The effects of CSF neurogranin and APOE ε4 on cognition and neuropathology in mild cognitive impairment and Alzheimer’s disease

doi:10.21203/rs.3.rs-66187/v1

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The effects of CSF neurogranin and APOE ε4 on cognition and neuropathology in mild cognitive impairment and Alzheimer’s disease

https://doi.org/10.21203/rs.3.rs-66187/v1

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Background: Cerebrospinal fluid (CSF) neurogranin (Ng) has emerged as a promising biomarker for cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Apolipoprotein E ε4 (APOE ε4) allele is by far the most consistent genetic risk factor for AD. However, it is not known whether the pathophysiological roles of Ng in MCI or AD are related to APOE ε4.

Methods: We stratified 250 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database into cognitively normal (CN) ɛ4 negative (CN ɛ4-, n = 48), CN ɛ4 positive (CN ɛ4+, n = 17), MCI ɛ4 negative (MCI ɛ4-, n = 50), MCI ɛ4 positive (MCI ɛ4+, n = 72), AD ɛ4 negative (AD ɛ4-, n = 17), and AD ɛ4 positive (AD ɛ4+, n = 46) according to whether the subjects carried ε4. Spearman correlation was used to test the relationships between biomarkers. Overall diagnostic accuracy (area under the curve (AUC)) was obtained from receiver operating curve (ROC) analyses. Cox proportional hazard models tested the effect of CSF Ng measures on the conversion from CN to MCI or AD and MCI to AD. Relationships between the CSF Ng levels and diagnostic groups were tested with linear regressions. Linear mixed-effects models and linear regression models were used to evaluate CSF Ng as predictors of AD features, including cognition as measured with the Mini-Mental State Examination (MMSE) and Alzheimer’s disease assessment scale (ADAS)-cog 11, brain structure as measured by magnetic resonance imaging (MRI), and functional measures of cortical glucose metabolism as measured with 18F-Fluorodeoxyglucose-PET (FDG-PET).

Results: CSF Ng levels were significantly increased in APOE ε4 carriers compared to APOE ε4 non-carriers with MCI. In addition, CSF Ng identified MCI ɛ4 + versus CN ɛ4-, but not MCI ɛ4- versus CN ɛ4-. Similarly, CSF Ng negatively correlated with MMSE scores at baseline in the MCI ɛ4 + group. Above phenomena between the APOE ε4 carriers and APOE ε4 non-carriers with CN and AD did not observe.

Conclusions: Our findings support the use of CSF Ng as a biomarker of synaptic pathology for AD. We propose that the roles of CSF Ng in the pathophysiology of MCI may be dependent of APOE ε4.

Neurology

Alzheimer's disease

Apolipoprotein E ε4

Mild cognitive impairment

Neurogranin

Alzheimer's disease (AD) is the most prominent cause of dementia and a major health problem all over the world. The main pathological features of AD include extracellular depositions of β-amyloid (Aβ) peptides and intracellular neurofibrillary tangles consisting of hyperphosphorylated tau in the forebrain, while there is almost no neurodegeneration in the cerebellum [1]. The etiology and pathogenesis of AD are still unclear, but more than 15 genome-wide studies have shown that apolipoprotein E ε4 (APOE ε4) allele is associated with AD and is by far the most consistent genetic risk factor [2, 3]. Compared with non-carriers, APOE ε4 carriers tend to show an accelerated decline in cognitive ability. A large number of evidences show that APOE ε4 affects clinical AD to a large extent through Aβ metabolism and triggers a cascade of events, which eventually leads to the formation or propagation of neurofibrillary tangles and cognitive impairment [4–7].

Synaptic dysfunction and degeneration are central events in AD pathology [8]. Synaptic loss has been identified as an early event of AD progression, as well as the basic cause of progressive cognitive decline [8]. Furthermore, compared with Aβ deposits and neurofibrillary tangles, synapse loss is more related to the degree of dementia, especially in certain areas of the brain, such as the hippocampus [9, 10]. Therefore, synaptic proteins are promising tools for early AD diagnosis in addition to monitoring disease progression and evaluating drug effects on synaptic dysfunction and degeneration in clinical trials of disease-modifying therapies for AD. Neurogranin (Ng) is a 78-amino acid-long post synaptic protein,which is enriched in dendritic spines and plays an important role in long-term potentiation and memory consolidation [11–13], where it regulates calmodulin levels in response to the levels of intracellular calcium after neuronal excitation [14–16]. Several studies have shown that the levels of Ng are increased in cerebrospinal fluid (CSF) [17–21] but reduced in the brain of AD patients [22–24]. In the mild cognitive impairment (MCI) patients, high baseline CSF Ng levels predict cognitive decline at clinical follow-up [20]. In addition, high baseline CSF Ng levels in MCI correlate with longitudinal reductions in cortical glucose metabolism and hippocampal volume at clinical follow-up [20]. Furthermore, within the progressive MCI group, elevated CSF Ng levels are associated with accelerated deterioration in Alzheimer’s disease assessment scale (ADAS)[20]. Another study has also demonstrated CSF Ng is associated with brain atrophy [25].

