Accelerated Brain Aging in Amnestic Mild Cognitive Impairment: Relationships with Individual Cognitive Decline, Risk Factors for Alzheimer Disease and Clinical Progression

Background: Amyloid PET allows for the assessment of amyloid β status in the brain, distinguishing true Alzheimer’s disease from Alzheimer’s disease-mimicking conditions. Around 15–20% of patients with clinically probable Alzheimer’s disease have been found to have no significant Alzheimer’s pathology on amyloid PET. However, a limited number of studies had been conducted this subpopulation in terms of clinical progression. Objective: We investigated the risk factors that could affect the progression to dementia in patients with amyloid-negative amnestic mild cognitive impairment (MCI). Methods: This study was a single-institutional, retrospective cohort study of patients over the age of 50 with amyloidnegative amnestic MCI who visited the memory clinic of Asan Medical Center with a follow-up period of more than 36 months. All participants underwent brain magnetic resonance imaging (MRI), detailed neuropsychological testing, and fluorine-18[F18]-florbetaben amyloid PET. Results: During the follow-up period, 39 of 107 patients progressed to dementia from amnestic MCI. In comparison with the stationary group, the progressed group had a more severe impairment in verbal and visual episodic memory function and hippocampal atrophy, which showed an Alzheimer’s disease-like pattern despite the lack of evidence for significant Alzheimer’s disease pathology. Voxel-based morphometric MRI analysis revealed that the progressed group had a reduced gray matter volume in the bilateral cerebellar cortices, right temporal cortex, and bilateral insular cortices. Conclusion: Considering the lack of evidence of amyloid pathology, clinical progression of these subpopulation may be caused by other neuropathologies such as TDP-43, abnormal tau or alpha synuclein that lead to neurodegeneration independent of amyloid-driven pathway. Further prospective studies incorporating biomarkers of Alzheimer’s diseasemimicking dementia are warranted.

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O1-11-02: FRACTION OF CASES OF AMNESTIC MILD COGNITIVE IMPAIRMENT PROGRESSION ATTRIBUTABLE TO THE INTERACTION BETWEEN THE SEVERITY OF COGNITIVE DECLINE AND CARDIOVASCULAR RISK FACTORS

Alzheimer s & Dementia ◽

10.1016/j.jalz.2014.04.117 ◽

2014 ◽

Vol 10 ◽

pp. P151-P151

Author(s):

Yuliya Bodryzlova ◽

Olivier Potvin ◽

Simon Duchesne

Keyword(s):

Risk Factors ◽

Cognitive Impairment ◽

Cardiovascular Risk ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Cardiovascular Risk Factors ◽

Amnestic Mild Cognitive Impairment

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P3-335: IMPACT OF SUBJECTIVE COGNITIVE COMPLAINTS ON INSTRUMENTAL ACTIVITIES OF DAILY LIVING IN PATIENTS WITH SUBJECTIVE COGNITIVE DECLINE AND AMNESTIC MILD COGNITIVE IMPAIRMENT: DATA FROM THE CZECH BRAIN AGING STUDY

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.1696 ◽

2006 ◽

Vol 14 (7S_Part_23) ◽

pp. P1210-P1210

Author(s):

Martin Vyhnalek ◽

Hana Markova ◽

Tomas Nikolai ◽

Adela Fendrych Mazancova ◽

Katerina Cechova ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Activities Of Daily Living ◽

Cognitive Decline ◽

Daily Living ◽

Brain Aging ◽

Amnestic Mild Cognitive Impairment ◽

Subjective Cognitive Decline ◽

Aging Study ◽

Subjective Cognitive Complaints

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Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study

Journal of the International Neuropsychological Society ◽

10.1017/s1355617720001046 ◽

2020 ◽

pp. 1-14

Author(s):

Dylan J. Jester ◽

Ross Andel ◽

Katerina Cechová ◽

Jan Laczó ◽

Ondrej Lerch ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Brain Aging ◽

Amnestic Mild Cognitive Impairment ◽

Subjective Cognitive Decline ◽

Classification Error ◽

Aging Study ◽

Risk Of Progression

Abstract Objective: To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning. Method: Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error. Results: Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified). Conclusions: Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

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Analyses of Visuospatial and Visuoperceptual Errors as Predictors of Dementia in Parkinson’s Disease Patients with Subjective Cognitive Decline and Mild Cognitive Impairment

Journal of the International Neuropsychological Society ◽

10.1017/s1355617720001216 ◽

2020 ◽

pp. 1-11

Author(s):

Iván Galtier ◽

Antonieta Nieto ◽

María Mata ◽

Jesús N. Lorenzo ◽

José Barroso

Keyword(s):

Risk Factors ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Poor Performance ◽

Subjective Cognitive Decline ◽

Independent Risk Factors

ABSTRACT Objective: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in Parkinson’s disease (PD) are considered as the risk factors for dementia (PDD). Posterior cortically based functions, such as visuospatial and visuoperceptual (VS-VP) processing, have been described as predictors of PDD. However, no investigations have focused on the qualitative analysis of the Judgment of Line Orientation Test (JLOT) and the Facial Recognition Test (FRT) in PD-SCD and PD-MCI. The aim of this work was to study the VS-VP errors in JLOT and FRT. Moreover, these variables are considered as predictors of PDD. Method: Forty-two PD patients and 19 controls were evaluated with a neuropsychological protocol. Patients were classified as PD-SCD and PD-MCI. Analyses of errors were conducted following the procedure described by Ska, Poissant, and Joanette (1990). Follow-up assessment was conducted to a mean of 7.5 years after the baseline. Results: PD-MCI patients showed a poor performance in JLOT and FRT total score and made a greater proportion of severe intraquadrant (QO2) and interquadrant errors (IQO). PD-SCD showed a poor performance in FRT and made mild errors in JLOT. PD-MCI and QO2/IQO errors were independent risk factors for PDD during the follow-up. Moreover, the combination of both PD-MCI diagnosis and QO2/IQO errors was associated with a greater risk. Conclusions: PD-MCI patients presented a greater alteration in VS-VP processing observable by the presence of severe misjudgments. PD-SCD patients also showed mild difficulties in VS-SP functions. Finally, QO2/IQO errors in PD-MCI are a useful predictor of PDD, more than PD-MCI diagnosis alone.

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