Association of type 2 diabetes mellitus and periodontal disease susceptibility with genome-wide association-identified risk variants in a Southeastern Brazilian population

Exploring the Impacts of Age at Menarche on Cognitive Aging in Late Adulthood: Evidence from a Mendelian Randomization Study on the Taiwanese Population

INTRODUCTION

The potential influence of age at menarche (AM) on cognitive aging remains inadequate, partly because of the difficulties presented by multiple confounders. To address this issue, Mendelian randomization (MR) analysis was used to explore the causal impacts of AM on cognitive aging.

METHODS

Using the publicly accessible Taiwan Biobank, we identified single nucleotide polymorphisms (SNPs) significantly associated with AM as instrumental variables to estimate the effects of instruments on cognitive function assessed with the Mini-Mental State Examination (MMSE). We employed several MR methods, including two-stage least squares, inverse variance weighting (IVW), MR-Egger, weighted median, weighted mode, and constrained maximum likelihood (cML) MR methods, to ensure the stability and reliability of the results.

RESULTS

MR analyses indicated no significant causal relationship between genetically determined AMs and total and subdomain MMSE scores, except the G5 subdomain (βIVW = 0.156, 95% confidence interval [CI]: 0.005, 0.307; βcML = 0.161, 95% CI: 0.014, 0.309). However, in a leave-one-out sensitivity analysis, we found a significant relationship between AM and cognitive aging after eliminating rs157863 and rs6758290, thus demonstrating the potential pleiotropic effects of these two SNPs. After these two SNPs were eliminated, we found a significant causal relationship between AM and overall MMSE scores (βIVW = 0.425, 95% CI: 0.011, 0.839), though.

CONCLUSION

Evidence from the present MR study did not fully support a causal relationship between AM and cognitive function decline in later life. Potential pleiotropic effects of the genes underlying these two traits are worthy of further investigation.

Correlation between whole salivary prostaglandin E2 and hemoglobin A1c levels among type-2 diabetic and non-diabetic patients with periodontal inflammation

BACKGROUND

It is hypothesized that whole salivary prostaglandin E2 (PgE2) levels are higher in patients with type-2 diabetes mellitus (type-2 DM) than non-diabetic individuals with periodontal inflammation; and that whole salivary expression of PgE2 is correlated with hemoglobin A1C (HbA1c) levels. The aim of the present study was to compare whole salivary PgE2 levels among patients with type-2 DM and non-diabetic individuals with periodontal inflammation.

METHODS

Sociodemographic data, duration since the diagnosis and management of type-2 DM, most recent hemoglobin A1C (HbA1c level), and any familial history of DM was retrieved from patient's healthcare records. Participants were divided into four groups: Group-1: type-2 diabetics with periodontal inflammation; Group-2: type-2 diabetics without periodontal inflammation; Group-3: non-diabetics with periodontal inflammation; and Group-4: non-diabetics without periodontal inflammation. Plaque and gingival indices (PI and GI), probing depth (PD), clinical attachment loss (CAL) and marginal bone loss (MBL) were measured. Unstimulated whole saliva samples were collected and PgE2 levels were measured. Group-comparisons were done and P < 0.05 were considered statistically significant.

RESULTS

One-hundred-sixty individuals were included. Mean HbA1c levels were higher in Group-1 than groups 2 (P < 0.05), 3 (P < 0.05) and 4 (P < 0.05). The PI (P < 0.05), GI (P < 0.05) and PD (P < 0.05) were higher in Group-1 than groups 2 and 4. The CAL was higher in Group-1 than groups 2 (P < 0.05) and 3 (P < 0.05). The PD (P < 0.05), PI (P < 0.05) and GI (P < 0.05) were higher in Group-3 than Group-4. The MBL was higher in Group-1 than groups 2 (P < 0.05), 3 (P < 0.05) and 4 (P < 0.05). The PgE2 levels were higher in Group-1 than groups 2 (P < 0.05), 3 (P < 0.05) and 4 (P < 0.05).

CONCLUSION

Hyperglycemia in patients with type-2 DM is associated with increased expression of whole salivary PgE2 levels and worsened periodontal inflammation compared with individuals with well-controlled type-2 DM and non-diabetic individuals.

Influence of polymorphisms in IRS1, IRS2, MC3R, and MC4R on metabolic and inflammatory status and food intake in Brazilian adults: An exploratory pilot study

Polymorphisms in genes of leptin-melanocortin and insulin pathways have been associated with obesity and type 2 diabetes. We hypothesized that polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory markers and food intake composition in Brazilian subjects. This exploratory pilot study included 358 adult subjects. Clinical, anthropometric, and laboratory data were obtained through interview and access to medical records. The variants IRS1 rs2943634 A˃C, IRS2 rs1865434 C>T, MC3R rs3746619 C>A, and MC4R rs17782313 T>C were analyzed by real-time polymerase chain reaction. Food intake composition was assessed in a group of subjects with obesity (n = 84) before and after a short-term nutritional counseling program (9 weeks). MC4R rs17782313 was associated with increased risk of obesity (P = .034). Multivariate linear regression analysis adjusted by covariates indicated associations of IRS2 rs1865434 with reduced low-density lipoprotein cholesterol and resistin, MC3R rs3746619 with high glycated hemoglobin, and IRS1 rs2943634 and MC4R rs17782313 with increased high-sensitivity C-reactive protein (P < .05). Energy intake and carbohydrate and total fat intakes were reduced after the diet-oriented program (P < .05). Multivariate linear regression analysis showed associations of IRS2 rs1865434 with high basal fiber intake, IRS1 rs2943634 with low postprogram carbohydrate intake, and MC4R rs17782313 with low postprogram total fat and saturated fatty acid intakes (P < .05). Although significant associations did not survive correction for multiple comparisons using the Benjamini-Hochberg method in this exploratory study, polymorphisms in IRS1, IRS2, MC3R, and MC4R influence metabolic and inflammatory status in Brazilian adults. IRS1 and MC4R variants may influence carbohydrate, total fat, and saturated fatty acid intakes in response to a diet-oriented program in subjects with obesity.

