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A new tetra-plex fluorimetric assay for the quantification of cerebrospinal fluid β-amyloid42, total-tau, phospho-tau and α-synuclein in the differential diagnosis of neurodegenerative dementia

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Abstract

Background

Differential diagnosis of neurodegenerative dementia is currently supported by biomarkers including cerebrospinal fluid (CSF) tests. Among them, CSF total-tau (t-tau), phosphorylated tau (p-tau) and β-amyloid42 (Aβ42) are considered core biomarkers of neurodegeneration. In the present work, we hypothesize that simultaneous assessment of these biomarkers together with CSF α-synuclein (α-syn) will significantly improve the differential diagnostic of Alzheimer’s disease and other dementias. To that aim, we characterized the analytical and clinical performance of a new tetra-plex immunoassay that simultaneously quantifies CSF Aβ42, t-tau, p-tau and α-syn in the differential diagnosis of neurodegenerative dementia.

Methods

Biomarkers’ concentrations were measured in neurological controls (n = 38), Alzheimer’s disease (n = 35), Creutzfeldt–Jakob disease (n = 37), vascular dementia (n = 28), dementia with Lewy bodies/Parkinson’s disease dementia (n = 27) and frontotemporal dementia (n = 34) using the new tetra-plex assay and established single-plex assays. Biomarker’s performance was evaluated and diagnostic accuracy in the discrimination of diagnostic groups was determined using partial least squares discriminant analysis.

Results

The tetra-plex assay presented accuracies similar to individual single-plex assays with acceptable analytical performance. Significant correlations were observed between tetra-plex and single-plex assays. Using partial least squares discriminant analysis, Alzheimer’s disease and Creutzfeldt–Jakob disease were well differentiated, reaching high accuracies in the discrimination from the rest of diagnostic groups.

Conclusions

The new tetra-plex assay coupled with multivariate analytical approaches becomes a valuable asset for the differential diagnosis of neurodegenerative dementia and related applications.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CSF:

Cerebrospinal fluid

AD:

Alzheimer’s disease

CJD:

Creutzfeldt–Jakob disease

t-tau:

Total-tau

p-tau:

Phosphorylated tau

Aβ42:

β-Amyloid42

RT-QuIC:

Real-time quacking-induced conversion

VaD:

Vascular dementia

LBD:

Lewy body diseases

FTD:

Fronto-temporal dementia

α-syn:

α-Synuclein

ND:

Neurological controls

DLB:

Dementia with Lewy bodies

PDD:

Parkinson’s disease dementia

bvFTD:

Behavioral variant FTD

LM:

Linear models

LRT:

Likelihood ratio tests

AUCs:

Areas under the curve

95% CI:

95% Confidence intervals

ROC:

Receiver operating characteristic

PLS-DA:

Partial least squares discriminant analysis

CV:

Intra-coefficient of variation

RT:

Room temperature

F/T:

Freeze/thawing

PCA:

Principal component analysis

VIP:

Variable importance for the projection

SD:

Standard deviation

f:

Female

m:

Male

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Funding

This study was funded by the Instituto Carlos III (Grants CP/00041 and PI19/00144) and by the Fundació La Marató de TV3 (201821-30-31-32) to FL and by the Robert Koch Institute through funds from the Federal Ministry of Health (Grant no. 1369-341) to IZ. JADR received funding from MICINN (RTI2018-099773-B-100) and La Caixa Banking Foundation under the project code HR18-00452. This project was also funded at 65% by the Fondo Europeo de Desarrollo Regional (FEDER) through the Interreg V-A-Spain-France-Andorra (POCTEFA 2014–2020) programme. AVP is supported by the Beatriu de Pinós programme (2018-BP-00129) from the Ministry of Business and Knowledge of the Government of Catalonia, cofunded by the EU Horizon 2020 programme under an MSCA grant agreement (801370). We thank the CERCA Programme of Generalitat de Catalunya for institutional support.

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Author notes

  1. Daniela Diaz-Lucena and Geòrgia Escaramis contributed equally.

Authors and Affiliations

  1. Center for Networked Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Barcelona, Spain

    Daniela Diaz-Lucena, Anna Villar-Piqué, José Antonio del Rio, Isidre Ferrer & Franc Llorens

  2. CIBER in Epidemiology and Public Health (CIBERESP), Barcelona, Spain

    Geòrgia Escaramis & Eulàlia Martí

  3. Department de Biomedical Sciences, Institute of Neuroscience, University de Barcelona, Barcelona, Spain

    Geòrgia Escaramis & Eulàlia Martí

  4. Department of Neurology, Clinical Dementia Center and National Reference Center for CJD Surveillance, University Medical School, Göttingen, Germany

    Peter Hermann, Matthias Schmitz, Daniela Varges, Inga Zerr & Franc Llorens

  5. German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany

    Matthias Schmitz & Inga Zerr

  6. Neurology Department, CHUC-Centro Hospitalar e Universitário de Coimbra, CNC-Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal

    Isabel Santana & Inês Baldeiras

  7. Institute of Neurosciences, University of Barcelona, Barcelona, Spain

    José Antonio del Rio

  8. Molecular and Cellular Neurobiotechnology, Institute of Bioengineering of Catalonia (IBEC), Barcelona Institute for Science and Technology, Parc Científic de Barcelona, Barcelona, Spain

