Brain-derived neurotrophic factor and its relationship to known biomarkers of Alzheimer's disease

Abstract

Background: Brain-derived neurotrophic factor (BDNF) is involved in neuronal survival and synaptic plasticity, but can be released by platelets, confounding its measurement. Objective: We aimed to compare BDNF concentrations following different processing strategies. Methods: We analyzed BDNF in 99 older adults with and without cognitive impairment. Processing EDTA plasma normally (PRP) resulted in different values than removing platelets (PPP). Results: Cognitively impaired participants had lower BDNF compared to controls in PPP, while PRP BDNF showed no differences. A positive relationship was evident between PPP BDNF and AD biomarkers measured in PRP. Conclusions: Our findings suggest that circulating BDNF may be a compensatory mechanism in impaired participants and underscore the importance of blood pre-processing strategies.

Keywords

Alzheimer's disease brain-derived neurotrophic factor neuroplasticity plasma biomarkers

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, predominantly manifesting with phenotypes such as memory loss^1,2 and behavioral changes.³ These symptoms arise from the underlying neuropathological changes that characterize AD, such as accumulating amyloid-β (Aβ).⁴ One of the most prominent features observed in AD is the reduction in brain volume, particularly in regions critical for memory and cognitive functions, such as the hippocampus and the cerebral cortex.^5–7 This atrophy is detectable through neuroimaging techniques and serves as a hallmark of disease progression.

However, the disease is highly heterogeneous, making it challenging to accurately identify patients with AD. Further advancements in techniques such as fluorodeoxyglucose positron emission tomography can reveal reductions in glucose metabolism in AD affected brain regions, even before significant clinical symptoms emerge.^8–11 Recent developments in the field have created the “A/T/N” characterization as a tool to better identify individuals with AD by classifying amyloid, tau and neurodegeneration. These biomarkers are invaluable for early diagnosis and for monitoring disease progression.

Brain-derived neurotrophic factor (BDNF) has long been known for its involvement in brain health, however its role in AD-related brain changes alongside its interaction with other known biomarkers are unknown. BDNF is a key protein involved in neuronal survival and synaptic plasticity,^12–14 which are all processes critically impaired in AD.^15,16 Our research along with others has shown that genes encoding BDNF are not directly associated with risk for AD, although genetic variation in AD does appear to play a role in AD -related brain neurodegeneration.¹⁷ BDNF in neurons may increase neuroprotection,^18,19 and reduction of Aβ peptide.²⁰ Furthermore, in primary neurons and animal models, pTau has been shown to decrease BDNF expression.^21,22 Research has also shown reductions in BDNF mRNA levels from human postmortem brain tissue, which may contribute to AD progression.^23–25 That is why it is important to further characterize the relationship between BDNF and pTau in individuals AD.

As the prevalence of AD continues to rise, there is an increasing demand for biomarkers that provide a less expensive and less invasive approach to diagnosis. When utilizing biomarkers to assess disease state, several factors must be considered, including the methods used to process blood samples prior to analysis. In this cross-sectional study, we first evaluate differences in BDNF levels derived from platelet-rich plasma (PRP) versus platelet-poor plasma (PPP). Next, we investigate variations in plasma-based biomarkers (Aβ₄₀, Aβ₄₂, pTau217, pTau181, NfL, GFAP, and BDNF) between cognitively impaired (CogI) and cognitively healthy (CH) individuals.

We hypothesized that implementing an additional centrifugation step to remove platelets will result in lower BDNF levels, potentially providing a more accurate representation of circulating BDNF rather than BDNF released from platelets. Additionally, we expected our findings to align with preclinical studies, showing that individuals with elevated AD biomarkers (e.g., pTau and Aβ) will exhibit lower BDNF levels. Furthermore, we aimed to characterize the relationship between BDNF and neurodegenerative plasma biomarkers (NfL and GFAP). This analysis seeks to underscore the significance of BDNF in the context of brain aging and AD pathology.

