Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders

CSF and Plasma Biomarkers in Patients With Iatrogenic Cerebral Amyloid Angiopathy

OBJECTIVES

Recently, a subset of patients affected by cerebral amyloid angiopathy (CAA) distinguished by atypical juvenile onset and a hypothesized iatrogenic origin (iatrogenic CAA, iCAA) has emerged. β-Amyloid (Aβ) accumulation evidenced by amyloid PET positivity or CSF Aβ decrease was included in the iCAA diagnostic criteria. Conversely, diagnostic criteria for sporadic CAA (sCAA) do not involve biomarker analysis. The aim of this study was to assess CSF and plasma levels of Aβ and tau in iCAA and sCAA cohorts.

METHODS

Patients affected by probable or possible CAA according to established criteria (Boston 2.0) were prospectively recruited at Fondazione IRCCS Carlo Besta and San Gerardo dei Tintori from May 2021 to January 2024. Patients with probable and possible iCAA or sCAA with available plasma and/or CSF samples were included. Clinical and neurologic data were collected, and levels of Aβ40, Aβ42, total tau, and phospho-tau (p-tau) were assessed in CSF and plasma by SiMoA and Lumipulse.

RESULTS

21 patients with iCAA (72% male, mean age at symptom onset 50 years [36-74]) and 32 patients with sCAA (44% male, mean age at symptom onset 68 years [52-80]) were identified. Cognitive impairment and cardiovascular risk factors in the sCAA cohort were more common compared with the iCAA cohort. Patients with sCAA and iCAA showed similar CSF levels for Aβ40 (p = 0.5 [sCAA, 95% CI 2,604-4,228; iCAA, 95% CI 1,958-3,736]), Aβ42 (p = 0.7 [sCAA, 95% CI 88-157; iCAA, 95% CI 83-155]), and total tau (p = 0.08 [sCAA, 95% CI 80-134; iCAA, 95% CI 37-99]). Plasma levels of Aβ40 (p = 0.08, 95% CI 181-222), Aβ42 (p = 0.3, 95% CI 6-8), and total tau (p = 0.4, 95% CI 3-6) were not statistically different in patients with sCAA compared with iCAA ones (Aβ40, 95% CI 153-193; Aβ42, 95% CI 6-7 and total tau, 95% CI 2-4).

DISCUSSION

Despite presenting with a younger age at onset, fewer cardiovascular risk factors, and lower cognitive impairment, patients with iCAA demonstrated Aβ and tau levels comparable with elderly patients with sCAA, supporting a common molecular paradigm between the 2 CAA forms.

Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project

BACKGROUND

In recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer's disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future.

METHODS

The ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50-85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aβ1-42, Aβ1-42/1-40 ratio, Aβ1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram.

DISCUSSION

This paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03834402. Registered on February 8, 2019.

CSF neurosecretory proteins VGF and neuroserpin in patients with Alzheimer's and Lewy body diseases

BACKGROUND

VGF and neuroserpin are neurosecretory proteins involved in the pathophysiology of neurodegenerative diseases. We aimed to evaluate their cerebrospinal fluid (CSF) concentrations in patients with Alzheimer's disease (AD) and Lewy body disease (LBD).

METHODS

We measured CSF VGF [AQEE] peptide and neuroserpin levels in 108 LBD patients, 76 AD patients and 37 controls, and tested their associations with clinical scores and CSF AD markers.

RESULTS

We found decreased CSF levels of VGF [AQEE] in patients with LBD and dementia compared to controls (p = 0.016) and patients with AD-dementia (p = 0.011), but with significant influence of age and sex distribution. Moreover, we observed, on the one hand, a significant associations between lower VGF [AQEE] and neuroserpin levels and poorer cognitive performance (i.e., lower Mini-Mental State Examination scores). On the other hand, higher levels of CSF tau proteins, especially pTau181, were significantly associated with higher concentrations of VGF [AQEE] and neuroserpin. Indeed, LBD patients with AD-like CSF profiles, especially T+ profiles, had higher levels of VGF [AQEE] and neuroserpin compared to controls and LBD/T- cases.

