Distinguishing Alzheimer's Disease Research Articles

Comparative Diagnostic Performance of Amyloid-β Positron Emission Tomography and Magnetic Resonance Imaging in Alzheimer's Disease: A Head-to-Head Meta-Analysis.

This meta-analysis aimed to evaluate the comparative diagnostic performance of amyloid-β positron emission tomography (Aβ PET) and magnetic resonance imaging (MRI) in diagnosing Alzheimer's disease (AD). An extensive search was conducted in the PubMed and Embase databases to identify available publications up to December 2023. Head-to-head comparative studies were included if they evaluated the diagnostic performance of Aβ PET and MRI in diagnosing Alzheimer's disease. Sensitivity and specificity were assessed using the DerSimonian and Laird method, followed by transformation via the Freeman-Tukey double inverse sine transformation. Six articles involving 560 patients were included in the meta-analysis. When distinguishing AD from mild cognitive impairment (MCI), both methods showed comparable sensitivity (Aβ PET: 0.71, MRI: 0.62) and specificity (Aβ PET: 0.68, MRI: 0.69), with no statistically significant differences observed (p = 0.34 and 0.99). When identifying AD from normal cognitive control (NC), both Aβ PET and MRI showed similar results, with comparable sensitivity (Aβ PET: 0.93, MRI: 0.85) and specificity (Aβ PET: 0.95, MRI: 0.82), without significant differences (p = 0.38 and 0.19). Similarly, in detecting MCI from NC, both Aβ PET and MRI demonstrated similar sensitivity (Aβ PET: 0.69, MRI: 0.64) and specificity (Aβ PET: 0.75, MRI: 0.76) without significant differences (p = 0.40 and 0.94). However, 18F-FMM seems to have a higher specificity compared to MRI when distinguishing AD from MCI (P = 0.03) and AD from NC (p = 0.04). Our meta-analysis indicates that Aβ PET demonstrates similar sensitivity and specificity to MRI in diagnosing Alzheimer's disease. However, the limited number of studies may impact the evidence of the current study; further larger sample prospective research is required to confirm these findings.

Plasma metabolites distinguish dementia with Lewy bodies from Alzheimer's disease: a cross-sectional metabolomic analysis.

In multifactorial diseases, alterations in the concentration of metabolites can identify novel pathological mechanisms at the intersection between genetic and environmental influences. This study aimed to profile the plasma metabolome of patients with dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), two neurodegenerative disorders for which our understanding of the pathophysiology is incomplete. In the clinical setting, DLB is often mistaken for AD, highlighting a need for accurate diagnostic biomarkers. We therefore also aimed to determine the overlapping and differentiating metabolite patterns associated with each and establish whether identification of these patterns could be leveraged as biomarkers to support clinical diagnosis. A panel of 630 metabolites (Biocrates MxP Quant 500) and a further 232 metabolism indicators (biologically informative sums and ratios calculated from measured metabolites, each indicative for a specific pathway or synthesis; MetaboINDICATOR) were analyzed in plasma from patients with probable DLB (n = 15; age 77.6 ± 8.2 years), probable AD (n = 15; 76.1 ± 6.4 years), and age-matched cognitively healthy controls (HC; n = 15; 75.2 ± 6.9 years). Metabolites were quantified using a reversed-phase ultra-performance liquid chromatography column and triple-quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode, or by using flow injection analysis in MRM mode. Data underwent multivariate (PCA analysis), univariate and receiving operator characteristic (ROC) analysis. Metabolite data were also correlated (Spearman r) with the collected clinical neuroimaging and protein biomarker data. The PCA plot separated DLB, AD and HC groups (R2 = 0.518, Q2 = 0.348). Significant alterations in 17 detected metabolite parameters were identified (q ≤ 0.05), including neurotransmitters, amino acids and glycerophospholipids. Glutamine (Glu; q = 0.045) concentrations and indicators of sphingomyelin hydroxylation (q = 0.039) distinguished AD and DLB, and these significantly correlated with semi-quantitative measurement of cardiac sympathetic denervation. The most promising biomarker differentiating AD from DLB was Glu:lysophosphatidylcholine (lysoPC a 24:0) ratio (AUC = 0.92; 95%CI 0.809-0.996; sensitivity = 0.90; specificity = 0.90). Several plasma metabolomic aberrations are shared by both DLB and AD, but a rise in plasma glutamine was specific to DLB. When measured against plasma lysoPC a C24:0, glutamine could differentiate DLB from AD, and the reproducibility of this biomarker should be investigated in larger cohorts.

