Consensus Report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer’s Disease” 11The names of the Working Group Members and the names of the Working Group Advisory Committee Members are listed in the Appendix A(section VI). 22The Reagan Institute Working Groups are planned and organized by Z. S. Khachaturian and T.S. Radebaugh; Fax: 301-879-2023; E-mail: zaven@idt.net

The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions

The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were "roundtable" discussion (n = 30); with facilitation (n = 16); that was iterative (n = 27); and frequently conducted in-person (n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease

OBJECTIVE

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial.

METHODS

We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls.

RESULTS

The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively.

CONCLUSION

The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Predicting the diagnosis of various mental disorders in a mixed cohort using blood-based multi-protein model: a machine learning approach

The lack of objective diagnostic methods for mental disorders challenges the reliability of diagnosis. The study aimed to develop an easily accessible and useable objective method for diagnosing major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BPD), and panic disorder (PD) using serum multi-protein. Serum levels of brain-derived neurotrophic factor (BDNF), VGF (non-acronymic), bicaudal C homolog 1 (BICC1), C-reactive protein (CRP), and cortisol, which are generally recognized to be involved in different pathogenesis of various mental disorders, were measured in patients with MDD (n = 50), SZ (n = 50), BPD (n = 55), and PD along with 50 healthy controls (HC). Linear discriminant analysis (LDA) was employed to construct a multi-classification model to classify these mental disorders. Both leave-one-out cross-validation (LOOCV) and fivefold cross-validation were applied to validate the accuracy and stability of the LDA model. All five serum proteins were included in the LDA model, and it was found to display a high overall accuracy of 96.9% when classifying MDD, SZ, BPD, PD, and HC groups. Multi-classification accuracy of the LDA model for LOOCV and fivefold cross-validation (within-study replication) reached 96.9 and 96.5%, respectively, demonstrating the feasibility of the blood-based multi-protein LDA model for classifying common mental disorders in a mixed cohort. The results suggest that combining multiple proteins associated with different pathogeneses of mental disorders using LDA may be a novel and relatively objective method for classifying mental disorders. Clinicians should consider combining multiple serum proteins to diagnose mental disorders objectively.

Molecular Biomarkers for the Diagnosis, Prognosis, and Pharmacodynamics of Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is a progressive degenerative illness that affects 1 in every 6 to 11,000 live births. This autosomal recessive disorder is caused by homozygous deletion or mutation of the SMN1 gene (survival motor neuron). As a backup, the SMN1 gene has the SMN2 gene, which produces only 10% of the functional SMN protein. Nusinersen and risdiplam, the first FDA-approved medications, act as SMN2 pre-mRNA splicing modifiers and enhance the quantity of SMN protein produced by this gene. The emergence of new therapies for SMA has increased the demand for good prognostic and pharmacodynamic (response) biomarkers in SMA. This article discusses current molecular diagnostic, prognostic, and pharmacodynamic biomarkers that could be assessed in SMA patients' body fluids. Although various proteomic, genetic, and epigenetic biomarkers have been explored in SMA patients, more research is needed to uncover new prognostic and pharmacodynamic biomarkers (or a combination of biomarkers).

Misfolded alpha-synuclein detection by RT-QuIC in dementia with lewy bodies: a systematic review and meta-analysis

Introduction: Dementia with Lewy Bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer's disease (AD), but the field is still lacking a specific biomarker for its core pathology: alpha synuclein (α-syn). Realtime quaking induced conversion (RT-QuIC) has recently emerged as a strong biomarker candidate to detect misfolded α-syn in DLB. However, the variability in the parameters of the technique and the heterogeneity of DLB patients make the reproducibility of the results difficult. Here, we provide an overview of the state-of-the-art research of α-syn RT-QuIC in DLB focused on: (1) the capacity of α-syn RT-QuIC to discriminate DLB from controls, Parkinson's disease (PD) and AD; (2) the capacity of α-syn RT-QuIC to identify prodromal stages of DLB; and (3) the influence of co-pathologies on α-syn RT-QuIC's performance. We also assessed the influence of different factors, such as technical conditions (e.g., temperature, pH, shaking-rest cycles), sample type, and clinical diagnosis versus autopsy confirmation. Methods: We conducted a systematic review following the PRISMA guidelines in August 2022, without any limits in publication dates. Search terms were combinations of "RT-QuIC" and "Lewy Bodies," "DLB" or "LBD". Results: Our meta-analysis shows that α-syn RT-QuIC reaches very high diagnostic performance in discriminating DLB from both controls (pooled sensitivity and specificity of 0.94 and 0.96, respectively) and AD (pooled sensitivity and specificity of 0.95 and 0.88) and is promising for prodromal phases of DLB. However, the performance of α-syn RT-QuIC to discriminate DLB from PD is currently low due to low specificity (pooled sensitivity and specificity of 0.94 and 0.11). Our analysis showed that α-syn RT-QuIC's performance is not substantially influenced by sample type or clinical diagnosis versus autopsy confirmation. Co-pathologies did not influence the performance of α-syn RT-QuIC, but the number of such studies is currently limited. We observed technical variability across published articles. However, we could not find a clear effect of technical variability on the reported results. Conclusion: There is currently enough evidence to test misfolded α-syn by RT-QuIC for clinical use. We anticipate that harmonization of protocols across centres and advances in standardization will facilitate the clinical establishment of misfolded α-syn detection by RT-QuIC.

A Candidate microRNA Profile for Early Diagnosis of Sporadic Alzheimer’s Disease

Background: Late-onset or sporadic Alzheimer’s disease (sAD) is a neurodegenerative disease leading to cognitive impairment and memory loss. The underlying pathological changes take place several years prior to the appearance of the first clinical symptoms, however, the early diagnosis of sAD remains obscure. Objective: To identify changes in circulating microRNA (miR) expression in an effort to detect early biomarkers of underlying sAD pathology. Methods: A set of candidate miRs, earlier detected in biofluids from subjects at early stage of sAD, was linked to the proposed tau-driven adverse outcome pathway for memory loss. The relative expression of the selected miRs in serum of 12 cases (mild cognitive impairment, MCI) and 27 cognitively normal subjects, recruited within the ongoing Aiginition Longitudinal Biomarker Investigation Of Neurodegeneration (ALBION) study, was measured by RT-qPCR. Data on the protein levels of amyloid-β (Aβ42) and total/phosphorylated tau (t-tau/p-tau), in cerebrospinal fluid (CSF), and the cognitive z-scores of the participants were also retrieved. Results: Each doubling in relative expression of 13 miRs in serum changed the odds of either having MCI (versus control), or having pathological Aβ42 or pathological Aβ42 and tau (versus normal) proteins in their CSF, or was associated with the global composite z-score. Conclusion: These candidate human circulating miRs may be of great importance in early diagnosis of sAD. There is an urgent need for confirming these proposed early predictive biomarkers for sAD, contributing not only to societal but also to economic benefits.

All the Tau We Cannot See

Alzheimer's disease (AD) was described in 1906 as a dementing disease marked by the presence of two types of fibrillar aggregates in the brain: neurofibrillary tangles and senile plaques. The process of aggregation and formation of the aggregates has been a major focus of investigation ever since the discoveries that the tau protein is the predominant protein in tangles and amyloid β is the predominant protein in plaques. The idea that smaller, oligomeric species of amyloid may also be bioactive has now been clearly established. This review examines the possibility that soluble, nonfibrillar, bioactive forms of tau-the "tau we cannot see"-comprise a dominant driver of neurodegeneration in AD.

