| 1. |
- Jansen, Willemijn J, et al.
(author)
-
Association of Cerebral Amyloid-β Aggregation With Cognitive Functioning in Persons Without Dementia.
- 2018
-
In: JAMA psychiatry. - : American Medical Association (AMA). - 2168-6238 .- 2168-622X. ; 75:1, s. 84-95
-
Journal article (peer-reviewed)abstract
- Cerebral amyloid-β aggregation is an early event in Alzheimer disease (AD). Understanding the association between amyloid aggregation and cognitive manifestation in persons without dementia is important for a better understanding of the course of AD and for the design of prevention trials.To investigate whether amyloid-β aggregation is associated with cognitive functioning in persons without dementia.This cross-sectional study included 2908 participants with normal cognition and 4133 with mild cognitive impairment (MCI) from 53 studies in the multicenter Amyloid Biomarker Study. Normal cognition was defined as having no cognitive concerns for which medical help was sought and scores within the normal range on cognitive tests. Mild cognitive impairment was diagnosed according to published criteria. Study inclusion began in 2013 and is ongoing. Data analysis was performed in January 2017.Global cognitive performance as assessed by the Mini-Mental State Examination (MMSE) and episodic memory performance as assessed by a verbal word learning test. Amyloid aggregation was measured with positron emission tomography or cerebrospinal fluid biomarkers and dichotomized as negative (normal) or positive (abnormal) according to study-specific cutoffs. Generalized estimating equations were used to examine the association between amyloid aggregation and low cognitive scores (MMSE score ≤27 or memory z score≤-1.28) and to assess whether this association was moderated by age, sex, educational level, or apolipoprotein E genotype.Among 2908 persons with normal cognition (mean [SD] age, 67.4 [12.8] years), amyloid positivity was associated with low memory scores after age 70 years (mean difference in amyloid positive vs negative, 4% [95% CI, 0%-7%] at 72 years and 21% [95% CI, 10%-33%] at 90 years) but was not associated with low MMSE scores (mean difference, 3% [95% CI, -1% to 6%], P=.16). Among 4133 patients with MCI (mean [SD] age, 70.2 [8.5] years), amyloid positivity was associated with low memory (mean difference, 16% [95% CI, 12%-20%], P<.001) and low MMSE (mean difference, 14% [95% CI, 12%-17%], P<.001) scores, and this association decreased with age. Low cognitive scores had limited utility for screening of amyloid positivity in persons with normal cognition and those with MCI. In persons with normal cognition, the age-related increase in low memory score paralleled the age-related increase in amyloid positivity with an intervening period of 10 to 15 years.Although low memory scores are an early marker of amyloid positivity, their value as a screening measure for early AD among persons without dementia is limited.
|
|
| 2. |
- Jansen, Willemijn J, et al.
(author)
-
Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum.
- 2022
-
In: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 79:3, s. 228-243
-
Journal article (peer-reviewed)abstract
- One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Alzheimer disease biomarkers detected on PET or in CSF.Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Among the 19097 participants (mean [SD] age, 69.1 [9.8] years; 10148 women [53.1%]) included, 10139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P=.04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P=.004), subjective cognitive decline (9%; 95% CI, 3%-15%; P=.005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P=.004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P=.18).This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies.
|
|
| 3. |
- Jansen, Willemijn J, et al.
(author)
-
Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis.
- 2015
-
In: JAMA. - : American Medical Association (AMA). - 1538-3598 .- 0098-7484. ; 313:19, s. 1924-38
-
Journal article (peer-reviewed)abstract
- Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies.
|
|
| 4. |
- Vos, Stephanie J B, et al.
(author)
-
Modifiable Risk Factors for Prevention ofDementia in Midlife, Late Life and the Oldest-Old: Validation of the LIBRA Index.
- 2017
-
In: Journal of Alzheimer's disease : JAD. - 1875-8908. ; 58:2, s. 537-547
-
Journal article (peer-reviewed)abstract
- Recently, the LIfestyle for BRAin health (LIBRA) index was developed to assess an individual's prevention potential for dementia.We investigated the predictive validity of the LIBRA index for incident dementia in midlife, late life, and the oldest-old.9,387 non-demented individuals were recruited from the European population-based DESCRIPA study. An individual's LIBRA index was calculated solely based on modifiable risk factors: depression, diabetes, physical activity, hypertension, obesity, smoking, hypercholesterolemia, coronary heart disease, and mild/moderate alcohol use. Cox regression was used to test the predictive validity of LIBRA for dementia at follow-up (mean 7.2y, range 1-16).In midlife (55-69y, n=3,256) and late life (70-79y, n=4,320), the risk for dementia increased with higher LIBRA scores. Individuals in the intermediate- and high-risk groups had a higher risk of dementia than those in the low-risk group. In the oldest-old (80-97y, n=1,811), higher LIBRA scores did not increase the risk for dementia.LIBRA might be a useful tool to identify individuals for primary prevention interventions of dementia in midlife, and maybe in late life, but not in the oldest-old.
|
|
| 5. |
- Xu, Jin, et al.