Levels of plasma brain-derived tau and p-tau181 in Alzheimer's disease and rapidly progressive dementias.

Rapidly progressive dementias (RPDs) are a group of neurological disorders characterized by a rapid cognitive decline. The diagnostic value of blood-based biomarkers for Alzheimer's disease (AD) in RPD has not been fully explored. We measured plasma brain-derived tau (BD-tau) and p-tau181 in 11 controls, 15 AD patients, and 33 with RPD, of which 19 were Creutzfeldt-Jakob disease (CJD). Plasma BD-tau differentiated AD from RPD and controls (p=0.002 and p=0.03, respectively), while plasma and cerebrospinal fluid (CSF) p-tau181 distinguished AD from RPD (p<0.001) but not controls from RPD (p>0.05). The correlation of CSF t-tau with plasma BD-tau was stronger (r=0.78, p<0.001) than the correlation of CSF and plasma p-tau181 (r=0.26, p=0.04). The ratio BD-tau/p-tau181 performed equivalently to the CSF t-tau/p-tau181 ratio, differentiating AD from CJD (p<0.0001). Plasma BD-tau and p-tau181 mimic their corresponding cerebrospinal fluid (CSF) markers. P-tau significantly increased in AD but not in RPD. Plasma BD-tau, like CSF t-tau, increases according to neurodegeneration intensity.

Metabolic Pathway Pairwise-Based Signature as a Potential Non-Invasive Diagnostic Marker in Alzheimer's Disease Patients.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood plasma, has been demonstrated as a promising approach to the diagnosis and prevention of AD. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in the whole blood transcriptome. Hence, we hypothesized that the establishment of a diagnostic model based on the metabolic signatures of blood is a workable strategy. To that end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, network analysis, etc., were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via the Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD groups, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation, fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression compared with S1 and the non-AD group. These findings indicated that S2 probably has a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 (95%CI: 0.70, 0.77) in the training dataset, 0.71 (95%CI: 0.65, 0.77) in the testing dataset, and an AUC of 0.99 (95%CI: 0.96, 1.00) in one external validation dataset. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using the blood transcriptome and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD.

The Markers in Neuropsychiatric Disorders Study (The MiND Study): moving neurofilament light and other markers into the clinic to improve care for people with symptoms and illnesses of the mind and brain

AbstractBackgroundAccurate, timely diagnosis of neuropsychiatric symptoms, in particular distinguishing primary psychiatric from neurological disorders and in younger people, can be challenging. Neurofilament light (NfL) and other blood biomarkers have shown promise to reduce the diagnostic odyssey and improve outcomes. The MiND Study in investigating the diagnostic utility of NfL and other markers, to distinguish neurological/neurodegenerative from psychiatric disorders, to lead to a widely available, routine screening blood test.MethodsWe assessed NfL, p‐tau181 and GFAP in broad cohorts, including: patients assessed for neurocognitive/psychiatric symptoms at Neuropsychiatry and other services, in a wide range of disorders including Alzheimer disease, frontotemporal dementia, schizophrenia, bipolar disorder, depression, functional neurological disorders, Niemann‐Pick Type C, epilepsy.ResultsUpdates on the The MiND Study progress and findings that NfL differentiated diverse neurodegenerative from psychiatric disorders, with 90+% accuracy, from over 500 patients/participants, will be presented, with real patient and family stories to demonstrate the challenges and potential clinical impact of The MiND Study. Plasma P‐tau181 levels distinguished Alzheimer disease (mainly younger sporadic), from non‐Alzheimer. As recruitment, sample analysis, data collection is ongoing, the most up to date results including GFAP, cognitive and neuroimaging markers, will be presented.ConclusionsNfL shows great promise as a diagnostic screening blood test, akin to a “CRP for the brain”. Plasma p‐tau181 shows strong diagnostic utility in younger‐onset Alzheimer disease. NfL could dramatically alter clinical care of patients with neuropsychiatric and neurological symptoms, improving outcomes for patients, their families, the healthcare system, and clinical trials, facilitating precision medicine algorithmic diagnostics incorporating other biomarkers and clinical/cognitive markers, for real‐world clinical settings.

