Dataset Information

Secondary prevention of Alzheimer's dementia: neuroimaging contributions.

ABSTRACT:

Background

In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on markers of molecular pathology and rates of neurodegeneration. This is of particular importance in pre-dementia AD trials, where clinical outcomes have a limited ability to detect treatment effects within the typical time frame of a clinical trial. We review available evidence for the use of neuroimaging in clinical trials in pre-dementia AD. We appraise currently available imaging markers for subject selection, stratification, outcome measures, and safety in the context of such populations.

Main body

Amyloid positron emission tomography (PET) is a validated in-vivo marker of fibrillar amyloid plaques. It is appropriate for inclusion in trials targeting the amyloid pathway, as well as to monitor treatment target engagement. Amyloid PET, however, has limited ability to stage the disease and does not perform well as a prognostic marker within the time frame of a pre-dementia AD trial. Structural magnetic resonance imaging (MRI), providing markers of neurodegeneration, can improve the identification of subjects at risk of imminent decline and hence play a role in subject inclusion. Atrophy rates (either hippocampal or whole brain), which can be reliably derived from structural MRI, are useful in tracking disease progression and have the potential to serve as outcome measures. MRI can also be used to assess comorbid vascular pathology and define homogeneous groups for inclusion or for subject stratification. Finally, MRI also plays an important role in trial safety monitoring, particularly the identification of amyloid-related imaging abnormalities (ARIA). Tau PET to measure neurofibrillary tangle burden is currently under development. Evidence to support the use of advanced MRI markers such as resting-state functional MRI, arterial spin labelling, and diffusion tensor imaging in pre-dementia AD is preliminary and requires further validation.

Conclusion

We propose a strategy for longitudinal imaging to track early signs of AD including quantitative amyloid PET and yearly multiparametric MRI.

SUBMITTER: Ten Kate M

PROVIDER: S-EPMC6208183 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Secondary prevention of Alzheimer's dementia: neuroimaging contributions.

Ten Kate Mara M Ingala Silvia S Schwarz Adam J AJ Fox Nick C NC Chételat Gaël G van Berckel Bart N M BNM Ewers Michael M Foley Christopher C Gispert Juan Domingo JD Hill Derek D Irizarry Michael C MC Lammertsma Adriaan A AA Molinuevo José Luis JL Ritchie Craig C Scheltens Philip P Schmidt Mark E ME Visser Pieter Jelle PJ Waldman Adam A Wardlaw Joanna J Haller Sven S Barkhof Frederik F

Alzheimer's research & therapy 20181030 1

<h4>Background</h4>In Alzheimer's disease (AD), pathological changes may arise up to 20 years before the onset of dementia. This pre-dementia window provides a unique opportunity for secondary prevention. However, exposing non-demented subjects to putative therapies requires reliable biomarkers for subject selection, stratification, and monitoring of treatment. Neuroimaging allows the detection of early pathological changes, and longitudinal imaging can assess the effect of interventions on mark ...[more]

PMID: 30376881

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Project description:The diagnosis of Alzheimer's disease (AD) in the oldest-old is complicated by the increasing prevalence of age-related neurofibrillary tangles, plaques and non-AD pathologies such as cerebrovascular disease (CVD), hippocampal sclerosis (HS), aging-related tau astrogliopathy (ARTAG), as well as TDP-43 and Lewy pathology. The contribution of these non-AD pathologies to dementia and cognitive resilience is unclear. We assessed the level of AD neuropathologic change (ADNPC) and non-AD pathology in 185 participants enrolled in The 90+ Study with available cognitive assessments and brain tissue. Logistic regression models-adjusting for age, sex and education-determined the association between each pathology and dementia or between subgroups. 53% had dementia, primarily AD or mixed AD; 23% had cognitive impairment without dementia (CIND); 23% were not impaired. Both AD and non-AD pathology was prevalent. 100% had tangles, 81% had plaques, and both tangles and plaques associated with dementia. ARTAG distributed across limbic (70%), brainstem (39%) and cortical regions (24%). 49% had possible CVD and 26% had definite CVD, while HS was noted in 15%. Cortical ARTAG, CVD and HS were each associated with dementia, but limbic and brainstem ARTAGs were not. TDP-43 and Lewy pathologies were found in 36 and 17% and both associated with dementia. No pathology distinguished CIND and the not impaired. By NIA-AA criteria and dementia status, the cohort was subdivided into four groups: those with minimal ADNPC included the not dementia (ND) and Not AD dementia groups; and those with significant ADNPC included the Resilient without dementia and AD dementia groups. Compared to the ND group, the Not AD dementia group had more HS, cortical ARTAG, TDP-43, and Lewy pathology. Compared to the AD dementia group, the Resilient group had less CVD, no HS and less cortical ARTAG, TDP-43 and Lewy pathology. Our findings imply that reductions in non-AD pathologies including CVD contribute to cognitive resilience in the oldest-old.

Project description:In this work we aimed to identify neural predictors of the efficacy of multimodal rehabilitative interventions in AD-continuum patients in the attempt to identify ideal candidates to improve the treatment outcome. Subjects in the AD continuum who participated in a multimodal rehabilitative treatment were included in the analysis [n = 82, 38 Males, mean age = 76 ± 5.30, mean education years = 9.09 ± 3.81, Mini Mental State Examination (MMSE) mean score = 23.31 ± 3.81]. All subjects underwent an MRI acquisition (1.5T) at baseline (T0) and a neuropsychological evaluation before (T0) and after intervention (T1). All subjects underwent an intensive multimodal cognitive rehabilitation (8-10 weeks). The MMSE and Neuropsychiatric Inventory (NPI) scores were considered as the main cognitive and behavioral outcome measures, and Delta change scores (T1-T0) were categorized in Improved (ΔMMSE > 0; ΔNPI < 0) and Not Improved (ΔMMSE ≤ 0; ΔNPI ≥ 0). Logistic Regression (LR) and Random Forest classification models were performed including neural markers (Medial Temporal Brain; Posterior Brain (PB); Frontal Brain (FB), Subcortical Brain indexes), neuropsychological (MMSE, NPI, verbal fluencies), and demographical variables (sex, age, education) at baseline. More than 50% of patients showed a positive effect of the treatment (ΔMMSE > 0: 51%, ΔNPI < 0: 52%). LR model on ΔMMSE (Improved vs. Not Improved) indicate a predictive role for MMSE score (p = 0.003) and PB index (p = 0.005), especially the right PB (p = 0.002) at baseline. The Random Forest analysis correctly classified 77% of cognitively improved and not improved AD patients. Concerning the NPI, LR model on ΔNPI (Improved vs. Not Improved) showed a predictive role of sex (p = 0.002), NPI (p = 0.005), PB index (p = 0.006), and FB index (p = 0.039) at baseline. The Random Forest reported a classification accuracy of 86%. Our data indicate that cognitive and behavioral status alone are not sufficient to identify best responders to a multidomain rehabilitation treatment. Increased neural reserve, especially in the parietal areas, is also relevant for the compensatory mechanisms activated by rehabilitative treatment. These data are relevant to support clinical decision by identifying target patients with high probability of success after rehabilitative programs on cognitive and behavioral functioning.

Dataset Information

Secondary prevention of Alzheimer's dementia: neuroimaging contributions.

Background

Main body

Conclusion

Publications

Secondary prevention of Alzheimer's dementia: neuroimaging contributions.

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