Browsing by Subject "Mild cognitive impairment (MCI)"

Now showing 1 - 7 of 7
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    Antiphospholipid autoantibodies as blood biomarkers for detection of early stage Alzheimer's disease
    (Taylor & Francis, 2015-08) McIntyre, John A.; Ramsey, Curtis J.; Gitter, Bruce D.; Saykin, Andrew J.; Wagenknecht, Dawn R.; Hyslop, Paul A.; Department of Radiology and Imaging Sciences, IU School of Medicine
    A robust blood biomarker is urgently needed to facilitate early prognosis for those at risk for Alzheimer's disease (AD). Redox reactive autoantibodies (R-RAAs) represent a novel family of antibodies detectable only after exposure of cerebrospinal fluid (CSF), serum, plasma or immunoglobulin fractions to oxidizing agents. We have previously reported that R-RAA antiphospholipid antibodies (aPLs) are significantly decreased in the CSF and serum of AD patients compared to healthy controls (HCs). These studies were extended to measure R-RAA aPL in serum samples obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). Serum samples from the ADNI-1 diagnostic groups from participants with mild cognitive impairment (MCI), AD and HCs were blinded for diagnosis and analyzed for R-RAA aPL by ELISA. Demographics, cognitive data at baseline and yearly follow-up were subsequently provided by ADNI after posting assay data. As observed in CSF, R-RAA aPL in sera from the AD diagnostic group were significantly reduced compared to HC. However, the sera from the MCI population contained significantly elevated R-RAA aPL activity relative to AD patient and/or HC sera. The data presented in this study indicate that R-RAA aPL show promise as a blood biomarker for detection of early AD, and warrant replication in a larger sample. Longitudinal testing of an individual for increases in R-RAA aPL over a previously established baseline may serve as a useful early sero-epidemiologic blood biomarker for individuals at risk for developing dementia of the Alzheimer's type.
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    Author Correction: Predicting Alzheimer’s disease progression using multi-modal deep learning approach
    (Springer Nature, 2023-08-01) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung‑Ah; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Correction to: Scientific Reports 10.1038/s41598-018-37769-z, published online 13 February 2019 This Article contains errors. A Supplementary Information file was omitted from the original version of this Article. The Supplementary Information file is now linked to this correction notice.
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    Critical review of the Appropriate Use Criteria for amyloid imaging: Effect on diagnosis and patient care
    (Elsevier, 2016-12-18) Apostolova, Liana G.; Haider, Janelle M.; Goukasian, Naira; Rabinovici, Gil D.; Chetelat, Gael; Ringman, John M.; Kremen, Sarah; Grill, Joshua; Restrepo, Lucas; Mendez, Mario F.; Silverman, Daniel H.; Department of Neurology, IU School of Medicine
    INTRODUCTION: The utility of the Appropriate Use Criteria (AUC) for amyloid imaging is not established. METHODS: Fifty-three cognitively impaired patients with clinical F18-florbetapir imaging were classified as early and late onset, as well as AUC-consistent or AUC-inconsistent. Chi-square statistics and t test were used to compare demographic characteristics and clinical outcomes as appropriate. RESULTS: Early-onset patients were more likely to be amyloid positive. Change in diagnosis was more frequent in late-onset cases. Change in therapy was more common in early-onset cases. AUC-consistent and AUC-inconsistent cases had comparable rates of amyloid positivity. We saw no difference in the rate of treatment changes in the AUC-consistent group as opposed to the AUC-inconsistent group. DISCUSSION: The primary role of amyloid imaging in the early-onset group was to confirm the clinically suspected etiology, and in the late-onset group in detecting amyloid-negative cases. The rate of therapeutic changes was significantly greater in the early-onset cases.
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    Florbetapir positron emission tomography and cerebrospinal fluid biomarkers
    (Elsevier, 2015-08) Hake, Ann Marie; Trzepacz, Paula T.; Wang, Shufang; Yu, Peng; Case, Michael; Hochstetler, Helen; Witte, Michael M.; Degenhardt, Elisabeth K.; Dean, Robert A.; Department of Neurology, IU School of Medicine
    BACKGROUND: We evaluated the relationship between florbetapir-F18 positron emission tomography (FBP PET) and cerebrospinal fluid (CSF) biomarkers. METHODS: Alzheimer's Disease Neuroimaging Initiative-Grand Opportunity and Alzheimer's Disease Neuroimaging Initiative 2 (GO/2) healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia subjects with clinical measures and CSF collected ±90 days of FBP PET data were analyzed using correlation and logistic regression. RESULTS: In HC and MCI subjects, FBP PET anterior and posterior cingulate and composite standard uptake value ratios correlated with CSF amyloid beta (Aβ1-42) and tau/Aβ1-42 ratios. Using logistic regression, Aβ1-42, total tau (t-tau), phosphorylated tau181P (p-tau), and FBP PET composite each differentiated HC versus AD. Aβ1-42 and t-tau distinguished MCI versus AD, without additional contribution by FBP PET. Total tau and p-tau added discriminative power to FBP PET when classifying HC versus AD. CONCLUSION: Based on cross-sectional diagnostic groups, both amyloid and tau measures distinguish healthy from demented subjects. Longitudinal analyses are needed.