Switchable Multiplex Photoelectrochemical Immunoassay of Aβ42 and Aβ40 Based on a pH-Responsive i-Motif Probe and Pyrene-Based MOF Photocathode.

In accurately diagnosing Alzheimer's disease (AD) and distinguishing AD from other dementia, the concentration ratio of amyloid-beta 42 (Aβ42) to Aβ40 is more reliable than the concentration of Aβ42 alone. For the multiplex PEC assay, generating an independent photocurrent of multiple targets on a single interface is a great challenge. Herein, an i-motif-based switchable sensing approach is proposed to construct a pH-regulated multiplex PEC immunosensor for Aβ42 and Aβ40 by using Bi-TBAPy as an efficient photoactive cathode material. An independent photocurrent signal of Aβ42 and Aβ40 is produced through the regulation of the electron-transfer tunneling distance by a pH-dependent configuration transition of the i-motif DNA. In a 96-well plate, immunological recognition of Aβ42 (or Aβ40) coupled with an enzymatic catalytic reaction produces an acidic (or alkaline) lysis solution, which triggers the formation and unravelment of the i-motif structure. The above configuration transition regulates the distance between Au NPs labeled SH-DNA and Bi-TBAPy, leading to PEC signal switching. Smart integration of the pH-responsive switchable DNA probe with a high-efficiency photocathode enables the precise monitoring of Aβ42 and Aβ40 at a single interface in a wide detection range (10 fg/mL ∼ 1 μg/mL and 1 pg/mL ∼ 1 μg/mL) with detection limit of 4.5 fg/mL and 0.52 pg/mL, respectively. The proposed i-motif-based switchable sensing strategy paves a new avenue for a multiplex PEC assay on a single interface, showing great prospects in bioanalysis and early disease diagnosis.

Neurodegeneration and Glial Activation Related CSF Biomarker as the Diagnosis of Alzheimer's Disease: A Systematic Review and an Updated Meta- analysis.

Recently, neuron specific enolase (NSE), Visinin-like protein-1 (VLP-1), neurogranin (Ng), and YKL-40 have been identified as candidates for neuronal degeneration and glial activation biomarkers. Therefore, we perform a comprehensive meta-analysis to assess the diagnostic value of CSF NSE, VLP-1, Ng and YKL-40 in Alzheimer's disease (AD). We searched Pubmed, MEDLINE, EMBASE databases for research about the levels of CSF NSE, VLP-1, Ng and YKL-40 in AD patients compared with controls or other dementia diseases until Dec 2020. The present meta-analysis contained a total of 51 studies comprising 6248 patients with dementia disorders and 3861 controls. Among them, there were 3262 patients with AD, 2456 patients with mild cognitive impairment (MCI), 173 patients with vascular dementia (VaD), 221 patients with frontotemporal dementia (FTD), and 136 with Lewy bodies dementia (DLB). Our study demonstrated that CSF NSE, VLP-1, Ng and YKL-40 levels were increased in AD as compared to healthy controls. We also observed that the CSF NSE level was higher in AD than VaD, suggesting CSF NSE might act as a key role in distinguishing between AD and VaD. Interestingly, there was a higher VLP-1 expression in AD, and a lower expression in DLB patients. Moreover, we found the CSF Ng level was increased in AD than MCI, implying CSF Ng might be a biomarker for identifying the progression of AD. Additionally, a significantly higher CSF YKL-40 level was detected not only in AD, but also in FTD, DLB, VaD, signifying YKL-40 was not sensitive in the diagnosis of AD. Our study confirmed that CSF levels of NSE, VLP-1, and Ng could be valuable biomarkers for identifying patients who are more susceptible to AD and distinguishing AD from other neurodegenerative dementia disorders.