The spectrum of clinical biomarkers in severe malaria and new avenues for exploration

Globally, malaria is a public health concern, with severe malaria (SM) contributing a major share of the disease burden in malaria endemic countries. In this context, identification and validation of SM biomarkers are essential in clinical practice. Some biomarkers (C-reactive protein, angiopoietin 2, angiopoietin-2/1 ratio, platelet count, histidine-rich protein 2) have yielded interesting results in the prognosis of Plasmodium falciparum severe malaria, but for severe P. vivax and P. knowlesi malaria, similar evidence is missing. The validation of these biomarkers is hindered by several factors such as low sample size, paucity of evidence-evaluating studies, suboptimal values of sensitivity/specificity, poor clinical practicality of measurement methods, mixed Plasmodium infections, and good clinical value of the biomarkers for concurrent infections (pneumonia and current COVID-19 pandemic). Most of these biomarkers are non-specific to pathogens as they are related to host response and hence should be regarded as prognostic/predictive biomarkers that complement but do not replace pathogen biomarkers for clinical evaluation of SM patients. This review highlights the importance of research on diagnostic/predictive/therapeutic biomarkers, neglected malaria species, and clinical practicality of measurement methods in future studies. Finally, the importance of omics technologies for faster identification/validation of SM biomarkers is also included.

Cerebrospinal Fluid Biomarker Profile in TDP-43-Related Genetic Frontotemporal Dementia

Cerebrospinal fluid (CSF) biomarkers, namely total tau, phospho-tau and amyloid beta peptides, have received much attention specifically regarding Alzheimer’s disease (AD), since they can detect the biochemical fingerprint of AD and serve as a diagnostic tool for accurate and early diagnosis during life. In the same way, biomarkers for other neurodegenerative disease pathologies are also needed. We present a case series of six patients with genetic frontotemporal dementia (FTD), with TDP-43 underlying proteinopathy, in an attempt to assess TDP-43 as a novel biomarker alone and in combination with established AD biomarkers for this specific patient group, based on the principles of personalized and precision medicine. Our results indicate that genetic TDP-43-FTD is characterized by increased CSF TPD-43 and increased TDP-43 × τΤ/τP-181 combination. Hence, TDP-43 combined with tau proteins could be a useful tool for the diagnosis of genetic FTD with TDP-43 underling histopathology, supplementing clinical, neuropsychological and imaging data.

Classification accuracy of blood-based and neurophysiological markers in the differential diagnosis of Alzheimer's disease and frontotemporal lobar degeneration

BACKGROUND

In the last decade, non-invasive blood-based and neurophysiological biomarkers have shown great potential for the discrimination of several neurodegenerative disorders. However, in the clinical workup of patients with cognitive impairment, it will be highly unlikely that any biomarker will achieve the highest potential predictive accuracy on its own, owing to the multifactorial nature of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD).

METHODS

In this retrospective study, performed on 202 participants, we analysed plasma neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and tau phosphorylated at amino acid 181 (p-Tau181) concentrations, as well as amyloid β42 to 40 ratio (Aβ1-42/1-40) ratio, using the ultrasensitive single-molecule array (Simoa) technique, and neurophysiological measures obtained by transcranial magnetic stimulation (TMS), including short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), long-interval intracortical inhibition (LICI), and short-latency afferent inhibition (SAI). We assessed the diagnostic accuracy of combinations of both plasma and neurophysiological biomarkers in the differential diagnosis between healthy ageing, AD, and FTLD.

RESULTS

We observed significant differences in plasma NfL, GFAP, and p-Tau181 levels between the groups, but not for the Aβ1-42/Aβ1-40 ratio. For the evaluation of diagnostic accuracy, we adopted a two-step process which reflects the clinical judgement on clinical grounds. In the first step, the best single biomarker to classify "cases" vs "controls" was NfL (AUC 0.94, p < 0.001), whilst in the second step, the best single biomarker to classify AD vs FTLD was SAI (AUC 0.96, p < 0.001). The combination of multiple biomarkers significantly increased diagnostic accuracy. The best model for classifying "cases" vs "controls" included the predictors p-Tau181, GFAP, NfL, SICI, ICF, and SAI, resulting in an AUC of 0.99 (p < 0.001). For the second step, classifying AD from FTD, the best model included the combination of Aβ1-42/Aβ1-40 ratio, p-Tau181, SICI, ICF, and SAI, resulting in an AUC of 0.98 (p < 0.001).

CONCLUSIONS

The combined assessment of plasma and neurophysiological measures may greatly improve the differential diagnosis of AD and FTLD.

Prediction of Alzheimer’s Disease Using Patterns of Methylation Levels in Key Immunologic-Related Genes

Background: DNA methylation is expected to become a kind of new diagnosis and treatment method of Alzheimer’s disease (AD). Neuroinflammation- and immune-related pathways represent one of the major genetic risk factors for AD. Objective: We aimed to investigate DNA methylation levels of 7 key immunologic-related genes in peripheral blood and appraise their applicability in the diagnosis of AD. Methods: Methylation levels were obtained from 222 participants (101 AD, 72 MC, 49 non-cognitively impaired controls). Logistic regression models for diagnosing AD were established after least absolute shrinkage and selection operator (LASSO) and best subset selection (BSS), evaluated by respondent working curve and decision curve analysis for sensitivity. Results: Six differentially methylated positions (DMPs) in the MCI group and 64 in the AD group were found, respectively. Among them, there were 2 DMPs in the MCI group and 30 DMPs in the AD group independent of age, gender, and APOE4 carriers (p <  0.05). AD diagnostic prediction models differentiated AD from normal controls both in a training dataset (LASSO: 8 markers, including methylation levels at ABCA7_1040077, CNR1_88166293, CX3CR1_39322324, LRRK2_40618505, LRRK2_40618493, NGFR_49496745, TARDBP_11070956, TARDBP_11070840, area under the curve [AUC] = 0.81; BSS: 2 markers, including methylation levels at ABCA7_1040077 and CX3CR1_39322324, AUC = 0.80) and a testing dataset (AUC = 0.84, AUC = 0.82, respectively). Conclusion: Our work indicated that methylation levels of 7 key immunologic-related genes (ABCA7, CNR1, CX3CR1, CSF1 R, LRRK2, NGFR, and TARDBP) in peripheral blood was altered in AD and the models including methylation of immunologic-related genes biomarkers improved prediction of AD.

Plasma P-Tau181 for the Discrimination of Alzheimer’s Disease from Other Primary Dementing and/or Movement Disorders

Blood phospho-tau181 may offer a useful biomarker for Alzheimer’s disease. However, the use of either serum or plasma phospho-tau181 and their diagnostic value are currently under intense investigation. In a pilot study, we measured both serum and plasma phospho-tau181 (pT181-Tau) by single molecule array (Simoa) in a group of patients with Alzheimer’s disease and a mixed group of patients with other primary dementing and/or movement disorders. Classical cerebrospinal fluid biomarkers were also measured. Plasma (but not serum) pT181-Tau showed a significant increase in Alzheimer’s disease and correlated significantly with cerebrospinal fluid amyloid and pT181-Tau. Receiver operating curve analysis revealed a significant discrimination of Alzheimer’s from non-Alzheimer’s disease patients, with an area under the curve of 0.83 and an excellent sensitivity but a moderate specificity. Plasma pT181-Tau is not an established diagnostic biomarker for Alzheimer’s disease, but it could become one in the future, or it may serve as a screening tool for specific cases of patients or presymptomatic subjects.