Methods

Participants

A total of 113 participants (n = 82 CH, n = 31 CogI) from the KU ADRC Clinical Cohort were recruited.²⁶ Participants had to be currently enrolled in the KU ADRC Cohort and willing to undergo an MRI and PET scan as well as a blood draw. All participants were evaluated with the Clinical Dementia Rating (CDR).^27,28 Eligible participants were grouped as either CH or CogI based on the etiological diagnosis from a clinician interview. All CH individuals had a CDR of 0, and all CogI had a CDR of 0.5 or 1. Trained psychometrist administered cognitive examination to all participants consisting of the Uniform Data Set version 3.0.²⁹ We used the UDS normative calculator to compute global normative values for each participant.³⁰ All participants in this study provided informed consent according to institutional guidelines and in accordance with the Declaration of Helsinki. We were unable to collect blood from 14 participants, resulting in a sample size of 99 participants, 74 CH and 25 CogI (11 probable AD, 7 amnestic MCI, 1 non-amnestic MCI, 5 impaired not MCI, 1 frontotemporal dementia).

BDNF plasma measurement

Blood was collected during the non-fasted state at the MRI visit. Blood was collected in a 10 mL ethylenediaminetetraacetic acid (EDTA) tube and was immediately centrifuged at 1500×g for 10 min at 4°C to generate platelet-rich plasma (PRP). BDNF has been shown to be highly concentrated in platelets and can be released into circulation.³¹ To better reflect circulating BDNF levels we removed platelets remaining through a second centrifugation at 1700×g for 15 min at 4°C to generate platelet-poor plasma (PPP). Both PRP and PPP were stored at −80°C until analysis. To compare BDNF in PRP and PPP, paired samples (n = 73) were thawed and analyzed on the same day using the same Lot#503658 to reduce variability in data results. BDNF assays were run on three plates according to manufacturer instructions with appropriate calibrators. Manufacturer provided quality control (QC) samples were analyzed in duplicate at the beginning and end of each plate to assess reproducibility of each assay. The within run CV for QC samples averaged 2.59% and across runs, the mean QC CV was 14.5%. The mean CV for all unknown samples across runs was 4.11%. The limit of detection for BDNF was 0.0042 pg/mL.

To process PPP, we first assessed (n = 5) samples at five different factors (1:5, 1:10, 1:25, 1:50 and 1:100) to determine the optimal dilution needed to have samples fall within the dynamic range of 0–120 pg/mL. The mean and standard deviation measured value for PPP BDNF at each dilution was 1:5 (14.8 [18]), 1:10 (8.21 [9.5]), 1:25 (3.32 [3.5]), 1:50 (1.73 [1.8]), 1:100 (0.870 [0.91]). This suggests that the 1:50 dilution would provide optimal readings, keeping most samples in the middle of the standard curve and avoiding saturation or falling below the detection threshold. We then evaluated BDNF in the remaining paired samples.

A sample of PRP was thawed and analyzed for BDNF by following the manufactures standard procedures. Briefly, calibrators, controls and samples were brought to room temperature. Bead reagent was washed and resuspended in diluent. Both Detector and streptavidin-β-galactosidase (SBG) Reagent were diluted from stock concentrate. Thawed samples were then vortexed and centrifuged at 10,000×for 5 min. A serial dilution of 10x followed by 50x was performed with vortexing prior to each transfer to ensure sample homogeneity, then plated and ran as neat. The BDNF Discovery Kit is a two-step immunoassay based on single-molecule array (Simoa) technique, in which the target antibody, coated on paramagnetic beads, is incubated with the sample and a detector antibody. After washing, SBG is added, enabling enzyme labeling of the captured target. A final wash with resorufin β-D-galactopyranoside (RGP) allows the complex to bind to the disc well, where a single-labeled target molecule generates a fluorescent signal. The Simoa optical system then detects this signal and interpolates a value from the calibration curve.³²

AD-related plasma biomarker measures

The apolipoprotein E (APOE) gene codes for a 299 amino acid protein that functions to deliver cholesterol and phospholipids throughout the body.³³ There are three polymorphisms of the APOE (E2, E3, and E4) and the APOE4 allele shows the greatest genetic risk for developing AD.^34–37 To determine APOE genotype, whole blood was collected in 8.5 mL vacutainer tube containing acid citrate dextrose as an anticoagulant. Genotyping of two participants failed. Participants were classified as noncarrier when no E4 allele was present and carrier was classified by the presence of one or two i alleles (i.e., E3/E4, E4/E4).