LncRNA HOTAIR in exercise-induced neuro-protective function in Alzheimer's disease.

Exercise is effective in Alzheimer's disease (AD), which is characterized by neuro-degenerative progress with increasing morbidity. The present study aimed to explore whether HOTAIR participated in the regulation of exercise in AD. Arelative expression of serum HOTAIR was detected using quantitative real-time polymerase chain reaction (PCR). The diagnostic significance of HOTAIR on distinguishing AD patients was evaluated by receiver operating characteristic (ROC) curve. Correlations between HOTAIR expression and Mini-Mental State Examination (MMSE) score or Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) score were analyzed with Pearson's test. Logistic regression analysis was applied to investigate factors as independent indicators for HOTAIR expression. In AD patients, the expression of HOTAIR was increased, and it could function as adiagnostic marker in AD patients. The expression of HOTAIR was associated with MMSE score and ADAS-Cog score in AD patients before exercise. Exercise ameliorated the cognitive impairment and reduced the relative serum expression of HOTAIR. Exercise was proved to be an independent indicator of HOTAIR expression. HOTAIR was apossible biomarker for indicating AD patients, and it was correlated with MMSE scores and ADAS-Cog results. Exercise might moderate AD progress via controlling HOTAIR.

Plasma neurofilament light chain and phosphorylated tau 181 in neurodegenerative and psychiatric disorders: moving closer towards a simple diagnostic test like a 'C‐reactive protein' for the brain?

AbstractBackgroundAccurate, timely diagnosis of neurodegenerative disorders, in particular distinguishing primary psychiatric from neurological disorders and in younger people, can be challenging. There is a need for biomarkers to reduce the diagnostic odyssey and improve outcomes. Neurofilament light (NfL) has shown promise as a diagnostic biomarker in a wide range of disorders. Our Markers in Neuropsychiatric Disorders (MiND) Study builds on our pilot (Eratne et al, ANZJP, 2020), to explore the diagnostic and broader utility of plasma and cerebrospinal fluid (CSF) NfL and other novel markers such as phosphorylated tau 181 (p‐tau181), in a broad range of psychiatric and neurodegenerative/neurological disorders, with a view of translation into routine clinical practice.MethodsWe assessed plasma and/or CSF NfL and p‐tau181 concentrations in broad cohorts, including: patients assessed for neurocognitive/psychiatric symptoms at Neuropsychiatry and Melbourne Young‐Onset Dementia services and other services, in a wide range of disorders including Alzheimer disease, frontotemporal dementia, schizophrenia, bipolar disorder, depression, Niemann‐Pick Type C, epilepsy, functional neurological disorders. The most recent primary consensus diagnosis informed by established diagnostic criteria was categorised: primary psychiatric disorder (PPD), neurodegenerative/neurological disorder (ND), or healthy controls (HC).ResultsFindings from over 500 patients/participants will be presented, which indicate that CSF and plasma NfL levels are significantly elevated in a broad range of ND compared to a broad range of PPD, and HC, and bvFTD progressors from phenocopy syndromes, differentiating with areas under the curve of &gt;0.90, sensitivity and specificity &gt;90%. Plasma P‐tau181 levels distinguished Alzheimer disease (mainly younger sporadic), compared to other neurodegenerative disorders, with AUC 0.90, 90% sensitivity and specificity. As recruitment, sample analysis, data collection is ongoing, the most up to date results will be presented.ConclusionsNfL shows great promise as a diagnostic test to assist with the common, challenging diagnostic dilemma of distinguishing neurodegenerative from non‐neurodegenerative and primary psychiatric disorders. Plasma p‐tau181 shows strong diagnostic utility in younger‐onset Alzheimer disease. A significantly elevated NfL in someone with a psychiatric diagnosis should prompt consideration of neurodegenerative differentials. Plasma NfL could dramatically alter clinical care of patients with neuropsychiatric and neurological symptoms, improving outcomes for patients, their families, the healthcare system, and clinical trials.

