Diagnostic Biomarkers For Alzheimer's Disease Research Articles

Surface protein profiling and subtyping of extracellular vesicles in body fluids reveals non-CSF biomarkers of Alzheimer's disease.

Noninvasive and effortless diagnosis of Alzheimer's disease (AD) remains challenging. Here we report the multiplexed profiling of extracellular vesicle (EV) surface proteins at the single EV level in five types of easily accessible body fluids using a proximity barcoding assay (PBA). A total of 183 surface proteins were detected on the EVs from body fluids collected from APP/PS1 transgenic mice and patients with AD. The AD-associated differentially expressed EV proteins could discriminate between the control and AD/AD model samples with high accuracy. Based on machine learning predictive models, urinary EV proteins exhibited the highest diagnostic potential compared to those on other biofluid EVs, both in mice and humans. Single EV analysis further revealed AD-associated EV subpopulations in the tested body fluids, and a urinary EV subpopulation with the signature proteins PLAU, ITGAX and ANXA1 could diagnose patients with AD in blinded datasets with 88% accuracy. Our results suggest that EVs and their subpopulations from noninvasive body fluids, particularly urine, are potential diagnostic biomarkers for AD.

ER-stress response in retinal Müller glia occurs significantly earlier than amyloid pathology in the Alzheimer's mouse brain and retina.

Alzheimer's Disease (AD) pathogenesis is thought to begin up to 20 years before cognitive symptoms appear, suggesting the need for more sensitive diagnostic biomarkers of AD. In this report, we demonstrated pathological changes in retinal Müller glia significantly earlier than amyloid pathology in AD mouse models. By utilizing the knock-in NLGF mouse model, we surprisingly discovered an increase in reticulon 3 (RTN3) protein levels in the NLGF retina as early as postnatal day 30 (P30). Despite RTN3 being a canonically neuronal protein, this increase was noted in the retinal Müller glia, confirmed by immunohistochemical characterization. Further unbiased transcriptomic assays of the P30 NLGF retina revealed that retinal Müller glia were the most sensitive responding cells in this mouse retina, compared with other cell types including photoreceptor cells and ganglion neurons. Pathway analyses of differentially expressed genes in glia cells showed activation of ER stress response via the upregulation of unfolded protein response (UPR) proteins such as ATF4 and CHOP. Early elevation of RTN3 in response to challenges by toxic Aβ likely facilitated UPR. Altogether, these findings suggest that Müller glia act as a sentinel for AD pathology in the retina and should aid for both intervention and diagnosis.

Entropy-driven catalysis-based lateral flow assay for sensitive detection of Alzheimer 's-associated MicroRNA

Alzheimer's disease (AD) is a degenerative disease of the brain worldwide. Currently, there is no effective cure. But accurate and early diagnosis of AD is critical to the development of patient care and future treatments. MiRNA-16 has been considered as an effective diagnostic biomarker for AD because of its regulatory effect on key proteins of AD. Herein, a colorimetric lateral flow assay (LFA) was developed for sensitive detection of miRNA-16 based on entropy-driven catalysis (EDC) amplification strategy. MiRNA-16 triggered EDC and released more linker DNAs (LDNA) of sandwich structure. Thus, AuNPs were enriched at the T-line to enhance the colorimetric signal and improve the sensitivity of visual assay. It showed good specificity and sensitivity for detecting miRNA-16 with a detection limit of 1.01 pM. The practical detection of miRNA-16 in human serum obtained satisfactory result. Significantly, EDC achieved signal amplification in homogeneous solution without enzyme and DNA labeling, leading to a cheap and easy detection of miRNA-16. Therefore, it provided a portable and rapid assay for AD-related nucleic acid, which holds a potential for point-of-care testing (POCT) of AD.

