| 31. |
- Caroli, Anna, et al.
(författare)
-
Mild cognitive impairment with suspected nonamyloid pathology (SNAP) Prediction of progression
- 2015
-
Ingår i: Neurology. - 0028-3878 .- 1526-632X. ; 84:5, s. 508-515
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives:The aim of this study was to investigate predictors of progressive cognitive deterioration in patients with suspected non-Alzheimer disease pathology (SNAP) and mild cognitive impairment (MCI).Methods:We measured markers of amyloid pathology (CSF -amyloid 42) and neurodegeneration (hippocampal volume on MRI and cortical metabolism on [F-18]-fluorodeoxyglucose-PET) in 201 patients with MCI clinically followed for up to 6 years to detect progressive cognitive deterioration. We categorized patients with MCI as A+/A- and N+/N- based on presence/absence of amyloid pathology and neurodegeneration. SNAPs were A-N+ cases.Results:The proportion of progressors was 11% (8/41), 34% (14/41), 56% (19/34), and 71% (60/85) in A-N-, A+N-, SNAP, and A+N+, respectively; the proportion of APOE epsilon 4 carriers was 29%, 70%, 31%, and 71%, respectively, with the SNAP group featuring a significantly different proportion than both A+N- and A+N+ groups (p 0.005). Hypometabolism in SNAP patients was comparable to A+N+ patients (p = 0.154), while hippocampal atrophy was more severe in SNAP patients (p = 0.002). Compared with A-N-, SNAP and A+N+ patients had significant risk of progressive cognitive deterioration (hazard ratio = 2.7 and 3.8, p = 0.016 and p < 0.001), while A+N- patients did not (hazard ratio = 1.13, p = 0.771). In A+N- and A+N+ groups, none of the biomarkers predicted time to progression. In the SNAP group, lower time to progression was correlated with greater hypometabolism (r = 0.42, p = 0.073).Conclusions:Our findings support the notion that patients with SNAP MCI feature a specific risk progression profile.
|
|
| 32. |
- Damian, Marinella, et al.
(författare)
-
Single-Domain Amnestic Mild Cognitive Impairment Identified by Cluster Analysis Predicts Alzheimer's Disease in the European Prospective DESCRIPA Study
- 2013
-
Ingår i: Dementia and Geriatric Cognitive Disorders. - : S. Karger AG. - 1420-8008 .- 1421-9824. ; 36:1-2, s. 1-19
-
Tidskriftsartikel (refereegranskat)abstract
- Background/Aims: To identify prodromal Alzheimer's disease (AD) subjects using a data-driven approach to determine cognitive profiles in mild cognitive impairment (MCI). Methods: A total of 881 MCI subjects were recruited from 20 memory clinics and followed for up to 5 years. Outcome measures included cognitive variables, conversion to AD, and biomarkers (e. g. CSF, and MRI markers). Two hierarchical cluster analyses (HCA) were performed to identify clusters of subjects with distinct cognitive profiles. The first HCA included all subjects with complete cognitive data, whereas the second one selected subjects with very mild MCI (MMSE >= 28). ANOVAs and ANCOVAs were computed to examine whether the clusters differed with regard to conversion to AD, and to AD-specific biomarkers. Results: The HCAs identified 4-cluster solutions that best reflected the sample structure. One cluster (aMCIsingle) had a significantly higher conversion rate (19%), compared to subjective cognitive impairment (SCI, p < 0.0001), and non-amnestic MCI (naMCI, p = 0.012). This cluster was the only one showing a significantly different biomarker profile (A beta(42), t-tau, APOE epsilon 4, and medial temporal atrophy), compared to SCI or naMCI. Conclusion: In subjects with mild MCI, the single-domain amnestic MCI profile was associated with the highest risk of conversion, even if memory impairment did not necessarily cross specific cut-off points. A cognitive profile characterized by isolated memory deficits may be sufficient to warrant applying prevention strategies in MCI, whether or not memory performance lies below specific z-scores. This is supported by our preliminary biomarker analyses. However, further analyses with bigger samples are needed to corroborate these findings. Copyright (C) 2013 S. Karger AG, Basel
|
|
| 33. |
- Dubois, Bruno, et al.
(författare)
-
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria.
- 2014
-
Ingår i: Lancet neurology. - 1474-4465. ; 13:6, s. 614-29
-
Forskningsöversikt (refereegranskat)abstract
- In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimer's Association have contributed criteria for the diagnosis of Alzheimer's disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimer's pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD.
|
|
| 34. |
- Etminani, Kobra, 1984-, et al.