Tau as a Biomarker of Neurodegeneration

Less than 50 years since tau was first isolated from a porcine brain, its detection in femtolitre concentrations in biological fluids is revolutionizing the diagnosis of neurodegenerative diseases. This review highlights the molecular and technological advances that have catapulted tau from obscurity to the forefront of biomarker diagnostics. Comprehensive updates are provided describing the burgeoning clinical applications of tau as a biomarker of neurodegeneration. For the clinician, tau not only enhances diagnostic accuracy, but holds promise as a predictor of clinical progression, phenotype, and response to drug therapy. For patients living with neurodegenerative disorders, characterization of tau dysregulation could provide much-needed clarity to a notoriously murky diagnostic landscape.

Neuronal and Neuroaxonal Damage Cerebrospinal Fluid Biomarkers in Autoimmune Encephalitis Associated or Not with the Presence of Tumor

The aim of this study was to evaluate the association of neuronal damage biomarkers (neurofilament light chain (NFL) and total tau protein (T-tau)) in the CSF of patients with autoimmune encephalitis (AE) with the presence of an underlying malignancy and to determine correlations with patient characteristics. The study comprised 21 patients with encephalitis associated with antibodies against intracellular (n = 11) and surface/synaptic antigens (extracellular, n = 10) and non-inflammatory disease controls (n = 10). Patients with AE associated with intracellular antigens had increased CSF-NFL (p = 0.003) but not T-tau levels compared to controls. When adjusted for age, CSF-NFL but not CSF-T-tau was higher in patients with encephalitis associated with intracellular antigens as compared to those with encephalitis associated with extracellular antigens (p = 0.032). Total tau and NFL levels were not significantly altered in patients with encephalitis associated with extracellular antigens compared to controls. NFL in the total cohort correlated with neurological signs of cerebellar dysfunction, peripheral neuropathy, presence of CV2 positivity, presence of an underlying tumor and a more detrimental clinical outcome. AE patients with abnormal MRI findings displayed higher NFL levels compared to those without, albeit with no statistical significance (p = 0.07). Using receiver operating characteristic curve analysis, CSF-NFL levels with a cut-off value of 969 pg/mL had a sensitivity and specificity of 100% and 76.19%, respectively, regarding the detection of underlying malignancies. Our findings suggest that neuronal integrity is preserved in autoimmune encephalitis associated with extracellular antigens and without the presence of tumor. However, highly increased NFL is observed in AE associated with intracellular antigens and presence of an underlying tumor. CSF-NFL could potentially be used as a diagnostic biomarker of underlying malignancies in the clinical setting of AE.

A Comparison of Cerebrospinal Fluid Beta-Amyloid and Tau in Idiopathic Normal Pressure Hydrocephalus and Neurodegenerative Dementias

Purpose

Idiopathic normal pressure hydrocephalus (iNPH) is the leading reversible cause of cognitive impairment and gait disturbance that has similar clinical manifestations and accompanies to major neurodegenerative disorders in older adults. We aimed to investigate whether cerebrospinal fluid (CSF) biomarker for Alzheimer’s disease (AD) may be useful in the differential diagnosis of iNPH.

Patients and Methods

Amyloid-beta (Aß) 42 and 40, total tau (t-tau), phosphorylated tau (p-tau) were measured via ELISA in 192 consecutive CSF samples of patients with iNPH (n=80), AD (n=48), frontotemporal dementia (FTD) (n=34), Lewy body diseases (LBDs) (n=30) consisting of Parkinson’s disease dementia and dementia with Lewy bodies.

Results

The mean age of the study population was 75.6±7.7 years, and 54.2% were female. CSF Aβ₄₂ levels were significantly higher, and p-tau and t-tau levels were lower in iNPH patients than in those with AD and LBDs patients. Additionally, iNPH patients had significantly higher levels of t-tau than those with FTD. Age and sex-adjusted multi-nominal regression analysis revealed that the odds of having AD relative to iNPH decreased by 37% when the Aβ₄₂ level increased by one standard deviation (SD), and the odds of having LBDs relative to iNPH decreased by 47%. The odds of having LBDs relative to iNPH increased 76% when the p-tau level increased 1SD. It is 2.5 times more likely for a patient to have LBD relative to NPH and 2.1 times more likely to have AD relative to iNPH when the t-tau value increased 1SD.

Conclusion

Our results suggest that levels of CSF Aβ₄₂, p-tau, and t-tau, in particularly decreased t-tau, are of potential value in differentiating iNPH from LBDs and also confirm previous studies reporting t-tau level is lower and Aβ₄₂ level is higher in iNPH than in AD.

Transcriptional response of murine microglia in Alzheimer’s disease and inflammation

Background

Alzheimer’s disease (AD) is a neurodegenerative disorder and is the most common cause of late-onset dementia. Microglia, the primary innate immune cells of the central nervous system (CNS), have a complex role in AD neuropathology. In the initial stages of AD, microglia play a role in limiting pathology by removing amyloid-β (Aβ) by phagocytosis. In contrast, microglia also release pro-inflammatory cytokines and chemokines to promote neuroinflammation and exacerbate AD neuropathology. Therefore, investigating microglial gene networks could identify new targets for therapeutic strategies for AD.

Results

We identified 465 differentially expressed genes (DEG) in 5XFAD versus wild-type mice by microarray, 354 DEG in lipopolysaccharide (LPS)-stimulated N9 microglia versus unstimulated control cells using RNA-sequencing (RNA-seq), with 32 DEG common between both datasets. Analyses of the 32 common DEG uncovered numerous molecular functions and pathways involved in Aβ phagocytosis and neuroinflammation associated with AD. Furthermore, multiplex ELISA confirmed the induction of several cytokines and chemokines in LPS-stimulated microglia.

Conclusions

In summary, AD triggered multiple signaling pathways that regulate numerous genes in microglia, contributing to Aβ phagocytosis and neuroinflammation. Overall, these data identified several regulatory factors and biomarkers in microglia that could be useful in further understanding AD neuropathology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08417-8.

Cerebrospinal Fluid EV Concentration and Size Are Altered in Alzheimer’s Disease and Dementia with Lewy Bodies

Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD) represent the three major neurodegenerative dementias characterized by abnormal brain protein accumulation. In this study, we investigated extracellular vesicles (EVs) and neurotrophic factors in the cerebrospinal fluid (CSF) of 120 subjects: 36 with AD, 30 with DLB, 34 with FTD and 20 controls. Specifically, CSF EVs were analyzed by Nanoparticle Tracking Analysis and neurotrophic factors were measured with ELISA. We found higher EV concentration and lower EV size in AD and DLB groups compared to the controls. Classification tree analysis demonstrated EV size as the best parameter able to discriminate the patients from the controls (96.7% vs. 3.3%, respectively). The diagnostic performance of the EV concentration/size ratio resulted in a fair discrimination level with an area under the curve of 0.74. Moreover, the EV concentration/size ratio was associated with the p-Tau181/Aβ42 ratio in AD patients. In addition, we described altered levels of cystatin C and progranulin in the DLB and AD groups. We did not find any correlation between neurotrophic factors and EV parameters. In conclusion, the results of this study suggest a common involvement of the endosomal pathway in neurodegenerative dementias, giving important insight into the molecular mechanisms underlying these pathologies.

