Clinical Diagnosis Of Alzheimer's Disease Research Articles

Potential Utility of Plasma Biomarker Panels in Differential Diagnosis of Alzheimer's Disease.

Blood tests are in need, in the clinical diagnosis of Alzheimer's disease (AD) as minimally invasive and less expensive alternatives to cerebrospinal fluid and neuroimaging methods. On these lines, single molecule array (Simoa) analysis of amyloid-β (Aβ42), total tau (t-tau), phospho-tau (p-tau 181), and neurofilament L (NfL) in the plasma samples of AD subjects, healthy controls (HC), and non-AD subjects was conducted. Findings from this study suggest that a panel of multiple plasma biomarkers (NfL, Aβ42, t-tau, and p-tau 181) combined with the clinical assessments could support differential diagnosis of AD and other dementias from healthy controls.

Use of modern classification systems for complex diagnostics of Alzheimer's disease

To compare the content of β-amyloid (Aβ) peptides Aβ40, Aβ42, total and threonine phosphorylated 181 tau-protein in cerebrospinal fluid (CSF) of patients with the clinical diagnosis of Alzheimer's disease (AD). The study was performed on 64 patients with a diagnosis of dementia and MMSE scores of 24 or lower. All patients underwent lumbar puncture. Aβ40, Aβ42, Aβ42/40 ratio, total tau, phosphorylated tau at threonine 181 were determined in the CSF using a multiplex assay according to the manufacturer's protocol, the concentration was determined in pkg/ml. The preliminary diagnosis of AD was made in 3 patients (5%). As a result of the study of protein content in the CSF, signs of AD were detected in 48 (75%) people. The findings suggest that the diagnosis of AD is made 10-14 times less frequently than it should be according to the World Health Organization data. The discrepancy between clinical diagnosis and laboratory findings is confirmed by our study. Differences in the therapy of dementias and the development of new drugs targeting specific links in the pathogenesis of different types of dementias require accurate and complete diagnosis of dementias, especially AD, as the most common type of dementia.

Cerebrospinal fluid biomarker profiling of diverse pathophysiological domains in Alzheimer's disease.

While Alzheimer's disease (AD) is defined by amyloid-β plaques and tau tangles in the brain, it is evident that many other pathophysiological processes such as inflammation, neurovascular dysfunction, oxidative stress, and metabolic derangements also contribute to the disease process and that varying contributions of these pathways may reflect the heterogeneity of AD. Here, we used a previously validated panel of cerebrospinal fluid (CSF) biomarkers to explore the degree to which different pathophysiological domains are dysregulated in AD and how they relate to each other. Twenty-five CSF biomarkers were analyzed in individuals with a clinical diagnosis of AD verified by positive CSF AD biomarkers (AD, n = 54) and cognitively unimpaired controls negative for CSF AD biomarkers (CU-N, n = 26) using commercial single- and multi-plex immunoassays. We noted that while AD was associated with increased levels of only three biomarkers (MMP-10, FABP3, and 8OHdG) on a group level, half of all AD participants had increased levels of biomarkers belonging to at least two pathophysiological domains reflecting the diversity in AD. LASSO modeling showed that a panel of FABP3, 24OHC, MMP-10, MMP-2, and 8OHdG constituted the most relevant and minimally correlated set of variables differentiating AD from CU-N. Interestingly, factor analysis showed that two markers of metabolism and oxidative stress (24OHC and 8OHdG) contributed independent information separate from MMP-10 and FABP3 suggestive of two independent pathophysiological pathways in AD, one reflecting neurodegeneration and vascular pathology, and the other associated with metabolism and oxidative stress. Better understanding of the heterogeneity among individuals with AD and the different contributions of pathophysiological processes besides amyloid-β and tau will be crucial for optimizing personalized treatment strategies. A panel of 25 highly validated biomarker assays were measured in CSF.MMP10, FABP3, and 8OHdG were increased in AD in univariate analysis.Many individuals with AD had increased levels of more than one biomarker.Markers of metabolism and oxidative stress contributed to an AD multianalyte profile.Assessing multiple biomarker domains is important to understand disease heterogeneity.

