Cognitive Changes associated with Alzheimer’s disease in Down syndrome

AbstractObjectiveIndividuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimer’s disease (AD) however the course of cognitive decline associated with progression to dementia is ill-defined. Data-driven methods can estimate long-term trends from cross-sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS.MethodsWe applied an event-based model to cognitive test data and informant-rated questionnaire data from 283 adults with DS (the largest study of cognitive functioning in DS to date) to estimate the sequence of cognitive decline and individuals’ disease stage.ResultsDecline in tests of memory, sustained attention / motor coordination, and verbal fluency occurred early, demonstrating that AD in DS follows a similar pattern of change to other forms of AD. Later decline was found for informant measures. Using the resulting staging model, we showed that adults with a clinical diagnosis of dementia and those with APOE 3:4 or 4:4 genotype were significantly more likely to be staged later, suggesting the model is valid.InterpretationOur results identify tests of memory and sustained attention may be particularly useful measures to track decline in the preclinical/prodromal stages of AD in DS whereas informant-measures may be useful in later stages (i.e. during conversion to dementia, or post-diagnosis). These results have implications for the selection of outcome measures of treatment trials to delay or prevent cognitive decline due to AD in DS. As clinical diagnoses are generally made late into AD progression, early assessment is essential.

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Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline

10.1101/2019.12.13.19014860 ◽

2019 ◽

Author(s):

Lars Lau Raket ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Disease Progression ◽

Latent Variables ◽

Likelihood Estimation ◽

Disease Stage ◽

Cognitive Test ◽

Nonlinear Mixed Effects Model ◽

Cognitive Capability

AbstractBackgroundThe characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration will not directly reflect the disease state, but rather the effect of the cognitive decline on the patient’s predisease cognitive capability. Patients with high predisease cognitive capabilities tend to score better on cognitive tests relative to patients with low predisease cognitive capabilities at the same disease stage. Thus, a single assessment with a cognitive test is not adequate for determining the stage of an AD patient.Methods and FindingsI developed a joint statistical model that explicitly modeled disease stage, baseline cognition, and the patients’ individual changes in cognitive ability as latent variables. The developed model takes the form of a nonlinear mixed-effects model. Maximum-likelihood estimation in this model induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer’s Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline in AD that spans approximately 15 years from the earliest subjective cognitive deficits to severe AD dementia. It was demonstrated how direct modeling of latent factors that modify the observed data patterns provide a scaffold for understanding disease progression, biomarkers and treatment effects along the continuous time progression of disease.ConclusionsThe suggested framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

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Preclinical Alzheimer's disease

Reviews in Clinical Gerontology ◽

10.1017/s095925981400001x ◽

2014 ◽

Vol 24 (2) ◽

pp. 117-121

Author(s):

P Gil-Gregorio ◽

R Yubero-Pancorbo

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Diagnostic Criteria ◽

Stage I ◽

Disease Stage ◽

Synaptic Dysfunction ◽

Preclinical Alzheimer’S Disease ◽

Cerebral Amyloidosis ◽

Three Stages

SummaryRecently, diagnostic criteria for preclinical Alzheimer's disease have been proposed. These describe and define three stages of disease. Stage I is focused on asymptomatic cerebral amyloidosis. Stage II includes evidence of synaptic dysfunction and/or early degeneration. Finally, stage III of the disease is characterized by the beginning of cognitive decline.

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Impaired Sustained Attention and Error-Induced Stereotypy in the Aged Ts65Dn Mouse: A Mouse Model of Down Syndrome and Alzheimer's Disease.

