Performance of Plasma Amyloid β, Total Tau, and Neurofilament Light Chain in the Identification of Probable Alzheimer's Disease in South China

Microarray Chip-Based High-Throughput Screening of Neurofilament Light Chain Self-Assembling Peptide for Noninvasive Monitoring of Alzheimer's Disease

Alzheimer's disease (AD) starts decades before cognitive symptoms develop. Easily accessible and cost-effective biomarkers that accurately reflect AD pathology are essential for both monitoring and therapeutics of AD. Neurofilament light chain (NfL) levels in blood and cerebrospinal fluid are increased in AD more than a decade before the expected onset, thus providing one of the most promising blood biomarkers for monitoring of AD. The clinical practice of employing single-molecule array (Simoa) technology for routine use in patient care is limited by the high costs. Herein, we developed a microarray chip-based high-throughput screening method and screened an attractive self-assembling peptide targeting NfL. Through directly "imprinting" and further analyzing the sequences, morphology, and affinity of the identified self-assembling peptides, the Pep-NfL peptide nanosheet with high binding affinity toward NfL (KD = 1.39 × 10-9 mol/L), high specificity, and low cost was characterized. The superior binding ability of Pep-NfL was confirmed in AD mouse models and cell lines. In the clinical setting, the Pep-NfL peptide nanosheets hold great potential for discriminating between patients with AD (P < 0.001, n = 37), mild cognitive impairment (P < 0.05, n = 26), and control groups (n = 30). This work provides a high-throughput, high-sensitivity, and economical system for noninvasive tracking of AD to monitor neurodegeneration at different stages of disease. The obtained Pep-NfL peptide nanosheet may be useful for assessing dynamic changes in plasma NfL concentrations to evaluate disease-modifying therapies as a surrogate end point of neurodegeneration in clinical trials.

A randomized, double-blind, placebo-controlled study of Cistanche tubulosa and Ginkgo biloba extracts for the improvement of cognitive function in middle-aged and elderly people

Mild cognitive impairment poses an increasing challenge to middle-aged and elderly populations. Traditional Chinese medicinal herbs like Cistanche tubulosa and Ginkgo biloba (CG) have been proposed as potential agents to improve cognitive and memory functions. A randomized controlled trial involving 100 Chinese middle-aged and elderly participants was conducted to investigate the potential synergistic effects of CG on cognitive function in individuals at risk of neurodegenerative diseases. Over 90 days, both CG group and placebo group received two tablets daily, with each pair of CG tablets containing 72 mg echinacoside and 27 mg flavonol glycosides. Cognitive functions were assessed using multiple scales and blood biomarkers were determined at baseline, Day 45, and Day 90. The CG group exhibited significant improvements in the scores of Mini-Mental State Examination (26.5 at baseline vs. 27.1 at Day 90, p < 0.001), Montreal Cognitive Assessment (23.4 at baseline vs. 25.3 at Day 90, p < 0.001), and World Health Organization Quality of Life (81.6 at baseline vs. 84.2 at Day 90, p < 0.001), all surpassing scores in placebo group. Notably, both the Cognitrax matrix test and the Wechsler Memory Scale-Revised demonstrated enhanced memory functions, including long-term and delayed memory, after CG intervention. Moreover, cognitive-related blood biomarkers, including total tau, pT181, pS199, pT231, pS396, and thyroid-stimulating hormone, significantly decreased, whereas triiodothyronine and free triiodothyronine significantly increased. No treatment-related adverse events were reported, and routine blood and urine tests remained stable. These findings indicated that CG supplementation could potentially serve as an effective supplementary solution for enhancing cognitive and memory functions.

Application of blood-based biomarkers of Alzheimer's disease in clinical practice: Recommendations from Taiwan Dementia Society

Blood-based biomarkers (BBM) are potentially powerful tools that assist in the biological diagnosis of Alzheimer's disease (AD) in vivo with minimal invasiveness, relatively low cost, and good accessibility. This review summarizes current evidence for using BBMs in AD, focusing on amyloid, tau, and biomarkers for neurodegeneration. Blood-based phosphorylated tau and the Aβ42/Aβ40 ratio showed consistent concordance with brain pathology measured by CSF or PET in the research setting. In addition, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are neurodegenerative biomarkers that show the potential to assist in the differential diagnosis of AD. Other pathology-specific biomarkers, such as α-synuclein and TAR DNA-binding protein 43 (TDP-43), can potentially detect AD concurrent pathology. Based on current evidence, the working group from the Taiwan Dementia Society (TDS) achieved consensus recommendations on the appropriate use of BBMs for AD in clinical practice. BBMs may assist clinical diagnosis and prognosis in AD subjects with cognitive symptoms; however, the results should be interpreted by dementia specialists and combining biochemical, neuropsychological, and neuroimaging information. Further studies are needed to evaluate BBMs' real-world performance and potential impact on clinical decision-making.