DMA-HPCNet: Dual Multi-level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural network (CNN)-based methods are highly sensitive to subtle changes caused by brain atrophy in medical images (e.g., magnetic resonance imaging, MRI). Due to computational resource constraints, most CAD methods focus on quantitative features in specific regions, neglecting the holistic nature of the images, which poses a challenge for a comprehensive understanding of pathological changes in AD. To address this issue, we propose a lightweight dual multi-level hybrid pyramid convolutional neural network (DMA-HPCNet) to aid clinical diagnosis of AD. Specifically, we introduced ResNet as the backbone network and modularly extended the hybrid pyramid convolution (HPC) block and the dual multi-level attention (DMA) module. Among them, the HPC block is designed to enhance the acquisition of information at different scales, and the DMA module is proposed to sequentially extract different local and global representations from the channel and spatial domains. Our proposed DMA-HPCNet method was evaluated on baseline MRI slices of 443 subjects from the ADNI dataset. Experimental results show that our proposed DMA-HPCNet model performs efficiently in AD-related classification tasks with low computational cost.

Alzheimers disease course prediction methods based on deep learning methods

Alzheimer 's disease (AD) is a primary disease of the nervous system with memory and cognitive impairment as the main symptoms. It is the most common type of senile dementia and seriously threatens the quality of life and life safety of the elderly. Due to the pathological characteristics of AD and the limitations of medical technology, there is currently no very effective treatment worldwide. Therefore, early prediction and diagnosis of AD has always been a research hotspot worldwide. Deep learning may finish the joint classification of several stages by mining the abundant information implicit in the visual data collected from patients. It is a hub for computer-assisted diagnosis research. Therefore, many studies have applied computer-aided methods such as deep learning to the medical image data of Alzheimer 's disease, so as to provide ideas and help for prediction and diagnosis of diseases. This paper analyzes and compares various network structures, summarizes the use of the current, comparatively new deep learning network models applied in the classification of Alzheimer's disease course, and discusses the difficulties and potential future research directions for deep learning in the field of AD diagnosis. Thus, it is very important to raise the efficiency of the clinical diagnosis of AD and the accuracy of early forecasts.

Striatal Amyloid deposition in sporadic Alzheimer’s Disease

AbstractBackgroundIt has been shown that disorders associated with early‐life alterations in amyloid production or processing such as in autosomal dominant Alzheimer’s disease (AD) and down syndrome‐associated dementia have a different pattern of Aβ deposition, with early striatal accumulation, as compared to sporadic late‐onset cases, in which striatal deposition has been suggested to be a marker of late and advanced amyloidosis. It is not clear, however, if the pattern of amyloid accumulation is related to any other factors or if it has any impact on brain metabolism. We aimed to determine if there are any clinical or demographic predictors of striatal tau accumulation in sporadic AD and whether its presence affects the subjects´ brain metabolism.MethodSubjects that underwent positron emission tomography (PET) scans with 18F‐FDG and 11C‐PIB were analyzed retrospectively. Only those with clinical diagnosis of AD and mild cognitive impairment (MCI) due to AD were considered for the analysis. Visual analyses were performed to exclude subjects without cortical amyloid accumulation. Mean values from cortical volumes of interest (VOIs) and striatum were obtained. Amyloid deposition was correlated with age and disease duration via Pearson’s correlations. Amyloid deposition was also correlated to brain metabolism as assessed by FDG PET.Result150 subjects were included in this study. Neither age nor disease duration was correlated with striatal amyloid accumulation. Subjects with striatal Pib accumulation showed higher cortical amyloid burden. Moreover, higher striatal amyloid burden was positively correlated with higher frontal amyloid burden specifically. Amyloid burden in the striatum did not seem to have an impact on the glucose metabolism in the striatum itself. However, variations in cortical glucose metabolism could be observed. A higher striatal amyloid burden was correlated with cortical glucose hypometabolism, specifically in the precuneus and temporal areas (Figure 1).ConclusionThere seems to be a stage of sporadic Alzheimer’s disease in which amyloid accumulates in the striatum. This, in turn, appears to have an impact on brain functioning. Whether this has a real impact on cognitive performance remains to be elucidated.

