Data-Driven vs Consensus Diagnosis of MCI: Enhanced Sensitivity for Detection of Clinical, Biomarker, and Neuropathologic Outcomes.

Aged Aged, 80 and over Alzheimer Disease / cerebrospinal fluid Amyloid beta-Peptides / cerebrospinal fluid Biomarkers / cerebrospinal fluid Brain / pathology Cognitive Dysfunction / cerebrospinal fluid Consensus Disease Progression Female Humans Male tau Proteins / cerebrospinal fluid

Journal

Neurology
ISSN: 1526-632X
Titre abrégé: Neurology
Pays: United States
ID NLM: 0401060

Informations de publication

Date de publication:
28 09 2021
Historique:
received: 05 02 2021
accepted: 01 07 2021
pubmed: 12 8 2021
medline: 30 10 2021
entrez: 11 8 2021
Statut: ppublish

Résumé

Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego Alzheimer's Disease Research Center (ADRC) in order to identify cognitive subgroups within ADRC participants without dementia and to examine cognitive, biomarker, and neuropathologic trajectories. Cluster analysis was performed on baseline neuropsychological data (n = 738; mean age 71.8). Survival analysis examined progression to dementia (mean follow-up 5.9 years). CSF Alzheimer disease (AD) biomarker status and neuropathologic findings at follow-up were examined in a subset with available data. Five clusters were identified: optimal cognitively normal (CN; n = 130) with above-average cognition, typical CN (n = 204) with average cognition, nonamnestic MCI (naMCI; n = 104), amnestic MCI (aMCI; n = 216), and mixed MCI (mMCI; n = 84). Progression to dementia differed across MCI subtypes (mMCI > aMCI > naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy. We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathologic findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathologic signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.

Sections du résumé

BACKGROUND AND OBJECTIVES
Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego Alzheimer's Disease Research Center (ADRC) in order to identify cognitive subgroups within ADRC participants without dementia and to examine cognitive, biomarker, and neuropathologic trajectories.
METHODS
Cluster analysis was performed on baseline neuropsychological data (n = 738; mean age 71.8). Survival analysis examined progression to dementia (mean follow-up 5.9 years). CSF Alzheimer disease (AD) biomarker status and neuropathologic findings at follow-up were examined in a subset with available data.
RESULTS
Five clusters were identified: optimal cognitively normal (CN; n = 130) with above-average cognition, typical CN (n = 204) with average cognition, nonamnestic MCI (naMCI; n = 104), amnestic MCI (aMCI; n = 216), and mixed MCI (mMCI; n = 84). Progression to dementia differed across MCI subtypes (mMCI > aMCI > naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy.
DISCUSSION
We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathologic findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathologic signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.

Identifiants

DOI: 10.1212/WNL.0000000000012600 PMID: 34376506 PMC: PMC8480404
pubmed: 34376506
pii: WNL.0000000000012600
doi: 10.1212/WNL.0000000000012600
pmc: PMC8480404
doi:

Substances chimiques

Amyloid beta-Peptides 0
Biomarkers 0
MAPT protein, human 0
tau Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1288-e1299

Subventions

Organisme : CSRD VA
ID : IK2 CX001415
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG049810
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG062429
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG063782
Pays : United States
Organisme : CSRD VA
ID : IK2 CX001865
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG070435
Pays : United States

Informations de copyright

© 2021 American Academy of Neurology.

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Auteurs

Emily C Edmonds (EC)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla. ecedmonds@ucsd.edu.

Denis S Smirnov (DS)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Kelsey R Thomas (KR)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Lisa V Graves (LV)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Katherine J Bangen (KJ)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Lisa Delano-Wood (L)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Douglas R Galasko (DR)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

David P Salmon (DP)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

Mark W Bondi (MW)

From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.

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Classifications MeSH

questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Sujet âgé de 80 ans ou plus › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Démence › Maladie d'Alzheimer › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Peptides bêta-amyloïdes › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Facteurs biologiques › Marqueurs biologiques › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Troubles mentaux › Troubles neurocognitifs › Troubles de la cognition › Dysfonctionnement cognitif › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Phénomènes psychologiques › Processus mentaux › Pensée (activité mentale) › Prise de décision › Consensus › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Évolution de la maladie › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Data-Driven vs Consensus Diagnosis of MCI:...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines de tissu nerveux › Protéines associées aux microtubules › Protéines tau › Data-Driven vs Consensus Diagnosis of MCI:...