Four Common Late-Life Cognitive Trajectories Patterns Associate with Replicable Underlying Neuropathologies

Shama D. Karanth; Frederick A. Schmitt; Peter T. Nelson; Yuriko Katsumata; Richard J. Kryscio; David W. Fardo; Jordan P. Harp; Erin L. Abner; Gabriella Macron

doi:10.3233/JAD-210293

Four Common Late-Life Cognitive Trajectories Patterns Associate with Replicable Underlying Neuropathologies

Shama D. Karanth, Frederick A. Schmitt, Peter T. Nelson, Yuriko Katsumata, Richard J. Kryscio, David W. Fardo, Jordan P. Harp, Erin L. Abner, Gabriella Macron

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Background: Late-life cognitive function is heterogeneous, ranging from no decline to severe dementia. Prior studies of cognitive trajectories have tended to focus on a single measure of global cognition or individual tests scores, rather than considering longitudinal performance on multiple tests simultaneously. Objective: The current study aimed to examine cognitive trajectories from two independent datasets to assess whether similar patterns might describe longitudinal cognition in the decade preceding death, as well as what participant characteristics were associated with trajectory membership. Methods: Data were drawn from autopsied longitudinally followed participants of two cohorts (total N = 1,346), community-based cohort at the University of Kentucky Alzheimer's Disease Research Center (n = 365) and National Alzheimer's Coordinating Center (n = 981). We used group-based multi-trajectory models (GBMTM) to identify cognitive trajectories over the decade before death using Mini-Mental State Exam, Logical Memory-Immediate, and Animal Naming performance. Multinomial logistic and Random Forest analyses assessed characteristics associated with trajectory groups. Results: GBMTM identified four similar cognitive trajectories in each dataset. In multinomial models, death age, Braak neurofibrillary tangles (NFT) stage, TDP-43, and α-synuclein were associated with declining trajectories. Random Forest results suggested the most important trajectory predictors were Braak NFT stage, cerebral atrophy, death age, and brain weight. Multiple pathologies were most common in trajectories with moderate or accelerated decline. Conclusion: Cognitive trajectories associated strongly with neuropathology, particularly Braak NFT stage. High frequency of multiple pathologies in trajectories with cognitive decline suggests dementia treatment and prevention efforts must consider multiple diseases simultaneously.

Original language	English
Pages (from-to)	647-659
Number of pages	13
Journal	Journal of Alzheimer's Disease
Volume	82
Issue number	2
DOIs	https://doi.org/10.3233/JAD-210293
State	Published - 2021

Bibliographical note

Funding Information:
The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Say-kin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Tro-janowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).

Funding Information:
UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This

Funding Information:
The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This work was also partially supported by NIA grant R01 AG038651 and NICHD grant R01 HD064993

Publisher Copyright:
© 2021 - IOS Press. All rights reserved.

Keywords

Cognitive decline
Dementia
Neurodegenerative disorders
Neuropsychological tests
Trajectories

ASJC Scopus subject areas

Neuroscience (all)
Clinical Psychology
Geriatrics and Gerontology
Psychiatry and Mental health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3233/JAD-210293

Cite this

@article{f2782d8ad781425cbe2f853ec3f1ff0a,

title = "Four Common Late-Life Cognitive Trajectories Patterns Associate with Replicable Underlying Neuropathologies",

abstract = "Background: Late-life cognitive function is heterogeneous, ranging from no decline to severe dementia. Prior studies of cognitive trajectories have tended to focus on a single measure of global cognition or individual tests scores, rather than considering longitudinal performance on multiple tests simultaneously. Objective: The current study aimed to examine cognitive trajectories from two independent datasets to assess whether similar patterns might describe longitudinal cognition in the decade preceding death, as well as what participant characteristics were associated with trajectory membership. Methods: Data were drawn from autopsied longitudinally followed participants of two cohorts (total N = 1,346), community-based cohort at the University of Kentucky Alzheimer's Disease Research Center (n = 365) and National Alzheimer's Coordinating Center (n = 981). We used group-based multi-trajectory models (GBMTM) to identify cognitive trajectories over the decade before death using Mini-Mental State Exam, Logical Memory-Immediate, and Animal Naming performance. Multinomial logistic and Random Forest analyses assessed characteristics associated with trajectory groups. Results: GBMTM identified four similar cognitive trajectories in each dataset. In multinomial models, death age, Braak neurofibrillary tangles (NFT) stage, TDP-43, and α-synuclein were associated with declining trajectories. Random Forest results suggested the most important trajectory predictors were Braak NFT stage, cerebral atrophy, death age, and brain weight. Multiple pathologies were most common in trajectories with moderate or accelerated decline. Conclusion: Cognitive trajectories associated strongly with neuropathology, particularly Braak NFT stage. High frequency of multiple pathologies in trajectories with cognitive decline suggests dementia treatment and prevention efforts must consider multiple diseases simultaneously.",

keywords = "Cognitive decline, Dementia, Neurodegenerative disorders, Neuropsychological tests, Trajectories",

