Growth Associated Protein 43 (GAP-43) predicts brain amyloidosis in Alzheimer’s Dementia Continuum: an [18F] AV45 study

doi:10.21203/rs.3.rs-5004381/v1

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Research Article

Growth Associated Protein 43 (GAP-43) predicts brain amyloidosis in Alzheimer’s Dementia Continuum: an [18F] AV45 study

https://doi.org/10.21203/rs.3.rs-5004381/v1

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Background

Alzheimer's disease (AD) is a global health concern with a rising prevalence. Growth Associated Protein 43 (GAP-43) is a crucial protein for neuronal growth and synaptic plasticity, essential for maintaining healthy brain function. In AD, changes in GAP-43 levels have been observed, potentially indicating synaptic dysfunction and neurodegeneration. This study investigates the potential of GAP-43 as a biomarker in AD by analyzing its correlation with amyloid-beta (Aβ) pathology, a hallmark feature of the disease using [18F] AV45.

Methods

We examined 1639 participants using a dataset extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Results

A total of 226 subjects meeting the eligibility criteria were recruited from the ADNI dataset for enrollment. These individuals were categorized into three groups: 77 cognitively normal (CN) individuals, 111 with mild cognitive impairment (MCI), and 38 AD. Our results reveal elevated CSF GAP-43 levels in AD, and GAP-43 exhibited a stronger association with tau pathology than with Aβ. The study establishes a robust positive correlation between GAP-43 and [18F] florbetapir PET ([18F] AV45), a marker for Aβ plaques, independent of cognitive status. Additionally, logistic regression identified GAP-43) as significant predictors of AD.

Conclusion

The diagnostic accuracy of [18F] AV45, combined with GAP-43, enhances understanding of AD pathology. This study sets the stage for future research on GAP-43's trajectory in disease progression and the molecular mechanisms linking GAP-43 and amyloid-beta. The findings suggest promising avenues for novel therapeutic targets, contributing to advancements in early detection and treatment strategies for AD.

Alzheimer's disease

Growth Associated Protein 43

ADNI

[18F] AV45

Estimates show that about 46.8 million people are living with dementia worldwide. This number will rise to 74.7 million by 2030 and 131.5 million by 2050. The estimated incidence of new cases is 9.9 million each year across the world (1). The exact pathogenesis of Alzheimer’s disease (AD) is still not recognized. However, based on our current knowledge, AD is caused by extracellular accumulation of plaques containing amyloid beta protein (Aβ) and intracellular accumulation of neurofibrillary tangles (NFTs) containing hyperphosphorylated tau protein (2, 3). These protein accumulations cause neuronal damage and synaptic loss. Early diagnosis of AD is essential considering that the suggested disease-modifying treatments are known to be most effective in the mild cognitive impairment (MCI) stage (4). Due to the complexity of AD and overlapping clinical manifestations with other forms of dementia, especially in the earliest phases of the disease, recognizing disease patterns and early diagnostic markers are crucial.

Aβ denotes a peptide of 36–43 amino acids which is manufactured from amyloid precursor protein (APP) cleavage catalyzed by β- and γ-secretase (5–8). Aβ is present in neurons and extracellular space (9–11). Aβ peptides possess an inherent capability to spontaneously form oligomers, protofibrils, or mature amyloid fibrils through self-assembly (12–14). Aβ plaques, protofibrils, and fibrils are observed in symptomatic AD and pathologically defined preclinical AD (15–19). Evidently, plasma Aβ concentrations and cerebral β-amyloidosis can predict AD (20–22). These accumulations are also sensitive to the stage of the disease, meaning that patients in the pre-clinical phases of AD show lower levels of Aβ42 in their CSF (23, 24). Studies have shown that the levels of GAP-43 are significantly higher in the brains of AD patients compared to healthy individuals (25). This elevation in GAP-43 levels has been observed in regions of the brain affected by AD pathology, including the hippocampus, amygdala, and cortex (26). The correlation between GAP-43 levels and the presence of NFT and Aβ plaques suggests that it may reflect the extent of disease progression. (27). GAP-43 is primarily found in neurons and is involved in regulating synaptic plasticity and axonal growth. As AD is characterized by synaptic dysfunction and neurodegeneration, the altered expression of GAP-43 in AD brains indicates its potential relevance as a biomarker (28, 29). GAP-43 is critical during early developmental stages of the brain, including prenatal and early postnatal periods. It plays a crucial role in neurite outgrowth, synaptogenesis, and neuronal plasticity. Since AD pathology begins years before clinical symptoms appear, the detection of altered GAP-43 levels in the early stages of the disease suggests its potential as an early biomarker (30–32).

