Background
Ethno‐racial differences are observed in amyloid (A), tau (T), and neurodegeneration (N) plasma biomarkers of Alzheimer’s disease (AD). In neuroimaging studies, structural and social ...determinant of health (SSDOH) factors and associated health outcomes may mediate observed racial differences in neurodegeneration markers. However, little is known regarding the potential effects of SSDOH and downstream comorbidities on ethno‐racial differences in AT(N) plasma biomarkers. This study A) assessed potential differences in plasma Aβ42, Aβ40, Aβ42/40, t‐tau, and NfL among Mexican Americans (MA), non‐Hispanic Blacks (NHB), and non‐Hispanic Whites (NHW); B) determined whether SSDOH and comorbidity factors mediate potential ethno‐racial differences in AD plasma biomarkers.
Method
Data were obtained from the diverse and well‐characterized Health and Aging Brain Study – Health Disparities (HABS‐HD) and included MAs (n = 931), NHBs (n = 258), and NHWs (n = 942). Group differences in AT(N) plasma biomarkers were assessed using analysis of covariance. Age, sex, and cognitive status (Clinical Dementia Rating Scale Sum of Boxes) were included as covariates. Two multiple mediation models were performed with SSDOH (acculturation, chronic stress, income, area deprivation index) and comorbidity factors (body mass index BMI, blood pressure, diabetes history) as the potential mediators, respectively. Models accounted for age, sex, and cognitive status, assumed nonlinear relationships, and allowed for mediators to be considered simultaneously.
Result
Plasma Aβ42 was significantly lower in MAs compared to NHWs (p<0.05). Plasma NfL was significantly higher in MAs compared to NHBs (p<0.01) and for NHWs compared to NHBs (p<0.05). No other significant differences in plasma variables were observed (p’s>0.05). Group differences in plasma Aβ42 were mediated by comorbidity factors (BMI and history of diabetes), but not SSDOH. MAs had greater BMIs and history of diabetes compared to NHWs, both of which were associated with lower plasma Aβ42 levels. SSDOH and comorbidity factors did not mediate group differences in plasma NfL.
Conclusion
Using a large and diverse community‐based cohort, we demonstrate that ethno‐racial differences in AD‐specific plasma biomarkers are, in part, due to modifiable health and comorbidity factors. This work informs the conditions that give rise to ethno‐racial differences in plasma ATN biomarkers while highlighting potential ethno‐racial disparities in AD research.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Trisomy 21 causes Down syndrome (DS) and is a recognized cause of Alzheimer’s disease (AD). The triplication of the amyloid precursor protein gene leads to an overexpression of ...amyloid‐beta (Aβ), which accumulates into extracellular plaques, followed by intracellular neurofibrillary tau tangles (Lott & Head, 2019). However, variability in the age of AD onset in DS spans 25+ years (Iulita et al., 2022). As the DS field moves to AD clinical trials, it is important to identify factors related to this variability, such as premorbid severity of intellectual disability (ID). We compared the age‐trajectory of the AD biomarkers Aβ and tau and cognitive decline across premorbid ID levels (mild, moderate, and severe/profound), in models controlling trisomy type (full, mosaic, translocation) and APOE status
Method
Analyses involved 361 adults with DS (M = 45.22 years, SD = 9.92) from the Alzheimer’s Biomarkers Consortium‐Down Syndrome. Participants completed measures of memory, mental status, and visuospatial ability. Premorbid ID level was recorded. PET was acquired using 11C PiB for Aβ, and 18F AV‐1451 for tau in 162 participants. Aβ was quantified using the centiloid method and tau was quantified as SUVR (reference: cerebellar cortex) in a composite region determined as the volume‐weighted average of select FreeSurfer 5.3, T1 MR‐based components.
Results
General linear models controlling for site, age, trisomy type and APOE status indicated no significant effect of premorbid ID level by age on the cognitive outcomes (Figure 1a‐e). There was not a significant effect of premorbid ID by age on PET Aβ or on tau PET (Figure 2a‐b). There was not a significant difference in age of those with mild cognitive impairment‐DS (MCI‐DS) or dementia by premorbid ID level (Figure 3).