To assess the relationship between BDNF and AD-related plasma-based biomarkers, PRP was analyzed using the Neurology 4-Plex Advantage (Aβ₄₂, Aβ₄₀, NfL, GFAP), ALZpath p-Tau 217 v2 (pTau217). pTau181 levels were measured using the p-Tau181 assay versions 2.0 and 2.1. Data from samples analyzed with the version 2.0 assay were converted to align with the version 2.1 data, following the manufacturer's guidelines. All plasma-based biomarkers were processed following the manufactures protocols and analyzed on the SIMOA HD-X platform (Quanterix, Bilerica, MA).

Statistical analysis

We applied a logarithmic transformation to all plasma-based biomarkers to achieve normality in their distributions. To test our primary hypothesis that an additional centrifugation step to remove platelets will result in lower BDNF levels across the whole cohort, paired sample t-test was used to assess the difference between BDNF in PRP compared to PPP. Diagnostic differences in outcome measures were assessed using ANOVA for continuous variables and chi square for categorical variables. Furthermore, linear regression was used to test the hypothesis that individuals with elevated AD biomarkers will exhibit lower BDNF levels. All statistical analyses were performed using IBM SPSS (version 29.0) and ANOVA and linear regression models included age, sex and education as covariates. All results were considered significant at p < 0.05.

Results

Participants

Demographic information is provided in Table 1. There were no differences in number of females and E4 carriers between groups. CH were slightly older than CogI group (p = 0.047). Also, CH had higher educational levels compared to CogI (p = 0.041). The Gini-Simpson Diversity Index (DI) is a measure of diversity within a population, where a zero reflects no diversity and 1 represents high diversity.³⁸ The DI did not differ between groups (0.17 for CH participants and 0.15 for CogI). We also characterized Area Deprivation Index (ADI),³⁹ which did not differ between groups. As expected, CogI had lower global cognitive z-scores compared to CH (p < 0.001; Table 1).

Table 1.

Participant demographics and blood biomarker values.

	Cognitively Healthy (n = 74)	Cognitively Impaired (n = 25)	p
Age at visit, y [SD]	77 [6]	74 [8]	0.047
Female, total [%]	42 [57]	12 [48]	0.298
Education, y [SD]	16 [3]	15 [3]	0.032
*E4 carrier, No. [%]	24 [33]	8 [32]	0.555
Race, No. non-white [%]	7 [9]	2 [8]	0.747
Area Deprivation Index, Percentile [SD]	39.8 [24]	43.8 [21]	0.469
**Global cognition, z-score [SD]	0.565 [0.72]	−0.438 [1.2]	<0.001
PRP Aβ₄₂ pg/ml [SD]	8.83 [10]	6.82 [1.5]	0.318
PRP Aβ₄₀ pg/ml [SD]	141 [107]	126 [30]	0.874
Aβ_42/40 ratio [SD]	0.0592 [0.013]	0.0552 [0.008]	0.050
PRP pTau217, pg/ml [SD]	0.752 [0.53]	1.11 [1.1]	0.087
pTau217/Aβ₄₂ ratio [SD]	0.113 [0.10]	0.175 [0.21]	0.080
PRP pTau181, pg/ml [SD]	2.32 [1.4]	2.83 [1.7]	0.209
PRP NfL, pg/ml [SD]	28.5 [19]	31.5 [13]	0.008
PRP GFAP, pg/ml [SD]	181 [79]	221 [122]	0.031
PPP BDNF, pg/ml [SD]	403 [1105]	67.3 [62]	0.040