Although FDG PET can diagnose AD with good accuracy, Amyloid PET is the ultimate gold standard of AD diagnosis. V45 PET is primarily used to detect and visualize amyloid-beta (Aβ) plaques, which are one of the hallmark pathological features of AD. It allows for the quantification and localization of Aβ deposition in the brain. Florbetapir F 18 ([18F] AV45) is a PET ligand that binds Aβ42 with high affinity and specificity (33). Evidence shows that there is an association between findings of Florbetapir-PET and postmortem beta-amyloid burden. Florbetapir-PET images can provide a precise assessment of amyloid burden in the brain of living subjects (34).

Eventually, since GAP-43 is involved in synaptic plasticity and axonal growth, while Aβ plaques, detected through [18F] AV45, are a hallmark feature of AD, Studying the association between GAP-43 and Aβ pathology can provide insights into the mechanisms by which synaptic dysfunction and neurodegeneration occur in AD. Establishing a link between GAP-43 and Aβ deposition could have diagnostic implications and contribute to the development of more accurate diagnostic methods and biomarker panels for AD, aiding in early detection and differential diagnosis. We hypothesized that GAP-43 can accurately predict the [18F] AV45 findings independent of the stage of the disease and other potential confounders.

Design and Participants

Data was extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. ADNI is a comprehensive and longitudinal collection of clinical, neuroimaging, genetic, and biomarker data from participants with AD, MCI, and healthy individuals (35, 36). ADNI researchers gather, analyze, and use data, such as PET and MRI images, genetic material, cognitive tests, CSF, blood biomarkers, plasma, serum, urine, and brain tissue as predictors of AD disease. This data that is gathered from AD patients, MCI subjects, and elderly controls, is managed by the Resource Allocation Review Committee (RARC) or Biospecimen Review Committee (BRC). Participants in ADNI are 55 to 90 years old and their data is obtained from 59 research centers in Canada and the United States. After obtaining informed consent, following an initial series of tests, they repeat them annually. These tests include genetic testing, a clinical evaluation, lumbar puncture, neuropsychological tests, MRI, and PET scans.

In this research, a total of 1639 patients including 757 women and 882 men were screened for inclusion at baseline. Among these, 391 patients were found eligible for inclusion. Of these 391 subjects first, the control group was cognitively normal subjects who had no signs of dementia(38subjects). The other groups were the SMC subjects with significant memory complaints (35 subjects), early MCI (173 subjects), and late MCI (72 subjects) groups. The last group was the patients who were diagnosed as having Alzheimer's disease (73 subjects).

CSF sampling, storage and measurement

Lumbar punctures were performed to obtain CSF biomarkers using either 20- or 24-gauge spinal needles, following the guidelines specified in the ADNI procedures manual (http://www.adni-info.org/). Within the first hour after the samples were taken, they were put into the collection tubes, moved to polypropylene tubes, and then frozen on dry ice. Aliquots of 0.5 ml were made at the ADNI biomarker core laboratory and kept at -80°C. On an entirely automated Cobas e 601, aliquots of CSF were evaluated using electrochemiluminescence immunoassays (ECLIA) Elecsys® β-Amyloid (1–42), Elecsys® Phospho-Tau(181p), and Elecsys® Total-Tau according to the preliminary kit manufacturer’s instruction. We used Baseline Aβ, t-tau, p-tau and GAP-43.

Positron Emission Tomography (PET)

The imaging data from the ADNI dataset underwent a standardized preprocessing pipeline. The specific details concerning image acquisition can be found on the ADNI website (http://adni.loni.usc.edu/). For the PET scan, [18F] florbetapir (florbetapir) was used as the tracer to assess Aβ burden in the brain. The scan was performed within a time frame of 50 to 70 minutes after injection. The resulting images were then averaged, spatially aligned, interpolated to a standard voxel size, and smoothed. This process was implemented to achieve a common resolution of 8mm full width at half maximum (37). To estimate the cross-sectional brain Aβ burden, the average standardized uptake value ratio (SUVR) was calculated for specific regions of interest. These regions included the precuneus, cingulate, inferior parietal, medial prefrontal, lateral temporal, and orbitofrontal cortices. The pons region served as the reference region for comparison. By analyzing the SUVR values, the global Aβ load in the brain was determined, providing insights into the extent of Aβ deposition in individuals (38).