However, so far, the relationship between APOE ε4 and Ng is poorly understood, and it is not known whether the above-mentioned roles of Ng are related to APOE ε4. In this study, we show results on CSF Ng in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, which includes cognitively normal (CN) control, participants with MCI, and dementia due to AD. We verified the specific hypotheses that CSF Ng is increased in APOE ε4 positive subjects compared to APOE ε4 negative participants in each diagnostic group and CSF Ng reflects neurodegeneration dependently of APOE ε4.

Database description

Data used in preparation of this article were obtained from the ADNI database. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD. Further information can be found at http://www.adni-info.org.

From the dataset, we selected all participants between 55–90 (inclusive) years of age who had completed lumbar puncture, Mini–Mental State Examination (MMSE), ADAS-cog 11, Clinical Dementia Rating scale (CDR), MRI, and 18F-Fluorodeoxyglucose-PET (FDG-PET). Selected individuals were classified as CN (n=65), MCI (n=122), and dementia due to AD (n=63) according to clinical and behavioral measures provided by ADNI. Individuals who have at least one ε4 allele were considered as ε4 carriers. According to whether the subjects carried ε4, they were divided into CN ɛ4 negative (CN ɛ4-, n=48), CN ɛ4 positive (CN ɛ4+, n=17), MCI ɛ4 negative (MCI ɛ4-, n=50), MCI ɛ4 positive (MCI ɛ4+, n=72), AD ɛ4 negative (AD ɛ4-, n=17), and AD ɛ4 positive (AD ɛ4+, n=46).

Classification criteria

The criteria for CN included an MMSE score ranging between 24–30, and a CDR score of 0 [26, 27]. The criteria for MCI included the presence of a subjective memory complaint, with an MMSE score between 24–30, a CDR of 0.5, preserved activities of daily living, and an absence of dementia [28]. In addition to the NINCDS/ADRDA criteria for probable AD, AD dementia subjects had MMSE scores between 20–26 and a CDR of 0.5 or 1.0 [29]. (Further information about the inclusion/exclusion criteria may be found at www.adni-info.org [accessed July 2020].)

Standard protocol approvals and patient consents

The ADNI study was approved by the Institutional Review Boards of all the participating institutions. Informed written consent was obtained from all subjects at each center.

CSF analyses

CSF Aβ42, total-tau (T-tau), and phosphorylated-tau at threonine 181 (P-tau) were measured by using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX) and Innogenetics INNO-BIA AlzBio3 (Innogenetics, Ghent, Belgium) immunoassay reagents as described previously [30]. CSF Ng was analyzed by electrochemiluminescence technology (Meso Scale Discovery, Gaithersburg, Maryland, USA) using Ng7, which is a monocloncal antibody specific for Ng, as coating antibody and polyclonal Ng anti-rabbit (ab 23570, Upstate) as detector antibody. Values are given as pg/ml. All of the CSF data used in this study were obtained from the ADNI files “UPENNBIOMK5-8.csv” and “BLENNOWCSFNG.csv”, (accessed July 2020). Further details of ADNI methods for CSF acquisition and measurements and quality control procedures can be found at www.adni-info.org.

Cognitive assessment

To assess the global cognitive performance, we used MMSE and ADAS-Cog 11 scores. Because of the lack of many data during the follow-up period, we only collected MMSE and ADAS-cog 11 scores at baseline. The data used in this study was obtained from the ADNI files “MMSE.csv” and “ADAS_ADNI1. csv”, (accessed July 2020).