DISCUSSION

CSF VGF [AQEE] and neuroserpin may show a complex relationship with cognitive decline when the levels are reduced, and with AD pathology when levels are increased. They may represent novel markers of neurosecretory impairment in neurodegenerative disorders.

Exploring the Aβ1-42 fibrillogenesis timeline by atomic force microscopy and surface enhanced Raman spectroscopy

Introduction: Alzheimer's disease (AD) is a progressive debilitating neurological disorder representing the most common neurodegenerative disease worldwide. Although the exact pathogenic mechanisms of AD remain unresolved, the presence of extracellular amyloid-β peptide 1-42 (Aβ1-42) plaques in the parenchymal and cortical brain is considered one of the hallmarks of the disease. Methods: In this work, we investigated the Aβ1-42 fibrillogenesis timeline up to 48 h of incubation, providing morphological and chemo-structural characterization of the main assemblies formed during the aggregation process of Aβ1-42, by atomic force microscopy (AFM) and surface enhanced Raman spectroscopy (SERS), respectively. Results: AFM topography evidenced the presence of characteristic protofibrils at early-stages of aggregation, which form peculiar macromolecular networks over time. SERS allowed to track the progressive variation in the secondary structure of the aggregation species involved in the fibrillogenesis and to determine when the β-sheet starts to prevail over the random coil conformation in the aggregation process. Discussion: Our research highlights the significance of investigating the early phases of fibrillogenesis to better understand the molecular pathophysiology of AD and identify potential therapeutic targets that may prevent or slow down the aggregation process.

Predictive value for cerebrospinal fluid Alzheimer's disease profile of different measures of verbal episodic memory in patients with MCI

Neuropsychological evidence of memory impairment represents the main feature of the clinical onset of typical Alzheimer's disease (AD). Rey's Auditory Verbal Learning Test (RAVLT) and Logical Memory (LM) are two tests both assessing verbal episodic memory, widely used in clinical practice. Our aim was to investigate the added value of their combined use in predicting cerebrospinal fluid (CSF) AD biomarkers positivity in a retrospective consecutive series of patients with mild cognitive impairment (MCI). 169 MCI patients were included. For all of them neuropsychological assessment and CSF analysis were available. According to CSF A/T/(N) profile, 109 were defined as MCI due to AD (A+T+), and 60 were non-AD MCI (A-T-). Logistic regression model and receiver-operating characteristic (ROC) curves were analyzed to evaluate the discriminatory power of single and combined sub-measures between AD and non-AD patients. The combination of RAVLT-del with LM could acceptably discriminate the two groups (AUC: 0.69, CI 95% 0.617-0.761, sens: 0.75, spec. 0.58, p < 0.001), while the single tests did not show sufficient discriminative performance. Our study shows that the combination of RAVLT delayed recall with LM better predicts the biological AD diagnosis (A+T+), showing a good discriminative power between MCI-AD from non-AD MCI. Since RAVLT and LM assess different components of verbal episodic memory, they should be considered as complementary, rather than interchangeable, tests.

Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort

BACKGROUND

A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic diseases, such as cancer, cardiovascular disease and type 2 diabetes, has not been sufficiently explored.

METHODS

We measured concentrations of 13 biomarkers in a random subcohort of 2,500 participants in the European Prospective Investigation into Cancer and Nutrition Potsdam study. Chronic disease-free ageing was defined as reaching the age of 70 years within study follow-up without major chronic diseases, including cardiovascular disease, type 2 diabetes or cancer. Using a novel machine-learning technique, we aimed to identify biomarker clusters and explore their association with chronic disease-free ageing in multivariable-adjusted logistic regression analysis taking socio-demographic, lifestyle and anthropometric factors into account.

RESULTS

Of the participants who reached the age of 70 years, 321 met our criteria for chronic-disease free ageing. Machine learning analysis identified three distinct biomarker clusters, among which a signature characterised by high concentrations of high-density lipoprotein cholesterol, adiponectin and insulin-like growth factor-binding protein 2 and low concentrations of triglycerides was associated with highest odds for ageing free of major chronic diseases. After multivariable adjustment, the association was attenuated by socio-demographic, lifestyle and adiposity indicators, pointing to the relative importance of these factors as determinants of healthy ageing.

