Diagnostic value of cerebrospinal fluid Aβ ratios in preclinical Alzheimer's disease

Ultrasensitive and Multiple Biomarker Discrimination for Alzheimer's Disease via Plasmonic & Microfluidic Sensing Technologies

As the population ages, the worldwide prevalence of Alzheimer's disease (AD) as the most common dementia in the elderly is increasing dramatically. However, a long-term challenge is to achieve rapid and accurate early diagnosis of AD by detecting hallmarks such as amyloid beta (Aβ42). Here, a multi-channel microfluidic-based plasmonic fiber-optic biosensing platform is established for simultaneous detection and differentiation of multiple AD biomarkers. The platform is based on a gold-coated, highly-tilted fiber Bragg grating (TFBG) and a custom-developed microfluidics. TFBG excites a high-density, narrow-cladding-mode spectral comb that overlaps with the broad absorption of surface plasmons for high-precision interrogation, enabling ultrasensitive monitoring of analytes. In situ detection and in-parallel discrimination of different forms of Aβ42 in cerebrospinal fluid (CSF) are successfully demonstrated with a detection of limit in the range of ≈30-170 pg mL-1, which is one order of magnitude below the clinical cut-off level in AD onset, providing high detection sensitivity for early diagnosis of AD. The integration of the TFBG sensor with multi-channel microfluidics enables simultaneous detection of multiple biomarkers using sub-µL sample volumes, as well as combining initial binding rate and real-time response time to differentiate between multiple biomarkers in terms of binding kinetics. With the advantages of multi-parameter, low consumption, and highly sensitive detection, the sensor represents an urgently needed potentials for large-scale diagnosis of diseases at early stage.

How far is the goal of applying β-amyloid in cerebrospinal fluid for clinical diagnosis of Alzheimer's disease with standardization of measurements?

Cerebrospinal fluid (CSF) β-amyloid (Aβ) is important for early diagnosis of Alzheimer's disease (AD). However, the cohort distributions and cut-off values have large variation across different analytical assays, kits, and laboratories. In this review, we summarize the cut-off values and diagnostic performance for CSF Aβ1-42 and Aβ1-42/Aβ1-40, and explore the important effect factors. Based on the Alzheimer's Association external quality control program (AAQC program), the peer group coefficient of variation of manual ELISA assays for CSF Aβ1-42 was unsatisfied (>20%). Fully automated platforms with better performance have recently been developed, but still not widely applied. In 2020, the certified reference material (CRM) for CSF Aβ1-42 was launched; however, the AAQC 2021-round results did not show effective improvements. Thus, further development and popularization of CRM for CSF Aβ1-42 and Aβ1-40 are urgently required. Standardizing the diagnostic procedures of AD and related status and the pre-analytical protocols of CSF samples, improving detection performance of analytical assays, and popularizing the application of fully automated platforms are also important for the establishment of uniform cut-off values. Moreover, each laboratory should verify the applicability of uniform cut-off values, and evaluate whether it is necessary to establish its own population- and assay-specific cut-off values.

Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort

Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST^® ELISA and Lumipulse G^® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815⁻²⁷). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.

Unravelling the Association Between Amyloid-PET and Cerebrospinal Fluid Biomarkers in the Alzheimer's Disease Spectrum: Who Really Deserves an A+?

BACKGROUND

Association between cerebrospinal fluid (CSF)-amyloid-β (Aβ)42 and amyloid-PET measures is inconstant across the Alzheimer's disease (AD) spectrum. However, they are considered interchangeable, along with Aβ 42/40 ratio, for defining 'Alzheimer's Disease pathologic change' (A+).

OBJECTIVE

Herein, we further characterized the association between amyloid-PET and CSF biomarkers and tested their agreement in a cohort of AD spectrum patients.

METHODS

We include ed 23 patients who underwent amyloid-PET, MRI, and CSF analysis showing reduced levels of Aβ 42 within a 365-days interval. Thresholds used for dichotomization were: Aβ 42 <  640 pg/mL (Aβ 42+); pTau >  61 pg/mL (pTau+); and Aβ 42/40 <  0.069 (ADratio+). Amyloid-PET scans were visually assessed and processed by four pipelines (SPMCL, SPMAAL, FSGM, FSWC).

RESULTS

Different pipelines gave highly inter-correlated standardized uptake value ratios (SUVRs) (rho = 0.93-0.99). The most significant findings were: pTau positive correlation with SPMCL SUVR (rho = 0.56, p = 0.0063) and Aβ 42/40 negative correlation with SPMCL and SPMAAL SUVRs (rho = -0.56, p = 0.0058; rho = -0.52, p = 0.0117 respectively). No correlations between CSF-Aβ 42 and global SUVRs were observed. In subregion analysis, both pTau and Aβ 42/40 values significantly correlated with cingulate SUVRs from any pipeline (R2 = 0.55-0.59, p <  0.0083), with the strongest associations observed for the posterior/isthmus cingulate areas. However, only associations observed for Aβ 42/40 ratio were still significant in linear regression models. Moreover, combining pTau with Aβ 42 or using Aβ 42/40, instead of Aβ 42 alone, increased concordance with amyloid-PET status from 74% to 91% based on visual reads and from 78% to 96% based on Centiloids.

CONCLUSION

We confirmed that, in the AD spectrum, amyloid-PET measures show a stronger association and a better agreement with CSF-Aβ 42/40 and secondarily pTau rather than Aβ 42 levels.

Treatment of Dementia With Bosutinib: An Open-Label Study of a Tyrosine Kinase Inhibitor

OBJECTIVE

The pursuit of an effective therapeutic intervention for dementia has inspired interest in the class of medications known as tyrosine kinase inhibitors such as bosutinib.

METHODS

Thirty-one patients with probable Alzheimer dementia or Parkinson spectrum disorder with dementia completed 12 months of bosutinib therapy and an additional 12 months of follow-up. The Clinical Dementia Rating scale (as estimated by the Quick Dementia Rating System [QDRS]) was the primary cognitive status outcome measure. Secondary outcome measures included the Repeatable Battery Assessment of Neuropsychological Status (RBANS) and the Montreal Cognitive Assessment. Cox regression methods were used to compare results with population-based estimates of cognitive decline.

RESULTS

The present article reports on cognitive outcomes obtained at 12 months for 31 participants and up to 24 months for a 16-participant subset. Safety and tolerability of bosutinib were confirmed among the study population (Mage = 73.7 years, SDage = 14 years). Bosutinib was associated with less worsening in Clinical Dementia Rating (CDR) scores (hazard ratio = -0.62, p < 0.001, 95% confidence interval [CI]: -1.02 to -0.30) and less decline in RBANS performance (hazard ratio = -3.42, p < 0.001, 95% CI: -3.59 to -3.72) during the year of treatment than population-based estimates of decline. In the 24-month follow-up, wherein 16 patients were observed after 1 year postintervention, 31.2% of participants exhibited worsened CDR levels compared with their 12-month performances.

CONCLUSIONS

Results support an overall positive outcome after 1 year of bosutinib. Future studies should explore the relationship between tyrosine kinases and neurodegenerative pathology as well as related avenues of treatment.

Prognostic value of amyloid/tau/neurodegeneration (ATN) classification based on diagnostic cerebrospinal fluid samples for Alzheimer's disease

Objective

The primary study objective of this retrospective academic memory clinic-based observational longitudinal study was to investigate the prognostic value of a cerebrospinal fluid (CSF)-based ATN classification for subsequent cognitive decline during the 3 years following lumbar puncture in a clinical, real-life setting. The secondary objective was to investigate the prognostic value of CSF biomarkers as continuous variables.