Alzheimer’s disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling

Amyloid-β, tau pathology, and biomarkers of neurodegeneration make up the core diagnostic biomarkers of Alzheimer disease (AD). However, these proteins represent only a fraction of the complex biological processes underlying AD, and individuals with other brain diseases in which AD pathology is a comorbidity also test positive for these diagnostic biomarkers. More AD-specific early diagnostic and disease staging biomarkers are needed. In this study, we performed tandem mass tag proteomic analysis of paired cerebrospinal fluid (CSF) and serum samples in a discovery cohort comprising 98 participants. Candidate biomarkers were validated by parallel reaction monitoring-based targeted proteomic assays in an independent multicenter cohort comprising 288 participants. We quantified 3,238 CSF and 1,702 serum proteins in the discovery cohort, identifying 171 and 860 CSF proteins and 37 and 323 serum proteins as potential early diagnostic and staging biomarkers, respectively. In the validation cohort, 58 and 21 CSF proteins, as well as 12 and 18 serum proteins, were verified as early diagnostic and staging biomarkers, respectively. Separate 19-protein CSF and an 8-protein serum biomarker panels were built by machine learning to accurately classify mild cognitive impairment (MCI) due to AD from normal cognition with areas under the curve of 0.984 and 0.881, respectively. The 19-protein CSF biomarker panel also effectively discriminated patients with MCI due to AD from patients with other neurodegenerative diseases. Moreover, we identified 21 CSF and 18 serum stage-associated proteins reflecting AD stages. Our findings provide a foundation for developing blood-based tests for AD screening and staging in clinical practice.

Identification of Candidate Biomarkers of Alzheimer's Disease via Multiplex Cerebrospinal Fluid and Serum Proteomics.

Alzheimer's disease (AD) is the most prevalent form of dementia among elderly people worldwide. Cerebrospinal fluid (CSF) is the optimal fluid source for AD biomarkers, while serum biomarkers are much more achievable. To search for novel diagnostic AD biomarkers, we performed a quantitative proteomic analysis of CSF and serum samples from AD and normal cognitive controls (NC). CSF and serum proteomes were analyzed via data-independent acquisition quantitative mass spectrometry. Our bioinformatic analysis was based on Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. In comparison to the controls, 8 proteins were more abundant in AD CSF, and 60 were less abundant in AD CSF, whereas 55 proteins were more and 10 were less abundant in the serum samples. ATPase-associated activity for CSF and mitochondrial functions for CSF and serum were the most enriched GO terms of the DEPs. KEGG enrichment analysis showed that the most significant pathways for the differentially expressed proteins were the N-glycan biosynthesis pathways. The area under the curve (AUC) values for CSF sodium-/potassium-transporting ATPase subunit beta-1 (AT1B1), serglycin (SRGN), and thioredoxin-dependent peroxide reductase, mitochondrial (PRDX3) were 0.867 (p = 0.004), 0.833 (p = 0.008), and 0.783 (p = 0.025), respectively. A panel of the above three CSF proteins accurately differentiated AD (AUC = 0.933, p = 0.001) from NC. The AUC values for serum probable phospholipid-transporting ATPase IM (AT8B4) and SRGN were moderate. The AUC of the CSF SRGN + serum SRGN was 0.842 (p = 0.007). These novel AD biomarker candidates are mainly associated with inflammation, ATPase activity, oxidative stress, and mitochondrial dysfunction. Further studies are needed to investigate the molecular mechanisms by which these potential biomarkers are involved in AD.

Recent Breakthroughs in the Diagnosis and Treatment of Neurodegenerative Disorders

This paper covers recent breakthroughs in diagnosis and treatment of neurodegenerative disorders, specifically diagnostic biomarkers and disease modifying treatments for Alzheimer's Disease (AD), Parkinson's Disease (PD) and Amyotrophic Lateral Sclerosis (ALS). Biomarkers in the brain, spinal fluid and blood are helping identify the pathologic misfolded proteins that specifically characterize these neurodegenerative disorders. The signature proteins include beta-amyloid and hyperphosphorylated tau (P-tau) in AD, alpha-synuclein in PD, and recently discovered in ALS, TDP-43. As new biomarkers are validated, there is increasing optimism that innovative disease modifying treatments will prevent, reverse or delay the progression of these neurodegenerative diseases. The growing trend of FDA approval of medications through the Accelerated Approval program based on biomarker indicators is expediting drug development for AD, PD, and ALS. Using this pathway, the FDA has granted conditional approval for an AD treatment that lowers brain amyloid and another AD treatment that is associated in studies with slowing of both cognitive and functional loss. The FDA also granted conditional approval for an ALS treatment demonstrating reduction in a neurodegeneration biomarker. The FDA granted Breakthrough Device designation for a blood test as a diagnostic biomarker for AD and the recent breakthrough in the ability to diagnose PD with an accurate biomarker is a major milestone. Continued collaboration among the drug discovery and clinical research communities is needed to accelerate our understanding of the underlying biology and develop disease modifying treatments for these neurodegenerative disorders.

Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies.

Alzheimer's disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance. The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging. The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets. The pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.

Uncovering neuroinflammation-related modules and potential repurposing drugs for Alzheimer's disease through multi-omics data integrative analysis.

