Proton‐Nuclear Magnetic Resonance Metabolomics of Gingival Crevicular Fluid During Orthodontic Tooth Movement With Aligners - Domini, Maria Chiara; Castroflorio, Tommaso; Deregibus, Andrea; Ravera, Serena; Migliaretti, Giuseppe; Costalonga, Massimo (Orthodontics & craniofacial research, 2025)
ABSTRACT Objectives To determine the correlation between orthodontic tooth movement and a pre‐defined set of gingival crevicular fluid (GCF) metabolites through proton nuclear magnetic resonance (1H‐NMR) spectroscopy. Materials and Methods A clinical randomised prospective split‐mouth study comparing the GCF metabolites around stationary and moving second maxillary molars. Twenty‐four healthy subjects diagnosed with dental class II malocclusion undergoing orthodontic clear aligner treatment (CAT) were enrolled. GCF samples from the mesial and distal sulcus of second molars under stationary conditions or under 1 N of distalising force were harvested at baseline, 1 h, 7 days and 21 days after the application of CAT. 1H‐NMR was utilised for GCF sample analysis. The 2‐dimensional total correlation spectroscopy spectral signature of 35 known GCF metabolites was compared in moving and stationary teeth. Principal component analysis (PCA), partial least squares‐discriminant analysis (PLS‐DA), variable importance in projection (VIP) score and area under the curve (AUC) were computed utilising MetaboAnalyst 5.0 software. Results VIP‐score values showed statistically significant differences between the metabolites involved in moving and stationary molars (p < 0.05). PCA and PLS‐DA results showed potential differences between the metabolite clusters. The variation of the 1H‐NMR signals of Glutamine, Uracil, N‐Acetylneuraminate and alpha‐ketoglutarate contributes primarily to the variance across metabolites in moving versus stationary teeth at 1 h, 7 days and 21 days. Conclusion High values of Glutamine and low values of Uracil, N‐Acetylneurinamate and alpha‐ketoglutarate could be utilised to predict the progress of orthodontic tooth movement over time. Knowledge of metabolites predictive of tooth movement could contribute to the design of tailored orthodontic treatment planning, reducing time, costs and side‐effects.