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. Author manuscript; available in PMC: 2017 May 1.
Published in final edited form as: JAMA Neurol. 2016 May 1;73(5):561–571. doi: 10.1001/jamaneurol.2016.0086

Diagnostic and Prognostic Utility of the Synaptic Marker Neurogranin in Alzheimer Disease

Rawan Tarawneh 1, Gina D’Angelo 1, Dan Crimmins 1, Elizabeth Herries 1, Terry Griest 1, Anne M Fagan 1, Gregory J Zipfel 1, Jack H Ladenson 1, John C Morris 1, David M Holtzman 1
PMCID: PMC4861689  NIHMSID: NIHMS774455  PMID: 27018940

Abstract

IMPORTANCE

Synaptic loss is an early pathologic substrate of Alzheimer disease (AD). Neurogranin is a postsynaptic neuronal protein that has demonstrated utility as a cerebrospinal fluid (CSF) marker of synaptic loss in AD.

OBJECTIVE

To investigate the diagnostic and prognostic utility of CSF neurogranin levels in a large, well-characterized cohort of individuals with symptomatic AD and cognitively normal controls.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional and longitudinal observational study of cognitive decline in patients with symptomatic AD and cognitively normal controls was performed. Participants were individuals with a clinical diagnosis of early symptomatic AD and cognitively normal controls who were enrolled in longitudinal studies of aging and dementia at the Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, from January 21, 2000, through March 21, 2011. Data analysis was performed from November 1, 2013, to March 31, 2015.

MAIN OUTCOMES AND MEASURES

Correlations between baseline CSF biomarker levels and future cognitive decline in patients with symptomatic AD and cognitively normal controls overtime.

RESULTS

A total of 302 individuals (mean [SE] age, 73.1 [0.4] years) were included in this study (95 patients [52 women and 43 men] with AD and 207 controls [125 women and 82 men]). The CSF neurogranin levels differentiated patients with early symptomatic AD from controls with comparable diagnostic utility (mean [SE] area under the receiver operating characteristic curve, 0.71 [0.03]; 95% CI, 0.64–0.77) to the other CSF biomarkers. The CSF neurogranin levels correlated with brain atrophy (normalized whole-brain volumes: adjusted r = −0.38, P = .02; hippocampal volumes: adjusted r = −0.36, P = .03; entorhinal volumes: adjusted r = −0.46, P = .006; and parahippocampal volumes: adjusted r = −0.47, P = .005, n = 38) in AD and with amyloid load (r = 0.39, P = .02, n = 36) in preclinical AD. The CSF neurogranin levels predicted future cognitive impairment (adjusted hazard ratio, 1.89; 95% CI, 1.29–2.78; P = .001 as a continuous measure, and adjusted hazard ratio, 2.78; 95% CI, 1.13–5.99; P = .02 as a categorical measure using the 85th percentile cutoff value) in controls and rates of cognitive decline (Clinical Dementia Rating sum of boxes score: β estimate, 0.29; P = .001; global composite scores: β estimate, −0.11; P = .001; episodic memory scores: β estimate, −0.18; P < .001; and semantic memory scores: β estimate, −0.06; P = .04, n = 57) in patients with symptomatic AD over time, similarly to the CSF proteins VILIP-1, tau, and p-tau181.

CONCLUSIONS AND RELEVANCE

The CSF levels of the synaptic marker neurogranin offer diagnostic and prognostic utility for early symptomatic AD that is comparable to other CSF markers of AD. Importantly, CSF neurogranin complements the collective ability of these markers to predict future cognitive decline in cognitively normal individuals and, therefore, will be a useful addition to the current panel of AD biomarkers.


The aggregation and deposition of amyloid-β (Aβ) and tau, the 2 key proteins involved in Alzheimer disease (AD) pathogenesis, are estimated to begin years before the onset of cognitive impairment1,2 However, the first signs of cognitive impairment only appear after significant neuronal and synaptic loss has occurred in vulnerable brain regions3 Neuronal and synaptic loss reflects the cumulative outcome of different pathologic substrates in AD and, therefore, may provide the best surrogate for clinical and radiologic disease progression2,47

Synaptic dysfunction is an early and prominent pathologic feature of AD that precedes frank neuronal loss in several brain regions812 Cortical synaptic density is reduced by 25% to 30% and synaptic density per neuron by 15% to 35% in even the earliest symptomatic stages of the disease5,7 Presynaptic, synaptic, and postsynaptic protein expression levels are reduced in postmortem AD brains compared with controls13,14

Neurogranin is a calmodulin-binding postsynaptic15 neuronal protein16 that is abundantly expressed in perikaryal and dendritic cytoplasm15 Neurogranin is thought to be involved in activity-dependent synaptic plasticity and long-term potentiation through the modulation of calcium-mediated signaling pathways1719 Because of its abundant and preferential neuronal expression, neurogranin has been identified as a potential marker of neurodegeneration in large-scale gene arrays,20 along with other candidate markers, such as visinin-like protein-1 (VILIP-1)2123 Previous studies suggest that cerebrospinal fluid (CSF) neurogranin levels are elevated in AD24 and predict conversion from mild cognitive impairment (MCI) to AD dementia2527

We investigate the diagnostic and prognostic utility of CSF neurogranin levels in a large cohort of well-characterized individuals with early AD and controls who were followed up for 2 to 3 years. Our results are consistent with previous reports25,26 of increased CSF neurogranin levels in AD. Furthermore, we found that CSF neurogranin levels correlate with whole-brain and regional atrophy in AD and with amyloid load in preclinical AD. Importantly, in our cohort, CSF neurogranin levels predicted rates of cognitive decline in patients with early symptomatic AD and future cognitive impairment in cognitively normal controls similarly to the CSF proteins VILIP-1, tau, and p-tau181 over time.

Methods

Participants

Participants were community-dwelling volunteers enrolled in longitudinal studies of healthy aging and dementia through the Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, from January 21, 2000, through March 21, 2011. Data analysis was performed from November 1, 2013, to March 31, 2015. All participants in this study were included in a previous study of CSF VILIP-1 in AD to allow comparison of markers (eMethods in the Supplement)21,22 Participants were in good general health with no other medical illness that could contribute to dementia and no contraindication to lumbar puncture (LP) or magnetic resonance imaging (MRI). APOE genotypes were obtained as previously described28

The Clinical Dementia Rating (CDR) was used to denote the presence or absence of symptomatic AD and, when present, its severity29,30 A CDR score of 0, indicating no dementia, characterizes individuals who are cognitively normal. In the cohort being studied, a CDR score of 0.5 denotes very mild symptomatic AD (encompassing MCI caused by AD31), whereas a CDR score of 1 and a CDR score of 2 denote mild and moderate symptomatic AD,32 respectively. Annual clinical assessments included assignment of CDR, CDR sum of boxes (CDR-SB),33 Mini-Mental State Examination,34 and a 1.5-hour psychometric test battery (eMethods in the Supplement)22,30 The CDR scores and clinical diagnoses were based on the cognitive assessment closest to the time of the LP (median interval, 3.4 months).

For comparison, research participants with a clinical diagnosis of frontotemporal lobar degeneration, progressive supranuclear palsy, or Lewy body dementia at the University of California, San Francisco (UCSF) Memory and Aging Center were included in this study.

All clinical diagnoses were made in accordance with standard criteria3540 Studies were approved by the Human Research Protection Office at Washington University and the UCSF Committee on Human Research. Written informed consent was obtained from all participants. All data were deidentified.