Conclusion
This study adds to the characterization of the time course of AD pathology and clinical symptomology in DS. Findings provide robust evidence of a similar time course in AD trajectory across severity of ID, laying the groundwork for the inclusion of individuals with DS with a variety of IQ levels in clinical AD trials. Premorbid IQ may have little effect on AD biomarkers in DS in contrast to findings in neurotypical populations.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•Longitudinal analysis of tau and amyloid-β interaction in Down syndrome.•Tau as an independent predictor of cognitive and functional change.•Tau as a better predictor of cognitive and functional ...change than amyloid-β status.•Evidence that tau and amyloid-β contribute synergistically to cognitive decline.
This study investigates whether tau has (i) an independent effect from amyloid-β on changes in cognitive and functional performance and (ii) a synergistic relationship with amyloid-β in the exacerbation of decline in aging Down syndrome (DS).
105 participants with DS underwent baseline PET 18F-AV1451 and PET 11CPiB scans to quantify tau deposition in Braak regions II-VI and the Striatum and amyloid-β status respectively. Linear Mixed Effects models were implemented to assess how tau and amyloid-β deposition are related to change over three time points.
Tau was a significant independent predictor of cognitive and functional change. The three-way interaction between time, 11CPiB status and tau was significant in the models of episodic memory and visuospatial cognition.
Baseline tau is a significant predictor of cognitive and functional decline, over and above the effect of amyloid-β status. Results suggest a synergistic relationship between amyloid-β status and tau as predictors of change in memory and visuospatial cognition.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Down's syndrome results from trisomy of chromosome 21, a genetic change which also confers a probable 100% risk for the development of Alzheimer's disease neuropathology (amyloid plaque and ...neurofibrillary tangle formation) in later life. We aimed to assess the effectiveness of diffusion-weighted imaging and connectomic modelling for predicting brain amyloid plaque burden, baseline cognition and longitudinal cognitive change using support vector regression. Ninety-five participants with Down's syndrome successfully completed a full Pittsburgh Compound B (PiB) PET-MR protocol and memory assessment at two timepoints. Our findings indicate that graph theory metrics of node degree and strength based on the structural connectome are effective predictors of global amyloid deposition. We also show that connection density of the structural network at baseline is a promising predictor of current cognitive performance. Directionality of effects were mainly significant reductions in the white matter connectivity in relation to both PiB+ status and greater rate of cognitive decline. Taken together, these results demonstrate the integral role of the white matter during neuropathological progression and the utility of machine learning methodology for non-invasively evaluating Alzheimer's disease prognosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Trisomy 21 causes Down syndrome (DS) and is a recognized cause of early-onset Alzheimer's disease (AD).
The current study sought to determine if premorbid intellectual disability level (ID) was ...associated with variability in age-trajectories of AD biomarkers and cognitive impairments. General linear mixed models compared the age-trajectory of the AD biomarkers PET Aβ and tau and cognitive decline across premorbid ID levels (mild, moderate, and severe/profound), in models controlling trisomy type, APOE status, biological sex, and site.
Analyses involved adults with DS from the Alzheimer's Biomarkers Consortium-Down Syndrome. Participants completed measures of memory, mental status, and visuospatial ability. Premorbid ID level was based on IQ or mental age scores prior to dementia concerns. PET was acquired using 11C PiB for Aβ, and 18F AV-1451 for tau.
Cognitive data was available for 361 participants with a mean age of 45.22 (SD = 9.92) and PET biomarker data was available for 154 participants. There was not a significant effect of premorbid ID level by age on cognitive outcomes. There was not a significant effect of premorbid ID by age on PET Aβ or on tau PET. There was not a significant difference in age at time of study visit of those with mild cognitive impairment-DS or dementia by premorbid ID level.
Findings provide robust evidence of a similar time course in AD trajectory across premorbid ID levels, laying the groundwork for the inclusion of individuals with DS with a variety of IQ levels in clinical AD trials.
Background
Activation of microglial cells in the brain, more commonly known as neuroinflammation, has often been linked to the pathophysiology of Alzheimer’s disease (AD). However, how microglial ...activation is associated with longitudinal tau tangle accumulation and consequent cognitive decline is poorly understood. Here, we aimed to investigate whether baseline microglial activation impacts tau tangle deposition and cognitive decline in individuals across the AD continuum.
