BMC Endocrine Disorders最新文献

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.

Background: Being overweight is a growing concern worldwide, characterized by a body mass index (BMI) ranging from 25 to 29.9 kg/m². This condition can arise from various factors, including poor dietary choices, sedentary lifestyles, genetic predispositions, and environmental influences. The impact of dietary amino acids on metabolic health remains a topic of debate. In this study, we explored the correlation between the ratio of dietary branched-chain amino acids (BCAAs) to aromatic amino acids (AAAs) and metabolic profiles, including C-Peptide levels in overweight individuals.

Methods: A total of 221 overweight participants were enrolled in this study. Dietary intake was assessed using a validated food frequency questionnaire (FFQ). We conducted laboratory tests to measure metabolic variables, lipid profiles, glycemic indicators, and C-Peptide levels.

Results: Participants in the highest tertile of the dietary BCAAs/AAAs ratio exhibited significantly lower blood sugar levels and higher concentrations of high-density lipoprotein (HDL) compared to those in the lowest tertiles, with p-values of 0.033 and 0.003, respectively.

Conclusion: Our findings suggest that a higher dietary ratio of BCAAs to AAAs is linked to improved metabolic health among overweight individuals. However, further longitudinal studies are necessary to clarify the causal relationships between these dietary factors and metabolic outcomes.

Clinical trial number: Not applicable.

Background: Diabetes mellitus (DM) is a complex metabolic disease with numerous consequences that are especially linked to it. Because microRNAs (miRNAs) are produced specifically in the pancreas and remain stable in various bodily fluids, they can be used for early diagnosis as well as tracking the course and severity of the disease. This study examined the serum expression of miR-410, miR-133, and miR-582 in Egyptian patients with Type 1 Diabetes Mellitus.

Methods: There were two groups of 120 participants in this study: 40 healthy children and 80 diabetic children. Samples of venous blood were obtained from every participant. The quantitative real-time PCR (qRT-PCR) method was used to determine the serum expressions of miR-410, miR-133, and miR-582.

Results: Our findings showed a statistically significant difference in the median relative expression levels of miR-410, miR-133, and miR-582 between the T1DM group and the control group. T1DM patients had higher levels of miR-410 (P < .0001), miR- 133 (P = .006), and miR-582 (P < .0001) compared to controls. ROC curve analysis indicated that miR-410, miR-133, and miR-582 can distinguish T1DM patients from healthy subjects. For miR-410, at a cutoff point of 0.0009, sensitivity was 62%, specificity was 80%, and the area under the curve (AUC) was 0.713. For miR-133, the cutoff was 0.0016, with AUC 0.628, sensitivity 63%, and specificity 60%. For miR-582, the cutoff was 0.00028, with AUC 0.668, sensitivity 71.0%, and specificity 50%.

The periprocedural period represents a critical time for children and adolescents with diabetes, during which they are at increased risk of complications. The management of diabetes in this context has become increasingly complex due to the wide range of available insulin regimens and glucose monitoring technologies. The Australasian Periprocedural Guideline for Children and Adolescents with Diabetes Mellitus has been co-developed by New Zealand Paediatric Endocrine Society, Australia and New Zealand Society for Paediatric Endocrinology and Diabetes, Society for Paediatric Anaesthesia in New Zealand and Australia, and consumer representatives. The guideline emphasises the importance of involving individuals with diabetes and their caregivers in the development of management plans, adopting a multidisciplinary approach when appropriate. It provides detailed guidance on procedure classifications, the perioperative use of existing continuous glucose monitors and insulin pumps, fasting protocols, perioperative intravenous fluid and insulin administration, and hypoglycaemia management strategies. By adhering to this guideline, healthcare providers can deliver safe, comprehensive, and patient-centred care throughout the periprocedural period.

Background: Cardiovascular-kidney-metabolic (CKM) syndrome is a systemic disorder characterized by the interrelated dysfunction of metabolic abnormalities, chronic kidney disease, and cardiovascular injury, significantly increasing the risk of cardiovascular events and all-cause mortality. Insulin resistance (IR) plays an important role in the development and progression of CKM syndrome, but the relationship between estimated glucose disposal rate (eGDR) and mortality risk in CKM syndrome patients remains unclear, particularly across different glucose metabolic states.

Methods: This cohort study used data from the National Health and Nutrition Examination Survey (NHANES 1999-2018) for CKM syndrome patients. We employed Cox regression and restricted cubic spline (RCS) analysis to assess the relationship between eGDR and mortality. Stratified analyses by glucose metabolism status and ROC curves compared the predictive performance of eGDR with TyG and HOMA-IR.

Results: eGDR was significantly associated with all-cause and cause-specific mortality (P < 0.05). RCS analysis revealed a nonlinear relationship between eGDR and all-cause (P for non-linear = 0.041) and diabetes-specific mortality (P for non-linear = 0.003), while a linear relationship was found with cardiovascular mortality P for non-linear = 0.278). Stratified analysis showed eGDR's strongest predictive value for all-cause mortality in diabetes, cardiovascular mortality in prediabetes, and multiple mortality outcomes in normal glucose regulation (all P < 0.05). ROC analysis demonstrated superior predictive performance of eGDR compared to TyG and HOMA-IR, especially in the CKM syndrome and normal glucose regulation groups.

Conclusion: Lower eGDR levels were independently associated with increased risks of all-cause, cardiovascular-specific, and diabetes-specific mortality in CKM syndrome patients. The relationship between eGDR and mortality was nonlinear for some outcomes. eGDR showed superior predictive performance, especially in individuals with normal glucose regulation and prediabetes, suggesting its potential as a biomarker for risk re-stratification and early intervention in CKM syndrome.

Clinical trial number: Not applicable.