Statistical Methods

All the statistical analyses were performed by SPSS Statistics version 26. To account for the non-normal distribution of all variables, we employed the Kruskal-Wallis test for continuous variables and the Chi-Square test for categorical variables to compare values among the groups. We utilized a Python Matplotlib Framework to generate a violin plot for visualizing the comparison of CSF GAP-43 and AV-45 among the groups. To examine the correlation between CSF GAP-43, [18F] AV45, MMSE, ADAS-13 and CSF biomarkers, we calculated Pearson's correlation coefficient (r) and p-values. Additionally, we obtained AUC values with 95% confidence intervals (CIs) from ROC curve analyses to assess the diagnostic capability of CSF biomarkers, namely GAP-43 and [18F] AV45.

We also conducted cross-sectional correlation multiple linear regression (MLP) to investigate the relationship between CSF GAP-43 and AV-45, and MMSE scores, and also [18F] AV45 and CSF-GAP43, and MMSE. To ensure normality, we transformed CSF GAP-43, [18F] AV45, and MMSE values into z-scores before inputting them into the model. Age, sex, and education were considered as covariates. P values less than 0.05 were considered statistically significant.

Ethical Considerations

We used deidentified data obtained from the ADNI databased, and no patients identifying information was accessed by any of the co-authors. As per ADNI protocols about human ethical approval, written full consent from the participants at each location was obtained before the study and all procedures performed in studies that involved human participants were following the ethical protocols of the national or/and institutional research committee, and with the Helsinki declaration (1964) and its amendments. More information about ADNI ethical protocols can be found at adni.loni.usc.edu.

Demographic Characteristics

A total of 226 subjects were found eligible for enrollment and were categorized into three groups: 77 cognitively normal (CN) individuals (mean age = 71.9 ± 6.7 years, 49.4% female, mean education = 17.2 ± 2.2 years), 111 patients with MCI (mean age = 70.6 ± 7.6 years, 54.1% female, mean education = 16.1 ± 2.6 years), and 38 patients with AD (mean age = 73.1 ± 6.6 years, 16.8% female, mean education = 16.5 ± 2.6 years). The current cross-sectional study revealed that the mean scores for Mini-Mental State Examination (MMSE) were 29.14, 27.85, and 22.11; Montreal Cognitive Assessment (MOCA) scores were 26.65, 24.69, and 16.66; Alzheimer's Disease Assessment Scale-Cognitive subscale 13 (ADAS-Cog13) scores were 7.01, 12.44, and 28.32; and Clinical Dementia Rating-Sum of Boxes (CDRSB) scores were 0.09, 1.17, and 5.01 for the CN, MCI, and AD groups, respectively. There was no significant difference in age among the groups (p = 0.1). Additionally, no significant difference was observed in education between the MCI and dementia or MCI and AD groups (p > 0.5). However, CN individuals had significantly higher levels of education than MCI patients (p < 0.5). A more comprehensive summary of the findings can be found in Table 1.

Table 1

Sample characteristics
Demographic	CN (n = 77)	MCI (n = 111)	AD (n = 38)	P value
Age (years)	71.9 ± 6.7	70.6 ± 7.6	73.1 ± 6.6	0.071
Female, N (%)	49.4%	54.1%	16.8%	0.579
Education (years)	17.2 ± 2.2	16.1 ± 2.6	16.5 ± 2.6	0.029
MMSE	29.14	27.85	22.11	< 0.001
ADAS13	7.01	12.44	28.32	< 0.001
CDRSB	0.09	1.17	5.01	< 0.001
CSF T-tau (pg/mL)	68.9 ± 36.5	77.4 ± 44	151.7 ± 80.6	< 0.001
CSF P-tau (pg/mL)	39.4 ± 26.7	43.4 ± 24	73.6 ± 36.2	< 0.001
CSF Aβ42 (pg/mL)	200.6 ± 52.4	182 ± 55.3	132.2 ± 35.2	< 0.001
Abbreviations: CN: cognitively normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; MMSE: Mini-Mental State Examination; CDRSB: Clinical Dementia Rating Scale Sum of Boxes; ADAS13: Alzheimer’s Disease Assessment Scale-Cognitive 13; T-tau: total tau; P-tau: phosphorylated tau; Aβ: amyloid-β; CSF: cerebrospinal fluid