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    Key inflammatory pathway activations in the MCI stage of Alzheimer's disease
    (Wiley, 2019-07) Pillai, Jagan A.; Maxwell, Sean; Bena, James; Bekris, Lynn M.; Rao, Stephen M.; Chance, Mark; Lamb, Bruce T.; Leverenz, James B.; Neurology, School of Medicine
    OBJECTIVE: To determine the key inflammatory pathways that are activated in the peripheral and CNS compartments at the mild cognitive impairment (MCI) stage of Alzheimer's disease (AD). METHODS: A cross-sectional study of patients with clinical and biomarker characteristics consistent with MCI-AD in a discovery cohort, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Inflammatory analytes were measured in the CSF and plasma with the same validated multiplex analyte platform in both cohorts and correlated with AD biomarkers (CSF Aβ42, total tau (t-tau), phosphorylated tau (p-tau) to identify key inflammatory pathway activations. The pathways were additionally validated by evaluating genes related to all analytes in coexpression networks of brain tissue transcriptome from an autopsy confirmed AD cohort to interrogate if the same pathway activations were conserved in the brain tissue gene modules. RESULTS: Analytes of the tumor necrosis factor (TNF) signaling pathway (KEGG ID:4668) in the CSF and plasma best correlated with CSF t-tau and p-tau levels, and analytes of the complement and coagulation pathway (KEGG ID:4610) best correlated with CSF Aβ42 levels. The top inflammatory signaling pathways of significance were conserved in the peripheral and the CNS compartments. They were also confirmed to be enriched in AD brain transcriptome gene clusters. INTERPRETATION: A cell-protective rather than a proinflammatory analyte profile predominates in the CSF in relation to neurodegeneration markers among MCI-AD patients. Analytes from the TNF signaling and the complement and coagulation pathways are relevant in evaluating disease severity at the MCI stage of AD.
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    Plasma amyloid beta levels are associated with cerebral amyloid and tau deposition
    (Elsevier, 2019-07-26) Risacher, Shannon L.; Fandos, Noelia; Romero, Judith; Sherriff, Ian; Pesini, Pedro; Saykin, Andrew J.; Apostolova, Liana G.; Radiology and Imaging Sciences, School of Medicine
    Introduction: We investigated the relationship of plasma amyloid beta (Aβ) with cerebral deposition of Aβ and tau on positron emission tomography (PET). Methods: Forty-four participants (18 cognitively normal older adults [CN], 10 mild cognitive impairment, 16 Alzheimer's disease [AD]) underwent amyloid PET and a blood draw. Free and total plasma Aβ40 and Aβ42 were assessed using a validated assay. Thirty-seven participants (17 CN, 8 mild cognitive impairment, 12 AD) also underwent a [18F]flortaucipir scan. Scans were preprocessed by standard techniques, and mean global and regional amyloid and tau values were extracted. Free Aβ42/Aβ40 (Aβ F42:F40) and total Aβ42/Aβ40 (Aβ T42:T40) were evaluated for differences by diagnosis and relation to PET Aβ positivity. Relationships between these measures and cerebral Aβ and tau on both regional and voxel-wise basis were also evaluated. Results: Lower Aβ T42:T40 was associated with diagnosis and PET Aβ positivity. Lower plasma Aβ T42:T40 ratios predicted cerebral Aβ positivity, both across the full sample and in CN only. Finally, lower plasma Aβ T42:T40 ratios were associated with increased cortical Aβ and tau in AD-related regions on both regional and voxel-wise analyses. Discussion: Plasma Aβ measures may be useful biomarkers for predicting cerebral Aβ and tau. Additional studies in larger samples are warranted.
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    Predicting Alzheimer's disease progression using multi-modal deep learning approach
    (Springer Nature, 2019-02-13) Lee, Garam; Nho, Kwangsik; Kang, Byungkon; Sohn, Kyung-Ah; Kim, Dokyoon; Radiology and Imaging Sciences, School of Medicine
    Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.