CSF Collection and Processing

The CSF samples (20–30 mL) were collected from all participants and analyzed for total tau, p-tau181, and Aβ1–42 (Aβ42) by enzyme-linked immunosorbent assays (Innotest, Fujire-bio [formerly Innogenetics])41 The CSF samples were analyzed for VILIP-1 by a microparticle-based immunoassay (Erenna, Singulex)21,22

The CSF neurogranin levels were measured using a 2-site immunoassay that uses an affinity-efficient trapping and purification technique for polyclonal antibodies developed in the Laboratory of Jack H. Ladenson, PhD, Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri (eMethods in the Supplement)42

Regional and Whole-Brain Volumetry

A subset of the control and AD cohorts underwent MRI within 1.1 years of their LP (median interval, 1.7 months) (eMethods in the Supplement)43,44

In Vivo Amyloid Imaging

A subset of the control and AD cohorts underwent amyloid imaging via positron emission tomography (PET) using Pittsburgh Compound B (PiB) within 1.1 years of their LP (median interval, 2.7 months) (eMethods in the Supplement)45

Statistical Analysis

Analysis of variance, t tests, Fisher exact tests, Wilcoxon rank sum tests, or χ2 tests were used to assess differences in demographic, clinical, genotype, MRI, or CSF biomarker variables between the clinical groups. The Bonferroni correction was performed for all multiple comparisons. Receiver operating characteristic curve analyses assessed rates of agreement between CSF biomarkers and clinical diagnoses or PiB-positivity (SPSS, statistical software, version 15; SPSS Inc).

Pearson correlations examined associations among CSF biomarkers and between CSF biomarkers and mean cortical binding potential (MCBP) on PET-PiB (SPSS statistical software, version 15). Partial correlations examined associations between CSF and MRI measures, adjusting for age, sex, and scanner type (SPSS statistical software, version 15).

Cox proportional hazards regression models tested the effects of CSF biomarkers, individually or in combination (using principal components analyses), on the conversion rate from a CDR score of 0 to a CDR score of 0.5 or higher (SAS Institute, Inc). The CSF biomarker measures were analyzed as continuous and categorical (dichotomized at the 85th percentile value) variables, adjusting for age, sex, educational level, and APOE ε4 genotype. The bootstrap method was used to compare CSF biomarkers (individually or in combination) as predictors of conversion in nonnested models46,47 (R Project for Statistical Computing, R Foundation).

Mixed linear models (PROC MIXED, SAS Institute Inc) tested the ability of CSF biomarkers to predict annual change in CDR-SB, global, episodic memory, semantic memory, working memory, or visual-spatial composite scores in AD over time (SAS statistical software, version 9.2). Analyses were adjusted for age, educational level, sex, APOE ε4 genotype, and baseline dementia severity (eMethods in the Supplement). Statistical significance was defined as P < .05 for all analyses.

Results

Participants

A total of 302 individuals (mean [SE] age, 73.1 [0.4] years) were included in this study (95 patients with AD and 207 controls). Of the 302 participants from the Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, included in this study, 221 participants (164 controls and 57 patients with AD) had more than 1 annual cognitive assessment during follow-up. For comparison, 19 research participants with a clinical diagnosis of frontotemporal lobar degeneration (n = 11), progressive supranuclear palsy (n = 7),or Lewy body dementia(n = 1) at the UCSF Memory and Aging Center were included in this study.

The demographic, clinical, psychometric, genotype, and CSF biomarker characteristics of the study participants are summarized in the Table. Individuals with symptomatic AD were older than controls and included a higher percentage of individuals with the APOE ε4 genotype or with cortical amyloid binding on PET-PiB. Baseline Mini-Mental State Examination and psychometric composite scores were lower and baseline CDR-SB scores were higher in patients with AD than in controls. The CSF neurogranin levels did not differ by age or sex in this cohort (eResults in the Supplement).

Table.

Demographic, Clinical, Psychometric, Genotype, and CSF Biomarker Characteristics of Study Participantsa

Characteristic Controls
(n = 207)
AD
(n = 95)
P Value
Age at LP, y 72.3 (0.5) 75.0 (0.8) .003
Female/male sex, No. (% female)b,c 125/82 (60) 52/43 (55) .38
Educational level, y 15.6 (0.2) 14.4 (0.3) .001
APOE genotype (ε4+/ε4−), No. (% ε4+)c,d 59/148 (29) 56/39 (59) <.001
PiB+/PiB−, No. (% PiB+)c,e 36/92 (28) 17/7 (71) <.001
Baseline scores
  CDR-SB 0.03 (0.01) 3.4 (0.25) <.001
  MMSE 28.9 (0.09) 25.3 (0.40) <.001
  Global composite 0.19 (0.04) −0.95 (0.09) <.001
  Memory composite
    Episodic 0.18 (0.06) −1.75 (0.10) <.001
    Semantic 0.10 (0.05) −1.13 (0.13) <.001
    Working 0.14 (0.04) −0.52 (0.09) <.001
  Visual spatial composite 0.32 (0.06) −0.72 (0.12) <.001
CSF levels
  Neurogranin, ng/mL 1.47 (0.06) 2.02 (0.10) <.001
  VILIP-1, pg/mL 397 (10) 514 (17) <.001
  tau, pg/mLf 296 (11) 602 (29) <.001
  p-tau181, pg/mLf 54 (2) 90 (4) <.001
  Aβ42, pg/mLg 613 (18) 390 (18) <.001
  Neurogranin/Aβ42g 0.003 (0.0002) 0.006 (0.0004) <.001
  VILIP-1/Aβ42g 0.75 (0.03) 1.54 (0.08) <.001
  tau/Aβ42g 0.60 (0.04) 1.87 (0.13) <.001
  p-tau181/Aβ42g 0.11 (0.01) 0.28 (0.02) <.001

Abbreviations: Aβ42, amyloid-β 42; AD, Alzheimer disease; CDR-SB, Clinical Dementia Rating sum of boxes; CSF, cerebrospinal fluid; LP, lumbar puncture; MMSE, Mini-Mental State Examination; PiB, Pittsburgh Compound B.

a

Data are presented as mean (SE) unless otherwise indicated.

b

Mean CSF neurogranin levels were 1.6 and 1.7 ng/mL for men and women, respectively.

c

χ2 Tests were used for group comparisons. All other group comparisons were performed using t tests.

d

APOE ε4+ genotype was defined by the presence of at least one APOE ε4 allele.

e

Individuals who underwent positron emission tomography with PiB (n = 152) included cognitively normal controls (n = 128) and patients with AD (n = 24).

f

n = 289.

g

n = 288.

Participants with very mild (CDR score of 0.5, n = 70),mild (CDR score of 1, n = 22), and moderate (CDR score of 2, n = 3) symptomatic AD exhibited the typical CSF biomarker phenotype of AD with higher mean levels of CSF tau, p-tau181, tau/Aβ42, and p-tau181/Aβ42 and lower mean levels of CSF Aβ42 compared with controls (Figure 1 and eFigure 1 in the Supplement). As previously reported in this cohort, mean CSF VILIP-1 and CSF VILIP-1/Aβ42 levels were higher in patients with AD than in controls.

Figure 1. Scatterplots of Cerebrospinal Fluid (CSF) Biomarker Levels by Clinical Diagnosis and Clinical Dementia Rating (CDR) Scores.