Methods

Data from 228 patients (median age 67 (47–85) years), who presented at the Neurology Memory Clinic UZ/KU Leuven between September 2011 and December 2016, were included with a follow-up period of up to 36 months. Patients underwent a CSF AD biomarker test for amyloid-beta 1–42 (Aβ₄₂)_, hyperphosphorylated tau (p₁₈₁-tau) and total tau (t-tau) in the clinical work-up for diagnostic reasons. Patients were divided into ATN classes based on CSF biomarkers: Aβ₄₂ for amyloid (A), p₁₈₁-tau for tau (T), and t-tau as a measure for neurodegeneration (N). Based on retrospective data analysis, cognitive performance was evaluated by Mini Mental State Examination (MMSE) scores every 6 months over a period up to 36 months following the lumbar puncture. The statistical analysis was based on linear mixed-effects modeling (LME).

Results

The distribution in the current clinical sample was as follows: A−/T−/N− 32.02%, A+/T−/N− 33.33%, A+/T+/N+ 17.11%, A+/T−/N+ 11.84%, A−/T−/N+ 4.39%, A−/T+/N+ 1.32% (3 cases), with no cases in the A−/T+/N− and A+/T+/N− class. Hence, the latter 3 classes were excluded from further analyses. The change of MMSE relative to A−/T−/N− over a 36-month period was significant in all four ATN classes: A+/T+/N+ = − 4.78 points on the MMSE; A−/T−/N+ = − 4.76; A+/T−/N+ = − 2.83; A+/T−/N− = − 1.96. The earliest significant difference was seen in the A+/T+/N+ class at 12 months after baseline. The effect of ATN class on future cognitive decline was confirmed for a different set of CSF thresholds. All individual baseline CSF biomarkers including the Aβ₄₂/t-tau ratio showed a significant correlation with subsequent cognitive decline, with the highest correlation seen for Aβ₄₂/t-tau.

Conclusion

ATN classification based on CSF biomarkers has a statistically significant and clinically relevant prognostic value for the course of cognitive decline in a 3-year period in a clinical practice setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00817-4.

Baseline cognition is the best predictor of 4-year cognitive change in cognitively intact older adults

BACKGROUND

We examined in cognitively intact older adults the relative weight of cognitive, genetic, structural and amyloid brain imaging variables for predicting cognitive change over a 4-year time course.

METHODS

One hundred-eighty community-recruited cognitively intact older adults (mean age 68 years, range 52-80 years, 81 women) belonging to the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK) longitudinal observational cohort underwent a baseline evaluation consisting of detailed cognitive assessment, structural MRI and 18F-flutemetamol PET. At inclusion, subjects were stratified based on Apolipoprotein E (APOE) ε4 and Brain-Derived Neurotrophic Factor (BDNF) val66met polymorphism according to a factorial design. At inclusion, 15% were amyloid-PET positive (Centiloid >23.4). All subjects underwent 2-yearly follow-up of cognitive performance for a 4-year time period. Baseline cognitive scores were analysed using factor analysis. The slope of cognitive change over time was modelled using latent growth curve analysis. Using correlation analysis, hierarchical regression and mediation analysis, we examined the effect of demographic (age, sex, education) and genetic variables, baseline cognition, MRI volumetric (both voxelwise and region-based) as well as amyloid imaging measures on the longitudinal slope of cognitive change.

RESULTS

A base model of age and sex explained 18.5% of variance in episodic memory decline. This increased to 41.6% by adding baseline episodic memory scores. Adding amyloid load or volumetric measures explained only a negligible additional amount of variance (increase to 42.2%). A mediation analysis indicated that the effect of age on episodic memory scores was partly direct and partly mediated via hippocampal volume. Amyloid load did not play a significant role as mediator between age, hippocampal volume and episodic memory decline.

CONCLUSION

In cognitively intact older adults, the strongest baseline predictor of subsequent episodic memory decline was the baseline episodic memory score. When this score was included, only very limited explanatory power was added by brain volume or amyloid load measures. The data warn against classifications that are purely biomarker-based and highlight the value of baseline cognitive performance levels in predictive models.

Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users

Prescription opioid use disorder (POUD) has reached epidemic proportions in the United States, raising an urgent need for diagnostic biological tools that can improve predictions of disease characteristics. The use of neuroimaging methods to develop such biomarkers have yielded promising results when applied to neurodegenerative and psychiatric disorders, yet have not been extended to prescription opioid addiction. With this long-term goal in mind, we conducted a preliminary study in this understudied clinical group. Univariate and multivariate approaches to distinguishing between POUD (n = 26) and healthy controls (n = 21) were investigated, on the basis of structural MRI (sMRI) and resting-state functional connectivity (restFC) features. Univariate approaches revealed reduced structural integrity in the subcortical extent of a previously reported addiction-related network in POUD subjects. No reliable univariate between-group differences in cortical structure or edgewise restFC were observed. Contrasting these mixed univariate results, multivariate machine learning classification approaches recovered more statistically reliable group differences, especially when sMRI and restFC features were combined in a multi-modal model (classification accuracy = 66.7%, p < .001). The same multivariate multi-modal approach also yielded reliable prediction of individual differences in a clinically relevant behavioral measure (persistence behavior; predicted-to-actual overlap r = 0.42, p = .009). Our findings suggest that sMRI and restFC measures can be used to reliably distinguish the neural effects of long-term opioid use, and that this endeavor numerically benefits from multivariate predictive approaches and multi-modal feature sets. This can serve as theoretical proof-of-concept for future longitudinal modeling of prognostic POUD characteristics from neuroimaging features, which would have clearer clinical utility.

Longitudinal pathways of cerebrospinal fluid and positron emission tomography biomarkers of amyloid-β positivity

Mismatch between CSF and PET amyloid-β biomarkers occurs in up to ≈20% of preclinical/prodromal Alzheimer's disease individuals. Factors underlying mismatching results remain unclear. In this study we hypothesized that CSF/PET discordance provides unique biological/clinical information. To test this hypothesis, we investigated non-demented and demented participants with CSF amyloid-β₄₂ and [18F]Florbetapir PET assessments at baseline (n = 867) and at 2-year follow-up (n = 289). Longitudinal trajectories of amyloid-β positivity were tracked simultaneously for CSF and PET biomarkers. In the longitudinal cohort (n = 289), we found that participants with normal CSF/PET amyloid-β biomarkers progressed more frequently toward CSF/PET discordance than to full CSF/PET positivity (χ²₍₁₎ = 5.40; p < 0.05). Progression to CSF+/PET+ status was ten times more frequent in cases with discordant biomarkers, as compared to csf-/pet- cases (χ²₍₁₎ = 18.86; p < 0.001). Compared to the CSF+/pet- group, the csf-/PET+ group had lower APOE-ε4ε4 prevalence (χ²₍₆₎ = 197; p < 0.001; n = 867) and slower rate of brain amyloid-β accumulation (F_(3,600) = 12.76; p < 0.001; n = 608). These results demonstrate that biomarker discordance is a typical stage in the natural history of amyloid-β accumulation, with CSF or PET becoming abnormal first and not concurrently. Therefore, biomarker discordance allows for identification of individuals with elevated risk of progression toward fully abnormal amyloid-β biomarkers, with subsequent risk of neurodegeneration and cognitive decline. Our results also suggest that there are two alternative pathways ("CSF-first" vs. "PET-first") toward established amyloid-β pathology, characterized by different genetic profiles and rates of amyloid-β accumulation. In conclusion, CSF and PET amyloid-β biomarkers provide distinct information, with potential implications for their use as biomarkers in clinical trials.

Quantitative Measurement of Cerebrospinal Fluid Amyloid-β Species by Mass Spectrometry

Background:

High sensitivity liquid chromatography mass spectrometry (LC-MS/MS) was recently introduced to measure amyloid-β (Aβ) species, allowing for a simultaneous assay that is superior to ELISA, which requires more assay steps with multiple antibodies.