Development of molecular tools for diagnosis of Alzheimer's disease that are based on detection of amyloidogenic proteins

Alzheimer's disease (AD) is the most common form of dementia that usually occurs among older people. AD results from neuronal degeneration that leads to the cognitive impairment and death. AD is incurable, typically develops over the course of many years and is accompanied by a loss of functional autonomy, making a patient completely dependent on family members and/or healthcare workers. Critical features of AD are pathological polymerization of Aβ peptide and microtubule-associated protein tau, accompanied by alterations of their conformations and resulting in accumulation of cross-β fibrils (amyloids) in human brains. AD apparently progresses asymptomatically for years or even decades before the appearance of symptoms. Therefore, development of the early AD diagnosis at a pre-symptomatic stage is essential for potential therapies. This review is focused on current and potential molecular tools (including non-invasive methods) that are based on detection of amyloidogenic proteins and can be applicable to early diagnosis of AD.Abbreviations: Aβ - amyloid-β peptide; AβO - amyloid-β oligomers; AD - Alzheimer's disease; ADRDA - Alzheimer's Disease and Related Disorders Association; APH1 - anterior pharynx defective 1; APP - amyloid precursor protein; BACE1 - β-site APP-cleaving enzyme 1; BBB - brain blood barrier; CJD - Creutzfeldt-Jakob disease; CRM - certified reference material; CSF - cerebrospinal fluid; ELISA - enzyme-linked immunosorbent assay; FGD - 18F-fluorodesoxyglucose (2-deoxy-2-[18F]fluoro-D-glucose); IP-MS - immunoprecipitation-mass spectrometry assay; MCI - mild cognitive impairment; MDS - multimer detection system; MRI - magnetic resonance imaging; NIA-AA - National Institute on Ageing and Alzheimer's Association; NINCDS - National Institute of Neurological and Communicative Disorders and Stroke; PEN2 - presenilin enhancer 2; PET - positron emission tomography; PiB - Pittsburgh Compound B; PiB-SUVR - PIB standardized uptake value ratio; PMCA - Protein Misfolding Cycling Amplification; PrP - Prion Protein; P-tau - hyperphosphorylated tau protein; RMP - reference measurement procedure; RT-QuIC - real-time quaking-induced conversion; SiMoA - single-molecule array; ThT - thioflavin T; TSEs - Transmissible Spongiform Encephslopathies; T-tau - total tau protein.

Fibrinogen isoforms as potential blood-based biomarkers of Alzheimer's disease using a proteomics approach

Objective: Alzheimer's disease (AD), the commonest form of dementia which is characterized by progressive decline in cognitive function, can only be definitively diagnosed after death. Although biomarkers may aid diagnosis, currently available AD biomarkers, which are predominantly based on cerebrospinal fluid and neuroimaging facilities, are either invasive or costly. Blood-based biomarkers for AD diagnosis are highly sought after due to its practicality at the clinic. This study was undertaken to determine the differential protein expression in plasma amongst Malaysian AD, mild cognitive impairment (MCI) and non-AD individuals. Methods: A proteomic approach which utilized two-dimensional differential in gel electrophoresis (2 D DIGE) was performed for blood samples from 15 AD, 14 MCI and 15 non-AD individuals. Results: Mass spectrometry (MS)-based protein identification via MALDI ToF/ToF showed that fibrinogen-β-chain (spot 64) and fibrinogen-γ-chain (spot 91) with differential expression ratio >1.5 were significantly upregulated (p < 0.05) in AD patients when compared to non-AD individuals. Further data analysis using Pearson correlation found that the upregulated fibrinogen-γ-chain was weakly but significantly (p < 0.05) and inversely correlated with cognitive decline. Conclusion: Fibrinogen isoforms may play important roles in the vascular pathology of AD as well as neuroinflammation. As such, fibrinogen appears to be a promising blood-based biomarker for AD. Further validation of the present findings in larger population is now warranted.

Latent Class and Transition Analysis of Alzheimer's Disease Data

This study uses independent latent class analysis (LCA) and latent transition analysis (LTA) to explore accurate diagnosis and disease status change of a big Alzheimer's disease Neuroimaging Initiative (ADNI) data of 2,132 individuals over a 3-year period. The data includes clinical and neural measures of controls (CN), individuals with subjective memory complains (SMC), early-onset mild cognitive impairment (EMCI), late-onset mild cognitive impairment (LMCI), and Alzheimer's disease (AD). LCA at each time point yielded 3 classes: Class 1 is mostly composed of individuals from CN, SMC, and EMCI groups; Class 2 represents individuals from LMCI and AD groups with improved scores on memory, clinical, and neural measures; in contrast, Class 3 represents LMCI and from AD individuals with deteriorated scores on memory, clinical, and neural measures. However, 63 individuals from Class 1 were diagnosed as AD patients. This could be misdiagnosis, as their conditional probability of belonging to Class 1 (0.65) was higher than that of Class 2 (0.27) and Class 3 (0.08). LTA results showed that individuals had a higher probability of staying in the same class over time with probability &gt;0.90 for Class 1 and 3 and probability &gt;0.85 for Class 2. Individuals from Class 2, however, transitioned to Class 1 from time 2 to time 3 with a probability of 0.10. Other transition probabilities were not significant. Lastly, further analysis showed that individuals in Class 2 who moved to Class 1 have different memory, clinical, and neural measures to other individuals in the same class. We acknowledge that the proposed framework is sophisticated and time-consuming. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritize an accurate diagnosis. Our findings show that LCA can provide a more accurate prediction for classifying and identifying the progression of AD compared to traditional clinical cut-off measures on neuropsychological assessments.

Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models.

Association of glial and neuronal degeneration markers with Alzheimer's disease cerebrospinal fluid profile and cognitive functions

BACKGROUND

Neuroinflammation has gained increasing attention as a potential contributing factor in the onset and progression of Alzheimer's disease (AD). The objective of this study was to examine the association of selected cerebrospinal fluid (CSF) inflammatory and neuronal degeneration markers with signature CSF AD profile and cognitive functions among subjects at the symptomatic pre- and early dementia stages.

METHODS

In this cross-sectional study, 52 subjects were selected from an Icelandic memory clinic cohort. Subjects were classified as having AD (n = 28, age = 70, 39% female, Mini-Mental State Examination [MMSE] = 27) or non-AD (n = 24, age = 67, 33% female, MMSE = 28) profile based on the ratio between CSF total-tau (T-tau) and amyloid-β_1-42 (Aβ₄₂) values (cut-off point chosen as 0.52). Novel CSF biomarkers included neurofilament light (NFL), YKL-40, S100 calcium-binding protein B (S100B) and glial fibrillary acidic protein (GFAP), measured with enzyme-linked immunosorbent assays (ELISAs). Subjects underwent neuropsychological assessment for evaluation of different cognitive domains, including verbal episodic memory, non-verbal episodic memory, language, processing speed, and executive functions.

