ADAS-Cog score did not obliterate the relationship between any CSF biomarker and rate of change in FAQ score. However, the relationships were attenuated—17% for p-tau181, 13% for t-tau:Aβ42 and p-tau:Aβ42 ratios, 12% for Aβ42, and 7% for t-tau—consistent with partial mediation.

COMBINATION OF TAU AND Aβ42 ABNORMALITIES AND RATE OF FUNCTIONAL DECLINE

Within each diagnostic group, the abnormal t-tau/abnormal Aβ42 subgroup experienced the steepest rate of functional decline. However, within the AD group, this subgroup's rate of decline was statistically indistinguishable from that of the other 3 subgroups. Among patients with MCI, those in the normal t-tau/abnormal Aβ42 subgroup declined faster than those in the normal t-tau/normal Aβ42 subgroup, whereas those in the abnormal t-tau/normal Aβ42 subgroup did not. These findings were essentially replicated in the p-tau181 and Aβ42 analyses (Table 4 and Figure 4).

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Figure 4.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of concurrent tau and β-amyloid 1-42 (Aβ42) abnormalities. AD indicates Alzheimer disease; MCI, mild cognitive impairment; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

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Table Graphic Jump LocationTable 4. Rate of Change in FAQ for Groups Defined by Combination of Tau and Aβ42 Abnormalitiesa

With reference to the core questions this study investigated, our key findings were as follows: (1) All CSF analytes were associated with functional decline in MCI and all but t-tau:Aβ42 ratio were associated with functional decline in controls, whereas no CSF analyte was associated with functional decline in AD. (2) Among controls, p-tau181 concentration was the most sensitive to functional decline, whereas in MCI it was Aβ42 concentration. (3) The CSF biomarkers were more sensitive than ADAS-Cog scores among controls and variably so in MCI, whereas the ADAS-Cog score was unequivocally more sensitive than CSF biomarkers in AD. (4) The impact of CSF biomarkers on functional decline in MCI is partially mediated by their effect on cognitive status. (5) Across all diagnostic groups, persons with a combination of tau and Aβ42 abnormalities exhibited the fastest rate of functional decline.

Progressive diminution in, and eventual loss of, the ability to perform daily activities is a hallmark feature of AD.11 Consequently, decline in everyday function is a veritable measure of disease progression in AD.13 The findings from this study therefore suggest that p-tau181 level is the strongest predictor of possible disease progression among controls, whereas Aβ42 level is most potent in MCI. This conclusion is consistent with histopathological studies that suggest a temporal sequence in the manifestation of AD-related brain lesions wherein intraneuronal alterations precede the deposition of amyloid plaques.2325 Even so, we acknowledge that the temporal ordering of AD lesions and their presumed downstream effects on CSF analytes remain controversial issues deserving continued investigation.6,7,26,27 For instance, it may be that t-tau and p-tau181 levels were stronger correlates of FAQ score decline (compared with Aβ42 concentration) among controls because Aβ42 levels were already reduced in the earliest phase of AD.7,28,29 Nonetheless, because levels of p-tau181 reflect hyperphosphorylation of tau (a putatively AD-specific process),3,30,31 our control findings suggest that, among cognitively intact elderly individuals, functional decline and eventual progression to AD may be most probable for individuals who already demonstrate pathognomonic features of AD.

Within the MCI and control groups, we found that ratio of tau protein to Aβ42 was strongly correlated with functional decline. Previous reports have suggested that biomarker ratios may be more promising AD biomarkers compared with absolute biomarker levels.5,3235 However, a potential drawback to their application is that, by virtue of being ratios, they mask a likely nontrivial distinction between individuals who have normal tau/abnormal Aβ42 findings and those who have abnormal tau/normal Aβ42 findings. For instance, in the present study we found that patients with MCI who had normal tau/abnormal Aβ42 findings experienced a more rapid functional decline compared with those with normal tau/normal Aβ42 findings, whereas those with abnormal tau/normal Aβ42 findings did not. This observation buttresses the earlier-noted finding that, among patients with MCI, abnormal Aβ42 levels were a better prognostic indicator of functional degradation and disease progression than tau alterations.3638

