Alassy Hatem, Kersten Elias, Hamilton Janelle, Botorous Barbara, Iuorio Angelomaria, Rappe Todd, Costalonga Massimo
Division of Basic Sciences, Department of Diagnostic and Biological Sciences, School of Dentistry, University of Minnesota, Minneapolis, Minnesota, USA.
Private Practice, Aversa, Italy.
J Periodontal Res. 2025 Mar 26. doi: 10.1111/jre.13400.
Poor accuracy of diagnostic and prognostic tools prevents the prediction of peri-implant disease stability or progression. We analyzed metabolites from peri-implant crevicular fluid (PICF) samples from healthy and diseased implants to identify those diagnostic of health and peri-implant disease and predictive of peri-implant bone loss over time.
Clinical, radiographic examinations and PICF samples were collected from 59 healthy implants, 33 implants with peri-implantitis, and 38 implants with peri-implant mucositis in 71 subjects. A subset of implants was evaluated at 6, 12, 18, and 24 months. Over time, all initially healthy implants remained stable (Group B, N = 28), whereas 6 initially diseased implants continued to lose bone and 8 did not (Group C). PICF metabolites were measured using proton-nuclear magnetic resonance (1H-NMR) 2-dimensional Total Correlation Spectroscopy. PCA and PLS-DA tested the cross-sectional clustering and importance of each metabolite, while the AUC summarized the accuracy of predicting radiographic bone changes ≥ 1 mm at 6-month intervals.
At baseline, the Cadaverine/Lysine and Putrescine/Lysine signatures diagnosed peri-implantitis (AUC = 0.76 and 0.70; p < 0.000) with good accuracy, while alpha-ketoglutarate diagnosed implant health (AUC = 0.706; p = 0.002). Combining metabolites increased diagnostic accuracy (AUC = 0.81; p < 0.01). Proline and 1-3-diaminopropane predicted future bone loss (AUC = 0.917 and AUC = 0.854). ANOVA post hoc analysis established that biotin and propionate levels were higher in Group C compared to Groups A and B (p < 0.001; AUC = 0.889; AUC = 0.87). Valine levels were higher in Groups A and C compared to Group B (p = 0.002; AUC = 0.841).
H-NMR 2-dimensional spectroscopy identified PICF metabolites diagnostic of peri-implantitis with high accuracy. Despite the small number of affected implants, metabolite signatures that predict future bone loss in peri-implantitis appear to be different from those diagnostic of peri-implantitis.
诊断和预后工具的准确性较差，无法预测种植体周围疾病的稳定性或进展情况。我们分析了来自健康和患病种植体的种植体周围龈沟液（PICF）样本中的代谢物，以确定那些可诊断健康状况和种植体周围疾病，并能预测种植体周围骨量随时间流失情况的代谢物。
收集了71名受试者中59个健康种植体、33个患有种植体周炎的种植体和38个患有种植体周围黏膜炎的种植体的临床、影像学检查结果及PICF样本。对一部分种植体在6、12、18和24个月时进行评估。随着时间推移，所有最初健康的种植体保持稳定（B组，N = 28），而6个最初患病的种植体继续骨质流失，8个则没有（C组）。使用质子核磁共振（1H-NMR）二维全相关谱测量PICF代谢物。主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）测试了每种代谢物的横断面聚类情况及其重要性，而曲线下面积（AUC）总结了每隔6个月预测影像学骨变化≥1 mm的准确性。
在基线时，尸胺/赖氨酸和腐胺/赖氨酸特征对种植体周炎的诊断准确性良好（AUC = 0.76和0.70；p < 0.000），而α-酮戊二酸可诊断种植体健康状况（AUC = 0.706；p = 0.002）。联合代谢物可提高诊断准确性（AUC = 0.81；p < 0.01）。脯氨酸和1,3-二氨基丙烷可预测未来的骨量流失（AUC = 0.917和AUC = 0.854）。方差分析的事后检验表明，与A组和B组相比，C组中的生物素和丙酸盐水平更高（p < 0.001；AUC = 0.889；AUC = 0.87）。与B组相比，A组和C组中的缬氨酸水平更高（p = 0.002；AUC = 0.841）。
1H-NMR二维光谱法能高精度地识别出可诊断种植体周炎的PICF代谢物。尽管受影响的种植体数量较少，但预测种植体周炎未来骨量流失的代谢物特征似乎与诊断种植体周炎的特征不同。
