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      Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood

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          Abstract

          <p id="d5629772e333">Vibrational spectroscopy is an ideal technique for analysis of biofluids, as it provides a “spectral fingerprint” of all of the molecules present within a biological sample, thus generating a holistic picture of the sample’s status. Neurodegenerative diseases lack early and accurate diagnosis, and tests currently used for their detection are either invasive or expensive and time-consuming. This study used blood plasma to diagnose and differentiate various neurodegenerative diseases; the achieved sensitivities and specificities are equal to, or even higher than, the ones obtained by clinical/molecular methods. Herein, we show that spectroscopy could provide a simple and robust diagnostic test. Additional work should include asymptomatic individuals for an early screening test and exploration of neurodegenerative diseases at all stages of severity. </p><p class="first" id="d5629772e336">The progressive aging of the world’s population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer’s disease (AD; <i>n</i> = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype ( <i>APOE</i> ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases ( <i>n</i> = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; <i>n</i> = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; <i>n</i> = 30), Parkinson’s disease (PD; <i>n</i> = 32), and progressive supranuclear palsy (PSP; <i>n</i> = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias. </p>

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          Most cited references29

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          The role of apolipoprotein E in Alzheimer's disease.

          The epsilon4 allele of apolipoprotein E (APOE) is the major genetic risk factor for Alzheimer's disease (AD). Although there have been numerous studies attempting to elucidate the underlying mechanism for this increased risk, how apoE4 influences AD onset and progression has yet to be proven. However, prevailing evidence suggests that the differential effects of apoE isoforms on Abeta aggregation and clearance play the major role in AD pathogenesis. Other potential mechanisms, such as the differential modulation of neurotoxicity and tau phosphorylation by apoE isoforms as well as its role in synaptic plasticity and neuroinflammation, have not been ruled out. Inconsistent results among studies have made it difficult to define whether the APOE epsilon4 allele represents a gain of toxic function, a loss of neuroprotective function, or both. Therapeutic strategies based on apoE propose to reduce the toxic effects of apoE4 or to restore the physiological, protective functions of apoE.
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            Using Fourier transform IR spectroscopy to analyze biological materials.

            IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.
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              Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues

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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                September 19 2017
                September 19 2017
                : 114
                : 38
                : E7929-E7938
                Article
                10.1073/pnas.1701517114
                5617251
                28874525
                eef7f954-4dc8-45c9-bd9d-41b879d18a6a
                © 2017
                History

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