RESULTS

Accuracy coefficient for distinguishing between the two CSF profiles was calculated for each CSF marker and test. Novel CSF markers performed poorly (area under curve [AUC] coefficients ranging from 0.61 to 0.64) compared to tests reflecting verbal episodic memory, which all performed fair (AUC > 70). LASSO regression with a stability approach was applied for the selection of CSF markers and demographic variables predicting performance on each cognitive domain, both among all subjects and only those with a CSF AD profile. Relationships between CSF markers and cognitive domains, where the CSF marker reached stability selection criteria of > 75%, were visualized with scatter plots. Before calculations of corresponding Pearson's correlations coefficients, composite scores for cognitive domains were adjusted for age and education. GFAP correlated with executive functions (r = - 0.37, p = 0.01) overall, while GFAP correlated with processing speed (r = - 0.68, p < 0.001) and NFL with verbal episodic memory (r = - 0.43, p = 0.02) among subjects with a CSF AD profile.

CONCLUSIONS

The novel CSF markers NFL and GFAP show potential as markers for cognitive decline among individuals with core AD pathology at the symptomatic pre- and early stages of dementia.

Neuropsychological profile of amyloid-positive versus amyloid-negative amnestic Mild Cognitive Impairment

INTRODUCTION

Patients diagnosed with amnestic mild cognitive impairment (aMCI) are at high risk of progressing to dementia. It became possible, through the use of biomarkers, to diagnose those patients with aMCI who have Alzheimer's disease. However, it is presently unfeasible that all patients undergo biomarker testing. Since neuropsychological testing is required to make a formal diagnosis of aMCI, it would be interesting if it could be used to predict the amyloid status of patients with aMCI.

METHODS

Participants with aMCI, known amyloid status (Aβ+ or Aβ-) and a comprehensive neuropsychological evaluation, were selected from the Cognitive Complaints Cohort database for this study. Neuropsychological tests were compared in Aβ+ and Aβ- aMCI patients. A binary logistic regression analysis was conducted to model the probability of being amyloid positive.

RESULTS

Of the 216 aMCI patients studied, 117 were Aβ+ and 99 were Aβ-. Aβ+ aMCI patients performed worse on several memory tests, namely Word Total Recall, Logical Memory Immediate and Delayed Free Recall, and Verbal Paired Associate Learning, as well as on Trail Making Test B, an executive function test. In a binary logistic regression model, only Logical Memory Delayed Free Recall retained significance, so that for each additional score point in this test, the probability of being amyloid positive decreased by 30.6%. The resulting model correctly classified 64.6% of the aMCI cases regarding their amyloid status.

CONCLUSIONS

The neuropsychological assessment remains an essential step to diagnose and characterize patients with aMCI; however, neuropsychological tests have limited value to distinguish the aMCI patients who have amyloid pathology from those who might suffer from other clinical conditions.

Alzheimer's Disease Diagnosis Using Misfolding Proteins in Blood

Alzheimer's disease (AD) is pathologically characterized by a long progressive phase of neuronal changes, including accumulation of extracellular amyloid-β (Aβ) and intracellular neurofibrillary tangles, before the onset of observable symptoms. Many efforts have been made to develop a blood-based diagnostic method for AD by incorporating Aβ and tau as plasma biomarkers. As blood tests have the advantages of being highly accessible and low cost, clinical implementation of AD blood tests would provide preventative screening to presymptomatic individuals, facilitating early identification of AD patients and, thus, treatment development in clinical research. However, the low concentration of AD biomarkers in the plasma has posed difficulties for accurate detection, hindering the development of a reliable blood test. In this review, we introduce three AD blood test technologies emerging in South Korea, which have distinctive methods of heightening detection sensitivity of specific plasma biomarkers. We discuss in detail the multimer detection system, the self-standard analysis of Aβ biomarkers quantified by interdigitated microelectrodes, and a biomarker ratio analysis comprising Aβ and tau.

The discriminative capacity of CSF β-amyloid 42 and Tau in neurodegenerative diseases in the Chinese population

INTRODUCTION

In the past few years, the β-amyloid 42 peptide and tau protein in cerebrospinal fluid (CSF) have become primary diagnostic biomarkers in differentiating Alzheimer's disease (AD) and cognitive normal controls. As we know, several neurodegenerative diseases have been reported to overlap with AD in neuropathology and clinical symptoms. To examine the discriminative utility of these biomarkers in AD and other neurodegenerative diseases, we measured them in a cohort of Chinese population.

METHODS

We measured CSF Aβ₄₂, t-tau and p-tau₁₈₁ by ELISA tests and calculated the ratios of t-tau/Aβ₄₂ and p-tau₁₈₁/Aβ₄₂ in 240 Chinese Han patients with AD (n = 82), frontotemporal dementia (FTD, n = 20), Huntington's disease (HD, n = 27), multiple system atrophy (MSA, n = 24), spinocerebellar ataxia type-3 (SCA3, n = 27), amyotrophic lateral sclerosis (ALS, n = 36) and controls (n = 24).

RESULTS

As expected, all biomarkers showed high discriminative capacity between AD and non-AD groups (p < .05) except for the elevated CSF t-tau in FTD (p > .05). Comparing with the controls, tau related biomarkers significantly elevated in the FTD (p < .001) and MSA (p < .05) groups. Surprisingly, comparing with controls, we found that CSF Aβ₄₂ increased remarkably in the SCA3 (p < .05), HD and ALS groups (p < .001), achieving a high specificity, respectively.

CONCLUSION

To our best knowledge, this is the first comprehensive study in the Han Chinese population that confirmed the discriminative utility of CSF Aβ₄₂ and tau biomarkers between AD and other neurodegenerative diseases.

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background

Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.

Methods

Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination.

Results

The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment.

Conclusions

The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

Behavioral and cognitive markers of mild cognitive impairment: diagnostic value of saccadic eye movements and Simon task

BACKGROUND

Mild Cognitive Impairment (MCI) has been considered as a prodromal stage of Alzheimer disease (AD). Subtle changes in specific aspects of executive function like inhibitory control have been found in MCI.

AIMS

We examined attentional and inhibitory control with the aim to distinguish between amnestic MCI patients and healthy controls.

METHOD

Using neuropsychological, behavioral, and oculomotor function experiments, we examined executive function in 59 normal control, 49, multiple domain amnestic MCI (a-MCI) subjects, and 21 early stage AD patients using eye tracking and Simon task as measures of attentional control, to determine which saccade and behavioral tasks were sensitive enough to identify a-MCI. Saccades were investigated in gap and overlap pro-saccade and anti-saccade tasks.

RESULTS

Scores on the Simon task were inversely correlated with general cognitive status and can distinguish a-MCI from controls with excellent specificity (AUC = 0.65 for reaction time and 0.59 for false responses). More importantly, our results showed that saccadic gains were affected in a-MCI and were the most sensitive measures to distinguish a-MCI from normal participants AST gap task AUC = 0.7, PST gap task AUC = 0.63, AST overlap task (AUC = 0.73). Moreover, these parameters were strongly correlated with neuropsychological measures. Using tests in parallel model, improved sensitivity up to 0.97.

CONCLUSION

The present results enable us to suggest eye tracking along with behavioral data as a possible sensitive tools to detect a-MCI in preclinical stage.

