Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers: An INSIGHT‐preAD study

Teipel, Stefan J.; Grothe, Michel J.; Epelbaum, Stéphane; Sambati, Luisa; Toschi, Nicola; Greicius, Michael D.; Dubois, Bruno; Harald, Hampel; Audrain, C.; Auffret, Alexandra; Bakardjian, Hovagim; Baldacci, Filippo; Batrancourt, Bénédicte; Benakki, I.; Benali, Habib; Bertin, Hugo; Bertrand, Anne; Boukadida, Laurie; Cacciamani, Federica; Causse, V.; Cavedo, Enrica; Touil, S. Cherif; Chiesa, Patrizia Andrea; Colliot, Olivier; Barba, G. Dalla; Depaulis, M.; Santos, Antonio; Dubois, M.; Fontaine, Bertrand; Francisque, Hélène; Geoffroy, Gagliardi; Genin, A.; Genthon, Rémy; Glasman, Pauline; Gombert, F.; Habert, Marie‐Odile; Hampel, Harald; Hewa, H.; Houot, Marion; Jungalee, N.; Kas, Aurélie; Kilani, M.; Corte, Valentina La; Roy, Florence Le; Lehericy, Stéphane; Letondor, Claire; Lévy, Marcel; Lista, Simone; Lowrey, Mark; Ly, Juliette; Makiese, O.; Masetti, I.; Mendes, Alexandre; Metzinger, Christiane; Michon, Agnès; Mochel, Fanny; Arab, R. Nait; Nyasse, Francis; Perrin, Claire; Poirier, Frédéric J.A.M.; Poisson, Catherine; Potier, Marie‐Claude; Ratovohery, Stéphie; Revillon, Marie; Rojkova, Katrine; Santos-Andrade, K.; Schindler, Rachel; Servera, M.C.; Seux, L.; Simon, V.; Skovronsky, Daniel; Thiébaut, M.; Uspenskaya, Olga; Vlaincu, M.

doi:10.1016/j.jalz.2018.04.004

Cited by 26 publications

(22 citation statements)

References 48 publications

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“…Similarly, one of these studies observed a decline in the class which started highest, while there was an increase in the class starting lowest [33]. Another study found that all classes, except for the worst class, improved in performance over time [34]. Finally, one slope-based study observed similar cognitive function between classes at baseline, but they diverged over follow-up [35].…”

Section: Number and Shape Of Trajectoriesmentioning

confidence: 84%

“…The strongest evidence for this 'stable-high' class was for a measure of general cognitive function, but this was also because most studies focused on this cognitive assessment. However, the 'stable-high' class was also observed in terms of some other cognitive domains such as memory, processing speed, and executive function [23,34,43,45,[49][50][51]. Of note, these are cognitive domains that are thought to decline more rapidly than others during the 'normal' aging process [2].…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the study of Yokoyama et al was the only study that reported on genetic factors of cognitive maintenance [29]. There is a total of 11 studies that looked at biological measures in relation to cognitive trajectories including neuroimaging, genetic, blood and autoptic biomarkers [20,28,29,31,34,35,38,43,50,52,69]. However, APOE 4 allele and amyloid load are the only two biomarkers which were tested in two or more studies.…”

Section: Discussionmentioning

confidence: 99%

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Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Phyo

Alharbi

et al. 2020

ADR

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Background: Cognitive aging is a dynamic process in late life with significant heterogeneity across individuals. Objective: To review the evidence for latent classes of cognitive trajectories and to identify the associated predictors and outcomes. Methods: A systematic search was performed in MEDLINE and EMBASE for articles that identified two or more cognitive trajectories in adults. The study was conducted following the PRISMA statement. Results: Thirty-seven studies were included, ranging from 219 to 9,704 participants, with a mean age of 60 to 93.4 years. Most studies (n = 30) identified distinct cognitive trajectories using latent class growth analysis. The trajectory profile commonly consisted of three to four classes with progressively decreasing baseline and increasing rate of decline—a ‘stable-high’ class characterized as maintenance of cognitive function at high level, a ‘minor-decline’ class or ‘stable-medium’ class that declines gradually over time, and a ‘rapid-decline’ class with the steepest downward slope. Generally, membership of better classes was predicted by younger age, being female, more years of education, better health, healthier lifestyle, higher social engagement and lack of genetic risk variants. Some factors (e.g., education) were found to be associated with cognitive function over time only within individual classes. Conclusion: Cognitive aging in late life is a dynamic process with significant inter-individual variability. However, it remains unclear whether similar patterns of cognitive aging are observed across all cognitive domains. Further research into unique factors which promote the maintenance of high-cognitive function is needed to help inform public policy.

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Section: Number and Shape Of Trajectoriesmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Phyo

Alharbi

et al. 2020

ADR

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“…In this context, AD-related neuropathological markers have been observed several years before clinical manifestation of memory symptoms (Braak and Braak, 1996;Delacourte et al, 1999;Morris et al, 1996;Serrano-Pozo et al, 2011;Mosconi et al, 2007), which suggests that AD development could be predicted before clinical onset via in vivo biomarker analysis (e.g. PET and MR imaging as well as blood or cerebrospinal fluid (CSF) biomarkers) (Markesbery, 2010;Baldacci et al, 2018;Teipel et al, 2018). Magnetic resonance imaging (MRI) has garnered interest in AD diagnosis as well as prediction of MCI to AD conversion.…”