CONCLUSION

These data underline the importance of exploring combinations of biomarkers rather than single molecules in understanding complex biological pathways underpinning healthy ageing.

Investigating alpha-synuclein co-pathology in Alzheimer's disease by means of cerebrospinal fluid alpha-synuclein seed amplification assay

INTRODUCTION

Lewy body disease, a frequently observed co-pathology in Alzheimer's disease (AD), can be identified antemortem in cerebrospinal fluid (CSF) by α-synuclein seed amplification assay (αS-SAA). The prevalence and clinical impact of CSF αS-SAA positivity in AD are still unknown.

METHODS

αS-SAA was performed on CSF samples from 240 AD patients (preclinical, prodromal, and dementia stages), 85 controls, 84 patients with Parkinson's disease (PD), and 21 patients with PD with dementia or dementia with Lewy bodies. In AD patients, associations between αS-SAA positivity and cognitive changes were also evaluated.

RESULTS

In agreement with available neuropathological studies, αS-SAA positivity was observed in 30% of AD patients (vs 9% in controls), and was associated with cognitive decline, visuospatial impairment, and behavioral disturbances.

DISCUSSION

αS-SAA positivity in AD patients reflects the prevalence observed in neuropathological series and is associated with a worse clinical outcome. These data confirm the validity of CSF αS-SAA positivity as biomarker of synucleinopathy.

Fully automated measurement of plasma Aβ42/40 and p-tau181: Analytical robustness and concordance with cerebrospinal fluid profile along the Alzheimer's disease continuum in two independent cohorts

INTRODUCTION

For routine clinical implementation of Alzheimer's disease (AD) plasma biomarkers, fully automated random-access platforms are crucial to ensure reproducible measurements. We aimed to perform an analytical validation and to establish cutoffs for AD plasma biomarkers measured with Lumipulse.

METHODS

Two cohorts were included. UNIPG: n = 450 paired cerebrospinal fluid (CSF)/plasma samples from subjects along the AD-continuum, subjects affected by other neurodegenerative diseases, and controls with known CSF profile; AMS: n = 40 plasma samples from AD and n = 40 controls. Plasma amyloid β (Aβ)42, Aβ40, and p-tau181 were measured with Lumipulse. We evaluated analytical and diagnostic performance.

RESULTS

Lumipulse assays showed high analytical performance. Plasma p-tau181 levels accurately reflected CSF A+/T+ profile in AD-dementia and mild cognitive impairment (MCI)-AD, but not in asymptomatic-AD. Plasma and CSF Aβ42/40 values were concordant across clinical AD stages. Cutoffs and probability-based models performed satisfactorily in both cohorts.

DISCUSSION

The identified cutoffs and probability-based models represent a significant step toward plasma AD molecular diagnosis.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers

AIMS

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

METHODS

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

RESULTS

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia.

CONCLUSION

We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

CSF and plasma Aβ42/40 across Alzheimer's disease continuum: comparison of two ultrasensitive Simoa® assays targeting distinct amyloid regions

OBJECTIVES

Decreased cerebrospinal fluid (CSF) amyloid beta 42/40 ratio (Aβ42/40) is one of the core Alzheimer's disease (AD) biomarkers. Measurement of Aβ42/40 in plasma has also been proposed as a surrogate marker for amyloidosis, however the validity and the diagnostic performance of this biomarker is still uncertain. Here we evaluated two immunoassays targeting distinct regions of the amyloid peptides by (a) performing a method comparison in both CSF and plasma, and (b) assessing the diagnostic performance across the AD continuum.

METHODS

We used N4PE and N3PA Simoa® assays to measure Aβ42/40 in CSF and plasma of 134 patients: preclinical AD (pre-AD, n=19), mild cognitive impairment due to AD (MCI-AD, n=41), AD at the dementia stage (AD-dem, n=35), and a control group (CTRL, n=39). The N4PE includes a detector antibody targeting the amyloid N-terminus, while the N3PA uses a detector targeting amyloid mid-region.

