Amyloid Stage Research Articles

Optimal blood tau species for the detection of Alzheimer’s disease neuropathology: an immunoprecipitation mass spectrometry and autopsy study

Plasma-to-autopsy studies are essential for validation of blood biomarkers and understanding their relation to Alzheimer’s disease (AD) pathology. Few such studies have been done on phosphorylated tau (p-tau) and those that exist have made limited or no comparison of the different p-tau variants. This study is the first to use immunoprecipitation mass spectrometry (IP-MS) to compare the accuracy of eight different plasma tau species in predicting autopsy-confirmed AD. The sample included 123 participants (AD = 69, non-AD = 54) from the Boston University Alzheimer’s disease Research Center who had an available ante-mortem plasma sample and donated their brain. Plasma samples proximate to death were analyzed by targeted IP-MS for six different tryptic phosphorylated (p-tau-181, 199, 202, 205, 217, 231), and two non-phosphorylated tau (195–205, 212–221) peptides. NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Binary logistic regressions tested the association between each plasma peptide and autopsy-confirmed AD status. Area under the receiver operating curve (AUC) statistics were generated using predicted probabilities from the logistic regression models. Odds Ratio (OR) was used to study associations between the different plasma tau species and CERAD and Braak classifications. All tau species were increased in AD compared to non-AD, but p-tau217, p-tau205 and p-tau231 showed the highest fold-changes. Plasma p-tau217 (AUC = 89.8), p-tau231 (AUC = 83.4), and p-tau205 (AUC = 81.3) all had excellent accuracy in discriminating AD from non-AD brain donors, even among those with CDR < 1). Furthermore, p-tau217, p-tau205 and p-tau231 showed the highest ORs with both CERAD (ORp-tau217 = 15.29, ORp-tau205 = 5.05 and ORp-tau231 = 3.86) and Braak staging (ORp-tau217 = 14.29, ORp-tau205 = 5.27 and ORp-tau231 = 4.02) but presented increased levels at different amyloid and tau stages determined by neuropathological examination. Our findings support plasma p-tau217 as the most promising p-tau species for detecting AD brain pathology. Plasma p-tau231 and p-tau205 may additionally function as markers for different stages of the disease.

Progressive dysregulation of circular RNAs in the hippocampus of a transgenic mouse model of amyloidosis

AbstractBackgroundCircular RNAs (circRNAs) are a class of RNAs that act as transcriptional regulators. They have a circular structure and are produced from back‐splicing events, which makes them more stable than linear RNAs. In Alzheimer’s disease (AD), the role of circRNAs remains elusive. Here, we aimed to evaluate whether the transcription of circRNAs is altered in the hippocampus, a region highly vulnerable to AD, of a transgenic amyloid mouse model.MethodWe selected hippocampal RNA‐sequencing or microarray datasets of circRNAs of the APPswe/PSEN1dE9, a widely used transgenic amyloid mouse model, from the NCBI databases. CircRNAs were detected with the CIRI2 algorithm by using the gene symbol of the parent transcript and the transcript ID. We identified the differently expressed circRNAs between APPswe/PSEN1dE9 and age‐matched wild‐types using the DESeq2 method (p‐value &lt; 0.01). The packages UpSetR and VennDiagram were used to uncover the circRNAs commonly altered between the studies. All analyses were performed in R.ResultFrom 12 databases, three had hippocampal circRNA data of 4, 6, and 8‐month‐old APPswe/PSEN1dE9 mice (GSE166393; GSE158995; and PRJNA712946, n = 3 per group). Amyloid‐beta (Aβ) starts to deposit at six months in the APPswe/PSEN1dE9 hippocampus. The number of dysregulated circRNAs increased with age: 41 (4‐month‐old), 82 (6‐month‐old), and 425 (8‐month‐old) (Figure 1). Interestingly, only one circRNA was shared between the three studies at all time points, originating from the CCDC141 gene. Additionally, 15 circRNAs were shared in at least two ages, such as CD2AP, FMN1, HOMER1, IL1RAP, NDST3, and TET1 (Please, see the full list of intersections in Figure 2). By dividing groups into pre‐plaque (4‐month‐old) and amyloid (6 and 8‐month‐old) phases, we found that four altered circRNAs were shared in both stages, 37 exclusively in pre‐amyloid stage and 490 in amyloid stage (Figure 3).ConclusionOur results demonstrate an increasing dysregulation in circRNAs profiles in the hippocampus of the APP/PS1 mice. Considering the role of circRNAs as transcriptional regulators, one could argue that these progressive changes may impact gene expression in AD‐related brain regions.