(författare)
-
A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET
- 2022
-
Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models performance to that of multiple expert nuclear medicine physicians readers. Materials and methods Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimers disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The models performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
|
|
| 35. |
|
|
| 36. |
|
|
| 37. |
- Frisoni, Giovanni B, et al.
(författare)
-
The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium.
- 2008
-
Ingår i: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279. ; 4:4, s. 255-64
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: In North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI). METHODS: Seven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales. RESULTS: Recruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects. CONCLUSIONS: Academic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.
|
|
| 38. |
- Groot, Colin, et al.
(författare)
-
Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment
- 2024
-
Ingår i: JAMA Neurology. - 2168-6149. ; 81:8, s. 845-856
-
Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. OBJECTIVE To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. DESIGN, SETTING, AND PARTICIPANTS This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. EXPOSURES Tau PET, Aβ PET, and MRI. MAIN OUTCOMES AND MEASURES Positive results on tau PET (temporal meta–region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. RESULTS In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. CONCLUSIONS AND RELEVANCE In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.
|
|
| 39. |
- Hampel, Harald, et al.
(författare)
-
Biomarkers for Alzheimer's disease therapeutic trials.
- 2011
-
Ingår i: Progress in neurobiology. - : Elsevier BV. - 1873-5118 .- 0301-0082. ; 95:4, s. 579-593
-
Forskningsöversikt (refereegranskat)abstract
- The development of disease-modifying treatments for Alzheimer's disease requires innovative trials with large numbers of subjects and long observation periods. The use of blood, cerebrospinal fluid or neuroimaging biomarkers is critical for the demonstration of disease-modifying therapy effects on the brain. Suitable biomarkers are those which reflect the progression of AD related molecular mechanisms and neuropathology, including amyloidogenic processing and aggregation, hyperphosphorylation, accumulation of tau and neurofibrillary tangles, progressive functional, metabolic and structural decline, leading to neurodegeneration, loss of brain tissue and cognitive symptoms. Biomarkers should be used throughout clinical trial phases I-III of AD drug development. They can be used to enhance inclusion and exclusion criteria, or as baseline predictors to increase the statistical power of trials. Validated and qualified biomarkers may be used as outcome measures to detect treatment effects in pivotal clinical trials. Finally, biomarkers can be used to identify adverse effects. Questions regarding which biomarkers should be used in clinical trials, and how, are currently far from resolved. The Oxford Task Force continues and expands the work of our previous international expert task forces on disease-modifying trials and on endpoints for Alzheimer's disease clinical trials. The aim of this initiative was to bring together a selected number of key international opinion leaders and experts from academia, regulatory agencies and industry to condense the current knowledge and state of the art regarding the best use of biological markers in Alzheimer's disease therapy trials and to propose practical recommendations for the planning of future AD trials.
|
|
| 40. |
- Herukka, Sanna-Kaisa, et al.
(författare)
-
Recommendations for cerebrospinal fluid Alzheimer's disease biomarkers in the diagnostic evaluation of mild cognitive impairment.
- 2017
-
Ingår i: Alzheimer's & dementia : the journal of the Alzheimer's Association. - : Wiley. - 1552-5279. ; 13:3, s. 285-295
-
Tidskriftsartikel (refereegranskat)abstract
- This article presents recommendations, based on the Grading of Recommendations, Assessment, Development, and Evaluation method, for the clinical application of cerebrospinal fluid (CSF) amyloid-β1-42, tau, and phosphorylated tau in the diagnostic evaluation of patients with mild cognitive impairment (MCI). The recommendations were developed by a multidisciplinary working group and based on the available evidence and consensus from focused group discussions for 1) prediction of clinical progression to Alzheimer's disease (AD) dementia, 2) cost-effectiveness, 3) interpretation of results, and 4) patient counseling. The working group recommended using CSF AD biomarkers in the diagnostic workup of MCI patients, after prebiomarker counseling, as an add-on to clinical evaluation to predict functional decline or conversion to AD dementia and to guide disease management. Because of insufficient evidence, it was uncertain whether CSF AD biomarkers outperform imaging biomarkers. Furthermore, the working group provided recommendations for interpretation of ambiguous CSF biomarker results and for pre- and post-biomarker counseling.
|
|