Neuroimaging methods

To investigate neurodegeneration, we used the hippocampal and ventricular volumes. Those data were obtained from the ADNI files “FOXLABBSI_08_04_17.csv” and “UCSDVOL.csv”, (accessed July 2020). All the imaging data were selected at baseline because there were too many missing data during the follow-up period. The neuroimaging methods used by ADNI have been described previously [31]. Further details for ADNI image acquisition and processing can be found at www.adni-info.org/methods.

18F-Fluorodeoxyglucose-PET

ADNI PET image data were acquired at baseline and processed as described online (adni.loni.ucla.edu/ about-data-samples/image-data/ and adni.loni.ucla.edu/research/pet-post-processing/, respectively). For detailed instructions, see Landau et al. [7]. Briefly, we used the average PET counts of the lateral and medial frontal, anterior cingulate, posterior cingulate, lateral parietal, and lateral temporal regions [20].

Statistical methods

Analysis of covariance (ANOVA) and chi-square analyses were performed to test for significant differences between groups on baseline demographics. Associations between the CSF Ng and diagnostic groups were tested with multiple-variable linear regression, adjusted for age and gender. To evaluate whether APOE ε4 influenced these associations, we included the interaction between diagnosis and APOE ε4 positivity as a predictor in the model.

Spearman correlation was used to test associations between Ng and other core biomarkers. Overall diagnostic accuracy (area under the receiver operator characteristics curve, AUC) was obtained for each biomarker by using Receiver operating curve (ROC) analyses. The differences between two AUCs derived from all pairs of two different variables were tested by using bootstrap methods.

The associations of Ng with the incidence of AD were assessed by calculating hazard ratios (HR) with 95% CIs using Cox proportional hazard regression analysis with adjustment of age, education, and gender. Ng was categorized into two groups by the median of each biomarker when conducting COX proportional hazard regression analysis.

For MMSE, ADAS-cog 11, hippocampal and ventricular volumes, and FDG-PET, intercepts (baseline values) were derived by using linear mixed effects models. The intercept was then used as outcomes in linear regression models with Ng as predictor (adjusted for age and gender; and for education for MMSE and ADAS-cog 11; and for intracranial volume for hippocampal and ventricular volumes) within diagnostic groups. All statistics were done using R (v. 3.4.2) and SPSS version 20. Statistical significance was defined as p<0.05 for all analyses.

Characteristics of subjects

The demographics and biomarker characteristics of the study participants are listed in Table 1. There were no differences in age and education among the groups. Compared with the CN ɛ4- and AD ɛ4+ groups, there were significantly fewer female subjects in the MCI ɛ4- group. Between CN ɛ4- and CN ɛ4+ and MCI ɛ4- and MCI ɛ4+, Aβ42 levels in APOE ε4 positive subjects decreased significantly. There was no similar phenomenon between AD ɛ4- and AD ɛ4+. Between MCI ɛ4- and MCI ɛ4+, the levels of T-tau and P-tau in APOE ε4 positive participants increased significantly. However, there was no similar finding between CN ɛ4- and CN ɛ4+, and between AD ɛ4- and AD ɛ4+. MMSE scores were lower in MCI ɛ4-, MCI ɛ4+, AD ɛ4-, and AD ɛ4+ groups compared with CN ɛ4- and CN ɛ4+ subjects, and lower in AD ɛ4- and AD ɛ4+ groups compared with MCI ɛ4- and MCI ɛ4+. On the contrary, ADAS-Cog 11 scores were higher in MCI ɛ4-, MCI ɛ4+, AD ɛ4-, and AD ɛ4+ groups compared with CN ɛ4- and CN ɛ4+ participants, and higher in AD ɛ4- and AD ɛ4+ groups compared with MCI ɛ4- and MCI ɛ4+ patients.

CSF Ng levels in APOE ε4 positive and negative participants in different diagnostic groups

CSF Ng levels were significantly higher in patients with MCI ɛ4+, AD ɛ4-, and AD ɛ4+ (all p < 0.001) compared with CN ɛ4-. Higher Ng levels were also found in both MCI ɛ4+ (p < 0.05) and AD ɛ4- (p < 0.05) compared with CN ɛ4+. Between MCI ɛ4- and MCI ɛ4+, CSF Ng levels in APOE ε4 positive participants increased significantly (p < 0.001). However, there were no differences between CN ɛ4- and CN ɛ4+, and similarly between AD ɛ4- and AD ɛ4+ (Fig. 1).