Neuroinflammation is one of the key factors leading to neuron death and synapse dysfunction in Alzheimer's disease (AD). Amyloid-β (Aβ) is thought to have an association with microglia activation and trigger neuroinflammation in AD. However, inflammation response in brain disorders is heterogenous, and thus, it is necessary to unveil the specific gene module of neuroinflammation caused by Aβ in AD, which might provide novel biomarkers for AD diagnosis and help understand the mechanism of the disease. Transcriptomic datasets of brain region tissues from AD patients and the corresponding normal tissues were first used to identify gene modules through the weighted gene co-expression network analysis (WGCNA) method. Then, key modules highly associated with Aβ accumulation and neuroinflammatory response were pinpointed by combining module expression score and functional information. Meanwhile, the relationship of the Aβ-associated module to the neuron and microglia was explored based on snRNA-seq data. Afterward, transcription factor (TF) enrichment and the SCENIC analysis were performed on the Aβ-associated module to discover the related upstream regulators, and then a PPI network proximity method was employed to repurpose the potential approved drugs for AD. A total of 16 co-expression modules were primarily obtained by the WGCNA method. Among them, the green module was significantly correlated with Aβ accumulation, and its function was mainly involved in neuroinflammation response and neuron death. Thus, the module was termed the amyloid-β induced neuroinflammation module (AIM). Moreover, the module was negatively correlated with neuron percentage and showed a close association with inflammatory microglia. Finally, based on the module, several important TFs were recognized as potential diagnostic biomarkers for AD, and then 20 possible drugs including ibrutinib and ponatinib were picked out for the disease. In this study, a specific gene module, termed AIM, was identified as a key sub-network of Aβ accumulation and neuroinflammation in AD. Moreover, the module was verified as having an association with neuron degeneration and inflammatory microglia transformation. Moreover, some promising TFs and potential repurposing drugs were presented for AD based on the module. The findings of the study shed new light on the mechanistic investigation of AD and might make benefits the treatment of the disease.

MiR-191-5p Attenuates Tau Phosphorylation, Aβ Generation, and Neuronal Cell Death by Regulating Death-Associated Protein Kinase 1.

Dysregulation of microRNAs has been implicated in diverse diseases, including Alzheimer's disease (AD). MiR-191-5p in plasma/serum has been identified as a novel and promising noninvasive diagnostic biomarker for AD. However, whether miR-191-5p is involved in AD pathogenesis is largely unknown, and its levels in human AD brains are undetermined. Herein, we demonstrated that miR-191-5p downregulated tau phosphorylation at multiple AD-related sites and promoted neurite outgrowth using immunoblotting, immunofluorescence, and neurite outgrowth assays. Moreover, immunoblotting and enzyme-linked immunosorbent assays indicated that miR-191-5p decreased amyloid precursor protein phosphorylation levels and beta-amyloid (Aβ) generation. Furthermore, miR-191-5p reduced ceramide-induced neuronal cell death analyzed by trypan blue staining, the in situ cell death detection kit, and Annexin V-FITC/PI flow cytometry. Next, we verified that death-associated protein kinase 1 (DAPK1) was a direct target of miR-191-5p through the dual luciferase reporter assay and confirmed that the effects of miR-191-5p were antagonized by restoration of DAPK1 expression. Finally, the hippocampal miR-191-5p level was found to be decreased in humans with AD compared with controls and was inversely correlated with the DAPK1 expression level. Collectively, these findings suggest that miR-191-5p might exert inhibitory effects on tau phosphorylation, Aβ secretion, and neuronal cell death by directly targeting DAPK1, providing an attractive therapeutic option for AD.

Diabetes: Risk factor and translational therapeutic implications for Alzheimer's disease.

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) commonly co-occur. T2DM increases the risk for AD by approximately twofold. Animal models provide one means of interrogating the relationship of T2DM to AD and investigating brain insulin resistance in the pathophysiology of AD. Animal models show that persistent hyperglycaemia results in chronic low-grade inflammation that may contribute to the development of neuroinflammation and accelerate the pathobiology of AD. Epidemiological studies suggest that patients with T2DM who received treatment with specific anti-diabetic agents have a decreased risk for the occurrence of AD and all-cause dementia. Agents such as metformin ameliorate T2DM and may have other important systemic effects that lower the risk of AD. Glucagon-like peptide 1 (GLP-1) agonists have been associated with a decreased risk for AD in patients with T2DM. Both insulin and non-insulin anti-diabetic treatments have been evaluated for the treatment of AD in clinical trials. In most cases, patients included in the trials have clinical features of AD but do not have T2DM. Many of the trials were conducted prior to the use of diagnostic biomarkers for AD. Trials have had a wide range of durations and population sizes. Many of the agents used to treat T2DM do not cross the blood brain barrier, and the effects are posited to occur via lowering of peripheral hyperglycaemia and reduction of peripheral and central inflammation. Clinical trials of anti-diabetic agents to treat AD are ongoing and will provide insight into the therapeutic utility of these agents.