Associations of Fully Automated CSF and Novel Plasma Biomarkers With Alzheimer Disease Neuropathology at Autopsy.

ObjectiveTo study CSF biomarkers of Alzheimer disease (AD) analyzed by fully automated Elecsys immunoassays compared to neuropathologic gold standards and to compare their accuracy to plasma phosphorylated tau (p-tau181) measured with a novel single molecule array method.MethodsWe studied antemortem Elecsys-derived CSF biomarkers in 45 individuals who underwent standardized postmortem assessments of AD and non-AD neuropathologic changes at autopsy. In a subset of 26 participants, we also analyzed antemortem levels of plasma p-tau181 and neurofilament light (NfL). Reference biomarker values were obtained from 146 amyloid-PET–negative healthy controls (HC).ResultsAll CSF biomarkers clearly distinguished pathology-confirmed AD dementia (n = 27) from HC (area under the curve [AUC] 0.86–1.00). CSF total tau (t-tau), p-tau181, and their ratios with β-amyloid1-42 (Aβ1-42) also accurately distinguished pathology-confirmed AD from non-AD dementia (n = 8; AUC 0.94–0.97). In pathology-specific analyses, intermediate to high Thal amyloid phases were best detected by CSF Aβ1-42 (AUC [95% confidence interval] 0.91 [0.81–1]), while intermediate to high scores for Consortium to Establish a Registry for Alzheimer's Disease neuritic plaques and Braak tau stages were best detected by CSF p-tau181 (AUC 0.89 [0.79–0.99] and 0.88 [0.77–0.99], respectively). Optimal Elecsys biomarker cutoffs were derived at 1,097, 229, and 19 pg/mL for Aβ1-42, t-tau, and p-tau181. In the plasma subsample, both plasma p-tau181 (AUC 0.91 [0.86–0.96]) and NfL (AUC 0.93 [0.87–0.99]) accurately distinguished those with pathology-confirmed AD (n = 14) from HC. However, only p-tau181 distinguished AD from non-AD dementia cases (n = 4; AUC 0.96 [0.88–1.00]) and showed a similar, although weaker, pathologic specificity for neuritic plaques (AUC 0.75 [0.52–0.98]) and Braak stage (AUC 0.71 [0.44–0.98]) as CSF p-tau181.ConclusionElecsys-derived CSF biomarkers detect AD neuropathologic changes with very high discriminative accuracy in vivo. Preliminary findings support the use of plasma p-tau181 as an easily accessible and scalable biomarker of AD pathology.Classification of EvidenceThis study provides Class II evidence that fully automated CSF t-tau and p-tau181 measurements discriminate between autopsy-confirmed AD and other dementias.