Time‐encoded ASL shows higher sensitivity to detect cerebral blood flow reductions in AD

AbstractBackgroundPseudo continuous arterial spin labelling (pcASL) is an MRI technique to quantify cerebral blood flow (CBF). Time encoded ASL (te‐ASL) is a multiple time‐point pcASL variant that by acquiring several post‐label delays (PLD) to have more accurate CBF estimation allowing to measure arterial‐transit time (ATT). The aim of this study was to assess the performance of te‐ASL in detecting CBF and ATT changes in the Alzheimer’s disease (AD) continuum.MethodThe sample consisted of 59 subjects: 25 healthy controls (HC), 17 cognitively unimpaired individuals with positive cerebrospinal fluid (CSF) biomarkers (Ab42<1.098pg/mL; p‐Tau181>19pg/mL; Elecsys® ‐Roche Diagnostics) (preAD) and 17 patients with clinical diagnosis of AD (Ab42/p‐Tau181 ratio <10.25; Lumipulse‐Fujirebio). The MRI protocol comprised of anatomical T1, and ASL sequences (M0 and te‐ASL). CBF and ATT maps were calculated in the oxford_asl toolbox in FSL. CBF and ATT maps were spatially normalized to the MNI, smoothed (12mm FWHM) and intensity normalized to the cerebellar gray matter. To detect patterns of lower CBF, voxel‐wise one way 3‐group ANOVA and post‐hoc paired t‐tests were performed using age and sex as confounders (SPM12; statistical threshold: p<0.001 uncorrected; k>100 voxels (∼0.33cm3) in all analyses). We compared results with those obtained using a single PLD by computing CBF from the last PLD (perfusion block, nominal PLD time of 2000ms) of the te‐ASL schema.ResultTable 1 displays the descriptive statistics of the sample. Figure 1 shows that, with te‐ASL, AD patients displayed lower CBF (yellow) in extensive bilateral pareto‐temporal, cingulate and occipital areas. Preclinical AD participants showed an overlapping pattern (green), albeit reduced in extension. When using pcASL, the detected pattern of lower CBF in the AD group was less extended and did not overlap with that of the PreAD groups. No ATT differences were found associated to AD.Conclusionte‐ASL was more sensitive to detect CBF differences across the AD continuum than single PLD ASL. The pattern of lower CBF in the AD group covered expected areas, and that in the preclinical AD group partially overlapped.

CSF Abeta 3‐42/1‐42 ratio and neurogranin as biomarkers of interest in dementia with Lewy bodies

AbstractBackgroundDementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are the among the most common neuropathologies. Amyloid and tau biomarker changes are often incorporated in the clinical diagnosis of AD. The current DLB criteria does not include robust biofluid biomarkers. However differences in amyloid plaque and CSF neurogranin in DLB has been of interest based on neuropathology and prior CSF reports. Our objective was to determine if Abeta modifications Abeta 3‐42 and Abeta 1‐42 along with neurogranin could be used to differentiate DLB from AD.MethodThis cohort study among 45 MCI stage of AD meeting NIA/AA 2011 criteria and 15 DLB participants meeting the 2017 Mc Keith criteria. We evaluated the CSF levels of Abeta 3‐42, Abeta 1‐42 by Liquid chromatography‐mass spectrometry (LC‐MS/MS) techniques and CSF neurogranin by standard ELISA to explore the differences between groups. Two sample t‐tests both without and with data imputation for samples not meeting lower levels of detection and linear regression models by considering the confounding variables of age, sex and education were assessed.ResultMean age 67.67 (SD5.13) years, 33% Female. Mean neurogranin levels in DLB 325.9 (SD 200.34 were lower than in MCI‐AD 707.7 (SD 489.94) pg/ml, (p = 0.0006 from two‐sample t‐test). Mean levels of Abeta 3‐42 were 51.63(SD = 19.6) and 43.63(SD = 13.5)fmol/l, and Abeta 1‐42 848.7 (SD = 248.4) fmol/l and 981.5(SD = 265.0) in DLB and MCI‐AD, respectively and were not significantly different. The ratio of Abeta 3‐42/Abeta 1‐42 was higher in DLB 0.06 (SD = 0.02) than MCI‐AD 0.04 (SD = 0.01) (p = 0.008 from two‐sample t‐test). In the multivariable regression models after adjusting for confounding variables the ratio of Abeta 3‐42/Abeta 1‐42 (Est = 67.19,SE = 29.62, p = 0.02) and neurogranin (Est = ‐0.0069,SE = 0.0026,p = 0.009) were significant discriminators between DLB and MCI‐AD .ConclusionHigher ratio of CSF Abeta 3‐42/Abeta 1‐42 and lower CSF levels of neurogranin were noted in the DLB group compared to MCI‐AD. Lower levels of CSF neurogranin in DLB has been reported, while higher ratio of CSF Abeta 3‐42/Abeta 1‐42 in DLB is a novel biomarker finding. Differences in Abeta species in DLB could be potential biomarkers in separating DLB from AD.

Usefulness of R2 maps to detect GM changes in AD continuum: a pilot study

AbstractBackgroundMRI multi‐echo sequences can be used to compute quantitative R2 maps (1/T2). This pilot study aimed to determine whether R2 maps might offer relevant information on GM changes in AD continuum.MethodThe sample consisted of 60 subjects: 20 healthy controls (HC), 20 cognitively unimpaired individuals with CSF Aβ42/40<0.071 (preAD) and 20 subjects with clinical diagnosis of Alzheimer disease (AD) validated by biomarkers (Ab42/p‐Tau181 ratio <10.25; Lumipulse‐Fujirebio). The MRI protocol included 3D T1W (voxel size: 1.2×1.2×1.2mm3, TR = 9.8ms, TE = 3ms, TI = 900ms) and multi‐echo (GRASE, voxel size: 2×2.2×2 mm3, TR = 2000ms, 32 echoes, TE: successive multiples of 9.3ms). R2 maps were obtained through linear regression of the logarithm of multi‐echo images. GM and R2 maps were spatially normalized to the MNI and smoothed (8mm FWHM). Voxel‐wise one‐way 3‐group ANOVA and post‐hoc paired t‐tests were performed on modulated GM (GMv) and R2 maps using age and sex as confounders and applying a common GM mask (SPM12, statistical threshold: p<0.001 uncorrected; k>100 voxels (∼0.33cm3) in all analyses).ResultTable 1 displays the descriptive statistics of the sample. Figure 1 shows the results from post‐hoc two‐sample t‐tests between HC and AD for GMv and R2 maps. GMv decrements were mostly located in temporal and parietal regions, as expected. Differences in R2 maps covered those regions but also others such anterior cingulate. No differences were found between HC and preAD neither in R2 maps nor in GMv.ConclusionResults might indicate that changes in R2 are not only reflecting atrophy but other undetermined tissular changes. Further research is warranted to further determine the mechanisms underlying R2 maps changes and the potential usefulness of R2 maps in AD characterization and diagnosis.

