Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer’s disease in late-middle-aged adults
Alzheimer’s disease (AD) is characterized by substantial neurodegeneration, including both cortical atrophy and loss of underlying white matter fiber tracts. Understanding longitudinal alterations to white matter may provide new insights into trajectories of brain change in both healthy aging and AD, and fluid biomarkers may be particularly useful in this effort. To examine this, 151 late-middle-aged participants enriched with risk for AD with at least one lumbar puncture and two diffusion tensor imaging (DTI) scans were selected for analysis from two large observational and longitudinally followed cohorts. Cerebrospinal fluid (CSF) was assayed for biomarkers of AD-specific pathology (phosphorylated-tau/Aβ42 ratio), axonal degeneration (neurofilament light chain protein, NFL), dendritic degeneration (neurogranin), and inflammation (chitinase-3-like protein 1, YKL-40). Linear mixed effects models were performed to test the hypothesis that biomarkers for AD, neurodegeneration, and inflammation, or two-year change in those biomarkers, would be associated with worse white matter health overall and/or progressively worsening white matter health over time. At baseline in the cingulum, phosphorylated-tau/Aβ42 was associated with higher mean diffusivity (MD) overall (intercept) and YKL-40 was associated with increases in MD over time. Two-year change in neurogranin was associated with higher mean diffusivity and lower fractional anisotropy overall (intercepts) across white matter in the entire brain and in the cingulum. These findings suggest that biomarkers for AD, neurodegeneration, and inflammation are potentially important indicators of declining white matter health in a cognitively healthy, late-middle-aged cohort.
KeywordsPreclinical Alzheimer’s disease Cerebrospinal fluid White matter Biomarkers Longitudinal Linear mixed effects
Diffusion Tensor Imaging
Wisconsin Registry for Alzheimer’s Prevention
- APOE ε4
apolipoprotein E gene
(parental) family history
FMRIB Software Library
Advanced Normalization Tools
cingulum adjacent to corpus callosum
The authors gratefully acknowledge Amy Hawley, Jennifer Oh, Chuck Illingworth, Nancy Davenport-Sis, Sandra Harding, and the support of researchers and staff at the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Alzheimer’s Institute, the Waisman Center, and the University of Wisconsin-Madison for their assistance in recruitment, data collection, and data analysis. Above all, we wish to thank our dedicated volunteers for their participation in this research.
Compliance with ethical standards
This research was supported by the National Institutes of Health awards (AG021155, AG027161, AG000213, P50 AG033514, AG037639 and UL1RR025011) to the University of Wisconsin, Madison; by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1256259 (APM); by the Neuroscience & Public Policy Program (SES-0849122); by the Neuroscience Training Program (T32GM007507); by the Medical Scientist Training Program (T32GM008692); by the Wisconsin Alzheimer’s Institute Lou Holland Fund; and by the Swedish Research Council, the Swedish Brain Foundation, the Knut and Alice Wallenberg Foundation, and Torsten Söderberg’s Foundation to the University of Gothenburg. N.A is supported in part by R01-EB022883, U54-HD090256, U54-AI117924, UF1-AG051216 and R56-AG052698. The project was also supported by the Clinical and Translational Science Award (CTSA) program by the National Center for Advancing Translational Sciences (NCATS) grant UL1TR000427 and NSF CAREER award (1252725). Portions of this research were supported by the Veterans Administration including facilities and resources at the Geriatric Research Education and Clinical Center of the William S. Middleton Memorial Veterans Hospital, Madison, WI. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the NIH, the Veterans Administration, or National Science Foundation.
Conflict of interest
KB and HZ are co-founders of Brain Biomarker Solutions in Gothenburg AB, a GU Venture-based platform company at the University of Gothenburg. KB has served as a consultant or at advisory boards for IBL International, Roche Diagnostics, Eli Lilly, Fujirebio Europe, and Novartis. The other authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Bendlin, B. B., Fitzgerald, M. E., Ries, M. L., Xu, G., Kastman, E. K., Thiel, B. W., et al. (2010). White matter in aging and cognition: A cross-sectional study of microstructure in adults aged eighteen to eighty-three. Developmental Neuropsychology, 35(3), 257–277.CrossRefPubMedPubMedCentralGoogle Scholar
- Bjerke, M., Portelius, E., Minthon, L., Wallin, A., Anckarsäter, H., Anckarsäter, R., et al. (2010). Confounding factors influencing amyloid beta concentration in cerebrospinal fluid. International journal of Alzheimer’s disease, 2010. doi:10.4061/2010/986310.