Method
We assessed 92 individuals from the TRIAD cohort (57 cognitively unimpaired and 35 cognitively impaired) with available baseline 11CPBR28‐PET, a measure of microglial activation and 18FNAV4694 Aß‐PET, and longitudinal 18FMK6240 Tau‐PET (mean follow‐up time = 1.93 years) and Mini‐Mental State Exam (MMSE) (mean follow‐up time = 1.84 years). We performed voxel‐wise associations using linear regressions accounting for age and sex and adjusted for multiple comparisons using Random Field Theory (RFT) (p < 0.05). We used the cuneus and superior temporal cortex as a composite ROI for 11CPBR28‐PET and Aß‐PET since these regions showed a higher association with longitudinal tau accumulation in the temporal meta‐ROI.
Result
Voxel‐wise analysis showed that baseline levels of 11CPBR28‐PET alone are not sufficient to predict longitudinal tau tangle accumulation (Fig. 1a). However, the interaction between 11CPBR28‐PET levels and Aß burden predicted an increased accumulation of tau tangle, mainly in the cuneus, inferior frontal and lateral occipital regions (Fig. 1b).Individuals with higher baseline 11CPBR28‐PET and Aß‐PET levels present higher rates of longitudinal tau accumulation in the temporal meta‐ROI (ß = 0.36, t = 3.46, p = 0.0009; Fig. 1c). Similarly, while 11CPBR28‐PET levels alone did not correlate with longitudinal changes in MMSE score (ß = ‐0.17, t = ‐1.69, p = 0.10), a significant interaction between 11CPBR28‐PET and Aß‐PET levels on MMSE annual decline was observed (ß = ‐0.24, t = ‐2.25, p = 0.028; Fig. 1d).
Conclusion
We found that baseline levels of microglial activation was associated with longitudinal tau tangle accumulation and cognitive decline in individuals across the AD continuum in the presence of Aß burden. Our results indicate that microglial activation might act potentiating the deleterious effects of Aß on forthcoming tau tangle deposition.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Biomarkers of astrocyte reactivity have the potential to improve diagnostic precision, disease monitoring, and treatment efficacy. Glial fibrillary acidic protein (GFAP) protein is ...expressed in astrocytes, which is important to synaptic plasticity, cell communication, and reactive gliosis.
Method
Cerebrospinal fluid (CSF) biomarkers were measured in participants of the McGill TRIAD cohort. 75 individuals (>50 years old, 44 cognitively unimpaired (CU), and 31 with cognitive impairment (CI)), had available Aβ and tau‐PET. We measured CSF GFAP and synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). Linear regressions adjusted for age, sex, clinical diagnosis, and Aβ/tau‐PET were used to test the associations between astrocyte reactivity and synaptic function.
Result
Demographic information is shown in Table 1. We found an association between CSF GFAP and presynaptic markers (GAP‐43: p<0.0001, β = 0.1076; SYT1: p<0.0001, β = 0.0024; SNAP‐25 long: p<0.0001, β = 0.0008) as well as postsynaptic markers (Ng: p<0.05, β = 0.0066) independently of Aβ and tau burden (Figure 1).
Conclusion
Our biomarker results support experimental literature suggesting that astrocyte reactivity plays a role in downstream synaptic dysfunction independent of the brain levels of Aβ and tau tangles pathologies. This supports in vitro literature suggesting that therapeutic interventions targeting astrocyte reactivity can contribute to halting AD progression.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Healthy synapses are the key to proper brain function, ensuring communication between neurons. Recent studies have associated synaptic dysfunction with Alzheimer’s disease (AD) proteins, ...however, little is known about the role of glial reactivity, another pathology closely linked to AD, in brain synaptic dysfunction (Figure 1).
Method
We evaluated 123 individuals (67 cognitively unimpaired (CU) and 56 cognitively impaired (CI)) who had available Aß‐ and Tau‐PET as well as cerebrospinal fluid measures of glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), and presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). ANCOVA adjusted for clinical diagnosis, age, and sex was used to compare levels of CSF biomarkers; whereas linear regressions adjusted for age, sex, clinical diagnosis, and Aß/tau‐PET were used to test the associations between glial reactivity and synaptic markers.
