Aβ Positivity Research Articles

Plasma Alzheimer's disease biomarker variability: Amyloid-independent and amyloid-dependent factors.

We aimed to investigate which factors affect plasma biomarker levels via amyloid beta (Aβ)-independent or Aβ-dependent effects and improve the predictive performance of these biomarkers for Aβ positivity on positron emission tomography (PET). A total of 2935 participants underwent blood sampling for measurements of plasma Aβ42/40 ratio, phosphorylated tau 217 (p-tau217; ALZpath), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels usingsingle-molecule array and Aβ PET. Laboratory findings were collected using a routine blood test battery. Aβ-independent factors included hemoglobin and estimated glomerular filtration rate (eGFR) for p-tau217 and hemoglobin, eGFR, and triiodothyronine (T3) for GFAP and NfL. Aβ-dependent factors included apolipoprotein E genotypes, body mass index status for Aβ42/40, p-tau217, GFAP, and NfL. However, these factors exhibited negligible or modest effects on Aβ positivity on PET. Our findings highlight the importance of accurately interpreting plasma biomarkers for predicting Aβ uptake in real-world settings. We investigated factor-Alzheimer's disease plasma biomarker associations in a large Korean cohort. Hemoglobin and estimated glomerular filtration rate affect the biomarkers independently of brain amyloid beta (Aβ). Apolipoprotein E genotypes and body mass index status affect the biomarkers dependent on brain Aβ. Addition of Aβ-independent factors shows negligible effect in predicting Aβ positivity. Adjusting for Aβ-dependent factors shows amodest effect in predicting Aβ positivity.

Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort.

Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations. RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography. DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data. Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies. Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network moduleswere associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβdeposition. Dysregulated genes were related to a NK cell-mediated immunity.

Experimental peri-implantitis induces neuroinflammation: An exploratory study in rats

PurposeCumulating evidence supports the close association between periodontal diseases, neuroinflammation and neurodegenerative pathologies, except for peri-implantitis (PI). Thus, this study explored the association between experimental PI and neuropathological changes in the rat brain.Materials and methodsAfter bilateral first molars extraction, experimental PI was induced at titanium implants placed in the maxillae by lipopolysaccharide injections and ligature placement. Following 28-weeks of disease progression, the maxillae and brains were retrieved from 6 rats. Healthy brains from 3 rats were used as control. Brains were analyzed by immunohistochemistry to detect signs of neuroinflammation (interleukin (IL)-6 and tumor necrosis factor (TNF)-α)), microglial activation (IBA-1) and astrogliosis (GFAP). To explore signs of neurodegeneration, hematoxylin/eosin and Nissl stainings were used. Also, four different antibodies against amyloid beta (Aβ 1–42) were tested.ResultsChronic PI lesions showed peri-implant bone resorption accompanied by large inflammatory infiltrates. IL-6+ and TNF-α+ cells were found within the CA1 and Dentate Gyrus regions of the hippocampus of the PI-affected group, while almost no immune-positivity was detected in the control (p < 0.05). Detection of activated GFAP+ microglia and IBA-1+ astrocytes surface were significantly higher at the CA areas, and cerebral cortex of the PI-affected group, in comparison with control (p < 0.05). Shrunk neurons with pyknotic nuclei were inconsistently found among the PI-affected group, and these were almost not detected in control. No positive Aβ reactivity was detected in any of the samples.ConclusionChronic experimental PI lesions led to an increased detection of IL-6 and TNF-α, GFAP+ microgliosis and IBA-1+ astrocytosis, and in some cases, neurodegeneration, in the rat brain.

The Association Between Plasma Amyloid-β 42/40 and Percentage of Semantic Intrusion Errors in Mild Cognitive Impairment.