We were surprised to find that no CSF biomarker was predictive of functional decline among patients with AD. The reason for this is not immediately clear, although it might be due to reduced variability in the CSF biomarkers. This would be consistent with previous studies that have shown that, on becoming abnormal, CSF biomarkers subsequently tend to remain stable for several years even as dementia progresses.7,9,3941 In addition, other studies have also failed to find associations between CSF biomarkers and indices of disease risk and burden in AD.42

Cerebrospinal fluid analytes hold great promise as biomarkers of AD30 and, therefore, have potentially pivotal clinical utility.4345 However, their routine implementation in clinical practice is hampered by several factors, including lumbar puncture's relative invasiveness and potential for iatrogenesis, although the latter may not be as inexorable as originally believed.4547 Thus, clinical measures and peripheral fluid biomarkers are increasingly explored as viable alternatives.31,32,48 In this study, we examined the comparative sensitivity of CSF biomarkers and scores on the ADAS-Cog, a brief measure of global cognition, to the rate of functional decline within each diagnostic group. Overall, our findings suggest that a cognitive screen that is brief, noninvasive, and easy to administer competes favorably with CSF biomarkers with regard to sensitivity to functional decline and hence disease progression, especially among patients with AD.49

Our mediation analyses showed that the greatest reduction in the variance accounted for by CSF biomarkers occurred for p-tau181. There is evidence that p-tau181 levels reflect neurofibrillary tangle formation3,31 and that the density of tangles correlates better with cognitive decline and dementia than plaque load.50,51 Therefore, it stands to reason that adjusting for cognition most attenuated the original relationship between p-tau181 level and rate of functional decline. Finally, consistent with reports from previous investigations,5,33,35 we found that, within each diagnostic group, individuals who had pathological concentrations of tau and Aβ42 experienced the steepest functional decline. This was most pronounced in the MCI group, in which those with abnormal tau/abnormal Aβ42 levels declined at about 2.5 times the rate of those with normal tau/normal Aβ42 levels (eg, abnormal t-tau/abnormal Aβ42 vs normal t-tau/normal Aβ42 = [0.90 + 1.40]/0.90). Because concurrent disturbances in tau and Aβ42 concentrations are considered diagnostic for AD, the accelerated decline in everyday function manifested by controls and patients with MCI who have these defining CSF alterations might represent a harbinger of their eventual progression to AD.52

Potential limitations of this study include the use of relatively gross measures of everyday function (FAQ) and cognition (ADAS-Cog) and the low ethnic diversity of the sample. In addition, the participants studied were enrolled in a clinical study, not an epidemiological study. It is unclear how these factors may have influenced our findings. Despite these limitations, this study is unique in being the first, to our knowledge, to examine several interrelated questions concerning the relationship between CSF biomarkers and rate of functional decline across the AD spectrum.

Correspondence: Ozioma C. Okonkwo, PhD, Department of Neurology, Johns Hopkins School of Medicine, 1620 McElderry St, Reed Hall East 2, Baltimore, MD 21205 (ozioma@jhmi.edu).

Accepted for Publication: October 28, 2009.

Author Contributions: Dr Okonkwo had full access to all the data reported in this manuscript and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Okonkwo, Alosco, Griffith, Trojanowski, and Tremont. Acquisition of data: Trojanowski. Analysis and interpretation of data: Okonkwo, Mielke, Shaw, Trojanowski, and Tremont. Drafting of the manuscript: Okonkwo, Alosco, Griffith, and Trojanowski. Critical revision of the manuscript for important intellectual content: Okonkwo, Griffith, Mielke, Shaw, Trojanowski, and Tremont. Statistical analysis: Okonkwo, Griffith, and Mielke. Obtained funding: Trojanowski. Administrative, technical, and material support: Alosco, Shaw, Trojanowski, and Tremont. Study supervision: Trojanowski and Tremont.

Financial Disclosure: None reported.