Figure 1

A, Mean CSF neurogranin levels were higher in those with CDR scores of 0.5 and those with CDR scores of 1 or higher compared with those with CDR scores of 0 (P < .001) or non-Alzheimer disease (AD) dementias (P < .001). B, Mean CSF neurogranin levels were higher in those with CDR scores of 0.5 and those with CDR scores of 1 or higher compared with those with negative Pittsburgh Compound B (PiB) test results and CDR scores of 0 (P < .001). C, Mean (SE) CSF VILIP-1 levels were higher in those with CDR scores of 0.5 (503 [20] pg/mL, n = 70) and those with CDR scores of 1 or higher (545 [33] pg/mL, n = 25) compared with those with CDR scores of 0 (397 [10] pg/mL, n = 207) (P < .001) and those with non-AD dementias (323 [40] pg/mL, n = 19) (P < .001). D, Mean (SE) CSF tau levels were higher in those with CDR scores of 0.5 (573 [34] pg/mL, n = 67) and those with CDR scores of 1 or higher (680 [57] pg/mL, n = 25) compared with those with CDR scores of 0 (296 [11] pg/mL, n = 197) (P < .001) and non-AD dementias (319 [48] pg/mL, n = 19) (P < .001). One-way analysis of variance with Welch correction for unequal variances and the Tukey post hoc test were used for all group comparisons. Similar results were obtained when Bonferroni corrections were used for all group comparisons. E and F, Receiver operating characteristic curves for the diagnostic utility of CSF biomarkers in differentiating AD from controls by clinical diagnosis and PiB status. Figure panels C and D are reproduced from Tarawneh et al21 with permission from John Wiley and Sons, Inc.

Diagnostic Utility of CSF Neurogranin in AD

Mean (SE) CSF neurogranin levels were higher in those with CDR scores of 0.5 (2.04 [0.12] ng/mL, n = 70) and CDR scores of 1 or higher (1.98 [0.18] ng/mL, n = 25) compared with those with CDR scores of 0 (1.47 [0.06] ng/mL, n = 207) (P < .001) or those with non-AD dementias (1.08 [0.23] ng/mL, n =19) (P < .001). Similarly, mean (SE) CSF neurogranin/Aβ42 levels were higher in those with CDR scores of 0.5 (0.006 [0.0005], n = 67) and CDR scores of 1 or higher (0.007 [0.001], n = 25) compared with those with CDR scores of 0 (0.003 [0.0002], n = 196) (P < .001) or those with non-AD dementias (0.0013 [0.0002], n = 19) (P < .001). No significant differences in mean neurogranin or neurogranin/Aβ42 levels were observed among the CDR categories in the AD cohort (Figure 1). The diagnostic accuracy (area under the receiver operating characteristic curve [AUC]) of CSF neurogranin in differentiating patients with AD from controls was comparable to that of the other markers (Figure 1E). The mean (SE) AUCs were 0.85 (0.02) for tau, 0.81 (0.03) for p-tau181, 0.77 (0.03) for Aβ42, 0.74 (0.03) for VILIP-1, and 0.71 (0.03) for neurogranin. The mean (SE) AUCs for the CSF marker ratios to Aβ42 were 0.88 (0.02) for tau/Aβ42, 0.86 (0.02) for p-tau181/Aβ42, 0.85 (0.02) for VILIP-1/Aβ42, and 0.81 (0.03) for neurogranin/Aβ42 (eResults in the Supplement).

The CSF neurogranin levels predicted PiB status with comparable utility to that of the other CSF biomarkers, irrespective of clinical diagnoses (Figure 1F). The mean (SE) AUC was 0.86 (0.03) for tau, 0.81 (0.04) for p-tau181, 0.87 (0.03) for Aβ42, 0.77 (0.04) for VILIP-1, and 0.73 (0.04) for neurogranin. The mean (SE) AUCs for the CSF marker ratios to Aβ42 were 0.95 (0.02) for tau/Aβ42, 0.95 (0.02) for p-tau181/Aβ42, 0.93 (0.02) for VILIP-1/Aβ42, and 0.89 (0.03) for neurogranin/Aβ42. The CSF neurogranin differentiated PiB-positive from PiB-negative individuals with a sensitivity of 79% and a specificity of 60%. The ratios of CSF tau, p-tau181, VILIP-1, and neurogranin to CSF Aβ42 levels provided higher diagnostic accuracy than each marker alone (respectively) and higher diagnostic accuracy for PiB status than for clinical diagnoses (Figure 1 and eTable 1 in the Supplement).

Correlation of CSF Neurogranin With CSF and Imaging Markers of AD

The CSF neurogranin levels correlated with CSF VILIP-1 (r = 0.76 and r = 0.83), tau (r = 0.81 and r = 0.77), and p-tau181 (r = 0.80 and r = 0.77) levels in patients with AD and controls, respectively (P < .001). No correlations were observed between CSF neurogranin levels and CSF Aβ42 levels in patients with AD (r = −0.03, P = .77) or controls (r = 0.12, P = .10) (Figure 2 and eFigure 2 in the Supplement). The CSF neurogranin levels negatively correlated with baseline normalized whole-brain (r = −0.38, P = .02), hippocampal (r = −0.36, P = .03), entorhinal (r = −0.46, P = .006), and parahippocampal volumes (r = −0.47, P = .005) in AD (n = 38), adjusting for age, sex, and scanner type (Figure 2 and eTable 2 in the Supplement). No correlations between the CSF neurogranin levels and brain volumes were observed in controls (n = 144) (eResults in the Supplement).

Figure 2. Correlations Between Cerebrospinal Fluid (CSF) Neurogranin Levels and CSF, Magnetic Resonance Imaging, or Amyloid Markers.

Figure 2

The CSF neurogranin levels correlated with CSF VILIP-1 (r = 0.82, P < .001) (A) and CSF tau levels (r = 0.78, P < .001) (B) in the combined cohort of patients with Alzheimer disease (AD) and controls. The CSF neurogranin levels negatively correlated with normalized whole-brain volume (nWBV) (unadjusted r = −0.38, P = .02; adjusted r = −0.38, P = .02) (C) and hippocampal volumes (unadjusted r = −0.34, P = .04; adjusted r = −0.36, P = .03) in patients with AD (n = 38) (D). Unadjusted linear regression lines are shown. The CSF neurogranin levels correlated with mean cortical binding potential (MCBP) on positron emission tomography with Pittsburgh Compound B (PiB) in the combined (patients with AD and controls) cohort (E) and cognitively normal controls who are PiB–positive (preclinical AD; ie, MCBP ≥0.18, n = 36) (F).

The CSF neurogranin levels correlated with MCBP on PET-PiB in the combined (r = 0.28, P < .001, n = 152) (Figure 2) and control cohorts (r = 0.29, P = .001, n = 128) but not in the AD cohort (r = −0.1, P = .68, n = 24). The CSF neurogranin/Aβ42 levels correlated with MCBP in the AD (r = 0.54, P = .01) and control (r = 0.65, P < .001) cohorts. The CSF neurogranin levels correlated with MCBP (r = 0.39, P = .02) in PiB- positive, cognitively normal controls (ie, MCBP ≥0.18, n = 36) (Figure 2).

Ability of CSF Neurogranin Levels to Predict Future Cognitive Impairment in Controls

Cox proportional hazards regression models assessed the ability of CSF biomarkers (as continuous or categorical variables) to predict future cognitive impairment in cognitively normal controls over time (eTable 3 in the Supplement), adjusting for age, sex, educational level, and APOE ε4 genotype (eTable 4 in the Supplement and Figure 3). Analyses included cognitively normal controls who had at least 1 follow-up annual cognitive assessment (n=164). Of these, 26 participants (15.9%) progressed from CDR scores of 0 to a CDR score of 0.5 or higher during follow-up. With the exception of CSF Aβ42, all CSF biomarkers predicted conversion from a CDR score of 0 to a CDR score of 0.5 or higher during follow-up (eTable 4 in the Supplement). The CSF neurogranin (adjusted hazard ratio, 1.89; 95% CI, 1.29–2.78; P = .001) and neurogranin/Aβ42 (adjusted hazard ratio, 27.9; 95% CI, 6.93–112.1; P < .001) levels predicted conversion from a CDR score of 0 to a CDR score of 0.5 or higher over time. Individuals whose neurogranin or neurogranin/Aβ42 levels were in the upper 15th percentile of values progressed more rapidly to cognitive impairment than individuals whose levels were in the lower 85th percentile (Figure 3).