CSF GAP-43 Levels in Different Diagnostic Groups

We found significant differences in the levels of GAP-43 between the diagnostic groups. Specifically, the levels of GAP-43 were significantly higher in the dementia group compared to both the CN and MCI groups (p < 0.001). However, there was no significant difference in the levels of GAP-43 between the CN and MCI groups (p = 1). These findings suggest that the measurement of CSF GAP-43 levels may be useful in differentiating individuals with dementia from those with normal cognition or mild cognitive impairment (Fig. 1).

Difference of [18F] AV45 in Diagnostic Groups

A significant difference was found in [18F] AV45 levels between the CN and MCI groups (p = 0.04). However, no statistically significant variance was detected between the CN and dementia groups, nor between the MCI and dementia groups (p < 0.05) (Fig. 2).

The present study identified significant correlations between various biomarkers in individuals with CN, MCI, and AD. Specifically, GAP-43 demonstrated positive correlations with T-tau in CN (r = 0.634, p < 0.05), MCI (r = 0.603, p < 0.05), and AD (r = 0.467, p = 0.003) groups, as well as with P-tau in CN (r = 0.376; p = 0.001), MCI (r = 0.487; p < 0.05), and AD (r = 0.443; p = 0.005) groups. However, GAP-43 did not show a significant correlation with Aβ in any of the three groups (CN: r = 0.025, p = 0.0829; MCI: r=-0.05; p = 0.602; AD: r=-0.197; p = 0.235). More details are available in Table 2 − 1.

Table 2

**− 1** Pearson’s Correlation of GAP-43 with T-tau, P-tau, Aβ42, MMSE, ADAS-13 and [18F] AV45.
	CN		MCI		AD
	r	P value	r	P value	r	P value
T-tau	0.634	< 0.05	0.603	< 0.05	0.467	0.003
P-tau	0.376	0.001	0.487	< 0.05	0.443	0.005
Aβ42	0.025	0.0829	-0.05	0.602	-0.197	0.235
MMSE	0.061	0.598	-0.30	0.757	0.102	0.542
ADAS13	-0.284	0.012	0.139	0.144	-0.061	0.714
[18F] AV45	0.331	0.003	0.218	0.022	0.318	0.051

Table 2

2. Correlation of [18F] AV45 with T-tau, P-tau, Aβ42, MMSE, ADAS-13 and GAP-43.
	CN		MCI		AD
	r	P value	r	P value	r	P value
T-tau	0.414	< 0.001	0.575	< 0.001	0.498	0.001
P-tau	0.478	< 0.001	0.462	< 0.001	0.335	< 0.04
Aβ42	-0.615	< 0.001	0.716	< 0.001	-0.670	< 0.001
MMSE	-0.107	0.355	-0.306	0.001	-0.183	0.272
ADAS13	0.144	0.213	0.170	0.074	0.272	0.098
GAP-43	0.331	0.003	0.218	0.022	0.318	0.051
Abbreviations: CN: cognitively normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; MMSE: Mini-Mental State Examination; ADAS13: Alzheimer’s Disease Assessment Scale-Cognitive 13; T-tau: total tau; P-tau: phosphorylated tau; Aβ: amyloid-β; CSF: cerebrospinal fluid

[18F] AV45 showed positive correlations with P-tau, T-tau, and Aβ in CN (r = 0.414, p < 0.001; r = 0.478, p < 0.001; r=-0.615, p < 0.001), MCI (r = 0.575, p < 0.001; r = 0.462, p < 0.001; r=-0.716, p < 0.001), and AD (r = 0.498, p = 0.001; r = 0.335, p < 0.04; r=-0.670, p < 0.001) groups. Additionally, [18F] AV45 showed a significant correlation with CN and MCI groups (r = 0.331, p = 0.003; r = 0.218, p = 0.022), while no correlation was found with the AD group (r = 0.318, p = 0.051). Additional information can be found in Table <link rid="tb3">2</link>–2.

