INTRODUCTION
This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) ...biomarkers.
METHODS
AD polygenic risk scores (PRS) were tested for association with DS‐related traits.
RESULTS
The AD risk PRS was associated with disease status in several cohorts of sporadic late‐ and early‐onset and familial late‐onset AD, but not in familial early‐onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE, p = 2.84 × 10−4; PRS excluding APOE, PRSnonAPOE, p = 1.60 × 10−2). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS.
DISCUSSION
These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits.
Highlights
Examination of the polygenic risk of AD in DS presented here is the first of its kind.
AD PRS influences memory aspects in DS individuals, independently of APOE genotype.
These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population.
APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Background
Past attempts to characterize the earliest cognitive changes as individuals with Down Syndrome (DS) transition from cognitively stable to mild cognitive impairment (MCI) have been ...equivocal (Garcia‐Alba et al., 2019; Lautarescu et al., 2017). Difficulties identifying MCI in this population are complicated by variability in pre‐morbid cognitive abilities, the use of neuropsychological tests that were created for the neurotypical population, and participants scoring at floor on the baseline assessment (Krinsky‐McHale and Silverman, 2013).
Method
We examined data from 151 individuals with Down Syndrome (M age=50.25, SD age=6.94). Their pre‐morbid level of intellectual impairment ranged from mild to severe. All participants received comprehensive evaluations. Following data collection, the clinical status of each participant was rated at consensus review that considered performance on a core neuropsychological test battery and the clinical data for each participant. Data from the non‐demented and MCI groups are examined: Cognitive Stable (N=107, 70.9%) and MCI‐DS (N=44, 29.1%). The full battery consists of 27 subtests that were hypothesized a priori to measure five cognitive domains: language, memory, executive function, visuospatial reasoning, and motor coordination.
Result
Factor analysis revealed 7 principal components that maximally discriminated between test scores in older adults with DS who have not reached clinical AD status: (1) general intelligence (2) sensorimotor, (3) memory, (4) language comprehension and expression, (5) executive function/speed, (6) attention/language expression, and (7) visuomotor. Cluster analysis for the MCI group produced 3 distinct groups: (1) dysexecutive (n=4), (2) dysnomic/visuospatial impaired (n=28), and (3) amnestic/motor impaired (n=12).
Conclusion
The neuropsychological battery assesses 7 distinct cognitive functions in older adults with DS. It can also capture cognitive decline, as we were able to empirically identify three distinct neuropsychological subtypes of MCI: amnestic/visuomotor impaired, dysexecutive, and dysnomic. These subtypes are generally consistent with those that have been found within the neurotypical population (Edmonds et al., 2015; Dick et al., 2016), strengthening the evidence that AD has a similar course in the DS population and late onset AD.
Full text
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Abstract
Background
Past attempts to characterize the earliest cognitive changes as individuals with Down Syndrome (DS) transition from cognitively stable to mild cognitive impairment (MCI) have been ...equivocal (Garcia‐Alba et al., 2019; Lautarescu et al., 2017). Difficulties identifying MCI in this population are complicated by variability in pre‐morbid cognitive abilities, the use of neuropsychological tests that were created for the neurotypical population, and participants scoring at floor on the baseline assessment (Krinsky‐McHale and Silverman, 2013).
Method
We examined data from 151 individuals with Down Syndrome (M age=50.25, SD age=6.94). Their pre‐morbid level of intellectual impairment ranged from mild to severe. All participants received comprehensive evaluations. Following data collection, the clinical status of each participant was rated at consensus review that considered performance on a core neuropsychological test battery and the clinical data for each participant. Data from the non‐demented and MCI groups are examined: Cognitive Stable (N=107, 70.9%) and MCI‐DS (N=44, 29.1%). The full battery consists of 27 subtests that were hypothesized
a priori
to measure five cognitive domains: language, memory, executive function, visuospatial reasoning, and motor coordination.
Result
Factor analysis revealed 7 principal components that maximally discriminated between test scores in older adults with DS who have not reached clinical AD status: (1) general intelligence (2) sensorimotor, (3) memory, (4) language comprehension and expression, (5) executive function/speed, (6) attention/language expression, and (7) visuomotor. Cluster analysis for the MCI group produced 3 distinct groups: (1) dysexecutive (n=4), (2) dysnomic/visuospatial impaired (n=28), and (3) amnestic/motor impaired (n=12).
Conclusion
The neuropsychological battery assesses 7 distinct cognitive functions in older adults with DS. It can also capture cognitive decline, as we were able to empirically identify three distinct neuropsychological subtypes of MCI: amnestic/visuomotor impaired, dysexecutive, and dysnomic. These subtypes are generally consistent with those that have been found within the neurotypical population (Edmonds et al., 2015; Dick et al., 2016), strengthening the evidence that AD has a similar course in the DS population and late onset AD.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
INTRODUCTION
Virtually all people with Down syndrome (DS) develop neuropathology associated with Alzheimer's disease (AD). Atrophy of the hippocampus and entorhinal cortex (EC), as well as elevated ...plasma concentrations of neurofilament light chain (NfL) protein, are markers of neurodegeneration associated with late‐onset AD. We hypothesized that hippocampus and EC gray matter loss and increased plasma NfL concentrations are associated with memory in adults with DS.
METHODS
T1‐weighted structural magnetic resonance imaging (MRI) data were collected from 101 participants with DS. Hippocampus and EC volume, as well as EC subregional cortical thickness, were derived. In a subset of participants, plasma NfL concentrations and modified Cued Recall Test scores were obtained. Partial correlation and mediation were used to test relationships between medial temporal lobe (MTL) atrophy, plasma NfL, and episodic memory.