Participant demographics and blood biomarker values are given for cognitively healthy and cognitively impaired groups. *2 participants excluded due to failed genotyping. **4 participants unable to complete cognitive testing due to COVID-19 pandemic. AD plasma biomarkers (Aβ_42/40 ratio, pTau217, pTau181, NfL, GFAP) were measured in platelet rich plasma (PRP), per conventional methods. Bold values indicate p < 0.05. Aβ: amyloid-beta (40 and 42 isoform); BDN: brain-derived neurotrophic factor; E4: Apolipoprotein E; GFAP: glial fibrillary acidic protein; NfL: neurofilament light; PPP: platelet poor plasma; PRP: platelet rich plasma; pTau: phosphorylated tau (181 and 217 isoform). Bold values indicate p < 0.05.

We found BDNF to be higher across all groups in PRP compared to PPP (p < 0.001; Figure 1A). Furthermore, there was no correlation between BDNF measured in PRP compared to PPP (R² = 0.013). Interestingly, PPP BDNF was lower in CogI compared to CH (p = 0.040), whereas no group difference in PRP BDNF was observed (p = 0.369; Figure 1B).

Figure 1.

BDNF varies with the processing protocol, influencing group differences. (A) BDNF is much higher in EDTA plasma containing platelets compared to plasma where platelets have been removed. (B) Group differences are apparent only when assaying platelet poor plasma. Intervals represent standard error of the mean. BDNF: brain-derived neurotrophic factor; CH: cognitively healthy; CogI: cognitively impaired, PPP: platelet poor plasma; PRP: platelet rich plasma.

In our group analysis of plasma-based biomarkers, NfL and GFAP were elevated in CogI compared to CH (p = 0.008, p = 0.031, respectively). As expected, Aβ ratio (Aβ_42/40) was lower in the CogI group compared to CH (p = 0.050). While not significant CogI was seen trending to have higher levels of pTau217 compared to CH (p = 0.087). No significant group difference in pTau181 levels were observed (p = 0.209).

Plasma relationship regressions

Due to group differences being observed only in PPP BDNF, further analysis to characterize the relationship between BDNF and Aβ_42/40, pTau217, pTau181, NfL and GFAP was only investigated in PPP. We found no significant associations between these biomarkers across the whole cohort.

When examining BDNF by group, we identified a positive association in the CogI group between PPP BDNF and pTau217 (β = 0.413, p = 0.040), pTau181 (β = 0.543, p = 0.007), and GFAP (β = 0.450, p = 0.039), while no relationship was observed in Aβ_42/40 (β = −0.068, p = 0.786), and NfL (β = 0.181, p = 0.432). In contrast, no significant associations were found in the CH group for these biomarkers: Aβ_42/40 (β = 0.034, p = 0.785), pTau217 (β = 0.045, p = 0.738), pTau181 (β = −0.036, p = 0.795), NfL (β = −0.045, p = 0.757), and GFAP (β = −0.027, p = 0.838) as described in Table 2 and illustrated in Supplemental Figure 1.

Table 2.

Relationship between PPP BDNF and AD plasma biomarkers.

	Cognitively Healthy (n = 74)			Cognitively Impaired (n = 25)
	F	Standardized Beta	p	F	Standardized Beta	p
Aβ_42/40	0.244	0.034	0.785	1.201	−0.068	0.786
pTau217	0.253	0.045	0.738	2.661	0.413	0 . 040
pTau181	0.242	−0.036	0.795	3.965	0.543	0.007
NfL	0.249	−0.045	0.757	1.376	0.181	0.432
GFAP	0.236	−0.027	0.838	2.693	0.450	0.039

Linear regression between PPP BDNF and AD-related EDTA PRP biomarkers by group controlled for age, sex and education. AD plasma biomarkers (Aβ_42/40 ratio, pTau217, pTau181, NfL, GFAP) were measured in platelet rich plasma (PRP), per conventional methods. Bold values represent p < 0.05. Aβ: amyloid-beta (40 and 42 isoform); BDNF: brain-derived neurotrophic factor; GFAP: glial fibrillary acidic protein: NfL: neurofilament light; PPP: platelet poor plasma; PRP: platelet rich plasma; pTau: phosphorylated tau (181 and 217 isoform). Bold values indicate p < 0.05.

