BMC Endocrine Disorders最新文献

Background: Hormonal changes, particularly in estrogen and follicle-stimulating hormone, during the menopausal transition affect lipid and glucose metabolism, thereby increasing the risk of dyslipidemia. Dyslipidemia is a well-recognized risk factor for cardiovascular disease, which remains the leading cause of death worldwide. Therefore, assessing dyslipidemia and its associated factors among women in the reproductive age group and those who have entered menopause may help reduce the risk of cardiovascular disease by promoting balanced lipid levels.

Objective: The aim of this study was to determine and compare prevalence of dyslipidemia and associated factors among premenopausal and post-menopausal women: a community-based comparative cross-sectional study in Debre Markos city, Northwest, Ethiopia, 2024.

Method: A community-based comparative cross-sectional study was conducted in Debre Markos city among 320 women. Participants were recruited using a multistage simple random sampling technique. Data were collected using a structured and pretested interviewer-administered questionnaire. Lipid profiles, estrogen, follicle-stimulating hormone, progesterone, luteinizing hormone, and glucose were measured using a Beckman Coulter analyzer. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 27. Multivariable logistic regression was employed to identify predictors of dyslipidemia. In the multivariable logistic regression, a significant association was considered at p < 0.05 with odds ratios and 95% confidence intervals.

Result: The prevalence of dyslipidemia was 41.9% (95% CI: 34.2-49.6) among menopausal women and 22.5% (95% CI: 16.3-28.7) among premenopausal women. In menopausal women, physical inactivity (AOR = 3.04; 95% CI: 1.18-7.85), being obese (AOR = 3.08; 95% CI: 1.14-8.34), and low estrogen levels (≤ 30 pg/ml: AOR = 5.97; 95% CI: 1.88-18.93; 30.63-39.39 pg/ml: AOR = 3.73; 95% CI: 1.30-10.71) were significantly associated with dyslipidemia.

Conclusion: Menopausal women demonstrated a higher prevalence of dyslipidemia compared to their premenopausal counterparts. Independent of age, decreased estrogen levels, physical inactivity, and being obese were significant predictors of adverse lipid changes. These findings underscore the need for targeted preventive strategies during the menopausal transition to reduce the burden of dyslipidemia and improve women's long-term cardiovascular health and quality of life.

Background: Atrial fibrillation (AF) is a common arrhythmia driven by chronic inflammation, which promotes myocardial remodeling. While diabetes mellitus (DM) is known to exacerbate AF inflammation, the specific factors linking DM to AF pathology are complex and often confounded by conditions like periodontitis. This study aimed to determine the true contribution of DM and periodontitis to systemic inflammation in older AF patients, using the Ferritin/Hemoglobin ratio-a surrogate marker for chronic inflammation and impaired iron utilization.

Methods: This single-center, retrospective observational study included older AF patients undergoing catheter ablation (CA), categorized into DM and Non-DM groups. We collected demographic data, cardiac parameters, metabolic indices (HbA1c, and periodontitis markers, including the number of Residual roots and periodontal pocket depth (PPD). Principal Component Analysis (PCA) and subsequent multiple regression were performed using the Ferritin/Hb ratio as the dependent variable to identify independent inflammatory predictors.

Results: The DM group exhibited a higher inflammatory and metabolic risk profile, including significantly higher Body Mass Index, HbA1c, and C-reactive protein, as well as significantly poorer oral hygiene (higher Residual roots). Multiple regression analysis demonstrated that the number of teeth with PPD > 4 mm was the strongest independent positive predictor of the Ferritin/Hb ratio. Other positive predictors included Left atrium diameter. Notably, the presence of DM itself was not the most influential factor, and the mean PPD showed a paradoxical negative association, suggesting that local lesion severity (PPD > 4 mm count) is more critical than the extent of the disease.

Conclusion: The presence of severe periodontal lesions -rather than DM status alone-is independently and significantly associated with systemic inflammation and impaired iron utilization in older AF patients. This indicates that periodontitis acts as a major mediating factor in DM-related AF pathology. We recommend that proactive periodontal treatment and oral hygiene management be integrated alongside optimal glycemic control as crucial therapeutic strategies to suppress AF inflammation.