Cerebrospinal Fluid, Imaging, and Physiological Biomarkers in Dementia With Lewy Bodies

Dementia with Lewy bodies is a progressive neurodegenerative disorder, clinically characterized by gradual cognitive impairment and fluctuating cognition, behavioral changes and recurrent visual hallucinations, and autonomic function and movement symptoms in the type of parkinsonism. It is the second most common type of dementia in the Western world after Alzheimer disease. Over the last 20 years, many neurophysiological, neuroimaging, and cerebrospinal fluid (CSF) biomarkers have been described toward a better discrimination between dementia with Lewy bodies, Alzheimer disease, and other neurodegenerative conditions.In the present review, we aim to describe the neurophysiological, imaging, and CSF biomarkers in dementia with Lewy bodies and to question whether they could be reliable tools for the clinical practice.

Cerebrospinal fluid biomarkers and cognitive status in differential diagnosis of frontotemporal dementia and Alzheimer's disease

Objective

This study aimed to determine the most appropriate cognitive and cerebrospinal fluid (CSF) biomarker setting to distinguish frontotemporal dementia (FTD) from Alzheimer’s disease (AD).

Method

Patients with FTD, those with AD, and those without dementia were enrolled in this study. CSF amyloid-ß 42 (Aß42), total (t)-tau, and phosphorylated (p)-tau concentrations were determined by enzyme-linked immunosorbent assays. Cognition was evaluated by the Mini-Mental State Examination (MMSE) and its domain scores. The associations of CSF biomarkers with cognitive measures were examined using regression models and the diagnostic value of CSF biomarkers was determined by receiver operating characteristics curves.

Results

CSF Aß42 levels were lower, whereas t-tau/Aß42 and p-tau/Aß42 ratios were higher in patients with AD compared with those with FTD. Some MMSE domain scores were different in FTD and AD, but they did not improve the ability to distinguish between the two pathologies. Poor temporal orientation scores were associated with low Aß42 levels only in patients with FTD. The p-tau/Aß42 ratio reached sufficient levels of sensitivity and specificity to discriminate FTD with primary progressive aphasia from AD.

Conclusions

The ratio of CSF p-tau/Aß42 is a sensitive and specific biomarker for discriminating patients with primary progressive aphasia from those with AD.

Plasma amyloid and tau as dementia biomarkers in Down syndrome: Systematic review and meta-analyses

Individuals with Down syndrome (DS) are at high risk of developing Alzheimer's disease (AD). Discovering reliable biomarkers which could facilitate early AD diagnosis and be used to predict/monitor disease course would be extremely valuable. To examine if analytes in blood related to amyloid plaques may constitute such biomarkers, we conducted meta‐analyses of studies comparing plasma amyloid beta (Aβ) levels between DS individuals and controls, and between DS individuals with and without dementia. PubMed, Embase, and Google Scholar were searched for studies investigating the relationship between Aβ plasma concentrations and dementia in DS and 10 studies collectively comprising >1,600 adults, including >1,400 individuals with DS, were included. RevMan 5.3 was used to perform meta‐analyses. Meta‐analyses showed higher plasma Aβ₄₀ (SMD = 1.79, 95% CI [1.14, 2.44], Z = 5.40, p < .00001) and plasma Aβ₄₂ levels (SMD = 1.41, 95% CI [1.15, 1.68], Z = 10.46, p < .00001) in DS individuals than controls, and revealed that DS individuals with dementia had higher plasma Aβ₄₀ levels (SMD = 0.23, 95% CI [0.05, 0.41], Z = 2.54, p = .01) and lower Aβ₄₂/Aβ₄₀ ratios (SMD = −0.33, 95% CI [−0.63, −0.03], Z = 2.15, p = .03) than DS individuals without dementia. Our results indicate that plasma Aβ₄₀ levels may constitute a promising biomarker for predicting dementia status in individuals with DS. Further investigations using new ultra‐sensitive assays are required to obtain more reliable results and to investigate to what extent these results may be generalizable beyond the DS population.

The CSF p-tau181/Aβ42 Ratio Offers a Good Accuracy “In Vivo” in the Differential Diagnosis of Alzheimer’s Dementia

Background:

The incoming disease-modifying therapies against Alzheimer’s disease (AD) require reliable diagnostic markers to correctly enroll patients all over the world. CSF AD biomarkers, namely amyloid-β 42 (Aβ42), total tau (t-tau), and tau phosphorylated at threonine 181 (p-tau181), showed good diagnostic accuracy in detecting AD pathology, but their real usefulness in daily clinical practice is still a matter of debate. Therefore, further validation in complex clinical settings, that is patients with different types of dementia, is needed to uphold their future worldwide adoption.

Methods:

We measured CSF AD biomarkers’ concentrations in a sample of 526 patients with a clinical diagnosis of dementia (277 with AD and 249 with Other Type of Dementia, OTD). Brain FDG-PET was also considered in a subsample of 54 patients with a mismatch between the clinical diagnosis and the CSF findings.

Results:

A p-tau181/Aβ42 ratio higher than 0.13 showed the best diagnostic performance in differentiating AD from OTD (86% accuracy index, 74% sensitivity, 81% specificity). In cases with a mismatch between clinical diagnosis and CSF findings, brain FDG-PET partially agreed with the p-tau181/Aβ42 ratio, thus determining an increase in CSF accuracy.

Conclusions:

The p-tau181/Aβ42 ratio alone might reliably detect AD pathology in heterogeneous samples of patients suffering from different types of dementia. It might constitute a simple, cost-effective and reproducible in vivo proxy of AD suitable to be adopted worldwide not only in daily clinical practice but also in future experimental trials, to avoid the enrolment of misdiagnosed AD patients.

Kallikrein-related peptidases 6 and 10 are elevated in cerebrospinal fluid of patients with Alzheimer's disease and associated with CSF-TAU and FDG-PET

Background

Alterations in the expression of human kallikrein-related peptidases (KLKs) have been described in patients with Alzheimer’s disease (AD). We elucidated the suitability of KLK6, KLK8 and KLK10 to distinguish AD from NC and explored associations with established AD biomarkers.

Methods

KLK levels in cerebrospinal fluid (CSF), as determined by ELISA, were compared between 32 AD patients stratified to A/T/(N) system with evidence for amyloid pathology and of 23 normal controls with normal AD biomarkers. Associations between KLK levels and clinical severity, CSF and positron emission tomography (PET) based AD biomarkers were tested for.

Results

Levels of KLK6 and KLK10 were significantly increased in AD. KLK6 differed significantly between AD A+/T+/N+ and AD A+/T−/N+ or NC with an AUC of 0.922. CSF pTau and tTau levels were significantly associated with KLK6 in AD.

Conclusions

KLK6 deserves further investigations as a potential biomarker of Tau pathology in AD.

Electronic supplementary material

The online version of this article (10.1186/s40035-019-0168-6) contains supplementary material, which is available to authorized users.

The Association Between Circulating Inflammatory Markers and the Progression of Alzheimer Disease in Norwegian Memory Clinic Patients With Mild Cognitive Impairment or Dementia

OBJECTIVE

Neuroinflammation may play an important role in the pathogenesis and progression of Alzheimer disease (AD). The aim of the present study was to detect whether increased inflammatory activity at baseline could predict cognitive and functional decline in patients with amnestic mild cognitive impairment (aMCI) or AD dementia after 2 years.