Cardiovascular health and the risk of Alzheimer’s dementia in a biracial population‐based study

AbstractBackgroundThe cardiovascular health (CVH) index, created by the American Heart Association (AHA) to assess and monitor the risk of cardiovascular disease, has been related to dementia risk. However, evidence is limited, and most studies originate from populations comprised of primarily white participants. We examined the association of the CVH index with the risk of incident Alzheimer’s dementia in a biracial population‐based study.MethodOur study consisted of 1,702 non‐Hispanic Black and white participants of the Chicago Health and Aging Project, a population‐based cohort. CVH was based on seven risk factors: diet, physical activity, body mass index, smoking, dyslipidemia, hypertension, and diabetes. Following AHA recommendations, we assigned points of 0, 1, or 2 based on adherence to each factor, with an overall score ranging from 0 to 14 – a higher score indicates better cardiovascular health. Clinical diagnosis of Alzheimer’s dementia was based on the NINCDS‐ADRDA criteria. Cox proportional hazard models were used to compute the hazard ratio and 95% confidence interval. Models were adjusted for age, sex, race, education, ApoE4 allele, cognitive activities, and cardiovascular disease.ResultThe average baseline age of the study population was 73 years and comprised 62% women and 53% black participants. During 14,226 person‐years of follow‐up, there were 351 incident Alzheimer’s dementia. In a multivariable‐adjusted model, CVH was linearly associated with a lower risk of incident Alzheimer’s dementia. For each additional CVH score, the HR was lower by 0.84 (95% CI 0.76 ‐ 0.94). Compared to individuals with inadequate CVH (score: 0‐6), the HRs (95% CIs) of incident Alzheimer’s dementia in participants with average (7‐9) and optimal CVH (10‐14) was 0.44 (95%CI 0.28 ‐ 0.69) and 0.37 (95%CI 0.21 ‐ 0.65), respectively. We found similar associations between CVH score, per one additional score, and risk of Alzheimer’s dementia in Black (HR 0.84, 95%CI 0.74 ‐ 0.95) and white (HR 0.83, 95%CI 0.7 ‐ 0.98) individuals.ConclusionIn a biracial cohort of older adults, the cardiovascular health burden was higher among Black individuals. However, promoting a better CVH in the general population may help lower dementia risk in Black and white older adults.

Discovery and optimization of the first‐in‐class TDP‐43 PET tracer

AbstractBackgroundIntracellular aggregation of TDP‐43 is found in most patients with amyotrophic lateral sclerosis (ALS), 45% of patients with frontotemporal dementia (FTD) and 30‐50% of patients with clinical diagnosis of Alzheimer’s disease. Sensitive fluid or imaging biomarkers of TDP‐43 pathology are currently not available. Direct detection of TDP‐43 aggregates by positron emission tomography (PET) holds promise for a more accurate diagnosis, patient stratification and assessment of therapeutic efficacy in clinical trials.MethodsUsing ACI’s Morphomer® small molecule library focused on brain penetrant and beta sheet binding compounds, initial hits were identified, and iterative medicinal chemistry applied to discover compounds with optimized properties. Radiobinding and autoradiography experiments on FTLD‐TDP brain samples were used to evaluate affinity and target engagement. These techniques were also used to assess selectivity over other aggregation‐prone proteins using brain material from Alzheimer’s (AD) and Parkinson’s disease (PD) cases. Brain pharmacokinetic (PK) profile was obtained in mice, followed by assessment of brain uptake, distribution and washout for selected 18F radiolabeled compounds in non‐human primates (NHP).ResultsSeveral chemical series showing nanomolar binding affinity to aggregated TDP‐43 from patient brain samples were identified. The specificity of binding was further confirmed by high resolution autoradiography enabling visualization of target engagement on TDP‐43 inclusions in FTLD‐TDP brain sections. In addition, the most advanced compounds differentiated FTLD‐TDP from control by classical autoradiography on patient brain sections. Selectivity over amyloid beta, alpha‐synuclein and Tau was observed. The compounds with the most favorable brain penetrantion properties demonstrated fast and substantial brain uptake and fast and complete washout in NHPs.ConclusionsFirst‐in‐class TDP‐43 PET ligands have been discovered with the potential to identify FTLD‐TDP pathology by PET. These tools may finally allow the ability to detect and follow the progression of TDP‐43 pathology in the brains of living patients with certain TDP‐43 proteinopathies.