Staging Neurodegeneration Across the AD Continuum

AbstractBackgroundWhile amyloid and tau staging are ubiquitous in Alzheimer’s disease (AD), there are no equivalent staging techniques for AD‐related neurodegeneration. Currently, neurodegeneration in AD is commonly assessed with structural MRI measures such as hippocampal volume and cortical thickness. However, recent work suggests that markers for microstructural neurodegeneration derived from diffusion MRI (DWI) may provide crucial additional information related to the onset and progression of AD. Neurite density index (NDI) and return to origin probability (RTOP) are two DWI metrics that have shown promise for characterizing these microstructural alterations.To investigate the utility of DWI for staging neurodegeneration in AD, we implemented logistic regression to compare the abilities of NDI, RTOP, and hippocampal volume to predict AD dementia in 481 late‐middle‐aged participants on the AD continuum.Methods481 participants (383 cognitively unimpaired [CU], 68 Mild Cognitive Impairment [MCI], 30 AD) from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with T1‐weighted MRI and multi‐shell DWI. After delineating hippocampal volume, average NDI and RTOP values were extracted from the hippocampus and a whole brain white matter skeleton. All measures were fit to a logistic model with age, sex, and scanner as covariates.Corresponding ROC curves were used to derive cutoff values for NDI and RTOP by maximizing the Youden Index.ResultsBox plots and ROC curves are provided in Figures 1‐2 and cutoff values for each DWI metric are provided in Table 1. NDI and RTOP outperformed hippocampal volume in predicting AD across brain regions. The best performing marker was WM NDI (AUC = 0.905), followed closely by WM RTOP (AUC = .878). Meanwhile, hippocampal RTOP (AUC = 0.861) slightly outperformed hippocampal NDI (AUC = 0.844).ConclusionOur work adds to a growing body of literature which suggests that neuroimaging markers of brain microstructure may capture unique signatures of AD progression. Furthermore, the fact that both DWI measures performed best in whole brain WM reinforces WM degeneration as a core feature of AD pathophysiology. Future work will incorporate CSF and PET measures of amyloid and tau to better characterize AD‐specific neurodegeneration.

Association between glial and synaptic fluid biomarkers and brain [18F]FDG‐PET signal

AbstractBackgroundBeyond the hypometabolic signature, indexed by [18F]FDG‐PET imaging, seen in the brain of Alzheimer’s disease (AD) patients, a transient brain glucose hypermetabolism has been recently identified in the early stages of AD. The cellular source of this dual metabolic response remains controversial, with glial cells gaining a lot of attention. In light of this, whether glial and synaptic fluid biomarkers are associated with [18F]FDG‐PET signal in the early stages of AD remains debatable. Thus, we evaluated the association of astrocyte, microglial and synaptic fluid biomarkers with [18F]FDG‐PET signal in an AD rat model.Method[18F]FDG‐PET imaging was conducted in ten‐month‐old (early amyloid stage) APP/PS1 (TgF344‐AD, n = 8‐17) and wild‐type (WT, n = 8‐15) rats. Glial (GFAP and sTREM2), synaptic (neurogranin), and amyloid biomarkers (Aß1‐40 and Aß1‐42) were quantified in the cerebrospinal fluid (CSF) and plasma through a multiplex immunoassay. T‐statistical maps of brain [18F]FDG‐PET and those fluid biomarkers were conducted at the voxel level using RMINC. Differences were considered statistically significant at p&lt;0.05 (t&gt;2).ResultA large hypermetabolic cluster was identified in ten‐month‐old TgF344‐AD (Fig. 1AB, local maxima, t(13) = 4.28). We found positive correlations between CSF TREM2 and brain [18F]FDG‐PET signal (Fig. 2AC, local maxima, t(13) = 4.12). In addition, we identified a positive correlation between plasma GFAP and brain [18F]FDG‐PET signal (Fig. 2BC, local maxima, t(13) = 10.62). Plasma TREM2, CSF GFAP, and plasma neurogranin did not associate with brain glucose metabolism.ConclusionOur findings suggest that CSF TREM2 and plasma GFAP are associated with brain metabolism before amyloid load reaches a plateau. Stronger association of plasma GFAP, rather than CSF GFAP, with brain pathology corroborates cross‐sectional human studies. One could argue that [18F]FDG‐PET hypermetabolism identified is likely a response to astrocyte and microglial changes.