RESULTS

Method comparison of N4PE and N3PA assays revealed discrepancies in assessment of plasma Aβ42/Aβ40. While the diagnostic performance of the two assays did not significantly differ in CSF, in plasma, N4PE assay provided better accuracy for AD discrimination than N3PA assay (AUC AD-dem vs. CTRL 0.77 N4PE, 0.68 N3PA).

CONCLUSIONS

While both Aβ42/40 assays allowed for an effective discrimination between CTRL and different AD stages, the assay targeting amyloid N-terminal region provided the best diagnostic performance in plasma. Differences observed in technical and diagnostic performance of the two assays may depend on matrix-specific amyloid processing, suggesting that further studies should be carried to standardize amyloid ratio measurement in plasma.

Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York

BACKGROUND

COVID-19 is a challenging disease to characterize given its wide-ranging heterogeneous symptomatology. Several studies have attempted to extract clinical phenotypes but often relied on data from small patient cohorts, usually limited to only one viral variant and utilizing a static snapshot of patient data.

OBJECTIVE

This study aimed to identify clinical phenotypes of hospitalized COVID-19 patients and investigate their longitudinal dynamics throughout the pandemic, with the goal to relate these phenotypes to clinical outcomes and treatment strategies.

METHODS

We utilized routinely collected demographic and clinical data throughout the hospitalization of 38,077 patients admitted between 3/2020 to 5/2022, in 12 New York hospitals. Uniform Manifold Approximation and Projection and agglomerative hierarchical clustering were used to derive the clusters, followed by exploratory data analysis to compare the prevalence of comorbidities and treatments per cluster.

RESULTS

4 distinct clinical phenotypes remained robust in multi-site validation and were associated with different mortality rates. The temporal progression of these phenotypes throughout the COVID-19 pandemic demonstrated increased variability across the waves of the three dominant viral variants (alpha, delta, omicron). Longitudinal analysis evaluating changes in clinical phenotypes of each patient throughout the course of a 4-week hospital stay exemplified the dynamic nature of the disease progression. Factors such as sex, race/ethnicity and specific treatment modalities revealed significant and clinically relevant differences between the observed phenotypes.

CONCLUSIONS

Our proposed methodology has the potential of enabling clinicians and policy makers to draw evidence-based conclusions for guiding treatment modalities in a dynamic fashion.

Potential diagnostic value of CSF metabolism-related proteins across the Alzheimer's disease continuum

BACKGROUND

Alzheimer's disease (AD) cerebrospinal fluid (CSF) core biomarkers (Aβ42/40 ratio, p-tau, and t-tau) provide high diagnostic accuracy, even at the earliest stage of disease. However, these markers do not fully reflect the complex AD pathophysiology. Recent large scale CSF proteomic studies revealed several new AD candidate biomarkers related to metabolic pathways. In this study we measured the CSF levels of four metabolism-related proteins not directly linked to amyloid- and tau-pathways (i.e., pyruvate kinase, PKM; aldolase, ALDO; ubiquitin C-terminal hydrolase L1, UCHL1, and fatty acid-binding protein 3, FABP3) across the AD continuum. We aimed at validating the potential value of these proteins as new CSF biomarkers for AD and their possible involvement in AD pathogenesis, with specific interest on the preclinical phase of the disease.

METHODS

CSF PKM and ALDO activities were measured with specific enzyme assays while UCHL1 and FABP3 levels were measured with immunoassays in a cohort of patients composed as follows: preclinical AD (pre-AD, n = 19, cognitively unimpaired), mild cognitive impairment due to AD (MCI-AD, n = 50), dementia due to AD (ADdem, n = 45), and patients with frontotemporal dementia (FTD, n = 37). Individuals with MCI not due to AD (MCI, n = 30) and subjective cognitive decline (SCD, n = 52) with negative CSF AD-profile, were enrolled as control groups.