Semantic intrusion errors (SIEs) are both sensitive and specific to PET amyloid-β (Aβ) burden in older adults with amnestic mild cognitive impairment (aMCI). Plasma Aβ biomarkers including the Aβ42/40 ratio using mass spectrometry are expected to become increasingly valuable in clinical settings. Plasma biomarkers are more clinically informative if linked to cognitive deficits that are salient to Alzheimer's disease (AD). This study included 119 older adults enrolled in the 1Florida Alzheimer's Disease Research Center (ADRC), 45 aMCI participants scored below the established Aβ42/40 ratio cut-off of 0.160 using the Quest AD-Detect™ assay indicating Aβ positivity (Aβ+), while 50 aMCI participants scored above this cut-off indicating Aβ negative status (Aβ-). Additionally, 24 cognitively unimpaired (CU) persons scored above the cut-off of 0.160 (Aβ-). The aMCI plasma Aβ+ group evidenced the greatest percentage of SIEs, followed by the aMCI Aβ-. The CU Aβ- group exhibited the lowest percentage of SIEs. After adjustment for global cognitive impairment, aMCI plasma Aβ+ continued to demonstrate greater SIEs on tests tapping the failure to recover from proactive semantic interference (frPSI) as compared to the aMCI Aβ-group. Using pre-established cut-offs for frPSI impairment, 8.3% of CU Aβ- participants evidenced deficits, compared to 37.8% of aMCI Aβ-, and 74.0% of aMCI Aβ+. SIEs reflecting frPSI were associated with aMCI Aβ+ status based on the Aβ42/40 ratio. Results suggest the importance of SIEs as salient cognitive markers that map onto underlying AD pathology in the blood.

Detecting Amyloid Positivity Using Morphometric Magnetic ResonanceImaging.

Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive. We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI). Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.3±7.2, 150 male; 246 Aβ-, age: 71.1±7.1, 131 male) were combined in different feature sets. We implemented a machine learning workflow to find the best Aβ+ classification model. In an AD+MCI+CN population scenario, the best-performing model selected 120 features (107 GT features, 12 regional morphometric features and the MMSE total score) and achieved a negative predictive value (NPVadj) of 68.4%, and a balanced accuracy (BAC) of 66.9%. In a AD+MCI scenario, the best model obtained NPVadj of 71.6%, and BAC of 70.7%, using 180 regional morphometric features (98 volumes, 52 areas and 29 thicknesses from temporal, parietal, and frontal brain regions). Although with currently limited clinical applicability, regional MRI morphometric features have clinical usefulness potential for detecting Aβ status, which may be augmented by a combination with cognitive data when cognitively normal subjects make up a substantial part of the population presenting for diagnosis.

18F]PI-2620 Tau PET signal across the aging and Alzheimer’s disease clinical spectrum

Abstract [18F]PI-2620 is a second generation tracer that has shown high binding affinity for tau aggregation in Alzheimer’s disease (AD). However, [18F]PI-2620 signal in a large sample spanning the healthy aging and AD clinical spectrum as well as the stability of signal across different acquisition time windows have not yet been examined. Here, amyloid negative (Aβ-) cognitively unimpaired (CU; n=49), amyloid positive (Aβ+) CU (n=37), CU individuals with unknown amyloid status (n=5), mild cognitive impairment (MCI; n=14), dementia due to AD (n=19), and non-AD neurodegenerative disorder (n=54) participants were scanned with [18F]PI-2620 using a 45-75 min and/or 60-90 min acquisition time window. The impact of acquisition time on standardized uptake value ratio (SUVR) magnitude was first quantified with linear mixed models and in participants and regions with high [18F]PI-2620 signal, SUVRs increased linearly up to 0.04 SUVR with each additional 5 min past injection time. We then accounted for differences in acquisition time using a voxelwise correction approach and showed high correlations (all rs&amp;gt;0.986) between SUVRs calculated from 45-75 min data and SUVRs from 60-90 min data that were interpolated to the 45-75 min scale in 15 participants who were scanned across both time windows. Using real and interpolated 45-75 min data, we next examined [18F]PI-2620 signal in Braak regions of interest and an off-target binding region (putamen) in Aβ- and Aβ+ CU, Aβ+ MCI, and Aβ+ AD dementia (n=115) and showed that SUVRs in all Braak regions increased with greater disease severity. Within CU, higher Braak I SUVR was significantly associated with greater CSF pTau-181 (n=35), and higher SUVRs were significantly associated with worse memory and language (n=57). Thus, voxelwise acquisition time corrections can be applied to combine [18F]PI-2620 datasets collected at different post-injection times, and once acquisition time is accounted for, [18F]PI-2620 signal shows the expected increases across the AD spectrum and can be used for detection of early tau elevations.