Evaluation of blood-based, extracellular vesicles as biomarkers for aging-related TDP-43 pathology.

Limbic predominant age related TDP-43 encephalopathy neuropathological change (LATE-NC) is a recently characterized brain disease that mimics Alzheimer's disease (AD) clinically. To date, LATE-NC is difficult to diagnose antemortem using clinical information or biomarkers. Recent studies suggest concentrations of extracellular vesicle (EVs) protein cargo derived from neuronal and glial cells may serve as useful diagnostic biomarkers for AD and other neurodegenerative diseases. TDP-43 was evaluated in neuronal (NDEVs), astrocyte (ADEVs), and microglial derived extracellular vesicles (MDEVs). EV preparations were isolated from the plasma of research subjects with autopsy-confirmed diagnoses, including many with LATE (n=22). Quantified TDP-43 concentrations were compared to the cohort that included healthy controls, mild cognitively impairment (MCI), and AD dementia with diagnoses other than LATE-NC (n=42). TDP-43 was significantly elevated in plasma ADEVs derived from autopsy confirmed LATE-NC subjects, with or without comorbid AD pathology. Measurable levels of TDP-43 were also detected in EV-depleted plasma; however, TDP-43 levels were not significantly different between persons with and without eventual autopsy confirmed LATE-NC. No correlation was observed between EV TDP-43 levels with cognition-based variables, sex, and APOE carrier status. Blood-based EVs, specifically measuring TDP-43 accumulation in ADEVs, may serve as a potential diagnostic tool to rapidly identify subjects who are currently living with LATE-NC.

Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer's patients: an EEG graph study.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T0), after treatment (T1), and after 40weeks (T2), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.

Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD‐specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co‐expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short‐chain acylcarnitines/amino acids and medium/long‐chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP‐AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co‐expression network analysis of the AMP‐AD brain RNA‐seq data suggests the CPT1A‐ and ABCA1‐centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short‐chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large‐scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.

ProNGF Measurement in Cerebrospinal Fluid Samples of a Large Cohort of Living Patients With Alzheimer's Disease by a New Automated Immunoassay.

The discovery of new biomarkers for Alzheimer's disease (AD) is essential for an accurate diagnosis, to conceive new strategies of treatments, and for monitoring the efficacy of potential disease-modifying therapies in clinical trials. proNGF levels in the cerebrospinal fluid (CSF) represent a promising diagnostic biomarker for AD, but its validation was hampered by the absence of a reliable immunoassay. In the literature, proNGF is currently measured in postmortem brain tissue by semiquantitative immunoblot. Here we describe the development and validation of a new method to measure proNGF in the CSF of living patients. This method, based on molecular size separation by capillary electrophoresis, is automated and shows a 40-fold increase in sensitivity with respect to the proNGF immunoblot, largely used in literature, and is robust, specific, and scalable to high-throughput. We have measured proNGF in the cerebrospinal fluid of 84 living patients with AD, 13 controls, and 15 subjective memory complaints (SMC) subjects. By comparing the proNGF levels in the three groups, we found a very significant difference between proNGF levels in AD samples compared with both controls and SMC subjects, while no significant difference was found between SMC and controls. Because of the development of this new immunoassay, we are ready to explore the potentiality of proNGF as a new biomarker for AD or subgroups thereof, as well as for other neurodegenerative diseases.

MicroRNAs as Potential Orchestrators of Alzheimer's Disease-Related Pathologies: Insights on Current Status and Future Possibilities.

