Comparative Evaluation of the Lipid Profile in the Serum of Patients with Type II Diabetes Mellitus and Healthy Individuals with Periodontitis

Exploring the bi-directional relationship between periodontitis and dyslipidemia: a comprehensive systematic review and meta-analysis

AIM

As periodontitis and dyslipidemia are diseases that occur with high incidence, the relationship between them has attracted much attention. Previous studies on these diseases have tended to focus on lipid parameters and periodontitis, we aimed to investigate the relationship between dyslipidemia and periodontitis.

MATERIALS AND METHODS

A comprehensive search to identify the studies investigating the relationship between dyslipidemia and periodontitis was performed on PubMed, Web of Science and Cochrane Library before the date of August, 2023. Studies were considered eligible if they contained data on abnormal blood lipid parameters and periodontitis. Studies that reported mean differences and 95% confidence intervals or odds ratios were used.

RESULTS

A total of 73 publications were included in the meta-analysis. Hyper total cholesterol (TC), triglycerides (TGs), low-density lipoprotein (LDL), very low-density lipoprotein (VLDL) and lower high-density lipoprotein (HDL) levels are risk factors for periodontitis. Periodontal disease is a risk factor for high TG and low HDL levels. Three months after periodontal treatment, the levels of TC, TG and HDL were significantly improved, and statin treatment only improved gingival index (GI) levels compared to that of the dietary control.

CONCLUSIONS

The findings reported here suggest that the mutual promotion of periodontitis and dyslipidemia can be confirmed. Non-surgical periodontal therapy may improve lipid abnormalities. It can't be demonstrated whether systematic application of statins have a better effect on the improvement in periodontal status in patients with dyslipidemia compared to that of the control.

The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and prevalence of periodontitis among US adults: a cross-sectional NHANES study

The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is a recently developed lipid parameter, but there is currently a lack of research exploring its relationship with periodontitis. This study aims to identify the potential association between NHHR and periodontitis. The association between NHHR and periodontitis were examined through univariate and multivariate weighted logistic regression utilizing the National Health and Nutrition Examination Survey data from 2009 to 2014. The participants were grouped based on the type of periodontitis. This study included a total of 9023 participants, with 1947 individuals having no periodontitis, and an additional 7076 individuals suffering from periodontitis. Patients in periodontitis group demonstrated a statistically significant elevation in NHHR values 2.82 (2.05-3.80) compared to those in no periodontitis group (p < 0.001). Logistic regression analysis of variables demonstrated a positive association between NHHR and periodontitis [1.07 (1.02, 1.12) p = 0.0067]. The study revealed a positive association between NHHR and an elevated prevalence of periodontitis development. For each unit increase in NHHR, there is a 7% increase in the prevalence of periodontitis. Further investigations into NHHR may enhance our understanding of preventing and treating periodontitis. However, additional studies are required to validate these findings.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is associated with periodontitis. Currently, there are few studies proposing predictive models for periodontitis in patients with T2DM.

AIM

To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models.

METHODS

In this a retrospective study, 300 patients with T2DM who were hospitalized at the First People's Hospital of Wenling from January 2022 to June 2022 were selected for inclusion, and their data were collected from hospital records. We used logistic regression to analyze factors associated with periodontitis in patients with T2DM, and random forest and logistic regression prediction models were established. The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

Of 300 patients with T2DM, 224 had periodontitis, with an incidence of 74.67%. Logistic regression analysis showed that age [odds ratio (OR) = 1.047, 95% confidence interval (CI): 1.017-1.078], teeth brushing frequency (OR = 4.303, 95%CI: 2.154-8.599), education level (OR = 0.528, 95%CI: 0.348-0.800), glycosylated hemoglobin (HbA1c) (OR = 2.545, 95%CI: 1.770-3.661), total cholesterol (TC) (OR = 2.872, 95%CI: 1.725-4.781), and triglyceride (TG) (OR = 3.306, 95%CI: 1.019-10.723) influenced the occurrence of periodontitis (P < 0.05). The random forest model showed that the most influential variable was HbA1c followed by age, TC, TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showed that in the training dataset, the AUC of the random forest model was higher than that of the logistic regression model (AUC = 1.000 vs AUC = 0.851; P < 0.05). In the validation dataset, there was no significant difference in AUC between the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915; P > 0.05).

CONCLUSION

Both random forest and logistic regression models have good predictive value and can accurately predict the risk of periodontitis in patients with T2DM.

Association between periodontitis and hyperlipidaemia: A systematic review and meta-analysis

To date, it has been reported that periodontitis (PD) may be associated with hyperlipidaemia in clinical practice. However, data on this issue are inconsistent and controversial. The purpose of this meta-analysis was to identify the association between PD and hyperlipidaemia. Here, 21 case-control and eight cross-sectional studies on PD and hyperlipidaemia were included in the random-effects meta-analysis, involving 2060 patients with PD and 2776 healthy controls (HC). Meta-analysis showed that serum triglyceride (TG) and total cholesterol (TC) levels in the PD group were significantly higher than those in the HC group [TG, weighted mean difference (WMD) = 19.4 mg/dL, 95% confidence interval (CI) 13.3-25.5 mg/dL, P = .000; TC, WMD = 15.4 mg/dL, 95%CI 10.2-20.6 mg/dL, P = .000]. Subgroup analysis stratified by study design validated that PD was associated with higher serum TG and TC levels. In addition, compared with the HC group, serum low-density lipoprotein (LDL) in patients with PD showed a markedly higher level (WMD = 11.7 mg/dL, 95% CI 8.3-15.0 mg/dL, P = .000), whereas serum high-density lipoprotein (HDL) in PD group exhibited a significantly lower level (WMD = -4.5 mg/dL, 95%CI -6.4 - -2.7 mg/dL, P = .000). Finally, no significant publication bias was observed and sensitivity analysis also confirmed the stability of our meta-analyses. In conclusion, the accumulated evidence suggests that PD is indeed associated with hyperlipidaemia in humans. More interventions for lowering lipids or increasing HDL may benefit the patients with PD, which need be further investigated in prospective clinical trials.