Figure 3. Baseline Cerebrospinal Fluid (CSF) Biomarker Levels as Predictors of Conversion From Clinical Dementia Rating (CDR) of 0 to 0.5 or Greater.

Figure 3

Kaplan-Meier estimates of the rates of conversion from CDR scores of 0 to 0.5 or greater over time as a function of CSF biomarker measures are shown. The CSF biomarkers or ratios were analyzed as categorical variables (dichotomized at the 85th percentile value), and analyses were adjusted for age, sex, educational level, and APOE ε4 genotype. The adjusted hazard ratios for the CSF biomarkers or ratios (dichotomized at the 85th percentile value) as predictors of future cognitive decline in cognitively normal individuals were 2.78 for neurogranin (95%CI, 1.13–5.99; P = .02), 3.74 for VILIP-1 (95%CI, 1.98–9.57; P = .002), 2.57 for tau (95%CI, 1.31–6.97; P = .03), 1.72 for p-tau181 (95%CI, 0.97–5.38; P = .06), 11.00 for neurogranin/amyloid-β (Aβ42) (95%CI, 4.41–27.39; P < .001), 13.00 for VILIP-1/Aβ42 (95%CI, 4.38–30.90; P < .001), 9.82 for tau/Aβ42 (95%CI, 3.11–21.28; P < .001), and 7.83 for p-tau181/Aβ42 (95%CI, 2.65–16.34; P < .001). The Kaplan-Meier curves for p-tau181 and p-tau181/Aβ42 are not shown in the figure. Figure panels C through F are from Tarawneh et al21 with permission from John Wiley and Sons, Inc.

Results from the bootstrap analyses indicate that the predictive ability for future cognitive impairment was 0.890 (P = .001) for neurogranin, 0.892 (P = .001) for VILIP-1,0.866 (P = .002) for tau, 0.452 (P = .04) for p-tau181, 0.328 (P = .11) for Aβ42, 0.993 (P < .001) for neurogranin/Aβ42, 0.998 (P < .001) for VILIP-1/Aβ42,0.974 (P < .001) for tau/Aβ42,and 0.902 (P = .002) for p-tau181/Aβ42. The combinations of CSF neurogranin and tau (0.885, P = .001) and of CSF neurogranin and p-tau181 (0.758, P = .007) were stronger predictors of conversion than tau (0.866, P = .002) or p-tau181 (0.452, P = .04) alone, respectively. When neurogranin was added to the combination of VILIP-1, tau, and p-tau181, the 4 markers together were stronger predictors of conversion (0.869, P = .002) than the combination of VILIP-1, tau, and p-tau181 (0.844, P = .002). When neurogranin was added to the combination of VILIP-1, tau, p-tau181, and Aβ42, the combination of all 5 markers (0.859, P = .002) was a stronger predictor of conversion than the combination of VILIP-1, tau, p-tau181, and Aβ42 (0.826, P = .002).

Ability of CSF Neurogranin Levels to Predict Rates of Cognitive Decline in AD

All CSF biomarkers except CSF Aβ42 predicted annual change in CDR-SB, global, episodic, and semantic memory scores in patients with symptomatic AD (n = 57) during follow-up (eTable 5 in the Supplement and Figure 4). Baseline CSF neurogranin levels (as continuous measures) predicted annual change in CDR-SB (β estimate, 0.29, P = .001), global (β estimate, −0.11, P = .001), episodic memory (β estimate, −0.18, P < .001), and semantic memory (β estimate, −0.06, P = .04) scores. Baseline CSF neurogranin/Aβ42 levels predicted annual change in CDR-SB (β estimate, 0.27, P = .001), global (β estimate, −0.13, P < .001), episodic memory (β estimate, −0.16, P < .001), and semantic memory (β estimate, −0.06, P = .02) scores. Individuals with AD whose CSF neurogranin or neurogranin/Aβ42 levels were in the upper tercile (corresponding to a CSF neurogranin level ≥2.0 ng/mL and a neurogranin/Aβ42 level >0.007) progressed more rapidly in CDR-SB(P = .03 and P = .02, respectively), global (P = .02 and P < .001, respectively), and episodic memory (P < .001 and P = .001,respectively) scores than those in the lower 2 terciles (eTable 5 in the Supplement).

Figure 4. Rates of Cognitive Decline as a Function of Cerebrospinal Fluid (CSF) Neurogranin and Neurogranin/Amyloid-β 42 (Aβ42) Terciles in Alzheimer Disease (AD).

Figure 4

Mixed linear models were used to estimate rates of decline in Clinical Dementia Rating sum of boxes (CDR-SB) (A and B), global composite (C and D), and episodic memory (E and F) scores over time in the symptomatic AD cohort as a function of CSF neurogranin and neurogranin/Aβ42 levels. The slope and intercept for each of the 3 terciles of CSF neurogranin and CSF neurogranin/Aβ42 are plotted. Adjusted rates of cognitive decline in the upper, middle, and lower terciles of neurogranin values were 1.40, 1.21, and 0.58 boxes per year, respectively, for CDR-SB; −0.37, −0.28, and −0.11 points per year, respectively, for global composite scores; and −0.49, −0.22, and −0.06 points per year, respectively, in episodic memory scores. Adjusted rates of cognitive decline in the upper, middle, and lower terciles of neurogranin/Aβ42 values were 1.39, 1.18, and 0.61 boxes per year, respectively, in CDR-SB; −0.44, −0.19, and −0.11 points per year, respectively, in global composite scores; and −0.47, −0.33, and −0.02 points per year, respectively, in episodic memory scores. LP indicates lumbar puncture.

Discussion

Neurogranin is a calmodulin-binding16 postsynaptic neuronal protein15 that is abundantly expressed in neuronal perikarya and dendritic spines15,16 Studies suggest that neurogranin is involved in synaptic plasticity, synaptic regeneration, and long-term potentiation through the modulation of calcium- and calmodulin-signaling pathways1719 and plays an important role in memory and learning16,4851

Neurogranin has been proposed as a potential marker of synaptic injury in large-scale gene arrays20 because of its preferential neuronal expression and widespread distribution in different brain regions52 Pathologic studies13,14,53 indicate that neurogranin immunoreactivity is reduced in patients with early symptomatic AD compared with controls. Because expression levels of other synaptic proteins are also decreased in AD13,14 and correlate with dementia severity,6,5355 reduced tissue neurogranin levels in AD are thought to reflect synaptic degeneration and loss of whole synaptic elements in the presence of AD13,14 The extracellular release of synaptic elements as a result of AD-associated synaptic degeneration likely explains previous reports25,26 of increased CSF neurogranin levels in AD.

We confirm the diagnostic utility of CSF neurogranin in a large, well-characterized cohort of AD and controls using a highly sensitive immunoassay developed in the Laboratory of Jack H. Ladenson, PhD, Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri. Furthermore, we found that CSF neurogranin levels correlate with brain atrophy in AD, with amyloid load in preclinical AD, and with other CSF markers of AD in patients with AD and controls. Importantly, we report for the first time, to our knowledge, that CSF neurogranin predicts future cognitive impairment in cognitively normal controls as well as the other CSF biomarkers and complements their predictive ability (collectively) for future cognitive impairment during a 2- to 3-year follow-up period.