CSF Ng levels in relation to Aβ and tau

Ng and Aβ42 were negatively correlated in CN ɛ4- participants (r = −0.353, p = 0.014). However, there were no significant associations between Ng and Aβ42 in CN ɛ4+, MCI ɛ4-, MCI ɛ4+, AD ɛ4-, and AD ɛ4+ subjects (r = −0.095, p = 0.717; r = −0.194, p = 0.177; r = −0.023, p = 0.845; r = 0.172, p = 0.509; r = 0.080, p = 0.596, respectively) (Fig. 2A). Ng was strongly correlated with T-tau and P-tau in CN ɛ4- (r = 0.550, p < 0.001 for T-tau; r = 0.519, p < 0.001 for P-tau), CN ɛ4+ (r = 0.858, p < 0.001 for T-tau; r = 0.841, p < 0.001 for P-tau), MCI ɛ4- (r = 0.799, p < 0.001 for T-tau; r = 0.784, p < 0.001 for P-tau), MCI ɛ4+ (r = 0.746, p < 0.001 for T-tau; r = 0.726, p < 0.001 for P-tau), AD ɛ4- (r = 0.869, p < 0.001 for T-tau; r = 0.906, p < 0.001 for P-tau), and AD ɛ4+ subjects (r = 0.747, p < 0.001 for T-tau; r = 0.726, p < 0.001 for P-tau) (Fig. 2B and C).

Diagnostic accuracy of CSF Ng and core CSF biomarkers

ROC analyses were performed to test CSF biomarkers in relation to clinical diagnoses for MCI ɛ4-, MCI ɛ4+, AD ɛ4-, and AD ɛ4+. All CSF biomarkers had significant diagnostic accuracy for MCI ɛ4+ (Table 2 and Fig. 3B), AD ɛ4- (Table 2 and Fig. 3C), and AD ɛ4+ (Table 2 and Fig. 3D) but not MCI ɛ4- (Table 2 and Fig. 3A) compared with CN ɛ4-. Compared with T-tau and P-tau, Ng had almost the same range of diagnostic accuracy for MCI ɛ4+, AD ɛ4-, and AD ɛ4+ (Table 2 and Fig. 3B-D). However, compared with T-tau and P-tau, the combination of Ng, T-tau or P-tau did not significantly improve the diagnostic accuracy for MCI ɛ4-, MCI ɛ4+, AD ɛ4-, and AD ɛ4+ (Table 2 and Fig. 3A-D).

CSF Ng predict conversion from CN to MCI or AD and MCI to AD

Among the subjects, 18 CN individuals progressed to MCI or AD and 73 MCI participants progressed to AD during follow-up. We investigated whether CSF Ng predicted conversion from CN to MCI or AD and MCI to AD. Cox proportional hazard models were performed for Ng as a continuous variable. HRs were then calculated for Ng as a dichotomous variable using the median values of Ng as a cutoff (adjusting for age, education, and gender). CSF Ng did not significantly predict conversion from CN to MCI or AD (Fig. 4A) and MCI to AD (Fig. 4B).

CSF Ng and APOE ε4 in relation to cognition

High CSF Ng levels associated with low MMSE scores at baseline in the MCI ɛ4+ group (β = −0.18, p = 0.036), but not in the MCI ɛ4- and other groups (Fig. 5A). CSF Ng did not correlate with ADAS-cog 11 scores at baseline in every diagnostic group (Fig. 5A). Although there was trend for associations between CSF Ng and with ADAS-cog 11 in the AD ɛ4- group, but this did not reach statistical significance (β = −0.22, p = 0.064) (Fig. 5B).

CSF Ng and APOE ε4 in relation to brain structure

CSF Ng did not correlate with baseline FDG-PET or ventricular volumes in different diagnostic groups (Fig. 6A and B). However, high CSF Ng levels were associated with low hippocampal volumes in the MCI ɛ4- group (β = −0.39, p = 0.007), the MCI ɛ4+ group (β = −0.25, p = 0.036), and the AD ɛ4+ group (β = −0.42, p = 0.003) (Fig. 6C), whereas in the AD ɛ4- and other groups, no such associations were found.

