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Prevention of Cognitive Decline in Alzheimer's Disease by Novel Antioxidative Supplements.

ABSTRACT: Oxidative stress plays a crucial role in Alzheimer's disease (AD) from its prodromal stage of mild cognitive impairment. There is an interplay between oxidative stress and the amyloid ? (A?) cascade via various mechanisms including mitochondrial dysfunction, lipid peroxidation, protein oxidation, glycoxidation, deoxyribonucleotide acid damage, altered antioxidant defense, impaired amyloid clearance, inflammation and chronic cerebral hypoperfusion. Based on findings that indicate that oxidative stress plays a major role in AD, oxidative stress has been considered as a therapeutic target of AD. In spite of favorable preclinical study outcomes, previous antioxidative components, including a single antioxidative supplement such as vitamin C, vitamin E or their mixtures, did not clearly show any therapeutic effect on cognitive decline in AD. However, novel antioxidative supplements can be beneficial for AD patients. In this review, we summarize the interplay between oxidative stress and the A? cascade, and introduce novel antioxidative supplements expected to prevent cognitive decline in AD.

SUBMITTER: Tadokoro K

PROVIDER: S-EPMC7139972 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Prevention of Cognitive Decline in Alzheimer's Disease by Novel Antioxidative Supplements.

Tadokoro Koh K Ohta Yasuyuki Y Inufusa Haruhiko H Loon Alan Foo Nyuk AFN Abe Koji K

International journal of molecular sciences 20200313 6

Oxidative stress plays a crucial role in Alzheimer's disease (AD) from its prodromal stage of mild cognitive impairment. There is an interplay between oxidative stress and the amyloid β (Aβ) cascade via various mechanisms including mitochondrial dysfunction, lipid peroxidation, protein oxidation, glycoxidation, deoxyribonucleotide acid damage, altered antioxidant defense, impaired amyloid clearance, inflammation and chronic cerebral hypoperfusion. Based on findings that indicate that oxidative s ...[more]

PMID: 32183152

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Project description:BACKGROUND:There is growing interest in identifying sensitive composite cognitive tests to serve as primary endpoints in preclinical Alzheimer's disease (AD) treatment trials. We reported previously a composite cognitive test score sensitive to tracking preclinical AD decline up to 5?years prior to clinical diagnosis. Here we expand upon and refine this work, empirically deriving a composite cognitive test score sensitive to tracking preclinical AD decline up to 11?years prior to diagnosis and suitable for use as a primary endpoint in a preclinical AD trial. METHODS:This study used a longitudinal approach to maximize sensitivity to tracking progressive cognitive decline in people who progressed to the clinical stages of AD (n =?868) compared to those who remained cognitively unimpaired during the same time period (n =?989), thereby correcting for normal aging and practice effects. Specifically, we developed the Alzheimer's Prevention Initiative Preclinical Composite Cognitive test (APCC) to measure very early longitudinal cognitive decline in older adults with preclinical AD. Data from three cohorts from Rush University were analyzed using a partial least squares (PLS) regression model to identify optimal composites within different time periods prior to diagnosis, up to 11?years prior to diagnosis. The mean-to-standard deviation ratio (MSDRs) is an indicator of sensitivity to change and was used to inform the final calculation of the composite score. RESULTS:The optimal composite, the APCC, is calculated: 0.26*Symbol Digit Modalities +?2.24*MMSE Orientation to Time?+?2.14*MMSE Orientation to Place +?0.53*Logical Memory Delayed Recall +?1.36* Word List-Delayed Recall +?0.68*Judgment of Line Orientation +?1.39*Raven's Progressive Matrices Matrices (subset of 9 items from A and B). The MSDR of the APCC in a population of preclinical AD individuals who eventually progress to cognitive impairment, compared to those who remained cognitively unimpaired during the same time period, was -?1.10 over 1 year. CONCLUSIONS:The APCC is an empirically derived composite cognitive test score with high face validity that is sensitive to preclinical AD decline up to 11?years prior to diagnosis of the clinical stages of AD. The components of the APCC are supported by theoretical understanding of cognitive decline that occurs during preclinical AD. The APCC was used as a primary outcome in the API Generation Program trials.

Project description:Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical ?-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

Dataset Information

Prevention of Cognitive Decline in Alzheimer's Disease by Novel Antioxidative Supplements.

Publications

Prevention of Cognitive Decline in Alzheimer's Disease by Novel Antioxidative Supplements.

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