Behavioral Neuroscience ◽

10.1037/0735-7044.118.6.1196 ◽

2004 ◽

Vol 118 (6) ◽

pp. 1196-1205 ◽

Cited By ~ 26

Author(s):

Lori L. Driscoll ◽

Jenna C. Carroll ◽

Jisook Moon ◽

Linda S. Crnic ◽

David A. Levitsky ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Down Syndrome ◽

Mouse Model ◽

Sustained Attention ◽

Ts65dn Mouse

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Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-210664 ◽

2021 ◽

pp. 1-8

Author(s):

Neda Shafiee ◽

Mahsa Dadar ◽

Simon Ducharme ◽

D. Louis Collins ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cognitive Decline ◽

Functional Decline ◽

Amyloid Β ◽

Cognitive Test ◽

Disease Heterogeneity ◽

Early Alzheimer’S Disease

Background: While both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer’s disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects. Objective: To investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer’s disease. Methods: Data used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-β, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods. Results: Combining both sets of features yields 77%accuracy (81%sensitivity and 75%specificity) to predict cognitive decline at 2 years (74%accuracy at 3 years with 75%sensitivity and 73%specificity). When used to select trial participants, this tool yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25%treatment effect to reduce cognitive decline. Conclusion: When used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.

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Neuropsychiatric Symptoms in Mild Cognitive Impairment and Dementia Due to AD: Relation With Disease Stage and Cognitive Deficits

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.707580 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wietse A. Wiels ◽

Mandy M. J. Wittens ◽

Dieter Zeeuws ◽

Chris Baeken ◽

Sebastiaan Engelborghs

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Depressive Symptoms ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Neuropsychiatric Symptoms ◽

Disease Stage ◽

Research Database ◽

Severe Dementia

Background: The interaction between neuropsychiatric symptoms, mild cognitive impairment (MCI), and dementia is complex and remains to be elucidated. An additive or multiplicative effect of neuropsychiatric symptoms such as apathy or depression on cognitive decline has been suggested. Unraveling these interactions may allow the development of better prevention and treatment strategies. In the absence of available treatments for neurodegeneration, a timely and adequate identification of neuropsychiatric symptom changes in cognitive decline is highly relevant and can help identify treatment targets.Methods: An existing memory clinic-based research database of 476 individuals with MCI and 978 individuals with dementia due to Alzheimer's disease (AD) was reanalyzed. Neuropsychiatric symptoms were assessed in a prospective fashion using a battery of neuropsychiatric assessment scales: Middelheim Frontality Score, Behavioral Pathology in Alzheimer's Disease Rating Scale (Behave-AD), Cohen-Mansfield Agitation Inventory, Cornell Scale for Depression in Dementia (CSDD), and Geriatric Depression Scale (30 items). We subtyped subjects suffering from dementia as mild, moderate, or severe according to their Mini-Mental State Examination (MMSE) score and compared neuropsychiatric scores across these groups. A group of 126 subjects suffering from AD with a significant cerebrovascular component was examined separately as well. We compared the prevalence, nature, and severity of neuropsychiatric symptoms between subgroups of patients with MCI and dementia due to AD in a cross-sectional analysis.Results: Affective and sleep-related symptoms are common in MCI and remain constant in prevalence and severity across dementia groups. Depressive symptoms as assessed by the CSDD further increase in severe dementia. Most other neuropsychiatric symptoms (such as agitation and activity disturbances) progress in parallel with severity of cognitive decline. There are no significant differences in neuropsychiatric symptoms when comparing “pure” AD to AD with a significant vascular component.Conclusion: Neuropsychiatric symptoms such as frontal lobe symptoms, psychosis, agitation, aggression, and activity disturbances increase as dementia progresses. Affective symptoms such as anxiety and depressive symptoms, however, are more frequent in MCI than mild dementia but otherwise remain stable throughout the cognitive spectrum, except for an increase in CSDD score in severe dementia. There is no difference in neuropsychiatric symptoms when comparing mixed dementia (defined here as AD + significant cerebrovascular disease) to pure AD.

