Single-vesicle profiling of multiple biomarkers on serum EVs via EV-CATCH and nano-flow cytometry for clinical stratification of Alzheimer's disease.

Objective To explore the visinin-like protein-1 (VILIP-1) levels in the blood in the Alzheimer disease (AD) and mild cognitive impairment (MCI) due to AD patients, and also to explore its feasibility as a biomarker for the early diagnosis of AD and for the detection of progression of AD. Methods Fifty-eighty participants were included: 20 cases for AD group, 19 cases for MCI due to AD group, 19 cases for normal control group. The level of VILIP-1 was tested by the ELISA method. Results The level of VILIP-1 in AD group was significantly higher than that in normal control group and MCI due to AD group: (9.0±2.9) ng/L vs. (3.3±1.7) and (6.5±3.1) ng/L, and that in MCI due to AD group was significantly higher than that in normal group, there were statistical differences (P<0.01) . The MMSE score in AD group was significantly lower than that in normal group and MCI due to AD group: (15±3) scores vs. (27±2) and (23±2) scores, and that in MCI due to AD group was significantly lower than that in normal group, there were statistical differences (P<0.01) . The level of VILIP-1 was negatively correlated with MMSE score (r=0.463,P<0.01) , but positively correlated with age (r=0.417,P=0.01) . Conclusions With the progression of the disease, the cognitive impairment of the AD patient is decreasing. VILIP-1 increased in the blood of the patients of AD and MCI due to AD. It means that the blood VILIP-1 could be a new and potential biomarker for the early diagnosis of AD, and it may be clinical useful for the early diagnosis and effective detection of AD to some extent. Key words: Alzheimer disease; Cognitive impairment; Visinin-like protein-1; Early diagnosis

Through the case control study on structural magnetic resonance imaging (sMRI) scanning, MR spectrum (MRS), and neuropsychological assessment of the intracranial structures of Alzheimer's disease (AD), patients of different degrees (early, middle, and late), the early clinical features, imaging features, and neuropsychological characteristics of patients with AD were analyzed to provide help for the early diagnosis of AD. The data of MR scanning of the brain, bilateral MRS scan of the hippocampus, thyroid function and other laboratory indicators, and neuropsychological evaluation analysis were collected in 50 patients who had been diagnosed with AD. According to CDR, 50 patients were divided into the early AD group and the middle and advanced AD group, with 23 patients in the early AD group and 27 patients in the middle and advanced AD group. Retrospective study was conducted to analyze the general conditions, medial temporal lobe atrophy (MTA) grading, and the metabolic changes of bilateral MRS in the hippocampus of patients in both groups, so did the mini-mental state examination (MMSE), activities of daily living scale (ADL), and other neuropsychological assessment results. Moreover, the comparative analysis was carried out. The results showed that the MTA grade of medial temporal atrophy increased with the progressive severity of the disease in both groups. A statistical test was conducted on the reduction of hippocampal volume in the two groups, and the P was less than 0.05. Therefore, the MTA scale was of great value in the diagnosis and staging of early AD. However, when the diagnosis of early AD was treated by MTA visual evaluation alone, there was 23.8% false negative diagnosis. If the judgment of early AD only depended on the metabolic changes of hippocampus MRS or MR scanning of intracranial structures, it was likely to cause false negative diagnosis. Therefore, the combination of MRS analysis and MR scanning of intracranial structures was favorable for the early diagnosis and treatment of AD. Combined with neuropsychological assessment, AD patients were staged more effectively, which greatly improved the accuracy of AD diagnosis in the early stage.

BackgroundEarly and accurate diagnosis of Alzheimer's disease (AD) is critical for ensuring access to targeted lifestyle recommendations and disease modifying therapies (DMT), which can slow disease progression. Historically, diagnosis has been slow, complex, costly, inaccurate, and inconsistent, with individuals often diagnosed in later stages of the disease. Today, AD diagnosis is evolving from symptom‐based exclusion to biomarker‐based inclusion, reflecting the underlying biological etiology. Primary care physicians (PCPs) are expected to play an increasingly important role in AD diagnosis and care. Novel assays employing blood biomarkers (BBM) could support an early and accurate AD diagnosis and be more accessible in primary care than traditional diagnostic tools, including positron‐emission tomography (PET) or cerebrospinal fluid (CSF) biomarker analysis. This study aimed to uncover PCPs’ attitudes on, perceptions of and barriers to AD diagnosis and incorporating BBMs into the diagnostic workflow.MethodRemote 60‐minute in‐depth interviews with 20 PCPs were conducted May 12‐26, 2023. Participants included generalists and geriatricians representing urban, suburban and rural US practices. Interviews focused on early AD diagnosis, PCP role and referral, BBMs, and key barriers or requirements for their clinical implementation.ResultMost PCPs believe that investigating cognitive decline is an important part of their role and are somewhat confident in diagnosing AD but would like to improve their skills. PCPs also report facing barriers such as the complexity and time‐consuming nature of current diagnostic methods, lack of effective treatments and the stigma of an AD diagnosis. PCPs responded positively to BBMs, viewing them as accurate and cost‐effective tools that could integrate easily into their practice. However, they expressed concerns about reimbursement for BBMs and the need for clarity on their place in the diagnostic pathway.ConclusionThis study highlights PCPs’ engagement and interest in supporting AD diagnosis as well as their receptivity towards BBMs. PCPs are generally positive about integrating BBMs into their clinical practice. However, clarity on healthcare coverage and context of use is needed before adoption. Continued medical education on diagnosing AD as well as interpretation and communication of BBM test results will be beneficial for primary care involvement in AD diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