In our cohort, the diagnostic utility of CSF neurogranin in differentiating patients with AD from controls was comparable to that of other CSF markers. Because most of our AD cohort includes individuals with very mild dementia (CDR score of 0.5), some of whom may elsewhere be classified as having MCI or pre-MCI, neurogranin may be a useful diagnostic marker for even the earliest symptomatic stages of the disease. The diagnostic accuracy of all CSF biomarkers and ratios was higher in relation to PiB status than in relation to clinical diagnoses, supporting the potential value of CSF biomarkers in identifying AD irrespective of clinical status.

Synaptic loss or dysfunction is an early and primary pathologic substrate of AD812 Cognitive deficits attributed to synaptic dysfunction occur in the absence of, or even before, neuronal loss in AD55 Cortical synaptic density is reduced by as much as 35% in even the earliest stages of AD56 and reflects neuronal loss and reduced synaptic density of viable neurons57 Synaptic loss is a good surrogate for cognitive decline and disease progression in AD5,7,10,58 because it appears to be more closely correlated with cognitive deficits than the numbers of plaques or tangles or extent of cortical gliosis in pathologic studies5,7 of postmortem AD brains.

We found that baseline CSF neurogranin, VILIP-1,21,22 tau, and p-tau181,5962 but not CSF Aβ42,61,63,64 levels predict future cognitive impairment in cognitively normal controls and rates of cognitive decline in patients with symptomatic AD over time. Importantly, CSF neurogranin levels predicted future cognitive impairment in cognitively normal controls similarly to other CSF markers of AD in this cohort and complemented their collective predictive ability for future cognitive decline. These findings are consistent with previous reports6567 and proposed models of disease progression that suggest the presence of significant synaptic disease years before symptom onset2,68,69 Although CSF Aβ42 levels begin to decrease a decade or more before cognitive impairment,1,2,41,70 they do not change substantially over time once this low set point has been reached71,72 On the other hand, ongoing deposition of neurofibrillary tangles and progressive neuronal or synaptic loss accompany disease progression into the symptomatic stages2,6870 Associations of neurofibrillary tangle load,7375 synaptic disease,6,9 and neuronal loss,76,77 but not cortical amyloid burden,63,78 with rates of cognitive decline have previously been reported. Therefore, CSF markers of neuronal or synaptic loss or tau disease are more closely associated with future cognitive decline than CSF markers of amyloid disease during short follow-up periods.

The CSF neurogranin levels correlate with brain atrophy in our AD cohort, with higher CSF neurogranin levels indicating more severe synaptic disease that accompanies brain volume loss in AD79 The correlation of CSF neurogranin, VILIP-1, tau, and p-tau18121,80 with amyloid load in preclinical AD is consistent with the notion that increasing amyloid deposition2,81 is associated with ongoing neuronal or synaptic loss before symptom onset. Because cortical amyloid deposition likely plateaus near the time of symptom onset,70,82 no correlations were observed between these CSF biomarkers and amyloid load in AD.

Synaptic disease in patients with AD is predominantly observed in the neuropil with no clear relation to amyloid plaques10,83,84 or neurofibrillary tangles14,58,84,85 Our findings that neurogranin immunoreactivity is detected in neuronal perikarya independently of neurofibrillary tangles and amyloid deposition are consistent with this notion. Associations between CSF neurogranin and CSF VILIP-1, tau, or p-tau181 levels in our cohort are likely caused by the ability of these markers to measure neurodegeneration because neither VILIP-121 nor neurogranin appears to be a component of neurofibrillary tangles. Furthermore, chronic alterations in synaptic input may influence the degree of phosphorylation of cytoskeletal proteins, including tau86 None of the CSF biomarkers correlated with CSF Aβ42 levels, which first decrease years before symptom onset and remain relatively stable with further disease progression41,71,72,80 Synaptic dysfunction is thought to be in part mediated by soluble Aβ oligomers in AD,87,88 with significant synaptic disease observed in areas devoid of or distant from insoluble amyloid deposits12,83,88,89

Our study is limited by the short duration of follow-up. It will be important to validate these findings across different centers. Because synaptic dysfunction may occur in the absence of synaptic loss,57 the identification of imaging markers of synaptic function may provide further insight into synaptic disease in AD and complement information provided by the CSF.

Conclusions

Our findings highlight the potential utility of CSF neurogranin as a biomarker surrogate for synaptic loss in AD. Markers of synaptic and neuronal injury, such as neurogranin and VILIP-1, may assist in monitoring response to potential therapies independently of effects on tau or amyloid disease. The CSF neurogranin levels may complement information provided by other CSF and imaging markers to guide diagnostic and prognostic decisions in clinical trials of disease-modifying therapies.

Supplementary Material

Supp. File

Key Points.

Question

What is the diagnostic and prognostic utility of cerebrospinal fluid (CSF) levels of the synaptic marker neurogranin in Alzheimer disease (AD)?

Findings

In this study comparing patients with symptomatic AD to a group of cognitively normal individuals, the CSF neurogranin levels differentiated patients with early symptomatic AD from controls with comparable diagnostic utility to other CSF markers of AD (tau, p-tau181, amyloid-β1–42, and VILIP-1).

Meaning

Cerebrospinal fluid neurogranin shows promise as a CSF biomarker for synaptic loss in AD.

Acknowledgments

Funding/Support: This work was supported by grants P50-AG05681, P01-AG03991, and P01-AG026276 from the National Institutes of Health (Dr Morris), a grant from Eli Lilly (Dr Holtzman), the JPB Foundation (Dr Holtzman), Siemens Health Care Diagnostics (Dr Ladenson), and the Charles F. and Joanne Knight Alzheimer Research Initiative. The frontotemporal lobar degeneration, progressive supranuclear palsy, and Lewy body dementia cerebrospinal fluid was generously provided by the University of California, San Francisco (UCSF) Memory and Aging Center (work supported by grants R01AG031278, K23-AG031861, P01AG019724, and P50 AG023501 from the National Institutes of Health, National Institute on Aging; grant P50 AG023501 from the UCSF Alzheimer's Disease Research Centers; CurePSP; and Association for Frontotemporal Dementias).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Additional Contributions: We acknowledge the contributions of the clinical, genetic, biomarker, imaging, and biostatistics cores of the Charles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine. We also acknowledge the altruism of all Alzheimer Disease Research Center research participants and their families.

Footnotes

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

Study concept and design: Tarawneh, D’Angelo, Ladenson, Morris, Holtzman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Tarawneh, Holtzman.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tarawneh, D’Angelo.

Obtained funding: Ladenson, Morris, Holtzman.

Administrative, technical, or material support: Crimmins, Herries, Griest, Fagan, Zipfel, Ladenson, Morris, Holtzman.

Study supervision: Tarawneh, Morris, Holtzman.

Conflict of Interest Disclosures: Dr Holtzman reported being a cofounder of and serving on the scientific advisory board of C2N Diagnostics and reported serving as a consultant for Genentech, AstraZeneca, AbbVie, Denali, and NeuroPhage. Dr Fagan reported serving on the scientific advisory boards of IBL International and Roche and working as a consultant for AbbVie and DiamR. Dr Ladenson reported being a coinventor of patents filed by Washington University concerning brain biomarkers. These patents are being managed by Washington University in accordance with university policy. Dr Morris reported previously or currently participating in the following clinical trials of antidementia drugs: SNIFF (Study of Nasal Insulin to Fight Forgetfullness) and A4 (Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease). Dr Morris reported serving as a consultant for Lilly USA and Takeda Pharmaceuticals. No other disclosures were reported.