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Identifying Sensitive Measures of Cognitive Decline at Different Clinical Stages of Alzheimer’s Disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617720000934 ◽

2020 ◽

pp. 1-13

Author(s):

Roos J. Jutten ◽

Sietske A.M. Sikkes ◽

Rebecca E. Amariglio ◽

Rachel F. Buckley ◽

Michael J. Properzi ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Neuropsychological Tests ◽

Clinical Stage ◽

Word List ◽

Time Interval ◽

Cognitive Changes ◽

Clinical Stages ◽

Stage 1

Abstract Objective: Alzheimer’s disease (AD) studies are increasingly targeting earlier (pre)clinical populations, in which the expected degree of observable cognitive decline over a certain time interval is reduced as compared to the dementia stage. Consequently, endpoints to capture early cognitive changes require refinement. We aimed to determine the sensitivity to decline of widely applied neuropsychological tests at different clinical stages of AD as outlined in the National Institute on Aging – Alzheimer’s Association (NIA-AA) research framework. Method: Amyloid-positive individuals (as determined by positron emission tomography or cerebrospinal fluid) with longitudinal neuropsychological assessments available were included from four well-defined study cohorts and subsequently classified among the NIA-AA stages. For each stage, we investigated the sensitivity to decline of 17 individual neuropsychological tests using linear mixed models. Results: 1103 participants (age = 70.54 ± 8.7, 47% female) were included: n = 120 Stage 1, n = 206 Stage 2, n = 467 Stage 3 and n = 309 Stage 4. Neuropsychological tests were differentially sensitive to decline across stages. For example, Category Fluency captured significant 1-year decline as early as Stage 1 (β = −.58, p < .001). Word List Delayed Recall (β = −.22, p < .05) and Trail Making Test (β = 6.2, p < .05) became sensitive to 1-year decline in Stage 2, whereas the Mini-Mental State Examination did not capture 1-year decline until Stage 3 (β = −1.13, p < .001) and 4 (β = −2.23, p < .001). Conclusions: We demonstrated that commonly used neuropsychological tests differ in their ability to capture decline depending on clinical stage within the AD continuum (preclinical to dementia). This implies that stage-specific cognitive endpoints are needed to accurately assess disease progression and increase the chance of successful treatment evaluation in AD.

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Longitudinal amyloid and tau accumulation in autosomal dominant Alzheimer’s disease: findings from the Colombia-Boston (COLBOS) biomarker study

Alzheimer s Research & Therapy ◽

10.1186/s13195-020-00765-5 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Justin S. Sanchez ◽

Bernard J. Hanseeuw ◽

Francisco Lopera ◽

Reisa A. Sperling ◽

Ana Baena ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Symptom Onset ◽

Autosomal Dominant ◽

Amyloid Β ◽

Cognitive Changes ◽

Cross Sectional ◽

Rates Of Change ◽

Autosomal Dominant Alzheimer’S Disease

Abstract Background Neuroimaging studies of autosomal dominant Alzheimer’s disease (ADAD) enable characterization of the trajectories of cerebral amyloid-β (Aβ) and tau accumulation in the decades prior to clinical symptom onset. Longitudinal rates of regional tau accumulation measured with positron emission tomography (PET) and their relationship with other biomarker and cognitive changes remain to be fully characterized in ADAD. Methods Fourteen ADAD mutation carriers (Presenilin-1 E280A) and 15 age-matched non-carriers from the Colombian kindred underwent 2–3 sessions of Aβ (11C-Pittsburgh compound B) and tau (18F-flortaucipir) PET, structural magnetic resonance imaging, and neuropsychological evaluation over a 2–4-year follow-up period. Annualized rates of change for imaging and cognitive variables were compared between carriers and non-carriers, and relationships among baseline measurements and rates of change were assessed within carriers. Results Longitudinal measurements were consistent with a sequence of ADAD-related changes beginning with Aβ accumulation (16 years prior to expected symptom onset, EYO), followed by entorhinal cortex (EC) tau (9 EYO), neocortical tau (6 EYO), hippocampal atrophy (6 EYO), and cognitive decline (4 EYO). Rates of tau accumulation among carriers were most rapid in parietal neocortex (~ 9%/year). EC tau PET signal at baseline was a significant predictor of subsequent neocortical tau accumulation and cognitive decline within carriers. Conclusions Our results are consistent with the sequence of biological changes in ADAD implied by cross-sectional studies and highlight the importance of EC tau as an early biomarker and a potential link between Aβ burden and neocortical tau accumulation in ADAD.