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

The relationship between cerebrovascular disease (CVD) and amyloid beta (Aβ) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers-including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)-would correlate with Aβ positivity on positron emission tomography (Aβ-PET). We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=1352). Logistic regression was used to calculate odds ratios (ORs), with Aβ-PET positivity as the standard-of-truth. Following adjustment, WMHs (OR=1.25) and superficial CMBs (OR=1.45) remained positively associated with Aβ-PET positivity (p<0.001). Deep CMBs and lacunes exhibited a varied relationship with Aβ-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ-PET (pseudo-R2=0.41). The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. gov: ADNI-2 (NCT01231971), ADNI-3 (NCT02854033). Cerebrovascular biomarkers linked to amyloid beta (Aβ) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aβ-PET positivity. Relationships with Aβ-PET vary by cognitive stage. Novel accessible model predicts Aβ-PET status. Study supports multimodal diagnostic approaches.

Ethnic differences in the prevalence of amyloid positivity and cognitive trajectories.

We investigated the prevalence of amyloid beta (Aβ) positivity (+) and cognitive trajectories in Koreans and non-Hispanic Whites (NHWs). We included 5121 Koreans from multiple centers across South Korea and 929 NHWs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants underwent Aβ positron emission tomography and were categorized into cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia stages. Age, sex, education, and apolipoprotein E. genotype were adjusted using multivariable logistic regression and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances in these variables. The prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (adjusted odds ratio 0.60). Aβ+ Koreans showed a faster cognitive decline than Aβ+ NHWs in the CU (B=-0.314, p=.004) and MCI stages (B=-0.385, p<.001). Ethnic characteristics of Aβ biomarkers should be considered in research and clinical application of Aβ-targeted therapies in diverse populations. Koreans have a lower prevalence of Aβ positivity compared to NHWs in the CU stage. The effects of Alzheimer's risk factors on Aβ positivity differ between Koreans and NHWs. Aβ-positive (Aβ+) Koreans show faster cognitive decline than Aβ+ NHWs in the CU and MCI stages.

Convergent Multimodal Imaging Abnormalities in the Dorsal Precuneus in Subjective Cognitive Decline.

A range of imaging modalities have reported Alzheimer's disease-related abnormalities in individuals experiencing subjective cognitive decline (SCD). However, there has been no consistent local abnormality identified across multiple neuroimaging modalities for SCD. We aimed to investigate the convergent local alterations in amyloid-β (Aβ) deposition, glucose metabolism, and resting-state functional MRI (RS-fMRI) metrics in SCD. Fifty SCD patients (66.4±5.7 years old, 19 men [38%]) and 15 normal controls (NC) (66.3±4.4 years old, 5 men [33.3%]) were scanned with both [18F]-florbetapir PET and [18F]-fluorodeoxyglucose PET, as well as simultaneous RS-fMRI from February 2018 to November 2018. Voxel-wise metrics were retrospectively analyzed, including Aβ deposition, glucose metabolism, amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality(DC). The SCD group showed increased Aβ deposition and glucose metabolism (p < 0.05, corrected), as well as decreased ALFF, ReHo, and DC (p < 0.05, uncorrected) in the left dorsal precuneus (dPCu). Furthermore, the dPCu illustrated negative resting-state functional connectivity with the default mode network. Regarding global Aβ deposition positivity, the Aβ deposition in the left dPCu showed a gradient change, i.e., Aβ positive SCD > Aβ negative SCD > Aβ negative NC. Additionally, both Aβ positive SCD and Aβ negative SCD showed increased glucose metabolism and decreased RS-fMRI metrics in the dPCu. The dorsal precuneus, an area implicated in early AD, shows convergent neuroimaging alterations in SCD, and might be more related to other cognitive functions (e.g., unfocused attention) than episodic memory.

