TY - JOUR
T1 - CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework
AU - Alzheimer’s Disease Neuroimaging Initiative (ADNI)
AU - Llano, Daniel A.
AU - Devanarayan, Priya
AU - Devanarayan, Viswanath
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012 ). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2023/1
Y1 - 2023/1
N2 - The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative (“N”) marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as “N” markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as “N” in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.
AB - The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative (“N”) marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as “N” markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as “N” in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.
KW - Alzheimer disease
KW - Biomarker
KW - CSF
KW - GAP43
KW - Mild cognitive impairment
KW - NPTX2
KW - VGF
UR - http://www.scopus.com/inward/record.url?scp=85140783048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140783048&partnerID=8YFLogxK
U2 - 10.1016/j.neurobiolaging.2022.07.015
DO - 10.1016/j.neurobiolaging.2022.07.015
M3 - Article
C2 - 36368195
AN - SCOPUS:85140783048
SN - 0197-4580
VL - 121
SP - 15
EP - 27
JO - Neurobiology of Aging
JF - Neurobiology of Aging
ER -