The present study performed a relatively comprehensive assessment about the characteristic of CSF Ng of subjects with MCI or AD from the ADNI database. We have the following main findings: first, the levels of Ng in APOE ε4 positive participants increased significantly between MCI ɛ4- and MCI ɛ4+. However, there was no similar finding between CN ɛ4- and CN ɛ4 + and between AD ɛ4- and AD ɛ4+. Secondly, CSF Ng was strongly correlated with T-tau and P-tau but not Aβ42 in every diagnostic group. Our third main finding is that CSF Ng had almost the same range of diagnostic accuracy for MCI ɛ4+, AD ɛ4-, and AD ɛ4 + compared with T-tau and P-tau. However, the combination of Ng, T-tau or P-tau did not significantly improve the diagnostic accuracy. Finally, high CSF Ng levels only associated with low MMSE scores at baseline in the MCI ɛ4 + group. Moreover, CSF Ng correlated with hippocampal volumes at baseline in the MCI ɛ4-, MCI ɛ4+, and AD ɛ4 + groups.

Previous studies have shown that, compared with healthy controls, CSF Ng was significantly increased in progressive MCI and AD, and progressive MCI participants had higher levels of CSF Ng than stable MCI subjects [20, 32]. To investigate whether the levels of CSF Ng can be used to identify MCI and AD subjects with an underlying APOE ε4 pathology, each group included in the present study was dichotomized into either APOE ε4-positive or APOE ε4-negative. The CSF Ng levels were significantly increased in the MCI ɛ4+, AD ɛ4-, and AD ɛ4 + groups compared to CN ɛ4- group. Interestingly, higher CSF Ng levels were also found in the MCI ɛ4 + group than MCI ɛ4- group, but there was no similar finding between CN ɛ4- and CN ɛ4 + and between AD ɛ4- and AD ɛ4+, validating that CSF Ng may be an early pathophysiological marker of AD-related synaptic degeneration [20], and suggesting the roles of CSF Ng in the pathophysiology of MCI may be related to APOE ε4.

Previous studies demonstrated that CSF Ng was particularly elevated in patients with MCI and AD with Aβ pathologic features [20, 33]. However, the relationship between CSF Ng and Aβ pathology remains controversial. Some studies have shown that CSF Ng positively or negatively correlated with Aβ42 or Aβ40 in AD patients [17, 19, 21, 34]. Moreover, other researchers have reported that the CSF Ng levels did not correlate to Aβ42 in AD samples [21, 35]. In the present study, except for CN ɛ4- group, no correlation between CSF Ng and Aβ42 was found in other diagnostic groups. Most likely, it indicates that the degeneration of synapses is weakly related to the axonal damage induced by Aβ. In line with previous studies [17, 19–21, 33–36], we have investigated that elevated levels of CSF Ng were positively associated with levels of T-tau and P-tau in every diagnostic group. Interestingly, T-tau and P-tau levels were significantly increased in APOE ε4 carriers compared to APOE ε4 non-carriers with MCI, but the levels of T-tau and P-tau between the APOE ε4 carriers and APOE ε4 non-carriers with CN and AD did not differ. These suggest that synaptic degeneration especially in MCI subjects may be related to the axonal damage induced by tau or APOE ε4.

We next sought to test whether CSF Ng could improve the differential diagnosis of MCI and AD dementia when compared to the standard AD biomarkers, CSF T-tau and P-tau. All biomarkers identified MCI ɛ4 + versus CN ɛ4-, AD ɛ4- versus CN ɛ4-, and AD ɛ4 + versus CN ɛ4-, but not MCI ɛ4- versus CN ɛ4-, and combinations did not improve the diagnostic accuracy compared with using individual biomarkers. In terms of diagnostic accuracy, the different performance of CSF Ng on MCI ɛ4- and MCI ɛ4 + may be due to the fact that less MCI ɛ4- subjects would progress to AD, while more MCI ɛ4 + subjects would progress to AD. In addition, unfortunately, our study indicated that CSF Ng did not significantly predict conversion from CN to MCI or AD and MCI to AD, suggesting that CSF Ng may be not sensitive in predicting progression in cognitively normal subjects or MCI patients.