Does Socioeconomic Status Have an Impact on Light Chain (AL) Amyloidosis Disease Characteristics and Outcomes?

Background: Evidence suggests that low socioeconomic status leads to unequal access to care and outcomes. This can be particularly problematic for rarer diseases such as light chain (AL) amyloidosis. Using the Area Deprivation Index (ADI) as a proxy for socioeconomic status (SES), we hypothesized that persons living in areas with greater deprivation will exhibit differences in AL disease characteristics at diagnosis and worse post-diagnosis outcomes. Methods: We conducted an IRB-approved, retrospective chart review of patients with systemic AL amyloidosis seen between January 1, 2010 and December 31, 2022 at a large and diverse health system in Milwaukee, Wisconsin. Patients with localized amyloidosis were excluded. Patients were identified using electronic health records. Demographic information (age, sex, race, zip code of residence, insurance, and marital status), disease characteristics (amyloid stage at diagnosis, type, and number of organs involved), and treatment indicators (stem cell transplant eligibility and utilization, time to first chemotherapy) were extracted for all eligible patients. The 2020 Area Deprivation Index (0-100, higher scores equating to higher deprivation) was calculated through the Neighborhood Atlas ® database using zip code and analyzed in quartiles. Spearman correlation was used to describe differences in sociodemographic and clinical characteristics across ADI quartiles and a Cox regression analysis was conducted for overall survival testing the association of ADI controlling for age, sex, race, disease stage, and number of involved organs. A p-value of &amp;lt;0.05 was considered statistically significant. Results: Of the 200 patients with systemic AL amyloidosis who met our inclusion criteria, 8 patients were excluded due to missing/other race. Of the remaining 192 patients which comprised our final cohort, 171 (89%) were White, 21 (11%) were Black. Over half of all patients (51.5%) lived in neighborhoods in the two highest ADI quartiles. Patients in different ADI quartile categories differed significantly by race (Black and White, p&amp;lt;0.001), age at diagnosis (p=0.012), marital status (p&amp;lt;0.001), and Medicaid insurance (p&amp;lt;0.001) (Table 1). Differences in diagnostic and treatment characteristics were not observed across ADI quartiles (Table 1). There were also no differences in overall survival at 1, 5, and 8 years across ADI quartiles (p=0.48). Multivariable analyses revealed that older age at diagnosis, but not ADI, were associated with worse survival (Table 2). Interestingly, while stage 4 diagnosis alone was significant for worse survival (p=&amp;lt;0.001), in general there was no significant correlation between staging and survival (overall p-value=0.497) (Table 2). Controlling for ADI and other factors, Black AL patients were twice as likely as White AL patients to die (OR=2.10; 95% CI: 0.98-4.51), with a p-value of 0.058, marginally short of statistical significance. Conclusion: Socioeconomic status, as measured by the Area Deprivation Index of the neighborhood of residence, was not associated with disease characteristics, treatment, and outcomes among patients with AL amyloidosis. Controlling for SES, however, Black AL patients had worse survival outcomes then their White counterparts. While there was no significant correlation between ADI and clinical characteristics, a substantial proportion of the cohort was living in highly socioeconomically disadvantaged neighborhoods. Given the nature of our study, we were only able to analyze patients who were seen at our institution and our sample size was small. Studies of larger, multicenter populations are needed to better assess the effects of socioeconomic status and other social determinants of health on outcomes among patients with AL amyloidosis.