IDENTIFICATION AND BIOLOGICAL CHARACTERIZATION OF EXTRACELLULAR VESICLE PROTEINS FOR PROPAGATION AND SPREAD OF TAU

Accumulation of hyper-phosphorylated tau aggregates are pathological hallmark of Alzheimer's disease (AD). Extracellular vesicles (EVs) derived from AD derived microglia contribute to tau propagation as previously shown in a mouse model of human disease. The data sets, derived from the entorhinal cortex to the dentate gyrus, support a role for EVs in tau-associated pathobiology. A quantitative proteomics profile was made from EVs isolated from cortical grey matter of AD (n=20) and control (CTRL) (n=18) brain tissues. Here, we have identified differentially expressed proteins between these brain tissue cohorts using predictive machine learning. Expressed EV proteins were validated by ELISA and by biological function then linked to AD progression. Differentially expressed EV proteins, recovered from AD and CTRL brain samples, were observed by quantitative proteomics employing machine learning. The candidate proteins were selected then validated from an independent cohort by a commercially available ELISA. We evaluated the effect of the identified proteins on microglial EV uptake. AD brain-derived EVs were labeled with PKH26 and preincubated with recombinant target proteins then tested with murine microglia for uptake measurements. Annexin V (ANXA5), neurosecretory protein VGF, and neuronal membrane glycoprotein M6-a (GPM6A) were differentially expressed in AD EVs. Each of the proteins were confirmed with 88% accuracy by machine learning analyses. There was significant difference in protein levels of ANXA5 (p = 4.72E-06, fold change = 2.10), VGF (p = 7.49E-07, fold change = 0.23), GPM6A (p = 5.00E-04, fold change = 1.56) between AD and CTRL brain-derived EVs. Increased protein level of ANXA5 in AD compared CTRL EVs (p = 4.80E-03) was confirmed by ELISA. Moreover, the ANXA5 and VGF recombinant proteins inhibited EV microglial uptake. ANXA5 and VGF distinguished AD from CTRL EVs. Preincubation of AD brain-derived EVs with ANXA5 or VGF inhibited EV microglial uptake. These data suggest an ANXA5 and VGF-dependent spread of pathogenic tau protein in the brain, and these molecules can be a potential therapeutic target for AD.

Identification of Temporal Characteristic Networks of Peripheral Blood Changes in Alzheimer's Disease Based on Weighted Gene Co-expression Network Analysis.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease. The study of blood-based biomarkers has lasted for a long time in AD, because it supports the concept that peripheral changes are involved in AD pathology. But it is still unclear how peripheral blood is involved in the temporal characteristic molecular mechanisms of AD from aging to mild cognitive impairment (MCI) and which cells are responsible for the molecular mechanisms. The main purpose of our study is to gain a systematic and comprehensive understanding of temporal characteristic networks of peripheral blood in AD using whole blood samples with 329 case-control samples, including 104 normal elderly control subjects (CTL), 80 MCI who are susceptible to AD, and 145 AD, by the weighted gene co-expression network analysis (WGCNA). The module-trait relationships were constructed and module preservation was validated with independent datasets GSE63061, GSE97760, GSE18309, GSE29378, GSE28146, and GSE29652. Our results indicate that the down-regulated protein modification and ubiquitin degradation systems, and the up-regulated insulin resistance both play a major role in MCI, while the up-regulated inflammatory cascade dominates in AD, which is mainly mediated by monocytes, macrophages. Although there is mixed activation of M1 and M2 macrophages in all stages of AD, the immune neutral state or M2 polarization may predominate in MCI, and M1 polarization may predominate in AD. Moreover, we found that TRPV2, NDUFV1, ATF4, HSPA8, STAT3 and LUC7L3 may mediate the pathological changes in MCI, while SIRPA, LAMP-2, NDUFB5, HSPA8, STAT3 and FPR2 may mediate the conversion from MCI-AD or the pathological changes in AD, which provide a basis for the treatment based on the peripheral blood system. In addition, we also found that the combined diagnosis based on a panel of genes from the red, blue, and brown modules have a moderate diagnostic effect on distinguishing MCI and AD from CTL, suggesting that those panels of genes may be used for detection of MCI and prediction of this conversion from MCI to AD. Our research emphasizes that pathological changes, based on temporal characteristics of peripheral blood, provide a theoretical basis for targeted peripheral treatment based on appropriate times and identified several diagnostic markers.

A metabolomic approach to identifying biomarkers in blood of Alzheimer's disease.