    José Antonio del Rio

  9. Department of Cell Biology, Physiology and Immunology, Faculty of Biology, University of Barcelona, Barcelona, Spain

    José Antonio del Rio

  10. Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain

    Anna Villar-Piqué, Isidre Ferrer & Franc Llorens

  11. Department of Pathology and Experimental Therapeutics, University of Barcelona, Hospitalet de Llobregat, Barcelona, Spain

    Isidre Ferrer

Authors
  1. Daniela Diaz-Lucena
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  2. Geòrgia Escaramis
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  3. Anna Villar-Piqué
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  4. Peter Hermann
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  7. Isabel Santana
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  8. José Antonio del Rio
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  9. Eulàlia Martí
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  11. Inês Baldeiras
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  12. Inga Zerr
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  13. Franc Llorens
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Contributions

FL designed the study. DD-L, GE, AV-P, JADR, EM, IF and FL participated in the acquisition and analysis of data. PH, MS, IS, IB, IZ and FL collected and characterized biological samples and contributed to data interpretation. GE and FL drafted the manuscript and the figures. All authors critically revised the manuscript.

Corresponding author

Correspondence to Franc Llorens.

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Conflicts of interest

The authors declare that they have no competing interests.

Ethical approval

Written informed consent was obtained from all study participants or their legal guardians. The study was conducted according to the revised Declaration of Helsinki and Good Clinical Practice guidelines, and was approved by all local Ethics committees (Reference numbers 11/11/93, 5/09/08, 9/06/08, 19/11/09, Universitätsmedizin Göttingen, Germany and HUC-43-09, University of Coimbra, Portugal).

Electronic supplementary material

Supplementary Figure 1. Analytical performance of new tetra-plex assay for quantification of CSF Aβ42, t-tau, p-tau and α-syn. (a) Analytical performance of the tetra-plex assay. Intra and inter coefficients of variation (CV), lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ) and linearity are indicated for Aβ42, t-tau, p-tau and α-syn. (b) Representative standard curves for each biomarker.

Supplementary Figure 2. Comparison on diagnostic performance between tetra-plex (fluorimetric) and single-plex (colorimetric) assays. Area Under the Curve (AUC) derived from receiver operating characteristic curves with 95% confidence interval for the comparison for the tetra-plex and single-plex assays. Accuracy was calculated for the four biomarkers (Aβ42, t-tau, p-tau and α-syn) in the comparison among neurodegenerative dementias (AD, Alzheimer’s disease; CJD, Creutzfeldt-Jakob disease; VaD, vascular dementia; DLB/PDD, dementia with Lewy bodies and Parkinson’s disease dementia; FTD, frontotemporal dementia). Statistical differences between AUCs derived from tetra-plex and single-plex assays is indicated, with statistically significant values (considered as p<0.05) highlighted in bold.

Supplementary Figure 3. Patient outlier detection in the agreement analysis of tetra-plex and single-plex assays based on Cook’s distance.

Supplementary Figure 4. Unsupervised classification of ND, AD and CJD patients based on Principal Component Analysis (PCA). (a) First two PCA components involving t-tau, p-tau, α-syn, and Aβ42. (b) First two PCA components of t-tau, p-tau and Aβ42 in the absence of α-syn.

Supplementary Table 1. Sensitivities and specificities associated to the AUC comparisons between ND and neurodegenerative dementias. Sensitivities and specificities in % based in Youden index are indicated for each comparison for the four biomarkers in the tetra-plex and single-plex assays.

Supplementary Table 2. Diagnostic accuracy of the tetra-plex assay using PLS-DA in the discrimination of AD. PLS-DAs were constructed based on training datasets. Variable importance for the projection (VIP) criterion was used to identify which biomarkers contribute most on the classification performance. VIP scores estimate the contribution of each biomarker in the in the PLS-DA model, according to the variance explained by each PLS component. A biomarker with a VIP score close to or greater than 1 is considered important in the given model. Accuracy, sensitivity and specificity diagnostic measures are indicated. The training and test sets random partitions were generated 1000 times and statistical summaries (median, 2.5th and 97.5th quintiles, termed here 95% Confidence Interval) were computed for each diagnostic measure. Accuracies with random non informative data were obtained based on a permutation test involving 1000 data sets constructed by randomly reassigning class labels at each individual, then performing a PLS-DA on the new randomized training data sets and computing diagnostic measures in their respective 1000 randomized test sets. AD, Alzheimer’s disease; VaD, vascular dementia; DLB/PDD, dementia with Lewy bodies and Parkinson’s disease dementia; FTD, frontotemporal dementia. Aβ42, β-amyloid42; t-tau, total-tau, p-tau, phospho-tau and α-syn, α-synuclein.

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Diaz-Lucena, D., Escaramis, G., Villar-Piqué, A. et al. A new tetra-plex fluorimetric assay for the quantification of cerebrospinal fluid β-amyloid42, total-tau, phospho-tau and α-synuclein in the differential diagnosis of neurodegenerative dementia. J Neurol 267, 2567–2581 (2020). https://doi.org/10.1007/s00415-020-09870-9

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