Result
Demographic information is shown in Table 1. Increased levels of GAP‐43, SNAP‐25, and Ng were observed in CI compared to CU individuals. CSF GFAP was highly associated with both presynaptic and postsynaptic biomarkers in CU and CI groups. CSF YKL‐40 was associated only with presynaptic biomarkers in both clinical groups. On the other hand, CSF sTREM2 showed an association with all synaptic markers but only in the CI group (Figure 2).
Conclusion
We found a heterogeneous association between synaptic markers and glial activation. The presence of GFAP+ astrocytes was associated with dysfunction of presynaptic/postsynaptic markers, whereas YKL‐40+ astrocytes specifically reflected presynaptic dysfunction across aging and AD spectrums. On the other hand, microglial activation reflected synaptic dysfunction associated with dementia symptoms. Our results support recent experimental observations suggesting that clarifying the heterogeneity of different glial cell phenotypes is crucial to advancing our understanding of the role of immune cells in cognitive decline.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Healthy synapses are the key to proper brain function, ensuring communication between neurons. Recent studies have associated synaptic dysfunction with Alzheimer’s disease (AD) proteins, ...however, little is known about the role of glial reactivity, another pathology closely linked to AD, in brain synaptic dysfunction (Figure 1).
Method
We evaluated 123 individuals (67 cognitively unimpaired (CU) and 56 cognitively impaired (CI)) who had available Aβ‐ and Tau‐PET as well as cerebrospinal fluid measures of glial fibrillary acidic protein (GFAP), chitinase‐3‐like protein 1 (YKL‐40), soluble triggering receptor expressed on myeloid cells 2 (sTREM2), synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), and presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). ANCOVA adjusted for clinical diagnosis, age, and sex was used to compare levels of CSF biomarkers; whereas linear regressions adjusted for age, sex, clinical diagnosis, and Aβ/tau‐PET were used to test the associations between glial reactivity and synaptic markers.
Result
Demographic information is shown in Table 1. Increased levels of GAP‐43, SNAP‐25, and Ng were observed in CI compared to CU individuals. CSF GFAP was highly associated with both presynaptic and postsynaptic biomarkers in CU and CI groups. CSF YKL‐40 was associated only with presynaptic biomarkers in both clinical groups. On the other hand, CSF sTREM2 showed an association with all synaptic markers but only in the CI group (Figure 2).
Conclusion
We found a heterogeneous association between synaptic markers and glial activation. The presence of GFAP+ astrocytes was associated with dysfunction of presynaptic/postsynaptic markers, whereas YKL‐40+ astrocytes specifically reflected presynaptic dysfunction across aging and AD spectrums. On the other hand, microglial activation reflected synaptic dysfunction associated with dementia symptoms. Our results support recent experimental observations suggesting that clarifying the heterogeneity of different glial cell phenotypes is crucial to advancing our understanding of the role of immune cells in cognitive decline.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Biomarkers of astrocyte reactivity have the potential to improve diagnostic precision, disease monitoring, and treatment efficacy. Glial fibrillary acidic protein (GFAP) protein is ...expressed in astrocytes, which is important to synaptic plasticity, cell communication, and reactive gliosis.
Method
Cerebrospinal fluid (CSF) biomarkers were measured in participants of the McGill TRIAD cohort. 75 individuals (>50 years old, 44 cognitively unimpaired (CU), and 31 with cognitive impairment (CI)), had available Aß and tau‐PET. We measured CSF GFAP and synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). Linear regressions adjusted for age, sex, clinical diagnosis, and Aß/tau‐PET were used to test the associations between astrocyte reactivity and synaptic function.
Result
Demographic information is shown in Table 1. We found an association between CSF GFAP and presynaptic markers (GAP‐43: p<0.0001, ß = 0.1076; SYT1: p<0.0001, ß = 0.0024; SNAP‐25 long: p<0.0001, ß = 0.0008) as well as postsynaptic markers (Ng: p<0.05, ß = 0.0066) independently of Aß and tau burden (Figure 1).
Conclusion
Our biomarker results support experimental literature suggesting that astrocyte reactivity plays a role in downstream synaptic dysfunction independent of the brain levels of Aß and tau tangles pathologies. This supports in vitro literature suggesting that therapeutic interventions targeting astrocyte reactivity can contribute to halting AD progression.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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