Discussion

In this study, we first conducted an a priori test to evaluate the hypothesis that BDNF levels are significantly greater in PRP compared to PPP. Secondly, we sought to characterize the relationship between BDNF and AD biomarkers.

Previous research has shown that platelets store BDNF, and upon activation can release it into circulation.⁴⁰ This is an important consideration because blood collected in a serum separation tube allows the blood to clot, thereby releasing its content. However, there remains considerable ambiguity in the field regarding the optimal methods for blood collection and processing to measure AD biomarkers. Several studies have validated the measurement of BDNF in serum.^41–43 Zeng et al. provided a comprehensive review outlining general guidelines for analyzing AD biomarkers, specifically addressing sample collection and processing procedures. While they concluded that maintaining consistent procedures throughout a study is best practice, they also noted that using serum can pose challenges and plasma remains the most widely used specimen for AD research.⁴⁴ Serum BDNF levels have been shown to be orders of magnitude higher than levels in PPP.⁴⁵ This is most likely as serum BDNF most likely reflect stored BDNF, not circulating BDNF. Given our focus was to measure BDNF using methods that better reflected circulating levels and the SIMOA HD-X ultrasensitive capabilities, we sought to characterize BDNF in plasma, as the anticoagulant in the collection tube prevents platelet activation and minimizes the release of additional contents, allowing for a more accurate understanding of circulating biomarker levels.

Here, we highlight the importance of processing procedures that should be considered when analyzing BDNF in plasma. Although both PRP and PPP were centrifuged at 10,000 × g after freeze-thaw cycles, which removes platelets, BDNF measurements differed between the two methods. In our present study, we demonstrated that PRP has elevated levels of BDNF compared to PPP, which aligns with the literature suggesting that platelets release BDNF into circulation. Furthermore, we identified that there is no correlation between circulating BDNF and the amount of BDNF released from platelets. This may be an important consideration when investigating BDNF, as circulating levels may better reflect molecules available to directly regulate neuronal function.

In the present study, we found CogI PPP BDNF to be lower compared to CH. Prior research has shown individuals with AD to have lower levels of BDNF compared to healthy older adults.^46,47 Moreover, lower BDNF levels have been associated cross-sectionally with poorer cognitive performance,⁴⁸ and longitudinal studies suggest that higher BDNF levels are linked to a reduced risk for dementia.⁴⁹ However, the literature is mixed and does not always show diagnostic differences.^50,51 One reason may be a compensatory mechanism, in which BDNF is elevated during the preclinical stages of dementia.⁵²

In the CogI, the positive relationship between BDNF and pTau181, pTau217, and GFAP suggests that BDNF may be upregulated as part of a compensatory mechanism in response to accumulating tau pathology and astrocytic activation. Thus, individuals at the same clinical stage with elevated BDNF may carry additional neuropathological load, but this remains to be tested. Tau is a protein primarily found in neurons and plays an important role in stabilizing microtubules.⁵³ In diseases such as AD, tau can become hyperphosphorylated, resulting in destabilization and interference with synaptic activity.^54,55 As tau pathology progresses, neurons experience increased synaptic dysfunction and microtubule instability, which may trigger a cellular response to maintain neuronal survival. One such response could be the upregulation of neurotrophic factors like BDNF, due to its role in promoting synaptic plasticity, neuronal survival, and repair mechanisms.