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Semantic network abnormality predicts rate of cognitive decline in patients with probable Alzheimer's disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617700000291 ◽

1995 ◽

Vol 1 (3) ◽

pp. 297-303 ◽

Cited By ~ 28

Author(s):

Agnes S. Chan ◽

David P. Salmon ◽

Nelson Butters ◽

Shannon A. Johnson

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Rating Scale ◽

Semantic Network ◽

Linear Regression Analysis ◽

Semantic Knowledge ◽

Cognitive Changes ◽

Structural Deterioration ◽

The Difference

AbstractThe present study examined the relationship between rate of cognitive decline in patients with Alzheimer's disease (AD) and the integrity of the network of associations that comprise their semantic knowledge. The integrity of the semantic network of 12 AD patients was determined by comparing their networks to a standard normal control network derived with Pathfinder analysis, a multidimensional graphic analysis technique. A simple linear regression analysis, comparing the degree of semantic network deterioration with rate of cognitive decline as measured by the difference between the Dementia Rating Scale (DRS) scores obtained at the time of the testing of semantic knowledge (Year 1) and one year later (Year 2), was highly significant (r2= .84;p< .001). These results suggest that a sensitive measure of the structural deterioration of semantic knowledge may be useful for predicting the rate of progression of cognitive changes in patients with AD. (JINS, 1995,I, 297–303.)

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Grey matter network trajectories across the Alzheimer’s disease continuum and relation to cognition

Brain Communications ◽

10.1093/braincomms/fcaa177 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Ellen Dicks ◽

Lisa Vermunt ◽

Wiesje M van der Flier ◽

Frederik Barkhof ◽

Philip Scheltens ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Executive Functioning ◽

Cognitive Decline ◽

Grey Matter ◽

Disease Stage ◽

Network Measures ◽

Cognitive Continuum ◽

Alzheimer’S Disease Dementia ◽

Over Time

Abstract Biomarkers are needed to monitor disease progression in Alzheimer’s disease. Grey matter network measures have such potential, as they are related to amyloid aggregation in cognitively unimpaired individuals and to future cognitive decline in predementia Alzheimer’s disease. Here, we investigated how grey matter network measures evolve over time within individuals across the entire Alzheimer’s disease cognitive continuum and whether such changes relate to concurrent decline in cognition. We included 190 cognitively unimpaired, amyloid normal (controls) and 523 individuals with abnormal amyloid across the cognitive continuum (preclinical, prodromal, Alzheimer’s disease dementia) from the Alzheimer’s Disease Neuroimaging Initiative and calculated single-subject grey matter network measures (median of five networks per individual over 2 years). We fitted linear mixed models to investigate how network measures changed over time and whether such changes were associated with concurrent changes in memory, language, attention/executive functioning and on the Mini-Mental State Examination. We further assessed whether associations were modified by baseline disease stage. We found that both cognitive functioning and network measures declined over time, with steeper rates of decline in more advanced disease stages. In all cognitive stages, decline in network measures was associated with concurrent decline on the Mini-Mental State Examination, with stronger effects for individuals closer to Alzheimer’s disease dementia. Decline in network measures was associated with concurrent cognitive decline in different cognitive domains depending on disease stage: In controls, decline in networks was associated with decline in memory and language functioning; preclinical Alzheimer’s disease showed associations of decline in networks with memory and attention/executive functioning; prodromal Alzheimer’s disease showed associations of decline in networks with cognitive decline in all domains; Alzheimer’s disease dementia showed associations of decline in networks with attention/executive functioning. Decline in grey matter network measures over time accelerated for more advanced disease stages and was related to concurrent cognitive decline across the entire Alzheimer’s disease cognitive continuum. These associations were disease stage dependent for the different cognitive domains, which reflected the respective cognitive stage. Our findings therefore suggest that grey matter measures are helpful to track disease progression in Alzheimer’s disease.

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