RESULTS

CSF UCHL1 and FABP3 levels, and PKM activity were significantly increased in AD patients, already at the pre-clinical stage. CSF PKM activity was also increased in FTD patients compared with control groups, being similar between AD and FTD patients. No difference was found in ALDO activity among the groups. UCHL1 showed good performance in discriminating early AD patients (pre-AD and MCI-AD) from controls (AUC ~ 0.83), as assessed by ROC analysis. Similar results were obtained for FABP3. Conversely, PKM provided the best performance when comparing FTD vs. MCI (AUC = 0.80). Combination of PKM, FABP3, and UCHL1 improved the diagnostic accuracy for the detection of patients within the AD continuum when compared with single biomarkers.

CONCLUSIONS

Our study confirmed the potential role of UCHL1 and FABP3 as neurodegenerative biomarkers for AD. Furthermore, our results validated the increase of PKM activity in CSF of AD patients, already at the preclinical phase of the disease. Increased PKM activity was observed also in FTD patients, possibly underlining similar alterations in energy metabolism in AD and FTD.

Impact of seed amplification assay and surface-enhanced Raman spectroscopy combined approach on the clinical diagnosis of Alzheimer's disease

BACKGROUND

The current diagnosis of Alzheimer's disease (AD) is based on a series of analyses which involve clinical, instrumental and laboratory findings. However, signs, symptoms and biomarker alterations observed in AD might overlap with other dementias, resulting in misdiagnosis.

METHODS

Here we describe a new diagnostic approach for AD which takes advantage of the boosted sensitivity in biomolecular detection, as allowed by seed amplification assay (SAA), combined with the unique specificity in biomolecular recognition, as provided by surface-enhanced Raman spectroscopy (SERS).

RESULTS

The SAA-SERS approach supported by machine learning data analysis allowed efficient identification of pathological Aβ oligomers in the cerebrospinal fluid of patients with a clinical diagnosis of AD or mild cognitive impairment due to AD.

CONCLUSIONS

Such analytical approach can be used to recognize disease features, thus allowing early stratification and selection of patients, which is fundamental in clinical treatments and pharmacological trials.

CSF Synaptic Biomarkers in AT(N)-Based Subgroups of Lewy Body Disease

BACKGROUND AND OBJECTIVES

Patients with Lewy body disease (LBD) often show a co-occurring Alzheimer disease (AD) pathology. CSF biomarkers allow the detection in vivo of AD-related pathologic hallmarks included in the amyloid-tau-neurodegeneration (AT(N)) classification system. Here, we aimed to investigate whether CSF biomarkers of synaptic and neuroaxonal damage are correlated with the presence of AD copathology in LBD and can be useful to differentiate patients with LBD with different AT(N) profiles.

METHODS

We retrospectively measured CSF levels of AD core biomarkers (Aβ42/40 ratio, phosphorylated tau protein, and total tau protein) and of synaptic (β-synuclein, α-synuclein, synaptosomal-associated protein 25 [SNAP-25], and neurogranin) and neuroaxonal proteins (neurofilament light chain [NfL]) in 28 cognitively unimpaired participants with nondegenerative neurologic conditions and 161 participants with a diagnosis of either LBD or AD (at both mild cognitive impairment, AD-MCI, and dementia stages, AD-dem). We compared CSF biomarker levels in clinical and AT(N)-based subgroups.

RESULTS

CSF β-synuclein, α-synuclein, SNAP-25, neurogranin, and NfL levels did not differ between LBD (n = 101, age 67.2 ± 7.8 years, 27.7% females) and controls (age 64.8 ± 8.6 years, 39.3% females) and were increased in AD (AD-MCI: n = 30, AD-dem: n = 30, age 72.3 ± 6.0 years, 63.3% females) compared with both groups (p < 0.001 for all comparisons). In LBD, we found increased levels of synaptic and neuroaxonal degeneration biomarkers in patients with A+T+ (LBD/A+T+) than with A-T- profiles (LBD/A-T-) (p < 0.01 for all), and β-synuclein showed the highest discriminative accuracy between the 2 groups (area under the curve 0.938, 95% CI 0.884-0.991). CSF β-synuclein (p = 0.0021), α-synuclein (p = 0.0099), and SNAP-25 concentrations (p = 0.013) were also higher in LBD/A+T+ than in LBD/A+T- cases, which had synaptic biomarker levels within the normal range. CSF α-synuclein was significantly decreased only in patients with LBD with T- profiles compared with controls (p = 0.0448). Moreover, LBD/A+T+ and AD cases did not differ in any biomarker level.

