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Raman spectroscopy to diagnose Alzheimer’s disease and dementia with Lewy bodies in blood Maria Paraskevaidi, Camilo L. M. Morais, Diane E. Halliwell, David M. A. Mann, David Allsop, Pierre L Martin-Hirsch, and Francis Luke Martin ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00198 • Publication Date (Web): 04 Jun 2018 Downloaded from http://pubs.acs.org on June 7, 2018
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ACS Chemical Neuroscience
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Raman spectroscopy to diagnose Alzheimer’s disease and
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dementia with Lewy bodies in blood
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Maria Paraskevaidia,*, Camilo L. M. Moraisa, Diane E. Halliwella, David M. A. Mannb, David
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Allsopc, Pierre L. Martin-Hirschd and Francis L. Martina,*
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a
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PR1 2HE, UK
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b
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University of Manchester, Greater Manchester Neurosciences Centre, Salford Royal Hospital,
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Salford M6 8HD, UK
School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston
Division of Neuroscience and Experimental Psychology, School of Biological Sciences,
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c
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University, Lancaster LA1 4YQ, UK
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d
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Foundation Trust, Preston PR2 9HT, UK
Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster
Department of Obstetrics and Gynaecology, Central Lancashire Teaching Hospitals NHS
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*
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[email protected] To
whom
correspondence
should
be
addressed:
[email protected] 1 ACS Paragon Plus Environment
or
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Abstract
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Accurate identification of Alzheimer’s disease (AD) is still of major clinical importance
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considering the current lack of non-invasive and low-cost diagnostic approaches. Detection of
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early-stage AD is particularly desirable as it would allow early intervention and/or
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recruitment of patients into clinical trials. There is also an unmet need for discrimination of
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AD from dementia with Lewy bodies (DLB), as many cases of the latter are misdiagnosed as
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AD. Biomarkers based on a simple blood test would be useful in research and clinical
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practice. Raman spectroscopy has been implemented to analyse blood plasma of a cohort that
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consisted of early-stage AD, late-stage AD, DLB and healthy controls. Classification
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algorithms achieved high accuracy for the different groups: early-stage AD vs healthy with
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84% sensitivity, 86% specificity; late-stage AD vs healthy with 84% sensitivity, 77%
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specificity; DLB vs healthy with 83% sensitivity, 87% specificity; early-stage AD vs DLB
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with 81% sensitivity, 88% specificity; late-stage AD vs DLB with 90% sensitivity, 93%
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specificity; and lastly, early-stage AD vs late-stage AD 66% sensitivity and 83% specificity.
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G-score values were also estimated between 74-91%, demonstrating that the overall
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performance of the classification model was satisfactory. The wavenumbers responsible for
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differentiation were assigned to important biomolecules which can serve as a panel of
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biomarkers.
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neurodegeneration in dementias.
These
results
suggest
a
cost-effective,
blood-based
biomarker
for
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Keywords: Alzheimer’s disease; Dementia with Lewy bodies; Raman spectroscopy; blood
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plasma; biomarkers 2 ACS Paragon Plus Environment
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ACS Chemical Neuroscience
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Introduction
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Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) constitute the two
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most common causes of dementia. AD and DLB can share common symptoms and clinical
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characteristics, which can lead to misdiagnosis. A clear distinction between these two causes
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of dementia is necessary in terms of pharmacological treatment and outcome evaluation
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The neuropathological hallmarks of AD include senile plaques (containing accumulated
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amyloid-beta (Aβ) peptide) and neurofibrillary tangles (composed of hyperphosphorylated
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tau protein), while in DLB the hallmark pathology is the abnormal aggregation of α-synuclein
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into Lewy bodies and Lewy neurites
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pathological features in very early stages (i.e., prodromal disease), or even before symptoms
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occur (i.e., pre-clinical disease), would allow an earlier intervention before irreversible
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neuronal death occurs, as well as facilitating early recruitment into clinical trials.
1, 2
.
3, 4
. The ability to index the presence of these
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Accurate detection of dementia is essential for improving the lives of those affected.
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Current diagnostic approaches employ neuroimaging techniques, such as magnetic resonance
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imaging (MRI) and positron emission tomography (PET) scans (amyloid-PET and more
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recently tau-PET), or cerebrospinal fluid (CSF) biomarkers, but these methods have many
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limitations
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memory and psychological tests is often required for diagnosis, but not all pathologically
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similar cases will present with the same “clinical phenotype”; many studies have shown
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contradictory results regarding the suitability of these biomarkers for accurate diagnosis.
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Recently, blood biomarkers have emerged as a potential means to test for neurodegenerative
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diseases, with some being capable of detecting early-stage disease
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the use of blood samples is based on the daily release of 500 ml CSF into the bloodstream,
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which potentially renders blood a rich source of brain biomarkers 12.
5-8
. A combination of family and clinical history, as well as a series of different
3 ACS Paragon Plus Environment
9-11
. The rationale behind
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Raman is a spectroscopic technique that extracts biological information by applying a
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monochromatic, laser light onto the sample under interrogation; electrons are thus excited to
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virtual energy levels. When these electrons return to the original energy level, in the form of a
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photon, there is no energy shift (known as elastic or Rayleigh scattering), whereas when they
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return to a lower or a higher energy level there is a gain or loss of energy, respectively
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(known as inelastic or Raman scattering) 13. The shift in the energy allows the generation of a
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spectrum which is indicative of the chemical bonds present in the sample. The characteristic
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spectra that are derived from Raman spectroscopy, represent a number of different
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biomolecules within a sample (e.g., proteins, carbohydrates, lipids, DNA) 14. Recent studies
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have employed Raman spectroscopy to study different diseases, such as malaria, oral and
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colorectal cancer, in biological fluids 15-17.
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The aim of the present study was to diagnose patients with Alzheimer’s disease, in
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early and late disease stages, and patients with DLB, as well as to discriminate between AD
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and DLB. To achieve this, blood plasma was analysed with Raman spectroscopy as a
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minimally invasive procedure that would also allow repeated measurements for follow-up of
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individuals.
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Results
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We enrolled 56 individuals into this study who were classified into 4 groups; early
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stage AD (n=11; age range: 50-74 years), late stage AD (n=15; age range: 50-79 years), DLB
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(n=15; age range: 23-73 years) and healthy controls (n=15; age range: 23-73 years) (Table 1).
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Early and late-stage AD was defined according to the duration of illness, from designated age
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at onset up to age at sample collection. P-values were calculated based on age and statistical
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differences were detected only for the following two subgroup comparisons: Late AD vs
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Healthy (P=0.004) and DLB vs Healthy (P 0.005) (Supplementary Table 1). Even
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though there was age difference between the controls and AD individuals, no correlation was
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observed between age and AD spectra after using partial least squares regression (R2 = 0.107,
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2 latent variables with 99.93% cumulative explained variance) and no statistical difference
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was observed in the spectra of AD patients with age lower and higher than 54 years of age
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(average control age) with a 95% confidence level (P> 0.005). This indicates that age did not
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affect the spectral distribution within the AD class. Similarly, no statistical differences were
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observed in the Raman spectra of the different groups due to gender (male vs female)
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(Supplementary Fig. 8).
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Early stage AD vs healthy individuals.
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principal component analysis followed by linear discriminant analysis (PCA-LDA) was
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applied to the derived dataset. A one-dimensional (1D) scores plot was generated to account
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for differences and similarities between early stage AD and healthy subjects (Fig. 1A); after
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statistical analysis, the two classes showed significant differences (P