RESULTS
Hippocampus volume, left anterolateral EC (alEC) thickness, and plasma NfL were correlated with each other and were associated with memory. Plasma NfL mediated the relationship between left alEC thickness and memory as well as hippocampus volume and memory.
DISCUSSION
The relationship between MTL gray matter and memory is mediated by plasma NfL levels, suggesting a link between neurodegenerative processes underlying axonal injury and frank gray matter loss in key structures supporting episodic memory in people with DS.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
INTRODUCTION
People with Down syndrome (DS) have a 75% to 90% lifetime risk of Alzheimer's disease (AD). AD pathology begins a decade or more prior to onset of clinical AD dementia in people with DS. ...It is not clear if plasma biomarkers of AD pathology are correlated with early cognitive and functional impairments in DS, and if these biomarkers could be used to track the early stages of AD in DS or to inform inclusion criteria for clinical AD treatment trials.
METHODS
This large cross‐sectional cohort study investigated the associations between plasma biomarkers of amyloid beta (Aβ)42/40, total tau, and neurofilament light chain (NfL) and cognitive (episodic memory, visual–motor integration, and visuospatial abilities) and functional (adaptive behavior) impairments in 260 adults with DS without dementia (aged 25–81 years).
RESULTS
In general linear models lower plasma Aβ42/40 was related to lower visuospatial ability, higher total tau was related to lower episodic memory, and higher NfL was related to lower visuospatial ability and lower episodic memory.
DISCUSSION
Plasma biomarkers may have utility in tracking AD pathology associated with early stages of cognitive decline in adults with DS, although associations were modest.
Highlights
Plasma Alzheimer's disease (AD) biomarkers correlate with cognition prior to dementia in Down syndrome.
Lower plasma amyloid beta 42/40 was related to lower visuospatial abilities.
Higher plasma total tau and neurofilament light chain were associated with lower cognitive performance.
Plasma biomarkers show potential for tracking early stages of AD symptomology.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Accurate identification of the prodromal stage of Alzheimer's disease (AD), known as mild cognitive impairment (MCI), in adults with Down syndrome (MCI-DS) has been challenging because there are no ...established diagnostic criteria that can be applied for people with lifelong intellectual disabilities (ID). As such, the sequence of cognitive decline in adults with DS has been difficult to ascertain, and it is possible that domain constructs characterizing cognitive function in neurotypical adults do not generalize to this high-risk population. The present study examined associations among multiple measures of cognitive function in adults with DS, either prior to or during the prodromal stage of AD to determine, through multiple statistical techniques, the measures that reflected the same underlying domains of processing. Participants included 144 adults with DS 40-82 years of age, all enrolled in a larger, multidisciplinary study examining biomarkers of AD in adults with DS. All participants had mild or moderate lifelong intellectual disabilities. Overall AD-related clinical status was rated for each individual during a personalized consensus conference that considered performance as well as health status, with 103 participants considered cognitively stable (CS) and 41 to have MCI-DS. Analyses of 17 variables derived from 10 tests of cognition indicated that performance reflected three underlying factors: language/executive function, memory, and visuomotor. All three domain composite scores significantly predicted MCI-DS status. Based upon path modeling, the language/executive function composite score was the most affected by prodromal AD. However, based upon structural equation modeling, tests assessing the latent construct of memory were the most impacted, followed by those assessing visuomotor, and then those assessing language/executive function. Our study provides clear evidence that cognitive functioning in older adults with DS can be characterized at the cognitive domain level, but the statistical methods selected and the inclusion or exclusion of certain covariates may lead to different conclusions. Best practice requires investigators to understand the internal structure of their variables and to provide evidence that their variables assess their intended constructs.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Primary care integration of Down syndrome (DS)-specific dementia screening is strongly advised. The current study employed principal components analysis (PCA) and classification and regression tree ...(CART) analyses to identify an abbreviated battery for dementia classification. Scale- and subscale-level scores from 141 participants (no dementia
= 68; probable Alzheimer's disease
= 73), for the Severe Impairment Battery (SIB), Dementia Scale for People with Learning Disabilities (DLD), and Vineland Adaptive Behavior Scales-Second Edition (Vineland-II) were analyzed. Two principle components (PC1, PC2) were identified with the odds of a probable dementia diagnosis increasing 2.54 times per PC1 unit increase and by 3.73 times per PC2 unit increase. CART analysis identified that the DLD sum of cognitive scores (SCS < 35 raw) and Vineland-II community subdomain (<36 raw) scores best classified dementia. No significant difference in the PCA versus CART area under the curve (AUC) was noted (D(65.196) = -0.57683;
= 0.57; PCA AUC = 0.87; CART AUC = 0.91). The PCA sensitivity was 80% and specificity was 70%; CART was 100% and specificity was 81%. These results support an abbreviated dementia screening battery to identify at-risk individuals with DS in primary care settings to guide specialized diagnostic referral.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Down syndrome (DS) is now viewed as a genetic type of Alzheimer's disease (AD), given the near-universal presence of AD pathology in middle adulthood and the elevated risk for developing clinical AD ...in DS. As the field of DS prepares for AD clinical intervention trials, there is a strong need to identify cognitive measures that are specific and sensitive to the transition from being cognitively stable to the prodromal (e.g., Mild Cognitive Impairment-Down syndrome) and clinical AD (e.g., Dementia) stages of the disease in DS. It is also important to determine cognitive measures that map onto biomarkers of early AD pathology during the transition from the preclinical to the prodromal stage of the disease, as this transition period is likely to be targeted and tracked in AD clinical trials. The present chapter discusses the current state of research on cognitive measures that could be used to screen/select study participants and as potential outcome measures in future AD clinical trials with adults with DS. In this chapter, we also identify key challenges that need to be overcome and questions that need to be addressed by the DS field as it prepares for AD clinical trials in the coming years.
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