- Blennow, K., & Zetterberg, H. (2015). The past and the future of Alzheimer's disease CSF biomarkers—A journey toward validated biochemical tests covering the whole spectrum of molecular events. Frontiers in Neuroscience, 9 (2015): 345. doi:10.3389/fnins.2015.00345.
- Buchhave, P., Minthon, L., Zetterberg, H., Wallin, Å. K., Blennow, K., & Hansson, O. (2012). Cerebrospinal fluid levels ofβ-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. Archives of General Psychiatry, 69(1), 98–106.CrossRefPubMedGoogle Scholar
- Canu, E., McLaren, D. G., Fitzgerald, M. E., Bendlin, B. B., Zoccatelli, G., Alessandrini, F., et al. (2011). Mapping the structural brain changes in Alzheimer's disease: The independent contribution of two imaging modalities. Journal of Alzheimer's Disease, 26(s3), 263–274.PubMedPubMedCentralGoogle Scholar
- Duits, F. H., Teunissen, C. E., Bouwman, F. H., Visser, P.-J., Mattsson, N., Zetterberg, H., et al. (2014). The cerebrospinal fluid “Alzheimer profile”: Easily said, but what does it mean? Alzheimer’s & Dementia, 10(6), 713-723. e712.Google Scholar
- Huang, J., Friedland, R., & Auchus, A. (2007). Diffusion tensor imaging of normal-appearing white matter in mild cognitive impairment and early Alzheimer disease: Preliminary evidence of axonal degeneration in the temporal lobe. American Journal of Neuroradiology, 28(10), 1943–1948.CrossRefPubMedPubMedCentralGoogle Scholar
- Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., et al. (2013). Tracking pathophysiological processes in Alzheimer's disease: An updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12(2), 207–216. doi:10.1016/s1474-4422(12)70291-0.CrossRefPubMedPubMedCentralGoogle Scholar
- Keihaninejad, S., Zhang, H., Ryan, N. S., Malone, I. B., Modat, M., Cardoso, M. J., et al. (2013). An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. NeuroImage, 72, 153–163.CrossRefPubMedGoogle Scholar
- Kester, M. I., Teunissen, C. E., Crimmins, D. L., Herries, E. M., Ladenson, J. H., Scheltens, P., et al. (2015). Neurogranin as a cerebrospinal fluid biomarker for synaptic loss in symptomatic Alzheimer disease. JAMA Neurology, 72(11), 1275–1280. doi:10.1001/jamaneurol.2015.1867.CrossRefPubMedPubMedCentralGoogle Scholar
- Kim, W. H., Pachauri, D., Hatt, C., Chung, M. K., Johnson, S. C., & Singh, V. (2012). Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination. Advance in Neural Information Processing Systems, 2012, 1241–1249.Google Scholar
- Kim, W. H., Adluru, N., Chung, M. K., Charchut, S., GadElkarim, J. J., Altshuler, L., et al. (2013). Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16(Pt 3), 643–651.Google Scholar
- Kim, W. H., Singh, V., Chung, M. K., Hinrichs, C., Pachauri, D., Okonkwo, O. C., et al. (2014). Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness. NeuroImage, 93(Pt 1), 107–123. doi:10.1016/j.neuroimage.2014.02.028.CrossRefPubMedPubMedCentralGoogle Scholar
- Kim, W. H., Adluru, N., Chung, M. K., Okonkwo, O. C., Johnson, S. C., Bendlin, B. B., et al. (2015). Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease. NeuroImage, 118, 103–117. doi:10.1016/j.neuroimage.2015.05.050.CrossRefPubMedPubMedCentralGoogle Scholar
- Koscik, R. L., La Rue, A., Jonaitis, E. M., Okonkwo, O. C., Johnson, S. C., Bendlin, B. B., et al. (2014). Emergence of mild cognitive impairment in late middle-aged adults in the Wisconsin Registry for Alzheimer's Prevention. Dementia and Geriatric Cognitive Disorders, 38(1–2), 16–30.CrossRefPubMedPubMedCentralGoogle Scholar
- Melah, K. E., Lu, S. Y., Hoscheidt, S. M., Alexander, A. L., Adluru, N., Destiche, D. J., et al. (2015). Cerebrospinal fluid markers of Alzheimer's disease pathology and microglial activation are associated with altered white matter microstructure in asymptomatic adults at risk for Alzheimer's disease. Journal of Alzheimer's disease : JAD, 50(3), 873–886. doi:10.3233/JAD-150897.CrossRefGoogle Scholar