Objective:

We validated the Aβ_1-38, Aβ_1-40, Aβ_1-42, and Aβ_1-43 assay by LC-MS/MS and compared it with ELISA using cerebrospinal fluid (CSF) samples to investigate its feasibility for clinical application.

Methods:

CSF samples from 120 subjects [8 Alzheimer’s disease (AD) with dementia (ADD), 2 mild cognitive dementia due to Alzheimer’s disease (ADMCI), 14 cognitively unimpaired (CU), and 96 neurological disease subjects] were analyzed. Aβ species were separated using the Shimadzu Nexera X2 system and quantitated using a Qtrap 5500 LC-MS/MS system. Aβ_1-40 and Aβ_1-42 levels were validated using ELISA.

Results:

CSF levels in CU were 666±249 pmol/L in Aβ_1-38, 2199±725 pmol/L in Aβ_1-40, 153.7±79.7 pmol/L in Aβ_1-42, and 9.78±4.58 pmol/L in Aβ_1-43. The ratio of the amounts of Aβ_1-38, Aβ_1-40, Aβ_1-42, and Aβ_1-43 was approximately 68:225:16:1. Linear regression analyses showed correlations among the respective Aβ species. Both Aβ_1-40 and Aβ_1-42 values were strongly correlated with ELISA measurements. No significant differences were observed in Aβ_1-38 or Aβ_1-40 levels between AD and CU. Aβ_1-42 and Aβ_1-43 levels were significantly lower, whereas the Aβ_1-38/1-42, Aβ_1-38/1-43, and Aβ_1-40/Aβ_1-43 ratios were significantly higher in AD than in CU. The basic assay profiles of the respective Aβ species were adequate for clinical usage.

Conclusion:

A quantitative LC-MS/MS assay of CSF Aβ species is as reliable as specific ELISA for clinical evaluation of CSF biomarkers for AD.

Comparison of ELISA- and SIMOA-based quantification of plasma Aβ ratios for early detection of cerebral amyloidosis

BACKGROUND

Blood-based amyloid biomarkers may provide a non-invasive, cost-effective and scalable manner for detecting cerebral amyloidosis in early disease stages.

METHODS

In this prospective cross-sectional study, we quantified plasma Aβ1-42/Aβ1-40 ratios with both routinely available ELISAs and novel SIMOA Amyblood assays, and provided a head-to-head comparison of their performances to detect cerebral amyloidosis in a nondemented elderly cohort (n = 199). Participants were stratified according to amyloid-PET status, and the performance of plasma Aβ1-42/Aβ1-40 to detect cerebral amyloidosis was assessed using receiver operating characteristic analysis. We additionally investigated the correlations of plasma Aβ ratios with amyloid-PET and CSF Alzheimer's disease biomarkers, as well as platform agreement using Passing-Bablok regression and Bland-Altman analysis for both Aβ isoforms.

RESULTS

ELISA and SIMOA plasma Aβ1-42/Aβ1-40 detected cerebral amyloidosis with identical accuracy (ELISA: area under curve (AUC) 0.78, 95% CI 0.72-0.84; SIMOA: AUC 0.79, 95% CI 0.73-0.85), and both increased the performance of a basic demographic model including only age and APOE-ε4 genotype (p ≤ 0.02). ELISA and SIMOA had positive predictive values of respectively 41% and 36% in cognitively normal elderly and negative predictive values all exceeding 88%. Plasma Aβ1-42/Aβ1-40 correlated similarly with amyloid-PET for both platforms (Spearman ρ = - 0.32, p <  0.0001), yet correlations with CSF Aβ1-42/t-tau were stronger for ELISA (ρ = 0.41, p = 0.002) than for SIMOA (ρ = 0.29, p = 0.03). Plasma Aβ levels demonstrated poor agreement between ELISA and SIMOA with concentrations of both Aβ1-42 and Aβ1-40 measured by SIMOA consistently underestimating those measured by ELISA.

CONCLUSIONS

ELISA and SIMOA demonstrated equivalent performances in detecting cerebral amyloidosis through plasma Aβ1-42/Aβ1-40, both with high negative predictive values, making them equally suitable non-invasive prescreening tools for clinical trials by reducing the number of necessary PET scans for clinical trial recruitment.

TRIAL REGISTRATION

EudraCT 2009-014475-45 (registered on 23 Sept 2009) and EudraCT 2013-004671-12 (registered on 20 May 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-004671-12/BE ).

Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer's disease patients

INTRODUCTION

The eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls.

METHODS

In a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups.

RESULTS

Hyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size.

CONCLUSIONS

This study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model.

Association of CSF Aβ, amyloid PET, and cognition in cognitively unimpaired elderly adults

OBJECTIVE

To compare CSF β-amyloid (Aβ) and florbetapir PET measurements in cognitively unimpaired (CU) elderly adults in order to detect the earliest abnormalities and compare their predictive effect for cognitive decline.

METHODS

A total of 259 CU individuals were categorized as abnormal (+) or normal (-) on CSF Aβ_1-42/Aβ_1-40 analyzed with mass spectrometry and Aβ PET measured with ¹⁸F-florbetapir. Simultaneous longitudinal measurements of CSF and PET were compared for 39 individuals who were unambiguously Aβ-negative at baseline (CSF-/PET-). We also examined the relationship between baseline CSF/PET group membership and longitudinal changes in CSF Aβ, Aβ PET, and cognition.

RESULTS

The proportions of individuals in each discordant group were similar (8.1% CSF+/PET- and 7.7% CSF-/PET+). Among baseline Aβ-negative (CSF-/PET-) individuals with longitudinal CSF and PET measurements, a larger proportion subsequently worsened on CSF Aβ (odds ratio 4 [95% confidence interval (CI) 1.1, 22.1], p = 0.035) than Aβ PET over 3.5 ± 1.0 years. Compared to CSF-/PET- individuals, CSF+/PET- individuals had faster (estimate 0.009 [95% CI 0.005, 0.013], p < 0.001) rates of Aβ PET accumulation over 4.4 ± 1.7 years, while CSF-/PET+ individuals had faster (estimate -0.492 [95% CI -0.861, -0.123], p = 0.01) rates of cognitive decline over 4.5 ± 1.9 years.

CONCLUSIONS

The proportions of discordant PET and CSF Aβ-positive individuals were similar cross-sectionally. However, unambiguously Aβ-negative (CSF-/PET-) individuals are more likely to show subsequent worsening on CSF than PET, supporting the idea that CSF detects the earliest Aβ changes. In discordant cases, only PET abnormality predicted cognitive decline, suggesting that abnormal Aβ PET changes are a later phenomenon in cognitively normal individuals.

Brain regions vulnerable and resistant to aging without Alzheimer's disease

'Normal aging' in the brain refers to age-related changes that occur independent of disease, in particular Alzheimer's disease. A major barrier to mapping normal brain aging has been the difficulty in excluding the earliest preclinical stages of Alzheimer's disease. Here, before addressing this issue we first imaged a mouse model and learn that the best MRI measure of dendritic spine loss, a known pathophysiological driver of normal aging, is one that relies on the combined use of functional and structural MRI. In the primary study, we then deployed the combined functional-structural MRI measure to investigate over 100 cognitively-normal people from 20-72 years of age. Next, to cover the tail end of aging, in secondary analyses we investigated structural MRI acquired from cognitively-normal people, 60-84 years of age, who were Alzheimer's-free via biomarkers. Collectively, the results from the primary functional-structural study, and the secondary structural studies revealed that the dentate gyrus is a hippocampal region differentially affected by aging, and that the entorhinal cortex is a region most resistant to aging. Across the cortex, the primary functional-structural study revealed and that the inferior frontal gyrus is differentially affected by aging, however, the secondary structural studies implicated other frontal cortex regions. Together, the results clarify how normal aging may affect the brain and has possible mechanistic and therapeutic implications.