Assessment of Plasma and Cerebrospinal Fluid Biomarkers in Different Stages of Alzheimer's Disease and Frontotemporal Dementia

Alzheimer's disease (AD) is the primary type of dementia, followed by frontotemporal lobar degeneration (FTLD). They share some clinical characteristics, mainly at the early stages. So, the identification of early, specific, and minimally invasive biomarkers is required. In this study, some plasma biomarkers (Amyloid β42, p-Tau181, t-Tau, neurofilament light (NfL), TAR DNA-binding protein 43 (TDP-43)) were determined by single molecule array technology (SIMOA^®) in control subjects (n = 22), mild cognitive impairment due to AD (MCI-AD, n = 33), mild dementia due to AD (n = 12), and FTLD (n = 11) patients. The correlations between plasma and cerebrospinal fluid (CSF) levels and the accuracy of plasma biomarkers for AD early diagnosis and discriminating from FTLD were analyzed. As result, plasma p-Tau181 and NfL levels correlated with the corresponding CSF levels. Additionally, plasma p-Tau181 showed good accuracy for distinguishing between the controls and AD, as well as discriminating between AD and FTLD. Moreover, plasma NfL could discriminate dementia-AD vs. controls, FTLD vs. controls, and MCI-AD vs. dementia-AD. Therefore, the determination of these biomarkers in plasma is potentially helpful in AD spectrum diagnosis, but also discriminating from FTLD. In addition, the accessibility of these potential early and specific biomarkers may be useful for AD screening protocols in the future.

Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease

Background

The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients.

Methods

In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve.

Results

Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%].

Conclusion

Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.

Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer’s disease

The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ_1–42, Aβ_1–40, Aβ_1–42/Aβ_1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ_1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.

Machine Learning Reveals a Multipredictor Nomogram for Diagnosing the Alzheimer's Disease Based on Chemiluminescence Immunoassay for Total Tau in Plasma

Background

Predicting amnestic mild cognitive impairment (aMCI) in conversion and Alzheimer's disease (AD) remains a daunting task. Standard diagnostic procedures for AD population are reliant on neuroimaging features (positron emission tomography, PET), cerebrospinal fluid (CSF) biomarkers (Aβ1-42, T-tau, P-tau), which are expensive or require invasive sampling. The blood-based biomarkers offer the opportunity to provide an alternative approach for easy diagnosis of AD, which would be a less invasive and cost-effective screening tool than currently approved CSF or amyloid β positron emission tomography (PET) biomarkers.

Methods

We developed and validated a sensitive and selective immunoassay for total Tau in plasma. Robust signatures were obtained based on several clinical features selected by multiple machine learning algorithms between the three participant groups. Subsequently, a well-fitted nomogram was constructed and validated, integrating clinical factors and total Tau concentration. The predictive performance was evaluated according to the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. Decision curve analysis and calibration curves are used to evaluate the net benefit of nomograms in clinical decision-making.

Results

Under optimum conditions, chemiluminescence analysis (CLIA) displays a desirable dynamic range within Tau concentration from 7.80 to 250 pg/mL with readily achieved higher performances (LOD: 5.16 pg/mL). In the discovery cohort, the discrimination between the three well-defined participant groups according to Tau concentration was in consistent agreement with clinical diagnosis (AD vs. non-MCI: AUC = 0.799; aMCI vs. non-MCI: AUC = 0.691; AD vs. aMCI: AUC = 0.670). Multiple machine learning algorithms identified Age, Gender, EMPG, Tau, ALB, HCY, VB12, and/or Glu as robust signatures. A nomogram integrated total Tau concentration and clinical factors provided better predictive performance (AD vs. non-MCI: AUC = 0.960, AD vs. aMCI: AUC = 0.813 in discovery cohort; AD vs. non-MCI: AUC = 0.938, AD vs. aMCI: AUC = 0.754 in validation cohort).