Different associations between body mass index and Alzheimer’s markers depending on metabolic health

BackgroundIncreasing evidence supports the association between body mass index (BMI), Alzheimer’s disease, and vascular markers. Recently, metabolically unhealthy conditions have been reported to affect the expression of these markers. We aimed to investigate the effects of BMI status on Alzheimer’s and vascular markers in relation to metabolic health status.MethodsWe recruited 1,736 Asians without dementia (71.6 ± 8.0 years). Participants were categorized into underweight, normal weight, or obese groups based on their BMI. Each group was further divided into metabolically healthy (MH) and unhealthy (MU) groups based on the International Diabetes Foundation definition of metabolic syndrome. The main outcome was Aβ positivity, defined as a Centiloid value of 20.0 or above and the presence of vascular markers, defined as severe white matter hyperintensities (WMH). Logistic regression analyses were performed for Aβ positivity and severe WMH with BMI status or interaction terms between BMI and metabolic health status as predictors. Mediation analyses were performed with hippocampal volume (HV) and baseline Mini-Mental State Examination (MMSE) scores as the outcomes, and linear mixed models were performed for longitudinal change in MMSE scores.ResultsBeing underweight increased the risk of Aβ positivity (odds ratio [OR] = 2.37, 95% confidence interval [CI] 1.13–4.98), whereas obesity decreased Aβ positivity risk (OR = 0.63, 95% CI 0.50–0.80). Especially, obesity decreased the risk of Aβ positivity (OR = 0.38, 95% CI 0.26–0.56) in the MH group, but not in the MU group. Obesity increased the risk of severe WMH (OR = 1.69, 1.16–2.47). Decreased Aβ positivity mediate the relationship between obesity and higher HV and MMSE scores, particularly in the MH group. Obesity demonstrated a slower decline in MMSE (β = 1.423, p = 0.037) compared to being normal weight, especially in the MH group.ConclusionsOur findings provide new evidence that metabolic health has a significant effect on the relationship between obesity and Alzheimer’s markers, which, in turn, lead to better clinical outcomes.

Risk factors for cognitive decline in non-demented elders with amyloid-beta positivity

BackgroundAs a currently incurable but preventable disease, the prevention and early diagnosis of Alzheimer’s disease (AD) has long been a research hotspot. Amyloid deposition has been shown to be a major pathological feature of AD. Notably, not all the people with amyloid-beta (Aβ) pathology will have significant cognitive declines and eventually develop AD. Therefore, the aim of this study was to explore the risk factors for cognitive decline in Aβ-positive participants.MethodsWe included 650 non-demented participants who were Aβ-positive at baseline from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Mixed effects and COX regression models were applied to assess 37 potential risk factors. Mixed effects models were employed to assess the temporal associations between potential risk factors and four cognitive assessment scales. COX regression models were used to assess the impact of potential risk factors on cognitive diagnosis conversion. Univariate and multivariate analyses were applied to the above models. Additionally, we used the Cochran-Armitage trend test to examine whether the incidence of cognitive decline increased with the number concurrent of risk factors.ResultsSix factors (low diastolic pressure, low body mass index, retired status, a history of drug abuse, Parkinsonism, and depression) were the identified risk factors and four factors (a history of urinary disease, musculoskeletal diseases, no major surgical history, and no prior dermatologic-connective tissue diseases) were found to be suggestive risk factors. The incidence of cognitive decline in the Aβ-positive participants gradually increased as the number of concurrent risk factors increased (p for trend = 0.0005).ConclusionsOur study may facilitate the understanding of the potential pathological processes in AD and provide novel targets for the prevention of cognitive decline among participants with Aβ positivity.

