Bone markers and bone mineral density associates with periodontitis in females with poly-cystic ovarian syndrome

Causal relationship between PCOS and related sex hormones with oral inflammatory diseases: a bidirectional Mendelian randomization study

Background

Observational studies have identified a strong association between polycystic ovary syndrome (PCOS) and hormone levels related to oral inflammatory diseases. To better understand the relationship between them, we conducted an analysis using a two-sample Mendelian randomization (MR) approach.

Methods

We gathered summary statistical data from previously published genome-wide association studies (GWAS) on PCOS and three sex hormones (AMH, Estradiol, LH) along with four oral inflammatory diseases (painful gums, loose teeth, mouth ulcers, and toothache). We selected single nucleotide polymorphisms (SNPs) as instrumental variables and employed four types of MR analysis methods to evaluate causal relationships between exposure and outcome. Finally, the robustness of our results was further validated through sensitivity tests and reverse MR.

Results

We observed that PCOS could increase the risk of mouth ulcers (ORIVW= 1.0013, 95%CI: 1.0001-1.0025, PIVW = 0.0278), painful gums (ORIVW= 1.0015, 95%CI:1.0003-1.0027, PIVW = 0.0163), and loose teeth (ORIVW= 1.0014, 95%CI: 1.0001-1.0027, PIVW = 0.0328). Moreover, LH was also found to increase the risk of mouth ulcers (ORIVW= 1.0031, 95%CI: 0.0001-1.0062, PIVW = 0.0457). MR-Egger regression, weighted mode, and WE indicated similar results. Additionally, we discovered no causal link between PCOS and toothache (PIVW>0.05), LH and painful gums, loose teeth, or toothache (PIVW>0.05), or AMH and Estradiol level with any of the four oral diseases (PIVW>0.05).

Conclusion

Our research provides new insights and references for exploring the effects of PCOS and related hormones on oral inflammatory lesions. For patients with PCOS, especially those with elevated LH levels, early intervention measures should be taken to prevent the occurrence of oral inflammatory diseases.

Comparative analysis of serum and saliva samples using Raman spectroscopy: a high-throughput investigation in patients with polycystic ovary syndrome and periodontitis

BACKGROUND

Polycystic ovary syndrome (PCOS) and periodontitis significantly affect women's oral and systemic health worldwide, and yet increase the risk of cardiovascular-metabolic diseases like diabetes and coronary heart disease. Regarding the PCOS-periodontitis connection, whether sex hormones, metabolic and inflammatory mediators could account for the underlying linking mechanism needs to be further investigated. This case-control study evaluated the hormonal, metabolic and inflammatory profiles in PCOS and non-PCOS subjects with various periodontal conditions, via assessing serum and saliva samples by Raman spectroscopy.

METHODS

A total of 66 females with PCOS and 22 systemically healthy female volunteers were recruited in a single hospital. Full-mouth periodontal examination was undertaken for identifying the subjects with periodontal health, gingivitis or periodontitis. The datasets of sex hormones and metabolic indicators were retrieved from the hospital information system. Both serum and saliva samples were collected for detecting inflammatory mediators and Raman spectroscopic assessment. The subjects were categorized into four groups according to their conditions of PCOS and periodontitis for Raman spectroscopic analysis. Partial least squares discriminant analysis was performed to examine the inter-group differences in Raman spectra.

RESULTS

PCOS patients exhibited greater mean probing depth (P < 0.05) and higher serum levels of triglycerides (P < 0.05) and matrix metalloproteinase-8 (P < 0.05) than those in non-PCOS participants. Both probing depth and triglyceride level were positively correlated with PCOS (P < 0.05). There was a significant difference in mean Raman spectra of saliva samples among the four groups with different conditions of PCOS and periodontitis (P < 0.05), while no significant inter-group difference existed in serum samples.

CONCLUSIONS

The present study shows that periodontal condition may affect the biomolecular profiles of Raman spectra in serum and saliva of PCOS patients. It underscores the importance of the collaborative teamwork of dentists and gynecologists for enhancing women's oral health, general wellbeing and quality of life.

Effects of distinct Polycystic Ovary Syndrome phenotypes on bone health

Polycystic Ovary Syndrome (PCOS) is a highly prevalent and heterogenous endocrinopathy affecting 5-18% of women. Although its cardinal features include androgen excess, ovulatory dysfunction, and/or polycystic ovarian morphology, women often display related metabolic manifestations, including hyperinsulinaemia, insulin resistance, and obesity. Emerging data reveal that the hormonal alterations associated with PCOS also impact bone metabolism. However, inconsistent evidence exists as to whether PCOS is a bone-protective or bone-hindering disorder with an accumulating body of clinical data indicating that hyperandrogenism, hyperinsulinaemia, insulin resistance, and obesity may have a relative protective influence on bone, whereas chronic low-grade inflammation and vitamin D deficiency may adversely affect bone health. Herein, we provide a comprehensive assessment of the endocrine and metabolic manifestations associated with PCOS and their relative effects on bone metabolism. We focus principally on clinical studies in women investigating their contribution to the alterations in bone turnover markers, bone mineral density, and ultimately fracture risk in PCOS. A thorough understanding in this regard will indicate whether women with PCOS require enhanced surveillance of bone health in routine clinical practice.

Bidirectional association between polycystic ovary syndrome and periodontal diseases

Polycystic ovary syndrome (PCOS) and periodontal disease (PDD) share common risk factors. The bidirectional interaction between PCOS and PDD has been reported, but until now, the underlying molecular mechanisms remain unclear. Endocrine disorders including hyperandrogenism (HA) and insulin resistance (IR) in PCOS disturb the oral microbial composition and increase the abundance of periodontal pathogens. Additionally, PCOS has a detrimental effect on the periodontal supportive tissues, including gingiva, periodontal ligament, and alveolar bone. Systemic low-grade inflammation status, especially obesity, persistent immune imbalance, and oxidative stress induced by PCOS exacerbate the progression of PDD. Simultaneously, PDD might increase the risk of PCOS through disturbing the gut microbiota composition and inducing low-grade inflammation and oxidative stress. In addition, genetic or epigenetic predisposition and lower socioeconomic status are the common risk factors for both diseases. In this review, we will present the latest evidence of the bidirectional association between PCOS and PDD from epidemiological, mechanistic, and interventional studies. A deep understanding on their bidirectional association will be beneficial to provide novel strategies for the treatment of PCOS and PDD.

Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis

Purpose

Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches.

Materials and Methods

Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models.

Results

The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively.

Conclusion

Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.