Application of Skin Gas GC/MS Analysis for Prediction of the Severity Scale of Parkinson’s Disease

Abstract

Parkinson's disease (PD) is diagnosed by neurological examinations, as well as by scintigraphy for the dopamine transporter and metaiodobenzylguanidine (MIBG). We studied possible application of the skin gas in diagnosis of PD. We analyzed chemical substances emanated from the skin by gas chromatograph/mass spectrometer (GC/MS) after on-line-pre-concentrator. We analyzed the skin gas in 61 PD patients and 61 controls. The GC/MS chromatograms were sectionalized every 30 sec. The retention time drift was shifted every 5 sec, and a similarity coefficient (Z score) between a reference chromatogram and each shifted chromatogram was calculated. Chromatograms with high Z scores were excluded from our analysis. Models were made with partial least square (PLS), support vector machine (SVM), and support vector regression (SVR) analyses. PLS modeling to predict the Unified Parkinson's Disease Rating Scale part 3 (UPDRS3), representing motor deficits in PD, with all the detected mass numbers yielded 50 mass numbers with high PLS coefficients. Among the 50 mass numbers, nine mass numbers (m/e 48, 63, 67, 70, 81, 93, 96, 104, and 105) had dependable signal-to-noise ratios. We then generated an SVM model to differentiate PD and controls. Our SVM model had a sensitivity of 90.2% and a specificity of 85.2% by leave-one-out cross-validation (LOOCV) analysis. We next generated an SVR model to predict UPDRS3 with the nine mass numbers, and obtained a Pearson's correlation coefficient of 0.834. LOOCV analysis of our SVR model similarly gave rise to a correlation coefficient of 0.710. We propose that chemical substances in the skin gas potentially serve as biomarkers for PD.

A fast and non‐invasive artificial intelligence olfactory‐like system that aids diagnosis of Parkinson's disease

“…For instance, using GC-MS, Trivedi et al investigated volatile metabolites of sebum samples obtained from PD patients and HCs, revealing the existence of differential sebum VOCs and identifying a distinct volatiles-associated signature for PD [16]. In addition, Tsuda et al used GC-MS to analyze the scent compounds of sebum and found that the types and concentrations of VOCs from PD patient sebum were related to UPDRS-III [17]. Their diagnostic model had a sensitivity of 90.2% and a specificity of 85.2%.…”

“…In addition, Tsuda et al. used GC–MS to analyze the scent compounds of sebum and found that the types and concentrations of VOCs from PD patient sebum were related to UPDRS‐III [17]. Their diagnostic model had a sensitivity of 90.2% and a specificity of 85.2%.…”

Artificial Intelligent Olfactory System for the Diagnosis of Parkinson’s Disease

“…The fast, easy-to-use, and portable AIO system can balance sensitivity, specificity, linearity, dynamic range, detection limit, detection efficiency, and discrimination ability. Compared with the GC–MS, liquid chromatography–MS, or paper spray ionization coupled with ion mobility-MS, the AIO system greatly improves the detection speed and reduces the detection cost. ,,,, Compared with traditional clinical PD diagnosis methods, the AIO system is a fast and noninvasive method. It can also be widely used in hospitals, clinics, and homes as a PD patient screening or family self-health check method.…”

“…Trivedi used gas chromatography–mass spectrometry (GC–MS) to prove that there were different VOCs in the sebum of PD and healthy people . These VOCs such as perillic aldehyde and eicosane may change with the increase of sebum secretion, and the interaction between sebum and the yeast of the microbiome can make human skin smelly. − Besides, Tsuda used GC–MS to analyze the smell of sebum and found that the types and concentrations of VOCs such as dodecane, acetone, and ethyl acetate released in the sebum of PD patients were related to UPDRS part 3, representing the severity of motor in PD. These studies indicated that sebum gas could be used for the detection of PD.…”