Multiple markers contribute to risk of progression from normal to mild cognitive impairment

Jennifer S. Rabin; Taylor E. Neal; Hannah E. Nierle; Sietske A.M. Sikkes; Rachel F. Buckley; Rebecca E. Amariglio; Kathryn V. Papp; Dorene M. Rentz; Aaron P. Schultz; Keith A. Johnson; Reisa A. Sperling; Trey Hedden

doi:10.1016/j.nicl.2020.102400

Multiple markers contribute to risk of progression from normal to mild cognitive impairment

Jennifer S. Rabin, Taylor E. Neal, Hannah E. Nierle, Sietske A.M. Sikkes, Rachel F. Buckley, Rebecca E. Amariglio, Kathryn V. Papp, Dorene M. Rentz, Aaron P. Schultz, Keith A. Johnson, Reisa A. Sperling, Trey Hedden^*

^*Corresponding author for this work

Clinical Neuropsychology

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Objective: To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI). Methods: 250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education. Results: 23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression. Conclusion: Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.

Original language	English
Article number	102400
Pages (from-to)	1-7
Number of pages	7
Journal	NeuroImage: Clinical
Volume	28
Early online date	28 Aug 2020
DOIs	https://doi.org/10.1016/j.nicl.2020.102400
Publication status	Published - 2020

Funding

This work was supported by the National Institute on Aging (NIA) at the National Institutes of Health (NIH; P01 AG036694 to R.A.S. and K.A.J, K24 AG035007 to R.A.S., R01 AG053509 and R01 AG054110 to T.H, and P50 AG005134), a Canadian Institutes of Health Research Postdoctoral Fellowship Award to J.S.R., and a NIH Pathway to Independence Award to R.F.B (K99AG061238). This research was carried out in part at the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, using resources provided by the Center for Functional Neuroimaging Technologies, P41EB015896, a P41 Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health. This work also involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program; specifically, grant numbers S10RR023401, S10RR019307, S10RR019254, and S10RR023043. In addition to the funding described above J.S.R., T.E.N., H.E.N., R.F.B., S.A.M.S. and T.H. report no other interests. K.A.J. has served as paid consultant, speaker, or site coinvestigator for Bayer, GE Healthcare, Janssen Alzheimer’s Immunotherapy, Siemens Medical Solutions, Genzyme, Novartis, Biogen, Roche, ISIS Pharma, AZTherapy, GEHC, Lundberg, Abbvie, Eli Lilly/Avid, Pfizer, and Navidea. He receives funding from the NIH and the Alzheimer’s Association. R.A.S. has served as a paid consultant, speaker, or co-investigator for Abbvie, Biogen, Bracket, Genentech, Lundbeck, Roche, and Sanofi, Eli Lilly/Avid, and Janssen Alzheimer Immunotherapy. She receives research support from Janssen Pharmaceuticals, and Eli Lilly and Co. These relationships are not related to the content in the manuscript. She receives research support from the NIH, Fidelity Biosciences, Harvard NeuroDiscovery Center, and the Alzheimer’s Association.

Funders	Funder number
Center for Functional Neuroimaging Technologies
National Institutes of Health	R01 AG054110, P50 AG005134, R01 AG053509, K24 AG035007
National Institute on Aging	P01AG036694, K99AG061238
National Institute of Biomedical Imaging and Bioengineering	S10RR019254, P41EB015896, S10RR023043, S10RR019307, S10RR023401
Alzheimer's Association
Harvard NeuroDiscovery Center
Massachusetts General Hospital
Fidelity Biosciences
Canadian Institutes of Health Research

Keywords

Aging
Alzheimer's disease
Amyloid
Clinical Dementia Rating
Memory
Mild cognitive impairment
Neuroimaging
Survival

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Rabin, J. S., Neal, T. E., Nierle, H. E., Sikkes, S. A. M., Buckley, R. F., Amariglio, R. E., Papp, K. V., Rentz, D. M., Schultz, A. P., Johnson, K. A., Sperling, R. A., & Hedden, T. (2020). Multiple markers contribute to risk of progression from normal to mild cognitive impairment. NeuroImage: Clinical, 28, 1-7. Article 102400. https://doi.org/10.1016/j.nicl.2020.102400

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title = "Multiple markers contribute to risk of progression from normal to mild cognitive impairment",

abstract = "Objective: To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI). Methods: 250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education. Results: 23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression. Conclusion: Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.",

keywords = "Aging, Alzheimer's disease, Amyloid, Clinical Dementia Rating, Memory, Mild cognitive impairment, Neuroimaging, Survival",

author = "Rabin, {Jennifer S.} and Neal, {Taylor E.} and Nierle, {Hannah E.} and Sikkes, {Sietske A.M.} and Buckley, {Rachel F.} and Amariglio, {Rebecca E.} and Papp, {Kathryn V.} and Rentz, {Dorene M.} and Schultz, {Aaron P.} and Johnson, {Keith A.} and Sperling, {Reisa A.} and Trey Hedden",

year = "2020",

doi = "10.1016/j.nicl.2020.102400",

language = "English",

volume = "28",

pages = "1--7",

journal = "NeuroImage: Clinical",

issn = "2213-1582",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Multiple markers contribute to risk of progression from normal to mild cognitive impairment

AU - Rabin, Jennifer S.

AU - Neal, Taylor E.

AU - Nierle, Hannah E.

AU - Sikkes, Sietske A.M.

AU - Buckley, Rachel F.

AU - Amariglio, Rebecca E.

AU - Papp, Kathryn V.

AU - Rentz, Dorene M.

AU - Schultz, Aaron P.

AU - Johnson, Keith A.

AU - Sperling, Reisa A.

AU - Hedden, Trey

PY - 2020

Y1 - 2020

N2 - Objective: To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI). Methods: 250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education. Results: 23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression. Conclusion: Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.

AB - Objective: To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI). Methods: 250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education. Results: 23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression. Conclusion: Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.

KW - Aging

KW - Alzheimer's disease

KW - Amyloid

KW - Clinical Dementia Rating

KW - Memory

KW - Mild cognitive impairment

KW - Neuroimaging

KW - Survival

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Multiple markers contribute to risk of progression from normal to mild cognitive impairment

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