Funding/Support: This study was supported by grant U01 AG024904 from the National Institutes of Health for data collection and sharing by the ADNI (principal investigator: Michael Weiner, MD). The ADNI is supported by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from the following: Pfizer Inc, Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co Inc, AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer's Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging, with participation from the US Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the AD Cooperative Study at the University of California, San Diego. The ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles.

Role of the Sponsors: Data used in the preparation of this article were obtained from the ADNI database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or in the writing of this report.

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PubMed
Korczyn  AD The amyloid cascade hypothesis. Alzheimers Dement 2008;4 (3) 176- 178
PubMed
Ingelsson  MFukumoto  HNewell  KL  et al.  Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 2004;62 (6) 925- 931
PubMed
Jack  CRJ  JrLowe  VJWeigand  SD  et al. Alzheimer's Disease Neuroimaging Initiative, Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain 2009;132 (pt 5) 1355- 1365
PubMed
Hampel  HMitchell  ABlennow  K  et al.  Core biological marker candidates of Alzheimer's disease: perspectives for diagnosis, prediction of outcome and reflection of biological activity. J Neural Transm 2004;111 (3) 247- 272
PubMed
Frank  RAGalasko  DHampel  H  et al. National Institute on Aging Biological Markers Working Group, Biological markers for therapeutic trials in Alzheimer's disease: proceedings of the biological markers working group; NIA initiative on neuroimaging in Alzheimer's disease. Neurobiol Aging 2003;24 (4) 521- 536
PubMed
Sonnen  JAMontine  KSQuinn  JFKaye  JABreitner  JCMontine  TJ Biomarkers for cognitive impairment and dementia in elderly people. Lancet Neurol 2008;7 (8) 704- 714
PubMed
Tapiola  TAlafuzoff  IHerukka  SK  et al.  Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol 2009;66 (3) 382- 389
PubMed
Kapaki  EParaskevas  GPZalonis  IZournas  C CSF tau protein and β-amyloid (1-42) in Alzheimer's disease diagnosis: discrimination from normal ageing and other dementias in the Greek population. Eur J Neurol 2003;10 (2) 119- 128
PubMed
Hansson  OZetterberg  HBuchhave  PLondos  EBlennow  KMinthon  L Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006;5 (3) 228- 234
PubMed
Hampel  HTeipel  SJFuchsberger  T  et al.  Value of CSF β-amyloid1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment. Mol Psychiatry 2004;9 (7) 705- 710
PubMed
Schoonenboom  SNVisser  PJMulder  C  et al.  Biomarker profiles and their relation to clinical variables in mild cognitive impairment. Neurocase 2005;11 (1) 8- 13
PubMed
Ivanoiu  ASindic  CJ Cerebrospinal fluid TAU protein and amyloid β42 in mild cognitive impairment: prediction of progression to Alzheimer's disease and correlation with the neuropsychological examination. Neurocase 2005;11 (1) 32- 39
PubMed
Blennow  KZetterberg  HMinthon  L  et al.  Longitudinal stability of CSF biomarkers in Alzheimer's disease. Neurosci Lett 2007;419 (1) 18- 22
PubMed
Sunderland  TWolozin  BGalasko  D  et al.  Longitudinal stability of CSF tau levels in Alzheimer patients. Biol Psychiatry 1999;46 (6) 750- 755
PubMed
Zetterberg  HPedersen  MLind  K  et al.  Intra-individual stability of CSF biomarkers for Alzheimer's disease over two years. J Alzheimers Dis 2007;12 (3) 255- 260
PubMed
Engelborghs  SSleegers  KCras  P  et al.  No association of CSF biomarkers with APOEε4, plaque and tangle burden in definite Alzheimer's disease. Brain 2007;130 (pt 9) 2320- 2326
PubMed
Hampel  HBuerger  KZinkowski  R  et al.  Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study. Arch Gen Psychiatry 2004;61 (1) 95- 102
PubMed
Riemenschneider  MWagenpfeil  SVanderstichele  H  et al.  Phospho-tau/total tau ratio in cerebrospinal fluid discriminates Creutzfeldt-Jakob disease from other dementias. Mol Psychiatry 2003;8 (3) 343- 347
PubMed
Andreasen  NMinthon  LDavidsson  P  et al.  Evaluation of CSF-tau and CSF-Aβ42 as diagnostic markers for Alzheimer disease in clinical practice. Arch Neurol 2001;58 (3) 373- 379
PubMed
Peskind  ENordberg  ADarreh-Shori  TSoininen  H Safety of lumbar puncture procedures in patients with Alzheimer's disease. Curr Alzheimer Res 2009;6 (3) 290- 292
PubMed
Peskind  ERRiekse  RQuinn  JF  et al.  Safety and acceptability of the research lumbar puncture. Alzheimer Dis Assoc Disord 2005;19 (4) 220- 225
PubMed
Carrillo  MCBlackwell  AHampel  H  et al.  Early risk assessment for Alzheimer's disease. Alzheimers Dement 2009;5 (2) 182- 196
PubMed
Fleisher  ASSun  STaylor  C  et al.  Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment. Neurology 2008;70 (3) 191- 199
PubMed
Trojanowski  JQShin  RWSchmidt  MLLee  VM Relationship between plaques, tangles, and dystrophic processes in Alzheimer's disease. Neurobiol Aging 1995;16 (3) 335- 345
PubMed
Giannakopoulos  PHerrmann  FRBussiere  T  et al.  Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease. Neurology 2003;60 (9) 1495- 1500
PubMed
Price  JLMorris  JC Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol 1999;45 (3) 358- 368
PubMed