Ng is a post synaptic protein involved in memory consolidation as well as a potential biomarker for cognitive decline and brain injury in AD [13, 37]. However, the correlation between CSF Ng and cognitive evaluation scores as measured by MMSE and ADAS-cog is still inconsistent. De Vos, et al. investigated that no correlations were found between CSF Ng and clinical parameters, such as MMSE scores (at baseline), nor the change in MMSE per year or disease duration in control, MCI, and AD groups [17]. Hellwig, et al also thought that the MMSE scores did not correlate to Ng levels in any of the groups [21]. Portelius, et al. found that CSF Ng did not relate to baseline MMSE and ADAS-cog scores, but high CSF Ng levels correlated significantly to a more rapid elevation in ADAS-cog scores in progressive MCI over time [20]. Mattsson, et al. demonstrated that Ng was associated with worsening MMSE and ADAS-cog scores during the clinical follow-up period in subjects with Aβ positive[34]. In the present study, we only collected MMSE and ADAS-cog 11 scores at baseline because there were so many missing data during the follow-up period. We found that CSF Ng negatively associated with MMSE scores at baseline in the MCI ɛ4 + group, whereas CSF Ng did not correlate with ADAS-cog 11 scores at baseline in every diagnostic group. This result again suggests that the pathophysiological effects of CSF Ng in MCI may be related to APOE ε4. Finally, we tested whether CSF Ng correlated with structural measures of hippocampal and ventricular volumes as measured by MRI and with functional measures of cortical glucose metabolism as measured with FDG-PET. CSF Ng did not correlate with baseline FDG-PET or ventricular volumes in different diagnostic groups, but CSF Ng was associated with hippocampal volumes in the MCI ɛ4-, MCI ɛ4+, and AD ɛ4 + groups. In this respect, the role of CSF Ng in MCI is not found to be related to APOE ε4.

This study has limitations. Firstly, our study did not include non-AD neurodegenerative diseases. In addition, the ADNI database was volunteered by highly educated individuals for research focused on AD research. This may give rise to bias in choice because the study population is a self-selected individual who may have concerns about their cognition. Finally, this study lacks follow-up data and cannot dynamically observe the relevant indicators.

In conclusion, our findings support the use of CSF Ng as a biomarker of synaptic pathology for AD. CSF Ng levels were significantly increased in APOE ε4 carriers compared to APOE ε4 non-carriers with MCI. In addition, CSF Ng identified MCI ɛ4 + versus CN ɛ4-, but not MCI ɛ4- versus CN ɛ4-. Similarly, CSF Ng negatively correlated with MMSE scores at baseline in the MCI ɛ4 + group. Above phenomena between the APOE ε4 carriers and APOE ε4 non-carriers with CN and AD did not observe. We propose that the roles of CSF Ng in the pathophysiology of MCI may be dependent of APOE ε4. Future studies will further explore the relationship between APOE ε4 and CSF Ng and related mechanisms, providing more evidence for the potential roles of Ng in clinical research, trials and practice of AD and other neurodegenerative diseases.

Acknowledgments

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

This study was supported by the Medical Research Project of Chongqing Healthy Committee (2018MSXM058) and Nursing Research Fund of the First Affiliated Hospital of Chongqing Medical University (HLJJ2014-24).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ Contributions

YF: study concept, design, analysis and interpretation of data, composition of figures, and manuscript drafting. YG: study design, composition of figures, manuscript drafting, and critical review of manuscript for intellectual content. JL: analysis and interpretation of data. MB: analysis and interpretation of data. HZ: study concept, design, study supervision, and critical review of manuscript for intellectual content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The ADNI study was approved by the Institutional Review Boards of all the participating institutions. Informed written consent was obtained from all participants at every center.

Consent for publication

Not applicable.

Competing interests

The authors report no competing interests.

Authors details

¹Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

² General Medical Wards, the Affiliated Children’s Hospital of Chongqing Medical University, Chongqing 400014, China.

³Department of Neurology, Yantai Yuhuangding Hospital Affiliated to Qingdao Medical University, Shandong, China.

Aβ: amyloid-β; AD: Alzheimer's disease; ADAS-cog: Alzheimer’s disease assessment scale-cog; ADNI: Alzheimer’s disease Neuroimaging Initiative; ANOVA: Analysis of covariance; APOE: Apolipoprotein E; AUC: area under the curve; CDR: Clinical Dementia Rating scale; CN: cognitively normal; CSF: cerebrospinal fluid; FDG-PET: 18F-Fluorodeoxyglucose-PET; HR: hazard ratios; MCI: mild cognitive impairment; MMSE: Mini-mental State Examination; MRI: magnetic resonance imaging; NFT: neurofibrillary tangles; Ng: neurogranin; PET: positron emission tomography; ROC: receiver operating curve

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Table 1. Main demographics of subjects at baseline.