Section: Introductionmentioning

confidence: 99%

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease

Spasov¹,

Passamonti²,

Duggento

et al. 2018

Preprint

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Some forms of mild cognitive impairment (MCI) can be the clinical precursor of severe dementia like Alzheimer's disease (AD), while other types of MCI tend to remain stable over-time and do not progress to AD pathology. To choose an effective and personalized treatment for AD, we need to identify which MCI patients are at risk of developing AD and which are not. Here, we present a novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying those people with MCI who have a high likelihood of developing AD. Our deep learning procedures combine structural magnetic resonance imaging (MRI), demographic, neuropsychological, and APOe4 genotyping data as input measures. The most novel characteristics of our machine learning model compared to previous ones are as follows: 1) multi-tasking, in the sense that our deep learning model jointly learns to simultaneously predict both MCI to AD conversion, and AD vs healthy classification which facilitates the relevant feature extraction for prognostication; 2) the neural network classifier employs relatively few parameters compared to other deep learning architectures (we use ~550,000 network parameters, orders of magnitude lower than other network designs) without compromising network complexity and hence significantly limits data-overfitting; 3) both structural MRI images and warp field characteristics, which quantify the amount of volumetric change compared to the common template, were used as separate input streams to extract as much information as possible from the MRI data. All the analyses were performed on a subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, for a total of n=785 participants (192 AD, 409 MCI, and184 healthy controls (HC)). We found that the most predictive combination of inputs included the structural MRI images and the demographic, neuropsychological, and APOe4 data, while the warp field metric added little predictive value. We achieved an area under the ROC curve (AUC) of 0.925 with a 10-fold cross-validated accuracy of 86%, a sensitivity of 87.5% and specificity of 85% in classifying MCI patients who developed AD in three years' time from those individuals showing stable MCI over the same time-period. To the best of our knowledge, this is the highest performance reported on a test set achieved in the literature using similar data. The same network provided an AUC of 1 and 100% accuracy, sensitivity and specificity when classifying NC from AD. We also demonstrated that our classification framework was robust to different co-registration templates and possibly irrelevant features / image sections. Our approach is flexible and can in principle integrate other imaging modalities, such as PET, and a more diverse group of clinical data. The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of any medical condition utilizing multi-modal imaging a...

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“…Numerous studies suggest that SCD is associated with an increased likelihood of AD-related biomarker abnormalities and with increased risk of future cognitive decline and developing clinical DAD [18][19][20][21][22]. A recent study found initial evidence that cholinergic BF atrophy is already detectable in cohorts with subjective cognitive complaints [23], and may be associated with both AD biomarkers and a lower cognitive performance trajectory [23,24]. Especially, SCD in combination with proven AD pathology may re ect progression from stage 1 (asymptomatic) to stage 2 (transitional cognitive impairment) of the Alzheimer's continuum (according to the recent National Institute on Aging-Alzheimer's Association [NIA-AA] Research Framework [25].…”

Section: Introductionmentioning

confidence: 99%

Basal forebrain atrophy along the Alzheimer’s disease spectrum and its relevance for subjective cognitive decline

Li¹,

Grothe²,

Daamen³

et al. 2020

Preprint

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Background There is growing evidence in the literature that the cholinergic basal forebrain might be one of the earliest affected structures in Alzheimer’s disease (AD). Recent data suggest that individuals with preclinical Alzheimer’s pathology already show atrophy of the posterior nucleus basalis of Meynert and that this even precedes the atrophy of the entorhinal cortex. Here we investigated whether basal forebrain volume reductions might not only be detectable in the mild cognitive impairment (MCI) and dementia stage of AD, but also in subjective cognitive decline (SCD) individuals who represent an at-risk population for preclinical AD, and examine the relationship with cognitive performance and amyloid-beta pathology. Methods Basal forebrain volumes of 341 participants from the multi-center German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study, including 135 healthy controls, 110 SCD, 60 MCI, and 36 AD, were analyzed using high-resolution T1-weighted images. Healthy controls and SCD participants were further grouped into amyloid-positive and amyloid-negative cases according to their cerebrospinal fluid Aβ42/40 ratio. Associations between basal forebrain volume, neuropsychological performance, and amyloid load were evaluated. Results Apart from confirming progressive basal forebrain atrophy from MCI to AD, atrophy of the posterior of nucleus basalis of Meynert was also observed in subjective cognitive decline with confirmed evidence for preclinical Alzheimer’s pathology, based on the Aβ42/40 ratio. This atrophy was neither evident in subjects with SCD without amyloid pathology nor in healthy controls with amyloid pathology. Additionally, the volume of the posterior of nucleus basalis of Meynert was significantly correlated with amyloid Aβ42/40 ratio in SCD but not in healthy controls. Conclusion Our results confirm that basal forebrain atrophy occurs early along the Alzheimer’s disease trajectory. The observed volume reduction of the cholinergic basal forebrain in Aβ-positive participants with subjective cognitive complains and the absence of any volume reductions in the Aβ-positive healthy controls suggests that these ‘subjective cognitive decline’ symptoms reflect progression from stage 1 (asymptomatic) to stage 2 (transitional cognitive impairment) of the Alzheimer’s continuum (according to the recent National Institute on Aging-Alzheimer’s Association Research Framework), revealing the beginning neurodegeneration on a macroscopic level.

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Effect of Alzheimer's disease risk and protective factors on cognitive trajectories in subjective memory complainers: An INSIGHT‐preAD study

Abstract: Distinct classes of cognitive trajectories were associated with risk and protective factors of AD. These associations support the notion that the identified cognitive trajectories reflect different risk for AD that may be useful for selecting high-risk individuals for intervention trials.

Cited by 26 publications

References 48 publications

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

Distinct Cognitive Trajectories in Late Life and Associated Predictors and Outcomes: A Systematic Review

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease

Basal forebrain atrophy along the Alzheimer’s disease spectrum and its relevance for subjective cognitive decline

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