Response of chronic gingivitis to hygiene therapy and experimental gingivitis. Clinical, microbiological and metabonomic changes.

PURPOSE: To compare the clinical, microbiological and metabonomic profiles of subjects with high and low levels of chronic gingival bleeding during a controlled oral hygiene regimen intervention including sequential phases of rigorous therapeutic oral hygiene followed by experimental gingivitis (EG). METHODS: Two cohorts of qualified study subjects with differences in gingival bleeding on probing levels at their baseline clinical examination were entered into the study. These two cohorts were followed through three separate study phases including a 1-week baseline phase, a 2-week phase of rigorous oral hygiene including dental prophylaxis, and a 3-week EG phase of no oral hygiene to encourage relapse of gingivitis. The 58 subjects were assessed during each phase of the study for clinical presentation of gingivitis and concurrently had plaque sampled for real-time polymerase chain reaction (RTPCR) microbiological characterization and salivary lavage samples for 'systems biology' metabonomics assessment by 1H-NMR. RESULTS: Subjects presenting with different levels of gingival bleeding on probing when they entered the study responded differently to rigorous oral hygiene and EG. Specifically, the high bleeding cohort responded sluggishly to rigorous oral hygiene and exhibited markedly greater relapse to gingivitis during EG. RTPCR analysis showed changes in bacterial populations that were associated with study phases, particularly the increases in putative periodontal pathogens during EG. However, the microbiological profiles of high- and low-susceptibility gingival bleeding patients were largely similar. Metabonomic analysis likewise revealed significant changes in metabolite composition during study phases associated with differences in plaque toxicity, especially the short chain carboxylic acids propionate and n-butyrate, which tracked clinical changes in gingivitis severity. Systems analysis of metabonomic changes suggested differences between cohorts, although analysis to date has not elucidated whether these differences are causative (population predictive) or simply diagnostic of clinical status within populations.

Molecular factor analysis applied to collections of NMR spectra.

It is often useful to identify and quantify mixture components by analyzing collections of NMR spectra. Such collections arise in metabonomics and many other applications. Many mixtures studied by NMR can contain hundreds of compounds, and it is challenging to analyze the resulting complex spectra. We have approached the problem of separating signals from different molecules in complex mixtures by using self-modeling curve resolution as implemented by the alternating least-squares algorithm. Alternating least squares uses nonnegativity criteria to generate spectra and concentrations from a collection of mixture spectra. Compared to previous applications of alternating least squares, NMR spectra of complex mixtures possess unique features, such as large numbers of components and sample-to-sample variability in peak positions. To deal with these features, we developed a set of data preprocessing methods, and we made modifications to the alternating least-squares algorithm. We use the term "molecular factor analysis" to refer to the preprocessing and modified alternating least-squares methods. Molecular factor analysis was tested using an artificial data set and spectra from a metabonomics study. The results show that the tools can extract valuable information on sample composition from sets of NMR spectra.