A retinal tauopathy with a characteristic molecular pattern points to the existence of a “Primary retinal tauopathy”

AbstractBackgroundPathogenic processes characteristic of neurodegenerative brain diseases, such as accumulation of phosphorylated Tau protein (pTau), can also be detected in the retina. Given the advanced state of retinal imaging technology and its potential to aid in the clinical diagnosis of Alzheimer’s disease (AD), it is imperative to investigate the part the retina plays in neurodegenerative diseases of the brain, as well as the associated eye‐specific degenerative processes.MethodsRetinas from 129 individuals who underwent eyeball resection/evisceration for severe ophthalmological conditions and from 35 post‐mortem donors with or without AD were analyzed. Paraffin‐embedded sections from cross‐sectioned retinas were stained with antibodies against different pTau species and Gallyas staining to detect neurofibrillary tangles. Retinal pTau accumulation, its distribution over the retinal layers, and molecular composition were determined neuropathologically and correlated with visual performance, age, and the presence of dementia.ResultsRetinal pTau pathology showed a consistent topographical expansion pattern that allowed distinguishing four stages: Stage 0: no pTau pathology; Stage 1: thread‐like pTau in the outer plexiform layer; Stage 2: additional cytoplasmic pTau in the inner nuclear layer; Stage 3: thread‐like pTau also in the inner plexiform layers. Antibodies against pTauS202/pT205 (AT8), pTauT231 (AT180), and anti‐MC‐1 Tau showed comparable results. Neurofibrillary tangles were not found. The stage of retinal tauopathy was correlated with age (r = 0.176, p = 0.024, n = 164), the underlying ophthalmologic diseases (p = 0.001; β = 0.292), and independently associated with vision impairment (p = 0.030; β = 0.192) in a linear regression model. Cases with a clinical diagnosis of dementia were associated with slightly higher stages of retinal tauopathy (binary logistic regression: p = 0.026; odds ratio (OR) = 1.685 [95% C.I.1.066‐2.664), although some demented cases were free of pTau pathology.ConclusionsThese results point towards the existence of a primary retinal tauopathy (PReT). It differs in its molecular composition from cerebral tauopathies by the lack of argyrophilic inclusions and the predominance of 3‐repeat Tau. However, PReT may be a prerequisite for developing AD pathology in the retina. Altogether, these findings support the presence of retinal neurodegenerative changes that may reflect changes in the brain.FundingSAO/FRA 2020/017

Transcriptomic profile across different brain areas in Alzheimer’s disease

AbstractBackgroundAging is often characterized by a progressive loss of cognitive abilities due to the onset of Alzheimer Disease (AD). In recent years, ‐omic sciences have allowed us to begin to shed light on its molecular pathways in order to speculate about new and attractive hypothesis on AD pathogenesis.MethodIn this study, a whole transcriptome analysis and a Gene Set Enrichment Analysis (GSEA) have been carried out on brain samples from hippocampus (HI), temporal and parietal cortex (TC, PC), cingulate cortex (CG), and substantia nigra (SN) from the Abbiategrasso Brain Bank (ABB) of 6 subjects with clinical and neuropathological diagnosis of AD and 3 healthy age‐matched controls in duplicate. Clinical and neuropsychological profile were obtained through serial evaluations, made at the baseline and in the following years. According to the ABB protocol, the neuropathological diagnosis was based on a complete histological characterization, including all the main proteinopathies and vascular scoring.ResultTranscriptomic results showed a greater number of differentially expressed genes (DEGs) in TC (1571) and CG (1210) and a least amount of DEGs in HI (206), PC (109) and SN (60). Furthermore, the GSEA showed a difference between the group of early affected brain areas (HI, TC) and the group of the subsequently involved areas (PC, CG, SN). Notably, in HI and TC there was a significant down‐regulation of shared DEGs primarily involved in the synaptic transmission and in the development of the central nervous system (CDK5R1, KCNC1, KCNC2, BSN), while in PC, CG and SN there was a significant down‐regulation of genes primarily involved in the proper protein folding and trafficking and inflammation (HSPA1B, HSPA1A, DNAJB1, FKBP4, CHORDC1, STIP1).ConclusionThe course of AD could follow a definite time and severity‐related pattern that arises from inflammatory processes and protein misfolding, observed in PC, CG and SN, and leads to synaptic impairment, observed in HI and TC. Therefore, we can relate neurobiological processes to clinical manifestations and outline a clearer picture of AD neuropathology and pathogenesis. This could help to discover novel biological targets in order to develop effective and well‐timed therapeutical approaches.