author = "Karanth, {Shama D.} and Schmitt, {Frederick A.} and Nelson, {Peter T.} and Yuriko Katsumata and Kryscio, {Richard J.} and Fardo, {David W.} and Harp, {Jordan P.} and Abner, {Erin L.} and Gabriella Macron",

note = "Funding Information: The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Say-kin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Tro-janowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). Funding Information: UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This Funding Information: The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This work was also partially supported by NIA grant R01 AG038651 and NICHD grant R01 HD064993 Publisher Copyright: {\textcopyright} 2021 - IOS Press. All rights reserved.",

year = "2021",

doi = "10.3233/JAD-210293",

language = "English",

volume = "82",

pages = "647--659",

number = "2",

}

TY - JOUR

T1 - Four Common Late-Life Cognitive Trajectories Patterns Associate with Replicable Underlying Neuropathologies

AU - Karanth, Shama D.

AU - Schmitt, Frederick A.

AU - Nelson, Peter T.

AU - Katsumata, Yuriko

AU - Kryscio, Richard J.

AU - Fardo, David W.

AU - Harp, Jordan P.

AU - Abner, Erin L.

AU - Macron, Gabriella

N1 - Funding Information: The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Say-kin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Tro-janowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). Funding Information: UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This Funding Information: The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG 008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG005131 (PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD). UK-ADRC is funded by grant P30 AG028383 from the NIA (PI Linda Van Eldik, PhD). This work was also partially supported by NIA grant R01 AG038651 and NICHD grant R01 HD064993 Publisher Copyright: © 2021 - IOS Press. All rights reserved.

PY - 2021

Y1 - 2021

N2 - Background: Late-life cognitive function is heterogeneous, ranging from no decline to severe dementia. Prior studies of cognitive trajectories have tended to focus on a single measure of global cognition or individual tests scores, rather than considering longitudinal performance on multiple tests simultaneously. Objective: The current study aimed to examine cognitive trajectories from two independent datasets to assess whether similar patterns might describe longitudinal cognition in the decade preceding death, as well as what participant characteristics were associated with trajectory membership. Methods: Data were drawn from autopsied longitudinally followed participants of two cohorts (total N = 1,346), community-based cohort at the University of Kentucky Alzheimer's Disease Research Center (n = 365) and National Alzheimer's Coordinating Center (n = 981). We used group-based multi-trajectory models (GBMTM) to identify cognitive trajectories over the decade before death using Mini-Mental State Exam, Logical Memory-Immediate, and Animal Naming performance. Multinomial logistic and Random Forest analyses assessed characteristics associated with trajectory groups. Results: GBMTM identified four similar cognitive trajectories in each dataset. In multinomial models, death age, Braak neurofibrillary tangles (NFT) stage, TDP-43, and α-synuclein were associated with declining trajectories. Random Forest results suggested the most important trajectory predictors were Braak NFT stage, cerebral atrophy, death age, and brain weight. Multiple pathologies were most common in trajectories with moderate or accelerated decline. Conclusion: Cognitive trajectories associated strongly with neuropathology, particularly Braak NFT stage. High frequency of multiple pathologies in trajectories with cognitive decline suggests dementia treatment and prevention efforts must consider multiple diseases simultaneously.

AB - Background: Late-life cognitive function is heterogeneous, ranging from no decline to severe dementia. Prior studies of cognitive trajectories have tended to focus on a single measure of global cognition or individual tests scores, rather than considering longitudinal performance on multiple tests simultaneously. Objective: The current study aimed to examine cognitive trajectories from two independent datasets to assess whether similar patterns might describe longitudinal cognition in the decade preceding death, as well as what participant characteristics were associated with trajectory membership. Methods: Data were drawn from autopsied longitudinally followed participants of two cohorts (total N = 1,346), community-based cohort at the University of Kentucky Alzheimer's Disease Research Center (n = 365) and National Alzheimer's Coordinating Center (n = 981). We used group-based multi-trajectory models (GBMTM) to identify cognitive trajectories over the decade before death using Mini-Mental State Exam, Logical Memory-Immediate, and Animal Naming performance. Multinomial logistic and Random Forest analyses assessed characteristics associated with trajectory groups. Results: GBMTM identified four similar cognitive trajectories in each dataset. In multinomial models, death age, Braak neurofibrillary tangles (NFT) stage, TDP-43, and α-synuclein were associated with declining trajectories. Random Forest results suggested the most important trajectory predictors were Braak NFT stage, cerebral atrophy, death age, and brain weight. Multiple pathologies were most common in trajectories with moderate or accelerated decline. Conclusion: Cognitive trajectories associated strongly with neuropathology, particularly Braak NFT stage. High frequency of multiple pathologies in trajectories with cognitive decline suggests dementia treatment and prevention efforts must consider multiple diseases simultaneously.

KW - Cognitive decline

KW - Dementia

KW - Neurodegenerative disorders

KW - Neuropsychological tests

KW - Trajectories

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Four Common Late-Life Cognitive Trajectories Patterns Associate with Replicable Underlying Neuropathologies

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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