Salivary Biomarker Discovery by Raman Spectroscopy in ALS (SalivALS)

Study Overview
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Brief Summary
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BACKGROUND/RATIONALE: The paucity of biomarkers for the diagnosis and monitoring of patients affected by Amyotrophic Lateral Sclerosis (ALS) is one of the greatest concerns in ALS clinics and research. Phenotypic signs, electrophysiological test and clinical scales are currently used for ALS diagnosis and follow up before and after treatments. Nowadays, the diagnosis and differential diagnosis used to discriminate ALS from other comparable neurodegenerative diseases, are time-consuming and complex processes that reduce the time for a prompt intervention. Thus, the scientific community is asked to strive for new, measurable, fast and objective biomarkers for the diagnosis and stratification of patients. Saliva is a complex biofluid composed of bioactive molecules that can be collected by means of a non-invasive procedure. The possibility to simultaneously monitor all the variations in the endocrine, electrolytic and metabolic messengers in saliva has recently suggested its use for the diagnosis of complex diseases, like neurodegenerative diseases, but only limited information are available on the potential of saliva as alternative carrier of ALS biomarkers. OBJECTIVES: The aim of the present project is to optimize an innovative, non-invasive and fast procedure for the ALS onset and for the stratification of ALS patients, taking advantage of the sensitivity of Raman Spectroscopy (RS) and of accessible saliva. Fondazione Don Gnocchi (FDG) preliminary results on a small cohort of subjects demonstrated the feasibility of the methodology and the ability of LABION protocol to obtain a reproducible Raman fingerprint of saliva that can be used for the discrimination of healthy subjects, ALS patients and subjects affected by other types of neurological diseases. METHODS: Starting from FDG preliminary results, the biochemical composition of saliva in patients with diagnosed ALS will be evaluated and statistically compared with the one obtained from age and sex-matched healthy subjects and from patients affected by other neurological diseases (Parkinson's and Alzheimer's diseases). Moreover, an intra-group ALS clustering will be analysed in order to verify a different Raman fingerprint obtained from ALS patients with a bulbar or spinal onset. The collected Raman data will be processed using a multivariate analysis approach through Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA). The classification model will be created using cross-validation and subset validation. Thanks to RS, the overall composition of saliva will be established with minimal sample preparation, providing comprehensive biochemical fingerprint of the sample. In parallel, routine salivary parameters will be measured including viscosity, pH, total protein and carbohydrates concentration, amylase and pepsin, cortisol and Chromogranin A. EXPECTED RESULTS: By the end of this study, the investigators expected to verify the possibility to use the Raman salivary pattern as new promising biomarker for ALS diagnosis and progression to be related with clinical scales for the personalized and fine tuning of the therapeutic approach. The intent of this project is to create a classification model able to: Determine the ALS onset Discriminate the signal obtained from ALS patients from the one collected from other neurodegenerative diseases Stratify ALS patients into bulbar and spinal onset Correlate the Raman data with clinical and paraclinical scales used nowadays for ALS diagnosis and monitoring

Detailed Description
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SAMPLE COLLECTION: Saliva collection from all the selected subjects will be performed following the Salivette (SARSTEDT) manufacturer's instructions. To limit variability in salivary content not related to ALS, saliva will be obtained from all subjects at a fixed time, after an appropriate lag time from feeding and teeth brushing. Pre-analytical parameters (i.e. storage temperature and time between collection and processing), dietary and smoking habit will be properly recorded. Briefly, the swab will be removed, placed in the mouth and chewed for 60 seconds to stimulate salivation. Then the swab will be centrifuged for 2 minutes at 1,000 g to remove cells fragments and food debris. Collected samples will be stored at -80° C. SAMPLE PROCESSING: Before the Raman acquisition, saliva samples will be filtered with different cut-off ranges (3, 10 and 30 kDa), collecting and analysing by RS the eluted sample and discarding the concentrated counterpart. For the Raman analysis, a drop of each sample will be casted on an aluminium foil in order to achieve the Surface Enhanced Raman Scattering (SERS). DATA COLLECTION: Raman and SERS spectra will be acquired using an Aramis Raman microscope (Horiba Jobin-Yvon, France) equipped with a laser light source operating at 785 nm with laser power ranging from 25-100% (Max power 512 mW). Acquisition time between 10-30 seconds will be used. The instrument will be calibrated before each analysis using the reference band of silicon at 520.7 cm-1. Raman spectra will be collected from 15 points following a line-map from the edge to the centre of the drop. Spectra will be acquired in the region between 400 and 1800 cm-1 using a 50x objective (Olympus, Japan). Spectra resolution is about 1.2 cm-1. The software package LabSpec 6 (Horiba Jobin-Yvon, France) will be used for map design and the acquisition of spectra. DATA PROCESSING: All the acquired spectra will be fit with a fifth-degree polynomial baseline and normalized by unit vector using the dedicated software LabSpec 6. The contribution of the substrate will be removed from each spectra. The statistical analysis to validate the method, will be performed using a multivariate analysis approach. Principal Component analysis (PCA) will be performed in order to reduce data dimensions and to evidence major trends. The first 20 resultant Principal Components (PCs) will be used in a classification model, Linear Discriminant Analysis (LDA), to discriminate the data maximizing the variance between the selected groups. The smallest number of PCs will be selected to prevent data overfitting. Leave-one-out cross-validation and confusion matrix test will be used to evaluate the method sensitivity, precision and accuracy of the LDA model. Mann-Whitney will be performed on PCs scores to verify the differences statistically relevant between the analysed groups. Correlation and partial correlation analysis will be performed using the Spearman's test, assuming as valid correlation only the coefficients with a p-value lower than 0.05. The statistical analysis will be performed using Origin2018 (OriginLab, USA).