When there is damage to the CNS, such as traumatic brain injury or neurodegeneration, astrocytes mount a reactive response, marked by elevated GFAP.^56–59 Initially, reactive astrocytes may attempt to limit damage by releasing neurotrophic factors, including BDNF. This has been shown in tissue derived from multiple sclerosis patients in which astrocytes responded to lesions by increasing BDNF expression.⁶⁰ This could explain our finding of BDNF tracking with GFAP due to a coordinated astrocytic response to neuronal injury associated with CogI but not CH. These findings highlight the potential role of BDNF expression in astrocyte and tau-related pathology.

Limitations

There are several experimental considerations that should be considered when interpreting our data. Due to limited availability of samples that could be analyzed for BDNF, only a subset of samples was included to compare PRP and PPP a large sample size would have provided greater power to detect more subtle relationships. Differences in the size and education levels of the cognitively healthy and cognitively impaired groups may have affected results. In addition, depression is a common comorbidity observed in dementia.⁶¹ Some work suggests that antidepressant use is associated with increased BDNF,^62,63 but other studies have shown no effect.^64,65 In this study we did not account for medication use, which is a potential limitation. Furthermore, a single nucleotide polymorphism, Val66Met, has been identified as a genetic component that may influence BDNF expression.⁶⁶ Therefore, it is unclear if the differences in circulating BDNF can be explained by group alone. While our findings reveal significant associations between BDNF levels and AD biomarkers in CogI, the cross-sectional design limits our ability to determine causality. Future longitudinal studies are essential to assess whether changes in BDNF precede, follow, or co-occur with disease-related pathology.

Conclusion

This study underscores the importance of considering methodological differences in BDNF processing and their impact on interpreting its role in AD. The differential measurement of BDNF in PRP and PPP highlights the need for standardized protocols, particularly given the influence of platelets on circulating BDNF levels. Our findings suggest that circulating BDNF, particularly in PPP, may better reflect its functional role in supporting neuronal integrity and synaptic resilience. The observed associations between BDNF, pTau, and GFAP levels further support the hypothesis that BDNF may serve as a compensatory mechanism in the face of neurodegenerative diseases such as AD.

Together, these findings reinforce the relevance of BDNF as both a biomarker and a potential therapeutic target in AD. Future studies should further explore the dynamic response of BDNF following exercise and follow up relationships between BDNF and biomarkers to further characterize the effect of aging.

Supplemental Material

sj-docx-1-alz-10.1177_13872877251362487 - Supplemental material for Brain-derived neurotrophic factor and its relationship to known biomarkers of Alzheimer's disease

Supplemental material, sj-docx-1-alz-10.1177_13872877251362487 for Brain-derived neurotrophic factor and its relationship to known biomarkers of Alzheimer's disease by Paul J Kueck, Robyn A Honea, Riley E Kemna, Jeffrey M Burns and Jill K Morris in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

We thank all the research participants who contributed their time for this study. We would also like to thank Alberto Miale Merola and Casey John for their support.

ORCID iD

Jill K Morris

Ethical considerations

All procedures were performed in accordance with the Declaration of Helsinki.

Consent to participate

All participants provided informed written consent for participation.

Consent for publication

All participants provided informed written consent for publication of deidentified information and findings.

Author contributions

Paul J Kueck: Data curation; Formal analysis; Investigation; Methodology; Writing - original draft; Writing - review & editing.

Robyn A Honea: Data curation; Investigation; Methodology; Writing - original draft; Writing - review & editing.

Riley E Kemna: Data curation; Formal analysis; Methodology; Writing - original draft; Writing - review & editing.

Jeffrey M Burns: Funding acquisition; Methodology; Writing - review & editing.

Jill K Morris: Conceptualization; Data curation; Methodology; Supervision; Validation; Writing - original draft; Writing - review & editing.

Funding

This work was supported by R01 AG062548, R01 AG081304, P30 AG035982 (KU Alzheimer's Disease Research Center) and P30 AG035982-10S1 (NACC-SCAN). Additional support was provided by the Margaret “Peg” McLaughlin and Lydia A. Walker Opportunity Fund.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data will be made available upon reasonable request.

Supplemental material

Supplemental material for this article is available online.

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