Figures

Place holder to copy figure label and caption
Figure 1.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among control subjects. Aβ42 indicates β-amyloid 1-42; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among patients with mild cognitive impairment. Aβ42 indicates β-amyloid 1-42; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of cerebrospinal fluid biomarker concentrations and Alzheimer Disease Assessment Scale–cognitive subscale (ADAS-Cog) scores among patients with Alzheimer disease. For other abbreviations, see Figure 2.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.

Change in Pfeffer Functional Activities Questionnaire (FAQ) scores as a function of concurrent tau and β-amyloid 1-42 (Aβ42) abnormalities. AD indicates Alzheimer disease; MCI, mild cognitive impairment; p-tau181, tau phosphorylated at threonine 181; and t-tau, total tau.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Characteristics of Study Participants at Baseline
Table Graphic Jump LocationTable 2. CSF Biomarker Concentrations and Ratios at Baseline
Table Graphic Jump LocationTable 3. Trajectories of Functional Change Across AD Spectrum as a Function of CSF Biomarkers and ADAS-Cog Scoresa
Table Graphic Jump LocationTable 4. Rate of Change in FAQ for Groups Defined by Combination of Tau and Aβ42 Abnormalitiesa

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Korczyn  AD The amyloid cascade hypothesis. Alzheimers Dement 2008;4 (3) 176- 178
PubMed
Ingelsson  MFukumoto  HNewell  KL  et al.  Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 2004;62 (6) 925- 931
PubMed
Jack  CRJ  JrLowe  VJWeigand  SD  et al. Alzheimer's Disease Neuroimaging Initiative, Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain 2009;132 (pt 5) 1355- 1365
PubMed
Hampel  HMitchell  ABlennow  K  et al.  Core biological marker candidates of Alzheimer's disease: perspectives for diagnosis, prediction of outcome and reflection of biological activity. J Neural Transm 2004;111 (3) 247- 272
PubMed
Frank  RAGalasko  DHampel  H  et al. National Institute on Aging Biological Markers Working Group, Biological markers for therapeutic trials in Alzheimer's disease: proceedings of the biological markers working group; NIA initiative on neuroimaging in Alzheimer's disease. Neurobiol Aging 2003;24 (4) 521- 536
PubMed
Sonnen  JAMontine  KSQuinn  JFKaye  JABreitner  JCMontine  TJ Biomarkers for cognitive impairment and dementia in elderly people. Lancet Neurol 2008;7 (8) 704- 714
PubMed
Tapiola  TAlafuzoff  IHerukka  SK  et al.  Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol 2009;66 (3) 382- 389
PubMed
Kapaki  EParaskevas  GPZalonis  IZournas  C CSF tau protein and β-amyloid (1-42) in Alzheimer's disease diagnosis: discrimination from normal ageing and other dementias in the Greek population. Eur J Neurol 2003;10 (2) 119- 128
PubMed
Hansson  OZetterberg  HBuchhave  PLondos  EBlennow  KMinthon  L Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 2006;5 (3) 228- 234