ObjectiveThis study aims to identify metabolites with altered levels of expression in patients with early and progressive stages of Alzheimer's disease (AD).MethodsAll participants of the study underwent genetic screening and were diagnosed using both neuropsychological assessment and amyloid imaging before metabolome analysis. According to these assessments, the patients were classified as normal (n = 15), with mild cognitive impairment (n = 10), and with AD (n = 15).ResultsUsing a targeted metabolomic approach, we found that plasma levels of C3, C5, and C5‐DC acylcarnitines, arginine, phenylalanine, creatinine, symmetric dimethylarginine (SDMA) and phosphatidylcholine ae C38:2 were significantly altered in patients with early and progressive stages of AD. We created a predictive model based on the decision tree that included three main parameters: age, arginine and C5 plasma concentrations. The model distinguished AD patients from other participants with 60% sensitivity and 86.7% specificity. For healthy controls, the sensitivity was 85.7% and specificity was 61.5%. Multivariate ROC analysis to develop a decision tree showed that our model reached moderate diagnostic power in differentiating between older adults who are cognitively normal (AUC = 0.77) and those with AD (AUC = 0.72).InterpretationThe plasma levels of arginine and valeryl carnitine, together with subject age, are promising as biomarkers for the diagnosis of AD in older adults.

Diagnostic manifestations of total hemispheric glucose metabolism ratio in neuronal network diaschisis: diagnostic implications in Alzheimer's disease and mild cognitive impairment.

We tested the hypothesis that lateralized hemispheric glucose metabolism may have diagnostic implications in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We performed FDG-PET/CT in 23 patients (mean age 63.7 years, range 50-78, 17 females) diagnosed with AD (n = 15) or MCI (n = 8) during a six-month period in 2014. Ten neurologically healthy individuals (HIs) (mean age 62.5 years, range 43-75, 5 females) served as controls. A neuroimaging expert provided visual assessment of diaschisis. The total hemispheric glucose metabolism ratio (THGr) was calculated, and with area-under the curve of receiver operating characteristics (AUC-ROC) we generated a "Network Diaschisis Test (NDT)". The qualitative detection of cerebral (Ce) and cerebellar (Cb) diaschisis was 7/15 (47%), 0/8 (0%), and 0/10 (0%) in AD, MCI, and HI groups, respectively. Median cerebral THGr was 0.68 (range 0.43-0.99), 0.86 (range 0.64-0.98), and 0.95 (range 0.65-1.00) for AD, MCI, and HI groups, respectively (p =0.04). Median cerebellar THGr was, respectively, 0.70 (range 0.18-0.98), 0.70 (range 0.48-0.81), and 0.84 (range 0.75-0.96) (p = 0.0138). A positive NDT yielded a positive predictive value of 100% for the presence of AD or MCI and a 86% negative predictive value for healthy brain. Moreover, the diagnostic manifestation of THGr between MCI and AD led to a positive predictive value of 100% for AD, but a negative predictive value of 42.9% for MCI. Patients with AD or MCI had more pronounced diaschisis, lateralized hemispheric glucose metabolism and lower THGr compared to healthy controls. The NDT distinguished AD and MCI patients from HIs, and AD from MCI patients with a high positive predictive value and moderate and low negative predictive values. THGr can be a straightforward source of investigating neuronal network diaschisis in AD and MCI and in other cerebral diseases, across institutions.

Improved Differential Diagnosis of Alzheimer's Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers.

Background: Alzheimer’s disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD.Objective: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects.Methods: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4–9 years and 21 of these converted to AD, whereas 53 remained stable.Results: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively.Conclusions: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease.

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

26 Copper Failure in Wilson and Alzheimer Disease

Copper failure is the cause of Wilson disease (WD), a rare disorder typified by increased levels of copper nonbound to ceruloplasmin (non-Cp Cu) and copper-ceruloplasmin ratio (Cu:Cp). Meta-analyses also show copper abnormalities in Alzheimer disease (AD); however, a direct comparison of copper biological status in the two diseases has never been carried out. An investigation of copper status in serum (copper, ceruloplasmin, non-Cp Cu and Cu:Cp, adjusting for sex and age) and a 24-hour copper urine assessment of patients with AD, WD (nine WD patients at baseline and 24 WD patients under D-penicillamine [D-pen] treatment)], and healthy controls (35 healthy controls) was carried out. In a sample of 385 AD cases and 336 healthy controls investigated in previous studies, non-Cp Cu and Cu:Cp were higher in AD than healthy controls (both P < .001), but lower than in WD patients at baseline. While non-Cp Cu was similar in AD and WD, mean value of Cu:Cp distinguished AD from WD (P < .016), with the Cu:Cp Cohen d value of 0.8 (indicating a large biological effect) in AD vs healthy controls, which further increases in WD to 2.2. Concentrations of 24-hour urine copper in 35 healthy controls were compared with those at baseline of an AD patient group analyzed in a previous study on the effect of D-pen on the disease progression. AD patients at baseline had higher concentrations of 24-hour urine copper (P < .001; AD 12.05 µg/day [median, interquartile range 7.85–22.50]) than healthy controls (4.82 µg/day (median, interquartile range: 3.31–7.43]). Values of 24-hour urine copper higher than the cutoff of 200 µg/day were found in 87% of WD and 78% of AD. The direct comparison of AD and WD patients shows a copper failure affecting both diseases, but at a different degree, with the Cu:Cp signifying a more severe grade in WD.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning.