Official Title
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Salivary Raman Fingerprint as Biomarker for the Diagnosis of Amyotrophic Lateral Sclerosis

Conditions
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Amyotrophic Lateral Sclerosis

Intervention / Treatment
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* Procedure: Raman analysis of saliva with data collection and analysis


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosis of Amyotrophic Lateral Sclerosis (ALS) according to the El Escorial criteria and classification based on the main genetic mutations for the ALS group Diagnosis of Alzheimer's disease according to the ATN criteria for the Alzheimer's group Diagnosis of Parkinson's disease according to the Unified Parkinson's Disease Rating Scale and with Hoehn and Yahr scores for the Parkinson's group Male Age between 50 and 70 years Exclusion Criteria: Oncologic Diseases Immune and hematological diseases Bacterial or fungal infections in progress (e.g. oral candidiasis) Female Age less than 50 years or above 70 years

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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Male

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Healthy Subjects<br>20 Healthy subjects in a good state of health comparable by age and sex with the other selected groups | Procedure: Raman analysis of saliva with data collection and analysis<br>* Saliva collection and processing to all the enrolled subjects and consecutive Raman analysis of the biological fluid. Statistical processing of the collected data.<br>|
| Amyotrophic Lateral Sclerosis with Bulbar onset<br>20 subjects affected by Amyotrophic Lateral Sclerosis with Bulbar onset, comparable by age and sex with the other selected groups | Procedure: Raman analysis of saliva with data collection and analysis<br>* Saliva collection and processing to all the enrolled subjects and consecutive Raman analysis of the biological fluid. Statistical processing of the collected data.<br>|
| Amyotrophic Lateral Sclerosis with Spinal onset<br>20 subjects affected by Amyotrophic Lateral Sclerosis with Spinal onset, comparable by age and sex with the other selected groups | Procedure: Raman analysis of saliva with data collection and analysis<br>* Saliva collection and processing to all the enrolled subjects and consecutive Raman analysis of the biological fluid. Statistical processing of the collected data.<br>|
| Parkinson's Disease<br>20 subjects affected by Parkinson's Disease comparable by age and sex with the other selected groups | Procedure: Raman analysis of saliva with data collection and analysis<br>* Saliva collection and processing to all the enrolled subjects and consecutive Raman analysis of the biological fluid. Statistical processing of the collected data.<br>|
| Alzheimer's Disease<br>20 subjects affected by Alzheimer's Disease comparable by age and sex with the other selected groups | Procedure: Raman analysis of saliva with data collection and analysis<br>* Saliva collection and processing to all the enrolled subjects and consecutive Raman analysis of the biological fluid. Statistical processing of the collected data.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of a new ALS biomarker in the saliva of people affected by Amyotrophic Lateral Sclerosis (ALS) using Raman Spectroscopy | Raman spectroscopy will be used to identify a new salivary biomarker for the fast discrimination between healthy subjects and patients affected by ALS. Saliva samples will be collected and processed without any labeling procedure, investigating the overall salivary biochemistry of the two investigated groups. Using multivariate analysis on the collected data we will be able to create a classification model that can discriminate ALS signature from healthy subjects. | One day | 
| Evaluation of the spectral differences in the saliva of patients with different neurological diseases | The Raman data collected from ALS patients will be compared with the one obtained from patients affected by Alzheimer's and Parkinson's disease, validating our methodology as differential diagnostic tool for the discrimination of ALS from other neurological diseases | One day | 
| Evaluation of the spectral differences in the saliva of patients with bulbar or spinal ALS onset | The intra-group analysis of recruited spinal and bulbar ALS patients will reveal the Raman fingerprint associated to the specific pathological subtype. This result will be important for the stratification of ALS patients after the ALS diagnosis. | One Day | 
| Correlation of Raman data with clinical and paraclinical data | Raman data related to the ALS group will be correlated with clinical and paraclinical data, validating in this way our methodology. Collected data will be correlated primarily with factors that can alterate the biochemical composition of saliva including age, smoking habit, time before the last meal, time before teeth brushing, percutaneous endoscopic gastrostomy, mechanical ventilation and others. Consequently the same data will be analysed finding correlations with ALS-FRS (Amyotrophic Lateral Sclerosis - Functional Rating Scale), WHO-QOL (World of Health Organization - Quality of Life), blood analysis outcome, time from the diagnosis and other | One Day | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Raman Spectroscopy, Amyotrophic Lateral Sclerosis, Biomarker, Diagnosis