Photodiagnosis and photodynamic therapy · 2022
Saliva may predict quality of life in psoriasis as measured by Fourier transform infrared spectroscopy (FTIR) and chemometrics.
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da Silva Jaquelini Barboza, de Carvalho André Esteves Vicente, Schneider Carolina, Corbellini Valeriano Antonio
Аннотация
BACKGROUND: Psoriasis is a chronic skin disease, with several comorbidities, such as psoriatic arthritis, inflammatory bowel disease, metabolic syndrome, and impaired quality of life and work activity. The Dermatology Life Quality Index (DLQI) is the most commonly used quality of life index in psoriatic patients, as it is a marker of severe disease. This study evaluated the association between salivary Fourier transform Infrared Spectroscopy (FTIR) metabolic fingerprints and severity of psoriasis as measured by DLQI, using chemometric algorithms.
MATERIALS AND METHODS: Saliva was collected from 56 (27 with DLQI ≤ 10 [GI]; 29 with DLQI > 10 [GII]) psoriatic patients diagnosed and assessed by DLQI for disease severity by a dermatologist and analyzed by the transflectance technique in mid-infrared. Hierarchic cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and orthogonal partial least squares (OPLS) algorithms were used to associate salivary FTIR spectra with the respective DLQI scores.
RESULTS: Second derivative (2D) discriminated GI and GII at 2522 cm-1 (p < 0.0001). HCA and PCA partially discriminated GI from GII at 4000-450 cm-1 (p = 0.042 and 0.00821, respectively). Data processing with 1st derivative (1D), 3 latent variables (LV) and 1 orthogonal signal correction (OSC) component at 2550-1801 cm-1 generated an FTIR/OPLS-DA model with 100% accuracy to classify the severity of psoriasis, and an FTIR/OPLS model to quantify DLQI (range 0-28) with high performance: root mean square error of prediction (RMSEP) < 0.01 and coefficient of determination (R2) > 0.9999.
CONCLUSIONS: Salivary FTIR combined with chemometric algorithms such as OPLS-DA and OPLS can be used as a clinical tool to classify or predict the severity of psoriasis according to DLQI in patients with confirmed psoriasis.
MATERIALS AND METHODS: Saliva was collected from 56 (27 with DLQI ≤ 10 [GI]; 29 with DLQI > 10 [GII]) psoriatic patients diagnosed and assessed by DLQI for disease severity by a dermatologist and analyzed by the transflectance technique in mid-infrared. Hierarchic cluster analysis (HCA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and orthogonal partial least squares (OPLS) algorithms were used to associate salivary FTIR spectra with the respective DLQI scores.
RESULTS: Second derivative (2D) discriminated GI and GII at 2522 cm-1 (p < 0.0001). HCA and PCA partially discriminated GI from GII at 4000-450 cm-1 (p = 0.042 and 0.00821, respectively). Data processing with 1st derivative (1D), 3 latent variables (LV) and 1 orthogonal signal correction (OSC) component at 2550-1801 cm-1 generated an FTIR/OPLS-DA model with 100% accuracy to classify the severity of psoriasis, and an FTIR/OPLS model to quantify DLQI (range 0-28) with high performance: root mean square error of prediction (RMSEP) < 0.01 and coefficient of determination (R2) > 0.9999.
CONCLUSIONS: Salivary FTIR combined with chemometric algorithms such as OPLS-DA and OPLS can be used as a clinical tool to classify or predict the severity of psoriasis according to DLQI in patients with confirmed psoriasis.