DISCUSSION

LBD/A+T+ and AD cases showed significantly increased CSF levels of synaptic and neuroaxonal biomarkers compared with LBD/A-T- and control subjects. Patients with LBD and AT(N)-based AD copathology showed, thus, a distinct signature of synaptic dysfunction from other LBD cases.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that CSF levels of β-synuclein, α-synuclein, SNAP-25, neurogranin, and NfL are higher in patients with AD than in patients with LBD.

Required improvements for cerebrospinal fluid-based biomarker tests of Alzheimer's disease

INTRODUCTION

Cerebrospinal fluid (CSF) biomarkers represent a well-established tool for diagnosing Alzheimer's disease (AD), independently from the clinical stage, by reflecting the presence of brain amyloidosis (A+) and tauopathy (T+). In front of this important achievement, so far, (i) CSF AD biomarkers have not yet been adopted for routine clinical use in all Centers dedicated to AD, mainly due to inter-lab variation and lack of internationally accepted cutoff values; (ii) we do need to add other biomarkers more suitable to correlate with the clinical stage and disease monitoring; (iii) we also need to detect the co-presence of other 'non-AD' pathologies.

AREAS COVERED

Efforts to establish standardized cutoff values based on large-scale multi-center studies are discussed. The influence of aging and comorbidities on CSF biomarker levels is also analyzed, and possible solutions are presented, i.e. complementing the A/T/(N) system with markers of axonal damage and synaptic derangement.

EXPERT OPINION

The first, mandatory need is to reach common cutoff values and defined (automated) methodologies for CSF AD biomarkers. To properly select subjects deserving CSF analysis, blood tests might represent the first-line approach. In those subjects undergoing CSF analysis, multiple biomarkers, able to give a comprehensive and personalized pathophysiological/prognostic information, should be included.

Cerebrospinal fluid lipoproteins inhibit α-synuclein aggregation by interacting with oligomeric species in seed amplification assays

BACKGROUND

Aggregation of α-synuclein (α-syn) is a prominent feature of Parkinson's disease (PD) and other synucleinopathies. Currently, α-syn seed amplification assays (SAAs) using cerebrospinal fluid (CSF) represent the most promising diagnostic tools for synucleinopathies. However, CSF itself contains several compounds that can modulate the aggregation of α-syn in a patient-dependent manner, potentially undermining unoptimized α-syn SAAs and preventing seed quantification.

METHODS

In this study, we characterized the inhibitory effect of CSF milieu on detection of α-syn aggregates by means of CSF fractionation, mass spectrometry, immunoassays, transmission electron microscopy, solution nuclear magnetic resonance spectroscopy, a highly accurate and standardized diagnostic SAA, and different in vitro aggregation conditions to evaluate spontaneous aggregation of α-syn.

RESULTS

We found the high-molecular weight fraction of CSF (> 100,000 Da) to be highly inhibitory on α-syn aggregation and identified lipoproteins to be the main drivers of this effect. Direct interaction between lipoproteins and monomeric α-syn was not detected by solution nuclear magnetic resonance spectroscopy, on the other hand we observed lipoprotein-α-syn complexes by transmission electron microscopy. These observations are compatible with hypothesizing an interaction between lipoproteins and oligomeric/proto-fibrillary α-syn intermediates. We observed significantly slower amplification of α-syn seeds in PD CSF when lipoproteins were added to the reaction mix of diagnostic SAA. Additionally, we observed a decreased inhibition capacity of CSF on α-syn aggregation after immunodepleting ApoA1 and ApoE. Finally, we observed that CSF ApoA1 and ApoE levels significantly correlated with SAA kinetic parameters in n = 31 SAA-negative control CSF samples spiked with preformed α-syn aggregates.