Clinical trial number: Not applicable.

Objectives: To determine whether glucometer-based glucose measurements are analytically and clinically comparable to the standard laboratory Glucose Oxidase-Peroxidase (GOD-POD) method in adult inpatients at a tertiary care hospital. Self-monitoring of blood glucose (SMBG) is the primary focus of Diabetes management as it helps the patient to monitor their plasma glucose levels on their own and maintain strict glycaemic control. Their affordability, portability, and convenience of use give them an edge over traditional laboratory-based reference methods.

Material & methods: We conducted our study (cross-sectional, method-comparison study) in a Tertiary Care Hospital affiliated to Rajiv Gandhi Medical College, Thane. Venipuncture was done for the collection of whole blood venous sample from the Median Cubital Vein randomly and the samples were analysed for glucose concentration by fully automated analyser in the central biochemistry laboratory. Simultaneously, capillary whole blood samples were analysed by the glucometer. This helped us to rule out bias of inherent difference. Sample size for this study was determined according to the International Organization for Standardization (ISO) guideline 15197:2013, Accordingly, a total of 104 paired capillary and venous blood samples were collected. Agreement was evaluated through the Bland-Altman plot. Parke's error grid and Surveillance error grid were plotted for the evaluation of clinical efficacy of the glucometer. The two methods were contrasted using the Passing-Bablok regression analysis and Deming Regression analysis. A scatter plot with a regression line was plotted to present the outcomes of the analysis.

Result: Statistical difference between the results of Glucometer (109.4 ± 56.54) and Glucose Oxidase-Peroxidase (120.4 ± 55.19) method was significant with p value < 0.001. The dissemination of results in Surveillance Error Grid were as follows with 47.1% of results showing no risk while 26% & 23.1% results showed slightly lower and slightly higher risk respectively. Around 3.8% of results show moderately lower risk. Only 41.3% of results met ISO 15197:2013 accuracy criteria; however, 96.1% fell within clinically acceptable Parke's zones A and B. Although most discrepancies had minimal impact on treatment, caution is warranted in critical care settings.

Conclusion: Our study demonstrates that a substantial gap exists in the analytical performance of the glucometer compared to the standard laboratory analyser when evaluated against ISO 15197:2013 criteria. However, error grid analysis suggests that the majority of discrepancies observed had minimal impact on clinical decision-making in our study setting.

Aim: To analyze data from non-intensive care unit (non-ICU) inpatients with diabetes to predict the risk of hypoglycemia using electronic health records (EHRs) and point-of-care (POC) blood glucose values.

Methods: Patient demographics, laboratory results, POC blood glucose, and procedures were performed during the hospital stays on Days 0-2 to predict hypoglycemic episodes (blood glucose ≤ 3.9 mmol/L) on Days 3-6. The dataset was randomly split into a training set and an independent verification set at a 7:3 ratio. Logistic Regression (LR) and Artificial Neural Network (ANN) were compared using the area under the curve (AUC). A nomogram plot was also constructed to display the predicted hypoglycemia probabilities.

Results: Data from 16,593 diabetic patients (January 2017 to June 2022) were analyzed. Predictive factors from the LR model included the use of insulin; previous hypoglycemia in Days 0-2; respiratory rate; blood urea nitrogen; potassium; D-dimer levels; coefficient variation of blood glucose (BG CV) > 31%; and blood glucose gap (BG gap, maximum of blood glucose - minimum of blood glucose) > 10 mmol/L. In the verification set, the AUC of ANN was 0.762 and the AUC of LR was 0.763. There was no significant difference in the effects of the models built by the two methods. The results showed that the probability predicted by the nomogram using LR is similar to the clinical results. Decision curve analysis (DCA) indicated potential clinical application for the LR model.

Conclusions: The LR model demonstrated considerable value in predicting hypoglycemia risk, comparable to ANN. Trials of such models should be conducted to evaluate their utility in reducing inpatient hypoglycemia.

Clinical trial number: Not applicable.