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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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Optimizing a Diagnostic Model of Periodontitis by Using Targeted Proteomics.

Stefan Lars Reckelkamm1, Inga Kamińska2, Sebastian-Edgar Baumeister1

  • 1Institute of Health Services Research in Dentistry, University of Münster, Münster 48149, Germany.

Journal of Proteome Research
|June 3, 2023
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Summary

Serum protein profiling using proteomics can significantly improve the assessment of periodontitis (PD) risk. This novel approach enhances diagnostic accuracy without needing direct dental examination, offering scalable solutions for periodontitis evaluation.

Keywords:
periodontitisprediction modelproteomicsserum biomarkers

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Area of Science:

  • Oral Health Research
  • Proteomics
  • Biomarker Discovery

Background:

  • Periodontitis (PD) is a prevalent chronic infectious disease impacting oral health and linked to systemic conditions.
  • The utility of serum protein profiling for assessing PD risk remains unclear.
  • Existing PD risk assessment relies on established predictors, but lacks comprehensive molecular insights.

Purpose of the Study:

  • To evaluate the incremental benefit of serum protein profiling in assessing periodontitis (PD) risk.
  • To determine if proteomics enhances the predictive accuracy of PD risk models.
  • To explore the potential of proteomic technologies for scalable PD diagnostic applications.

Main Methods:

  • Collected general health data and performed dental examinations on 654 participants.
  • Generated serum protein profiles using Proximity Extension Assay technology.
  • Constructed and compared logistic regression models with and without proteomic data to assess PD risk.

Main Results:

  • Identified 14 specific proteins that significantly improved the global fit and discrimination of PD risk models.
  • The proteomic-enhanced model showed improved discrimination (AUC 0.86 vs 0.82) compared to the model with established risk factors alone.
  • The enhanced model maintained reasonable calibration, indicating reliable risk prediction.

Conclusions:

  • Serum protein profiling via proteomics offers a valuable advancement for periodontitis (PD) risk assessment.
  • Proteomic technologies provide a scalable and potentially non-invasive diagnostic approach for PD.
  • These findings suggest future applications in developing easy-to-use diagnostic tools for periodontitis.