Analysis of metabolic profiles of generalized aggressive periodontitis.

BACKGROUND AND OBJECTIVE

The specific pathogenesis of generalized aggressive periodontitis (GAgP) has not yet been clarified, and few studies have focused on the association between GAgP and metabolomics. To elucidate the roles of metabolic profiles in the status of GAgP, this study aimed to identify the differential metabolic profiles between patients with GAgP 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 GAgP (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 GAgP 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 GAgP. 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 GAgP, 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 GAgP. Both serum and gingival crevicular fluid metabolomics are significantly different between patients with GAgP and healthy controls. These metabolic profiles have great potential in detecting GAgP and helping to understand its underlying mechanisms.