Introduction

Brain-derived neurotrophic factor (BDNF) is one of a family of neurotrophins which play an important role in the development, maintenance and function of neurons [1]–[4]. In humans, BDNF levels are lower in AD patients than controls in the hippocampus and cortical regions associated with AD; in the healthy adult mouse, BDNF levels are high in brain regions associated with Alzheimer’s disease (AD) [5]–[9]. BDNF also circulates in the blood, where it appears to be stored in platelets and is released through physical or chemical platelet activation [10]. While BDNF can cross the blood-brain barrier, the extent to which blood derived BDNF levels are a reasonable proxy for BDNF in the central nervous system is unclear [11], [12].

The research community hoped that circulating BDNF could provide a less invasive, inexpensive biomarker for dementia; but so far, there is conflicting evidence in the literature regarding the association between BDNF and cognition decline and dementia. Some studies indicate serum BDNF is higher in controls than persons with mild cognitive impairment (MCI), AD or dementia [13]–[15], others indicate the opposite direction of association [16], and some studies show no association [17]–[19]. Confounding may explain some of these discrepancies – many studies examining associations between BDNF and cognitive status used basic tests of group differences with minimal controls for confounding and few have examined the longitudinal association between BDNF and cognitive change. To our knowledge, only two other studies have examined cognitive change and blood based BDNF; one study in AD patients found lower BDNF predicted more rapid cognitive decline but the other study found no association in a group of healthy older adults [20], [21]. Additional studies with careful controls for potential confounders are necessary.

In this analysis, our goal was two-fold. First, we aimed to elucidate correlates of BDNF, as they show some inconsistencies in the literature. Substantial evidence supports an association between depression and lower serum BDNF but evidence regarding correlations between BDNF and basic characteristics like sex, age, and BMI is equivocal. Second, we aimed to evaluate the association between BDNF and cognitive decline, especially after consideration of potential confounders using a large prospective cohort of cognitively healthy older adults with cognitive performance evaluated at multiple time points over a lengthy follow-up. The cohort is biracial (black and white) which allows examination of possible differences in BDNF by race, which other studies have lacked sufficient diversity to address.

Methods

Results

Correlates of BDNF

BDNF measurements were generally normally distributed with a small number of extreme values in the right tail. As such, the data was trimmed to exclude the top and bottom 1% of BDNF observations. BDNF tertiles were significantly associated with platelet count, sex, race, Met carrier (Val66Met genotype), history of MI, and smoking (Table 1). Females, blacks, smokers, and those with high platelet counts had higher BDNF. History of myocardial infarction (MI) and carrying the rarer Met allele was associated with lower BDNF. Although depression did not quite meet the p<0.20 cut-off from Table 1 (p = 0.22), we included it in the multivariate model predicting BDNF because of its strong association with BDNF in the literature. We found that race, depression, and platelet count were significant predictors of BDNF after simultaneous adjustment in a multivariate model. BDNF levels in blacks were 1.81 ng/mL higher than whites (95% CI: 1.19, 2.43) and were 2.84 ng/mL lower in participants with depression (95% CI:−5.17, −0.51). A one standard deviation increase in platelet count was associated with a 1.81 ng/mL increase in BDNF levels (95% CI: 1.19, 2.43). The BDNF estimate for females was 1.04ng/mL higher, although it was no longer significant (95% CI:−0.17, 2.24). Smoking, history of MI, and Met allele carrier status were no longer significant after adjustment for other factors.

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Table 1. Distribution of participant characteristics by BDNF tertile.

https://doi.org/10.1371/journal.pone.0091339.t001

Cognitive Decline

The median number of 3MS observations per person was 4 (range: 1–5); the median number of DSST observations per person was 3 (range: 1– 4). In both unadjusted and adjusted analyses, we found no significant association between BDNF and performance on either cognitive test, cross-sectionally or longitudinally (Table 2). There was a slight trend toward higher cross-sectional performance on DSST associated with higher BDNF, but it was not significant (p = 0.09). Performing the same analysis using the log of BDNF, rather than trimmed BDNF values, resulted in the same conclusions. There were no significant interactions between race and BDNF for either cognitive test. A recent study on the association between dementia treatment and serum BDNF found that treatment increased BDNF levels in persons with AD [27]. Four participants in this sample were prescribed dementia medications throughout the follow-up period; results excluding these individuals did not differ appreciably from the primary results.