Evidence Linking PPARG Genetic Variants with Periodontitis and Type 2 Diabetes Mellitus in a Brazilian Population

The peroxisome proliferator-activated receptor gamma (PPARG) gene encodes a transcription factor involved in the regulation of complex metabolic and inflammatory diseases. We investigated whether single nucleotide polymorphisms (SNPs) and haplotypes of the PPARG gene could contribute with susceptibility to develop periodontitis alone or together with type 2 diabetes mellitus (T2DM). Moreover, we evaluated the gene-phenotype association by assessing the subjects' biochemical and periodontal parameters, and the expression of PPARG and other immune response-related genes. We examined 345 subjects with a healthy periodontium and without T2DM, 349 subjects with moderate or severe periodontitis but without T2DM, and 202 subjects with moderate or severe periodontitis and T2DM. PPARG SNPs rs12495364, rs1801282, rs1373640, and rs1151999 were investigated. Multiple logistic regressions adjusted for age, sex, and smoking status showed that individuals carrying rs1151999-GG had a 64% lower chance of developing periodontitis together with T2DM. The CCGT haplotype increased the risk of developing periodontitis together with T2DM. The rs1151999-GG and rs12495364-TC were associated with reduced risk of obesity, periodontitis, elevated triglycerides, and elevated glycated hemoglobin, but there was no association with gene expression. Polymorphisms of the PPARG gene were associated with developing periodontitis together with T2DM, and with obesity, lipid, glycemic, and periodontal characteristics.

The Bidirectional Relationship between Periodontal Disease and Diabetes Mellitus-A Review

Periodontitis is a chronic inflammatory disease caused by the presence of a bacterial biofilm known as dental plaque. This biofilm affects the supporting apparatus of the teeth, especially the periodontal ligaments and the bone surrounding the teeth. Periodontal disease and diabetes seem to be interrelated and in a bidirectional relationship, and have been increasingly studied in recent decades. For example, diabetes mellitus has a detrimental effect on periodontal disease, increasing its prevalence, extent, and severity. In turn, periodontitis negatively affects glycemic control and the course of diabetes. This review aims to present the most recently discovered factors that contribute to the pathogenesis, therapy, and prophylaxis of these two diseases. Specifically, the article focuses on microvascular complications, oral microbiota, pro- and anti-inflammatory factors in diabetes, and periodontal disease. As presented in this review, these two diseases require specific/ complementary therapeutic solutions when they occur in association, with new clinical trials and epidemiological research being necessary for better control of this interdependent pathogenic topic.

Association between KCNJ11 E23K polymorphism and the risk of type 2 diabetes mellitus: A global meta-analysis

BACKGROUND

Potassium inwardly rectifying channel, subfamily J member 11(KCNJ11) is considered to be a potential susceptible gene of type 2 diabetes mellitus (T2DM), and the association between KCNJ11 E23K polymorphism and T2DM risk is still controversial worldwide. This meta-analysis was performed to assess the association more accurately between KCNJ11 E23K polymorphism and T2DM risk.

METHODS

The up-to-data meta-analysis was conducted based on studies selected from eight databases (PubMed, Web of Science, Medline, Scopus, Embase, CNKI, WanFang, and Vip). Five gene models were included in our study: allele model (K-allele vs. E-allele), heterozygous model (EK vs. EE), homozygous model (KK vs. EE), dominant genetic model (EK + KK vs. EE), and recessive genetic model (EK + EE vs. KK). Association strength was evaluated by odds ratio (OR) and 95% confidence interval (CI), publication bias was evaluated by Begg's funnel plot and Egger's test, sensitivity analysis and trial sequential analysis (TSA) were used to evaluate the stability of the results.

RESULTS

According to the inclusion and exclusion criteria, 31 eligible articles were finally selected in our meta-analysis, including 8754 T2DM cases and 7587 controls. We found that allelic model (OR = 1.25, 95%CI: 1.15-1.35, P < 0.01), heterozygous model (OR = 1.31, 95% CI: 1.18-1.44, P < 0.01), homozygous model (OR = 1.48, 95% CI: 1.24-1.76, P < 0.01), and dominant genetic model (OR = 1.35, 95% CI: 1.22-1.50, P < 0.01) were significantly associated with increased risk of T2DM, but recessive genetic model (OR = 0.78, 95% CI: 0.67-0.91, P < 0.01) was considered as a protective factor for T2DM. No significant evidence of publication bias was found.

CONCLUSION

Our meta-analysis confirms the association between KCNJ11 E23K polymorphism and the risk of T2DM, highlighting that gene-gene interaction and gene-environment interaction should be investigated in future.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.