The relationship between traumatic brain injury and the neuropathology of dementia

AbstractBackgroundClinical studies indicate that traumatic brain injury (TBI) is linked to an increased risk of dementia during life. However, studies involving neuropathologically‐confirmed cases have shown conflicting results. This study was aimed at assessing a large brain bank cohort to identify any association between TBI and the presence of pathologies underlying the most commonly found dementias, Alzheimer’s disease (AD) and Lewy body disease (LBD).Method636 cases were assessed for this study (average age 76.2). Cases with a history of TBI were identified using donor records held at the Sydney Brain Bank. We use the term "TBI” to include a single or multiple TBIs, as well as repetitive mild neurotrauma that may be subconcussive (N = 109). Amyloid plaque and alpha synuclein pathologies were identified by immunohistochemistry. Neurofibrillary tangles were identified using modified Bielschowsky silver staining. Pathology was assessed according to current neuropathological diagnostic criteria. Multivariate regression statistics were used to assess the effect of TBI, age and gender on amyloid plaque (A score), neurofibrillary tangles (B score) and Lewy pathology stages. Multinomial logistic regression was used to examine the effect of TBI on pathological diagnosis.ResultsTBI was not significantly associated with increased amyloid (p = 0.05) or Lewy pathology stages (p = 0.65), however the odds of having a higher neurofibrillary tangle stage increased by 75% in the TBI group (p = 0.009)(Figure 1). Gender was not a significant predictor of amyloid (p = 0.29), neurofibrillary tangle (p = 0.89) or Lewy pathology (p = 0.15). Not unexpectedly, age was a significant predictor of AD pathologies, including amyloid (p = &lt;0.0000001) and neurofibrillary tangle (p = 0.00002) stages, although age was not a significant driver of Lewy body pathology (p = 0.1). There was no association between TBI and overall diagnosis of AD neuropathologic change or LBD (p = 0.12).ConclusionsIn this elderly neurodegenerative cohort, age remains a risk factor for AD, but not LBD pathology. Gender did not have a significant association with these pathologies in this cohort. TBI did not increase the risk of AD but did increase the frequency of neurofibrillary tangles, suggesting that abnormal tau protein accumulation following TBI is an important long‐term consequence that may contribute to poor brain aging and future neurodegeneration.

Amyloid accumulation and functional connectivity in normal aging and preclinical Alzheimer’s Disease: a longitudinal analysis of the OASIS‐3 cohort

AbstractBackgroundPathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer’s Disease (AD). The deposition of amyloid‐beta (Aβ) proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical AD has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on cross sectional differences in the brains of patients with mild cognitive impairment (MCI) or AD and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes early preclinical phases of neurodegeneration.MethodWe use a longitudinal approach to examine the brain changes that take place during early stages of cognitive decline in the OASIS‐3 dataset. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two repeated measures of cognitive status and amyloid levels). We use mixed‐effect linear models to examine rates of amyloid accumulation in those who showed cognitive decline compared to those who did not change in cognitive status.ResultOver 90% of participants were healthy at baseline. Over 8‐10 years, some participants progressed to very mild dementia (n = 48) while others stayed healthy (n = 226). Participants with cognitive decline have faster amyloid accumulation and greater increases in functional connectivity in similar brain regions, namely among lateral temporal, motor, and some lateral prefrontal cortex areas. These changes were linked to increases in functional connectivity of select default mode, frontoparietal and motor components.ConclusionOur findings advance the understanding of initial amyloid staging in preclinical Alzheimer’s, as well as contribute to the determination of the functional changes that occur among brain network components as the disease progresses.

Comparative pathological study of Carbon‐11 labeled Pittsburgh compound‐B PET Centiloid Scale and amyloid beta deposition in postmortem brains

AbstractBackgroundCentiloid Scale (CL) was developed to standardize the quantitative results of amyloid beta (Aβ) positron emission tomography (PET). CL can allow not only categorization of the binary results of amyloid PET as positive or negative, but also parametric weighting within each category. To date, several neuropathological studies evaluated the performance of CL, but further validation studies of the Asian population, elderly cohorts, and relatively short intervals between PET scanning and autopsy may be required.MethodTwenty‐two consecutive autopsy cases registered to the Brain Bank for Aging Research, who received Carbon‐11 labeled Pittsburgh compound‐B PET were included. Amyloid PET results of CL were compared with the final neuropathological diagnosis and the grading of Aβ deposition.ResultThe mean age of the 22 patients at death was 81.0 (SD: 8.4) years, seven of whom fulfilled pathological diagnosis of Alzheimer’s disease (AD). The mean interval from PET to autopsy was 17.5 (SD: 15.3) months. The mean CL was higher in the AD group (88.5 [SD: 30.9]) than in the non‐AD group (9.7 [SD: 25.6], p &lt;0.001). Cutoff value of 50 could segregated seven AD and two non‐AD patients above and 13 non‐AD patients below. CL correlated with CERAD plaque score, Thal phase, and Braak amyloid stage with polyserial correlation coefficient of 0.93, 0.87, and 0.87, respectively. The lower limit of the CL value corresponding to moderate to frequent CERAD plaque score or equal to or more than Thal phase 4 was 17.31.ConclusionThe CL value is in good agreement with the extent of Aβ accumulation. The cutoff value for predicting moderate or greater accumulation of Aβ was 15‐20, and the cutoff value for predicting AD pathology was 50‐60. Further accumulation and integration of data will allow us to establish more appropriate cutoff value.