PubMed
Hampel  HTeipel  SJFuchsberger  T  et al.  Value of CSF β-amyloid1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment. Mol Psychiatry 2004;9 (7) 705- 710
PubMed
Schoonenboom  SNVisser  PJMulder  C  et al.  Biomarker profiles and their relation to clinical variables in mild cognitive impairment. Neurocase 2005;11 (1) 8- 13
PubMed
Ivanoiu  ASindic  CJ Cerebrospinal fluid TAU protein and amyloid β42 in mild cognitive impairment: prediction of progression to Alzheimer's disease and correlation with the neuropsychological examination. Neurocase 2005;11 (1) 32- 39
PubMed
Blennow  KZetterberg  HMinthon  L  et al.  Longitudinal stability of CSF biomarkers in Alzheimer's disease. Neurosci Lett 2007;419 (1) 18- 22
PubMed
Sunderland  TWolozin  BGalasko  D  et al.  Longitudinal stability of CSF tau levels in Alzheimer patients. Biol Psychiatry 1999;46 (6) 750- 755
PubMed
Zetterberg  HPedersen  MLind  K  et al.  Intra-individual stability of CSF biomarkers for Alzheimer's disease over two years. J Alzheimers Dis 2007;12 (3) 255- 260
PubMed
Engelborghs  SSleegers  KCras  P  et al.  No association of CSF biomarkers with APOEε4, plaque and tangle burden in definite Alzheimer's disease. Brain 2007;130 (pt 9) 2320- 2326
PubMed
Hampel  HBuerger  KZinkowski  R  et al.  Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study. Arch Gen Psychiatry 2004;61 (1) 95- 102
PubMed
Riemenschneider  MWagenpfeil  SVanderstichele  H  et al.  Phospho-tau/total tau ratio in cerebrospinal fluid discriminates Creutzfeldt-Jakob disease from other dementias. Mol Psychiatry 2003;8 (3) 343- 347
PubMed
Andreasen  NMinthon  LDavidsson  P  et al.  Evaluation of CSF-tau and CSF-Aβ42 as diagnostic markers for Alzheimer disease in clinical practice. Arch Neurol 2001;58 (3) 373- 379
PubMed
Peskind  ENordberg  ADarreh-Shori  TSoininen  H Safety of lumbar puncture procedures in patients with Alzheimer's disease. Curr Alzheimer Res 2009;6 (3) 290- 292
PubMed
Peskind  ERRiekse  RQuinn  JF  et al.  Safety and acceptability of the research lumbar puncture. Alzheimer Dis Assoc Disord 2005;19 (4) 220- 225
PubMed
Carrillo  MCBlackwell  AHampel  H  et al.  Early risk assessment for Alzheimer's disease. Alzheimers Dement 2009;5 (2) 182- 196
PubMed
Fleisher  ASSun  STaylor  C  et al.  Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment. Neurology 2008;70 (3) 191- 199
PubMed
Trojanowski  JQShin  RWSchmidt  MLLee  VM Relationship between plaques, tangles, and dystrophic processes in Alzheimer's disease. Neurobiol Aging 1995;16 (3) 335- 345
PubMed
Giannakopoulos  PHerrmann  FRBussiere  T  et al.  Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer's disease. Neurology 2003;60 (9) 1495- 1500
PubMed
Price  JLMorris  JC Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease. Ann Neurol 1999;45 (3) 358- 368
PubMed

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