Relationship between cognitive function and phase angle measured with a bioelectrical impedance system.

The purpose of this study was to investigate the relationship between cognitive function and phase angle (PhA), an indicator of muscle quality. This cross-sectional study enrolled outpatients who visited a memory clinic at the Nagoya University hospital from January 2016 to June 2022. We enrolled 153 participants with body composition measurements. Inclusion criteria were a Mini-Mental State Examination score of 20-30 and a clinical diagnosis of Alzheimer's dementia (AD) or amnesic mild cognitive impairment (aMCI). The background characteristics of the participants were compared according to AD and aMCI. Next, linear regression analysis was performed with PhA as the objective variable. In addition, logistic regression analysis was performed for AD diagnosis. PhA was lower in the AD group (P = 0.009). In linear regression analysis, PhA consistently decreased with worsening ADAS score. In logistic regression analysis, high PhA was associated with absence of AD. Gender-specific analyses showed these associations existed only in men. Our study of patients with AD and aMCI found that PhA decreased with worsening of cognitive function. Compared with aMCI, AD was associated with significantly lower PhA. Our results strengthen the limited evidence in the literature showing that low muscle quality is associated with poor cognitive function.

Integrative multi-omics analysis reveals the critical role of the PBXIP1 gene in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder, and its strongest risk factor is aging. A few studies have explored the relationship between aging and AD, while the underlying mechanism remains unclear. We assembled data across multi-omics (i.e., epigenetics, transcriptomics, and proteomics, based on frozen tissues from the dorsolateral prefrontal cortex) and neuropathological and clinical traits from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Aging was assessed using six DNA methylation clocks (including the Horvath clock, Hannum clock, Levine clock, HorvathSkin clock, Lin clock, and Cortical clock) that capture mortality risk in literature. After accounting for age, we first identified a gene module (including 263 genes) that was related to the integrated aging measure of six clocks, as well as three neuropathological traits of AD (i.e., β-amyloid, Tau tangles, and tangle density). Interestingly, among 20 key genes with top intramodular connectivity of the module, PBXIP1 was the only one that was significantly associated with all three neuropathological traits of AD at the protein level after Bonferroni correction. Furthermore, PBXIP1 was associated with the clinical diagnosis of AD in both ROSMAP and three independent datasets. Moreover, PBXIP1 may be related to AD through its role in astrocytes and hippocampal neurons, and the mTOR pathway. The results suggest the critical role of PBXIP1 in AD and support the potential and feasibility of using multi-omics data to investigate mechanisms of complex diseases. However, more validations in different populations and experiments invitro and invivo are required in the future.

Research progress on biomarkers and detection methods for Alzheimer's disease diagnosis in vitro

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

Retina pathology as a target for biomarkers for Alzheimer's disease: Current status, ophthalmopathological background, challenges, and future directions.

There is emerging evidence that amyloid beta protein (Aβ) and tau-related lesions in the retina are associated with Alzheimer's disease (AD). Aβ and hyperphosphorylated (p)-tau deposits have been described in the retina and were associated with small amyloid spots visualized by in vivo imaging techniques as well as degeneration of the retina. These changes correlate with brain amyloid deposition as determined by histological quantification,positron emission tomography (PET) or clinical diagnosis of AD. However, the literature is not coherent on these histopathological and in vivo imaging findings. One important reason for this is the variability in the methods and the interpretation of findings across different studies. In this perspective, we indicate the critical methodological deviations among different groups and suggest a roadmap moving forward on how to harmonize (i) histopathologic examination of retinal tissue; (ii) in vivo imaging among different methods, devices, and interpretation algorithms; and (iii) inclusion/exclusion criteria for studies aiming at retinal biomarker validation.