REFERENCES

  • 1.Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999;45(3):358–368. doi: 10.1002/1531-8249(199903)45:3<358::aid-ana12>3.0.co;2-x. [DOI] [PubMed] [Google Scholar]
  • 2.Jack CR, Jr, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9(1):119–128. doi: 10.1016/S1474-4422(09)70299-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Price JL, Ko AI, Wade MJ, Tsou SK, McKeel DW, Morris JC. Neuron number in the entorhinal cortex and CA1 in preclinical Alzheimer disease. Arch Neurol. 2001;58(9):1395–1402. doi: 10.1001/archneur.58.9.1395. [DOI] [PubMed] [Google Scholar]
  • 4.Jack CR, Jr, Shiung MM, Weigand SD, et al. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology. 2005;65(8):1227–1231. doi: 10.1212/01.wnl.0000180958.22678.91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Terry RD, Masliah E, Salmon DP, et al. Physical basis of cognitive alterations in Alzheimer’s disease: synapse loss is the major correlate of cognitive impairment. Ann Neurol. 1991;30(4):572–580. doi: 10.1002/ana.410300410. [DOI] [PubMed] [Google Scholar]
  • 6.DeKosky ST, Scheff SW. Synapse loss in frontal cortex biopsies in Alzheimer’s disease: correlation with cognitive severity. Ann Neurol. 1990;27(5):457–464. doi: 10.1002/ana.410270502. [DOI] [PubMed] [Google Scholar]
  • 7.DeKosky ST, Scheff SW, Styren SD. Structural correlates of cognition in dementia: quantification and assessment of synapse change. Neurodegeneration. 1996;5(4):417–421. doi: 10.1006/neur.1996.0056. [DOI] [PubMed] [Google Scholar]
  • 8.Heinonen O, Soininen H, Sorvari H, et al. Loss of synaptophysin-like immunoreactivity in the hippocampal formation is an early phenomenon in Alzheimer’s disease. Neuroscience. 1995;64(2):375–384. doi: 10.1016/0306-4522(94)00422-2. [DOI] [PubMed] [Google Scholar]
  • 9.Masliah E, Mallory M, Hansen L, DeTeresa R, Alford M, Terry R. Synaptic and neuritic alterations during the progression of Alzheimer’s disease. Neurosci Lett. 1994;174(1):67–72. doi: 10.1016/0304-3940(94)90121-x. [DOI] [PubMed] [Google Scholar]
  • 10.Scheff SW, Price DA. Synaptic pathology in Alzheimer’s disease: a review of ultrastructural studies. Neurobiol Aging. 2003;24(8):1029–1046. doi: 10.1016/j.neurobiolaging.2003.08.002. [DOI] [PubMed] [Google Scholar]
  • 11.Small DH, Mok SS, Bornstein JC. Alzheimer’s disease and Aβ toxicity: from top to bottom. Nat Rev Neurosci. 2001;2(8):595–598. doi: 10.1038/35086072. [DOI] [PubMed] [Google Scholar]
  • 12.Hamos JE, DeGennaro LJ, Drachman DA. Synaptic loss in Alzheimer’s disease and other dementias. Neurology. 1989;39(3):355–361. doi: 10.1212/wnl.39.3.355. [DOI] [PubMed] [Google Scholar]
  • 13.Reddy PH, Mani G, Park BS, et al. Differential loss of synaptic proteins in Alzheimer’s disease: implications for synaptic dysfunction. J Alzheimers Dis. 2005;7(2):103–117. doi: 10.3233/jad-2005-7203. [DOI] [PubMed] [Google Scholar]
  • 14.Davidsson P, Blennow K. Neurochemical dissection of synaptic pathology in Alzheimer’s disease. Int Psychogeriatr. 1998;10(1):11–23. doi: 10.1017/s1041610298005110. [DOI] [PubMed] [Google Scholar]
  • 15.Watson JB, Szijan I, Coulter PM, II, et al. Localization of RC3 (neurogranin) in rat brain subcellular fractions. Brain Res Mol Brain Res. 1994;27(2):323–328. doi: 10.1016/0169-328x(94)90017-5. [DOI] [PubMed] [Google Scholar]
  • 16.Díez-Guerra FJ. Neurogranin, a link between calcium/calmodulin and protein kinase C signaling in synaptic plasticity. IUBMB Life. 2010;62(8):597–606. doi: 10.1002/iub.357. [DOI] [PubMed] [Google Scholar]
  • 17.Huang KP, Huang FL, Jäger T, Li J, Reymann KG, Balschun D. Neurogranin/RC3 enhances long-term potentiation and learning by promoting calcium-mediated signaling. J Neurosci. 2004;24(47):10660–10669. doi: 10.1523/JNEUROSCI.2213-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gerendasy D. Homeostatic tuning of Ca2+ signal transduction by members of the calpacitin protein family. J Neurosci Res. 1999;58(1):107–119. [PubMed] [Google Scholar]
  • 19.Gerendasy DD, Sutcliffe JG. RC3/neurogranin, a postsynaptic calpacitin for setting the response threshold to calcium influxes. Mol Neurobiol. 1997;15(2):131–163. doi: 10.1007/BF02740632. [DOI] [PubMed] [Google Scholar]
  • 20.Laterza OF, Modur VR, Crimmins DL, et al. Identification of novel brain biomarkers. Clin Chem. 2006;52(9):1713–1721. doi: 10.1373/clinchem.2006.070912. [DOI] [PubMed] [Google Scholar]
  • 21.Tarawneh R, D’Angelo G, Macy E, et al. Visinin-like protein-1: diagnostic and prognostic biomarker in Alzheimer disease. Ann Neurol. 2011;70(2):274–285. doi: 10.1002/ana.22448. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tarawneh R, Lee JM, Ladenson JH, Morris JC, Holtzman DM. CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease. Neurology. 2012;78(10):709–719. doi: 10.1212/WNL.0b013e318248e568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tarawneh R, Head D, Allison S, et al. Cerebrospinal fluid markers of neurodegeneration and rates of brain atrophy in early Alzheimer disease. JAMA Neurol. 2015;72(6):656–665. doi: 10.1001/jamaneurol.2015.0202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Thorsell A, Bjerke M, Gobom J, et al. Neurogranin in cerebrospinal fluid as a marker of synaptic degeneration in Alzheimer’s disease. Brain Res. 2010;1362:13–22. doi: 10.1016/j.brainres.2010.09.073. [DOI] [PubMed] [Google Scholar]
  • 25.Kvartsberg H, Duits FH, Ingelsson M, et al. Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer’s disease. Alzheimers Dement. 2015;11(10):1180–1190. doi: 10.1016/j.jalz.2014.10.009. [DOI] [PubMed] [Google Scholar]
  • 26.Kester MI, Teunissen CE, Crimmins DL, et al. Neurogranin as a cerebrospinal fluid biomarker for synaptic loss in symptomatic Alzheimer disease. JAMA Neurol. 2015;72(11):1275–1280. doi: 10.1001/jamaneurol.2015.1867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Portelius E, Zetterberg H, Skillback T, et al. Alzheimer’s Disease Neuroimaging Initiative. Cerebrospinal fluid neurogranin: relation to cognition and neurodegeneration in Alzheimer’s disease. Brain. 2015;138(pt 11):3373–3385. doi: 10.1093/brain/awv267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Talbot C, Lendon C, Craddock N, Shears S, Morris JC, Goate A. Protection against Alzheimer’s disease with apoE epsilon 2. Lancet. 1994;343(8910):1432–1433. doi: 10.1016/s0140-6736(94)92557-7. [DOI] [PubMed] [Google Scholar]
  • 29.Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 1993;43(11):2412–2414. doi: 10.1212/wnl.43.11.2412-a. [DOI] [PubMed] [Google Scholar]
  • 30.Storandt M, Grant EA, Miller JP, Morris JC. Longitudinal course and neuropathologic outcomes in original vs revised MCI and in pre-MCI. Neurology. 2006;67(3):467–473. doi: 10.1212/01.wnl.0000228231.26111.6e. [DOI] [PubMed] [Google Scholar]