Conclusion

The developed assay and a satisfactory nomogram model hold promising clinical potential for early diagnosis of aMCI and AD participants.

Identification and Validation of Aging-Related Genes in Alzheimer’s Disease

Aging is recognized as the key risk factor for Alzheimer’s disease (AD). This study aimed to identify and verify potential aging-related genes associated with AD using bioinformatics analysis. Aging-related differential expression genes (ARDEGs) were determined by the intersection of limma test, weighted correlation network analysis (WGCNA), and 1153 aging and senescence-associated genes. Potential biological functions and pathways of ARDEGs were determined by GO, KEGG, GSEA, and GSVA. Then, LASSO algorithm was used to identify the hub genes and the diagnostic ability of the five ARDEGs in discriminating AD from the healthy control samples. Further, the correlation between hub ARDEGs and clinical characteristics was explored. Finally, the expression level of the five ARDEGs was validated using other four GEO datasets and blood samples of patients with AD and healthy individuals. Five ARDEGs (GFAP, PDGFRB, PLOD1, MAP4K4, and NFKBIA) were obtained. For biological function analysis, aging, cellular senescence, and Ras protein signal transduction regulation were enriched. Diagnostic ability of the five ARDEGs in discriminating AD from the control samples demonstrated a favorable diagnostic value. Eventually, quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) validation test revealed that compared with healthy controls, the mRNA expression level of PDGFRB, PLOD1, MAP4K4, and NFKBIA were elevated in AD patients. In conclusion, this study identified four ARDEGs (PDGFRB, PLOD1, MAP4K4, and NFKBIA) associated with AD. They provide an insight into potential novel biomarkers for diagnosing AD and monitoring progression.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

18F-APN-1607 Tau Positron Emission Tomography Imaging for Evaluating Disease Progression in Alzheimer’s Disease

Purpose

¹⁸F-APN-1607 is a novel tau positron emission tomography (PET) tracer characterized with high binding affinity for 3− and 4-repeat tau deposits. The aim was to analyze the spatial distribution of ¹⁸F-APN-1607 PET imaging in Alzheimer’s disease (AD) subjects with different stages and to investigate the relationship between the change of tau deposition and overall disease progression.

Methods

We retrospectively analyzed the ¹⁸F-APN-1607 PET imaging of 31 subjects with clinically and imaging defined as AD. According to the Mini-Mental State Examination (MMSE) score, patients were divided into three groups, namely, mild (≥21, n = 7), moderate (10–20, n = 16), and severe (≤9, n = 8). PET imaging was segmented to 70 regions of interest (ROIs) and extracted the standard uptake value (SUV) of each ROI. SUV ratio (SUVR) was calculated from the ratio of SUV in different brain regions to the cerebellar cortex. The regions were defined as positive and negative with unsupervised cluster analysis according to SUVR. The SUVRs of each region were compared among groups with the one-way ANOVA or Kruskal–Wallis H test. Furthermore, the correlations between MMSE score and regional SUVR were calculated with Pearson or Spearman correlation analysis.

Results

There were no significant differences among groups in gender (χ² = 3.814, P = 0.161), age of onset (P = 0.170), age (P = 0.109), and education level (P = 0.065). With the disease progression, the ¹⁸F-APN-1607 PET imaging showed the spread of tau deposition from the hippocampus, posterior cingulate gyrus (PCG), and lateral temporal cortex (LTC) to the parietal and occipital lobes, and finally to the frontal lobe. Between the mild and moderate groups, the main brain areas with significant differences in ¹⁸F-APN-1607 uptake were supplementary motor area (SMA), cuneus, precuneus, occipital lobule, paracentral lobule, right angular gyrus, and parietal, which could be used for early disease progression assessment (P < 0.05). There were significant differences in the frontal lobe, right temporal lobe, and fusiform gyrus between the moderate and severe groups, which might be suitable for the late-stage disease progression assessment (P < 0.05).

Conclusion

¹⁸F-APN-1607 PET may serve as an effective imaging marker for visualizing the change pattern of tau protein deposition in AD patients, and its uptake level in certain brain regions is closely related to the severity of cognitive impairment. These indicate the potential of ¹⁸F-APN-1607 PET for the in vivo evaluation of the progression of AD.