Decreased Deposition of Beta-Amyloid 1-38 and Increased Deposition of Beta-Amyloid 1-42 in Brain Tissue of Presenilin-1 E280A Familial Alzheimer's Disease Patients

Familial Alzheimer's Disease (FAD) caused by Presenilin-1 (PS1) mutations is characterized by early onset, cognitive impairment, and dementia. Impaired gamma secretase function favors production of longer beta-amyloid species in PS1 FAD. The PS1 E280A mutation is the largest FAD kindred under study. Here, we studied beta-amyloid deposits in PS1 E280A FAD brains in comparison to sporadic Alzheimer's disease (SAD). We analyzed cortices and cerebellum from 10 FAD and 10 SAD brains using immunohistochemistry to determine total beta-amyloid, hyperphosphorylated tau (pTau), and specific beta-amyloid peptides 1-38, 1-40, 1-42, and 1-43. Additionally, we studied beta-amyloid subspecies by ELISA, and vessel pathology was detected with beta-amyloid 1-42 and truncated pyroglutamylated beta-amyloid antibodies. There were no significant differences in total beta-amyloid signal between SAD and FAD. Beta-amyloid 1-38 and 1-43 loads were increased, and 1-42 loads were decreased in frontal cortices of SAD when compared to FAD. Beta-amyloid species assessment by ELISA resembled our findings by immunohistochemical analysis. Differences in beta-amyloid 1-38 and 1-42 levels between SAD and FAD were evidenced by using beta-amyloid length-specific antibodies, reflecting a gamma secretase-dependent shift in beta-amyloid processing in FAD cases. The use of beta-amyloid length-specific antibodies for postmortem assessment of beta-amyloid pathology can differentiate between SAD and PS1 FAD cases and it can be useful for identification of SAD cases potentially affected with gamma secretase dysfunction.

Clinical significance of fluid biomarkers in Alzheimer's Disease

The number of patients with Alzheimer's Disease (AD) and other types of dementia disorders has drastically increased over the last decades. AD is a complex progressive neurodegenerative disease affecting about 14 million patients in Europe and the United States. The hallmarks of this disease are neurotic plaques consist of the Amyloid-β peptide (Aβ) and neurofibrillary tangles (NFTs) formed of hyperphosphorylated Tau protein (pTau). Currently, four CSF biomarkers: Amyloid beta 42 (Aβ42), Aβ42/40 ratio, Tau protein, and Tau phosphorylated at threonine 181 (pTau181) have been indicated as core neurochemical AD biomarkers. However, the identification of additional fluid biomarkers, useful in the prognosis, risk stratification, and monitoring of drug response is sorely needed to better understand the complex heterogeneity of AD pathology as well as to improve diagnosis of patients with the disease. Several novel biomarkers have been extensively investigated, and their utility must be proved and eventually integrated into guidelines for use in clinical practice. This paper presents the research and development of CSF and blood biomarkers for AD as well as their potential clinical significance. Upper panel: Aβ peptides are released from transmembrane Amyloid Precursor Protein (APP) under physiological conditions (blue arrow). In AD, however, pathologic accumulation of Aβ monomers leads to their accumulation in plaques (red arrow). This is reflected in decreased concentration of Aβ1-42 and decreased Aβ42/40 concentration ratio in the CSF. Lower panel: Phosphorylated Tau molecules maintain axonal structures; hyperphosphorylation of Tau (red arrow) in AD leads to degeneration of axons, and release of pTau molecules, which then accumulate in neurofibrillary tangles. This process is reflected by increased concentrations of Tau and pTau in the CSF.

No evidence for amyloid pathology as a key mediator of neurodegeneration post-stroke - a seven-year follow-up study

Background

Cognitive impairment (CI) with mixed vascular and neurodegenerative pathologies after stroke is common. The role of amyloid pathology in post-stroke CI is unclear. We hypothesize that amyloid deposition, measured with Flutemetamol (¹⁸F-Flut) positron emission tomography (PET), is common in seven-year stroke survivors diagnosed with CI and, further, that quantitatively assessed ¹⁸F-Flut-PET uptake after 7 years correlates with amyloid-β peptide (Aβ₄₂) levels in cerebrospinal fluid (CSF) at 1 year, and with measures of neurodegeneration and cognition at 7 years post-stroke.

Methods

208 patients with first-ever stroke or transient Ischemic Attack (TIA) without pre-existing CI were included during 2007 and 2008. At one- and seven-years post-stroke, cognitive status was assessed, and categorized into dementia, mild cognitive impairment or normal. Etiologic sub-classification was based on magnetic resonance imaging (MRI) findings, CSF biomarkers and clinical cognitive profile. At 7 years, patients were offered ¹⁸F-Flut-PET, and amyloid-positivity was assessed visually and semi-quantitatively. The associations between ¹⁸F-Flut-PET standardized uptake value ratios (SUVr) and measures of neurodegeneration (medial temporal lobe atrophy (MTLA), global cortical atrophy (GCA)) and cognition (Mini-Mental State Exam (MMSE), Trail-making test A (TMT-A)) and CSF Aβ₄₂ levels were assessed using linear regression.

Results

In total, 111 patients completed 7-year follow-up, and 26 patients agreed to PET imaging, of whom 13 had CSF biomarkers from 1 year. Thirteen out of 26 patients were diagnosed with CI 7 years post-stroke, but only one had visually assessed amyloid positivity. CSF Aβ₄₂ levels at 1 year, MTA grade, GCA scale, MMSE score or TMT-A at 7 years did not correlate with ¹⁸F-Flut-PET SUVr in this cohort.

Conclusions

Amyloid binding was not common in 7-year stroke survivors diagnosed with CI. Quantitatively assessed, cortical amyloid deposition did not correlate with other measures related to neurodegeneration or cognition. Therefore, amyloid pathology may not be a key mediator of neurodegeneration 7 years post-stroke.

Trial registration

Clinicaltrials.gov (NCT00506818). July 23, 2007. Inclusion from February 2007, randomization and intervention from May 2007 and trial registration in July 2007.

A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI

Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging in adoption with machine learning techniques are especially useful for pattern recognition of medical imaging to assist the physician in early diagnosis of AD. It is observed that the early abnormal brain atrophy and healthy brain atrophy are same. In our endeavor, we proposed a model that differentiation MCI and CN more accurately to escalate early diagnosis of AD. In this paper, we applied both binary and multi class classification, 4463 Slide are divided in to two groups one for training and another for testing at subject level, achieves 100 % of accuracy, 100 % of sensitivity and 100 % of Specificity in the case of AD-CN. 96.2 % of accuracy, 93 % Sensitivity and 100 % Specificity in the case of AD-MCI. 98.0 % of accuracy, 96 % of sensitivity, 100 specificity in the case of CN-MCI. 86.7 % accuracy, 89.6 % of sensitivity, 86.61 % of specificity in the case of AD-MCI-CN. The model is further tested using 10 fold cross validation and obtained 98.0 % of accuracy, to differentiate CN and MCI. Our proposed framework generated results are significantly improving prediction of AD from MCI and CN than compare to the previous work flows and used to differentiate the AD at early stage.

Convolution neural network-based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation

In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and cognitive normal (CN) has become popular. Neuroimaging and computer-aided diagnosis techniques are used for classification of AD by physicians in the early stage. Most of the previous machine learning techniques work on handpicked features. In the recent days, deep learning has been applied for many medical image applications. Existing deep learning systems work on raw magnetic resonance imaging (MRI) images and cortical surface as an input to the convolution neural network (CNN) to perform classification of AD. AD affects the brain volume and changes the gray matter texture. In our work, we used 1820 T2-weighted brain magnetic resonance volumes including 635 AD MRIs, 548 MCI MRIs, and 637 CN MRIs, sliced into 18,017 voxels. We proposed an approach to extract the gray matter from brain voxels and perform the classification using the CNN. A Gaussian filter is used to enhance the voxels, and skull stripping algorithm is used to remove the irrelevant tissues from enhanced voxels. Then, those voxels are segmented by hybrid enhanced independent component analysis. Segmented gray matter is used as an input to the CNN. We performed clinical valuation using our proposed approach and achieved 90.47% accuracy, 86.66% of recall, and 92.59% precision.