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Towards a routine application of Top-Down approaches for label-free discovery workflows

Thanks to proteomics investigations, our vision of the role of different protein isoforms in the pathophysiology of diseases has largely evolved. The idea that protein biomarkers like tau, amyloid peptides, ApoE, cystatin, or neurogranin are represented in body fluids as single species is obviously over-simplified, as most proteins are present in different isoforms and subjected to numerous processing and post-translational modifications. Measuring the intact mass of proteins by MS has the advantage to provide information on the presence and relative amount of the different proteoforms. Such Top-Down approaches typically require a high degree of sample pre-fractionation to allow the MS system to deliver optimal performance in terms of dynamic range, mass accuracy and resolution. In clinical studies, however, the requirements for pre-analytical robustness and sample size large enough for statistical power restrict the routine use of a high degree of sample pre-fractionation. In this study, we have investigated the capacities of current-generation Ultra-High Resolution Q-Tof systems to deal with high complexity intact protein samples and have evaluated the approach on a cohort of patients suffering from neurodegenerative disease. Statistical analysis has shown that several proteoforms can be used to distinguish Alzheimer disease patients from patients suffering from other neurodegenerative disease. SignificanceTop-down approaches have an extremely high biological relevance, especially when it comes to biomarker discovery, but the necessary pre-fractionation constraints are not easily compatible with the robustness requirements and the size of clinical sample cohorts. We have demonstrated that intact protein profiling studies could be run on UHR-Q-ToF with limited pre-fractionation. The proteoforms that have been identified as candidate biomarkers in the-proof-of concept study are derived from proteins known to play a role in the pathophysiology process of Alzheimer disease.

Transcranial magnetic stimulation distinguishes Alzheimer disease from frontotemporal dementia.

To determine whether a transcranial magnetic stimulation (TMS) multiparadigm approach can be used to distinguish Alzheimer disease (AD) from frontotemporal dementia (FTD). Paired-pulse TMS was used to investigate short-interval intracortical inhibition (SICI) and facilitation (ICF), long-interval intracortical inhibition, and short-latency afferent inhibition (SAI) to measure the activity of different intracortical circuits in patients with AD, patients with FTD, and healthy controls (HC). The primary outcome measures were sensitivity and specificity of TMS measures, derived from receiver operating curve analysis. A total of 175 participants met the inclusion criteria. We diagnosed 79 patients with AD, 64 patients with FTD, and 32 HC. We found that while patients with AD are characterized by a specific impairment of SAI, FTD shows a remarkable dysfunction of SICI-ICF intracortical circuits. With the use of the best indexes, TMS differentiated FTD from AD with a sensitivity of 91.8% and specificity of 88.6%, AD from HC with a sensitivity of 84.8% and specificity of 90.6%, and FTD from HC with a sensitivity of 90.2% and specificity of 78.1%. These results were confirmed in patients with mild disease. TMS is a noninvasive procedure that reliably distinguishes AD from FTD and HC and, if these findings are replicated in larger studies, could represent a useful additional diagnostic tool for clinical practice. This study provides Class III evidence that TMS measures can distinguish patients with AD from those with FTD.