Hippocampal injections of soluble amyloid-beta oligomers alter electroencephalographic activity during wake and slow-wave sleep in rats

BackgroundSoluble amyloid-beta oligomers (Aβo) begin to accumulate in the human brain one to two decades before a clinical diagnosis of Alzheimer’s disease (AD). The literature supports that soluble Aβo are implicated in synapse and neuronal losses in the brain regions such as the hippocampus. This region importantly contributes to explicit memory, the first type of memory affected in AD. During AD preclinical and prodromal stages, people are also experiencing wake/sleep alterations such as insomnia (e.g., difficulty initiating sleep, decreased sleep duration), excessive daytime sleepiness, and sleep schedule modifications. In addition, changes in electroencephalographic (EEG) activity during wake and sleep have been reported in AD patients and animal models. However, the specific contribution of Aβo to wake/sleep alterations is poorly understood and was investigated in the present study.MethodsChronic hippocampal injections of soluble Aβo were conducted in male rats and combined with EEG recording to determine the progressive impact of Aβ pathology specifically on wake/sleep architecture and EEG activity. Bilateral injections were conducted for 6 consecutive days, and EEG acquisition was done before, during, and after Aβo injections. Immunohistochemistry was used to assess neuron numbers in the hippocampal dentate gyrus (DG).ResultsAβo injections did not affect the time spent in wakefulness, slow wave sleep (SWS), and paradoxical sleep but altered EEG activity during wake and SWS. More precisely, Aβo increased slow-wave activity (SWA; 0.5–5 Hz) and low-beta activity (16–20 Hz) during wake and decreased theta (5–9 Hz) and alpha (9–12 Hz) activities during SWS. Moreover, the theta activity/SWA ratio during wake and SWS was decreased by Aβo. These effects were significant only after 6 days of Aβo injections and were found with alterations in neuron counts in the DG.ConclusionsWe found multiple modifications of the wake and SWS EEG following Aβo delivery to the hippocampus. These findings expose a specific EEG signature of Aβ pathology and can serve the development of non-invasive and cost-effective markers for the early diagnosis of AD or other amyloid-related diseases.

Biomarkers and Risk Assessment of Alzheimer's Disease in Low- and Middle-Income Countries.

Dementia is a chronic syndrome which is common among the elderly and is associated with significant morbidity and mortality for patients and their caregivers. Alzheimer's disease (AD), the most common form of clinical dementia, is biologically characterized by the deposition of amyloid-β plaques and neurofibrillary tangles in the brain. The onset of AD begins decades before manifestation of symptoms and clinical diagnosis, underlining the need to shift from clinical diagnosis of AD to a more objective diagnosis using biomarkers. Having performed a literature search of original articles and reviews on PubMed and Google Scholar, we present this review detailing the existing biomarkers and risk assessment tools for AD. The prevalence of dementia in low- and middle-income countries (LMICs) is predicted to increase over the next couple of years. Thus, we aimed to identify potential biomarkers that may be appropriate for use in LMICs, considering the following factors: sensitivity, specificity, invasiveness, and affordability of the biomarkers. We also explored risk assessment tools and the potential use of artificial intelligence/machine learning solutions for diagnosing, assessing risks, and monitoring the progression of AD in low-resource settings. Routine use of AD biomarkers has yet to gain sufficient ground in clinical settings. Therefore, clinical diagnosis of AD will remain the mainstay in LMICs for the foreseeable future. Efforts should be made towards the development of low-cost, easily administered risk assessment tools to identify individuals who are at risk of AD in the population. We recommend that stakeholders invest in education, research and development targeted towards effective risk assessment and management.

EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.

Despite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases. The research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods. The results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively. The study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD.