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Current Neurovascular Research

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ISSN (Print): 1567-2026
ISSN (Online): 1875-5739

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Research Article

Association between Plasma Brain-derived Neurotrophic Factor Level and Alzheimer’s Disease: A Mendelian Randomization Study

Author(s): Jiaxing You, Yinan Wang, Xinyue Chang, Yi Liu, Yu He, Xiya Zhou, Jinyan Zou, Meng Xiao, Mengyao Shi, Daoxia Guo, Ouxi Shen and Zhengbao Zhu*

Volume 20, Issue 5, 2023

Published on: 25 January, 2024

Page: [553 - 559] Pages: 7

DOI: 10.2174/0115672026281995231227070637

Price: $65

Abstract

Background: High brain-derived neurotrophic factor (BDNF) concentrations have been found to be associated with a decreased risk of Alzheimer’s disease (AD) in observational studies, but the causality for this association remains unclear. Therefore, we aimed to examine the association between genetically determined plasma BDNF levels and AD using a two-sample Mendelian randomization (MR) method.

Methods: Twenty single-nucleotide polymorphisms associated with plasma BDNF concentrations were identified as genetic instruments based on a genome-wide association study with 3301 European individuals. Summary-level data on AD were obtained from the International Genomics of Alzheimer’s Project, involving 21,982 AD cases and 41,944 controls of European ancestry. To evaluate the relationship between plasma BDNF concentrations and AD, we employed the inverse-variance weighted method along with a series of sensitivity analyses.

Results: The inverse-variance weighted MR analysis showed that genetically determined BDNF concentrations were associated with a decreased risk of AD (odds ratio per SD increase, 0.91; 95% confidence interval, 0.86-0.96; p =0.001). The association between plasma BDNF concentrations and AD was further confirmed through sensitivity analyses using different MR methods, and MR-Egger regression suggested no directional pleiotropy for this association.

Conclusion: Genetically determined BDNF levels were associated with a decreased risk of AD, suggesting that BDNF was implicated in the development of AD and might be a promising target for the prevention of AD.

Keywords: Brain-derived neurotrophic factor, Alzheimer’s disease, mendelian randomization, neurodegeneration, neuroprotection, amyloid β.

« Previous Next »
[1]
Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer’s disease. Lancet 2021; 397(10284): 1577-90.
[http://dx.doi.org/10.1016/S0140-6736(20)32205-4] [PMID: 33667416]
[2]
Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet 2015; 47(8): 856-60.
[http://dx.doi.org/10.1038/ng.3314] [PMID: 26121088]
[3]
Autry AE, Monteggia LM. Brain-derived neurotrophic factor and neuropsychiatric disorders. Pharmacol Rev 2012; 64(2): 238-58.
[http://dx.doi.org/10.1124/pr.111.005108] [PMID: 22407616]
[4]
Huang EJ, Reichardt LF. Neurotrophins: roles in neuronal development and function. Annu Rev Neurosci 2001; 24(1): 677-736.
[http://dx.doi.org/10.1146/annurev.neuro.24.1.677] [PMID: 11520916]
[5]
Mattson MP, Maudsley S, Martin B. BDNF and 5-HT: A dynamic duo in age-related neuronal plasticity and neurodegenerative disorders. Trends Neurosci 2004; 27(10): 589-94.
[http://dx.doi.org/10.1016/j.tins.2004.08.001] [PMID: 15374669]
[6]
Braun DJ, Kalinin S, Feinstein DL. Conditional depletion of hippocampal brain-derived neurotrophic factor exacerbates neuropathology in a mouse model of Alzheimer’s disease. ASN Neuro 2017; 9(2)
[http://dx.doi.org/10.1177/1759091417696161] [PMID: 28266222]
[7]
Hsiao YH, Hung HC, Chen SH, Gean PW. Social interaction rescues memory deficit in an animal model of Alzheimer’s disease by increasing BDNF-dependent hippocampal neurogenesis. J Neurosci 2014; 34(49): 16207-19.
[http://dx.doi.org/10.1523/JNEUROSCI.0747-14.2014] [PMID: 25471562]
[8]
Wang W, Li Y, Ma F, et al. Microglial repopulation reverses cognitive and synaptic deficits in an Alzheimer’s disease model by restoring BDNF signaling. Brain Behav Immun 2023; 113: 275-88.
[http://dx.doi.org/10.1016/j.bbi.2023.07.011] [PMID: 37482204]
[9]
Gao L, Zhang Y, Sterling K, Song W. Brain-derived neurotrophic factor in Alzheimer’s disease and its pharmaceutical potential. Transl Neurodegener 2022; 11(1): 4.
[http://dx.doi.org/10.1186/s40035-022-00279-0] [PMID: 35090576]
[10]
Erickson KI, Prakash RS, Voss MW, et al. Brain-derived neurotrophic factor is associated with age-related decline in hippocampal volume. J Neurosci 2010; 30(15): 5368-75.
[http://dx.doi.org/10.1523/JNEUROSCI.6251-09.2010] [PMID: 20392958]
[11]
Tsai MJ, Lin YS, Chen CY, Lee WJ, Fuh JL. Serum brain-derived neurotrophic factor levels as a predictor for Alzheimer disease progression. J Chin Med Assoc 2023; 86(11): 960-5.
[http://dx.doi.org/10.1097/JCMA.0000000000000991]
[12]
Weinstein G, Beiser AS, Choi SH, et al. Serum brain-derived neurotrophic factor and the risk for dementia: The Framingham Heart Study. JAMA Neurol 2014; 71(1): 55-61.
[http://dx.doi.org/10.1001/jamaneurol.2013.4781] [PMID: 24276217]
[13]
Qin X-Y, Cao C, Cawley NX, et al. Decreased peripheral brain-derived neurotrophic factor levels in Alzheimer’s disease: A meta-analysis study (N=7277). Mol Psychiatry 2017; 22(2): 312-20.
[http://dx.doi.org/10.1038/mp.2016.62] [PMID: 27113997]
[14]
Zheng J, Baird D, Borges MC, et al. Recent developments in mendelian randomization studies. Curr Epidemiol Rep 2017; 4(4): 330-45.
[http://dx.doi.org/10.1007/s40471-017-0128-6] [PMID: 29226067]
[15]
Wang Y, Jia Y, Xu Q, et al. Association between myeloperoxidase and the risks of ischemic stroke, heart failure, and atrial fibrillation: A Mendelian randomization study. Nutr Metab Cardiovasc Dis 2023; 33(1): 210-8.
[http://dx.doi.org/10.1016/j.numecd.2022.09.027] [PMID: 36411224]
[16]
Wang Y, Jia Y, Xu Q, et al. Association between prekallikrein and stroke: A mendelian randomization study. J Am Heart Assoc 2023; 12(16): e030525.
[http://dx.doi.org/10.1161/JAHA.123.030525] [PMID: 37581399]
[17]
Yeung CHC, Au Yeung SL, Kwok MK, Zhao JV, Schooling CM. The influence of growth and sex hormones on risk of alzheimer’s disease: A mendelian randomization study. Eur J Epidemiol 2023; 38(7): 745-55.
[http://dx.doi.org/10.1007/s10654-023-01015-2] [PMID: 37253999]
[18]
Yeung CHC, Schooling CM. Systemic inflammatory regulators and risk of Alzheimer’s disease: A bidirectional Mendelian-randomization study. Int J Epidemiol 2021; 50(3): 829-40.
[http://dx.doi.org/10.1093/ije/dyaa241] [PMID: 33313759]
[19]
Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature 2018; 558(7708): 73-9.
[http://dx.doi.org/10.1038/s41586-018-0175-2] [PMID: 29875488]
[20]
Kunkle BW, Grenier-Boley B, Sims R, et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ tau, immunity and lipid processing. Nat Genet 2019; 51(3): 414-30.
[http://dx.doi.org/10.1038/s41588-019-0358-2] [PMID: 30820047]
[21]
Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 2011; 40(3): 755-64.
[http://dx.doi.org/10.1093/ije/dyr036] [PMID: 21414999]
[22]
Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 2013; 45(12): 1452-8.
[http://dx.doi.org/10.1038/ng.2802] [PMID: 24162737]
[23]
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013; 37(7): 658-65.
[http://dx.doi.org/10.1002/gepi.21758] [PMID: 24114802]
[24]
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40(4): 304-14.
[http://dx.doi.org/10.1002/gepi.21965] [PMID: 27061298]
[25]
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. Epidemiology 2017; 28(1): 30-42.
[http://dx.doi.org/10.1097/EDE.0000000000000559] [PMID: 27749700]
[26]
Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018; 7: e34408.
[http://dx.doi.org/10.7554/eLife.34408] [PMID: 29846171]
[27]
Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat 2020; 48(3): 1742-69. 28
[http://dx.doi.org/10.1214/19-AOS1866]
[28]
Ong JS, MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner’s perspective. Genet Epidemiol 2019; 43(6): 609-16.
[http://dx.doi.org/10.1002/gepi.22207] [PMID: 31045282]
[29]
Bowden J, Smith DG, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015; 44(2): 512-25.
[http://dx.doi.org/10.1093/ije/dyv080] [PMID: 26050253]
[30]
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017; 32(5): 377-89.
[http://dx.doi.org/10.1007/s10654-017-0255-x] [PMID: 28527048]
[31]
Wang T, Li T, Hao S, Han Y, Cai Y. Association of plasma BDNF levels with different stages of Alzheimer’s disease: A cross-sectional study. Neurol Res 2023; 45(3): 234-40.
[http://dx.doi.org/10.1080/01616412.2022.2129760] [PMID: 36453692]
[32]
Li Y, Chen J, Yu H, Ye J, Wang C, Kong L. Serum brain-derived neurotrophic factor as diagnosis clue for Alzheimer’s disease: A cross-sectional observational study in the elderly. Front Psychiatry 2023; 14: 1127658.
[http://dx.doi.org/10.3389/fpsyt.2023.1127658] [PMID: 37009109]
[33]
Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med 2008; 27(8): 1133-63.
[http://dx.doi.org/10.1002/sim.3034] [PMID: 17886233]
[34]
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ 2018; 362: k601.
[http://dx.doi.org/10.1136/bmj.k601] [PMID: 30002074]
[35]
Harward SC, Hedrick NG, Hall CE, et al. Autocrine BDNF–TrkB signalling within a single dendritic spine. Nature 2016; 538(7623): 99-103.
[http://dx.doi.org/10.1038/nature19766] [PMID: 27680698]
[36]
Zhang Y, Song W. Islet amyloid polypeptide: Another key molecule in Alzheimer’s pathogenesis? Prog Neurobiol 2017; 153: 100-20.
[http://dx.doi.org/10.1016/j.pneurobio.2017.03.001] [PMID: 28274676]
[37]
Lee YJ, Jeong YJ, Kang EJ, et al. GAP-43 closely interacts with BDNF in hippocampal neurons and is associated with Alzheimer’s disease progression. Front Mol Neurosci 2023; 16: 1150399.
[http://dx.doi.org/10.3389/fnmol.2023.1150399] [PMID: 37143467]
[38]
Baranowski BJ, Mohammad A, Finch MS, Brown A, Dhaliwal R, Marko DM. Exercise training and BDNF injections alter amyloid precursor protein (APP) processing enzymes and improve cognition. J Appl Physiol 2023; 135(1): 121-35.
[39]
Cummings JL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures. Alzheimers Res Ther 2014; 6(4): 37.
[http://dx.doi.org/10.1186/alzrt269] [PMID: 25024750]

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