MMSE did not show significant correlations with GAP-43 in any of the three groups (CN: r = 0.61, p = 0.598; MCI: r=-0.30, p = 0.757; AD: r = 0.102, p = 0.542), but demonstrated a positive correlation with Aβ in the MCI group (r = 0.316, p = 0.001). MMSE did not show significant correlations with Aβ in CN (r = 0.046, p = 0.692) and AD (r = 0.199, p = 0.231) groups, nor with [18F] AV4 in CN (r=-0.107, p = 0.355) and AD (r=-0.183, p = 0.272) groups. However, it demonstrated a significant negative correlation with [18F] AV45 in the MCI group (r=-0.306, p = 0.001). It was found to be correlated with GAP-43 in CN (r=-0.284, p = 0.012), but not in MCI and AD groups (r = 0.139, p = 0.144; r=-0.061, p = 0.714). Additionally, ADAS13 did not show a significant correlation with [18F] AV4 in any of the three groups (CN: r = 0.144, p = 0.213; MCI: r = 0.170, p = 0.074; AD: r = 0.272, p = 0.098).

Cross-Sectional Correlations

We considered GAP-43 as the dependent variable and MMSE and [18F] AV45 as predictors. The results showed that the adjusted R-Squared for CN, MCI and AD was 0.07, 0.09 and 0.06, respectively, indicating that the independent variables included in the model may not be strong predictors of the dependent variable for these groups. Additionally, when [18F] AV45 was considered as the dependent variable, the adjusted R-Squared for CN, MCI and AD was 0.25, 0.10 and 0.06, respectively (Figs. 1 and 2).

A positive correlation was observed between GAP-43 and [18F] AV45 level in CN, MCI, and AD groups (β = 0.327, p = 0.012; β = 0.222, p = 0.022; β = 0.381, p = 0.026). However, no correlation was found between GAP-43 and MMSE in any of the mentioned groups, respectively (β = 0.118, p = 0.300; β = 0.104, p = 0.290; β = 0.249, p = 0.158). Furthermore, [18F] AV45 demonstrated a positive correlation with GAP-43 in CN, MCI, and AD groups (β = 0.264, p = 0.012; β = 0.220, p = 0.022; β = 0.381, p = 0.026). It did not show a correlation with MMSE in CN and AD groups (β = -0.126, p = 0.217; β = -0.289, p = 0.100), whereas a significant negative correlation was observed in the MCI group (MMSE: β = -0.306, p = 0.001).

Diagnostic Ability of GAP-43, [18F] AV45 and Core AD Biomarkers

In order to assess the diagnostic accuracy of GAP-43, [18F] AV45 and core AD biomarkers, we conducted ROC analyses and computed AUCs. Our findings revealed that Aβ showed the best diagnostic performance in both CN and MCI groups, while its performance was the poorest in the AD group. Conversely, T-tau demonstrated the most effective diagnostic capability for AD in comparison to the other biomarkers (Fig. 3). Additional information regarding these results can be found in Tables 3 − 1, 3 − 2 and 4.

Table 3

**− 1.** Coefficients of GAP-43 as dependent variable and MMSE and AV45 as predictors.
	CN		MCI		AD
	β (CI 95%)	P value	β (CI 95%)	P value	β (CI 95%)	P value
[18F] AV45	0.327	0.012	0.222	0.022	0.381	0.026
MMSE	0.118	0.300	0.104	0.290	0.249	0.158
Model R Square	0.075		0.096		0.064

Table 3

**− 2.** Coefficients of [18F] AV45 as dependent variable and MMSE and GAP-43 as predictors.
	CN		MCI		AD
	β (CI 95%)	P value	β (CI 95%)	P value	β (CI 95%)	P value
GAP-43	0.264	0.012	0.220	0.022	0.381	0.026
MMSE	0.126	0.217	-0.306	0.001	-0.289	0.100
Model R Square	0.253		0.103		0.064

Table 4

AUCs for diagnostic capability of CSF GAP-43, [18F] AV45 and core AD biomarkers.
	CN	MCI	AD
CSF GAP-43	0.425	0.433	0.739
[18F] AV45	0.350	0.479	0.778
Aβ42	0.668	0.523	0.190
CSF P-tau	0.377	0.452	0.782
CSF T-tau	0.360	0.450	0.813
Abbreviations: CN (A). MCI (B). AD (C). CN: cognitively normal; MCI: mild cognitive impairment; AD: Alzheimer’s disease; T-tau: total tau; P-tau: phosphorylated tau; Aβ: amyloid-β; CSF: cerebrospinal fluid; ROC: Receiver Operating Characteristic

Logistic Regression

In the current study, performing logistic regression revealed that GAP-43 (= 1.028, S.E.=0.310, p = 0.001) and MMSE (=-3.289, S.E = 0.591, p < 0.001) were significant predictors of AD. The modal correctly classified AD and non-AD cases at rates of 81.6% and 99.5%, respectively. In addition, the overall correct percentage was 96.5. Hosmer and Lemeshow Test was not significant (Chi-square = 10.354, p = 0.241). The model Amnibus was coefficient with chi-square of 137.505 and p < 0.001.