Amyloid-β PET-Correlation with cerebrospinal fluid biomarkers and prediction of Alzheimer´s disease diagnosis in a memory clinic

Background

Alzheimer’s disease (AD) remains a clinical diagnosis but biomarkers from cerebrospinal fluid (CSF) and more lately amyloid imaging with positron emission tomography (PET), are important to support a diagnosis of AD.

Objective

To compare amyloid-β (Aβ) PET imaging with biomarkers in CSF and evaluate the prediction of Aβ PET on diagnosis in a memory clinic setting.

Methods

We included 64 patients who had lumbar puncture and Aβ PET with ¹⁸F-Flutemetamol performed within 190 days. PET was binary classified (Flut+ or Flut-) and logistic regression analyses for correlation to each CSF biomarker; Aβ 42 (Aβ₄₂), total tau (T-tau) and phosphorylated tau (P-tau), were performed. Cut-off values were assessed by receiver operating characteristic (ROC) curves. Logistic regression was performed for prediction of clinical AD diagnosis. We assessed the interrater agreement of PET classification as well as for diagnoses, which were made both with and without knowledge of PET results.

Results

Thirty-two of the 34 patients (94%) in the Flut+ group and nine of the 30 patients (30%) in the Flut- group had a clinical AD diagnosis. There were significant differences in all CSF biomarkers in the Flut+ and Flut- groups. Aβ₄₂ showed the highest correlation with ¹⁸F-Flutemetamol PET with a cut-off value of 706.5 pg/mL, corresponding to sensitivity of 88% and specificity of 87%. ¹⁸F-Flutemetamol PET was the best predictor of a clinical AD diagnosis. We found a very high interrater agreement for both PET classification and diagnosis.

Conclusions

The present study showed an excellent correlation of Aβ₄₂ in CSF and ¹⁸F-Flutemetamol PET and the presented cut-off value for Aβ₄₂ yields high sensitivity and specificity for ¹⁸F-Flutemetamol PET. ¹⁸F-Flutemetamol PET was the best predictor of clinical AD diagnosis.

Alzheimer's Disease Computer-Aided Diagnosis: Histogram-Based Analysis of Regional MRI Volumes for Feature Selection and Classification

This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer's disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way.

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.

Oriented Immobilization and Quantitative Analysis Simultaneously Realized in Sandwich Immunoassay via His-Tagged Nanobody

Despite the advantages of the nanobody, the unique structure limits its use in sandwich immunoassay. In this study, a facile protocol of sandwich immunoassay using the nanobody was established. In brief, β amyloid and SH2, an anti-β amyloid nanobody, were used as capture antibody and antigen, respectively. The SH2 fused with His-tag was first purified and absorbed on Co²⁺-NTA functional matrix and then immobilized through H₂O₂ oxidation of Co²⁺ to Co³⁺ under the optimized conditions. Then, 150 mM imidazole and 20 mM EDTA were introduced to remove the unbound SH2. The immobilized SH2 showed highly-sensitive detection of β amyloid. It is interesting that the quantification of the sandwich immunoassay was carried out by determining the His-tag of the detection nanobody, without interference from the His-tag of the capture nanobody. The immobilized SH2 detached exhibited outstanding stability during 30 days of storage. Taken together, His6-tag facilitated both the oriented immobilization of capture antibody and quantitative assay of detection antibody in sandwich immunoassay. We propose a facile and efficient sandwich immunoassay method that opens new avenue to the study of His-tagged protein interactions.

Diagnosis of Alzheimer's disease utilizing amyloid and tau as fluid biomarkers

Current technological advancements in clinical and research settings have permitted a more intensive and comprehensive understanding of Alzheimer’s disease (AD). This development in knowledge regarding AD pathogenesis has been implemented to produce disease-modifying drugs. The potential for accessible and effective therapeutic methods has generated a need for detecting this neurodegenerative disorder during early stages of progression because such remedial effects are more profound when implemented during the initial, prolonged prodromal stages of pathogenesis. The aggregation of amyloid-β (Aβ) and tau isoforms are characteristic of AD; thus, they are considered core candidate biomarkers. However, research attempting to establish the reliability of Aβ and tau as biomarkers has culminated in an amalgamation of contradictory results and theories regarding the biomarker concentrations necessary for an accurate diagnosis. In this review, we consider the capabilities and limitations of fluid biomarkers collected from cerebrospinal fluid, blood, and oral, ocular, and olfactory secretions as diagnostic tools for AD, along with the impact of the integration of these biomarkers in clinical settings. Furthermore, the evolution of diagnostic criteria and novel research findings are discussed. This review is a summary and reflection of the ongoing concerted efforts to establish fluid biomarkers as a diagnostic tool and implement them in diagnostic procedures.

Markers from body fluids could help clinicians diagnose Alzheimer’s disease before cognitive decline appears. After numerous setbacks in treating advanced Alzheimer’s, researchers are eager to identify biological indicators that facilitate earlier disease detection and interception. A review by YoungSoo Kim and colleagues at Yonsei University in South Korea, explores the promise of ‘fluid biomarkers,’ which enables diagnosis using cerebrospinal fluid (CSF), blood, oral, ocular, and olfactory fluid samples. Shifts in CSF levels of amyloid beta and tau, two proteins central to Alzheimer’s pathology, can reliably monitor at-risk individuals. Although CSF collection is unpleasant for patients, it remains more promising than blood, where current data for candidate fluid biomarkers are relatively inconclusive. In this review, investigations to discover safer, cheaper, and more reliable diagnostic tools to shift treatment from alleviation to prevention are introduced.

Cerebrospinal fluid levels of synaptic and neuronal integrity correlate with gray matter volume and amyloid load in the precuneus of cognitively intact older adults

The main pathophysiological alterations of Alzheimer's disease (AD) include loss of neuronal and synaptic integrity, amyloidogenic processing, and neuroinflammation. Similar alterations can, however, also be observed in cognitively intact older subjects and may prelude the clinical manifestation of AD. The objectives of this prospective cross-sectional study in a cohort of 38 cognitively intact older adults were twofold: (i) to investigate the latent relationship among cerebrospinal fluid (CSF) biomarkers reflecting the main pathophysiological processes of AD, and (ii) to assess the correlation between these biomarkers and gray matter volume as well as amyloid load. All subjects underwent extensive neuropsychological examinations, CSF sampling, [¹⁸ F]-flutemetamol amyloid positron emission tomography, and T₁ -weighted magnetic resonance imaging. A factor analysis revealed one factor that explained most of the variance in the CSF biomarker dataset clustering t-tau, α-synuclein, p-tau₁₈₁ , neurogranin, BACE1, visinin-like protein 1, chitinase-3-like protein 1 (YKL-40), Aβ_1-40 and Aβ_1-38 . Higher scores on this factor correlated with lower gray matter volume and with higher amyloid load in the precuneus. At the level of individual CSF biomarkers, levels of visinin-like protein 1, neurogranin, BACE1, Aβ_1-40 , Aβ_1-38, and YKL-40 all correlated inversely with gray matter volume of the precuneus. These findings demonstrate that in cognitively intact older subjects, CSF levels of synaptic and neuronal integrity biomarkers, amyloidogenic processing and measures of innate immunity (YKL-40) display a latent structure of common variance, which is associated with loss of structural integrity of brain regions implicated in the earliest stages of AD. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript, and for *Preregistration* because the study was pre-registered at https://osf.io/7qm9t/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Preanalytical approaches to improve recovery of amyloid-β peptides from CSF as measured by immunological or mass spectrometry-based assays

Background

Amyloid-β 1–42 (Aβ_1–42) peptide is a well-established cerebrospinal fluid (CSF) biomarker for Alzheimer’s disease (AD). Reduced levels of Aβ_1–42 are indicative of AD, but significant variation in the absolute concentrations of this analyte has been described for both healthy and diseased populations. Preanalytical factors such as storage tube type are reported to impact Aβ recovery and quantification accuracy. Using complementary immunological and mass spectrometry-based approaches, we identified and characterized preanalytical factors that influence measured concentrations of CSF Aβ peptides in stored samples.