Characteristics	CN ɛ4- (n=48)	CN ɛ4+ (n=17)	MCI ɛ4- (n=50)	MCI ɛ4+ (n=72)	AD ɛ4- (n=17)	AD ɛ4+ (n=46)
Age (years)	74.9 (0.7)	75.7 (1.4)	72.9 (1.2)	73.0 (0.8)	73.1 (2.3)	74.2 (1.1)
Sex (F %)	26 (54.2%)^c	6 (35.3%)	14 (28.0%)^a,e	31 (43.1%)	10 (58.8%)^c	21 (45.7%)
Education (years)	15.7 (0.4)	15.7 (0.9)	15.5 (0.5)	15.7 (0.3)	15.7 (0.7)	14.7 (0.4)
Aβ42 (pg/ml)	1128.4 (53.8)^b,c,d,e,f	768.2 (70.0)^a,f	902.2 (57.2)^a,d,f	632.5 (30.3)^a,c	729.5 (67.7)^a,f	538.7 (20.7)^a,b,c,e
T-tau (pg/ml)	215.6 (8.0)^d,e,f	267.8 (25.5)^d,e,f	273.1 (16.8)^d,e	351.9 (14.3)^a,b,c	391.2 (42.1)^a,b,c	344.1 (15.3)^a,b
P-tau (pg/ml)	19.9 (0.9)^d,e,f	26.4 (2.8)^d,e	26.4 (1.9)^d,e	36.1 (1.7)^a,b,c	39.4 (4.8)^a,b,c	34.7 (1.6)^a
MMSE	29.3 (0.1)^c,d,e,f	29.0 (0.2)^c,d,e,f	26.8 (0.3)^a,b,e,f	26.9 (0.2)^a,b,e,f	23.9 (0.4)^a,b,c,d	23.1 (0.3)^a,b,c,d
ADAS-cog 11	6.4 (0.4)^c,d,e,f	7.5 (0.8)^c,d,e,f	10.9 (0.7)^a,b,e,f	12.4 (0.6)^a,b,e,f	18.8 (1.7)^a,b,c,d	18.4 (0.8)^a,b,c,d

Measurement data are expressed by mean and standard error. P values indicate the values assessed with analyses of variance for each variable except gender, where a contingency chi-square was performed. Post hoc analysis provided significant differences between groups: ^afrom CN ɛ4-; ^bfrom CN ɛ4+; ^cfrom MCI ɛ4-; ^dfrom MCI ɛ4+;^efrom AD ɛ4-; ^ffrom AD ɛ4+. Abbreviations: MMSE, Mini–Mental State Examination; ADAS-cog 11, Alzheimer’s disease assessment scale-cog 11; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer’s disease.

Table 2. AUC of CSF biomarkers.

T-tau

P-tau

Ng+T-tau

Ng+P-tau

Ng+T-tau+P-tau

MCI ɛ4-

0.585

(0.469-0.700) (p=0.148)

0.606

(0.493-0.720) (p=0.070)

0.613

(0.498-0.724) (p=0.058)

0.600

(0.486-0.715) (p=0.087)

0.612

(0.500-0.725) (p=0.055)

0.603

(0.489-0.717) (p=0.078)

MCI ɛ4+

0.808

(0.730-0.887) (p<0.001)

0.875

(0.812-0.937) (p<0.001)

0.881

(0.819-0.943) (p<0.001)

0.876

(0.814-0.938) (p<0.001)

0.881

(0.820-0.942) (p<0.001)

0.881

(0.820-0.942) (p<0.001)

AD ɛ4-

0.809

(0.676-0.941) (p<0.001)

0.827

(0.686-0.968) (p<0.001)

0.824

(0.677-0.970) (p<0.001)

0.825

(0.683-0.967) (p<0.001)

0.819

(0.672-0.965) (p<0.001)

0.833

(0.699-0.968) (p<0.001)

AD ɛ4+

0.783

(0.687-0.878)

(p<0.001)

0.879

(0.809-0.949) (p<0.001)

0.888

(0.821-0.955) (p<0.001)

0.880

(0.811-0.949) (p<0.001)

0.889

(0.822-0.956) (p<0.001)

0.888

(0.821-0.956) (p<0.001)

Abbreviations: AUC, area under the receiver operator characteristics curve; Ng, neurogranin; MCI, mild cognitive impairment; AD, Alzheimer’s

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The effects of CSF neurogranin and APOE ε4 on cognition and neuropathology in mild cognitive impairment and Alzheimer’s disease

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