Raman Analysis of Saliva as Biomarker of COPD

  • End date
    Jan 1, 2023
  • participants needed
  • sponsor
    Fondazione Don Carlo Gnocchi Onlus
Updated on 27 January 2021


Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and chronic lung syndrome that causes accelerated lung function decline and death in 20% of cases. The worsening of symptoms, as well as the patient's condition, strictly depends on the identified COPD phenotype, severity stages, exacerbation events, selected drugs, rehabilitation cures and on the adherence of patients to these therapies. Despite the efficient COPD diagnostic procedure, a new fast, sensitive and easily applicable approach must be developed in order to achieve the specific evaluation and monitoring of therapy adherence in COPD patients, stratifying the different COPD phenotypes and foreseeing the exacerbation events in order to optimize the COPD disease management. The application of Raman spectroscopy on saliva has been already proposed for different infective, neurological and cancer diseases, with promising results in the diagnostic and monitoring fields, representing saliva an easy collectable and highly informative biofluid. In this project we propose a combined Raman Spectroscopy - Machine Learning analysis of saliva collected from COPD patients and non-pathological and pathological controls for the development of a multifactorial device able to provide fast and sensitive information regarding COPD phenotypes, exacerbation risks, adherence and effectiveness of pharmacological and rehabilitation therapies, achieving the crucial target of the personalized medicine. Moreover, after the model development, we propose to test the Raman approach in hospital evaluating the creation of a COPD point of care, accompanying the clinicians in the disease management. Starting from FDG preliminary results, the biochemical composition of saliva in patients with diagnosed COPD will be evaluated and statistically compared with the one obtained from age and sex-matched healthy subjects and from patients affected by other respiratory chronic diseases (Asthma). Moreover, an intra-group COPD clustering will be analysed in order to verify a different Raman fingerprint obtained from COPD patients with different phenotypes. The collected Raman data will be processed using a multivariate analysis approach. 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 a comprehensive biochemical fingerprint of the sample. The expected results are I) Identification of the specific COPD Raman fingerprint through the comparison with healthy subjects and patients affected by asthma; II) Monitoring of the therapy adherence through the drug signal and/or biochemical modification in saliva; III) Stratification of the 4 COPD phenotypes on the base of the provided Raman fingerprint; IV) Monitoring of the rehabilitation procedures and effects; V) Association of an high exacerbation risk index to specific COPD patients; VI) Creation of a classification model of the created Raman database.


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 COPD, 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: 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: 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 35 points following a line-map from the edge to the centre of the drop. Spectra will be acquired in the region between 400 and 1600 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 fourth-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).

Condition Chronic Obstructive Lung Disease, COPD (Chronic Obstructive Pulmonary Disease), chronic obstructive pulmonary disease, COPD, chronic obstructive pulmonary disease (copd)
Treatment Collection and Raman analysis of saliva for the database
Clinical Study IdentifierNCT04628962
SponsorFondazione Don Carlo Gnocchi Onlus
Last Modified on27 January 2021


Yes No Not Sure

Inclusion Criteria

COPD patients will be defined as a postbronchodilator ratio of FEV1/FEV <0.7. The severity of airflow limitation and phenotypes will be defined as described by the GOLD grading system, including Grade 2, 3 or 4
Overlapped Asthma - COPD will be established by the presence of a combination of the following factors: history of asthma and/or atopy, reversibility in the bronchodilator test, notable eosinophilia in respiratory and/or peripheral secretions, high IgE, positive prick test to pneumoallergens and high concentrations of exhaled NO
Sex and age matched HC and AsP (bronchial asthma according to The Global Strategy for Asthma Management and Prevention 2018 from at least 6 months) will be recruited as controls

Exclusion Criteria

Exclusion criteria will be the combination with obstructive sleep apnea, cancer, MMSE<24, at least 4 weeks from the last acute exacerbation, cardiovascular, neurologic and kidney diseases, age<18
Bacterial or fungal oral infections in progress
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