We explored the association between GAP-43 and brain amyloidosis in AD Continuum using [18F] AV45 PET. Our findings show potential diagnostic implications for GAP-43 in relation to Aβ pathology and provide insights into the mechanisms underlying synaptic dysfunction and neurodegeneration in AD.

Previous research has demonstrated a close connection between cognitive function and synaptic decline in patients with early AD or MCI even before the clinical manifestations (39–41), which supports monitoring biomarkers reflecting synaptic pathology, such as amino acid form of Aβ42, T-tau, P-tau, and GAP-43 (42–45). However, there is limited research on the role of CSF GAP-43 in the AD continuum. GAP-43 is known for its role in synaptic plasticity and axonal growth and elevated levels of GAP-43 in regions affected by AD pathology hint at its potential involvement in the response to neurodegenerative processes (46, 47). Its critical role during early developmental stages suggests a link between GAP-43 dysregulation and the neurodevelopmental origins of AD.

Our results revealed significant elevations in CSF GAP-43 levels in individuals diagnosed with AD compared to cognitively normal and MCI groups. This findings is consistent with previous reports of elevated levels of GAP-43 in CSF in AD (48, 49). This finding suggests that GAP-43 might serve as a potential biomarker, aiding in the differentiation of individuals with dementia from those with normal cognition or MCI (50). Former reports of elevation in CSF GAP-43 levels in MCI and dementia patients at baseline, along with significant increases over time in preclinical, prodromal, and dementia stages of AD, corroborates our initial findings. This extended validation strengthens the argument for the diagnostic relevance of GAP-43 across various stages of AD (50).

GAP-43 levels in CSF correlate positively with tau levels, supporting a mechanistic model for AD. According to this model, changes in synapses are essential for the spread of tau pathology associated with Aβ. This process is a key factor in the development of neurodegeneration and cognitive decline in AD (51–53). The theory that Aβ negatively affects synaptic function is supported by evidence from various studies, including in vitro investigations, animal trials, and post-mortem analyses. These studies demonstrate that Aβ influences glutamate re-uptake and sensitivity to gamma-aminobutyric acid (GABA), resulting in adverse effects on synaptic function (54, 55).

Evidence suggests a correlation between tau spread and hyperexcitatory synaptic changes in AD. In vitro and animal studies have shown that increased neuronal activity accelerates tau secretion. This leads to the transsynaptic propagation of seeding-competent tau, which refers to abnormally folded tau proteins capable of initiating pathological aggregation. These seeding-competent tau proteins can travel across synapses between neurons, contributing to tau spread in AD (56, 57).

GAP-43, an enzyme that plays a role in presynaptic vesicle cycling, is overexpressed in AD due to hyperexcitation (58, 59). The studies provide evidence that when GAP-43 is inhibited, there is a significant reduction in synaptic glutamate release (60). This finding shows that GAP-43 plays a crucial role in neurotransmitter release and synaptic activity. In the context of AD, this role may be even more important (60). The findings suggest that inhibiting GAP-43 can have a significant impact on synaptic glutamate release, which in turn affects overall glutamate, gamma-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine release, and synaptic activity. This could potentially contribute to AD pathophysiology. Therefore, the increased levels of CSF GAP-43 in AD may indicate hyperexcitatory synaptic changes induced by Aβ (61). Our study findings reveal a significant positive association between GAP-43 and T-tau in cognitively normal individuals in all cognitive groups. Additionally, we observed a noteworthy positive correlation between GAP-43 and phosphorylated P-tau in all groups. Unexpectedly, GAP-43 did not show a significant correlation with Aβ in any of the three groups. The findings from our study align with previous research, providing additional support to the notion that CSF GAP-43 is more closely linked to tau pathology and neurodegeneration than to Aβ pathology (44, 58, 62).

