Abstract
Background: White matter (WM) beta-amyloid uptake has been used as a reference region to calculate the cortical standard uptake value ratio (SUVr). However, white matter hyperintensities (WMH) may have an influence on WM beta-amyloid uptake. Our study aimed to investigate the associations between WMH and WM beta-amyloid deposition in cognitively unimpaired elderly.
Methods: Data from 83 cognitively unimpaired individuals in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset were analyzed. All participants had complete baseline and four-year follow-up information about WMH volume, WM 18F-AV-45 SUVr, and cognitive function, including ADNI-Memory (ADNI-Mem) and ADNI-Executive function (ADNI-EF) scores. Cross-sectional and longitudinal linear regression analyses were used to determine the associations between WMH and WM SUVr and cognitive measures.
Results: Lower WM 18F-AV-45 SUVr at baseline was associated with younger age (β=0.01, P=0.037) and larger WMH volume (β=-0.049, P=0.048). The longitudinal analysis found an annual increase in WM 18F-AV-45 SUVr was associated with an annual decrease in WMH volume (β=-0.016, P=0.041). An annual decrease in the ADNI-Mem score was associated with an annual increase in WMH volume (β=-0.070, P=0.001), an annual decrease in WM 18F-AV-45 SUVr (β=0.559, P=0.030), and fewer years of education (β=0.011, P=0.044). There was no significant association between WM 18F-AV-45 SUVr and ADNI-EF (P>0.05).
Conclusion: Reduced beta-amyloid deposition in WM was associated with higher WMH load and memory decline in cognitively unimpaired elderly. WMH volume should be considered when WM 18F-AV-45 SUVr is used as a reference for evaluating cortical 18F-AV-45 SUVr.
Keywords: White matter hyperintensities, white matter beta-amyloid, cognitive function, cognitive impairment, memory dysfunction, hyperlipidemia.
[http://dx.doi.org/10.1016/j.neuroimage.2003.12.027] [PMID: 15110004]
[http://dx.doi.org/10.1111/j.1552-6569.2006.00047.x]
[http://dx.doi.org/10.1136/jnnp.70.1.9] [PMID: 11118240]
[http://dx.doi.org/10.1001/archneurol.2010.280] [PMID: 21060015]
[http://dx.doi.org/10.3389/fneur.2019.00238] [PMID: 30972001]
[http://dx.doi.org/10.1038/jcbfm.2015.121] [PMID: 26036933]
[http://dx.doi.org/10.1111/ggi.12666] [PMID: 26671155]
[http://dx.doi.org/10.1212/WNL.0000000000001283] [PMID: 25632094]
[http://dx.doi.org/10.1161/STROKEAHA.109.563502] [PMID: 20133919]
[http://dx.doi.org/10.1016/j.neurobiolaging.2016.08.014] [PMID: 27639120]
[http://dx.doi.org/10.3233/JAD-170950] [PMID: 29614655]
[http://dx.doi.org/10.1016/j.neurobiolaging.2012.01.016] [PMID: 22410648]
[http://dx.doi.org/10.1016/j.nicl.2017.08.011] [PMID: 28856092]
[http://dx.doi.org/10.1007/s00259-012-2088-x] [PMID: 22398958]
[http://dx.doi.org/10.1016/j.neurobiolaging.2007.03.029] [PMID: 17499392]
[http://dx.doi.org/10.1002/ana.22068] [PMID: 20687209]
[http://dx.doi.org/10.1002/acn3.741] [PMID: 31019992]
[http://dx.doi.org/10.1016/j.nicl.2015.09.009] [PMID: 26594630]
[http://dx.doi.org/10.1007/s11682-012-9186-z] [PMID: 22782295]
[http://dx.doi.org/10.1007/s11682-012-9176-1] [PMID: 22644789]
[http://dx.doi.org/10.1002/ana.20009] [PMID: 14991808]
[http://dx.doi.org/10.1002/mrm.21515] [PMID: 18383289]
[http://dx.doi.org/10.2967/jnumed.108.057984] [PMID: 19164220]
[http://dx.doi.org/10.2967/jnumed.117.204271] [PMID: 29674420]
[http://dx.doi.org/10.1113/JP271081] [PMID: 26435295]
[http://dx.doi.org/10.1002/ana.22320] [PMID: 21337603]
[http://dx.doi.org/10.1016/S1474-4422(19)30079-1] [PMID: 31097385]
[http://dx.doi.org/10.1097/NEN.0b013e318277387e] [PMID: 23147507]
[http://dx.doi.org/10.1212/WNL.0b013e3182a43e45] [PMID: 23935177]
[http://dx.doi.org/10.1038/nrn2459] [PMID: 18641668]
[http://dx.doi.org/10.1093/brain/124.5.849] [PMID: 11335690]
[http://dx.doi.org/10.1016/j.nicl.2019.102143] [PMID: 31887716]
[http://dx.doi.org/10.3233/JAD-191005] [PMID: 31839612]