Periodontal conditions, retinopathy, and serum markers in individuals with type 1 diabetes

Diabetic Retinopathy and Periodontitis: Implications from a Systematic Review and Meta-Analysis

BACKGROUND

Diabetes mellitus, a chronic metabolic disorder affecting millions worldwide, is associated with microvascular complications, including diabetic retinopathy (DR) and periodontitis. Understanding their interrelationship is crucial for comprehensive patient care.

OBJECTIVE

This systematic review and meta-analysis aim to investigate the association between DR and periodontitis in patients with Type 1 and Type 2 diabetes.

METHODOLOGY

Using the PECO framework, we searched PubMed/MEDLINE, Scopus, EMBASE, Google Scholar, and Web of Science databases (Inception to April 2023) for studies on the association between DR and periodontitis. Ten studies (n = 1828 participants), including observational and cross-sectional studies, met the inclusion criteria. We conducted qualitative synthesis, risk of bias analysis using the ROBINS-E tool, Grading of Recommendations, Assessment, Development, and Evaluations assessment (GRADE), and random-effects meta-analysis.

RESULTS

Eight studies found a significant association between severe periodontitis (pocket depth ≥5 mm) and DR, while two found no association. Meta-analysis of 843 participants showed diabetics with periodontitis had 4.48 times higher odds (95% confidence interval: 1.67-12.07, P = .003) of developing retinopathy compared to diabetics without periodontitis. High heterogeneity was observed (I2 = 86%). Subgroup analysis by diabetes type showed no significant difference. The overall GRADE level of evidence was very low.

CONCLUSION

While most included studies suggest an association between severe periodontitis and increased DR risk, the overall certainty of evidence is low. These findings highlight the potential importance of periodontal health in diabetic patients. High-quality longitudinal studies with adequate control of confounders are required to determine if periodontitis contributes to the progression of DR or if the conditions are merely coincidentally related.

Screening and evaluation of diabetic retinopathy via a deep learning network model: A prospective study

BACKGROUND

Diabetic retinopathy (DR) is one of the most common serious complications in diabetic patients, and early screening and diagnosis are essential to prevent visual impairment. With the rapid development of deep learning technology, network models based on attention mechanisms have shown significant advantages in medical image analysis, which can improve the accuracy and efficiency of screening.

AIM

To evaluate the efficacy of an attention mechanism-based deep learning network model in screening for DR in natural and diabetic populations, as well as in screening with unilateral and bilateral fundus photography.

METHODS

From January 2023 to June 2024, a stratified multistage cluster sampling method was adopted to select a representative sample of permanent residents aged 18-70 years from our hospital. A total of 948 fundus images from 474 participants were included in the "deep learning model" system for scoring. The fundus images were graded via the early treatment of DR [Early Treatment Diabetic Retinopathy Study (ETDRS)] scoring system as the gold standard for the diagnosis of DR. With "DR to be referred (ETDRS > 31)" as the reference variable, a receiver operating characteristic curve was drawn to evaluate the area under the curve (AUC), sensitivity and specificity of the "deep learning model" to determine the screening efficiency of the system.

RESULTS

For each subject, in the natural population, the AUC of using the "deep learning model system" to screen "DR-requiring referral" was 0.941, and the sensitivity and specificity were 98.15% and 90.08%, respectively. The sensitivity and specificity of two-directional fundus photography were 100% and 86.91%, respectively. In the diabetic population, the AUC, sensitivity and specificity were 0.901, 98.08% and 82.10%, respectively, when "wise eye sugar net" unilateral fundus photography was used to screen for "DR-requiring referrals".

CONCLUSION

In both the natural population and the diabetic population, the deep learning model system has shown high sensitivity and specificity and can be used as an auxiliary means of DR screening.

Impact of Matrix Metalloproteinase-9 during Periodontitis and Cardiovascular Diseases

Matrix metalloproteinase-9 (MMP-9) has been shown to play a key role in endothelial function and perhaps pivotal in the correlation between periodontal disease and cardiovascular disease (CVD). For the study, the impact of MMP-9 of periodontitis and CVD on serum and saliva concentrations was analyzed. For the study patients with periodontitis (n = 31), CVD (n = 31), periodontitis + CVD (n = 31), and healthy patients (n = 31) were enrolled. Clinical and demographic characteristics as well as serum and salivary MMP-9 were evaluated. MMP-9 concentrations in serum and saliva were statistically elevated in patients with CVD (p < 0.01) and in patients with periodontitis plus CVD (p < 0.001) compared to patients with periodontitis and healthy subjects. Multivariate regression analysis showed that c-reactive protein (hs-CRP) was the only significant predictor for MMP-9 serum (p < 0.001), whereas hs-CRP (p < 0.001) and total cholesterol (p = 0.029) were the statistically significant salivary MMP-9 predictors. This study evidenced that patients with CVD and periodontitis + CVD presented elevated MMP-9 concentrations in serum and saliva compared to patients with periodontitis and healthy subjects. Furthermore, hs-CRP was a negative predictor of serum and salivary MMP-9.