(author)
-
Sex-Specific Metabolic Pathways Were Associated with Alzheimer's Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort
- 2021
-
In: Biomedicines. - : MDPI. - 2227-9059. ; 9:11
-
Journal article (peer-reviewed)abstract
- BACKGROUND: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives.METHODS: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD.RESULTS: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046).CONCLUSIONS: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.
|
|
| 6. |
- Bauckneht, Matteo, et al.
(author)
-
Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern: A European-DLB consortium project
- 2021
-
In: Alzheimer's & Dementia. - : WILEY. - 1552-5260 .- 1552-5279. ; 17:8, s. 1277-1286
-
Journal article (peer-reviewed)abstract
- Introduction We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. Methods Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). Results Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). Discussion These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non-disease-related variables such as age and cognitive reserve.
|
|
| 7. |
- Bischof, Gérard N., et al.
(author)
-
Clinical validity of second-generation tau PET tracers as biomarkers for Alzheimer’s disease in the context of a structured 5-phase development framework
- 2021
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 48:7, s. 2110-2120
-
Research review (peer-reviewed)abstract
- Purpose: In 2017, the Geneva Alzheimer’s disease (AD) strategic biomarker roadmap initiative proposed a framework of the systematic validation AD biomarkers to harmonize and accelerate their development and implementation in clinical practice. Here, we use this framework to examine the translatability of the second-generation tau PET tracers into the clinical context. Methods: All available literature was systematically searched based on a set of search terms that related independently to analytic validity (phases 1–2), clinical validity (phase 3–4), and clinical utility (phase 5). The progress on each of the phases was determined based on scientific criteria applied for each phase and coded as fully, partially, preliminary achieved or not achieved at all. Results: The validation of the second-generation tau PET tracers has successfully passed the analytical phase 1 of the strategic biomarker roadmap. Assay definition studies showed evidence on the superiority over first-generation tau PET tracers in terms of off-target binding. Studies have partially achieved the primary aim of the analytical validity stage (phase 2), and preliminary evidence has been provided for the assessment of covariates on PET signal retention. Studies investigating of the clinical validity in phases 3, 4, and 5 are still underway. Conclusion: The current literature provides overall preliminary evidence on the establishment of the second-generation tau PET tracers into the clinical context, thereby successfully addressing some methodological issues from the tau PET tracer of the first generation. Nevertheless, bigger cohort studies, longitudinal follow-up, and examination of diverse disease population are still needed to gauge their clinical validity.
|
|
| 8. |
- Bocchetta, Martina, et al.
(author)
-
The use of biomarkers for the etiologic diagnosis of MCI in Europe: An EADC survey.
- 2015
-
In: Alzheimer's & Dementia. - : Wiley. - 1552-5279 .- 1552-5260. ; 11:2, s. 195-206
-
Journal article (peer-reviewed)abstract
- We investigated the use of Alzheimer's disease (AD) biomarkers in European Alzheimer's Disease Consortium centers and assessed their perceived usefulness for the etiologic diagnosis of mild cognitive impairment (MCI). We surveyed availability, frequency of use, and confidence in diagnostic usefulness of markers of brain amyloidosis (amyloid positron emission tomography [PET], cerebrospinal fluid [CSF] Aβ42) and neurodegeneration (medial temporal atrophy [MTA] on MR, fluorodeoxyglucose positron emission tomography [FDG-PET], CSF tau). The most frequently used biomarker is visually rated MTA (75% of the 37 responders reported using it "always/frequently") followed by CSF markers (22%), FDG-PET (16%), and amyloid-PET (3%). Only 45% of responders perceive MTA as contributing to diagnostic confidence, where the contribution was rated as "moderate". Seventy-nine percent of responders felt "very/extremely" comfortable delivering a diagnosis of MCI due to AD when both amyloid and neuronal injury biomarkers were abnormal (P < .02 versus any individual biomarker). Responders largely agreed that a combination of amyloidosis and neuronal injury biomarkers was a strongly indicative AD signature.
|
|
| 9. |
- Damian, Marinella, et al.
(author)
-
Single-Domain Amnestic Mild Cognitive Impairment Identified by Cluster Analysis Predicts Alzheimer's Disease in the European Prospective DESCRIPA Study
- 2013
-
In: Dementia and Geriatric Cognitive Disorders. - : S. Karger AG. - 1420-8008 .- 1421-9824. ; 36:1-2, s. 1-19
-
Journal article (peer-reviewed)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
|
|
| 10. |
- Etminani, Kobra, 1984-, et al.
(author)
-
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
-
In: European Journal of Nuclear Medicine and Molecular Imaging. - New York : Springer. - 1619-7070 .- 1619-7089. ; 49, s. 563-584
-
Journal article (peer-reviewed)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.
|
|