Effect of APOE ɛ4 Status on Brain Amyloid‐β and Cognitive Function in Amnestic and Nonamnestic Mild Cognitive Impairment: A [18F] Florbetapir PET‐CT Study

AbstractMild cognitive impairment (MCI) is recognized as a predementia syndrome caused by multiple etiologies and nonmemory symptoms in MCI have recently gained increasing attention. However, the pattern of Aβ deposition and the effect of APOE (apolipoprotein E, APOE) ε4 on cognitive impairment in amnestic MCI (aMCI) and nonamnestic MCI (naMCI) patients has not been demonstrated. In this work, the amyloid‐β (Aβ) load by [18F]florbetapir PET imaging and cognitive performance is compared by comprehensive neuropsychological scales in participants with different MCI types or different APOE ε4 carriage status. According to the Aβ positivity and results of voxel‐wise analysis, higher Aβ loads are observed in aMCI patients than naMCI patients, especially aMCI patients with APOE ε4. Additionally, it is observed that memory domain Z scores show a strong negative correlation with global florbetapir SUVR in the aMCI group (r = – 0.352, p &lt; 0.001) but not in the naMCI group (r = –0.016, p = 0.924). Moreover, this correlation is independent of APOE e4 carriage status. This study aims to identify high‐risk groups at an early stage of AD(Alzheimer's Disease, AD) through cognitive performance and APOE ε4 carrier status, which can be important for guiding clinical intervention trials.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer’s disease

BackgroundWith the approval of disease-modifying treatments (DMTs) for early Alzheimer’s disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).MethodsIn this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific and BioFINDER-2 training data, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.ResultsPotential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92–1]), with diagnostic accuracy ranging from 0.88 (0.76–0.95, p = 0.028) to 0.96 (0.86–1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required confirmatory testing.ConclusionsThis study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Associations between regional blood-brain barrier permeability, aging, and Alzheimer's disease biomarkers in cognitively normal older adults.

Increased blood-brain barrier permeability (BBBp) has been hypothesized as a feature of aging that may lead to the development of Alzheimer's disease (AD). We sought to identify the brain regions most vulnerable to greater BBBp during aging and examine their regional relationship with neuroimaging biomarkers of AD. We studied 31 cognitively normal older adults (OA) and 10 young adults (YA) from the Berkeley Aging Cohort Study (BACS). Both OA and YA received dynamic contrast-enhanced MRI (DCE-MRI) to quantify Ktrans values, as a measure of BBBp, in 37 brain regions across the cortex. The OA also received Pittsburgh compound B (PiB)-PET to create distribution volume ratios (DVR) images and flortaucipir (FTP)- PET to create partial volume corrected standardized uptake volume ratios (SUVR) images. Repeated measures ANOVA assessed the brain regions where OA showed greater BBBp than YA. In OA, Ktrans values were compared based on sex, Aβ positivity status, and APOE4 carrier status within a composite region across the areas susceptible to aging. We used linear models and sparse canonical correlation analysis (SCCA) to examine the relationship between Ktrans and AD biomarkers. OA showed greater BBBp than YA predominately in the temporal lobe, with some involvement of parietal, occipital and frontal lobes. Within an averaged ROI of affected regions, there was no difference in Ktrans values based on sex or Aβ positivity, but OA who were APOE4 carriers had significantly higher Ktrans values. There was no direct relationship between averaged Ktrans and global Aβ pathology, but there was a trend for an Ab status by tau interaction on Ktrans in this region. SCCA showed increased Ktrans was associated with increased PiB DVR, mainly in temporal and parietal brain regions. There was not a significant relationship between Ktrans and FTP SUVR. Our findings indicate that the BBB shows regional vulnerability during normal aging that overlaps considerably with the pattern of AD pathology. Greater BBBp in brain regions affected in aging is related to APOE genotype and may also be related to the pathological accumulation of Aβ.

Association of CHAT Gene Polymorphism rs3793790 and rs2177370 with Donepezil Response and the Risk of Alzheimer's Disease Continuum.

