Analysis of metabolic profiles of generalized aggressive periodontitis

Background and Objective The specific pathogenesis of generalized aggressive periodontitis ( GA gP) has not yet been clarified, and few studies have focused on the association between GA gP and metabolomics. To elucidate the roles of metabolic profiles in the status of GA gP, this study aimed to identify the differential metabolic profiles between patients with GA gP and healthy controls using an untargeted metabolomic profiling method. Material and Methods Serum and gingival crevicular fluid samples were collected from healthy controls (n = 20) and patients with GA gP (n = 20) in this cross‐sectional study. The relative levels of biomarkers in the samples were measured by gas chromatography‐mass spectrometry. Principal components analysis and orthogonal partial least‐squares discriminant analysis were used for statistical analysis. Metabolites were analysed qualitatively using the FiehnLib and NIST databases. Full‐mouth probing depth and clinical attachment loss were recorded as indexes of periodontal disease. Results A total of 349 metabolites were qualitatively detected in the gingival crevicular fluid samples, and 200 metabolites were detected in the serum samples. Compared with healthy controls, patients with GA gP showed significant increases in serum urea and allo‐inositol levels. In contrast, glutathione, 2,5‐dihydroxybenzaldehyde, adipic acid and 2‐deoxyguanosine levels were decreased in patients with GA gP. In the gingival crevicular fluid samples, noradrenaline, uridine, α‐tocopherol, dehydroascorbic acid, xanthine, galactose, glucose‐1‐phosphate and ribulose‐5‐phosphate levels were increased in patients with GA gP, while thymidine, glutathione and ribose‐5‐phosphate levels were decreased. Conclusion The metabolomics analysis by gas chromatography‐mass spectrometry is an effective and minimally non‐invasive way to differentiate the metabolites characteristic of patients with GA gP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GA gP and healthy controls. These metabolic profiles have great potential in detecting GA gP and helping to understand its underlying mechanisms.