METHODS

Serum samples from 242 memory clinic patients with an aMCI (n=88) or AD dementia (n=154) were analyzed for C-reactive protein and for 14 other inflammatory markers [interleukin (IL)-1β, interleukin-1 receptor antagonist, IL-6, IL-10, IL-12p40, IL-17a, IL-18, IL-22, IL-33, tumor necrosis factor, cluster of differentiation 40 ligand, interferon-γ, chemokine ligand (CCL) 2, and CCL4] by bead-based multiplex immunoassay. Disease progression was measured by the annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) and annual decrease in the score on the Mini-Mental State Examination (MMSE).

RESULTS

No association between increased levels of the inflammatory markers and change on the CDR-SB or MMSE score was found, but there was a significant difference in baseline IL-6 and interleukin-1 receptor antagonist levels between aMCI and AD dementia groups.

CONCLUSION

Increased levels of inflammatory markers were not associated with faster progression as measured by the annual change on the CDR-SB or MMSE score.

[Neurodegenerative diseases]

Neurodegenerative diseases are sporadic and rare hereditary disorders of the central nervous system, which cause a slowly progressive loss of function of specific neuron populations and their connections. Severe impairments and care dependency can be the sequelae. Neurodegenerative disorders are diseases of older people; therefore, the demographic shift leads to an increase in the number of affected patients. Radiologists will also become more involved. For this reason important neurodegenerative diseases are presented in this article. In addition to Alzheimer's and Parkinson's diseases these also include frontotemporal lobar degeneration, Lewy body dementia, vascular dementia, Creutzfeldt-Jakob disease and Huntington's chorea. The clinical symptoms and diagnostics are described, whereby the focus lies on typical results of morphological imaging.

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.

Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Introduction

We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases.

Methods

Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms.

Results

The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal.

Discussion

These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over the recent years, most of them learning their non-disease patterns from MCI that remained stable over 2–3 years. In this work, we analyzed whether these stable MCI over short-term periods are actually appropriate training examples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5 years of follow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures primarily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity in a trial sample of 248 MCI patients followed-up over 5 years. We further compared the sensitivity in those MCI that converted before 2 years and those that converted after 2 years. Our results indicate that 23% of the stable MCI at 2 years progressed in the next three years and that MRI volumetric measures are good predictors of conversion to AD dementia even at the mid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampus and entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC = 73% at 2 years vs. AUC = 84% at 5 years), as well as for specificity (56% vs. 71%). Sensitivity showed a non-significant slight decrease (81% vs. 78%). Remarkably, the performance of this model was comparable to machine learning models at the same follow-up times. MRI correctly identified most of the patients that converted after 2 years (with sensitivity >60%), and these patients showed a similar degree of abnormalities to those that converted before 2 years. This implies that most of the MCI patients that remained stable over short periods and subsequently progressed to AD dementia had evident atrophies at baseline. Therefore, machine learning models that use these patients to learn non-disease patterns are including an important fraction of patients with evident pathological changes related to the disease, something that might result in reduced performance and lack of biological interpretability.

•

MRI predicted AD dementia significantly better when extending follow-up from 2 to 5 years.

•

MRI was similarly able to detect AD in short and mid-term converters.

•

Predictive models should not learn non-disease patterns from stable MCI over short periods.

Clinical applicability of diagnostic biomarkers in early-onset cognitive impairment

BACKGROUND AND PURPOSE

Several diagnostic biomarkers are currently available for clinical use in early-onset cognitive impairment. The decision on which biomarker is used in each patient depends on several factors such as its predictive value or tolerability.

METHODS

There were a total of 40 subjects with early-onset cognitive complaints (<65 years of age): 26 with Alzheimer's disease (AD), five with frontotemporal dementia and nine with diagnostic suspicion of non-neurodegenerative disorder. Clinical and neuropsychological evaluation, lumbar puncture for cerebrospinal fluid (CSF) AD core biochemical marker determination, medial temporal atrophy evaluation on magnetic resonance imaging, amyloid-positron emission tomography (PET) and ¹⁸ F-fluorodeoxyglucose-PET were performed. Neurologists provided pre- and post-biomarker diagnosis, together with diagnostic confidence and clinical/therapeutic management. Patients scored the tolerability of each procedure.

RESULTS

Cerebrospinal fluid biomarkers and amyloid-PET increased diagnostic confidence in AD (77.4%-86.2% after CSF, 92.4% after amyloid-PET, P < 0.01) and non-neurodegenerative conditions (53.6%-75% after CSF, 95% after amyloid-PET, P < 0.05). Biomarker results led to diagnostic (32.5%) and treatment (32.5%) changes. All tests were well tolerated.

CONCLUSIONS

Biomarker procedures are well tolerated and have an important diagnostic/therapeutic impact on early-onset cognitive impairment.

The importance of age in the search for ERP biomarkers of aMCI

Alzheimer's Disease (AD) has become a major health issue in recent decades, and there is now growing interest in amnestic mild cognitive impairment (aMCI), an intermediate stage between healthy aging and dementia, usually AD. Event-related brain potential (ERP) studies have sometimes failed to detect differences between aMCI and control participants in the Go-P3 (or P3b, related to target classification processes in a variety of tasks) and NoGo-P3 (related to response inhibition processes, mainly in Go/NoGo tasks) ERP components. The aim of the present study was to evaluate whether the age factor, which is not usually taken into account in ERP studies, modulates group differences in these components. With this aim, we divided two groups of volunteer participants, 34 subjects with aMCI (51-87 years) and 31 controls (52-86 years), into two age subgroups: 69 years or less and 70 years or more. We recorded brain activity while the participants performed a distraction-attention auditory-visual (AV) task. Task performance was poorer in the older than in the younger group, and aMCI participants produced fewer correct responses than the matched controls; but no interactions of the age and group factors on performance were found. On the other hand, Go-P3 and NoGo-N2 latencies were longer in aMCI participants than in controls only in the younger subgroup. Thus, the younger aMCI participants categorized the Go stimuli in working memory and processed the NoGo stimuli (which required response inhibition) slower than the corresponding controls. Finally, the combination of the number of hits, Go-P3 latency and NoGo-N2 latency yielded acceptable sensitivity and specificity scores (0.70 and 0.92, respectively) as regards distinguishing aMCI participants aged 69 years or less from the age-matched controls. The findings indicate age should be taken into account in the search for aMCI biomarkers.