<b>Abstract ID 21807</b> <b>Poster Board 470</b> <b>Aim:</b> One of the essential factors for memory formation through neuroplasticity is brain-derived neurotrophic factor (BDNF)/TrkB signaling. Therefore, the evaluation of TrkB activity could be a biomarker of cognitive improvement by drugs, but a non-invasive method to evaluate TrkB activity has not yet been established in humans. We focused on extracellular vesicles (EVs), which are released from various tissues and reflect the contents of the derived cells. The present study aimed to evaluate the release of TrkB-containing EVs from the brain into the circulating blood and its potential as a biomarker for cognitive improvement using food-derived amino acid ergothioneine (ERGO), which activates TrkB and improves cognitive function. <b>Methods:</b> EVs were isolated by ultracentrifugation from culture medium and serum. In the clinical study, healthy volunteers and subjects with mild cognitive impairment were divided into two groups: ERGO (5 mg/day)-containing food extract tablets treated group (ERGO group) and the placebo group. Serum collection and cognitive function test (Cognitrax) were performed at weeks 0, 4, 8, and 12. Expression of phosphorylated TrkB, an active form of TrkB, in serum EVs was quantified by Western blotting, and the correlation between the expression and each cognitive domain score of Cognitrax was analyzed. To confirm the involvement of TrkB phosphorylation and ERGO-induced cognitive improvement, mice were treated with ERGO with/without a TrkB inhibitor ANA-12, three times a week. <b>Results and Discussion:</b> First, we checked the expression of TrkB in EVs derived from the neuronal cells Neuro2A transfected with a plasmid encoding a FLAG-tagged mouse TrkB (TrkB-FLAG). Expression of TrkB-FLAG and EVs markers CD63 was confirmed in EVs isolated from the cultured medium. Interestingly, TrkB-FLAG was detected in serum EVs after intrahippocampal injection of adeno-associated virus serotype PHP.eB vector enocoding the TrkB-FLAG gene in mice. This result suggests that TrkB-expressing EVs are secreted from the brain into the bloodstream. In the clinical study, the expression ratio of p-TrkB to TrkB (p-TrkB/TrkB) in serum EVs in the ERGO group was significantly higher than that in the placebo group at week 12. Analysis of the correlation between p-TrkB/TrkB and cognitive improvement in serum EVs showed that p-TrkB/TrkB positively correlated with blood ERGO concentration and some cognitive domains such as composite memory and verbal memory. In mice, the oral ERGO administration increased cognitive function assessed by a novel object recognition test, while simultaneous administration of a TrkB inhibitor significantly suppressed such increase. These results suggest that TrkB phosphorylation may be involved in ERGO-induced cognitive improvement, and p-TrkB/TrkB would be a biomarker for cognitive improvement. <b>Conclusion:</b> TrkB-containing EVs are secreted from the brain into the circulating blood, and TrkB phosphorylation would be a potential biomarker for ERGO-induced cognitive improvement.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

Battle against Alzheimer's Disease: The Scope and Potential Value of Magnetic Resonance Imaging Biomarkers

Alzheimer's disease(AD) is a neurodegenerative disease that progresses slowly but worsens gradually, also, the most common kinds of dementia. Clinically, the diagnosis of AD is mainly based on rating scales and neuroimaging technology which is invasive, costly and time-consuming. Other than that, the clinical pathology has become irreversible when neuroimaging characteristics appear. It is imperative to develop new noninvasive methods for early diagnosis of AD. Several studies indicated the probable association of cognitive decline with gait changes might shed light on potential features for distinction of AD. This paper aims to exploit the feasibility of gait features for early diagnosis of mild cognitive impairment(MCI) and AD by using machine learning methods. A device-free AD detection system is built, with a natural undisturbed gait collecting system and a well-performed Long Short-Term Memory(LSTM) based model, in this article. Moreover, it can serve as a simplified, non-invasive, and highly accurate clinical auxiliary tool for early diagnosis and distinction of AD. Experimental results showed a 90.48%, 92.00%, and 88.24% in accuracy, sensitivity, and specificity respectively for distinguishing AD by using the method with LSTM based model. Furthermore, the gait cycle and stride length in MCI or AD were more variable than in healthy controls through redefining and calculating the gait features with skeleton data obtained by Kinect devices. © 2021 IEEE.

