Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-Surgical Periodontal Therapy. A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Pattern analysis of salivary metabolic profile has been proven accurate to discriminate generalized periodontitis (GP) patients from healthy individuals (HI) as disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data in the literature, the aim of this study was to determine whether non-surgical periodontal therapy (NST) could change salivary metabolomic profile in GP to one more similar to HI. Unstimulated whole saliva of 11 HI and 12 GP patients were obtained prior to and 3 months after NST. Metabolic profiling was performed using Nuclear Magnetic Resonance (NMR) spectroscopy, followed by supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis. In GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in discriminating the metabolomic profile of each GP patient before and after NST. OPLS was able to discriminate the 3 groups of subjects with an accuracy of 85.6%. However the post-NST metabolic profile of GP patients could not be completely assimilated to that of HS. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

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Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Journal of Clinical Medicine ◽

10.3390/jcm9123977 ◽

2020 ◽

Vol 9 (12) ◽

pp. 3977

Author(s):

Filippo Citterio ◽

Federica Romano ◽

Gaia Meoni ◽

Giovanni Iaderosa ◽

Silvia Grossi ◽

...

Keyword(s):

Nuclear Magnetic Resonance ◽

Magnetic Resonance ◽

Metabolic Profile ◽

Periodontal Therapy ◽

Partial Least Square ◽

Least Square ◽

Resonance Spectroscopy ◽

Metabolic Profiles ◽

Multivariate Statistical ◽

Nuclear Magnetic

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

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DISCRIMINATION OF DENGUE DISEASE FROM HEALTHY BASED ON THE CHEMOMETRY OF 1H NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY

Jurnal Teknologi ◽

10.11113/jt.v77.6998 ◽

2015 ◽

Vol 77 (33) ◽

Author(s):

Nurul Shahfizaa ◽

Maulidiani Maulidiani ◽

Hasnah Osman ◽

Tang T. Hock ◽

Khozirah Shaari ◽

...

Keyword(s):

Nuclear Magnetic Resonance ◽

Nmr Spectroscopy ◽

Magnetic Resonance ◽

Specific Treatment ◽

Partial Least Square ◽

Least Square ◽

Resonance Spectroscopy ◽

Healthy Individuals ◽

1H Nuclear Magnetic Resonance ◽

Nuclear Magnetic

Dengue is the most important human viral disease transmitted by arthropod vectors and over half of the world's populations live in areas at risk of infection. The severity of the infection depends on the form of the disease, which can be symptomatic or asymptomatic. Currently there is neither specific treatment nor vaccine to tackle this emerging disease. Metabolomics applied in this study, aimed to provide a global snapshot of all small-molecule metabolites in urine as biological sample of choice to more focused studies of metabolism to distinguish between healthy and dengue infected subjects. Fifty-two patients diagnosed with dengue fever at Penang General Hospital and fourty-three healthy individuals were recruited in this study. 1H-nuclear magnetic resonance (NMR) spectroscopy combined with multivariate analysis (MVA) methods such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were employed for statistical data exploration. The model score plot results showed that all three MVAs showed very good spatial distributions with clear clusters/grouping between healthy individuals and dengue infected individuals. Also, statistically, the PLS-DA and OPLS-DA models had high reproducibility and predictivity values, > 0.5. In conclusion, this study established the potential of using a combination of 1H NMR spectroscopy and multivariate data analyses in differentiating healthy and non-healthy individuals, based on obtained score plots reflecting the metabolites pertubation, where spectral features contributing most to variation or separation are identified for further analysis.

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