Pathological, imaging and genetic characteristics support the existence of distinct TDP-43 types in non-FTLD brains

TDP-43 is present in a high proportion of aged brains that do not meet criteria for frontotemporal lobar degeneration (FTLD). We determined whether there are distinct TDP-43 types in non-FTLD brains. From a cohort of 553 brains (Braak neurofibrillary tangle (NFT) stage 0-VI), excluding cases meeting criteria for FTLD, we identified those that had screened positive for TDP-43. We reviewed 14 different brain regions in these TDP-43 positive cases and classified them into those with "typical" TDP-43 immunoreactive inclusions (TDP type-α), and those in which TDP-43 immunoreactivity was adjacent to/associated with NFTs in the same neuron (TDP type-β). We compared pathological, genetic (APOE4, TMEM106B and GRN variants), neuroimaging and clinical data between types, as well as compared neuroimaging between types and a group of TDP-43 negative cases (n = 309). Two-hundred forty-one cases were classified as TDP type-α (n = 131, 54%) or TDP type-β (n = 110, 46%). Type-α cases were older than type-β at death (median 89 years vs. 87 years; p = 0.02). Hippocampal sclerosis was present in 78 (60%) type-α cases and 16 (15%) type-β cases (p < 0.001). Type-α cases showed a pattern of widespread TDP-43 deposition commonly extending into temporal, frontal and brainstem regions (84% TDP-43 stage 4-6) while in type-β cases deposition was predominantly limbic, located in amygdala, entorhinal cortex and subiculum of the hippocampus (84% TDP-43 stages 1-3) (p < 0.001). There was a difference in the frequency of TMEM106B protective (GG) and risk (CC) haplotypes (SNP rs3173615 encoding p.T185S) in type-α cases compared to type-β cases (GG/CG/CC: 8%/42%/50% vs. 24%/49%/27%; p = 0.01). Type-α cases had smaller amygdala (- 10.6% [- 17.6%, - 3.5%]; p = 0.003) and hippocampal (- 14.4% [- 21.6%, - 7.3%]; p < 0.001) volumes on MRI at death compared to type-β cases, although both types had smaller amygdala and hippocampal volumes compared to TDP-43 negative cases (- 7.77%, - 21.6%; p < 0.001). These findings demonstrate that there is distinct heterogeneity of TDP-43 deposition in non-FTLD brains.

Reduction of aluminum ion neurotoxicity through a small peptide application - NAP treatment of Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia in late life. It is difficult to precisely diagnose AD at early stages, making biomarker search essential for further developments. The objective of this study was to identify protein biomarkers associated with aluminum ions toxicity (AD-like toxicity) in a human neuroblastoma cell model, SH-SY5Y and assess potential prevention by NAP (NAPVSIPQ). Complete proteomic techniques were implemented. Four proteins were identified as up-regulated with aluminum ion treatment, CBP80/20-dependent translation initiation factor (CTIF), Early endosome antigen 1 (EEA1), Leucine-rich repeat neuronal protein 4 (LRRN4) and Phosphatidylinositol 3-kinase regulatory subunit beta (PI3KR2). Of these four proteins, EEA1 and PI3KR2 were down-regulated after NAP-induced neuroprotective activity in neuroblastoma cells. Thus, aluminum ions may increase the risk for neurotoxicity in AD, and the use of NAP is suggested as a treatment to provide additional protection against the effects of aluminum ions, via EEA1 and PI3KR2, associated with sorting and processing of the AD amyloid precursor protein (APP) through the endosomal system.

Blood-Derived Plasma Protein Biomarkers for Alzheimer's Disease in Han Chinese

It is well known that Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases; it begins gradually, and therefore no effective medicine is administered in the beginning. Thus, early diagnosis and prevention of AD are crucial. The present study focused on comparing the plasma protein changes between patients with AD and their healthy counterparts, aiming to explore a specific protein panel as a potential biomarker for AD patients in Han Chinese. Hence, we recruited and collected plasma samples from 98 AD patients and 101 elderly healthy controls from Wuxi and Shanghai Mental Health Centers. Using a Luminex assay, we investigated the expression levels of fifty plasma proteins in these samples. Thirty-two out of 50 proteins were found to be significantly different between AD patients and healthy controls (P < 0.05). Furthermore, an eight-protein panel that included brain-derived neurotrophic factor (BDNF), angiotensinogen (AGT), insulin-like growth factor binding protein 2 (IGFBP-2), osteopontin (OPN), cathepsin D, serum amyloid P component (SAP), complement C4, and prealbumin (transthyretin, TTR) showed the highest determinative score for AD and healthy controls (all P = 0.00). In conclusion, these findings suggest that a combination of eight plasma proteins can serve as a promising diagnostic biomarker for AD with high sensitivity and specificity in Han Chinese populations; the eight plasma proteins were proven important for AD diagnosis by further cross-validation studies within the AD cohort.

Current state of Alzheimer's fluid biomarkers

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a complex and heterogeneous pathophysiology. The number of people living with AD is predicted to increase; however, there are no disease-modifying therapies currently available and none have been successful in late-stage clinical trials. Fluid biomarkers measured in cerebrospinal fluid (CSF) or blood hold promise for enabling more effective drug development and establishing a more personalized medicine approach for AD diagnosis and treatment. Biomarkers used in drug development programmes should be qualified for a specific context of use (COU). These COUs include, but are not limited to, subject/patient selection, assessment of disease state and/or prognosis, assessment of mechanism of action, dose optimization, drug response monitoring, efficacy maximization, and toxicity/adverse reactions identification and minimization. The core AD CSF biomarkers Aβ42, t-tau, and p-tau are recognized by research guidelines for their diagnostic utility and are being considered for qualification for subject selection in clinical trials. However, there is a need to better understand their potential for other COUs, as well as identify additional fluid biomarkers reflecting other aspects of AD pathophysiology. Several novel fluid biomarkers have been proposed, but their role in AD pathology and their use as AD biomarkers have yet to be validated. In this review, we summarize some of the pathological mechanisms implicated in the sporadic AD and highlight the data for several established and novel fluid biomarkers (including BACE1, TREM2, YKL-40, IP-10, neurogranin, SNAP-25, synaptotagmin, α-synuclein, TDP-43, ferritin, VILIP-1, and NF-L) associated with each mechanism. We discuss the potential COUs for each biomarker.

Novel CSF biomarkers to discriminate FTLD and its pathological subtypes

Objective

Frontotemporal lobar degeneration (FTLD) is the second most prevalent dementia in young patients and is characterized by the presence of two main protein aggregates in the brain, tau (FTLD‐Tau) or TDP43 (FTLD‐TDP), which likely require distinct pharmacological therapy. However, specific diagnosis of FTLD and its subtypes remains challenging due to largely overlapping clinical phenotypes. Here, we aimed to assess the clinical performance of novel cerebrospinal fluid (CSF) biomarkers for discrimination of FTLD and its pathological subtypes.

Methods

YKL40, FABP4, MFG‐E8, and the activities of catalase and specific lysosomal enzymes were analyzed in patients with FTLD‐TDP (n = 30), FTLD‐Tau (n = 20), AD (n = 30), DLB (n = 29), and nondemented controls (n = 29) obtained from two different centers. Models were validated in an independent CSF cohort (n = 188).

Results

YKL40 and catalase activity were increased in FTLD‐TDP cases compared to controls. YKL40 levels were also higher in FTLD‐TDP compared to FTLD‐Tau. We identified biomarker models able to discriminate FTLD from nondemented controls (MFG‐E8, tTau, and Aβ₄₂; 78% sensitivity and 83% specificity) and non‐FTLD dementia (YKL40, pTau, p/tTau ratio, and age; 90% sensitivity, 78% specificity), which were validated in an independent cohort. In addition, we identified a biomarker model differentiating FTLD‐TDP from FTLD‐Tau (YKL40, MFGE‐8, βHexA together with βHexA/tHex and p/tTau ratios and age) with 80% sensitivity and 82% specificity.

Interpretation

This study identifies CSF protein signatures distinguishing FTLD and the two main pathological subtypes with optimal accuracy (specificity/sensitivity > 80%). Validation of these models may allow appropriate selection of cases for clinical trials targeting the accumulation of Tau or TDP43, thereby increasing their efficiency and facilitating the development of successful therapies.