Methods

CSF from healthy control subjects and patients with AD was aliquoted into polypropylene tubes at volumes of 0.1 ml and 0.5 ml. CSF Aβ_1–42 concentrations were initially measured by immunoassay; subsequent determinations of CSF Aβ_1–42, Aβ_1–40, Aβ_1–38, Aβ_1–37, and Aβ_1–34 concentrations were made with an absolute quantitative mass spectrometry assay. In a second study, CSF from healthy control subjects and patients with dementia was denatured with guanidine hydrochloride (GuHCl) at different stages of the CSF collection and aliquoting process and then measured with the mass spectrometry assay.

Results

Two distinct immunoassays demonstrated that CSF Aβ_1–42 concentrations measured from 0.5-ml aliquots were higher than those from 0.1-ml aliquots. Tween-20 surfactant supplementation increased Aβ_1–42 recovery but did not effectively resolve measured concentration differences associated with aliquot size. A CSF Aβ peptide mass spectrometry assay confirmed that Aβ peptide recovery was linked to sample volume. Unlike the immunoassay experiments, measured differences were consistently eliminated when aliquots were denatured in the original sample tube. Recovery from a panel of low-retention polypropylene tubes was assessed, and 1.5-ml Eppendorf LoBind® tubes were determined to be the least absorptive for Aβ_1–42. A comparison of CSF collection and processing methods suggested that Aβ peptide recovery was improved by denaturing CSF earlier in the collection/aliquoting process and that the Aβ_1–42/Aβ_1–40 ratio was a useful method to reduce variability.

Conclusions

Analyte loss due to nonspecific sample tube adsorption is a significant preanalytical factor that can compromise the accuracy of CSF Aβ_1–42 measurements. Sample denaturation during aliquoting increases recovery of Aβ peptides and improves measurement accuracy. The Aβ_1–42/Aβ_1–40 ratio can overcome some of the quantitative variability precipitated by preanalytical factors affecting recovery.

Electronic supplementary material

The online version of this article (10.1186/s13195-018-0445-0) contains supplementary material, which is available to authorized users.

Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease

INTRODUCTION

The Alzheimer's Association convened a multidisciplinary workgroup to develop appropriate use criteria to guide the safe and optimal use of the lumbar puncture procedure and cerebrospinal fluid (CSF) testing for Alzheimer's disease pathology detection in the diagnostic process.

METHODS

The workgroup, experienced in the ethical use of lumbar puncture and CSF analysis, developed key research questions to guide the systematic review of the evidence and developed clinical indications commonly encountered in clinical practice based on key patient groups in whom the use of lumbar puncture and CSF may be considered as part of the diagnostic process. Based on their expertise and interpretation of the evidence from systematic review, members rated each indication as appropriate or inappropriate.

RESULTS

The workgroup finalized 14 indications, rating 6 appropriate and 8 inappropriate.

DISCUSSION

In anticipation of the emergence of more reliable CSF analysis platforms, the manuscript offers important guidance to health-care practitioners and suggestions for implementation and future research.

Automation vs. Experience: Measuring Alzheimer's Beta-Amyloid 1-42 Peptide in the CSF

Cerebrospinal fluid (CSF) biomarkers can enhance the early and accurate etiologic detection of Alzheimer’s disease (AD) even when symptoms are very mild, but are not yet widely available for clinical testing. There are a number of reasons for this, including the need for an experienced operator, the use of instruments mostly reserved for research, and low cost-effectiveness when patient samples do not completely fill each assay plate. Newer technology can overcome some of these issues through automated assays of a single patient sample on existing clinical laboratory platforms, but it is not known how these newer automated assays compare with previous research-based measurements. This is a critical issue in the clinical translation of CSF AD biomarkers because most cohort and clinicopathologic studies have been analyzed on older assays. To determine the correlation of CSF beta-amyloid 1–42 (Aβ42) measures derived from the automated chemiluminescent enzyme immunoassay (CLEIA, on Lumipulse^® G1200), a bead-based Luminex immunoassay, and a plate-based enzyme-linked immunoassay enzyme-linked immunosorbent assay (ELISA), we analyzed 30 CSF samples weekly on each platforms over 3 weeks. We found that, while CSF Aβ42 levels were numerically closer between CLEIA and ELISA measurements, levels differed between all three assays. CLEIA-based measures correlated linearly with the two other assays in the low and intermediate Aβ42 concentrations, while there was a linear correlation between Luminex assay and ELISA throughout all concentrations. For repeatability, the average intra-assay coefficient of variation (CV) was 2.0%. For intermediate precision, the inter-assay CV was lower in CLEIA (7.1%) than Luminex (10.7%, p = 0.009) and ELISA (10.8%, p = 0.009), primarily due to improved intermediate precision in the higher CSF Aβ42 concentrations. We conclude that the automated CLEIA generated reproducible CSF Aβ42 measures with improved intermediate precision over experienced operators using Luminex assays and ELISA, and are highly correlated with the manual Aβ42 measures.

Single-word comprehension deficits in the nonfluent variant of primary progressive aphasia

BACKGROUND

A subset of patients with the nonfluent variant of primary progressive aphasia (PPA) exhibit concomitant single-word comprehension problems, constituting a 'mixed variant' phenotype. This phenotype is rare and currently not fully characterized. The aim of this study was twofold: to assess the prevalence and nature of single-word comprehension problems in the nonfluent variant and to study multimodal imaging characteristics of atrophy, tau, and amyloid burden associated with this mixed phenotype.

METHODS

A consecutive memory-clinic recruited series of 20 PPA patients (12 nonfluent, five semantic, and three logopenic variants) were studied on neurolinguistic and neuropsychological domains relative to 64 cognitively intact healthy older control subjects. The neuroimaging battery included high-resolution volumetric magnetic resonance imaging processed with voxel-based morphometry, and positron emission tomography with the tau-tracer [18F]-THK5351 and amyloid-tracer [11C]-Pittsburgh Compound B.

RESULTS

Seven out of 12 subjects who had been classified a priori with nonfluent variant PPA showed deficits on conventional single-word comprehension tasks along with speech apraxia and agrammatism, corresponding to a mixed variant phenotype. These mixed variant cases included three females and four males, with a mean age at onset of 65 years (range 44-77 years). Object knowledge and object recognition were additionally affected, although less severely compared with the semantic variant. The mixed variant was characterized by a distributed atrophy pattern in frontal and temporoparietal regions. A more focal pattern of elevated [18F]-THK5351 binding was present in the supplementary motor area, the left premotor cortex, midbrain, and basal ganglia. This pattern was closely similar to that seen in pure nonfluent variant PPA. At the individual patient level, elevated [18F]-THK5351 binding in the supplementary motor area and premotor cortex was present in six out of seven mixed variant cases and in five and four of these cases, respectively, in the thalamus and midbrain. Amyloid biomarker positivity was present in two out of seven mixed variant cases, compared with none of the five pure nonfluent cases.

CONCLUSIONS

A substantial proportion of PPA patients with speech apraxia and agrammatism also have single-word comprehension deficits. At the neurobiological level, the mixed variant shows a high degree of similarity with the pure nonfluent variant of PPA.

TRIAL REGISTRATION

EudraCT, 2014-002976-10 . Registered on 13-01-2015.