Patterns of amyloid accumulation in amyloid-negative cases

Amyloid staging models showed that regional abnormality occurs before global positivity. Several studies assumed that the trajectory of amyloid spread is homogeneous, but clinical evidence suggests that it is highly heterogeneous. We tested whether different amyloid-β (Aβ) patterns exist by applying clustering on negative scans and investigating their demographics, clinical, cognitive, and biomarkers correlates, and cognitive trajectories. 151 individuals from Geneva and Zurich cohorts with T1-MRI, negative Aβ positron emission tomography (PET,centiloid<12) and clinical assessment were included. N=123 underwent tau PET, and N=65 follow-up neuropsychological assessment. We performed k-means clustering using 33 Aβ regional Standardized Uptake Vales ratio. Demographics, clinical, cognitive, and biomarkers differences were investigated. Longitudinal cognitive changes by baseline cluster status were estimated using a linear mixed model. The cluster analysis identified two clusters: temporal predominant (TP) and cingulate predominant (CP). TP tau deposition was higher than CP. A trend for a higher cognitive decline in TP compared to CP was observed. This study suggests the existence of two Aβ deposition patterns in the earliest phases of Aβ accumulation, differently prone to tau pathology and cognitive decline.

Predicting amyloid PET and tau PET stages with plasma biomarkers.

Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Resilience and resistance to the accumulation of amyloid plaques and neurofibrillary tangles in centenarians: An age-continuous perspective.

With increasing age, neuropathological substrates associated with Alzheimer's disease (AD) accumulate in brains of cognitively healthy individuals-are they resilient, or resistant to AD-associated neuropathologies? In 85 centenarian brains, we correlated NIA (amyloid) stages, Braak (neurofibrillary tangle) stages, and CERAD (neuritic plaque) scores with cognitive performance close to death as determined by Mini-Mental State Examination (MMSE) scores. We assessed centenarian brains against 2131 brains from AD patients, non-AD demented, and non-demented individuals in an age continuum ranging from 16 to 100+ years. With age, brains from non-demented individuals reached the NIA and Braak stages observed in AD patients, while CERAD scores remained lower. In centenarians, NIA stages varied (22.4% were the highest stage 3), Braak stages rarely exceeded stage IV (5.9% were V), and CERAD scores rarely exceeded 2 (4.7% were 3); within these distributions, we observed no correlation with the MMSE (NIA: P=0.60; Braak: P=0.08; CERAD: P=0.16). Cognitive health can be maintained despite the accumulation of high levels of AD-related neuropathological substrates. Cognitively healthy elderly have AD neuropathology levels similar to AD patients. AD neuropathology loads do not correlate with cognitive performance in centenarians. Some centenarians are resilient to the highest levels of AD neuropathology.

BACE inhibitor treatment at different stages of amyloid deposition

AbstractBackgroundWe are interested in the link between Aβ deposition and neurodegenerationMethodWe analyzed the trajectories of Aβ deposition, Aβ seeding activity, and neurodegeneration in APP trangenic mice, and probed the linkage by impeding Aβ production at different amyloid stages using BACE1 inhibition. To extend the findings to humans, we studied key comparative trajectories of Aβ deposition and neurodegeneration markers in cases of familial AD.ResultOutcome of BACE1 inhibition was dependent on the amyloid stageConclusionWe found a temporal uncoupling between Aβ deposition and neurodegeneration

The Clinical Features of Patients with Alzheimer’s Disease and a History of Traumatic Brain Injury

