Association of hyperglycaemia with periodontal status: Results of the Northern Finland Birth Cohort 1966 study

Higher haemoglobin levels are associated with impaired periodontal status

AIM

Cellular oxygen sensing mechanisms have been linked to periodontal condition, and levels of haemoglobin (Hb) (the main carrier of oxygen) can be used as a surrogate measure for hypoxia. We aimed to examine relations between Hb levels and key periodontal health parameters in a general population.

MATERIALS AND METHODS

The population comprised 1711 (47% male) subjects from the Northern Finland Birth Cohort 1966, for whom an oral health examination was carried out at 46 years of age and whose Hb levels were within the Finnish reference values. Relative risks (RRs) were estimated using Poisson regression models.

RESULTS

The low-Hb tertile (mean Hb 133 g/L) had healthier anthropometric, metabolic and periodontal health parameters than the high-Hb tertile (mean Hb 151 g/L). Multivariable regression models adjusted for risk factors showed Hb levels to be positively associated with alveolar bone loss (ABL) and periodontal pocket depth (PPD), although the associations were weaker after adjustment for key metabolic parameters and were strongly influenced by smoking status.

CONCLUSIONS

Hb levels within the normal variation are positively associated with PPD and ABL. The association between Hb levels and periodontal condition appeared to be more complex than had previously been anticipated.

Deep learning for the early identification of periodontitis: a retrospective, multicentre study

AIM

To develop a deep-learning model to help general dental practitioners diagnose periodontitis accurately and at an early stage.

MATERIALS AND METHODS

First, the panoramic radiographs (PARs) from the Second Affiliated Hospital of Nanchang University were input into the convolutional neural network (CNN) architecture to establish the PAR-CNN model for healthy controls and periodontitis patients. Then, the PARs from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were included in the second testing set to validate the effectiveness of the model with data from two centres. Heat maps were produced using a gradient-weighted class activation mapping method to visualise the regions of interest of the model. The accuracy and time required to read the PARs were compared between the model, periodontal experts, and general dental practitioners. Areas under the receiver operating characteristic curve (AUCs) were used to evaluate the performance of the model.

RESULTS

The AUC of the PAR-CNN model was 0.843, and the AUC of the second test set was 0.793. The heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. The accuracy of the model, periodontal experts, and general dental practitioners was 0.800, 0.813, and 0.693, respectively. The time required to read each PAR by periodontal experts (6.042 ± 1.148 seconds) and general dental practitioners (13.105 ± 3.153 seconds), which was significantly longer than the time required by the model (0.027 ± 0.002 seconds).

CONCLUSION

The ability of the CNN model to diagnose periodontitis approached the level of periodontal experts. Deep-learning methods can assist general dental practitioners to diagnose periodontitis quickly and accurately.

Association of long-term obesity and weight gain with periodontal pocketing: Results of the Northern Finland Birth Cohort 1966 study

AIM

To investigate whether obesity, central obesity, and weight gain are associated with periodontal pocketing.

MATERIALS AND METHODS

A never-smoking sub-population (n = 725) of the Northern Finland Birth Cohort 1966 was categorized based on body mass index (BMI; participants with normal weight, overweight, and obesity) and waist circumference (WC; participants without central obesity and with central obesity) at ages 31 and 46. The categories were combined to define whether the participants stayed in the respective BMI and WC categories or moved on to a higher category during follow-up. A periodontal examination was done at age 46.

RESULTS

WC was more consistently associated with periodontal pocketing than BMI. The relative risks for the number of sites with periodontal pocket depth (PPD) ≥4 mm and bleeding PPD ≥4 mm in participants with central obesity both at age 31 and at age 46 were 1.7 (95% confidence interval [CI] 1.4-2.0) and 2.1 (95% CI 1.6-2.6). The corresponding values for participants who had no central obesity at age 31 but had central obesity at age 46 were 1.6 (95% CI 1.4-1.8) and 1.9 (95% CI 1.6-2.3).

CONCLUSION

Of all the studied measures, central obesity appeared to be most strongly associated with the inflammatory condition of the periodontium.

Gut Microbiota May Mediate the Influence of Periodontitis on Prediabetes

Mounting evidence has shown that periodontitis is associated with diabetes. However, a causal relationship remains to be determined. Recent studies reported that periodontitis may be associated with gut microbiota, which plays an important role in the development of diabetes. Therefore, we hypothesized that gut microbiota might mediate the link between periodontitis and diabetes. Periodontitis was induced by ligatures. Glycemic homeostasis was evaluated through fasting blood glucose (FBG), serum glycosylated hemoglobin (HbA1c), and intraperitoneal glucose tolerance test. Micro-computed tomography and hematoxylin and eosin staining were used to evaluate periodontal destruction. The gut microbiota was analyzed using 16S ribosomal RNA gene sequencing and bioinformatics. Serum endotoxin, interleukin (IL) 6, tumor necrosis factor α (TNF-α), and IL-1β were measured to evaluate the systemic inflammation burden. We found that the levels of FBG, HbA1c, and glucose intolerance were higher in the periodontitis (PD) group than in the control (Con) group (P < 0.05). When periodontitis was eliminated, the FBG significantly decreased (P < 0.05). Several butyrate-producing bacteria were decreased in the gut microbiota of the PD group, including Lachnospiraceae_NK4A136_group, Eubacterium_fissicatena_group, Eubacterium_coprostanoligenes_group, and Ruminococcaceae_UCG-014 (P < 0.05), which were negatively correlated with serum HbA1c (P < 0.05). Subsequently, the gut microbiota was depleted using antibiotics or transplanted through cohousing. Compared with the PD group, the levels of HbA1c and glucose intolerance were decreased in the gut microbiota-depleted mice with periodontitis (PD + Abx) (P < 0.05), as well as the serum levels of endotoxin and IL-6 (P < 0.05). The serum levels of IL-6, TNF-α, and IL-1β in the PD + Abx group were higher than those of the Con group (P < 0.05). Antibiotics exerted a limited impact on the periodontal microbiota. When the PD mice were cohoused with healthy ones, the elevated FBG and HbA1c significantly recovered (P < 0.05), as well as the aforementioned butyrate producers (P < 0.05). Thus, within the limitations of this study, our data indicated that the gut microbiota may mediate the influence of periodontitis on prediabetes.