Concordance Between Cerebrospinal Fluid Biomarkers with Alzheimer's Disease Pathology Between Three Independent Assay Platforms

BACKGROUND

To enhance the accuracy of clinical diagnosis for Alzheimer's disease (AD), pre-mortem biomarkers have become increasingly important for diagnosis and for participant recruitment in disease-specific treatment trials. Cerebrospinal fluid (CSF) biomarkers provide a low-cost alternative to positron emission tomography (PET) imaging for in vivo quantification of different AD pathological hallmarks in the brains of affected subjects; however, consensus around the best platform, most informative biomarker and correlations across different methodologies are controversial.

OBJECTIVE

Assessing levels of Aβ-amyloid and tau species determined using three different versions of immunoassays, the current study explored the ability of CSF biomarkers to predict PET Aβ-amyloid (32 Aβ-amyloid-and 45 Aβ-amyloid+), as well as concordance between CSF biomarker levels and PET Aβ-amyloid imaging.

METHODS

Prediction and concordance analyses were performed using a sub-cohort of 77 individuals (48 healthy controls, 15 with mild cognitive impairment, and 14 with AD) from the Australian Imaging Biomarker and Lifestyle study of aging.

RESULTS

Across all three platforms, the T-tau/Aβ42 ratio biomarker had modestly higher correlation with SUVR/BeCKeT (ρ= 0.69-0.8) as compared with Aβ42 alone (ρ= 0.66-0.75). Differences in CSF biomarker levels between the PET Aβ-amyloid-and Aβ-amyloid+ groups were strongest for the Aβ42/Aβ40 and T-tau/Aβ42 ratios (p < 0.0001); however, comparison of predictive models for PET Aβ-amyloid showed no difference between Aβ42 alone and the T-tau/Aβ42 ratio.

CONCLUSION

This study confirms strong concordance between CSF biomarkers and PET Aβ-amyloid status is independent of immunoassay platform, supporting their utility as biomarkers in clinical practice for the diagnosis of AD and for participant enrichment in clinical trials.

Distinct [18F]THK5351 binding patterns in primary progressive aphasia variants

PURPOSE

To assess the binding of the PET tracer [¹⁸F]THK5351 in patients with different primary progressive aphasia (PPA) variants and its correlation with clinical deficits. The majority of patients with nonfluent variant (NFV) and logopenic variant (LV) PPA have underlying tauopathy of the frontotemporal lobar or Alzheimer disease type, respectively, while patients with the semantic variant (SV) have predominantly transactive response DNA binding protein 43-kDa pathology.

METHODS

The study included 20 PPA patients consecutively recruited through a memory clinic (12 NFV, 5 SV, 3 LV), and 20 healthy controls. All participants received an extensive neurolinguistic assessment, magnetic resonance imaging and amyloid biomarker tests. [¹⁸F]THK5351 binding patterns were assessed on standardized uptake value ratio (SUVR) images with the cerebellar grey matter as the reference using statistical parametric mapping. Whole-brain voxel-wise regression analysis was performed to evaluate the association between [¹⁸F]THK5351 SUVR images and neurolinguistic scores. Analyses were performed with and without partial volume correction.

RESULTS

Patients with NFV showed increased binding in the supplementary motor area, left premotor cortex, thalamus, basal ganglia and midbrain compared with controls and patients with SV. Patients with SV had increased binding in the temporal lobes bilaterally and in the right ventromedial frontal cortex compared with controls and patients with NFV. The whole-brain voxel-wise regression analysis revealed a correlation between agrammatism and motor speech impairment, and [¹⁸F]THK5351 binding in the left supplementary motor area and left postcentral gyrus. Analysis of [¹⁸F]THK5351 scans without partial volume correction revealed similar results.

CONCLUSION

[¹⁸F]THK5351 imaging shows a topography closely matching the anatomical distribution of predicted underlying pathology characteristic of NFV and SV PPA. [¹⁸F]THK5351 binding correlates with the severity of clinical impairment.

Amyloid-β, Tau, and Cognition in Cognitively Normal Older Individuals: Examining the Necessity to Adjust for Biomarker Status in Normative Data

We investigated whether amyloid-β (Aβ) and tau affected cognition in cognitively normal (CN) individuals, and whether norms for neuropsychological tests based on biomarker-negative individuals would improve early detection of dementia. We included 907 CN individuals from 8 European cohorts and from the Alzheimer's disease Neuroimaging Initiative. All individuals were aged above 40, had Aβ status and neuropsychological data available. Linear mixed models were used to assess the associations of Aβ and tau with five neuropsychological tests assessing memory (immediate and delayed recall of Auditory Verbal Learning Test, AVLT), verbal fluency (Verbal Fluency Test, VFT), attention and executive functioning (Trail Making Test, TMT, part A and B). All test except the VFT were associated with Aβ status and this influence was augmented by age. We found no influence of tau on any of the cognitive tests. For the AVLT Immediate and Delayed recall and the TMT part A and B, we calculated norms in individuals without Aβ pathology (Aβ- norms), which we validated in an independent memory-clinic cohort by comparing their predictive accuracy to published norms. For memory tests, the Aβ- norms rightfully identified an additional group of individuals at risk of dementia. For non-memory test we found no difference. We confirmed the relationship between Aβ and cognition in cognitively normal individuals. The Aβ- norms for memory tests in combination with published norms improve prognostic accuracy of dementia.

Analysis of neurotrophic and antioxidant factors related to midbrain dopamine neuronal loss and brain inflammation in the cerebrospinal fluid of the elderly

Midbrain dopamine neuronal loss and neuroinflammation are two phenomena that are associated with brain senescence. Neurotrophic factor changes and oxidative stress could subserve these phenomena. Aging-related brain changes can be well monitored through the cerebrospinal fluid (CSF). The objective was to analyze neurotrophic and oxidative parameters that could be related to midbrain dopamine neuronal loss or brain inflammation in the CSF of elderly subjects: 1) levels of the dopaminotrophic factors BDNF, GDNF, persephin, and neurturin, 2) levels of the proinflammatory factors TGFβ₁ and TGFβ₂; 3) activity of main antioxidant enzymes (catalases, glutathione-peroxidase, glutathione-reductase, glutathione-S-transferases, peroxirredoxins, and superoxide-dismutases), 4) ferritin content, antioxidant protein which reduces reactive free iron, and 5) antioxidant potential of the cerebrospinal fluid. ELISA and PAO tests were used. Subjects were also evaluated clinically, and the group of old subjects with mild cognitive impairment was studied separately. The findings indicate that normal elderly CSF is devoid of changes in either dopaminotrophic or proinflammatory factors. The antioxidant efficacy is slightly reduced with normal aging, through a reduction of glutathione-S-transferase activity in people older than 74 years (p < 0.05). However old people with mild cognitive impairment show reduced BDNF levels, and stronger signs of oxidative stress such as low antioxidant potential and glutathione-S-transferase activity (p < 0.05). To sum up, the present study demonstrates that, in CSF of normal senescence, dopaminotrophic factors and proinflammatory TGF-family ligands are not affected, and antioxidant efficacy is slightly reduced. CSF of elderly subjects with mild cognitive impairment shows more oxidative and trophic changes that are characterized by reduction of BDNF content, glutathione-S-transferase activity, and antioxidant potential.

Cerebrospinal Fluid Aβ42/40 Corresponds Better than Aβ42 to Amyloid PET in Alzheimer's Disease

Background: Decreased concentrations of amyloid-β 1-42 (Aβ₄₂) in cerebrospinal fluid (CSF) and increased retention of Aβ tracers in the brain on positron emission tomography (PET) are considered the earliest biomarkers of Alzheimer’s disease (AD). However, a proportion of cases show discrepancies between the results of the two biomarker modalities which may reflect inter-individual differences in Aβ metabolism. The CSF Aβ_42/40 ratio seems to be a more accurate biomarker of clinical AD than CSF Aβ₄₂ alone.