AbstractBackgroundTraumatic brain injury (TBI) has been associated with a greater risk of developing Alzheimer’s disease (AD). Previous studies have argued that AD patients with TBI history have a differences in age of disease onset, cognitive features and neuropsychiatric symptoms (NPS), but current evidence is still limited and did not always take into account injury characteristics, such as age at injury, TBI frequency, and severity. The primary objective of this study is to examine whether a history of TBI and specific injury characteristics are associated with differences in age of disease onset, cognitive features and NPS in biomarker proven AD patients.MethodBiomarker proven AD patients (CSF or amyloid PET, stage MCI or dementia) were selected from the Amsterdam Dementia Cohort. TBI events were classified by severity, frequency and age at injury (TBI &lt;25 or ≥25 years). Cognitive composite scores were calculated from results of a neuropsychological test battery. Neuropsychiatric symptoms (NPS) were assessed with the Neuropsychiatric Inventory Questionnaire (NPI‐Q). Linear regression analyses were utilized to examine the association between TBI, injury characteristics and clinical outcome measures.ResultAmong the 1755 selected AD patients (mean age 65.2), 166 patients (9.5%) reported ≥1 TBI in their history. Overall, a history of TBI was not related to differences in age at AD, but age at injury &lt;25 years old was associated with a 2.3 years earlier age at diagnosis (β = ‐2.34 P = 0.031), which was independent of APOE e4 status. No significant associations were found between TBI history or age at injury and differences in cognitive functioning or NPS.ConclusionThe findings of our study indicate that TBI during childhood, adolescence or early adulthood may influence age of disease onset in AD patients. This underscores previous findings on vulnerability of the brain during critical development and maturation phases and suggest that an early TBI may contribute to lower resilience or higher vulnerability to neurodegenerative changes. TBI history does not seem to affect cognitive characteristics and NPS profiles in AD patients.

Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis.

Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer's disease. The deposition of amyloid beta proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical Alzheimer's disease has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on the cross-sectional differences in the brains of patients with mild cognitive impairment or Alzheimer's disease and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes the early preclinical phases of neurodegeneration. We use a longitudinal approach to examine the brain changes that take place during the early stages of cognitive decline in the Open Access Series of Imaging Studies-3 data set. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two cognitive assessments and two amyloid scans). Over 90% of participants were healthy at baseline. Over 8-10 years, some participants progressed to very mild cognitive impairment (n = 48), while others stayed healthy (n = 226). Participants with cognitive decline show faster amyloid accumulation in the lateral temporal, motor and parts of the lateral prefrontal cortex. These changes in amyloid levels were linked to longitudinal increases in the functional connectivity of select networks, including default mode, frontoparietal and motor components. Our findings advance the understanding of amyloid staging and the corresponding changes in functional organization of large-scale brain networks during the progression of early preclinical Alzheimer's disease.

The AHEAD 3-45 Study: Design of a prevention trial for Alzheimer's disease.

The Alzheimer's disease (AD) continuum begins with a long asymptomatic or preclinical stage, during which amyloid beta (Aβ) is accumulating for more than a decade prior to widespread cortical tauopathy, neurodegeneration, and manifestation of clinical symptoms. The AHEAD 3-45 Study (BAN2401-G000-303) is testing whether intervention with lecanemab (BAN2401), a humanized immunoglobulin 1 (IgG1) monoclonal antibody that preferentially targets soluble aggregated Aβ, initiated during this asymptomatic stage can slow biomarker changes and/or cognitive decline. The AHEAD 3-45 Study is conducted as a Public-Private Partnership of the Alzheimer's Clinical Trial Consortium (ACTC), funded by the National Institute on Aging, National Institutes of Health (NIH), and Eisai Inc. The AHEAD 3-45 Study was launched on July 14, 2020, and consists of two sister trials (A3 and A45) in cognitively unimpaired (CU) individuals ages 55 to 80 with specific dosing regimens tailored to baseline brain amyloid levels on screening positron emission tomography (PET) scans: intermediate amyloid (≈20 to 40 Centiloids) for A3 and elevated amyloid (>40 Centiloids) for A45. Both trials are being conducted under a single protocol, with a shared screening process and common schedule of assessments. A3 is a Phase 2 trial with PET-imaging end points, whereas A45 is a Phase 3 trial with a cognitive composite primary end point. The treatment period is 4 years. The study utilizes innovative approaches to enriching the sample with individuals who have elevated brain amyloid. These include recruiting from the Trial-Ready Cohort for Preclinical and Prodromal Alzheimer's disease (TRC-PAD), the Australian Dementia Network (ADNeT) Registry, and the Japanese Trial Ready Cohort (J-TRC), as well as incorporation of plasma screening with the C2N mass spectrometry platform to quantitate the Aβ 42/40 ratio (Aβ42/40), which has been shown previously to reliably identify cognitively normal participants not likely to have elevated brain amyloid levels. A blood sample collected at a brief first visit is utilized to "screen out" individuals who are less likely to have elevated brain amyloid, and to determine the participant's eligibility to proceed to PET imaging. Eligibility to randomize into the A3 Trial or A45 Trial is based on the screening PET imaging results. The focus of this article is on the innovative design of the study. The AHEAD 3-45 Study will test whether with lecanemab (BAN2401) can slow the accumulation of tau and prevent the cognitive decline associated with AD during its preclinical stage. It is specifically targeting both the preclinical and the early preclinical (intermediate amyloid) stages of AD and is the first secondary prevention trial to employ plasma-based biomarkers to accelerate the screening process and potentially substantially reduce the number of screening PET scans.

Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78–94

Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/−) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8–17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.

In vivo amyloid staging in individuals with subjective cognitive decline in DELCODE Study

AbstractBackgroundIn previous research, a data‐driven in vivo staging model of amyloid accumulation was established based on regional frequencies of amyloid positivity in 18F‐florbetapir‐PET scans of cognitively normal older adults. The model was subsequently validated in an independent cohort of participants with subjective memory complaints. In the current study we aim to apply this staging approach in an independent sample of individuals with subjective cognitive decline (SCD) in the multicenter DZNE ‐ Longitudinal Cognitive Impairment and Dementia Study (DELCODE).MethodPET scans with 18F‐florbetaben were obtained in a group of 63 SCD participants (mean age = 71.84y, range: 60.83‐83y; 35% female) from the DELCODE cohort. Participants also underwent MRI acquisition, clinical and neuropsychological assessments. CSF biomarkers and APOE genotypes were assessed for subsets of participants. In the future analysis we will classify participants into amyloid accumulation stages based on regional amyloid positivity as assessed in PET images.ResultAmong participants with available CSF assessments, 38% had abnormal CSF Aβ42/Aβ40 ratio values indicating amyloid positivity. Frequency of APOE4 allele carriers of 39% was suggestive of APOE4 enrichment.ConclusionSCD participants studied within the DELCODE represent a group at an increased risk for preclinical AD. Consistent with that, the current SCD subsample with available 18F‐florbetaben‐PET scans demonstrated heightened rates of abnormal CSF amyloid‐β biomarkers and APOE4 prevalence. Investigation of regional amyloid accumulation in this sample would help assess the validity of the previous in vivo amyloid staging model in preclinical AD, as well as further characterize SCD. In the future analysis we will apply the previously established amyloid staging approach. We will additionally evaluate the hypothesized links between amyloid accumulation stages, cognitive performance, CSF biomarkers and prevalence of APOE genotypes.

Data‐driven evidence for three distinct patterns of amyloid‐β accumulation

AbstractBackgroundAmyloid burden staging models assume a single spatial‐temporal progression of amyloid accumulation across subjects, while disease heterogeneity suggests there might be subtypes with distinct trajectories. Here, we apply a data‐driven model which simultaneously resolves subtypes and stages (SuStaIn)1 to amyloid PET data to assess evidence for Aβ accumulation subtypes.MethodAmyloid PET data of 3010 subjects () were pooled from ALFA+, EMIF‐AD, ABIDE, OASIS, and ADNI. SUVr was extracted for 17 regions. Next, GMM was applied to regional SUVr of cognitively unimpaired subjects per cohort, per tracer, to derive z‐scores. Then, we applied SuStaIn to baseline data to determine the optimal number of subtypes and the most probable subtype/stage per scan. Subtypes and stages were related to the proportion of APOE‐ε4 carriers and diagnostic groups, mean age, and Centiloid. The final model was also applied to longitudinal scans (&gt;4 years after baseline) from ADNI and OASIS (N=519) for validation, where we assessed subtype stability and stage progression.ResultSuStaIn identified three subtypes, referred to as Frontal, Parietal and Occipital based on the first regions to show abnormality (). This 3‐subtype model was preferred to a traditional one‐trajectory model () based on the Cross‐Validation Information Criteria. Most subjects displayed no amyloid (stage 0: N=1810, 60.1%) or abnormal amyloid levels across all regions (stage 17: N=282, 9.4%), challenging subtyping. Of the N=835 (27.7%) with strong subtype assignment (&gt;50% probability), the majority was assigned to Frontal (55.0%), followed by Parietal (24.1%), and Occipital subtypes (21.0%). Statistically significant differences included lower age for the Parietal subtype (Frontal:71.9±8.3; Parietal:69.3±9.6; Occipital:72.1±8.1), and distinct proportions of APOE‐ε4 carriership (Frontal:61.8%, Parietal:57.1%, Occipital:49.4%) and subjects with dementia across subtypes (Frontal:19.7%, Parietal:19.1%, Occipital:31.0%). Older age, APOE‐ε4 carriership, and Centiloid burden were positively associated with stage independent of subtype. At follow‐up, most subjects (77.9%) maintained baseline subtype assignment, and 65.3% progressed to a later stage within their subtype.ConclusionWhile a one‐trajectory model recovers the established pattern of amyloid accumulation, SuStaIn identified three subtypes with distinct associations to AD risk factors. These initial results suggest subtype classification may have clinical relevance and/or improve risk assessment compared to previous staging models.

