Published in:
Open Access 01-12-2018 | Research article
Relationship between the burden of major periodontal bacteria and serum lipid profile in a cross-sectional Japanese study
Published in: BMC Oral Health | Issue 1/2018
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Background
The association of periodontal bacteria with lipid profile alteration remains largely unknown, although it has been suggested that chronic periodontitis increases the atherosclerotic risk. This cross-sectional study investigated the relationship between the prevalence and total burden of periodontal bacteria and serum lipid profile.
Methods
Saliva from enrolled participants was collected to detect 4 major periodontal bacteria (Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Prevotella intermedia) using Polymerase Chain Reaction method. High-density lipoprotein (HDL) cholesterol, triglycerides (TG), and low-density lipoprotein cholesterol were assessed using blood samples. We compared the averages of each lipid in association with the prevalence of each bacterial species, their burden (low, moderate, and high), and the combination of bacterial burden and periodontal status, defined as periodontitis, using the Community Periodontal Index, after adjustment for other potential confounding factors, by employing general linear models with least square means.
Results
A total of 385 Japanese individuals (176 men, 209 women; mean age 69.2 years) were enrolled. The number of bacterial species and their co-existence with periodontitis were significantly related to a decrease in HDL (p for trend < 0.01) and increase in TG (p for trend = 0.04). The adjusted mean HDL levels (mg/dL) in individuals with low, moderate, and high levels of bacterial species were 66.1, 63.0, and 58.9, respectively, and those in the 6 groups defined by combination of the two factors were 67.9, 64.6, 64.3, 65.4, 61.5, and 54.7, respectively.
Conclusion
Periodontal bacterial burden is suggested to be independently involved in lowering serum HDL level. Our findings suggest that bacterial tests in a clinical setting could be a useful approach for predicting the risk of HDL metabolism dysregulation.