Objective: We tested whether CSF Aβ₄₂ alone or the Aβ_42/40 ratio corresponds better with amyloid PET status and analyzed the distribution of cases with discordant CSF-PET results.

Methods: CSF obtained from a mixed cohort (n = 200) of cognitively normal and abnormal research participants who had undergone amyloid PET within 12 months (n = 150 PET-negative, n = 50 PET-positive according to a previously published cut-off) was assayed for Aβ₄₂ and Aβ₄₀ using two recently developed immunoassays. Optimal CSF cut-offs for amyloid positivity were calculated, and concordance was tested by comparison of the areas under receiver operating characteristic (ROC) curves (AUC) and McNemar’s test for paired proportions.

Results: CSF Aβ_42/40 corresponded better than Aβ₄₂ with PET results, with a larger proportion of concordant cases (89.4% versus 74.9%, respectively, p < 0.0001) and a larger AUC (0.936 versus 0.814, respectively, p < 0.0001) associated with the ratio. For both CSF biomarkers, the percentage of CSF-abnormal/PET-normal cases was larger than that of CSF-normal/PET-abnormal cases.

Conclusion: The CSF Aβ_42/40 ratio is superior to Aβ₄₂ alone as a marker of amyloid-positivity by PET. We hypothesize that this increase in performance reflects the ratio compensating for general between-individual variations in CSF total Aβ.

Performance Evaluation of an Automated ELISA System for Alzheimer's Disease Detection in Clinical Routine

The variability of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers undermines their full-fledged introduction into routine diagnostics and clinical trials. Automation may help to increase precision and decrease operator errors, eventually improving the diagnostic performance. Here we evaluated three new CSF immunoassays, EUROIMMUNtrademark amyloid-β 1-40 (Aβ1-40), amyloid-β 1-42 (Aβ1-42), and total tau (t-tau), in combination with automated analysis of the samples. The CSF biomarkers were measured in a cohort consisting of AD patients (n = 28), mild cognitive impairment (MCI, n = 77), and neurological controls (OND, n = 35). MCI patients were evaluated yearly and cognitive functions were assessed by Mini-Mental State Examination. The patients clinically diagnosed with AD and MCI were classified according to the CSF biomarkers profile following NIA-AA criteria and the Erlangen score. Technical evaluation of the immunoassays was performed together with the calculation of their diagnostic performance. Furthermore, the results for EUROIMMUN Aβ1-42 and t-tau were compared to standard immunoassay methods (INNOTESTtrademark). EUROIMMUN assays for Aβ1-42 and t-tau correlated with INNOTEST (r = 0.83, p < 0.001 for both) and allowed a similar interpretation of the CSF profiles. The Aβ1-42/Aβ1-40 ratio measured with EUROIMMUN was the best parameter for AD detection and improved the diagnostic accuracy of Aβ1-42 (area under the curve = 0.93). In MCI patients, the Aβ1-42/Aβ1-40 ratio was associated with cognitive decline and clinical progression to AD.The diagnostic performance of the EUROIMMUN assays with automation is comparable to other currently used methods. The variability of the method and the value of the Aβ1-42/Aβ1-40 ratio in AD diagnosis need to be validated in large multi-center studies.

Automation on an Open-Access Platform of Alzheimer's Disease Biomarker Immunoassays

The lack of (inter-)laboratory standardization has hampered the application of universal cutoff values for Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers and their transfer to general clinical practice. The automation of the AD biomarker immunoassays is suggested to generate more robust results than using manual testing. Open-access platforms will facilitate the integration of automation for novel biomarkers, allowing the introduction of the protein profiling concept. A feasibility study was performed on an automated open-access platform of the commercial immunoassays for the 42-amino-acid isoform of amyloid-β (Aβ_1-42), Aβ_1-40, and total tau in CSF. Automated Aβ_1-42, Aβ_1-40, and tau immunoassays were performed within predefined acceptance criteria for bias and imprecision. Similar accuracy was obtained for ready-to-use calibrators as for reconstituted lyophilized kit calibrators. When compared with the addition of a standard curve in each test run, the use of a master calibrator curve, determined before and applied to each batch analysis as the standard curve, yielded an acceptable overall bias of -2.6% and -0.9% for Aβ_1-42 and Aβ_1-40, respectively, with an imprecision profile of 6.2% and 8.4%, respectively. Our findings show that transfer of commercial manual immunoassays to fully automated open-access platforms is feasible, as it performs according to universal acceptance criteria.

A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer's Disease

BACKGROUND

Amyloid PET (aPET) imaging could improve patient outcomes in clinical practice, but the extent of impact needs quantification.

OBJECTIVE

To provide an aggregated quantitative analysis of the value added by aPET in cognitively impaired subjects.

METHODS

Systematic literature searches were performed in Embase and Medline until January 2017. 1,531 cases over 12 studies were included (1,142 cases over seven studies in the primary analysis where aPET was the key biomarker; the remaining cases included as defined groups in the secondary analysis). Data was abstracted by consensus among two observers and assessed for bias. Clinical utility was measured by diagnostic change, diagnostic confidence, and patient management before and after aPET. Three groups were further analyzed: control patients for whom feedback of aPET scan results was delayed; aPET Appropriate Use Criteria (AUC+) cases; and patients undergoing additional FDG/CSF testing.

RESULTS

For 1,142 cases with only aPET, 31.3% of diagnoses were revised, whereas 3.2% of diagnoses changed in the delayed aPET control group (p < 0.0001). Increased diagnostic confidence following aPET was found for 62.1% of 870 patients. Management changes with aPET were found in 72.2% of 740 cases and in 55.5% of 299 cases in the control group (p < 0.0001). The diagnostic value of aPET in AUC+ patients or when FDG/CSF were additionally available did not substantially differ from the value of aPET alone in the wider population.

CONCLUSIONS

Amyloid PET contributed to diagnostic revision in almost a third of cases and demonstrated value in increasing diagnostic confidence and refining management plans.

A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer’s disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer’s disease neuroimaging initiative (ADNI).

CSF complement 3 and factor H are staging biomarkers in Alzheimer's disease

Introduction

CSF levels of established Alzheimer’s disease (AD) biomarkers remain stable despite disease progression, and non-amyloid non-tau biomarkers have the potential of informing disease stage and progression. We previously identified complement 3 (C3) to be decreased in AD dementia, but this change was not found by others in earlier AD stages. We hypothesized that levels of C3 and associated factor H (FH) can potentially distinguish between mild cognitive impairment (MCI) and dementia stages of AD, but we also found their levels to be influenced by age and disease status.

Results

We developed a biochemical/bioinformatics pipeline to optimize the handling of complex interactions between variables in validating biochemical markers of disease. We used data from the Alzheimer’s Disease Neuro-imaging Initiative (ADNI, n = 230) to build parallel machine learning models, and objectively tested the models in a test cohort (n = 73) of MCI and mild AD patients independently recruited from Emory University. Whereas models incorporating age, gender, APOE ε4 status, and CSF amyloid and tau levels failed to reliably distinguish between MCI and mild AD in ADNI, introduction of CSF C3 and FH levels reproducibly improved the distinction between the two AD stages in ADNI (p < 0.05) and the Emory cohort (p = 0.014). Within each AD stage, the final model also distinguished between fast vs. slower decliners (p < 0.001 for MCI, p = 0.007 for mild AD), with lower C3 and FH levels associated with more advanced disease and faster progression.

Conclusions

We propose that CSF C3 and FH alterations may reflect stage-associated biomarker changes in AD, and can complement clinician diagnosis in diagnosing and staging AD using the publically available ADNI database as reference.

Electronic supplementary material

The online version of this article (doi:10.1186/s40478-016-0277-8) contains supplementary material, which is available to authorized users.