In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer\u2019s disease

BackgroundWe investigated regional amyloid staging characteristics in 11C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer’s Prevention.MethodsWe analyzed partial volume effect-corrected 11C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.9–76.8 years). Regional amyloid positivity was established using region-specific thresholds. We used four stages from the frequency-based staging of amyloid positivity to characterize individual amyloid deposition. Longitudinal PET data was used to assess the temporal progression of stages and to evaluate the emergence of regional amyloid positivity in participants who were amyloid-negative at baseline. We also assessed the effect of amyloid stage on longitudinal cognitive trajectories.ResultsThe staging model suggested progressive accumulation of amyloid from associative to primary neocortex and gradually involving subcortical regions. Longitudinal PET measurements supported the cross-sectionally estimated amyloid progression. In mixed-effects longitudinal analysis of cognitive follow-up data obtained over an average period of 6.5 years following the baseline PET measurement, amyloid stage II showed a faster decline in executive function, and advanced amyloid stages (III and IV) showed a faster decline across multiple cognitive domains compared to stage 0.ConclusionsOverall, the 11C-PiB-PET-based staging model was generally consistent with previously derived models from 18F-labeled amyloid PET scans and a longitudinal course of amyloid accumulation. Differences in longitudinal cognitive decline support the potential clinical utility of in vivo amyloid staging for risk stratification of the preclinical phase of AD even in middle-aged to older individuals at risk for AD.

The kinetics of islet amyloid polypeptide phase-separated system and hydrogel formation are critically influenced by macromolecular crowding.

Many protein misfolding diseases (e.g. type II diabetes and Alzheimer's disease) are characterised by amyloid deposition. Human islet amyloid polypeptide (hIAPP, involved in type II diabetes) spontaneously undergoes liquid–liquid phase separation (LLPS) and a kinetically complex hydrogelation, both catalysed by hydrophobic–hydrophilic interfaces (e.g. air–water interface and/or phospholipids–water interfaces). Gelation of hIAPP phase-separated liquid droplets initiates amyloid aggregation and the formation of clusters of interconnected aggregates, which grow and fuse to eventually percolate the whole system. Droplet maturation into irreversible hydrogels via amyloid aggregation is thought to be behind the pathology of several diseases. Biological fluids contain a high volume fraction of macromolecules, leading to macromolecular crowding. Despite crowding agent addition in in vitro studies playing a significant role in changing protein phase diagrams, the mechanism underlying enhanced LLPS, and the effect(s) on stages beyond LLPS remain poorly or not characterised.We investigated the effect of macromolecular crowding and increased viscosity on the kinetics of hIAPP hydrogelation using rheology and the evolution of the system beyond LLPS by microscopy. We demonstrate that increased viscosity exacerbated the kinetic variability of hydrogelation and of the phase separated-aggregated system, whereas macromolecular crowding abolished heterogeneity. Increased viscosity also strengthened the gel meshwork and accelerated aggregate cluster fusion. In contrast, crowding either delayed cluster fusion onset (dextran) or promoted it (Ficoll). Our study highlights that an in vivo crowded environment would critically influence amyloid stages beyond LLPS and pathogenesis.