- Molinuevo, J. L., Ripolles, P., Simó, M., Lladó, A., Olives, J., Balasa, M., et al. (2014). White matter changes in preclinical Alzheimer's disease: A magnetic resonance imaging-diffusion tensor imaging study on cognitively normal older people with positive amyloid β protein 42 levels. Neurobiology of Aging, 35(12), 2671–2680.CrossRefPubMedGoogle Scholar
- Mori, S., Wakana, S., Van Zijl, P. C., & Nagae-Poetscher, L. (2005). MRI atlas of human white matter (Vol. 16): Am Soc neuroradiology.Google Scholar
- Pak, J. H., Huang, F. L., Li, J., Balschun, D., Reymann, K. G., Chiang, C., et al. (2000). Involvement of neurogranin in the modulation of calcium/calmodulin-dependent protein kinase II, synaptic plasticity, and spatial learning: A study with knockout mice. Proceedings of the National Academy of Sciences of the United States of America, 97(21), 11232–11237. doi:10.1073/pnas.210184697.CrossRefPubMedPubMedCentralGoogle Scholar
- Portelius, E., Zetterberg, H., Skillbäck, T., Törnqvist, U., Andreasson, U., Trojanowski, J. Q., et al. (2015). Cerebrospinal fluid neurogranin: Relation to cognition and neurodegeneration in Alzheimer’s disease. Brain, 138(11), 3373–3385. doi:10.1093/brain/awv267.
- Racine, A. M., Koscik, R. L., Berman, S. E., Nicholas, C. R., Clark, L. R., Okonkwo, O. C., et al. (2016a). Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife. Brain, 139(8), 2261–2274. doi:10.1093/brain/aww142.
- Racine, A. M., Koscik, R. L., Nicholas, C. R., Clark, L. R., Okonkwo, O. C., Oh, J. M., et al. (2016b). Cerebrospinal fluid ratios with Aβ 42 predict preclinical brain β-amyloid accumulation. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2, 27–38.Google Scholar
- Rosén, C., Andersson, C.-H., Andreasson, U., Molinuevo, J. L., Bjerke, M., Rami, L., et al. (2014). Increased levels of chitotriosidase and YKL-40 in cerebrospinal fluid from patients with Alzheimer's disease. Dementia and Geriatric Cognitive Disorders Extra, 4(2), 297–304.CrossRefPubMedPubMedCentralGoogle Scholar
- Starks, E. J., Patrick O’Grady, J., Hoscheidt, S. M., Racine, A. M., Carlsson, C. M., Zetterberg, H., et al. (2015). Insulin resistance is associated with higher cerebrospinal fluid tau levels in asymptomatic APOEɛ4 carriers. Journal of Alzheimer's Disease, 46(2), 525–533.CrossRefPubMedPubMedCentralGoogle Scholar
- Sutphen, C. L., Jasielec, M. S., Shah, A. R., Macy, E. M., Xiong, C., Vlassenko, A. G., et al. (2015). Longitudinal cerebrospinal fluid biomarker changes in preclinical Alzheimer disease during middle age. JAMA Neurology. doi:10.1001/jamaneurol.2015.1285.
- Yushkevich, P. A., Avants, B. B., Das, S. R., Pluta, J., Altinay, M., Craige, C., et al. (2010). Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3 T MRI data. NeuroImage, 50(2), 434–445.CrossRefPubMedGoogle Scholar
- Zetterberg, H., Skillbäck, T., Mattsson, N., Trojanowski, J. Q., Portelius, E., Shaw, L. M., et al. (2015). Association of Cerebrospinal Fluid Neurofilament Light Concentration with Alzheimer Disease Progression. JAMA Neurology, 1–8.Google Scholar
- Zetterberg, H., Skillback, T., Mattsson, N., Trojanowski, J. Q., Portelius, E., Shaw, L. M., et al. (2016). Association of Cerebrospinal Fluid Neurofilament Light Concentration with Alzheimer Disease Progression. JAMA Neurology, 73(1), 60–67. doi:10.1001/jamaneurol.2015.3037.CrossRefPubMedGoogle Scholar
- Zhang, H., Avants, B. B., Yushkevich, P. A., Woo, J. H., Wang, S., McCluskey, L. F., et al. (2007). High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: An example study using amyotrophic lateral sclerosis. Medical Imaging, IEEE Transactions on, 26(11), 1585–1597.CrossRefGoogle Scholar