Genetic variation plays an important role in drug response, there are few relevant studies on patients with Alzheimer's disease continuum (ADC). This study focused on the associations between two single nucleotide polymorphisms (SNPs) (rs3793790 and rs2177370) located in the CHAT gene and donepezil response in ADC patients, and further evaluated the associations between the two SNPs and ADC. According to 2018 National Institute on Aging and Alzheimer's Association (NIA-AA) standard, amyloid β-protein positive (Aβ+) and negative (Aβ-) patients were recruited according to the Aβ-PET/CT standard. rs3793790 and rs2177370 were genotyped in buccal swab samples by using the MassARRAY system. We used the Mini Mental State Examination (MMSE) in Chinese version, caregiver evaluation, and prescribing behavior to assess therapeutic response during the 9-month period. Using logistic regression models, we analyzed the relationship between the two SNPs and donepezil response in 58 Aβ+ patients treated with donepezil alone at the initial diagnosis of ADC. We also explored a probable link between the two SNPs and ADC in 147 Aβ+ and 73 Aβ- patients using a logistic regression analysis. The chance of donepezil response was higher in patients with the G allele of rs3793790 and/or the A allele of rs2177370 than in those without (odds ratio (OR) 6.83, 95% confidence interval (CI): 1.64-28.49). Additionally, the rs3793790 variant was not associated with ADC, whereas the A allele in rs2177370 increased 1.51-fold the ADC risk (OR 2.51, 95% CI: 1.28-4.95). The genetic variants of rs3793790 and rs2177370 were associated with the donepezil response, and rs2177370 may have a moderate relationship with the risk of ADC.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer's disease.

With the approval of disease-modifying treatments (DMTs) for early Alzheimer's disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP). In this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific training data and BioFINDER-2, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth. Potential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92-1]), with diagnostic accuracy ranging from 0.88 (0.76-0.95, p = 0.028) to 0.96 (0.86-1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required from confirmatory testing. This study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

The relationship between cerebrovascular disease (CVD) and amyloid-β (Aβ) in Alzheimer disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers, including cerebral microbleeds (CMBs), ischemic infarction, and white matter hyperintensities (WMH), would correlate with Aβ positivity on positron emission tomography (Aβ-PET). We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=1,352). Logistic regression was used to calculate odds ratios (ORs), with Aβ-PET positivity as the standard-of-truth. Following adjustment, WMH (OR=1.25) and superficial CMBs (OR=1.45) remained positively associated with Aβ-PET positivity (p<.001). Deep CMBs and infarcts exhibited a varied relationship with Aβ-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ-PET (pseudo-R 2 =.41). The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. ClinicalTrials.gov: ADNI-2 ( NCT01231971 ), ADNI-3 ( NCT02854033 ).

Prediction of Amyloid Positivity in Patients with Subcortical Vascular Cognitive Impairment.

Amyloid-β (Aβ) commonly coexists and impacts prognosis in subcortical vascular cognitive impairment (SVCI). This study aimed to examine the differences in clinical and neuroimaging variables between Aβ-positive and Aβ-negative SVCI and to propose a prediction model for Aβ positivity in clinically diagnosed SVCI patients. A total of 130 patients with SVCI were included in model development, and a separate cohort of 70 SVCI patients was used in external validation. The variables for the prediction model were selected by comparing the characteristics of the Aβ-negative and Aβ-positive SVCI groups. The final model was determined using a stepwise method. The model performance was evaluated using the receiver operating characteristic (ROC) curve and a calibration curve. A nomogram was used for visualization. Among 130 SVCI patients, 70 (53.8%) were Aβ-positive. The Aβ-positive SVCI group was characterized by older age, tendency to be in the dementia stage, a higher prevalence of APOEɛ4, a lower prevalence of lacune, and more severe medial temporal atrophy (MTA). The final prediction model, which excluded MTA grade following the stepwise method for variable selection, demonstrated good accuracy in distinguishing between Aβ-positive and Aβ-negative SVCI, with an area under the curve (AUC) of 0.80. The external validation demonstrated an AUC of 0.71. The findings suggest that older age, dementia stage, APOEɛ4 carrier, and absence of lacunes may be predictive of Aβ positivity in clinically diagnosed SVCI patients.

Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid.

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.