Evaluation of optimal extracellular vesicle small RNA isolation and qRT-PCR normalisation for serum and urine

Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive decline in multiple cognitive domains. Its pathological hallmarks include senile plaques and neurofibrillary tangles. Mild cognitive impairment (MCI) is the earliest detectable stage of AD with limited symptomology and no dementia. The yearly conversion rate of patients from MCI to AD is 10-15%, although conversion back to normal is possible in a small percentage. Early diagnosis of AD is important in an attempt to intervene or slow the advancement of the disease. Early AD (EAD) is a stage following MCI and characterized by full-blown dementia; however, information involving EAD is limited. Oxidative stress is well-established in MCI and AD, including protein oxidation. Protein nitration also is an important oxidative modification observed in MCI and AD, and proteomic analysis from our laboratory identified nitrated proteins in both MCI and AD. Therefore, in the current study, a proteomics approach was used to identify nitrated brain proteins in the inferior parietal lobule from four subjects with EAD. Eight proteins were found to be significantly nitrated in EAD: peroxiredoxin 2, triose phosphate isomerase, glutamate dehydrogenase, neuropolypeptide h3, phosphoglycerate mutase1, H(+)- transporting ATPase, alpha-enolase and fructose-1,6-bisphosphate aldolase. Many of these proteins are also nitrated in MCI and late-stage AD, making this study the first to our knowledge to link nitrated proteins in all stages of AD. These results are discussed in terms of potential involvement in the progression of this dementing disorder.

P2-133: Determination of tau, phospho-tau and Abeta1-42 levels in cerebrospinal fluid of patients as a help for the early diagnosis of AD: The french experience in lille

Growing evidence supports that receptor for advanced glycation end products (RAGE) and glyoxalase-1 (GLO-1) are implicated in the pathophysiology of Alzheimer's disease (AD). Extracellular vesicles (EVs) are nanovesicles secreted by almost all cell types, contribute to cellular communication, and are implicated in AD pathology. Recently, EVs are considered as promising tools to identify reliable biomarkers in AD. The aim of our study was to determine the levels of RAGE and GLO-1 in circulating EVs from mild cognitive impairment (MCI) and AD patients and to analyze their correlation with the clinical Mini-Mental State Examination and Montreal Cognitive Assessment scores. We have studied the possibility that neuronal cells could release and transfer GLO-1 through EVs. RAGE and GLO-1 levels were measured in circulating EVs, respectively, by Luminex assay and western blot. Released-EVs from SK-N-SH neuronal cells were isolated and GLO-1 levels were determined by western blot. Our data showed higher levels of RAGE in EVs from late AD patients while GLO-1 levels in EVs from early AD were lower as compared to control and MCI patients. Interestingly, levels of RAGE and GLO-1 in EVs were correlated with the cognitive scores regardless of age. For the first time, we demonstrated that GLO-1 was released from neuronal cells through EVs. Although more samples will be needed, our preliminary results support the use of peripheral EVs cargo as new tools for the discovery of peripheral AD biomarkers.

Oncogenes can alter metabolism by changing the balance between anabolic and catabolic processes. However, how oncogenes regulate tumor cell biomass remains poorly understood. Using isogenic MCF10A cells transformed with nine different oncogenes, we show that specific oncogenes reduce the biomass of cancer cells by promoting extracellular vesicle (EV) release. While MYC and AURKB elicited the highest number of EVs, each oncogene selectively altered the protein composition of released EVs. Likewise, oncogenes alter secreted miRNAs. MYC-overexpressing cells require ceramide, whereas AURKB requires ESCRT to release high levels of EVs. We identify an inverse relationship between MYC upregulation and activation of the RAS/MEK/ERK signaling pathway for regulating EV release in some tumor cells. Finally, lysosome genes and activity are downregulated in the context of MYC and AURKB, suggesting that cellular contents, instead of being degraded, were released via EVs. Thus, oncogene-mediated biomass regulation via differential EV release is a new metabolic phenotype.

The literature on cognitive markers in preclinical AD is reviewed. The findings demonstrate that impairment in multiple cognitive domains is typically observed several years before clinical diagnosis. Measures of executive functioning, episodic memory and perceptual speed appear to be most effective at identifying at-risk individuals. The fact that these cognitive domains are most implicated in normal cognitive aging suggests that the cognitive deficit observed preclinically is not qualitatively different from that observed in normal aging. The degree of cognitive impairment prior to the diagnosis of Alzheimer's disease (AD) appears to generalize relatively well across major study characteristics, including sample ascertainment procedures, age and cognitive status of participants, as well as time to diagnosis of dementia. In episodic memory, there is evidence that the size of the preclinical deficit increases with increasing cognitive demands. The global cognitive impairment observed is highly consistent with observations that multiple brain structures and functions are affected long before the diagnosis of AD. However, there is substantial overlap in the distribution of cognitive scores between those who will and those who will not be diagnosed with AD, hence limiting the clinical utility of cognitive markers for early identification of cases. Future research should consider combining cognitive indicators with other types of markers (i.e. social, somatic, genetic, brain-based) in order to increase prediction accuracy.