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Table 2. Estimates of the association between BDNF and cross-sectional and longitudinal cognitive test scores (change in test score per 1 SD increase in BDNF).

https://doi.org/10.1371/journal.pone.0091339.t002

Discussion

Serum BDNF showed strong associations with race, platelet count, and depression after adjusting for other factors, and weak associations with a number of other participant characteristics. We found that blacks had higher levels of BDNF than whites, a unique finding which has not been addressed in the literature so far. It is unclear whether the race differential in BDNF is due to unmeasured factors differing between blacks and whites or true biological differences. The strong association between BDNF and platelet count is consistent with theory that BDNF in serum is derived directly from platelets. Age did not differ significantly among serum BDNF tertiles which is in concordance with results from some studies [13]–[16], [28]–[30] and in conflict with others [19], [31], [32], although two of the studies which found an age effect included a wider age range than our study. Initially, we found that sex differed significantly between BDNF tertiles but sex was not a significant predictor of BDNF in a model adjusted for other covariates.

We did not identify any longitudinal associations between BDNF and cognition; this is in agreement with the other longitudinal study of blood based BDNF in cognitively normal older adults [21]. However, a longitudinal study using CSF-derived BDNF found that lower baseline CSF BDNF levels were significantly associated with greater annual decline on several tests of memory and category fluency in cognitively normal older adults [33]. This contrast may suggest that blood based measures of BDNF are not representative of CSF BDNF levels. A study comparing CSF and serum BDNF measurements in a sample of AD patients found that they were not correlated (r² = 0.03, n = 27); however, a study comparing blood and BDNF levels in pigs and rats found significant positive correlation between the two in both pigs and rats (r² = 0.41, 0.44, respectively) [13], [34]. Further study will be required to better understand the relationship between cortical, cerebrospinal fluid, and circulating levels of BDNF in humans. The trend toward a cross-sectional association and the lack of longitudinal association, combined with previous published work, may suggest that changes in BDNF occur contemporaneously with cognitive decline, as opposed to preceding decline. This would limit the utility of BDNF as a biomarker for early detection but does not preclude the potential for BDNF to play a useful diagnostic role.

Comparison of our raw and adjusted analyses demonstrates that confounders may have a profound effect on the estimate of the relationship between BDNF and cognition, particularly cross-sectionally. Many previous studies chose to use nonparametric group tests due to concerns that the BDNF distribution showed departures from normality. While extreme values and departures from normality are a concern, our results suggest that confounding may be a much greater concern, especially in the presence of an adequate sample size.

Strengths of this analysis include the prospective nature of the study, large biracial sample, and ability to control for multiple potential confounders. This analysis was limited by data collection occurring at different time points for some variables, such as platelet count. The negative impact of this is likely minimal as no covariate assessment occurred more than a year from BDNF blood draw and comparisons of platelet counts measured in years 3 and 11 suggest high intra-person correlation of platelet count over time (Spearman correlation coefficient 0.73). It is possible that our relatively healthy sample of older adults without dementia at baseline lacked sufficient variability in BDNF to detect associations reported elsewhere, although the serum BDNF range exhibited in our data is consistent with other similar studies. There is evidence that treatment for depression raises BDNF levels [35]. Our depression definition was partially based on treatment for depression, which makes it difficult to untangle the separate associations with depression and antidepressants. It is possible that our estimate of the association between BDNF and depression was attenuated due to the mixture of treated and untreated individuals. We were limited to evaluating two cognitive tests with repeated assessment in this sample (DSST and 3MS), which may not provide a complete picture of cognitive status and change.

After adjustment for confounding, our analysis provides no evidence that serum BDNF is prospectively associated with decline on DSST or 3MS. We found that serum BDNF was associated with a number of characteristics which also associated with dementia, which suggests that future studies should control for these potential confounders.

Acknowledgments

Principle investigators for the Health ABC: Tamara B. Harris, MD (lead investigator for Health ABC, harris99@nia.nih.gov), National Institute on Aging; Melissa E. Garcia, MPH, National Institute on Aging; Anne B. Newman, MD, MPH, University of Pittsburgh; Suzanne Satterfield, MD, MPH, University of Tennessee - Memphis; Steven R. Cummings, MD University of California – San Francisco; Michael C. Nevitt, PhD, University of California – San Francisco. Health ABC study coordinators: Diane Ives, University of Pittsburgh; Jan Elam, University of Tennessee – Memphis. Health ABC project director: Susan M. Rubin, MPH, University of California – San Francisco.