A key aspect of our investigation was establishing a link between GAP-43 and Aβ deposition, as detected through [18F] AV45 PET. We found that positive correlation observed between GAP-43 and [18F] AV45 levels in individuals across CN, MCI, and AD groups suggests a potential association between synaptic dysfunction and Aβ pathology. This correlation remained consistent even when adjusting for MMSE scores, indicating that the link between GAP-43 and Aβ is independent of the cognitive status. Comparing the diagnostic performance of GAP-43 and [18F] AV45 with core AD biomarkers. In line with our result there was some studies indicate that significant correlation between CSF GAP-43 concentration and [18F] AV45 (50, 63). These findings emphasize the complementary nature of various biomarkers in understanding the complex landscape of AD pathology. Our study lays the groundwork for further research into the intricate interplay between GAP-43, Aβ pathology, and cognitive decline in AD. Future longitudinal studies should explore the trajectory of GAP-43 alterations in relation to disease progression, considering its potential as an early biomarker. Additionally, investigating the molecular mechanisms linking GAP-43 and Aβ could unveil novel therapeutic targets for mitigating synaptic dysfunction in AD.

Limitations

The study sample was taken from the ADNI database, which may not fully represent the general population. Additionally, while the sample size was substantial, it might not capture the full range of AD progression. Although [18F] AV45 PET imaging is commonly used to detect Aβ plaques, it does not provide information on other AD-related pathologies like tau pathology or synaptic dysfunction. Utilizing multiple imaging modalities and biomarkers may provide a more comprehensive understanding of AD pathology.

Our study highlights the potential of GAP-43 as a valuable biomarker in AD. Elevated levels of GAP-43 in the CSF of AD patients suggest its effectiveness in distinguishing dementia from CN or MCI. We found a strong correlation between GAP-43 and tau pathology. Our findings demonstrate the complementary nature of various biomarkers. When comparing the diagnostic performance of GAP-43 and [18F] AV45 with core AD biomarkers, we observed a consistent positive correlation between GAP-43 and [18F] AV45 levels across different cognitive states. The diagnostic accuracy of [18F] AV45, combined with GAP-43, provides a more comprehensive understanding of AD pathology. A multifaceted approach in biomarker research is necessary to address the complexity of AD progression and our findings lay the foundation for future research on GAP-43 and AD progression and the molecular mechanisms linking GAP-43 and Aβ.

cognitively normal

MCI

mild cognitive impairment

Alzheimer’s disease

MMSE

Mini-Mental State Examination

ADAS13

Alzheimer’s Disease Assessment Scale-Cognitive 13

T-tau

total tau

P-tau

phosphorylated tau

Aβ

amyloid-β

CSF

cerebrospinal fluid

Ethics approval and consent to participate

The data used in this study were obtained from the ADNI database (adni.loni.usc.edu). The ADNI study was approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Funding

No funding

Author Contribution

M.M. was responsible for the conceptualization and design of the study, providing the foundational framework and guiding the overall research direction. M.S. arranged the study framework, ensuring the structure and methodology were coherent and effectively aligned with the study’s objectives. P.S.meticulously edited the manuscript, refining the content, ensuring clarity, and enhancing the overall quality of the text. A.S.A. , S.M., A.S.J.H., A.Y., A.H., N.K., and Y.R. gathered the data and wrote the manuscript. All authors reviewed and approved the final manuscript.

Acknowledgement

Data collection and sharing for this study 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: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol- Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. 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 Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). 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 writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Data Availability

Data AvailabilityThe data used in this study are not publicly available as they were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI data are available to qualified researchers upon request and approval from ADNI. Interested researchers can apply for access to the ADNI data through the ADNI website (http://adni.loni.usc.edu/data-samples/access-data/). The authors of this study do not have permission to redistribute the ADNI data directly.

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Growth Associated Protein 43 (GAP-43) predicts brain amyloidosis in Alzheimer’s Dementia Continuum: an [18F] AV45 study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Methodology

Design and Participants

CSF sampling, storage and measurement

Positron Emission Tomography (PET)

Statistical Methods

Ethical Considerations

Results

Demographic Characteristics

CSF GAP-43 Levels in Different Diagnostic Groups

Difference of [18F] AV45 in Diagnostic Groups

Cross-Sectional Correlations

Diagnostic Ability of GAP-43, [18F] AV45 and Core AD Biomarkers

Logistic Regression

Discussion

Limitations

Conclusion

Abbreviations

Declarations

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1