CONCLUSIONS

Our results describe a novel interaction between lipoproteins and α-syn aggregates that inhibits the formation of α-syn fibrils and could have relevant implications. Indeed, the donor-specific inhibition of CSF on α-syn aggregation explains the lack of quantitative results from analysis of SAA-derived kinetic parameters to date. Furthermore, our data show that lipoproteins are the main inhibitory components of CSF, suggesting that lipoprotein concentration measurements could be incorporated into data analysis models to eliminate the confounding effects of CSF milieu on α-syn quantification efforts.

Clinical usefulness of cerebrospinal fluid biomarkers in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a complex disease that shares clinical features with other dementias. It is important to establish a specific and reliable diagnosis. Nowadays, AD diagnosis is based on cerebrospinal fluid (CSF) biomarkers. However, the corresponding cut-offs differ amongst studies. This study aims to evaluate the CSF biomarkers in the AD differential diagnosis.

METHODS

Clinical relevant biomarkers (amyloid β42 (Aβ42), t-Tau, p-Tau, amyloid β40 (Aβ40), neurofilament light chain (NfL)) were determined in CSF samples from participants classified as AD (n = 124) and non-AD (n = 148) patients from the Neurology Unit. They were included and evaluated consecutively (August 2018-October 2020). The clinical utility of these biomarkers was evaluated by AUC-ROC curves and the corresponding cut-off points were defined.

RESULTS

The results showed satisfactory accuracy (AUC-ROC 0.91 for Aβ42, 0.890 for t-Tau and 0.933 for p-Tau); whilst Aβ40 and NfL did not show good discriminatory capacity (AUC-ROC 0.557 and 0.738, respectively). The ratios Aβ42/Aβ40 and t-Tau/Aβ42 improved the diagnosis indices of each individual biomarker, with AUC-ROC of 0.980 and 0.971, respectively. Also, elevated levels of NfL were found in the frontotemporal dementia group compared with the other participant groups.

CONCLUSIONS

The ratio Aβ42/Aβ40 showed the highest discriminating capacity between AD and non-AD patients and might be useful in clinical practice. Regarding NfL, it is not a specific biomarker for AD; however, it might be helpful for the differential diagnosis of frontotemporal dementia. Nevertheless, further analysis in an external cohort is required in order to validate these results.

A Novel Automated Chemiluminescence Method for Detecting Cerebrospinal Fluid Amyloid-Beta 1-42 and 1-40, Total Tau and Phosphorylated-Tau: Implications for Improving Diagnostic Performance in Alzheimer's Disease

Recently, a fully automated instrument for the detection of the Cerebrospinal Fluid (CSF) biomarker for Alzheimer’s disease (AD) (low concentration of Amyloid-beta 42 (Aβ42), high concentration of total tau (T-tau) and Phosphorylated-tau (P-tau181)), has been implemented, namely CLEIA. We conducted a comparative analysis between ELISA and CLEIA methods in order to evaluate the analytical precision and the diagnostic performance of the novel CLEIA system on 111 CSF samples. Results confirmed a robust correlation between ELISA and CLEIA methods, with an improvement of the accuracy with the new CLEIA methodology in the detection of the single biomarkers and in their ratio values. For Aβ42 regression analysis with Passing−Bablok showed a Pearson correlation coefficient r = 0.867 (0.8120; 0.907% 95% CI p < 0.0001), T-tau analysis: r = 0.968 (0.954; 0.978% 95% CI p < 0.0001) and P-tau181: r = 0.946 (0.922; 0.962 5% 95% CI p < 0.0001). The overall ROC AUC comparison between ROC in ELISA and ROC in CLEIA confirmed a more accurate ROC AUC with the new automatic method: T-tau AUC ELISA = 0.94 (95% CI 0.89; 0.99 p < 0.0001) vs. AUC CLEIA = 0.95 (95% CI 0.89; 1.00 p < 0.0001), and P-tau181 AUC ELISA = 0.91 (95% CI 0.85; 0.98 p < 0.0001) vs. AUC CLEIA = 0.98 (95% CI 0.95; 1.00 p < 0.0001). The performance of the new CLEIA method in automation is comparable and, for tau and P-tau181, even better, as compared with standard ELISA. Hopefully, in the future, automation could be useful in clinical diagnosis and also in the context of clinical studies.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.