Alzheimer's disease (AD) is a progressive and deleterious neurodegenerative disease, strongly affecting the cognitive functions and memory of seniors worldwide. Around 58% of the affected patients live in low and middle-income countries, with estimates of increasing deaths caused by AD in the coming decade. AD is a multifactor pathology. Mitochondrial function declines in AD brain and is currently emerging as a hallmark of this disease. It has been considered as one of the intracellular processes severely compromised in AD. Many mitochondrial parameters decline already during aging; mitochondrial efficiency for energy production, reactive oxygen species (ROS) metabolism and the de novo synthesis of pyrimidines, to reach an extensive functional failure, concomitant with the onset of neurodegenerative conditions. Besides its impact on cognitive functions, AD is characterized by loss of synapses, extracellular amyloid plaques composed of the amyloid-β peptide (Aβ), and intracellular aggregates of hyperphosphorylated Tau protein, accompanied by drastic sleep disorders, sensory function alterations and pain sensitization. Unfortunately, till date, effective management of AD-related disorders and early, non-invasive AD diagnostic markers are yet to be found. MicroRNAs (miRNAs) are small non-coding nucleic acids that regulate key signaling pathway(s) in various disease conditions. About 70% of experimentally detectable miRNAs are expressed in the brain where they regulate neurite outgrowth, dendritic spine morphology, and synaptic plasticity. Increasing studies suggest that miRNAs are intimately involved in synaptic function and specific signals during memory formation. This has been the pivotal key for considering miRNAs crucial molecules to be studied in AD. MicroRNAs dysfunctions are increasingly acknowledged as a pivotal contributor in AD via deregulating genes involved in AD pathogenesis. Moreover, miRNAs have been proved to control pain sensitization processes and regulate circadian clock system that affects the sleep process. Interestingly, the differential expression of miRNA panels implies their emerging potential as diagnostic AD biomarkers. In this review, we will present an updated analysis of miRNAs role in regulating signaling processes that are involved in AD-related pathologies. We will discuss the current challenges against wider use of miRNAs and the future promising capabilities of miRNAs as diagnostic and therapeutic means for better management of AD.

Meta-analysis of ICU Delirium Biomarkers and Their Alignment With the NIA-AA Research Framework.

Between 30% and 80% of survivors of critical illness experience cognitive impairment, but the underlying mechanisms remain unknown. To determine whether intensive care unit (ICU) delirium biomarkers align with the National Institute on Aging-Alzheimer's Association (NIA-AA) research framework for diagnostic biomarkers for Alzheimer disease and other related dementias (ADRD). Ovid MEDLINE, PsycInfo, Embase, and the Cochrane Library were systematically searched for articles published between January 1, 2000, and February 20, 2020, on the relationship between delirium and biomarkers listed in the NIA-AA framework. Only studies that addressed delirium in the ICU setting and fluid biomarkers were included in these analyses. Of 61 256 records screened, 38 studies met inclusion criteria, 8 of which were suitable for meta-analysis. In pooled analysis, significant associations were found between ICU delirium and amyloid β-peptide 1-40 (standard mean difference [SMD], 0.42; 95% CI, 0.09-0.75), interleukin (IL)-1 receptor antagonist (SMD, 0.58; 95% CI, 0.21-0.94), and IL-6 (SMD, 0.31; 95% CI, 0.06-0.56). No significant association was observed in pooled analyses between ICU delirium and the other biomarkers. Few studies have examined ICU delirium and pathologic tau or neurodegeneration biomarkers. Inflammatory biomarkers and amyloid β are associated with ICU delirium and point to potential overlapping mechanisms between delirium and ADRD. Critical care providers should consider integrating diagnostic approaches used in ADRD in their assessment of post-ICU cognitive dysfunction.

Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma.

African American/Black adults have a disproportionate incidence of Alzheimer's disease (AD) and are underrepresented in biomarker discovery efforts. This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

Advances in fluorescent probes for detection and imaging of amyloid-β peptides in Alzheimer's disease.

Amyloid plaques generated from the accumulation of amyloid-β peptides (Aβ) fibrils in the brain is one of the main hallmarks of Alzheimer's disease (AD), a most common neurodegenerative disorder. Aβ aggregation can produce neurotoxic oligomers and fibrils, which has been widely accepted as the causative factor in AD pathogenesis. Accordingly, both soluble oligomers and insoluble fibrils have been considered as diagnostic biomarkers for AD. Among the existing analytical methods, fluorometry using fluorescent probes has exhibited promising potential in quantitative detection and imaging of both soluble and insoluble Aβ species, providing a valuable approach for the diagnosis and drug development of AD. In this review, the most recent advances in the fluorescent probes for soluble or insoluble Aβ aggregates are discussed in terms of design strategy, probing mechanism, and potential applications. In the end, future research directions of fluorescent probes for Aβ species are also proposed.

Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data.

The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.

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