  • 31.Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270–279. doi: 10.1016/j.jalz.2011.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Morris JC, Blennow K, Froelich L, et al. Harmonized diagnostic criteria for Alzheimer’s disease: recommendations. J Intern Med. 2014;275(3):204–213. doi: 10.1111/joim.12199. [DOI] [PubMed] [Google Scholar]
  • 33.Berg L, Miller JP, Baty J, Rubin EH, Morris JC, Figiel G. Mild senile dementia of the Alzheimer type. 4. Evaluation of intervention. Ann Neurol. 1992;31(3):242–249. doi: 10.1002/ana.410310303. [DOI] [PubMed] [Google Scholar]
  • 34.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  • 35.Morris JC, Weintraub S, Chui HC, et al. The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Dis Assoc Disord. 2006;20(4):210–216. doi: 10.1097/01.wad.0000213865.09806.92. [DOI] [PubMed] [Google Scholar]
  • 36.Berg L, McKeel DW, Jr, Miller JP, et al. Clinicopathologic studies in cognitively healthy aging and Alzheimer’s disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Arch Neurol. 1998;55(3):326–335. doi: 10.1001/archneur.55.3.326. [DOI] [PubMed] [Google Scholar]
  • 37.Neary D, Snowden JS, Gustafson L, et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546–1554. doi: 10.1212/wnl.51.6.1546. [DOI] [PubMed] [Google Scholar]
  • 38.Litvan I, Agid Y, Calne D, et al. Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology. 1996;47(1):1–9. doi: 10.1212/wnl.47.1.1. [DOI] [PubMed] [Google Scholar]
  • 39.McKeith IG, Dickson DW, Lowe J, et al. Consortium on DLB. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology. 2005;65(12):1863–1872. doi: 10.1212/01.wnl.0000187889.17253.b1. [DOI] [PubMed] [Google Scholar]
  • 40.McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34(7):939–944. doi: 10.1212/wnl.34.7.939. [DOI] [PubMed] [Google Scholar]
  • 41.Fagan AM, Mintun MA, Mach RH, et al. Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans. Ann Neurol. 2006;59(3):512–519. doi: 10.1002/ana.20730. [DOI] [PubMed] [Google Scholar]
  • 42.Crimmins DL, Brada NA, Lockwood CM, et al. ETRAP (efficient trapping and purification) of target protein polyclonal antibodies from GST-protein immune sera. Biotechnol Appl Biochem. 2010;57(4):127–138. doi: 10.1042/BA20100279. [DOI] [PubMed] [Google Scholar]
  • 43.Buckner RL, Head D, Parker J, et al. A unified approach for morphometric and functional data analysis in young, old, demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage. 2004;23(2):724–738. doi: 10.1016/j.neuroimage.2004.06.018. [DOI] [PubMed] [Google Scholar]
  • 44.Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968–980. doi: 10.1016/j.neuroimage.2006.01.021. [DOI] [PubMed] [Google Scholar]
  • 45.Mintun MA, Larossa GN, Sheline YI, et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology. 2006;67(3):446–452. doi: 10.1212/01.wnl.0000228230.26044.a4. [DOI] [PubMed] [Google Scholar]
  • 46.Davison AC, Hinkley DV. Bootstrap Methods and Their Application. Cambridge: Cambridge University Press; 1997. [Google Scholar]
  • 47.Austin PC, Tu JV. Bootstrap methods for developing predictive models. Am Stat. 2004;58:131–137. [Google Scholar]
  • 48.Zhong L, Gerges NZ. Neurogranin targets calmodulin and lowers the threshold for the induction of long-term potentiation. PLoS One. 2012;7(7):e41275. doi: 10.1371/journal.pone.0041275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Wang JH, Kelly PT. Postsynaptic injection of CA2+/CaM induces synaptic potentiation requiring CaMKII and PKC activity. Neuron. 1995;15(2):443–452. doi: 10.1016/0896-6273(95)90048-9. [DOI] [PubMed] [Google Scholar]
  • 50.Miyakawa T, Yared E, Pak JH, Huang FL, Huang KP, Crawley JN. Neurogranin null mutant mice display performance deficits on spatial learning tasks with anxiety related components. Hippocampus. 2001;11(6):763–775. doi: 10.1002/hipo.1092. [DOI] [PubMed] [Google Scholar]
  • 51.Pak JH, Huang FL, Li J, et al. Involvement of neurogranin in the modulation of calcium/calmodulin-dependent protein kinase II, synaptic plasticity, and spatial learning: a study with knockout mice. Proc Natl Acad Sci U S A. 2000;97(21):11232–11237. doi: 10.1073/pnas.210184697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Watson JB, Battenberg EF, Wong KK, Bloom FE, Sutcliffe JG. Subtractive cDNA cloning of RC3, a rodent cortex-enriched mRNA encoding a novel 78 residue protein. J Neurosci Res. 1990;26(4):397–408. doi: 10.1002/jnr.490260402. [DOI] [PubMed] [Google Scholar]
  • 53.Masliah E, Mallory M, Alford M, et al. Altered expression of synaptic proteins occurs early during progression of Alzheimer’s disease. Neurology. 2001;56(1):127–129. doi: 10.1212/wnl.56.1.127. [DOI] [PubMed] [Google Scholar]
  • 54.Sze CI, Troncoso JC, Kawas C, Mouton P, Price DL, Martin LJ. Loss of the presynaptic vesicle protein synaptophysin in hippocampus correlates with cognitive decline in Alzheimer disease. J Neuropathol Exp Neurol. 1997;56(8):933–944. doi: 10.1097/00005072-199708000-00011. [DOI] [PubMed] [Google Scholar]
  • 55.Knobloch M, Mansuy IM. Dendritic spine loss and synaptic alterations in Alzheimer’s disease. Mol Neurobiol. 2008;37(1):73–82. doi: 10.1007/s12035-008-8018-z. [DOI] [PubMed] [Google Scholar]
  • 56.Davies CA, Mann DM, Sumpter PQ, Yates PO. A quantitative morphometric analysis of the neuronal and synaptic content of the frontal and temporal cortex in patients with Alzheimer’s disease. J Neurol Sci. 1987;78(2):151–164. doi: 10.1016/0022-510x(87)90057-8. [DOI] [PubMed] [Google Scholar]
  • 57.Coleman PD, Yao PJ. Synaptic slaughter in Alzheimer’s disease. Neurobiol Aging. 2003;24(8):1023–1027. doi: 10.1016/j.neurobiolaging.2003.09.001. [DOI] [PubMed] [Google Scholar]
  • 58.Lassmann H, Fischer P, Jellinger K. Synaptic pathology of Alzheimer’s disease. Ann N Y Acad Sci. 1993;695:59–64. doi: 10.1111/j.1749-6632.1993.tb23028.x. [DOI] [PubMed] [Google Scholar]
  • 59.Henneman WJ, Vrenken H, Barnes J, et al. Baseline CSF p-tau levels independently predict progression of hippocampal atrophy in Alzheimer disease. Neurology. 2009;73(12):935–940. doi: 10.1212/WNL.0b013e3181b879ac. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Hampel H, Bürger K, Pruessner JC, et al. Correlation of cerebrospinal fluid levels of tau protein phosphorylated at threonine 231 with rates of hippocampal atrophy in Alzheimer disease. Arch Neurol. 2005;62(5):770–773. doi: 10.1001/archneur.62.5.770. [DOI] [PubMed] [Google Scholar]
  • 61.Wahlund LO, Blennow K. Cerebrospinal fluid biomarkers for disease stage and intensity in cognitively impaired patients. Neurosci Lett. 2003;339(2):99–102. doi: 10.1016/s0304-3940(02)01483-0. [DOI] [PubMed] [Google Scholar]
  • 62.Tosun D, Schuff N, Shaw LM, Trojanowski JQ, Weiner MW Alzheimer’s Disease NeuroImaging Initiative. Relationship between CSF biomarkers of Alzheimer’s disease and rates of regional cortical thinning in ADNI data. J Alzheimers Dis. 2011;26(suppl 3):77–90. doi: 10.3233/JAD-2011-0006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Josephs KA, Whitwell JL, Ahmed Z, et al. β-Amyloid burden is not associated with rates of brain atrophy. Ann Neurol. 2008;63(2):204–212. doi: 10.1002/ana.21223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Schuff N, Woerner N, Boreta L, et al. Alzheimer’s Disease Neuroimaging Initiative. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(pt 4):1067–1077. doi: 10.1093/brain/awp007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Vemuri P, Wiste HJ, Weigand SD, et al. Alzheimer’s Disease Neuroimaging Initiative. MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology. 2009;73(4):294–301. doi: 10.1212/WNL.0b013e3181af79fb. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Snider BJ, Fagan AM, Roe C, et al. Cerebrospinal fluid biomarkers and rate of cognitive decline in very mild dementia of the Alzheimer type. Arch Neurol. 2009;66(5):638–645. doi: 10.1001/archneurol.2009.55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Fagan AM, Roe CM, Xiong C, Mintun MA, Morris JC, Holtzman DM. Cerebrospinal fluid tau/β-amyloid42 ratio as a prediction of cognitive decline in nondemented older adults. Arch Neurol. 2007;64(3):343–349. doi: 10.1001/archneur.64.3.noc60123. [DOI] [PubMed] [Google Scholar]
  • 68.Tarawneh R, Holtzman DM. Biomarkers in translational research of Alzheimer’s disease. Neuropharmacology. 2010;59(4–5):310–322. doi: 10.1016/j.neuropharm.2010.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Tarawneh R, Holtzman DM. Critical issues for successful immunotherapy in Alzheimer’s disease: development of biomarkers and methods for early detection and intervention. CNS Neurol Disord Drug Targets. 2009;8(2):144–159. doi: 10.2174/187152709787847324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Ingelsson M, Fukumoto H, Newell KL, et al. Early Aβ accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology. 2004;62(6):925–931. doi: 10.1212/01.wnl.0000115115.98960.37. [DOI] [PubMed] [Google Scholar]
  • 71.Blennow K, Zetterberg H, Minthon L, et al. Longitudinal stability of CSF biomarkers in Alzheimer’s disease. Neurosci Lett. 2007;419(1):18–22. doi: 10.1016/j.neulet.2007.03.064. [DOI] [PubMed] [Google Scholar]
  • 72.Andreasen N, Hesse C, Davidsson P, et al. Cerebrospinal fluid β-amyloid(1–42) in Alzheimer disease: differences between early- and late-onset Alzheimer disease and stability during the course of disease. Arch Neurol. 1999;56(6):673–680. doi: 10.1001/archneur.56.6.673. [DOI] [PubMed] [Google Scholar]
  • 73.Csernansky JG, Hamstra J, Wang L, et al. Correlations between antemortem hippocampal volume and postmortem neuropathology in AD subjects. Alzheimer Dis Assoc Disord. 2004;18(4):190–195. [PubMed] [Google Scholar]
  • 74.Whitwell JL, Josephs KA, Murray ME, et al. MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study. Neurology. 2008;71(10):743–749. doi: 10.1212/01.wnl.0000324924.91351.7d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Silbert LC, Quinn JF, Moore MM, et al. Changes in premorbid brain volume predict Alzheimer’s disease pathology. Neurology. 2003;61(4):487–492. doi: 10.1212/01.wnl.0000079053.77227.14. [DOI] [PubMed] [Google Scholar]
  • 76.Bobinski M, de Leon MJ, Wegiel J, et al. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000;95(3):721–725. doi: 10.1016/s0306-4522(99)00476-5. [DOI] [PubMed] [Google Scholar]
  • 77.Zarow C, Vinters HV, Ellis WG, et al. Correlates of hippocampal neuron number in Alzheimer’s disease and ischemic vascular dementia. Ann Neurol. 2005;57(6):896–903. doi: 10.1002/ana.20503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Chételat G, Villemagne VL, Bourgeat P, et al. Australian Imaging Biomarkers and Lifestyle Research Group. Relationship between atrophy and β-amyloid deposition in Alzheimer disease. Ann Neurol. 2010;67(3):317–324. doi: 10.1002/ana.21955. [DOI] [PubMed] [Google Scholar]
  • 79.Risacher SL, Saykin AJ. Neuroimaging and other biomarkers for Alzheimer’s disease: the changing landscape of early detection. Annu Rev Clin Psychol. 2013;9:621–648. doi: 10.1146/annurev-clinpsy-050212-185535. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Fagan AM, Mintun MA, Shah AR, et al. Cerebrospinal fluid tau and ptau(181) increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer’s disease. EMBO Mol Med. 2009;1(8–9):371–380. doi: 10.1002/emmm.200900048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Perrin RJ, Fagan AM, Holtzman DM. Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature. 2009;461(7266):916–922. doi: 10.1038/nature08538. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Rowe CC, Ng S, Ackermann U, et al. Imaging β-amyloid burden in aging and dementia. Neurology. 2007;68(20):1718–1725. doi: 10.1212/01.wnl.0000261919.22630.ea. [DOI] [PubMed] [Google Scholar]
  • 83.Scheff SW, Price DA. Synapse loss in the temporal lobe in Alzheimer’s disease. Ann Neurol. 1993;33(2):190–199. doi: 10.1002/ana.410330209. [DOI] [PubMed] [Google Scholar]
  • 84.Blennow K, Bogdanovic N, Alafuzoff I, Ekman R, Davidsson P. Synaptic pathology in Alzheimer’s disease: relation to severity of dementia, but not to senile plaques, neurofibrillary tangles, or the ApoE4 allele. J Neural Transm (Vienna) 1996;103(5):603–618. doi: 10.1007/BF01273157. [DOI] [PubMed] [Google Scholar]
  • 85.Masliah E, Ellisman M, Carragher B, et al. Three-dimensional analysis of the relationship between synaptic pathology and neuropil threads in Alzheimer disease. J Neuropathol Exp Neurol. 1992;51(4):404–414. doi: 10.1097/00005072-199207000-00003. [DOI] [PubMed] [Google Scholar]
  • 86.Aoki C, Siekevitz P. Ontogenetic changes in the cyclic adenosine 3′,5′-monophosphate-stimulatable phosphorylation of cat visual cortex proteins, particularly of microtubule-associated protein 2 (MAP 2): effects of normal and dark rearing and of the exposure to light. J Neurosci. 1985;5(9):2465–2483. doi: 10.1523/JNEUROSCI.05-09-02465.1985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Selkoe DJ. Alzheimer’s disease is a synaptic failure. Science. 2002;298(5594):789–791. doi: 10.1126/science.1074069. [DOI] [PubMed] [Google Scholar]
  • 88.Mucke L, Masliah E, Yu GQ, et al. High-level neuronal expression of Aβ1–42 in wild-type human amyloid protein precursor transgenicmice: synaptotoxicity without plaque formation. J Neurosci. 2000;20(11):4050–4058. doi: 10.1523/JNEUROSCI.20-11-04050.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Hsia AY, Masliah E, McConlogue L, et al. Plaque-independent disruption of neural circuits in Alzheimer’s disease mouse models. Proc Natl Acad Sci U S A. 1999;96(6):3228–3233. doi: 10.1073/pnas.96.6.3228. [DOI] [PMC free article] [PubMed] [Google Scholar]

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